diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..556a7df5ea13836d87176ea7d2aa0f8bd875b6e0 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..aec0b0b351c3ae2cd276a4360c3550cc29fbb7f8 --- /dev/null +++ b/.gitignore @@ -0,0 +1,213 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[codz] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py.cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock +#poetry.toml + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. +# https://pdm-project.org/en/latest/usage/project/#working-with-version-control +#pdm.lock +#pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. +#pixi.lock +# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one +# in the .venv directory. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md index 59b32d06c60410181d9d1dafab2e11cf95f759ac..fe1e282686331f5be087dd2e99e3dc760fe6c583 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,10 @@ --- title: SceneDINO -emoji: 📈 +emoji: 🦕 colorFrom: blue colorTo: pink sdk: gradio +python_version: 3.10 sdk_version: 5.35.0 app_file: app.py pinned: false diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..9f13fe770cb5c8b04370e57e8aa0d8b8ade4e15f --- /dev/null +++ b/app.py @@ -0,0 +1,213 @@ +from demo_utils.utils import (load_modules, + load_sample_from_path, + load_sample_from_dataset, + get_fov_mask, + inference_3d, + inference_rendered_2d) + +import tempfile +import os +import sys +import yaml + +sys.path.append("./sscbench") +from sscbench.gen_voxelgrid_npy import save_as_voxel_ply, classes_to_colors +from download_checkpoint_hf import download_scenedino_checkpoint + +import torch + +import numpy as np +import gradio as gr +import open3d as o3d +import spaces + + +# Load checkpoints from Hugging Face +download_scenedino_checkpoint("ssc-kitti-360-dino") +download_scenedino_checkpoint("ssc-kitti-360-dinov2") + +# Load model, ray sampler, datasets +ckpt_path = "out/scenedino-pretrained/seg-best-dino/" +ckpt_name = "checkpoint.pt" +net_v1, renderer_v1, ray_sampler_v1, test_dataset = load_modules(ckpt_path, ckpt_name) +renderer_v1.eval() + +ckpt_path = "out/scenedino-pretrained/seg-best-dinov2/" +ckpt_name = "checkpoint.pt" +net_v2, renderer_v2, ray_sampler_v2, _ = load_modules(ckpt_path, ckpt_name) +renderer_v2.eval() + + +def convert_voxels(arr, map_dict): + f = np.vectorize(map_dict.__getitem__) + return f(arr) + +with open("sscbench/label_maps.yaml", "r") as f: + label_maps = yaml.safe_load(f) + + +@spaces.GPU(duration=60) +def demo_run(image: str, + backbone: str, + mode: str, + sigma_threshold: float, + resolution: float, + x_range: int, + y_range: int, + z_range: int): + + if backbone == "DINO (ViT-B)": + net, renderer, ray_sampler = net_v1, renderer_v1, ray_sampler_v1 + elif backbone == "DINOv2 (ViT-B)": + net, renderer, ray_sampler = net_v2, renderer_v2, ray_sampler_v2 + + prediction_mode = "stego_kmeans" + if mode == "Feature PCA 1-3": + segmentation = False + rgb_from_pca_dim = 0 + elif mode == "Feature PCA 4-6": + segmentation = False + rgb_from_pca_dim = 3 + elif mode == "Feature PCA 7-9": + segmentation = False + rgb_from_pca_dim = 6 + elif mode == "SSC (unsup.)": + segmentation = True + elif mode == "SSC (linear)": + segmentation = True + prediction_mode = "direct_linear" + + # Necessary when reading from examples? cast from str + sigma_threshold, resolution = float(sigma_threshold), float(resolution) + x_range, y_range, z_range = int(x_range), int(y_range), int(z_range) + + # Too many voxels + max_voxel_count = 5000000 + voxel_count = (x_range//resolution + 1) * (y_range//resolution + 1) * (z_range//resolution + 1) + if voxel_count > max_voxel_count: + raise gr.Error(f"Too many voxels ({int(voxel_count) / 1_000_000:.1f}M > {max_voxel_count / 1_000_000:.1f}M).\n" + + "Reduce voxel resolution or range.", duration=5) + + with torch.no_grad(): + images, poses, projs = load_sample_from_path(image, intrinsic=None) + + net.encode(images, projs, poses, ids_encoder=[0]) + net.set_scale(0) + + # 2D Features output + dino_full_2d, depth_2d, seg_2d = inference_rendered_2d(net, poses, projs, ray_sampler, renderer, prediction_mode) + net.encoder.fit_visualization(dino_full_2d.flatten(0, -2)) + + if segmentation: + output_2d = convert_voxels(seg_2d.detach().cpu(), label_maps["cityscapes_to_label"]) + output_2d = classes_to_colors[output_2d].cpu().detach().numpy() + else: + output_2d = net.encoder.transform_visualization(dino_full_2d, from_dim=rgb_from_pca_dim) + output_2d -= output_2d.min() + output_2d /= output_2d.max() + output_2d = output_2d.cpu().detach().numpy() + + # Chunking + max_chunk_size = 100000 + z_layers_per_chunk = max_chunk_size // ((x_range//resolution + 1) * (y_range//resolution + 1)) + + # 3D Features output + x_range = (-x_range/2, x_range) + y_range = (-y_range/2, y_range) + z_range = (0, z_range) + + is_occupied, output_3d, fov_mask = [], [], [] + current_z = 0 + + while current_z <= z_range[1]: + z_range_chunk = (current_z, min(current_z + z_layers_per_chunk*resolution, z_range[1])) + current_z += (z_layers_per_chunk+1) * resolution + + xyz_chunk, dino_full_3d_chunk, sigma_3d_chunk, seg_3d_chunk = inference_3d(net, x_range, y_range, z_range_chunk, resolution, prediction_mode) + fov_mask_chunk = get_fov_mask(projs[0, 0], xyz_chunk) + + is_occupied_chunk = sigma_3d_chunk > sigma_threshold + + if segmentation: + output_3d_chunk = seg_3d_chunk + else: + output_3d_chunk = net.encoder.transform_visualization(dino_full_3d_chunk, from_dim=rgb_from_pca_dim) + output_3d_chunk -= output_3d_chunk.min() + output_3d_chunk /= output_3d_chunk.max() + + output_3d_chunk = torch.clamp(output_3d_chunk*1.2 - 0.1, 0.0, 1.0) + output_3d_chunk = (255*output_3d_chunk).int() + + fov_mask_chunk = fov_mask_chunk.reshape(is_occupied_chunk.shape) + + is_occupied.append(is_occupied_chunk) + output_3d.append(output_3d_chunk) + fov_mask.append(fov_mask_chunk) + + is_occupied = torch.cat(is_occupied, dim=2) + output_3d = torch.cat(output_3d, dim=2) + fov_mask = torch.cat(fov_mask, dim=2) + + temp_dir = tempfile.gettempdir() + ply_path = os.path.join(temp_dir, "output.ply") + + if segmentation: + # mapped to "unlabeled" + is_occupied[output_3d == 10] = 0 + is_occupied[output_3d == 12] = 0 + + save_as_voxel_ply(ply_path, + is_occupied.detach().cpu(), + voxel_size=resolution, + size=is_occupied.size(), + classes=torch.Tensor( + convert_voxels( + output_3d.detach().cpu(), + label_maps["cityscapes_to_label"])), + fov_mask=fov_mask) + else: + save_as_voxel_ply(ply_path, + is_occupied.detach().cpu(), + voxel_size=resolution, + size=is_occupied.size(), + colors=output_3d.detach().cpu(), + fov_mask=fov_mask) + + mesh = o3d.io.read_triangle_mesh(ply_path) + glb_path = os.path.join(temp_dir, "output.glb") + o3d.io.write_triangle_mesh(glb_path, mesh, write_ascii=True) + + del dino_full_2d, depth_2d, seg_2d + del dino_full_3d_chunk, sigma_3d_chunk, seg_3d_chunk, is_occupied_chunk + del is_occupied, output_3d, fov_mask + + torch.cuda.empty_cache() + + return output_2d, glb_path + + +demo = gr.Interface( + demo_run, + inputs=[ + gr.Image(label="Input image", type="filepath"), + gr.Radio(label="Backbone", choices=["DINO (ViT-B)", "DINOv2 (ViT-B)"]), + gr.Radio(label="Mode", choices=["Feature PCA 1-3", "Feature PCA 4-6", "Feature PCA 7-9", "SSC (unsup.)", "SSC (linear)"]), + gr.Slider(label="Density threshold", minimum=0, maximum=1, step=0.05, value=0.2), + gr.Slider(label="Resolution [m]", minimum=0.05, maximum=0.5, step=0.1, value=0.2), + gr.Slider(label="X Range [m]", minimum=1, maximum=50, step=1, value=10), + gr.Slider(label="Y Range [m]", minimum=1, maximum=50, step=1, value=10), + gr.Slider(label="Z Range [m]", minimum=1, maximum=100, step=1, value=20), + ], + outputs=[ + gr.Image(label="Rendered 2D Visualization"), + gr.Model3D(label="Voxel Surface 3D Visualization", + zoom_speed=0.5, pan_speed=0.5, + clear_color=[0.0, 0.0, 0.0, 0.0], + camera_position=[-90, 80, None], + display_mode="solid"), + ], + title="SceneDINO Demo", + examples="demo_utils/examples", +) + +demo.launch() diff --git a/configs/dataset/bdd_seg.yaml b/configs/dataset/bdd_seg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4665684dd0fefcb7f83bf9fe6e6f5a018cfbc3df --- /dev/null +++ b/configs/dataset/bdd_seg.yaml @@ -0,0 +1,2 @@ +type: "BDD_seg" +data_path: "" diff --git a/configs/dataset/cityscapes_seg.yaml b/configs/dataset/cityscapes_seg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ba097cc97beed34d6479a341c2545680a466234 --- /dev/null +++ b/configs/dataset/cityscapes_seg.yaml @@ -0,0 +1,2 @@ +type: "Cityscapes_seg" +data_path: "" diff --git a/configs/dataset/kitti_360_sscbench.yaml b/configs/dataset/kitti_360_sscbench.yaml new file mode 100644 index 0000000000000000000000000000000000000000..004cd428262d9990cc4cda7bbaf8b64a9187e96b --- /dev/null +++ b/configs/dataset/kitti_360_sscbench.yaml @@ -0,0 +1,15 @@ +type: "old_KITTI_360" +data_path: "" +pose_path: "" +split_path: "datasets/kitti_360/splits/sscbench" +image_size: [ 192, 640 ] +data_stereo: true +data_fisheye: true +data_fc: 2 +# dilation: 10 +# color_aug: true +fisheye_offset: [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] +stereo_offset: [0] +is_preprocessed: true +fisheye_rotation: -15 +data_segmentation: true diff --git a/configs/dataset/realestate10k.yaml b/configs/dataset/realestate10k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7bd631cc7168378249330bbe741fc558b19613a6 --- /dev/null +++ b/configs/dataset/realestate10k.yaml @@ -0,0 +1,3 @@ +type: "RealEstate10K" +data_path: ".pickle" +image_size: [288, 512] diff --git a/configs/downstream/semantic.yaml b/configs/downstream/semantic.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3906f1751af510ee2b1e11e9529e90cd5b4e0bda --- /dev/null +++ b/configs/downstream/semantic.yaml @@ -0,0 +1,13 @@ +type: "segmentation" + +n_classes: 19 +gt_classes: 19 +input_dim: 384 +code_dim: 64 + +knn_neighbors: 4 +buffer_size: 256 +patch_sample_size: 576 + +mode: "3d" +apply_crf: False diff --git a/configs/evaluate_semantic_bdd.yaml b/configs/evaluate_semantic_bdd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4e3e8932461bae257cfc1f9ee9546a2df8896c9b --- /dev/null +++ b/configs/evaluate_semantic_bdd.yaml @@ -0,0 +1,50 @@ +defaults: + - dataset: bdd_seg + - model: dino_downsampler + - renderer: pixelnerf + - training: semantic + - validation: semantic + - downstream: semantic + - _self_ + +training_type: "downstream_training" +mode: "nvs" +seed: 0 +backend: null +nproc_per_node: null +with_amp: false +name: "training" +batch_size: 1 +gradient_accum_factor: 1 +num_workers: 6 + +renderer: + n_coarse : 32 + n_fine : 0 + n_fine_depth : 0 + depth_std : 1.0 + sched : [] + white_bkgd : false + lindisp: true + hard_alpha_cap: true + render_mode: volumetric + eval_batch_size: 65536 + normalize_dino: true + +# eval_visualize: [0, 1, 2, 3] + +output: + path: "out/evaluation-paper" + unique_id: evaluation-bdd + +checkpoint: ".pt" + +evaluations: + - type: seg + agg_type: unsup_seg + args: + n_classes: 19 + gt_classes: 19 + +downstream: + input_dim: 768 diff --git a/configs/evaluate_semantic_cityscapes.yaml b/configs/evaluate_semantic_cityscapes.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f52d73b188932277cff79da433a8ceb179767267 --- /dev/null +++ b/configs/evaluate_semantic_cityscapes.yaml @@ -0,0 +1,50 @@ +defaults: + - dataset: cityscapes_seg + - model: dino_downsampler + - renderer: pixelnerf + - training: semantic + - validation: semantic + - downstream: semantic + - _self_ + +training_type: "downstream_training" +mode: "nvs" +seed: 0 +backend: null +nproc_per_node: null +with_amp: false +name: "training" +batch_size: 1 +gradient_accum_factor: 1 +num_workers: 6 + +renderer: + n_coarse : 32 + n_fine : 0 + n_fine_depth : 0 + depth_std : 1.0 + sched : [] + white_bkgd : false + lindisp: true + hard_alpha_cap: true + render_mode: volumetric + eval_batch_size: 65536 + normalize_dino: true + +# eval_visualize: [0, 1, 2, 3] + +output: + path: "out/evaluation-paper" + unique_id: evaluation-cityscapes + +checkpoint: ".pt" + +evaluations: + - type: seg + agg_type: unsup_seg + args: + n_classes: 19 + gt_classes: 19 + +downstream: + input_dim: 768 diff --git a/configs/evaluate_semantic_kitti_360.yaml b/configs/evaluate_semantic_kitti_360.yaml new file mode 100644 index 0000000000000000000000000000000000000000..995f7f553c81ae8b22c07c20d630274c197553b2 --- /dev/null +++ b/configs/evaluate_semantic_kitti_360.yaml @@ -0,0 +1,50 @@ +defaults: + - dataset: kitti_360_sscbench + - model: dino_downsampler + - renderer: pixelnerf + - training: semantic + - validation: semantic + - downstream: semantic + - _self_ + +training_type: "downstream_training" +mode: "nvs" +seed: 0 +backend: null +nproc_per_node: null +with_amp: false +name: "training" +batch_size: 1 +gradient_accum_factor: 1 +num_workers: 6 + +renderer: + n_coarse : 32 + n_fine : 0 + n_fine_depth : 0 + depth_std : 1.0 + sched : [] + white_bkgd : false + lindisp: true + hard_alpha_cap: true + render_mode: volumetric + eval_batch_size: 65536 + normalize_dino: true + +# eval_visualize: [0, 1, 2, 3] + +output: + path: "out/evaluation-paper" + unique_id: evaluation-kitti-360-sscbench + +checkpoint: ".pt" + +evaluations: + - type: seg + agg_type: unsup_seg + args: + n_classes: 19 + gt_classes: 19 + +downstream: + input_dim: 768 diff --git a/configs/model/dino_downsampler.yaml b/configs/model/dino_downsampler.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bb79e26bf3c76590add4571dec10a498d1463709 --- /dev/null +++ b/configs/model/dino_downsampler.yaml @@ -0,0 +1,64 @@ +arch: "BTSNet" +use_code: true +prediction_mode: default + +predict_dino: true +dino_dims: 64 # == encoder.pca_dino_out + +compensate_artifacts: true +flip_augmentation: true + +encoder: + type: "dinov2" + mode: "downsample-prediction" # upsample-gt, downsample-pred + decoder_arch: "dpt" + # upsampler_arch: "multiscale-crop" # multiscale-crop, nearest + downsampler_arch: "featup" # featup, bilinear + encoder_arch: "vit-b" # vit-s, vit-b + version: "v1" # v1, v2, reg, fit3d + separate_gt_version: "v1" # v1, v2, reg, fit3d, None + encoder_freeze: false + flip_avg_gt: false + dim_reduction_arch: "mlp" + num_ch_enc: [64, 64, 128, 256] + intermediate_features: [3, 6, 9] + decoder_out_dim: 256 + dino_pca_dim: 64 # == dino_dims + image_size: [192, 640] + key_features: false + +code: + num_freqs: 6 + freq_factor: 1.5 + include_input: true + +decoder_heads: + - type: "resnet" + name: "normal_head" + freeze: false + args: + n_blocks: 0 + d_hidden: 128 +final_prediction_head: "normal_head" + +encoding_strategy: + name: "default" + args: {} +eval_encoding_strategy: + name: "default" + args: null +loss_renderer_strategy: + name: "kitti_360" + args: null +eval_loss_renderer_strategy: + name: "single_renderer" + args: + shuffle_frames: false + all_frames: true + +inv_z: true + +learn_empty: false +code_mode: z + +n_frames_render: 4 # number of frames to render among v==8 diff --git a/configs/model/dino_upsampler.yaml b/configs/model/dino_upsampler.yaml new file mode 100644 index 0000000000000000000000000000000000000000..796449c3f92073f697f684d9f95358612389b635 --- /dev/null +++ b/configs/model/dino_upsampler.yaml @@ -0,0 +1,64 @@ +arch: "BTSNet" +use_code: true +prediction_mode: default + +predict_dino: true +dino_dims: 64 # == encoder.pca_dino_out + +compensate_artifacts: false +flip_augmentation: true + +encoder: + type: "dinov2" + mode: "upsample-gt" # upsample-gt, downsample-pred + decoder_arch: "dpt" + upsampler_arch: "multiscale-crop" # multiscale-crop, nearest + # downsampler_arch: "featup" # featup, bilinear + encoder_arch: "vit-b" # vit-s, vit-b + version: "v1" # v1, v2, reg, fit3d + separate_gt_version: "v1" # v1, v2, reg, fit3d, None + encoder_freeze: false + flip_avg_gt: false + dim_reduction_arch: "mlp" + num_ch_enc: [64, 64, 128, 256] + intermediate_features: [3, 6, 9] + decoder_out_dim: 256 + dino_pca_dim: 64 # == dino_dims + image_size: [192, 640] + key_features: false + +code: + num_freqs: 6 + freq_factor: 1.5 + include_input: true + +decoder_heads: + - type: "resnet" + name: "normal_head" + freeze: false + args: + n_blocks: 0 + d_hidden: 128 +final_prediction_head: "normal_head" + +encoding_strategy: + name: "default" + args: {} +eval_encoding_strategy: + name: "default" + args: null +loss_renderer_strategy: + name: "kitti_360" + args: null +eval_loss_renderer_strategy: + name: "single_renderer" + args: + shuffle_frames: false + all_frames: true + +inv_z: true + +learn_empty: false +code_mode: z + +n_frames_render: 4 # number of frames to render among v==8 diff --git a/configs/model/dinov2_downsampler.yaml b/configs/model/dinov2_downsampler.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c966b3ece832a57b041e2bef644ddd2647084426 --- /dev/null +++ b/configs/model/dinov2_downsampler.yaml @@ -0,0 +1,64 @@ +arch: "BTSNet" +use_code: true +prediction_mode: default + +predict_dino: true +dino_dims: 64 # == encoder.pca_dino_out + +compensate_artifacts: true +flip_augmentation: true + +encoder: + type: "dinov2" + mode: "downsample-prediction" # upsample-gt, downsample-pred + decoder_arch: "dpt" + # upsampler_arch: "multiscale-crop" # multiscale-crop, nearest + downsampler_arch: "featup" # featup, bilinear + encoder_arch: "vit-b" # vit-s, vit-b + version: "v2" # v1, v2, reg, fit3d + separate_gt_version: "v2" # v1, v2, reg, fit3d, None + encoder_freeze: false + flip_avg_gt: false + dim_reduction_arch: "mlp" + num_ch_enc: [64, 64, 128, 256] + intermediate_features: [3, 6, 9] + decoder_out_dim: 256 + dino_pca_dim: 64 # == dino_dims + image_size: [192, 640] + key_features: false + +code: + num_freqs: 6 + freq_factor: 1.5 + include_input: true + +decoder_heads: + - type: "resnet" + name: "normal_head" + freeze: false + args: + n_blocks: 0 + d_hidden: 128 +final_prediction_head: "normal_head" + +encoding_strategy: + name: "default" + args: {} +eval_encoding_strategy: + name: "default" + args: null +loss_renderer_strategy: + name: "kitti_360" + args: null +eval_loss_renderer_strategy: + name: "single_renderer" + args: + shuffle_frames: false + all_frames: true + +inv_z: true + +learn_empty: false +code_mode: z + +n_frames_render: 4 # number of frames to render among v==8 diff --git a/configs/renderer/pixelnerf.yaml b/configs/renderer/pixelnerf.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c93bf797debe406c299bc70c7dba60aee445f42c --- /dev/null +++ b/configs/renderer/pixelnerf.yaml @@ -0,0 +1,9 @@ +n_coarse : 32 +n_fine : 0 +n_fine_depth : 0 +depth_std : 1.0 +sched : [] +white_bkgd : false +lindisp: true +hard_alpha_cap: true +eval_batch_size: 65536 diff --git a/configs/train_scenedino_kitti_360.yaml b/configs/train_scenedino_kitti_360.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b142b48db13e2fac192ac5f30dd094a929ff1cfd --- /dev/null +++ b/configs/train_scenedino_kitti_360.yaml @@ -0,0 +1,43 @@ +defaults: + - dataset: kitti_360_sscbench + - model: dino_downsampler + - renderer: pixelnerf + - training: scenedino + - validation: scenedino + - _self_ + +training_type: "full_training" +mode: "nvs" +seed: 0 +backend: null +nproc_per_node: null +with_amp: true +name: "training" +batch_size: 4 +num_workers: 4 + +output: + path: "out/features-paper" + unique_id: scenedino-kitti-360-sscbench + +renderer: + n_coarse : 32 + n_fine : 0 + n_fine_depth : 0 + depth_std : 1.0 + sched : [] + white_bkgd : false + lindisp: true + hard_alpha_cap: true + render_mode: volumetric + eval_batch_size: 65536 + normalize_dino: true + +training: + ray_sampler: + args: + patch_size: 8 + # ray_batch_size: 512 + + scheduler: + step_size: 50000 \ No newline at end of file diff --git a/configs/train_scenedino_re10k.yaml b/configs/train_scenedino_re10k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bec10c8bdd82a9a80df7db5694d463bcf2985bd0 --- /dev/null +++ b/configs/train_scenedino_re10k.yaml @@ -0,0 +1,49 @@ +defaults: + - dataset: realestate10k + - model: dino_downsampler + - renderer: pixelnerf + - training: scenedino + - validation: scenedino + - _self_ + +training_type: "full_training" +mode: "nvs" +seed: 0 +backend: null +nproc_per_node: null +with_amp: true +name: "training" +batch_size: 4 +num_workers: 4 + +output: + path: "out/features-paper" + unique_id: scenedino-re10k + +renderer: + n_coarse : 32 + n_fine : 0 + n_fine_depth : 0 + depth_std : 1.0 + sched : [] + white_bkgd : false + lindisp: true + hard_alpha_cap: true + render_mode: volumetric + eval_batch_size: 65536 + normalize_dino: true + +model: + encoder: + image_size: [288, 512] + loss_renderer_strategy: + name: "alternate" + +training: + ray_sampler: + args: + patch_size: 8 + # ray_batch_size: 512 + + scheduler: + step_size: 50000 \ No newline at end of file diff --git a/configs/train_semantic_kitti_360.yaml b/configs/train_semantic_kitti_360.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d48087bda22513e6f16a321bae695b903f1508ec --- /dev/null +++ b/configs/train_semantic_kitti_360.yaml @@ -0,0 +1,52 @@ +defaults: + - dataset: kitti_360_sscbench + - model: dino_downsampler + - renderer: pixelnerf + - training: semantic + - validation: semantic + - downstream: semantic + - _self_ + +training_type: "downstream_training" +mode: "nvs" +seed: 0 +backend: null +nproc_per_node: null +with_amp: true +name: "training" +batch_size: 4 +gradient_accum_factor: 1 +num_workers: 6 + +renderer: + n_coarse : 32 + n_fine : 0 + n_fine_depth : 0 + depth_std : 1.0 + sched : [] + white_bkgd : false + lindisp: true + hard_alpha_cap: true + render_mode: volumetric + eval_batch_size: 65536 + normalize_dino: true + +output: + path: "out/ssc-paper" + unique_id: ssc-kitti-360-sscbench + +training: + epoch_length: 1000 + resume_from: ".pt" + + optimizer: + args: + lr: 5e-4 + +model: + sample_radius_3d: 0.5 + +downstream: + input_dim: 768 + mode: "3d" + # mlp_head: true diff --git a/configs/training/loss/scenedino.yaml b/configs/training/loss/scenedino.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a13cd618669146124574072ae0d21bfc6dc706ca --- /dev/null +++ b/configs/training/loss/scenedino.yaml @@ -0,0 +1,15 @@ +- type: reconstruction + coarse: + criterion: "l1+ssim" + dino_criterion: "cosine" + invalid_policy: weight_guided + + reconstruct_dino: true + lambda_dino_coarse: 0.2 + temperature_dino: 5 + + regularizations: + - type: edge_aware_smoothness + lambda: 0.001 + - type: dino_edge_aware_smoothness + lambda: 0.25 diff --git a/configs/training/loss/semantic.yaml b/configs/training/loss/semantic.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e1d1bb069ba123897ceeb0681d6f925209d7c192 --- /dev/null +++ b/configs/training/loss/semantic.yaml @@ -0,0 +1,10 @@ +- type: stego + + random_weight: 0.6702352279261414 + knn_weight: 0.4156436438453117 + self_weight: 0.08146997886146659 + random_shift: 0.8709334888837256 + knn_shift: 0.18458300726748128 + self_shift: 0.43610463774158115 + + pointwise: false diff --git a/configs/training/optimizer/scenedino.yaml b/configs/training/optimizer/scenedino.yaml new file mode 100644 index 0000000000000000000000000000000000000000..245cd7f214c7893cd43fc202e3ab0d7572974c2b --- /dev/null +++ b/configs/training/optimizer/scenedino.yaml @@ -0,0 +1,7 @@ +type: "adam" +args: + lr: 1e-4 + betas: [0.9, 0.999] + eps: 1e-08 + weight_decay: 0.0 + amsgrad: false \ No newline at end of file diff --git a/configs/training/optimizer/semantic.yaml b/configs/training/optimizer/semantic.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b3fd190c9f6e1e1919f2a7e19096ecb0c87e570c --- /dev/null +++ b/configs/training/optimizer/semantic.yaml @@ -0,0 +1,7 @@ +type: "adam" +args: + lr: 5e-4 + betas: [0.9, 0.999] + eps: 1e-08 + weight_decay: 0.0 + amsgrad: false \ No newline at end of file diff --git a/configs/training/scenedino.yaml b/configs/training/scenedino.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9d58538917acb28ff20163c6a1d512568404c5bb --- /dev/null +++ b/configs/training/scenedino.yaml @@ -0,0 +1,21 @@ +defaults: + - optimizer: scenedino + - scheduler: scenedino + - loss: scenedino + - _self_ + +num_epochs: 50 +continue: false + +checkpoint_every: 10000 +log_every_iters: 100 + +ray_sampler: + z_near: 3 + z_far: 80 + sample_mode: "patch" + args: + patch_size: 16 + ray_batch_size: 2048 + snap_to_grid: true + dino_upscaled: false \ No newline at end of file diff --git a/configs/training/scheduler/scenedino.yaml b/configs/training/scheduler/scenedino.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b8e32f6f4121044170cbaa868458596bebc10d3c --- /dev/null +++ b/configs/training/scheduler/scenedino.yaml @@ -0,0 +1,3 @@ +type: step +step_size: 100000 +gamma: 0.1 \ No newline at end of file diff --git a/configs/training/semantic.yaml b/configs/training/semantic.yaml new file mode 100644 index 0000000000000000000000000000000000000000..301ba9aa780f8555ad30a7b822d86c00ba126999 --- /dev/null +++ b/configs/training/semantic.yaml @@ -0,0 +1,16 @@ +defaults: + - optimizer: semantic + - loss: semantic + - _self_ + +num_epochs: 1 +epoch_length: 2500 +continue: false + +checkpoint_every: 5000 +log_every_iters: 250 + +ray_sampler: + z_near: 3 + z_far: 80 + sample_mode: "image" diff --git a/configs/validation/scenedino.yaml b/configs/validation/scenedino.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6bda218fc67f96a7cff32fb8f29d365feba64354 --- /dev/null +++ b/configs/validation/scenedino.yaml @@ -0,0 +1,78 @@ +validation: + metrics: + - type: depth + args: null + - type: dino + args: null + subset: + type: range + args: + start: 0 + end: 128 + save_best: + metric: dino_cos_sim + sign: 1 + log_loss: false + global_step: + type: "trainer iteration" + events: + # - type: STARTED + # args: null + - type: ITERATION_COMPLETED + args: + every: 5000 + # - type: EPOCH_COMPLETED + # args: + # every: 1 + - type: COMPLETED + args: null + +visualization: + metrics: + - type: depth + args: null + subset: + type: range + args: + start: 200 + end: 201 + visualize: + input_imgs: null + reconstructed_imgs: null + reconstruction_rmse: null + dino_gt: null + reconstructed_dino: null + reconstructed_dino_downsampled: null + batch_dino_gt: null + batch_dino_artifacts: null + batch_dino_features_kmeans: null + batch_dino_gt_kmeans: null + batch_reconstructed_dino: null + batch_reconstructed_dino_downsampled: null + dino_downsampling_salience: null + dino_downsampling_weight: null + dino_downsampling_per_patch_weight: null + dino_cos_sim_downsampled: null + depth: null + depth_profile: null + alpha_sum: null + ray_entropy: null + ray_entropy_weights: null + invalids: null + rendered_flow: null + predicted_occlusions: null + uncertainty: null + log_loss: false + global_step: + type: "trainer iteration" + events: + - type: STARTED + args: null + - type: ITERATION_COMPLETED + args: + every: 5000 + # - type: EPOCH_COMPLETED + # args: + # every: 1 + - type: COMPLETED + args: null \ No newline at end of file diff --git a/configs/validation/semantic.yaml b/configs/validation/semantic.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c39451f9310a8691fcd6582f0ff5e68bbc276f39 --- /dev/null +++ b/configs/validation/semantic.yaml @@ -0,0 +1,80 @@ +validation: + metrics: + - type: seg + agg_type: unsup_seg + args: + n_classes: 19 + gt_classes: 19 + - type: stego + agg_type: concat + subset: + type: random + args: + size: 32 + save_best: + metric: "stego_cluster_weighted_miou" + update_model: true + dry_run: false + log_loss: false + global_step: + type: "trainer iteration" + events: + # - type: STARTED + # args: null + - type: ITERATION_COMPLETED + args: + every: 100 + # - type: EPOCH_COMPLETED + # args: + # every: 1 + # - type: COMPLETED + # args: null + +visualization_seg: + metrics: {} + subset: + type: range + args: + start: 300 + end: 301 + visualize: + input_imgs: null + # reconstructed_imgs: null + # reconstruction_rmse: null + dino_gt: null + # reconstructed_dino: null + # reconstructed_dino_downsampled: null + batch_dino_gt: null + batch_dino_artifacts: null + segs_gt: null + segs_pred: null + batch_reconstructed_dino: null + batch_dino_features_kmeans: null + # batch_reconstructed_dino_downsampled: null + # dino_downsampling_salience: null + # dino_downsampling_weight: null + # dino_downsampling_per_patch_weight: null + # dino_cos_sim_downsampled: null, + depth: null + # depth_profile: null + # alpha_sum: null + # ray_entropy: null + # ray_entropy_weights: null + # invalids: null + # rendered_flow: null + # predicted_occlusions: null + # uncertainty: null + log_loss: false + global_step: + type: "trainer iteration" + events: + - type: STARTED + args: null + - type: ITERATION_COMPLETED + args: + every: 100 + # - type: EPOCH_COMPLETED + # args: + # every: 1 + # - type: COMPLETED + # args: null \ No newline at end of file diff --git a/datasets/__init__.py b/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/datasets/bdd/bdd_dataset.py b/datasets/bdd/bdd_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..2164637a633d58f475554cc0033015d15a22e2ce --- /dev/null +++ b/datasets/bdd/bdd_dataset.py @@ -0,0 +1,164 @@ + +import numpy as np +import time +import torch +import os + +from PIL import Image + +from torchvision import transforms +from torch.utils.data import Dataset + +from collections import namedtuple +from datasets.kitti_360.labels import trainId2label + + +Label = namedtuple( + "Label", + [ + "name", + "id", + "trainId", + "category", + "categoryId", + "hasInstances", + "ignoreInEval", + "color", + "to_cs27", + ], +) + +BDD_LABEL = [ + Label("unlabeled", 0, 255, "void", 0, False, True, (0, 0, 0), 255), + Label("dynamic", 1, 255, "void", 0, False, True, (111, 74, 0), 255), + Label("ego vehicle", 2, 255, "void", 0, False, True, (0, 0, 0), 255), + Label("ground", 3, 255, "void", 0, False, True, (81, 0, 81), 255), + Label("static", 4, 255, "void", 0, False, True, (0, 0, 0), 255), + Label("parking", 5, 255, "flat", 1, False, True, (250, 170, 160), 2), + Label("rail track", 6, 255, "flat", 1, False, True, (230, 150, 140), 3), + Label("road", 7, 0, "flat", 1, False, False, (128, 64, 128), 0), + Label("sidewalk", 8, 1, "flat", 1, False, False, (244, 35, 232), 1), + Label("bridge", 9, 255, "construction", 2, False, True, (150, 100, 100), 8), + Label("building", 10, 2, "construction", 2, False, False, (70, 70, 70), 4), + Label("fence", 11, 4, "construction", 2, False, False, (190, 153, 153), 6), + Label("garage", 12, 255, "construction", 2, False, True, (180, 100, 180), 255), + Label("guard rail", 13, 255, "construction", 2, False, True, (180, 165, 180), 7), + Label("tunnel", 14, 255, "construction", 2, False, True, (150, 120, 90), 9), + Label("wall", 15, 3, "construction", 2, False, False, (102, 102, 156), 5), + Label("banner", 16, 255, "object", 3, False, True, (250, 170, 100), 255), + Label("billboard", 17, 255, "object", 3, False, True, (220, 220, 250), 255), + Label("lane divider", 18, 255, "object", 3, False, True, (255, 165, 0), 255), + Label("parking sign", 19, 255, "object", 3, False, False, (220, 20, 60), 255), + Label("pole", 20, 5, "object", 3, False, False, (153, 153, 153), 10), + Label("polegroup", 21, 255, "object", 3, False, True, (153, 153, 153), 11), + Label("street light", 22, 255, "object", 3, False, True, (220, 220, 100), 255), + Label("traffic cone", 23, 255, "object", 3, False, True, (255, 70, 0), 255), + Label("traffic device", 24, 255, "object", 3, False, True, (220, 220, 220), 255), + Label("traffic light", 25, 6, "object", 3, False, False, (250, 170, 30), 12), + Label("traffic sign", 26, 7, "object", 3, False, False, (220, 220, 0), 13), + Label("traffic sign frame", 27, 255, "object", 3, False, True, (250, 170, 250), 255), + Label("terrain", 28, 9, "nature", 4, False, False, (152, 251, 152), 15), + Label("vegetation", 29, 8, "nature", 4, False, False, (107, 142, 35), 14), + Label("sky", 30, 10, "sky", 5, False, False, (70, 130, 180), 16), + Label("person", 31, 11, "human", 6, True, False, (220, 20, 60), 17), + Label("rider", 32, 12, "human", 6, True, False, (255, 0, 0), 18), + Label("bicycle", 33, 18, "vehicle", 7, True, False, (119, 11, 32), 26), + Label("bus", 34, 15, "vehicle", 7, True, False, (0, 60, 100), 21), + Label("car", 35, 13, "vehicle", 7, True, False, (0, 0, 142), 19), + Label("caravan", 36, 255, "vehicle", 7, True, True, (0, 0, 90), 22), + Label("motorcycle", 37, 17, "vehicle", 7, True, False, (0, 0, 230), 25), + Label("trailer", 38, 255, "vehicle", 7, True, True, (0, 0, 110), 23), + Label("train", 39, 16, "vehicle", 7, True, False, (0, 80, 100), 24), + Label("truck", 40, 14, "vehicle", 7, True, False, (0, 0, 70), 20), +] + + +def resize_with_padding(img, target_size, padding_value, interpolation): + target_h, target_w = target_size + width, height = img.size + aspect = width / height + + if aspect > (target_w / target_h): + new_w = target_w + new_h = int(target_w / aspect) + else: + new_h = target_h + new_w = int(target_h * aspect) + + img = transforms.functional.resize(img, (new_h, new_w), interpolation) + + pad_h = target_h - new_h + pad_w = target_w - new_w + padding = (pad_w // 2, pad_h // 2, pad_w - pad_w // 2, pad_h - pad_h // 2) + + return transforms.functional.pad(img, padding, fill=padding_value) + + +class BDDSeg(Dataset): + def __init__(self, root, image_set, image_size=(192, 640)): + super(BDDSeg, self).__init__() + self.split = image_set + self.root = root + + self.image_transform = transforms.Compose([ + #transforms.Lambda(lambda img: resize_with_padding(img, image_size, padding_value=0, interpolation=transforms.InterpolationMode.BILINEAR)), + + transforms.Resize((320, 640), interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop(image_size), + transforms.ToTensor(), + ]) + + self.target_transform = transforms.Compose([ + #transforms.Lambda(lambda img: resize_with_padding(img, image_size, padding_value=-1, interpolation=transforms.InterpolationMode.NEAREST)), + + transforms.Resize((320, 640), interpolation=transforms.InterpolationMode.NEAREST), + transforms.CenterCrop(image_size), + transforms.PILToTensor(), + transforms.Lambda(lambda x: x.long()), + ]) + + self.images, self.targets = [], [] + + image_dir = os.path.join(self.root, "images/10k", self.split) + target_dir = os.path.join(self.root, "labels/pan_seg/bitmasks", self.split) + for file_name in os.listdir(image_dir): + image_path = os.path.join(image_dir, file_name) + + target_filename = os.path.splitext(file_name)[0] + ".png" + target_path = os.path.join(target_dir, target_filename) + assert os.path.isfile(target_path) + + self.images.append(image_path) + self.targets.append(target_path) + + self.class_mapping = torch.Tensor([trainId2label[c.trainId].id for c in BDD_LABEL]).int() + + def __getitem__(self, index): + _start_time = time.time() + + image = Image.open(self.images[index]).convert("RGB") + target = Image.open(self.targets[index]) + + image = self.image_transform(image) + target = self.target_transform(target) + + image = 2.0 * image - 1.0 + poses = torch.eye(4) # (4, 4) + projs = torch.eye(3) # (3, 3) + target = target[0] # ("instance", "semantic", "polygon", "color") + target = self.class_mapping[target] + + _proc_time = time.time() - _start_time + + data = { + "imgs": [image.numpy()], + "poses": [poses.numpy()], + "projs": [projs.numpy()], + "segs": [target.numpy()], + "t__get_item__": np.array([_proc_time]), + "index": [np.array([index])], + } + return data + + def __len__(self): + return len(self.images) diff --git a/datasets/cityscapes/cityscapes_dataset.py b/datasets/cityscapes/cityscapes_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..7c8a2567a9064390ddcbfb79f2448c1cb25ad1a3 --- /dev/null +++ b/datasets/cityscapes/cityscapes_dataset.py @@ -0,0 +1,82 @@ + +import numpy as np +import time +import torch + +from torchvision import transforms +from torchvision.datasets.cityscapes import Cityscapes +from torch.utils.data import Dataset + + +def resize_with_padding(img, target_size, padding_value, interpolation): + target_h, target_w = target_size + width, height = img.size + aspect = width / height + + if aspect > (target_w / target_h): + new_w = target_w + new_h = int(target_w / aspect) + else: + new_h = target_h + new_w = int(target_h * aspect) + + img = transforms.functional.resize(img, (new_h, new_w), interpolation) + + pad_h = target_h - new_h + pad_w = target_w - new_w + padding = (pad_w // 2, pad_h // 2, pad_w - pad_w // 2, pad_h - pad_h // 2) + + return transforms.functional.pad(img, padding, fill=padding_value) + +class CityscapesSeg(Dataset): + def __init__(self, root, image_set, image_size=(192, 640)): + super(CityscapesSeg, self).__init__() + self.split = image_set + self.root = root + + transform = transforms.Compose([ + #transforms.Lambda(lambda img: resize_with_padding(img, image_size, padding_value=0, interpolation=transforms.InterpolationMode.BILINEAR)), + + transforms.Resize((320, 640), interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop(image_size), + transforms.ToTensor(), + ]) + + target_transform = transforms.Compose([ + #transforms.Lambda(lambda img: resize_with_padding(img, image_size, padding_value=-1, interpolation=transforms.InterpolationMode.NEAREST)), + + transforms.Resize((320, 640), interpolation=transforms.InterpolationMode.NEAREST), + transforms.CenterCrop(image_size), + transforms.PILToTensor(), + transforms.Lambda(lambda x: x.long()), + ]) + + self.inner_loader = Cityscapes(self.root, image_set, + mode="fine", + target_type="semantic", + transform=transform, + target_transform=target_transform) + + def __getitem__(self, index): + _start_time = time.time() + image, target = self.inner_loader[index] # (3, h, w) / (1, h, w) + + image = 2.0 * image - 1.0 + poses = torch.eye(4) # (4, 4) + projs = torch.eye(3) # (3, 3) + target = target.squeeze(0) # (h, w) + + _proc_time = time.time() - _start_time + + data = { + "imgs": [image.numpy()], + "poses": [poses.numpy()], + "projs": [projs.numpy()], + "segs": [target.numpy()], + "t__get_item__": np.array([_proc_time]), + "index": [np.array([index])], + } + return data + + def __len__(self): + return len(self.inner_loader) diff --git a/datasets/data_util.py b/datasets/data_util.py new file mode 100644 index 0000000000000000000000000000000000000000..cfbde5158c0091ae8d4f500b417c9a01c0cbfccb --- /dev/null +++ b/datasets/data_util.py @@ -0,0 +1,307 @@ +import os + +from datasets.kitti_360.kitti_360_dataset import Kitti360Dataset +from datasets.kitti_odom.kitti_odometry_dataset import KittiOdometryDataset +from datasets.kitti_raw.kitti_raw_dataset import KittiRawDataset +from datasets.nyu_depth_v2.nyu_depth_v2_dataset import NYUDepthV2Dataset +from datasets.realestate10k.realestate10k_dataset import RealEstate10kDataset +from datasets.waymo.waymo_dataset import WaymoDataset + + +def make_datasets(config): + type = config.get("type", "KITTI_Raw") + if type == "KITTI_Odometry": + train_dataset = KittiOdometryDataset( + base_path=config["data_path"], + frame_count=config.get("data_fc", 1), + target_image_size=config.get("image_size", (128, 256)), + return_stereo=config.get("data_stereo", False), + sequences=config.get("train_sequences", ("00",)), + custom_pose_path=config.get("custom_pose_path", None), + keyframe_offset=0 #-(config.get("data_fc", 1) // 2) + ) + test_dataset = KittiOdometryDataset( + base_path=config["data_path"], + frame_count=config.get("data_fc", 1), + target_image_size=config.get("image_size", (128, 256)), + return_stereo=config.get("data_stereo", False), + sequences=config.get("val_sequences", ("00",)), + custom_pose_path=config.get("custom_pose_path", None), + keyframe_offset=0 #-(config.get("data_fc", 1) // 2) + ) + return train_dataset, test_dataset + + elif type == "KITTI_Raw": + train_dataset = KittiRawDataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=os.path.join(config["split_path"], "train_files.txt"), + target_image_size=config.get("image_size", (192, 640)), + frame_count=config.get("data_fc", 1), + return_stereo=config.get("data_stereo", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + color_aug=config.get("color_aug", False) + ) + test_dataset = KittiRawDataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=os.path.join(config["split_path"], "val_files.txt"), + target_image_size=config.get("image_size", (192, 640)), + frame_count=config.get("data_fc", 1), + return_stereo=config.get("data_stereo", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + ) + return train_dataset, test_dataset + + elif type == "KITTI_360": + if config.get("split_path", None) is None: + train_split_path = None + test_split_path = None + else: + train_split_path = os.path.join(config["split_path"], "train_files.txt") + test_split_path = os.path.join(config["split_path"], "val_files.txt") + + train_dataset = Kitti360Dataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=train_split_path, + target_image_size=tuple(config.get("image_size", (192, 640))), + frame_count=config.get("data_fc", 3), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + return_3d_bboxes=config.get("data_3d_bboxes", False), + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=config.get("fisheye_offset", 1), + color_aug=config.get("color_aug", False), + is_preprocessed=config.get("is_preprocessed", False) + ) + test_dataset = Kitti360Dataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=test_split_path, + target_image_size=tuple(config.get("image_size", (192, 640))), + frame_count=config.get("data_fc", 3), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + return_3d_bboxes=config.get("data_3d_bboxes", False), + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=config.get("keyframe_offset", 0), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=config.get("fisheye_offset", 1), + dilation=config.get("dilation", 1), + is_preprocessed=config.get("is_preprocessed", False) + ) + return train_dataset, test_dataset + + elif type == "RealEstate10k": + train_dataset = RealEstate10kDataset( + data_path=config["data_path"], + split_path=None, + target_image_size=config.get("image_size", (256, 384)), + frame_count=config.get("data_fc", 2), + keyframe_offset=0, #-(config.get("data_fc", 1) // 2), + dilation=config.get("dilation", 10), + color_aug=config.get("color_aug", False) + ) + test_dataset = RealEstate10kDataset( + data_path=config["data_path"], + split_path=os.path.join(config["split_path"], "val_files.txt"), + target_image_size=config.get("image_size", (256, 384)), + frame_count=config.get("data_fc", 2), + keyframe_offset=0, #-(config.get("data_fc", 1) // 2), + dilation=config.get("dilation", 10), + color_aug=False + ) + return train_dataset, test_dataset + + elif type == "Waymo": + if config.get("split_path", None) is None: + train_split_path = None + test_split_path = None + else: + train_split_path = os.path.join(config["split_path"], "train_files.txt") + test_split_path = os.path.join(config["split_path"], "val_files.txt") + + train_dataset = WaymoDataset( + data_path=config["data_path"], + mode="training", + split_path=train_split_path, + target_image_size=tuple(config.get("image_size", (320, 480))), + frame_count=config.get("data_fc", 2), + keyframe_offset=config.get("keyframe_offset", 0), + return_45=config.get("return_45", True), + return_90=config.get("return_90", True), + offset_45=config.get("offset_45", 5), + offset_90=config.get("offset_90", 10), + dilation=config.get("dilation", 1), + color_aug=config.get("color_aug", True), + correct_exposure=config.get("correct_exposure", True), + ) + test_dataset = WaymoDataset( + data_path=config["data_path"], + mode="validation", + split_path=test_split_path, + target_image_size=tuple(config.get("image_size", (320, 480))), + frame_count=config.get("data_fc", 2), + keyframe_offset=config.get("keyframe_offset", 0), + return_45=config.get("return_45", True), + return_90=config.get("return_90", True), + offset_45=config.get("offset_45", 5), + offset_90=config.get("offset_90", 10), + dilation=config.get("dilation", 1), + color_aug=False, + return_depth=True, + correct_exposure=config.get("correct_exposure", True), + ) + return train_dataset, test_dataset + + elif type == "KITTI_Raw_DFT": + train_dataset = KittiRawDataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=os.path.join(config["split_path"], "train_files.txt"), + target_image_size=config.get("image_size", (192, 640)), + frame_count=config.get("data_fc", 1), + return_stereo=config.get("data_stereo", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + color_aug=config.get("color_aug", False) + ) + test_dataset = KittiRawDataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=os.path.join(config["split_path"], "val_files.txt"), + target_image_size=config.get("image_size", (192, 640)), + frame_count=config.get("data_fc", 1), + return_stereo=config.get("data_stereo", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + ) + return train_dataset, test_dataset + + elif type == "KITTI_360_DFT": + if config.get("split_path", None) is None: + train_split_path = None + test_split_path = None + else: + train_split_path = os.path.join(config["split_path"], "train_files.txt") + test_split_path = os.path.join(config["split_path"], "val_files.txt") + + train_dataset = Kitti360Dataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=train_split_path, + target_image_size=tuple(config.get("image_size", (192, 640))), + frame_count=config.get("data_fc", 3), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + return_3d_bboxes=config.get("data_3d_bboxes", False), + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=config.get("fisheye_offset", 1), + stereo_offset=config.get("stereo_offset", 1), + color_aug=config.get("color_aug", False), + is_preprocessed=config.get("is_preprocessed", False) + ) + test_dataset = Kitti360Dataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=test_split_path, + target_image_size=tuple(config.get("image_size", (192, 640))), + frame_count=config.get("data_fc", 3), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + return_3d_bboxes=config.get("data_3d_bboxes", False), + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=config.get("keyframe_offset", 0), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=config.get("fisheye_offset", [10])[0], ## this modifies the offsets for all datasets including the training dataset + stereo_offset= config.get("stereo_offset", [1])[0], ## This is to set consistent evaluation with test and viz + dilation=config.get("dilation", 1), + is_preprocessed=config.get("is_preprocessed", False) + ) + return train_dataset, test_dataset + + else: + raise NotImplementedError(f"Unsupported dataset type: {type}") + + +def make_test_dataset(config): + type = config.get("type", "KITTI_Raw") + if type == "KITTI_Raw": + test_dataset = KittiRawDataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=os.path.join(config["split_path"], "test_files.txt"), + target_image_size=config.get("image_size", (192, 640)), + return_depth=True, + frame_count=config.get("data_fc", 1), + return_stereo=config.get("data_stereo", False), + keyframe_offset=0 + ) + return test_dataset + elif type == "KITTI_360": + test_dataset = Kitti360Dataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=os.path.join(config.get("split_path", None), "test_files.txt"), + target_image_size=tuple(config.get("image_size", (192, 640))), + frame_count=config.get("data_fc", 1), + return_stereo=config.get("data_stereo", False), + return_fisheye=config.get("data_fisheye", False), + return_3d_bboxes=config.get("data_3d_bboxes", False), + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=0, + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=config.get("fisheye_offset", 1), + dilation=config.get("dilation", 1), + is_preprocessed=config.get("is_preprocessed", False) + ) + return test_dataset + elif type == "KITTI_360_DFT": + test_dataset = Kitti360Dataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=os.path.join(config.get("split_path", None), "test_files.txt"), + target_image_size=tuple(config.get("image_size", (192, 640))), + frame_count=config.get("data_fc", 1), + return_stereo=config.get("data_stereo", False), + return_fisheye=config.get("data_fisheye", False), + return_3d_bboxes=config.get("data_3d_bboxes", False), + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=0, + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=config.get("fisheye_offset", [10])[0], ## this modifies the offsets for all datasets including the training dataset + stereo_offset= config.get("stereo_offset", [1])[0], ## This is to set consistent evaluation with test and viz + dilation=config.get("dilation", 1), + is_preprocessed=config.get("is_preprocessed", False), + + return_depth=True + ) + return test_dataset + elif type == "RealEstate10k": + test_dataset = RealEstate10kDataset( + data_path=config["data_path"], + split_path=os.path.join(config["split_path"], "test_files.txt"), + target_image_size=config.get("image_size", (256, 384)), + frame_count=config.get("data_fc", 2), + keyframe_offset=0, + dilation=config.get("dilation", 10), + color_aug=False + ) + return test_dataset + elif type == "NYU_Depth_V2": + test_dataset = NYUDepthV2Dataset( + data_path=config["data_path"], + target_image_size=config.get("image_size", (256, 384)), + ) + return test_dataset + else: + raise NotImplementedError(f"Unsupported dataset type: {type}") \ No newline at end of file diff --git a/datasets/kitti_360/__init__.py b/datasets/kitti_360/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/datasets/kitti_360/annotation.py b/datasets/kitti_360/annotation.py new file mode 100644 index 0000000000000000000000000000000000000000..5c8d6ae88bec5c65e78faf9418d23f6eb1188bd1 --- /dev/null +++ b/datasets/kitti_360/annotation.py @@ -0,0 +1,538 @@ +#!/usr/bin/python +# + +from __future__ import print_function, absolute_import, division + +import glob +import json +import os +import struct +import xml.etree.ElementTree as ET +from collections import defaultdict +from collections import namedtuple + +import numpy as np +from matplotlib import cm +from skimage import io, filters + +# get current date and time + +# A point in a polygon +Point = namedtuple('Point', ['x', 'y']) + + +from abc import ABCMeta +from datasets.kitti_360.labels import labels, id2label, kittiId2label, name2label + +MAX_N = 1000 +def local2global(semanticId, instanceId): + globalId = semanticId*MAX_N + instanceId + if isinstance(globalId, np.ndarray): + return globalId.astype(np.int) + else: + return int(globalId) + +def global2local(globalId): + semanticId = globalId // MAX_N + instanceId = globalId % MAX_N + if isinstance(globalId, np.ndarray): + return semanticId.astype(np.int), instanceId.astype(np.int) + else: + return int(semanticId), int(instanceId) + +annotation2global = defaultdict() + +# Abstract base class for annotation objects +class KITTI360Object: + __metaclass__ = ABCMeta + + def __init__(self): + # the label + self.label = "" + + # colormap + self.cmap = cm.get_cmap('Set1') + self.cmap_length = 9 + + def getColor(self, idx): + if idx==0: + return np.array([0,0,0]) + return np.asarray(self.cmap(idx % self.cmap_length)[:3])*255. + + def assignColor(self): + if self.semanticId>=0: + self.semanticColor = id2label[self.semanticId].color + if self.instanceId>0: + self.instanceColor = self.getColor(self.instanceId) + else: + self.instanceColor = self.semanticColor + + +# Class that contains the information of a single annotated object as 3D bounding box +class KITTI360Bbox3D(KITTI360Object): + # Constructor + def __init__(self): + KITTI360Object.__init__(self) + # the polygon as list of points + self.vertices = [] + self.faces = [] + self.lines = [[0,5],[1,4],[2,7],[3,6], + [0,1],[1,3],[3,2],[2,0], + [4,5],[5,7],[7,6],[6,4]] + + # the ID of the corresponding object + self.semanticId = -1 + self.instanceId = -1 + self.annotationId = -1 + + # the window that contains the bbox + self.start_frame = -1 + self.end_frame = -1 + + # timestamp of the bbox (-1 if statis) + self.timestamp = -1 + + # projected vertices + self.vertices_proj = None + self.meshes = [] + + # name + self.name = '' + + def __str__(self): + return self.name + + def generateMeshes(self): + self.meshes = [] + if self.vertices_proj: + for fidx in range(self.faces.shape[0]): + self.meshes.append( [ Point(self.vertices_proj[0][int(x)], self.vertices_proj[1][int(x)]) for x in self.faces[fidx]] ) + + def parseOpencvMatrix(self, node): + rows = int(node.find('rows').text) + cols = int(node.find('cols').text) + data = node.find('data').text.split(' ') + + mat = [] + for d in data: + d = d.replace('\n', '') + if len(d)<1: + continue + mat.append(float(d)) + mat = np.reshape(mat, [rows, cols]) + return mat + + def parseVertices(self, child): + transform = self.parseOpencvMatrix(child.find('transform')) + R = transform[:3,:3] + T = transform[:3,3] + vertices = self.parseOpencvMatrix(child.find('vertices')) + faces = self.parseOpencvMatrix(child.find('faces')) + + vertices = np.matmul(R, vertices.transpose()).transpose() + T + self.vertices = vertices + self.faces = faces + self.R = R + self.T = T + + def parseBbox(self, child): + semanticIdKITTI = int(child.find('semanticId').text) + self.semanticId = kittiId2label[semanticIdKITTI].id + self.instanceId = int(child.find('instanceId').text) + self.name = kittiId2label[semanticIdKITTI].name + + self.start_frame = int(child.find('start_frame').text) + self.end_frame = int(child.find('end_frame').text) + + self.timestamp = int(child.find('timestamp').text) + + self.annotationId = int(child.find('index').text) + 1 + + global annotation2global + annotation2global[self.annotationId] = local2global(self.semanticId, self.instanceId) + self.parseVertices(child) + + def parseStuff(self, child): + classmap = {'driveway': 'parking', 'ground': 'terrain', 'unknownGround': 'ground', + 'railtrack': 'rail track', 'bigPole': 'pole', 'unknownObject': 'unknown object', + 'smallPole': 'smallpole', 'trafficSign': 'traffic sign', 'trashbin': 'trash bin', + 'guardrail': 'guard rail', 'trafficLight': 'traffic light', 'pedestrian': 'person', + 'vendingmachine': 'vending machine', 'unknownConstruction': 'unknown construction', + 'unknownVehicle': 'unknown vehicle'} + label = child.find('label').text + if label in classmap.keys(): + label = classmap[label] + + self.start_frame = int(child.find('start_frame').text) + self.end_frame = int(child.find('end_frame').text) + + self.timestamp = int(child.find('timestamp').text) + + self.semanticId = name2label[label].id + self.name = label + self.parseVertices(child) + +# Class that contains the information of the point cloud a single frame +class KITTI360Point3D(KITTI360Object): + # Constructor + def __init__(self): + KITTI360Object.__init__(self) + + self.vertices = [] + + self.vertices_proj = None + + # the ID of the corresponding object + self.semanticId = -1 + self.instanceId = -1 + self.annotationId = -1 + + # name + self.name = '' + + # color + self.semanticColor = None + self.instanceColor = None + + def __str__(self): + return self.name + + + def generateMeshes(self): + pass + + +# The annotation of a whole image, including semantic and instance +class Annotation2D: + # Constructor + def __init__(self, colormap='Set1'): + # the width of that image and thus of the label image + self.imgWidth = 0 + # the height of that image and thus of the label image + self.imgHeight = 0 + + self.instanceId = None + self.semanticId = None + self.instanceImg = None + self.semanticImg = None + + # savedId = semanticId*N + instanceId + self.N = 1000 + + # colormap + self.cmap = cm.get_cmap(colormap) + + if colormap == 'Set1': + self.cmap_length = 9 + else: + raise "Colormap length need to be specified!" + + def getColor(self, idx): + if idx==0: + return np.array([0,0,0]) + return np.asarray(self.cmap(idx % self.cmap_length)[:3])*255. + + # Load confidence map + def loadConfidence(self, imgPath): + self.confidenceMap = io.imread(imgPath) + self.confidenceMap = np.asarray(self.confidenceMap).astype(np.float)/255. + + # Load instance id + def loadInstance(self, imgPath, gtType='instance', toImg=True, contourType='instance', semanticCt=True, instanceCt=True): + instanceId = io.imread(imgPath) + self.instanceId = np.asarray( instanceId % self.N ) + self.semanticId = np.asarray( instanceId // self.N ) + + if not toImg: + return + + if gtType=='semantic': + self.toSemanticImage() + + elif gtType=='instance': + self.toInstanceImage() + + if semanticCt or instanceCt: + self.getBoundary() + + if gtType=='semantic' and semanticCt: + boundaryImg = self.toBoundaryImage(contourType=contourType, instanceOnly=False) + self.semanticImg = self.semanticImg * (1-boundaryImg) + \ + np.ones_like(self.semanticImg) * boundaryImg * 255 + + if gtType=='instance' and instanceCt: + boundaryImg = self.toBoundaryImage(contourType=contourType, instanceOnly=True) + self.instanceImg = self.instanceImg * (1-boundaryImg) + \ + np.ones_like(self.instanceImg) * boundaryImg * 255 + + + def toSemanticImage(self): + self.semanticImg = np.zeros((self.semanticId.size, 3)) + for label in labels: + mask = self.semanticId==label.id + mask = mask.flatten() + self.semanticImg[mask] = np.asarray(label.color) + self.semanticImg = self.semanticImg.reshape(*self.semanticId.shape, 3) + + def toInstanceImage(self): + self.instanceImg = np.zeros((self.instanceId.size, 3)) + + uniqueId = np.unique(self.instanceId) + for uid in uniqueId: + mask = self.instanceId==uid + mask = mask.flatten() + self.instanceImg[mask] = np.asarray(self.getColor(uid)) + + self.instanceImg = self.instanceImg.reshape(*self.instanceId.shape, 3) + + def getBoundary(self): + # semantic contours + uniqueId = np.unique(self.semanticId) + self.semanticContours = {} + for uid in uniqueId: + mask = (self.semanticId==uid).astype(np.uint8) * 255 + mask_filter = filters.laplace(mask) + self.semanticContours[uid] = np.expand_dims(np.abs(mask_filter)>0, 2) + + # instance contours + globalId = local2global(self.semanticId, self.instanceId) + uniqueId = np.unique(globalId) + self.instanceContours = {} + for uid in uniqueId: + mask = (globalId==uid).astype(np.uint8) * 255 + mask_filter = filters.laplace(mask) + self.instanceContours[uid] = np.expand_dims(np.abs(mask_filter)>0, 2) + + def toBoundaryImage(self, contourType='instance', instanceOnly=True): + if contourType=='semantic': + contours = self.semanticContours + assert(instanceOnly==False) + elif contourType=='instance': + contours = self.instanceContours + else: + raise ("Contour type can only be 'semantic' or 'instance'!") + + if not instanceOnly: + boundaryImg = [contours[k] for k in contours.keys()] + else: + boundaryImg = [contours[k] for k in contours.keys() if global2local(k)[1]!=0] + boundaryImg = np.sum(np.asarray(boundaryImg), axis=0) + boundaryImg = boundaryImg>0 + return boundaryImg + + +class Annotation2DInstance: + def __init__(self, gtPath, cam=0): + + # trace the instances in all images + self.instanceDict = defaultdict(list) + + # + instanceDictCached = os.path.join(gtPath, 'instanceDict.json') + print(instanceDictCached) + if os.path.isfile(instanceDictCached) and os.path.getsize(instanceDictCached)>0: + cachedDict = json.load( open(instanceDictCached) ) + for k,v in cachedDict.items(): + self.instanceDict[int(k)] = v + return + + obj = Annotation2D() + + gtPaths = glob.glob( os.path.join(gtPath, 'instance', '*.png') ) + print (f'Found {len(gtPaths)} label images...') + + for i,imgPath in enumerate(gtPaths): + if i%1000==0: + print(f'Processed {i}/{len(gtPaths)} label images...') + obj.loadInstance(imgPath, toImg=False) + globalId = local2global(obj.semanticId, obj.instanceId) + globalIdUnique = np.unique(globalId) + for idx in globalIdUnique: + self.instanceDict[int(idx)].append(os.path.basename(imgPath)) + + json.dump( self.instanceDict, open(instanceDictCached, 'w')) + + # returns the paths that contains the specific instance + def __call__(self, semanticId, instanceId): + globalId = local2global(semanticId, instanceId) + return self.instanceDict[globalId] + +# Meta class for KITTI360Bbox3D +class Annotation3D: + # Constructor + def __init__(self, labelDir='', sequence=''): + + labelPath = glob.glob(os.path.join(labelDir, '*', '%s.xml' % sequence)) # train or test + if len(labelPath)!=1: + raise RuntimeError('%s does not exist! Please specify KITTI360_DATASET in your environment path.' % labelPath) + else: + labelPath = labelPath[0] + print('Loading %s...' % labelPath) + + self.init_instance(labelPath) + + def init_instance(self, labelPath): + # load annotation + tree = ET.parse(labelPath) + root = tree.getroot() + + self.objects = defaultdict(dict) + + self.num_bbox = 0 + + for child in root: + if child.find('transform') is None: + continue + obj = KITTI360Bbox3D() + obj.parseBbox(child) + globalId = local2global(obj.semanticId, obj.instanceId) + self.objects[globalId][obj.timestamp] = obj + self.num_bbox+=1 + + globalIds = np.asarray(list(self.objects.keys())) + semanticIds, instanceIds = global2local(globalIds) + for label in labels: + if label.hasInstances: + print(f'{label.name:<30}:\t {(semanticIds==label.id).sum()}') + print(f'Loaded {len(globalIds)} instances') + print(f'Loaded {self.num_bbox} boxes') + + + def __call__(self, semanticId, instanceId, timestamp=None): + globalId = local2global(semanticId, instanceId) + if globalId in self.objects.keys(): + # static object + if len(self.objects[globalId].keys())==1: + if -1 in self.objects[globalId].keys(): + return self.objects[globalId][-1] + else: + return None + # dynamic object + else: + return self.objects[globalId][timestamp] + else: + return None + +class Annotation3DPly: + # parse fused 3D point cloud + def __init__(self, labelDir='', sequence='', isLabeled=True, isDynamic=False, showStatic=True): + + if isLabeled and not isDynamic: + # x y z r g b semanticId instanceId isVisible confidence + self.fmt = '=fffBBBiiBf' + self.fmt_len = 28 + elif isLabeled and isDynamic: + # x y z r g b semanticId instanceId isVisible timestamp confidence + self.fmt = '=fffBBBiiBif' + self.fmt_len = 32 + elif not isLabeled and not isDynamic: + # x y z r g b + self.fmt = '=fffBBBB' + self.fmt_len = 16 + else: + raise RuntimeError('Invalid binary format!') + + # True for training data, False for testing data + self.isLabeled = isLabeled + # True for dynamic data, False for static data + self.isDynamic = isDynamic + # True for inspecting static data, False for inspecting dynamic data + self.showStatic = showStatic + + pcdFolder = 'static' if self.showStatic else 'dynamic' + trainTestDir = 'train' if self.isLabeled else 'test' + self.pcdFileList = sorted(glob.glob(os.path.join(labelDir, trainTestDir, sequence, pcdFolder, '*.ply'))) + + print('Found %d ply files in %s' % (len(self.pcdFileList), sequence)) + + def readBinaryPly(self, pcdFile, n_pts=None): + + with open(pcdFile, 'rb') as f: + plyData = f.readlines() + + headLine = plyData.index(b'end_header\n')+1 + plyData = plyData[headLine:] + plyData = b"".join(plyData) + + n_pts_loaded = len(plyData)/self.fmt_len + # sanity check + if n_pts: + assert(n_pts_loaded==n_pts) + n_pts_loaded = int(n_pts_loaded) + + data = [] + for i in range(n_pts_loaded): + pts=struct.unpack(self.fmt, plyData[i*self.fmt_len:(i+1)*self.fmt_len]) + data.append(pts) + data=np.asarray(data) + + return data + + def writeBinaryPly(self, pcdFile, data): + fmt = '=fffBBBiiB' + fmt_len = 24 + n_pts = data.shape[0] + + with open(pcdFile, 'wb') as f: + f.write(b'ply\n') + f.write(b'format binary_little_endian 1.0\n') + f.write(b'comment author Yiyi Liao\n') + f.write(b'element vertex %d\n' % n_pts) + f.write(b'property float x\n') + f.write(b'property float y\n') + f.write(b'property float z\n') + f.write(b'property uchar red\n') + f.write(b'property uchar green\n') + f.write(b'property uchar blue\n') + f.write(b'property int semantic\n') + + +class Annotation3DInstance(object): + instance_id = 0 + labelId = 0 + vert_count = 0 + med_dist = -1 + dist_conf = 0.0 + + def __init__(self, mesh_vert_instances, instance_id): + if (instance_id == -1): + return + self.instance_id = int(instance_id) + self.labelId = int(self.get_labelId(instance_id)) + self.vert_count = int(self.get_instance_verts(mesh_vert_instances, instance_id)) + + def get_labelId(self, instance_id): + return int(instance_id // 1000) + + def get_instance_verts(self, mesh_vert_instances, instance_id): + return (mesh_vert_instances == instance_id).sum() + + def to_json(self): + return json.dumps(self, default=lambda o: o.__dict__, sort_keys=True, indent=4) + + def to_dict(self): + dict = {} + dict["instance_id"] = self.instance_id + dict["labelId"] = self.labelId + dict["vert_count"] = self.vert_count + dict["med_dist"] = self.med_dist + dict["dist_conf"] = self.dist_conf + return dict + + def from_json(self, data): + self.instance_id = int(data["instance_id"]) + self.labelId = int(data["labelId"]) + self.vert_count = int(data["vert_count"]) + if ("med_dist" in data): + self.med_dist = float(data["med_dist"]) + self.dist_conf = float(data["dist_conf"]) + + def __str__(self): + return "("+str(self.instance_id)+")" + +# a dummy example +if __name__ == "__main__": + + ann = Annotation3D() + + diff --git a/datasets/kitti_360/compute_kitti_360_bbox_split.py b/datasets/kitti_360/compute_kitti_360_bbox_split.py new file mode 100644 index 0000000000000000000000000000000000000000..d9326f0c6c5b83376a37724e84b5cf1a71d2bfd4 --- /dev/null +++ b/datasets/kitti_360/compute_kitti_360_bbox_split.py @@ -0,0 +1,110 @@ +import argparse + +from pathlib import Path + +DRY_RUN = False + +CUT = [7, 9, 10, 15, 19, 31, 43, 69, 87, 107, 118, 154, 156, 167, 168, 170, 171, 172, 173, 174, 178, 179, 180, 181, + 182, 183, 184, 185, 187, 188, 193, 194, 195, 196, 201, 202, 203, 209, 210, 212, 213, 214, 215, 216, 217, 218, + 219, 220, 221, 222, 224, 225, 226, 229, 230, 231, 234, 235, 236, 237, 238, 256, 257, 258, 267, 278, 283, 293, + 294, 295, 296, 297, 298, 299, 310, 315, 317, 318, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, + 334, 335, 336, 337, 340, 341, 349, 353, 354, 361, 362, 365, 366, 368, 371, 372, 376, 380, 386, 387, 394, 402, + 403, 404, 411, 414, 415, 416, 420, 438, 441, 448, 452, 456, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, + 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 519, 520, 554, 562, 593, 594, + 596] + + +def check_integrity(data_path, seq, img_id): + persp = data_path / "data_2d_raw" / seq / "image_00" / "data_rect" / f"{img_id:010d}.png" + fish = data_path / "data_2d_raw" / seq / "image_02" / "data_rgb" / f"{img_id:010d}.png" + + return fish.exists() and persp.exists() + + +def main(): + parser = argparse.ArgumentParser("KITTI Raw NVS Split") + parser.add_argument("--data_path", "-d", type=str) + parser.add_argument("--out_path", "-o", type=str) + parser.add_argument("--offset", type=int, default=20) + + args = parser.parse_args() + + data_path = Path(args.data_path) + out_path = Path(args.out_path) + offset = args.offset + + print("Setting up folders...") + Path(out_path).mkdir(parents=True, exist_ok=True) + + segmentation_train_file = data_path / "data_2d_semantics" / "train" / "2013_05_28_drive_train_frames.txt" + segmentation_val_file = data_path / "data_2d_semantics" / "train" / "2013_05_28_drive_val_frames.txt" + + with open(segmentation_train_file, "r") as f: + train_lines = f.readlines() + with open(segmentation_val_file, "r") as f: + val_lines = f.readlines() + + train_files = [] + val_files = [] + test_files = [] + + invalid = 0 + + for i in range(len(train_lines)): + parts = train_lines[i].split(" ") + img_path = parts[0] + + parts = img_path.split("/") + sequence = parts[1] + img_id = int(parts[-1][-14:-4]) + + if not check_integrity(data_path, sequence, img_id): + invalid += 1 + continue + + train_files.append(f"{sequence} {img_id:010d} l") + train_files.append(f"{sequence} {img_id:010d} r") + + for i in range(0, len(val_lines)): + parts = val_lines[i].split(" ") + img_path = parts[0] + seg_path = parts[1][:-1] + + parts = img_path.split("/") + sequence = parts[1] + img_id = int(parts[-1][-14:-4]) + + is_test = (i % offset) == 0 + + if not check_integrity(data_path, sequence, img_id): + invalid += 1 + continue + + if not is_test: + val_files.append(f"{sequence} {img_id:010d} l") + else: + test_files.append(f"{sequence} {img_id:010d} l") + + print(f"Found: Train={len(train_files)}, Val={len(val_files)}, Test={len(test_files)} test files.") + print(f"Found: {invalid} invalids.") + + test_files = [s for i, s in enumerate(test_files) if not i in CUT] + + print(f"{len(CUT)} test files removed. {len(test_files)} remaining.") + + train_file = out_path / f"train_files.txt" + val_file = out_path / f"val_files.txt" + test_file = out_path / f"test_files.txt" + + with open(train_file, "w") as f: + f.writelines("\n".join(train_files)) + + with open(val_file, "w") as f: + f.writelines("\n".join(val_files)) + + with open(test_file, "w") as f: + f.writelines("\n".join(test_files)) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/datasets/kitti_360/kitti_360_dataset.py b/datasets/kitti_360/kitti_360_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..89a8af7ee1beaf8fd165b5f7665c600cc02e3f30 --- /dev/null +++ b/datasets/kitti_360/kitti_360_dataset.py @@ -0,0 +1,1263 @@ +import os +import time +import xml.etree.ElementTree as ET +from collections import Counter, defaultdict +from pathlib import Path +from typing import Optional + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +import yaml +from scipy.spatial.transform import Rotation +from torch.utils.data import Dataset +from torchvision.transforms import ColorJitter + +from datasets.kitti_360.annotation import KITTI360Bbox3D +from scenedino.common.augmentation import get_color_aug_fn + +import omegaconf + + +class FisheyeToPinholeSampler: + def __init__(self, K_target, target_image_size, calibs, rotation=None): + self._compute_transform(K_target, target_image_size, calibs, rotation) + + def _compute_transform(self, K_target, target_image_size, calibs, rotation=None): + x = ( + torch.linspace(-1, 1, target_image_size[1]) + .view(1, -1) + .expand(target_image_size) + ) + y = ( + torch.linspace(-1, 1, target_image_size[0]) + .view(-1, 1) + .expand(target_image_size) + ) + z = torch.ones_like(x) + xyz = torch.stack((x, y, z), dim=-1).view(-1, 3) + + # Unproject + xyz = (torch.inverse(torch.tensor(K_target)) @ xyz.T).T + + if rotation is not None: + xyz = (torch.tensor(rotation) @ xyz.T).T + + # Backproject into fisheye + xyz = xyz / torch.norm(xyz, dim=-1, keepdim=True) + x = xyz[:, 0] + y = xyz[:, 1] + z = xyz[:, 2] + + xi_src = calibs["mirror_parameters"]["xi"] + x = x / (z + xi_src) + y = y / (z + xi_src) + + k1 = calibs["distortion_parameters"]["k1"] + k2 = calibs["distortion_parameters"]["k2"] + + r = x * x + y * y + factor = 1 + k1 * r + k2 * r * r + x = x * factor + y = y * factor + + gamma0 = calibs["projection_parameters"]["gamma1"] + gamma1 = calibs["projection_parameters"]["gamma2"] + u0 = calibs["projection_parameters"]["u0"] + v0 = calibs["projection_parameters"]["v0"] + + x = x * gamma0 + u0 + y = y * gamma1 + v0 + + xy = torch.stack((x, y), dim=-1).view(1, *target_image_size, 2) + self.sample_pts = xy + + def resample(self, img): + img = img.unsqueeze(0) + resampled_img = F.grid_sample(img, self.sample_pts, align_corners=True).squeeze( + 0 + ) + return resampled_img + + +# TODO: probably move to KITTI-360 dataset +# The KITTI 360 cameras have a 5 degrees negative inclination. We need to account for that. +cam_incl_adjust = torch.tensor( + [ + [1.0000000, 0.0000000, 0.0000000, 0], + [0.0000000, 0.9961947, 0.0871557, 0], + [0.0000000, -0.0871557, 0.9961947, 0], + [0.0000000, 000000000, 0.0000000, 1], + ], + dtype=torch.float32, +).view(1, 1, 4, 4) + + +def get_pts(x_range, y_range, z_range, ppm, ppm_y, y_res=None): ## ppm:=pts_per_meter + x_res = abs(int((x_range[1] - x_range[0]) * ppm)) + if y_res is None: + y_res = abs(int((y_range[1] - y_range[0]) * ppm_y)) + z_res = abs(int((z_range[1] - z_range[0]) * ppm)) + x = ( + torch.linspace(x_range[0], x_range[1], x_res) + .view(1, 1, x_res) + .expand(y_res, z_res, -1) + ) + z = ( + torch.linspace(z_range[0], z_range[1], z_res) + .view(1, z_res, 1) + .expand(y_res, -1, x_res) + ) + if y_res == 1: + y = ( + torch.tensor([y_range[0] * 0.5 + y_range[1] * 0.5]) + .view(y_res, 1, 1) + .expand(-1, z_res, x_res) + ) + else: + y = ( + torch.linspace(y_range[0], y_range[1], y_res) + .view(y_res, 1, 1) + .expand(-1, z_res, x_res) + ) + xyz = torch.stack((x, y, z), dim=-1) + + return xyz, (x_res, y_res, z_res) + + +# This function takes all points between min_y and max_y and projects them into the x-z plane. +# To avoid cases where there are no points at the top end, we consider also points that are beyond the maximum z distance. +# The points are then converted to polar coordinates and sorted by angle. + + +def get_lidar_slices(point_clouds, velo_poses, y_range, y_res, max_dist): + slices = [] + ys = torch.linspace(y_range[0], y_range[1], y_res) + if y_res > 1: + slice_height = ys[1] - ys[0] + else: + slice_height = 0 + n_bins = 360 + + for y in ys: + if y_res == 1: + min_y = y + max_y = y_range[-1] + else: + min_y = y - slice_height / 2 + max_y = y + slice_height / 2 + + slice = [] + + for pc, velo_pose in zip(point_clouds, velo_poses): + pc_world = (velo_pose @ pc.T).T + + mask = ((pc_world[:, 1] >= min_y) & (pc_world[:, 1] <= max_y)) | ( + torch.norm(pc_world[:, :3], dim=-1) >= max_dist + ) + + slice_points = pc[mask, :2] + + angles = torch.atan2(slice_points[:, 1], slice_points[:, 0]) + dists = torch.norm(slice_points, dim=-1) + + slice_points_polar = torch.stack((angles, dists), dim=1) + # Sort by angles for fast lookup + slice_points_polar = slice_points_polar[torch.sort(angles)[1], :] + + slice_points_polar_binned = torch.zeros_like(slice_points_polar[:n_bins, :]) + bin_borders = torch.linspace( + -math.pi, math.pi, n_bins + 1, device=slice_points_polar.device + ) + + dist = slice_points_polar[0, 1] + + # To reduce noise, we bin the lidar points into bins of 1deg and then take the minimum distance per bin. + border_is = torch.searchsorted(slice_points_polar[:, 0], bin_borders) + + for i in range(n_bins): + left_i, right_i = border_is[i], border_is[i + 1] + angle = (bin_borders[i] + bin_borders[i + 1]) * 0.5 + if right_i > left_i: + dist = torch.min(slice_points_polar[left_i:right_i, 1]) + slice_points_polar_binned[i, 0] = angle + slice_points_polar_binned[i, 1] = dist + + slice_points_polar = slice_points_polar_binned + + # Append first element to last to have full 360deg coverage + slice_points_polar = torch.cat( + ( + torch.tensor( + [ + [ + slice_points_polar[-1, 0] - math.pi * 2, + slice_points_polar[-1, 1], + ] + ], + device=slice_points_polar.device, + ), + slice_points_polar, + torch.tensor( + [ + [ + slice_points_polar[0, 0] + math.pi * 2, + slice_points_polar[0, 1], + ] + ], + device=slice_points_polar.device, + ), + ), + dim=0, + ) + + slice.append(slice_points_polar) + + slices.append(slice) + + return slices + + +def check_occupancy(pts, slices, velo_poses, min_dist=3): + is_occupied = torch.ones_like(pts[:, 0]) + is_visible = torch.zeros_like(pts[:, 0], dtype=torch.bool) + + thresh = (len(slices[0]) - 2) / len(slices[0]) + + pts = torch.cat((pts, torch.ones_like(pts[:, :1])), dim=-1) + + world_to_velos = torch.inverse(velo_poses) + + step = pts.shape[0] // len(slices) + + for i, slice in enumerate(slices): + for j, (lidar_polar, world_to_velo) in enumerate(zip(slice, world_to_velos)): + pts_velo = (world_to_velo @ pts[i * step : (i + 1) * step, :].T).T + + # Convert query points to polar coordinates in velo space + angles = torch.atan2(pts_velo[:, 1], pts_velo[:, 0]) + dists = torch.norm(pts_velo, dim=-1) + + indices = torch.searchsorted(lidar_polar[:, 0].contiguous(), angles) + + left_angles = lidar_polar[indices - 1, 0] + right_angles = lidar_polar[indices, 0] + + left_dists = lidar_polar[indices - 1, 1] + right_dists = lidar_polar[indices, 1] + + interp = (angles - left_angles) / (right_angles - left_angles) + surface_dist = left_dists * (1 - interp) + right_dists * interp + + is_occupied_velo = (dists > surface_dist) | (dists < min_dist) + + is_occupied[i * step : (i + 1) * step] += is_occupied_velo.float() + + if j == 0: + is_visible[i * step : (i + 1) * step] |= ~is_occupied_velo + + is_occupied /= len(slices[0]) + + is_occupied = is_occupied > thresh + + return is_occupied, is_visible + + +class KITTIVelodyn: + def __init__(self, config) -> None: + self.config = config + self.occ_pts, self.yd = self._gen_pts() + + def _gen_pts(self) -> torch.Tensor: + q_pts, (xd, yd, zd) = get_pts( + self.x_range, self.y_range, self.z_range, self.ppm, self.ppm_y, self.y_res + ) + return q_pts, yd + + def check_occupancy(self, points_all, velo_poses): + slices = get_lidar_slices( + points_all, + velo_poses, + self.config["y_range"], + self.yd, + (self.self.config["z_range"][0] ** 2 + self.self.config["x_range"][0] ** 2) + ** 0.5, + ) + is_occupied, is_visible = check_occupancy(self.occ_pts, slices, velo_poses) + + return is_occupied, is_visible + + +class Kitti360Dataset(Dataset): + def __init__( + self, + data_path: str, + pose_path: str, + split_path: Optional[str], + target_image_size=(192, 640), + return_stereo=False, + return_depth=False, + return_fisheye=True, ## default: True + return_3d_bboxes=False, + return_segmentation=False, + frame_count=2, + keyframe_offset=0, + dilation=1, + fisheye_rotation=0, + fisheye_offset=0, + stereo_offset=0, + eigen_depth=True, + color_aug=False, + is_preprocessed=False, + kitti_velodyn: KITTIVelodyn | None = None, + ): + self.data_path = data_path + self.pose_path = pose_path + self.split_path = split_path + self.target_image_size = target_image_size + self.return_stereo = return_stereo + self.return_fisheye = return_fisheye + self.return_depth = return_depth + self.return_3d_bboxes = return_3d_bboxes + self.return_segmentation = return_segmentation + self.frame_count = frame_count + self.dilation = dilation + self.fisheye_rotation = fisheye_rotation + self.fisheye_offset = fisheye_offset + self.stereo_offset = stereo_offset + self.keyframe_offset = keyframe_offset + self.eigen_depth = eigen_depth + self.color_aug = color_aug + self.is_preprocessed = is_preprocessed + self.kitti_velodyn = kitti_velodyn + + if isinstance(self.fisheye_rotation, float) or isinstance( + self.fisheye_rotation, int + ): + self.fisheye_rotation = (0, self.fisheye_rotation) + self.fisheye_rotation = tuple(self.fisheye_rotation) + + # if additional_random_front_offset and not self.random_fisheye_offset: + # raise ValueError("Random Fisheye Offset needs to be active for additional random front offset!") + # else: + # self.additional_random_front_offset = additional_random_front_offset + + # Support random fisheye offset + if type(self.fisheye_offset) == int: + self.random_fisheye_offset = False + self.fisheye_offset = (self.fisheye_offset,) + elif type(self.fisheye_offset) in [ + tuple, + list, + omegaconf.listconfig.ListConfig, + ]: + self.random_fisheye_offset = True + self.fisheye_offset = tuple(sorted(self.fisheye_offset)) + else: + raise ValueError( + f"Invalid datatype for fisheye offset: {type(self.fisheye_offset)}" + ) + + if type(self.stereo_offset) == int: + self.random_stereo_offset = False + self.stereo_offset = (self.stereo_offset,) + elif type(self.stereo_offset) in [tuple, list, omegaconf.listconfig.ListConfig]: + self.random_stereo_offset = True + self.stereo_offset = tuple(sorted(self.stereo_offset)) + else: + raise ValueError( + f"Invalid datatype for fisheye offset: {type(self.stereo_offset)}" + ) + + self._sequences = self._get_sequences(self.data_path) + + self._calibs = self._load_calibs(self.data_path, self.fisheye_rotation) + self._resampler_02, self._resampler_03 = self._get_resamplers( + self._calibs, self._calibs["K_fisheye"], self.target_image_size + ) + self._img_ids, self._poses = self._load_poses(self.pose_path, self._sequences) + self._left_offset = ( + (self.frame_count - 1) // 2 + self.keyframe_offset + ) * self.dilation + + self._perspective_folder = ( + "data_rect" + if not self.is_preprocessed + else f"data_{self.target_image_size[0]}x{self.target_image_size[1]}" + ) + self._fisheye_folder = ( + "data_rgb" + if not self.is_preprocessed + else f"data_{self.target_image_size[0]}x{self.target_image_size[1]}_{self.fisheye_rotation[0]}x{self.fisheye_rotation[1]}" + ) + + if self.split_path is not None: + self._datapoints = self._load_split(self.split_path, self._img_ids) + elif self.return_segmentation: + self._datapoints = self._semantics_split( + self._sequences, self.data_path, self._img_ids + ) + else: + self._datapoints = self._full_split( + self._sequences, self._img_ids, self.check_file_integrity + ) + + if self.return_3d_bboxes: + self._3d_bboxes = self._load_3d_bboxes( + Path(data_path) / "data_3d_bboxes" / "train_full", self._sequences + ) + + if self.return_segmentation: + # Segmentations are only provided for the left camera + self._datapoints = [dp for dp in self._datapoints if not dp[2]] + + self._skip = 0 + self.length = len(self._datapoints) + + def check_file_integrity(self, seq, id): + dp = Path(self.data_path) + image_00 = dp / "data_2d_raw" / seq / "image_00" / self._perspective_folder + image_01 = dp / "data_2d_raw" / seq / "image_01" / self._perspective_folder + image_02 = dp / "data_2d_raw" / seq / "image_02" / self._fisheye_folder + image_03 = dp / "data_2d_raw" / seq / "image_03" / self._fisheye_folder + + seq_len = self._img_ids[seq].shape[0] + + ids = [id] + [ + max(min(i, seq_len - 1), 0) + for i in range( + id - self._left_offset, + id - self._left_offset + self.frame_count * self.dilation, + self.dilation, + ) + if i != id + ] + ids_fish = [max(min(id + self.fisheye_offset, seq_len - 1), 0)] + [ + max(min(i, seq_len - 1), 0) + for i in range( + id + self.fisheye_offset - self._left_offset, + id + + self.fisheye_offset + - self._left_offset + + self.frame_count * self.dilation, + self.dilation, + ) + if i != id + self.fisheye_offset + ] + + img_ids = [self.get_img_id_from_id(seq, id) for id in ids] + img_ids_fish = [self.get_img_id_from_id(seq, id) for id in ids_fish] + + for img_id in img_ids: + if not ( + (image_00 / f"{img_id:010d}.png").exists() + and (image_01 / f"{img_id:010d}.png").exists() + ): + return False + if self.return_fisheye: + for img_id in img_ids_fish: + if not ( + (image_02 / f"{img_id:010d}.png").exists() + and (image_03 / f"{img_id:010d}.png").exists() + ): + return False + return True + + @staticmethod + def _get_sequences(data_path): + all_sequences = [] + + seqs_path = Path(data_path) / "data_2d_raw" + for seq in seqs_path.iterdir(): + if not seq.is_dir(): + continue + all_sequences.append(seq.name) + + return all_sequences + + @staticmethod + def _full_split(sequences, img_ids, check_integrity): + datapoints = [] + for seq in sorted(sequences): + ids = [id for id in range(len(img_ids[seq])) if check_integrity(seq, id)] + datapoints_seq = [(seq, id, False) for id in ids] + [ + (seq, id, True) for id in ids + ] + datapoints.extend(datapoints_seq) + return datapoints + + @staticmethod + def _semantics_split(sequences, data_path, img_ids): + datapoints = [] + for seq in sorted(sequences): + datapoints_seq = [(seq, id, False) for id in range(len(img_ids[seq]))] + datapoints_seq = [ + dp + for dp in datapoints_seq + if os.path.exists( + os.path.join( + data_path, + "data_2d_semantics", + "train", + seq, + "image_00", + "semantic_rgb", + f"{img_ids[seq][dp[1]]:010d}.png", + ) + ) + ] + datapoints.extend(datapoints_seq) + return datapoints + + @staticmethod + def _load_split(split_path, img_ids): + img_id2id = { + seq: {id: i for i, id in enumerate(ids)} for seq, ids in img_ids.items() + } + + with open(split_path, "r") as f: + lines = f.readlines() + + def split_line(l): + segments = l.split(" ") + seq = segments[0] + id = img_id2id[seq][int(segments[1])] + return seq, id, segments[2][0] == "r" + + return list(map(split_line, lines)) + + @staticmethod + def _load_calibs(data_path, fisheye_rotation=0): + data_path = Path(data_path) + + calib_folder = data_path / "calibration" + cam_to_pose_file = calib_folder / "calib_cam_to_pose.txt" + cam_to_velo_file = calib_folder / "calib_cam_to_velo.txt" + intrinsics_file = calib_folder / "perspective.txt" + fisheye_02_file = calib_folder / "image_02.yaml" + fisheye_03_file = calib_folder / "image_03.yaml" + + cam_to_pose_data = {} + with open(cam_to_pose_file, "r") as f: + for line in f.readlines(): + key, value = line.split(":", 1) + try: + cam_to_pose_data[key] = np.array( + [float(x) for x in value.split()], dtype=np.float32 + ) + except ValueError: + pass + + cam_to_velo_data = None + with open(cam_to_velo_file, "r") as f: + line = f.readline() + try: + cam_to_velo_data = np.array( + [float(x) for x in line.split()], dtype=np.float32 + ) + except ValueError: + pass + + intrinsics_data = {} + with open(intrinsics_file, "r") as f: + for line in f.readlines(): + key, value = line.split(":", 1) + try: + intrinsics_data[key] = np.array( + [float(x) for x in value.split()], dtype=np.float32 + ) + except ValueError: + pass + + with open(fisheye_02_file, "r") as f: + f.readline() # Skips first line that defines the YAML version + fisheye_02_data = yaml.safe_load(f) + + with open(fisheye_03_file, "r") as f: + f.readline() # Skips first line that defines the YAML version + fisheye_03_data = yaml.safe_load(f) + + im_size_rect = ( + int(intrinsics_data["S_rect_00"][1]), + int(intrinsics_data["S_rect_00"][0]), + ) + im_size_fish = (fisheye_02_data["image_height"], fisheye_02_data["image_width"]) + + # Projection matrices + # We use these projection matrices also when resampling the fisheye cameras. + # This makes downstream processing easier, but it could be done differently. + P_rect_00 = np.reshape(intrinsics_data["P_rect_00"], (3, 4)) + P_rect_01 = np.reshape(intrinsics_data["P_rect_01"], (3, 4)) + + # Rotation matrices from raw to rectified -> Needs to be inverted later + R_rect_00 = np.eye(4, dtype=np.float32) + R_rect_01 = np.eye(4, dtype=np.float32) + R_rect_00[:3, :3] = np.reshape(intrinsics_data["R_rect_00"], (3, 3)) + R_rect_01[:3, :3] = np.reshape(intrinsics_data["R_rect_01"], (3, 3)) + + # Rotation matrices from resampled fisheye to raw fisheye + fisheye_rotation = np.array(fisheye_rotation).reshape((1, 2)) + R_02 = np.eye(4, dtype=np.float32) + R_03 = np.eye(4, dtype=np.float32) + R_02[:3, :3] = ( + Rotation.from_euler("xy", fisheye_rotation[:, [1, 0]], degrees=True) + .as_matrix() + .astype(np.float32) + ) + R_03[:3, :3] = ( + Rotation.from_euler( + "xy", fisheye_rotation[:, [1, 0]] * np.array([[1, -1]]), degrees=True + ) + .as_matrix() + .astype(np.float32) + ) + + # Load cam to pose transforms + T_00_to_pose = np.eye(4, dtype=np.float32) + T_01_to_pose = np.eye(4, dtype=np.float32) + T_02_to_pose = np.eye(4, dtype=np.float32) + T_03_to_pose = np.eye(4, dtype=np.float32) + T_00_to_velo = np.eye(4, dtype=np.float32) + + T_00_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_00"], (3, 4)) + T_01_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_01"], (3, 4)) + T_02_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_02"], (3, 4)) + T_03_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_03"], (3, 4)) + T_00_to_velo[:3, :] = np.reshape(cam_to_velo_data, (3, 4)) + + # Compute cam to pose transforms for rectified perspective cameras + T_rect_00_to_pose = T_00_to_pose @ np.linalg.inv(R_rect_00) + T_rect_01_to_pose = T_01_to_pose @ np.linalg.inv(R_rect_01) + + # Compute cam to pose transform for fisheye cameras + T_02_to_pose = T_02_to_pose @ R_02 + T_03_to_pose = T_03_to_pose @ R_03 + + # Compute velo to cameras and velo to pose transforms + T_velo_to_rect_00 = R_rect_00 @ np.linalg.inv(T_00_to_velo) + T_velo_to_pose = T_rect_00_to_pose @ T_velo_to_rect_00 + T_velo_to_rect_01 = np.linalg.inv(T_rect_01_to_pose) @ T_velo_to_pose + + # Calibration matrix is the same for both perspective cameras + K = P_rect_00[:3, :3] + + # Normalize calibration + f_x = K[0, 0] / im_size_rect[1] + f_y = K[1, 1] / im_size_rect[0] + c_x = K[0, 2] / im_size_rect[1] + c_y = K[1, 2] / im_size_rect[0] + + # Change to image coordinates [-1, 1] + K[0, 0] = f_x * 2.0 + K[1, 1] = f_y * 2.0 + K[0, 2] = c_x * 2.0 - 1 + K[1, 2] = c_y * 2.0 - 1 + + # Convert fisheye calibration to [-1, 1] image dimensions + fisheye_02_data["projection_parameters"]["gamma1"] = ( + fisheye_02_data["projection_parameters"]["gamma1"] / im_size_fish[1] + ) * 2.0 + fisheye_02_data["projection_parameters"]["gamma2"] = ( + fisheye_02_data["projection_parameters"]["gamma2"] / im_size_fish[0] + ) * 2.0 + fisheye_02_data["projection_parameters"]["u0"] = ( + fisheye_02_data["projection_parameters"]["u0"] / im_size_fish[1] + ) * 2.0 - 1.0 + fisheye_02_data["projection_parameters"]["v0"] = ( + fisheye_02_data["projection_parameters"]["v0"] / im_size_fish[0] + ) * 2.0 - 1.0 + + fisheye_03_data["projection_parameters"]["gamma1"] = ( + fisheye_03_data["projection_parameters"]["gamma1"] / im_size_fish[1] + ) * 2.0 + fisheye_03_data["projection_parameters"]["gamma2"] = ( + fisheye_03_data["projection_parameters"]["gamma2"] / im_size_fish[0] + ) * 2.0 + fisheye_03_data["projection_parameters"]["u0"] = ( + fisheye_03_data["projection_parameters"]["u0"] / im_size_fish[1] + ) * 2.0 - 1.0 + fisheye_03_data["projection_parameters"]["v0"] = ( + fisheye_03_data["projection_parameters"]["v0"] / im_size_fish[0] + ) * 2.0 - 1.0 + + # Use same camera calibration as perspective cameras for resampling + # K_fisheye = np.eye(3, dtype=np.float32) + # K_fisheye[0, 0] = 2 + # K_fisheye[1, 1] = 2 + + K_fisheye = K + + calibs = { + "K_perspective": K, + "K_fisheye": K_fisheye, + "T_cam_to_pose": { + "00": T_rect_00_to_pose, + "01": T_rect_01_to_pose, + "02": T_02_to_pose, + "03": T_03_to_pose, + }, + "T_velo_to_cam": { + "00": T_velo_to_rect_00, + "01": T_velo_to_rect_01, + }, + "T_velo_to_pose": T_velo_to_pose, + "fisheye": { + "calib_02": fisheye_02_data, + "calib_03": fisheye_03_data, + "R_02": R_02[:3, :3], + "R_03": R_03[:3, :3], + }, + "im_size": im_size_rect, + } + + return calibs + + @staticmethod + def _get_resamplers(calibs, K_target, target_image_size): + resampler_02 = FisheyeToPinholeSampler( + K_target, + target_image_size, + calibs["fisheye"]["calib_02"], + calibs["fisheye"]["R_02"], + ) + resampler_03 = FisheyeToPinholeSampler( + K_target, + target_image_size, + calibs["fisheye"]["calib_03"], + calibs["fisheye"]["R_03"], + ) + + return resampler_02, resampler_03 + + @staticmethod + def _load_poses(pose_path, sequences): + ids = {} + poses = {} + + for seq in sequences: + pose_file = Path(pose_path) / seq / f"poses.txt" + + try: + pose_data = np.loadtxt(pose_file) + except FileNotFoundError: + print(f"Ground truth poses are not avaialble for sequence {seq}.") + + ids_seq = pose_data[:, 0].astype(int) + poses_seq = pose_data[:, 1:].astype(np.float32).reshape((-1, 3, 4)) + poses_seq = np.concatenate( + (poses_seq, np.zeros_like(poses_seq[:, :1, :])), axis=1 + ) + poses_seq[:, 3, 3] = 1 + + ids[seq] = ids_seq + poses[seq] = poses_seq + return ids, poses + + @staticmethod + def _load_3d_bboxes(bbox_path, sequences): + bboxes = {} + + for seq in sequences: + with open(Path(bbox_path) / f"{seq}.xml", "rb") as f: + tree = ET.parse(f) + root = tree.getroot() + + objects = defaultdict(list) + + num_bbox = 0 + + for child in root: + if child.find("transform") is None: + continue + obj = KITTI360Bbox3D() + if child.find("semanticId") is not None: + obj.parseBbox(child) + else: + obj.parseStuff(child) + # globalId = local2global(obj.semanticId, obj.instanceId) + # objects[globalId][obj.timestamp] = obj + objects[obj.timestamp].append(obj) + num_bbox += 1 + + # globalIds = np.asarray(list(objects.keys())) + # semanticIds, instanceIds = global2local(globalIds) + # for label in labels: + # if label.hasInstances: + # print(f'{label.name:<30}:\t {(semanticIds==label.id).sum()}') + # print(f'Loaded {len(globalIds)} instances') + # print(f'Loaded {num_bbox} boxes') + + bboxes[seq] = objects + + return bboxes + + def get_img_id_from_id(self, sequence, id): + return self._img_ids[sequence][id] + + def load_images(self, seq, img_ids, load_left, load_right, img_ids_fish=None): + imgs_p_left = [] + imgs_f_left = [] + imgs_p_right = [] + imgs_f_right = [] + + if img_ids_fish is None: + img_ids_fish = img_ids + + for id in img_ids: + if load_left: + img_perspective = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_00", + self._perspective_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_p_left += [img_perspective] + + if load_right: + img_perspective = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_01", + self._perspective_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_p_right += [img_perspective] + + for id in img_ids_fish: + if load_left: + img_fisheye = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_02", + self._fisheye_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_f_left += [img_fisheye] + if load_right: + img_fisheye = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_03", + self._fisheye_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_f_right += [img_fisheye] + + return imgs_p_left, imgs_f_left, imgs_p_right, imgs_f_right + + def process_img( + self, + img: np.array, + color_aug_fn=None, + resampler: FisheyeToPinholeSampler = None, + ): + if resampler is not None and not self.is_preprocessed: + img = torch.tensor(img).permute(2, 0, 1) + img = resampler.resample(img) + else: + if self.target_image_size: + img = cv2.resize( + img, + (self.target_image_size[1], self.target_image_size[0]), + interpolation=cv2.INTER_LINEAR, + ) + img = np.transpose(img, (2, 0, 1)) + img = torch.tensor(img) + + if color_aug_fn is not None: + img = color_aug_fn(img) + + img = img * 2 - 1 + return img + + def load_occ(self, seq, poses): + world_transform = torch.inverse(poses[:1, :, :]) + world_transform = cam_incl_adjust @ world_transform + seq_len = self._img_ids[seq].shape[0] + # Load lidar pointclouds + points_all, velo_poses = [], [] + for id in range(id, min(id + self.aggregate_timesteps, seq_len)): + points = np.fromfile( + os.path.join( + self.data_path, + "data_3d_raw", + seq, + "velodyne_points", + "data", + f"{self._img_ids[seq][id]:010d}.bin", + ), + dtype=np.float32, + ).reshape(-1, 4) + points[:, 3] = 1.0 + points = torch.tensor(points) + velo_pose = ( + world_transform.squeeze() + @ torch.tensor(self._poses[seq][id]) + @ torch.tensor(self._calibs["T_velo_to_pose"]) + ) + points_all.append(points) + velo_poses.append(velo_pose) + + velo_poses = torch.stack(velo_poses, dim=0) + + return self.kitti_velodyn.check_occupancy(points_all, velo_poses) + + def get_3d_bboxes(self, seq, img_id, pose, projs): + seq_3d_bboxes = self._3d_bboxes[seq] + pose_w2c = np.linalg.inv(pose) + + def filter_bbox(bbox): + verts = bbox.vertices + verts = (projs @ (pose_w2c[:3, :3] @ verts.T + pose_w2c[:3, 3, None])).T + verts[:, :2] /= verts[:, 2:3] + valid = ( + ((verts[:, 0] >= -1) & (verts[:, 0] <= 1)) + & ((verts[:, 1] >= -1) & (verts[:, 1] <= 1)) + & ((verts[:, 2] > 0) & (verts[:, 2] <= 80)) + ) + valid = np.any(valid, axis=-1) + return valid + + bboxes = seq_3d_bboxes[-1] + seq_3d_bboxes[img_id] + + bboxes = list(filter(filter_bbox, bboxes)) + + bboxes = [ + { + "vertices": bbox.vertices, + "faces": bbox.faces, + "semanticId": bbox.semanticId, + "instanceId": bbox.instanceId, + } + for i, bbox in enumerate(bboxes) + ] # if valid[i] + + return bboxes + + def load_segmentation(self, seq, img_id): + seg = cv2.imread( + os.path.join( + self.data_path, + "data_2d_semantics", + "train", + seq, + "image_00", + "semantic", + f"{img_id:010d}.png", + ), + cv2.IMREAD_UNCHANGED, + ) + seg = cv2.resize( + seg, + (self.target_image_size[1], self.target_image_size[0]), + interpolation=cv2.INTER_NEAREST, + ) + return seg + + def load_depth(self, seq, img_id, is_right): + points = np.fromfile( + os.path.join( + self.data_path, + "data_3d_raw", + seq, + "velodyne_points", + "data", + f"{img_id:010d}.bin", + ), + dtype=np.float32, + ).reshape(-1, 4) + points[:, 3] = 1.0 + + T_velo_to_cam = self._calibs["T_velo_to_cam"]["00" if not is_right else "01"] + K = self._calibs["K_perspective"] + + # project the points to the camera + velo_pts_im = np.dot(K @ T_velo_to_cam[:3, :], points.T).T + velo_pts_im[:, :2] = velo_pts_im[:, :2] / velo_pts_im[:, 2][..., None] + + # the projection is normalized to [-1, 1] -> transform to [0, height-1] x [0, width-1] + velo_pts_im[:, 0] = np.round( + (velo_pts_im[:, 0] * 0.5 + 0.5) * self.target_image_size[1] + ) + velo_pts_im[:, 1] = np.round( + (velo_pts_im[:, 1] * 0.5 + 0.5) * self.target_image_size[0] + ) + + # check if in bounds + val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0) + val_inds = ( + val_inds + & (velo_pts_im[:, 0] < self.target_image_size[1]) + & (velo_pts_im[:, 1] < self.target_image_size[0]) + ) + velo_pts_im = velo_pts_im[val_inds, :] + + # project to image + depth = np.zeros(self.target_image_size) + depth[ + velo_pts_im[:, 1].astype(np.int32), velo_pts_im[:, 0].astype(np.int32) + ] = velo_pts_im[:, 2] + + # find the duplicate points and choose the closest depth + inds = ( + velo_pts_im[:, 1] * (self.target_image_size[1] - 1) + velo_pts_im[:, 0] - 1 + ) + dupe_inds = [item for item, count in Counter(inds).items() if count > 1] + for dd in dupe_inds: + pts = np.where(inds == dd)[0] + x_loc = int(velo_pts_im[pts[0], 0]) + y_loc = int(velo_pts_im[pts[0], 1]) + depth[y_loc, x_loc] = velo_pts_im[pts, 2].min() + depth[depth < 0] = 0 + + return depth[None, :, :] + + def __getitem__(self, index: int): + _start_time = time.time() + + if index >= self.length: + raise IndexError() + + if self._skip != 0: + index += self._skip + + sequence, id, is_right = self._datapoints[index] + seq_len = self._img_ids[sequence].shape[0] + + load_left, load_right = ( + not is_right + ) or self.return_stereo, is_right or self.return_stereo + + ## randomly sample fisheye in the time steps where it can see the occlusion with the stereo + if self.random_fisheye_offset: + fisheye_offset = self.fisheye_offset[ + torch.randint(0, len(self.fisheye_offset), (1,)).item() + ] ## randomly select among the given list of fisheye_ids from config + else: + fisheye_offset = self.fisheye_offset[-1] + + if self.random_stereo_offset: + stereo_offset = self.stereo_offset[ + torch.randint(0, len(self.stereo_offset), (1,)).item() + ] + else: + stereo_offset = self.stereo_offset[0] + + # ids = [id] + [max(min(i, seq_len-1), 0) for i in range(id - self._left_offset, id - self._left_offset + self.frame_count * self.dilation, self.dilation) if i != id] + # ids_fish = [max(min(id + self.fisheye_offset, seq_len-1), 0)] + [max(min(i, seq_len-1), 0) for i in range(id + self.fisheye_offset - self._left_offset, id + self.fisheye_offset - self._left_offset + self.frame_count * self.dilation, self.dilation) if i != id + self.fisheye_offset] + # img_ids = [self.get_img_id_from_id(sequence, id) for id in ids] + # img_ids_fish = [self.get_img_id_from_id(sequence, id) for id in ids_fish] + + id_st = ( + id + stereo_offset - 1 + ) ## TODO: find out how to deal with 3 steps ahead without -1 => as we sample scenes with the amount of stereo_offset + ids = [id] + [ + max(min(i, seq_len - 1), 0) + for i in range( + id_st - self._left_offset, + id_st - self._left_offset + self.frame_count * self.dilation, + self.dilation, + ) + if i != id_st + ] + ids_fish = [max(min(id + fisheye_offset, seq_len - 1), 0)] + [ + max(min(i, seq_len - 1), 0) + for i in range( + id + fisheye_offset - self._left_offset, + id + + fisheye_offset + - self._left_offset + + self.frame_count * self.dilation, + self.dilation, + ) + if i != id + fisheye_offset + ] + ## and now ids_fish is 5 steps ahead of ids with 2 fisheye scenes + img_ids = [self.get_img_id_from_id(sequence, id) for id in ids] + img_ids_fish = [self.get_img_id_from_id(sequence, id) for id in ids_fish] + + if not self.return_fisheye: + ids_fish, img_ids_fish = [], [] + + if self.color_aug: + color_aug_fn = get_color_aug_fn( + ColorJitter.get_params( + brightness=(0.8, 1.2), + contrast=(0.8, 1.2), + saturation=(0.8, 1.2), + hue=(-0.1, 0.1), + ) + ) + else: + color_aug_fn = None + + _start_time_loading = time.time() + imgs_p_left, imgs_f_left, imgs_p_right, imgs_f_right = self.load_images( + sequence, img_ids, load_left, load_right, img_ids_fish=img_ids_fish + ) + _loading_time = np.array(time.time() - _start_time_loading) + + _start_time_processing = time.time() + imgs_p_left = [ + self.process_img(img, color_aug_fn=color_aug_fn) for img in imgs_p_left + ] + imgs_f_left = [ + self.process_img( + img, color_aug_fn=color_aug_fn, resampler=self._resampler_02 + ) + for img in imgs_f_left + ] + imgs_p_right = [ + self.process_img(img, color_aug_fn=color_aug_fn) for img in imgs_p_right + ] + imgs_f_right = [ + self.process_img( + img, color_aug_fn=color_aug_fn, resampler=self._resampler_03 + ) + for img in imgs_f_right + ] + _processing_time = np.array(time.time() - _start_time_processing) + + # These poses are camera to world !! + poses_p_left = ( + [ + self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["00"] + for i in ids + ] + if load_left + else [] + ) + poses_f_left = ( + [ + self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["02"] + for i in ids_fish + ] + if load_left + else [] + ) + poses_p_right = ( + [ + self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["01"] + for i in ids + ] + if load_right + else [] + ) + poses_f_right = ( + [ + self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["03"] + for i in ids_fish + ] + if load_right + else [] + ) + + projs_p_left = [self._calibs["K_perspective"] for _ in ids] if load_left else [] + projs_f_left = ( + [self._calibs["K_fisheye"] for _ in ids_fish] if load_left else [] + ) + projs_p_right = ( + [self._calibs["K_perspective"] for _ in ids] if load_right else [] + ) + projs_f_right = ( + [self._calibs["K_fisheye"] for _ in ids_fish] if load_right else [] + ) + + imgs = ( + imgs_p_left + imgs_p_right + imgs_f_left + imgs_f_right + if not is_right + else imgs_p_right + imgs_p_left + imgs_f_right + imgs_f_left + ) + projs = ( + projs_p_left + projs_p_right + projs_f_left + projs_f_right + if not is_right + else projs_p_right + projs_p_left + projs_f_right + projs_f_left + ) + poses = ( + poses_p_left + poses_p_right + poses_f_left + poses_f_right + if not is_right + else poses_p_right + poses_p_left + poses_f_right + poses_f_left + ) + ids = np.array(ids + ids + ids_fish + ids_fish, dtype=np.int32) + + if self.return_depth: + depths = [self.load_depth(sequence, img_ids[0], is_right)] + else: + depths = [] + + if self.return_3d_bboxes: + bboxes_3d = [self.get_3d_bboxes(sequence, img_ids[0], poses[0], projs[0])] + else: + bboxes_3d = [] + + if self.return_segmentation: + segs = [self.load_segmentation(sequence, img_ids[0])] + else: + segs = [] + + if self.kitti_velodyn: + is_occupied, is_visible = self.load_occ(sequence, poses) + else: + is_occupied, is_visible = [], [] + + _proc_time = np.array(time.time() - _start_time) + + # print(_loading_time, _processing_time, _proc_time) + + data = { + "imgs": imgs, + "projs": projs, + "poses": poses, + "depths": depths, + "ts": ids, + "3d_bboxes": bboxes_3d, + "segs": segs, + "is_occupied": is_occupied, + "is_visible": is_visible, + "t__get_item__": np.array([_proc_time]), + "index": np.array([index]), + } + + return data + + def __len__(self) -> int: + # return 10 + return self.length diff --git a/datasets/kitti_360/labels.py b/datasets/kitti_360/labels.py new file mode 100644 index 0000000000000000000000000000000000000000..86f45151b8e7c967512339a5e2b4098be8b1ed7e --- /dev/null +++ b/datasets/kitti_360/labels.py @@ -0,0 +1,200 @@ +#!/usr/bin/python +# +# KITTI-360 labels +# + +from collections import namedtuple + + +#-------------------------------------------------------------------------------- +# Definitions +#-------------------------------------------------------------------------------- + +# a label and all meta information +Label = namedtuple( 'Label' , [ + + 'name' , # The identifier of this label, e.g. 'car', 'person', ... . + # We use them to uniquely name a class + + 'id' , # An integer ID that is associated with this label. + # The IDs are used to represent the label in ground truth images + # An ID of -1 means that this label does not have an ID and thus + # is ignored when creating ground truth images (e.g. license plate). + # Do not modify these IDs, since exactly these IDs are expected by the + # evaluation server. + + 'kittiId' , # An integer ID that is associated with this label for KITTI-360 + # NOT FOR RELEASING + + 'trainId' , # Feel free to modify these IDs as suitable for your method. Then create + # ground truth images with train IDs, using the tools provided in the + # 'preparation' folder. However, make sure to validate or submit results + # to our evaluation server using the regular IDs above! + # For trainIds, multiple labels might have the same ID. Then, these labels + # are mapped to the same class in the ground truth images. For the inverse + # mapping, we use the label that is defined first in the list below. + # For example, mapping all void-type classes to the same ID in training, + # might make sense for some approaches. + # Max value is 255! + + 'category' , # The name of the category that this label belongs to + + 'categoryId' , # The ID of this category. Used to create ground truth images + # on category level. + + 'hasInstances', # Whether this label distinguishes between single instances or not + + 'ignoreInEval', # Whether pixels having this class as ground truth label are ignored + # during evaluations or not + + 'ignoreInInst', # Whether pixels having this class as ground truth label are ignored + # during evaluations of instance segmentation or not + + 'color' , # The color of this label + ] ) + + +#-------------------------------------------------------------------------------- +# A list of all labels +#-------------------------------------------------------------------------------- + +# Please adapt the train IDs as appropriate for your approach. +# Note that you might want to ignore labels with ID 255 during training. +# Further note that the current train IDs are only a suggestion. You can use whatever you like. +# Make sure to provide your results using the original IDs and not the training IDs. +# Note that many IDs are ignored in evaluation and thus you never need to predict these! + +labels = [ + # name id kittiId, trainId category catId hasInstances ignoreInEval ignoreInInst color + Label( 'unlabeled' , 0 , -1 , 255 , 'void' , 0 , False , True , True , ( 0, 0, 0) ), + Label( 'ego vehicle' , 1 , -1 , 255 , 'void' , 0 , False , True , True , ( 0, 0, 0) ), + Label( 'rectification border' , 2 , -1 , 255 , 'void' , 0 , False , True , True , ( 0, 0, 0) ), + Label( 'out of roi' , 3 , -1 , 255 , 'void' , 0 , False , True , True , ( 0, 0, 0) ), + Label( 'static' , 4 , -1 , 255 , 'void' , 0 , False , True , True , ( 0, 0, 0) ), + Label( 'dynamic' , 5 , -1 , 255 , 'void' , 0 , False , True , True , (111, 74, 0) ), + Label( 'ground' , 6 , -1 , 255 , 'void' , 0 , False , True , True , ( 81, 0, 81) ), + Label( 'road' , 7 , 1 , 0 , 'flat' , 1 , False , False , False , (128, 64,128) ), + Label( 'sidewalk' , 8 , 3 , 1 , 'flat' , 1 , False , False , False , (244, 35,232) ), + Label( 'parking' , 9 , 2 , 255 , 'flat' , 1 , False , True , True , (250,170,160) ), + Label( 'rail track' , 10 , 10, 255 , 'flat' , 1 , False , True , True , (230,150,140) ), + Label( 'building' , 11 , 11, 2 , 'construction' , 2 , True , False , False , ( 70, 70, 70) ), + Label( 'wall' , 12 , 7 , 3 , 'construction' , 2 , False , False , False , (102,102,156) ), + Label( 'fence' , 13 , 8 , 4 , 'construction' , 2 , False , False , False , (190,153,153) ), + Label( 'guard rail' , 14 , 30, 255 , 'construction' , 2 , False , True , True , (180,165,180) ), + Label( 'bridge' , 15 , 31, 255 , 'construction' , 2 , False , True , True , (150,100,100) ), + Label( 'tunnel' , 16 , 32, 255 , 'construction' , 2 , False , True , True , (150,120, 90) ), + Label( 'pole' , 17 , 21, 5 , 'object' , 3 , True , False , True , (153,153,153) ), + Label( 'polegroup' , 18 , -1 , 255 , 'object' , 3 , False , True , True , (153,153,153) ), + Label( 'traffic light' , 19 , 23, 6 , 'object' , 3 , True , False , True , (250,170, 30) ), + Label( 'traffic sign' , 20 , 24, 7 , 'object' , 3 , True , False , True , (220,220, 0) ), + Label( 'vegetation' , 21 , 5 , 8 , 'nature' , 4 , False , False , False , (107,142, 35) ), + Label( 'terrain' , 22 , 4 , 9 , 'nature' , 4 , False , False , False , (152,251,152) ), + Label( 'sky' , 23 , 9 , 10 , 'sky' , 5 , False , False , False , ( 70,130,180) ), + Label( 'person' , 24 , 19, 11 , 'human' , 6 , True , False , False , (220, 20, 60) ), + Label( 'rider' , 25 , 20, 12 , 'human' , 6 , True , False , False , (255, 0, 0) ), + Label( 'car' , 26 , 13, 13 , 'vehicle' , 7 , True , False , False , ( 0, 0,142) ), + Label( 'truck' , 27 , 14, 14 , 'vehicle' , 7 , True , False , False , ( 0, 0, 70) ), + Label( 'bus' , 28 , 34, 15 , 'vehicle' , 7 , True , False , False , ( 0, 60,100) ), + Label( 'caravan' , 29 , 16, 255 , 'vehicle' , 7 , True , True , True , ( 0, 0, 90) ), + Label( 'trailer' , 30 , 15, 255 , 'vehicle' , 7 , True , True , True , ( 0, 0,110) ), + Label( 'train' , 31 , 33, 16 , 'vehicle' , 7 , True , False , False , ( 0, 80,100) ), + Label( 'motorcycle' , 32 , 17, 17 , 'vehicle' , 7 , True , False , False , ( 0, 0,230) ), + Label( 'bicycle' , 33 , 18, 18 , 'vehicle' , 7 , True , False , False , (119, 11, 32) ), + Label( 'garage' , 34 , 12, 2 , 'construction' , 2 , True , True , True , ( 64,128,128) ), + Label( 'gate' , 35 , 6 , 4 , 'construction' , 2 , False , True , True , (190,153,153) ), + Label( 'stop' , 36 , 29, 255 , 'construction' , 2 , True , True , True , (150,120, 90) ), + Label( 'smallpole' , 37 , 22, 5 , 'object' , 3 , True , True , True , (153,153,153) ), + Label( 'lamp' , 38 , 25, 255 , 'object' , 3 , True , True , True , (0, 64, 64) ), + Label( 'trash bin' , 39 , 26, 255 , 'object' , 3 , True , True , True , (0, 128,192) ), + Label( 'vending machine' , 40 , 27, 255 , 'object' , 3 , True , True , True , (128, 64, 0) ), + Label( 'box' , 41 , 28, 255 , 'object' , 3 , True , True , True , (64, 64,128) ), + Label( 'unknown construction' , 42 , 35, 255 , 'void' , 0 , False , True , True , (102, 0, 0) ), + Label( 'unknown vehicle' , 43 , 36, 255 , 'void' , 0 , False , True , True , ( 51, 0, 51) ), + Label( 'unknown object' , 44 , 37, 255 , 'void' , 0 , False , True , True , ( 32, 32, 32) ), + Label( 'license plate' , -1 , -1, -1 , 'vehicle' , 7 , False , True , True , ( 0, 0,142) ), +] + +#-------------------------------------------------------------------------------- +# Create dictionaries for a fast lookup +#-------------------------------------------------------------------------------- + +# Please refer to the main method below for example usages! + +# name to label object +name2label = { label.name : label for label in labels } +# id to label object +id2label = { label.id : label for label in labels } +# trainId to label object +trainId2label = { label.trainId : label for label in reversed(labels) } +# KITTI-360 ID to cityscapes ID +kittiId2label = { label.kittiId : label for label in labels } +# category to list of label objects +category2labels = {} +for label in labels: + category = label.category + if category in category2labels: + category2labels[category].append(label) + else: + category2labels[category] = [label] + +#-------------------------------------------------------------------------------- +# Assure single instance name +#-------------------------------------------------------------------------------- + +# returns the label name that describes a single instance (if possible) +# e.g. input | output +# ---------------------- +# car | car +# cargroup | car +# foo | None +# foogroup | None +# skygroup | None +def assureSingleInstanceName( name ): + # if the name is known, it is not a group + if name in name2label: + return name + # test if the name actually denotes a group + if not name.endswith("group"): + return None + # remove group + name = name[:-len("group")] + # test if the new name exists + if not name in name2label: + return None + # test if the new name denotes a label that actually has instances + if not name2label[name].hasInstances: + return None + # all good then + return name + +#-------------------------------------------------------------------------------- +# Main for testing +#-------------------------------------------------------------------------------- + +# just a dummy main +if __name__ == "__main__": + # Print all the labels + print("List of KITTI-360 labels:") + print("") + print(" {:>21} | {:>3} | {:>7} | {:>14} | {:>10} | {:>12} | {:>12}".format( 'name', 'id', 'trainId', 'category', 'categoryId', 'hasInstances', 'ignoreInEval' )) + print(" " + ('-' * 98)) + for label in labels: + # print(" {:>21} | {:>3} | {:>7} | {:>14} | {:>10} | {:>12} | {:>12}".format( label.name, label.id, label.trainId, label.category, label.categoryId, label.hasInstances, label.ignoreInEval )) + print(" \"{:}\"".format(label.name)) + print("") + + print("Example usages:") + + # Map from name to label + name = 'car' + id = name2label[name].id + print("ID of label '{name}': {id}".format( name=name, id=id )) + + # Map from ID to label + category = id2label[id].category + print("Category of label with ID '{id}': {category}".format( id=id, category=category )) + + # Map from trainID to label + trainId = 0 + name = trainId2label[trainId].name + print("Name of label with trainID '{id}': {name}".format( id=trainId, name=name )) diff --git a/datasets/kitti_360/orb_slam_poses/2013_05_28_drive_0000_sync/poses.txt b/datasets/kitti_360/orb_slam_poses/2013_05_28_drive_0000_sync/poses.txt new file mode 100644 index 0000000000000000000000000000000000000000..788272821c071c1a7582769398df2f38b6fd73ce --- /dev/null +++ b/datasets/kitti_360/orb_slam_poses/2013_05_28_drive_0000_sync/poses.txt @@ -0,0 +1,11518 @@ +0 1.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 1.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 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0.0689518591 0.0840610729 2014.8320683939 -0.0760187087 -0.1119580174 0.9908009680 -164.6579988732 +8218 0.0776124333 -0.9912148857 -0.1070951008 62.0198715480 0.9941712106 0.0688829143 0.0829382186 2015.2707549061 -0.0748325741 -0.1129079029 0.9907834735 -164.6920747727 +8219 0.0773137590 -0.9910367850 -0.1089434417 62.0663437861 0.9942376479 0.0684989113 0.0824584665 2015.6860898532 -0.0742568664 -0.1146908451 0.9906219904 -164.7160335002 +8220 0.0769845713 -0.9911735989 -0.1079271633 62.1062424918 0.9944889399 0.0686100364 0.0792742790 2016.1123184668 -0.0711696858 -0.1134352665 0.9909930959 -164.7757137159 +8221 0.0779529425 -0.9912848850 -0.1061961187 62.1270811202 0.9944311893 0.0697353550 0.0790163910 2016.5469954430 -0.0709221300 -0.1117642928 0.9912006832 -164.8083326021 +8222 0.0802230489 -0.9915292310 -0.1021471811 62.1534086416 0.9942823294 0.0723553973 0.0785324509 2016.9576348502 -0.0704763207 -0.1078632497 0.9916645640 -164.8430918895 +8223 0.0838255359 -0.9914855606 -0.0996476932 62.1805560417 0.9940176720 0.0761729860 0.0782722425 2017.3658498516 -0.0700153358 -0.1056127806 0.9919394101 -164.8740742019 +8224 0.0877945254 -0.9911980309 -0.0990887728 62.2184532049 0.9936876922 0.0801705583 0.0784694338 2017.7819165135 -0.0698347460 -0.1053524806 0.9919798199 -164.9025813394 +8225 0.0925599052 -0.9903790385 -0.1028689652 62.2556941844 0.9933328999 0.0847148648 0.0781865823 2018.1929311863 -0.0687198217 -0.1094200701 0.9916172822 -164.9319992001 +8226 0.0963947482 -0.9897157766 -0.1056916938 62.2995385025 0.9931076163 0.0885215813 0.0768192166 2018.6026716660 -0.0666731947 -0.1123681950 0.9914272913 -164.9676069029 +8227 0.1008271100 -0.9894168581 -0.1043464172 62.3423615321 0.9926644227 0.0930120734 0.0772405206 2018.9966342424 -0.0667175966 -0.1113689144 0.9915370529 -164.9955586238 +8228 0.1053911573 -0.9893039477 -0.1008484158 62.3751446330 0.9922039595 0.0978299170 0.0772049871 2019.3963042253 -0.0665132063 -0.1081989203 0.9919017023 -165.0307459483 +8229 0.1103685158 -0.9891764366 -0.0966890271 62.4093522557 0.9916046822 0.1029979213 0.0781766101 2019.8041816895 -0.0673716917 -0.1045055284 0.9922397138 -165.0582338336 +8230 0.1137994251 -0.9888177165 -0.0963805704 62.4594309825 0.9912660662 0.1065002148 0.0777771829 2020.1659695106 -0.0666429049 -0.1043897875 0.9923011113 -165.0905590743 +8231 0.1176360029 -0.9879883219 -0.1002040247 62.5023495928 0.9908218436 0.1100062786 0.0785537579 2020.5596040314 -0.0665871235 -0.1085250865 0.9918611095 -165.1074558245 +8232 0.1211052896 -0.9871677485 -0.1040833563 62.5569965978 0.9904014883 0.1131287531 0.0794152207 2020.9510857805 -0.0666213243 -0.1127019142 0.9913928977 -165.1326696954 +8233 0.1249811268 -0.9863714738 -0.1070095024 62.6088915824 0.9898990062 0.1166931839 0.0805149569 2021.3608544816 -0.0669303771 -0.1159914500 0.9909925873 -165.1514046390 +8234 0.1291485643 -0.9859583414 -0.1058621620 62.6531208914 0.9895503413 0.1212393059 0.0780458370 2021.7684655084 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-0.0564102185 -227.4288050165 0.5958406014 0.7932669371 0.1253058032 2013.4048591533 -0.0296715040 -0.1341762234 0.9905131715 -180.8234139852 +9146 0.7995692584 -0.5978423535 -0.0572146957 -226.5180526314 0.5998366689 0.7902367270 0.1253869451 2014.0912112664 -0.0297484725 -0.1345750191 0.9904567596 -180.8579791645 +9147 0.7959323233 -0.6026805253 -0.0571657348 -225.6231963539 0.6045789583 0.7864469466 0.1264337113 2014.7514569946 -0.0312413179 -0.1351938779 0.9903265095 -180.8826320877 +9148 0.7927135522 -0.6070885384 -0.0552153127 -224.7283923436 0.6086601292 0.7832326518 0.1268048118 2015.4376412692 -0.0337353121 -0.1341272521 0.9903897258 -180.9087201701 +9149 0.7895040015 -0.6113100165 -0.0546213819 -223.8448673350 0.6127064391 0.7798637464 0.1280755893 2016.1273688244 -0.0356966551 -0.1345830626 0.9902591318 -180.9438640641 +9150 0.7851511872 -0.6168565629 -0.0550053991 -222.9475005480 0.6181105485 0.7750292624 0.1314115377 2016.8154681576 -0.0384312755 -0.1371773422 0.9898006940 -180.9779690583 +9151 0.7816973650 -0.6212120003 -0.0551804338 -222.0619053730 0.6222703086 0.7710001835 0.1354192755 2017.5316756654 -0.0415799544 -0.1401940364 0.9892505949 -181.0156310901 +9152 0.7768490111 -0.6271429949 -0.0565444771 -221.1496918366 0.6281507081 0.7655542693 0.1391163136 2018.2183239424 -0.0439579557 -0.1435908239 0.9886603934 -181.0476553681 +9153 0.7725539666 -0.6320429846 -0.0606797679 -220.2590014902 0.6334006614 0.7604725174 0.1431263515 2018.9360543736 -0.0443167105 -0.1490074356 0.9878425043 -181.0953480773 +9154 0.7673243986 -0.6379274195 -0.0652830353 -219.3669129411 0.6399671375 0.7553406090 0.1410766723 2019.6626710055 -0.0406857498 -0.1500305699 0.9878438631 -181.1357375113 +9155 0.7619520970 -0.6435023317 -0.0730325337 -218.4756683397 0.6467735794 0.7502778195 0.1369931771 2020.4091229300 -0.0333607388 -0.1516177517 0.9878760643 -181.1763746120 +9156 0.7571381262 -0.6487526028 -0.0765631646 -217.5685577610 0.6526176880 0.7460114841 0.1325029013 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-0.1820208765 -403.9709058215 0.9888705922 -0.1192688657 0.0889375603 2010.5544325179 -0.1086932524 -0.1709598372 0.9792642702 -196.6864926949 +9996 -0.0627287413 -0.9823067394 -0.1764612559 -404.0239694843 0.9930120865 -0.0791388359 0.0875445069 2011.1759711738 -0.0999604974 -0.1697366031 0.9804067444 -196.7473679792 +9997 -0.0285773267 -0.9847601957 -0.1715537626 -404.0577882603 0.9953829552 -0.0437671448 0.0854237064 2011.8123282241 -0.0916302842 -0.1683205100 0.9814642617 -196.8063834527 +9998 0.0010432528 -0.9857239501 -0.1683662848 -404.0764339397 0.9964588958 -0.0131306667 0.0830497110 2012.4806952034 -0.0840748508 -0.1678567240 0.9822197003 -196.8684375398 +9999 0.0275005051 -0.9856655050 -0.1664549020 -404.0727679879 0.9963858722 0.0136411057 0.0838398109 2013.1784352282 -0.0803673806 -0.1681589498 0.9824783212 -196.9272899482 +10000 0.0515172672 -0.9853209481 -0.1627531889 -404.0490145865 0.9955131123 0.0377156433 0.0867823342 2013.8912995535 -0.0793701105 -0.1664937223 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-31.0292990654 +3834 -0.4354401901 -0.8987749067 -0.0509461286 352.7295107369 0.9001251203 -0.4355090849 -0.0103249524 2039.2731049408 -0.0129076936 -0.0503537893 0.9986480298 -31.0302058776 +3835 -0.4354793629 -0.8987492656 -0.0510635104 352.7294491932 0.9001048997 -0.4355517876 -0.0102863858 2039.2721856308 -0.0129959215 -0.0504420246 0.9986424326 -31.0299727057 diff --git a/datasets/kitti_360/preprocess_kitti_360.py b/datasets/kitti_360/preprocess_kitti_360.py new file mode 100644 index 0000000000000000000000000000000000000000..307903cfafa7ff4274a2abf274506a4e338f4d78 --- /dev/null +++ b/datasets/kitti_360/preprocess_kitti_360.py @@ -0,0 +1,81 @@ +import argparse +import sys +sys.path.append(".") + +from pathlib import Path +import subprocess + +import cv2 +import numpy as np +from tqdm import tqdm + +from datasets.kitti_360.kitti_360_dataset import Kitti360Dataset + +DRY_RUN = False + + +def main(): + parser = argparse.ArgumentParser("KITTI 360 Preprocessing") + parser.add_argument("--data_path", "-d", type=str) + parser.add_argument("--resolution", "-r", default=(192, 640)) + parser.add_argument("--fisheye_rotation", "-f", default=(0, -15)) + parser.add_argument("--only_fisheye", "-o", action="store_true") + + args = parser.parse_args() + + data_path = Path(args.data_path) + resolution = args.resolution + rotation = args.fisheye_rotation + only_fisheye = args.only_fisheye + + print("Setting up dataset") + dataset = Kitti360Dataset( + data_path=data_path, + pose_path=data_path / "data_poses", + split_path=None, + return_stereo=True, + frame_count=1, + fisheye_rotation=rotation, + color_aug=False, + return_segmentation=False, + ) + + print("Setting up folders...") + + for i in tqdm(range(len(dataset))): + sequence, id, is_right = dataset._datapoints[i] + + if is_right: + continue + + image_00 = data_path / "data_2d_raw" / sequence / "image_00" / f"data_{resolution[0]}x{resolution[1]}" + image_01 = data_path / "data_2d_raw" / sequence / "image_01" / f"data_{resolution[0]}x{resolution[1]}" + image_02 = data_path / "data_2d_raw" / sequence / "image_02" / f"data_{resolution[0]}x{resolution[1]}_{rotation[0]}x{rotation[1]}" + image_03 = data_path / "data_2d_raw" / sequence / "image_03" / f"data_{resolution[0]}x{resolution[1]}_{rotation[0]}x{rotation[1]}" + + img_id = dataset._img_ids[sequence][id] + + if (image_00 / f"{img_id:010d}.png").exists(): + continue + + data = dataset[i] + + image_00.mkdir(exist_ok=True, parents=True) + image_01.mkdir(exist_ok=True, parents=True) + image_02.mkdir(exist_ok=True, parents=True) + image_03.mkdir(exist_ok=True, parents=True) + + img_00 = (np.transpose(data["imgs"][0].numpy(), (1, 2, 0)) * .5 + .5) * 255. + img_01 = (np.transpose(data["imgs"][1].numpy(), (1, 2, 0)) * .5 + .5) * 255. + img_02 = (np.transpose(data["imgs"][2].numpy(), (1, 2, 0)) * .5 + .5) * 255. + img_03 = (np.transpose(data["imgs"][3].numpy(), (1, 2, 0)) * .5 + .5) * 255. + + if not only_fisheye: + cv2.imwrite(str(image_00 / f"{img_id:010d}.png"), cv2.cvtColor(img_00, cv2.COLOR_RGB2BGR)) + cv2.imwrite(str(image_01 / f"{img_id:010d}.png"), cv2.cvtColor(img_01, cv2.COLOR_RGB2BGR)) + cv2.imwrite(str(image_02 / f"{img_id:010d}.png"), cv2.cvtColor(img_02, cv2.COLOR_RGB2BGR)) + cv2.imwrite(str(image_03 / f"{img_id:010d}.png"), cv2.cvtColor(img_03, cv2.COLOR_RGB2BGR)) + + +if __name__ == "__main__": + main() diff --git a/datasets/kitti_360/splits/seg/test_files.txt b/datasets/kitti_360/splits/seg/test_files.txt new file mode 100644 index 0000000000000000000000000000000000000000..1bb31f71271f89796cffe6abb54d2f629f334e22 --- /dev/null +++ b/datasets/kitti_360/splits/seg/test_files.txt @@ -0,0 +1,446 @@ +2013_05_28_drive_0000_sync 0000000386 l +2013_05_28_drive_0000_sync 0000000406 l +2013_05_28_drive_0000_sync 0000000426 l +2013_05_28_drive_0000_sync 0000000446 l +2013_05_28_drive_0000_sync 0000000466 l +2013_05_28_drive_0000_sync 0000000486 l +2013_05_28_drive_0000_sync 0000000506 l 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a/datasets/realestate10k/__init__.py b/datasets/realestate10k/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/datasets/realestate10k/download_realestate10k.py b/datasets/realestate10k/download_realestate10k.py new file mode 100644 index 0000000000000000000000000000000000000000..97f7755db16ea5dcab304cdec0fa7567d0b74285 --- /dev/null +++ b/datasets/realestate10k/download_realestate10k.py @@ -0,0 +1,194 @@ +import argparse +import os +import subprocess +from multiprocessing import Pool +from pathlib import Path +from time import sleep + +from pytube import YouTube +import tqdm +from subprocess import call + + +class Data: + def __init__(self, url, seqname, list_timestamps): + self.url = url + self.list_seqnames = [] + self.list_list_timestamps = [] + + self.list_seqnames.append(seqname) + self.list_list_timestamps.append(list_timestamps) + + def add(self, seqname, list_timestamps): + self.list_seqnames.append(seqname) + self.list_list_timestamps.append(list_timestamps) + + def __len__(self): + return len(self.list_seqnames) + + +def process(data, seq_id, videoname, output_root): + seqname = data.list_seqnames[seq_id] + out_path = output_root / seqname + if not out_path.exists(): + out_path.mkdir(exist_ok=True, parents=True) + else: + print("[INFO] Something Wrong, stop process") + return True + + list_str_timestamps = [] + for timestamp in data.list_list_timestamps[seq_id]: + timestamp = int(timestamp / 1000) + str_hour = str(int(timestamp / 3600000)).zfill(2) + str_min = str(int(int(timestamp % 3600000) / 60000)).zfill(2) + str_sec = str(int(int(int(timestamp % 3600000) % 60000) / 1000)).zfill(2) + str_mill = str(int(int(int(timestamp % 3600000) % 60000) % 1000)).zfill(3) + _str_timestamp = str_hour + ":" + str_min + ":" + str_sec + "." + str_mill + list_str_timestamps.append(_str_timestamp) + + # extract frames from a video + for idx, str_timestamp in enumerate(list_str_timestamps): + call(("ffmpeg", "-ss", str_timestamp, "-i", str(videoname), "-vframes", "1", "-f", "image2", str(out_path / f'{data.list_list_timestamps[seq_id][idx]}.jpg')), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + + return False + + +def wrap_process(list_args): + return process(*list_args) + + +class DataDownloader: + def __init__(self, data_path: Path, out_path: Path, tmp_path: Path, mode='test'): + print("[INFO] Loading data list ... ", end='') + self.data_path = data_path + self.out_path = out_path + self.tmp_path = tmp_path + self.mode = mode + + self.list_seqnames = sorted(self.data_path.glob('*.txt')) + + self.is_done = out_path.exists() + + if self.is_done: + print("[INFO] The output dir has already existed.") + + out_path.mkdir(exist_ok=True, parents=True) + + self.list_data = {} + if not self.is_done: + for txt_file in tqdm.tqdm(self.list_seqnames): + dir_name = txt_file.parent.name + seq_name = txt_file.stem + + # extract info from txt + with open(txt_file, "r") as seq_file: + lines = seq_file.readlines() + youtube_url = "" + list_timestamps = [] + for idx, line in enumerate(lines): + if idx == 0: + youtube_url = line.strip() + else: + timestamp = int(line.split(' ')[0]) + list_timestamps.append(timestamp) + + if youtube_url in self.list_data: + self.list_data[youtube_url].add(seq_name, list_timestamps) + else: + self.list_data[youtube_url] = Data(youtube_url, seq_name, list_timestamps) + + print(" Done! ") + print("[INFO] {} movies are used in {} mode".format(len(self.list_data), self.mode)) + + def run(self): + print("[INFO] Start downloading {} movies".format(len(self.list_data))) + + for global_count, data in enumerate(self.list_data.values()): + print("[INFO] Downloading {} ".format(data.url)) + current_file = self.tmp_path / f"current_{self.mode}" + + call(("rm", "-r", str(current_file))) + + try: + # sometimes this fails because of known issues of pytube and unknown factors + yt = YouTube(data.url) + stream = yt.streams.filter(res='360p').first() + stream.download(str(current_file)) + except: + with open(os.path.join(str(self.data_path.parent), 'failed_videos_' + self.mode + '.txt'), 'a') as f: + for seqname in data.list_seqnames: + f.writelines(seqname + '\n') + continue + + sleep(1) + + current_file = next(current_file.iterdir()) + + if len(data) == 1: # len(data) is len(data.list_seqnames) + process(data, 0, current_file, self.out_path) + else: + with Pool(processes=4) as pool: + pool.map(wrap_process, [(data, seq_id, current_file, self.out_path) for seq_id in range(len(data))]) + + print(f"[INFO] Extracted {sum(map(len, data.list_list_timestamps))}") + + # remove videos + call(("rm", str(current_file))) + # os.system(command) + + if self.is_done: + return False + + return True + + def show(self): + print("########################################") + global_count = 0 + for data in self.list_data.values(): + # print(" URL : {}".format(data.url)) + for idx in range(len(data)): + # print(" SEQ_{} : {}".format(idx, data.list_seqnames[idx])) + # print(" LEN_{} : {}".format(idx, len(data.list_list_timestamps[idx]))) + global_count = global_count + 1 + # print("----------------------------------------") + + print("TOTAL : {} sequnces".format(global_count)) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-m", "--mode", type=str) + parser.add_argument("-d", "--data_path", type=str) + parser.add_argument("-o", "--out_path", type=str) + parser.add_argument("-t", "--tmp_path", default="/dev/shm", type=str) + + args = parser.parse_args() + mode = args.mode + data_path = Path(args.data_path) + out_path = Path(args.out_path) + tmp_path = Path(args.tmp_path) + + if mode not in ["test", "train"]: + raise ValueError(f"Invalid split mode: {mode}") + + data_path = data_path / mode + out_path = out_path / mode + downloader = DataDownloader( + data_path=data_path, + out_path=out_path, + tmp_path=tmp_path, + mode=mode) + + downloader.show() + is_ok = downloader.run() + + if is_ok: + print("Done!") + else: + print("Failed") + + +if __name__ == "__main__": + main() + + diff --git a/datasets/realestate10k/process_mine_split.py b/datasets/realestate10k/process_mine_split.py new file mode 100644 index 0000000000000000000000000000000000000000..94c4ffc04ea8b92388723388d3ec30a70e3fbc5d --- /dev/null +++ b/datasets/realestate10k/process_mine_split.py @@ -0,0 +1,63 @@ +import argparse +import json +import pickle + +from pathlib import Path + +import numpy as np + +DRY_RUN = False + + +# Corresponds to Tulsiani et al. +def main(): + parser = argparse.ArgumentParser("KITTI Raw NVS Split") + parser.add_argument("--data_path", "-d", type=str) + parser.add_argument("--out_path", "-o", type=str) + parser.add_argument("--split_path", "-s", type=str) + + args = parser.parse_args() + + data_path = Path(args.data_path) + out_path = Path(args.out_path) + split_path = Path(args.split_path) + + print("Setting up folders...") + Path(out_path).mkdir(parents=True, exist_ok=True) + + file_path = Path(data_path) / f"test.pickle" + with open(file_path, "rb") as f: + seq_data = pickle.load(f) + + modes = ["val", "test"] + + def get_key_id(sample): + key = sample["sequence_id"] + if key not in seq_data: + return None + timestamp = int(sample["src_img_obj"]["frame_ts"]) + id = np.searchsorted(seq_data[key]["timestamps"], timestamp) + return key, id + + for mode in modes: + split_file_name = "test_pairs.json" if mode == "test" else "validation_pairs.json" + with open(split_path / split_file_name, "r") as f: + lines = f.readlines() + split_data = list(map(json.loads, lines)) + + print(f"Loaded {len(split_data)} samples.") + + key_id_pairs = list(map(get_key_id, split_data)) + key_id_pairs_filtered = [pair for pair in key_id_pairs if pair is not None] + print(f"Skipped {len(key_id_pairs)-len(key_id_pairs_filtered)} frames.") + + key_id_strs = [f"{key} {id}" for key, id in key_id_pairs_filtered] + + print(f"Processed {len(split_data)} samples.") + + with open(out_path / f"{mode}_files.txt", "w") as f: + f.write("\n".join(key_id_strs)) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/datasets/realestate10k/process_realestate10k.py b/datasets/realestate10k/process_realestate10k.py new file mode 100644 index 0000000000000000000000000000000000000000..df218b4a103f300dfd4c9f5ffd3484d893fd8d99 --- /dev/null +++ b/datasets/realestate10k/process_realestate10k.py @@ -0,0 +1,75 @@ +import argparse +import pickle +from pathlib import Path + +from tqdm import tqdm + +import numpy as np + + +def load_data(mode_path: Path, failures_path: Path): + seq_files = sorted(mode_path.glob('*.txt')) + with open(failures_path, "r") as f: + failures = set(l[:-1] for l in f.readlines()) + + skipped = 0 + + seq_data = {} + + for seq_file in tqdm(seq_files): + seq_name = seq_file.stem + + if seq_name in failures: + # print(f"Skipping sequence {seq_name} because the download had failed.") + skipped += 1 + continue + + try: + timestamps = np.loadtxt(seq_file, dtype=int, skiprows=1).reshape((-1, 19))[:, 0] + except: + print(seq_file) + pass + data = np.loadtxt(seq_file, dtype=float, skiprows=1).reshape((-1, 19))[:, 1:] + intrinsics = data[:, :6] + pose = np.reshape(data[:, 6:], (-1, 3, 4)) + + seq_data[seq_name] = { + "timestamps": timestamps, + "intrinsics": intrinsics, + "poses": pose + } + + print(f"Skipped {skipped} sequences because the video download had failed.") + + return seq_data + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-m", "--mode", type=str) + parser.add_argument("-d", "--data_path", type=str) + + args = parser.parse_args() + mode = args.mode + data_path = Path(args.data_path) + + if mode not in ["test", "train"]: + raise ValueError(f"Invalid split mode: {mode}") + + mode_path = data_path / mode + failures_path = data_path / f"failed_videos_{mode}.txt" + + print("Loading data") + data = load_data(mode_path, failures_path) + + print() + print("Saving data into a single file.") + with open(data_path / f"{mode}.pickle", "wb") as f: + pickle.dump(data, f) + + print() + print("Done") + + +if __name__ == "__main__": + main() diff --git a/datasets/realestate10k/realestate10k_dataset.py b/datasets/realestate10k/realestate10k_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..d7f61d027c9fc2e103dc51736b9451d86b484d3c --- /dev/null +++ b/datasets/realestate10k/realestate10k_dataset.py @@ -0,0 +1,220 @@ +import os +import pickle +import time +from pathlib import Path +from typing import Optional + +import cv2 +import numpy as np +import torch +from torch.utils.data import Dataset +from torchvision.transforms import ColorJitter + +from scenedino.common.augmentation import get_color_aug_fn + + +class RealEstate10kDataset(Dataset): + def __init__( + self, + data_path: str, + split_path: Optional[str] = None, + target_image_size=(256, 384), + frame_count=2, + dilation=1, + keyframe_offset=0, + color_aug=False, + ): + self.data_path = data_path + self.split_path = split_path + self.target_image_size = target_image_size + self.frame_count = frame_count + self.dilation = dilation + self.keyframe_offset = keyframe_offset + self.color_aug = color_aug + + if self.split_path is None: + self.split = "train" + else: + self.split = "test" + + self._seq_data = self._load_seq_data(self.data_path, self.split) + self._seq_keys = list(self._seq_data.keys()) + + if isinstance(self.dilation, int): + self._left_offset = ( + (self.frame_count - 1) // 2 + self.keyframe_offset + ) * self.dilation + dilation = self.dilation + else: + self._left_offset = 0 + dilation = 0 + + if self.split == "train": + self._key_id_pairs = self._full_index( + self._seq_keys, + self._seq_data, + self._left_offset, + (self.frame_count - 1) * dilation, + ) + else: + self._key_id_pairs = self._load_index(split_path) + + self._skip = 0 + self.length = len(self._key_id_pairs) + + @staticmethod + def _load_seq_data(data_path, split): + file_path = Path(data_path) / f"{split}.pickle" + with open(file_path, "rb") as f: + seq_data = pickle.load(f) + return seq_data + + @staticmethod + def _full_index(seq_keys, seq_data, left_offset, extra_frames): + key_id_pairs = [] + for k in seq_keys: + seq_len = len(seq_data[k]["timestamps"]) + seq_key_id_pairs = [ + (k, i + left_offset) for i in range(seq_len - extra_frames) + ] + key_id_pairs += seq_key_id_pairs + return key_id_pairs + + @staticmethod + def _load_index(index_path): + def get_key_id(s): + parts = s.split(" ") + key = parts[0] + id = int(parts[1]) + return key, id + + with open(index_path, "r") as f: + lines = f.readlines() + key_id_pairs = list(map(get_key_id, lines)) + return key_id_pairs + + def load_images(self, key, ids): + imgs = [] + + for id in ids: + timestamp = self._seq_data[key]["timestamps"][id] + img = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "frames", + self.split, + key, + f"{timestamp}.jpg", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs += [img] + + return imgs + + def process_img(self, img: np.array, color_aug_fn=None): + if self.target_image_size: + img = cv2.resize( + img, + (self.target_image_size[1], self.target_image_size[0]), + interpolation=cv2.INTER_LINEAR, + ) + img = np.transpose(img, (2, 0, 1)) + + if color_aug_fn is not None: + img = color_aug_fn(torch.tensor(img)) + + img = img * 2 - 1 + return img + + @staticmethod + def process_pose(pose): + pose = np.concatenate( + (pose.astype(np.float32), np.array([[0, 0, 0, 1]], dtype=np.float32)), + axis=0, + ) + pose = np.linalg.inv(pose) + return pose + + @staticmethod + def process_projs(proj): + K = np.eye(3, dtype=np.float32) + K[0, 0] = 2 * proj[0] + K[1, 1] = 2 * proj[1] + K[0, 2] = 2 * proj[2] - 1 + K[1, 2] = 2 * proj[3] - 1 + return K + + def __getitem__(self, index: int): + _start_time = time.time() + + if index >= self.length: + raise IndexError() + + if self._skip != 0: + index += self._skip + + if self.color_aug: + color_aug_fn = get_color_aug_fn( + ColorJitter.get_params( + brightness=(0.8, 1.2), + contrast=(0.8, 1.2), + saturation=(0.8, 1.2), + hue=(-0.1, 0.1), + ) + ) + else: + color_aug_fn = None + + key, index = self._key_id_pairs[index] + seq_len = len(self._seq_data[key]["timestamps"]) + + if self.dilation == "random": + dilation = torch.randint(1, 30, (1,)).item() + left_offset = self._left_offset + if self.frame_count > 2: + left_offset = dilation * (self.frame_count // 2) + else: + dilation = self.dilation + left_offset = self._left_offset + + ids = [index] + [ + max(min(i, seq_len - 1), 0) + for i in range( + index - left_offset, + index - left_offset + self.frame_count * dilation, + dilation, + ) + if i != index + ] + + imgs = self.load_images(key, ids) + imgs = [self.process_img(img, color_aug_fn=color_aug_fn) for img in imgs] + + # These poses are camera to world !! + poses = [self.process_pose(self._seq_data[key]["poses"][i, :, :]) for i in ids] + projs = [ + self.process_projs(self._seq_data[key]["intrinsics"][i, :]) for i in ids + ] + + depths = [np.ones_like(imgs[0][:1, :, :])] + + _proc_time = np.array(time.time() - _start_time) + + data = { + "imgs": imgs, + "projs": projs, + "poses": poses, + "depths": depths, + "t__get_item__": np.array([_proc_time]), + } + + return data + + def __len__(self) -> int: + return self.length diff --git a/datasets/realestate10k/splits/mine/test_files.txt 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0000000000000000000000000000000000000000..9e78a7bd85122593c3817af3f05b494f662a3a62 --- /dev/null +++ b/demo_utils/examples/log.csv @@ -0,0 +1,2 @@ +image, mode, sigma_threshold, resolution, x_range, y_range, z_range +"kitti-360.png","DINO (ViT-B)","Feature PCA 1-3",0.25,0.5,20,20,50 diff --git a/demo_utils/utils.py b/demo_utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9f08c780b84c126f049dff690e511d9a107c4121 --- /dev/null +++ b/demo_utils/utils.py @@ -0,0 +1,237 @@ +import torch +from torch import Tensor +from torch.utils.data import Dataset +from torchvision.io import read_image +from torchvision.transforms.functional import center_crop, resize + +import os +from hydra import compose, initialize + +from scenedino.models.bts import BTSNet +from scenedino.common.ray_sampler import ImageRaySampler +from scenedino.models import make_model +from scenedino.renderer.nerf import NeRFRenderer +from scenedino.training.trainer import BTSWrapper +from scenedino.datasets import make_datasets +from scenedino.common.array_operations import map_fn, unsqueezer + + +device = "cuda" + + +def load_modules( + ckpt_path: str, + ckpt_name: str +) -> tuple[BTSNet, NeRFRenderer, ImageRaySampler, Dataset]: + """ + Loads relevant modules with a SceneDINO checkpoint (*.pt) and corresponding config (training_config.yaml) file. + + Args: + ckpt_path (str): Relative path to the directory containing the files. + ckpt_name (str): File name of the checkpoint. + + Returns: + net (BTSNet): The SceneDINO network. + renderer (NeRFRenderer): Volume rendering module. + ray_sampler (ImageRaySampler): Camera ray sampler for whole images. + test_dataset (Dataset): Test set of the dataset trained on. + """ + with initialize(version_base=None, config_path="../" + ckpt_path, job_name="demo_script"): + config = compose(config_name="training_config") + + net = make_model(config["model"], config.get("downstream", None)) + + renderer = NeRFRenderer.from_conf(config["renderer"]) + renderer.hard_alpha_cap = False + renderer = renderer.bind_parallel(net, gpus=None).eval() + + height, width = config["dataset"]["image_size"] + ray_sampler = ImageRaySampler(z_near=3, z_far=80, width=width, height=height) + + model = BTSWrapper(renderer, ray_sampler, config["model"]) + cp = torch.load(ckpt_path + ckpt_name) + # cp = cp["model"] # Some older checkpoints have this structure + + model.load_state_dict(cp, strict=False) + model = model.to(device) + + test_dataset = make_datasets(config["dataset"])[1] + + return net, renderer, ray_sampler, test_dataset + + +def load_sample_from_path( + path: str, + intrinsic: Tensor | None +) -> tuple[Tensor, Tensor, Tensor]: + """ + Loads a test image from a provided path. + + Args: + path (str): Image path. + + Returns: + images (Tensor): RGB image normalized to [-1, 1]. + poses (Tensor): Camera pose (unit matrix). + projs (Tensor): Camera matrix (unit matrix). + """ + images = read_image(path) + + if not (images.size(1) == 192 and images.size(2) == 640): + scale = max(192 / images.size(1), 640 / images.size(2)) + new_h, new_w = int(images.size(1) * scale), int(images.size(2) * scale) + + images_resized = resize(images, [new_h, new_w]) + images = center_crop(images_resized, (192, 640)) + print("WARNING: Custom image does not have correct dimensions! Taking center crop.") + + if images.dtype == torch.uint8: + images = 2 * (images / 255) - 1 + elif images.dtype == torch.uint16: + images = 2 * (images / (2**16 - 1)) - 1 + + if images.size(0) == 4: + images = images[:3] + + images = images.unsqueeze(0).unsqueeze(1) + poses = torch.eye(4).unsqueeze(0).unsqueeze(1) + + if intrinsic: + projs = intrinsic.unsqueeze(0).unsqueeze(1) + else: + projs = torch.Tensor([ + [0.7849, 0.0000, -0.0312], + [0.0000, 2.9391, 0.2701], + [0.0000, 0.0000, 1.0000]]).unsqueeze(0).unsqueeze(1) + print("WARNING: Custom image has no provided intrinsics! Using KITTI-360 values.") + + return images.to(device), poses.to(device), projs.to(device) + + +def load_sample_from_dataset( + idx: int, + dataset: Dataset +) -> tuple[Tensor, Tensor, Tensor]: + """ + Loads a data point from the provided dataset. In this demo, we just load the front view. + + Args: + idx (int): Index in the dataset. + dataset (Dataset): The dataset. + + Returns: + images (Tensor): RGB image normalized to [-1, 1]. + poses (Tensor): Camera pose (since just front view, unit matrix). + projs (Tensor): Camera matrix. + """ + data = dataset[idx] + + data_batch = map_fn(map_fn(data, torch.tensor), unsqueezer) + images = torch.stack(data_batch["imgs"], dim=1) + poses = torch.stack(data_batch["poses"], dim=1) + projs = torch.stack(data_batch["projs"], dim=1) + + poses = torch.inverse(poses[:, :1, :, :]) @ poses + + # Just front view + images = images[:, :1] + poses = poses[:, :1] + projs = projs[:, :1] + + return images.to(device), poses.to(device), projs.to(device) + + +def inference_3d( + net: BTSNet, + x_range: tuple[float, float], + y_range: tuple[float, float], + z_range: tuple[float, float], + resolution: float, + prediction_mode: str = "stego_kmeans" +) -> tuple[Tensor, Tensor, Tensor]: + """ + Inference in a uniform 3D grid. All units are provided in meters. + + Args: + net (BTSNet): The SceneDINO network. + x_range (tuple[float, float]): Range along the X dimension. + y_range (tuple[float, float]): Range along the Y dimension. + z_range (tuple[float, float]): Range along the Z dimension, the viewing direction. + resolution (float): Resolution of the grid. + + Returns: + dino_full (Tensor): SceneDINO features [n_X, n_Y, n_Z, 768]. + sigma (Tensor): Volumentric density [n_X, n_Y, n_Z]. + seg (Tensor): Predicted semantic classes [n_X, n_Y, n_Z]. + """ + n_pts_x = int((x_range[1] - x_range[0]) / resolution) + 1 + n_pts_y = int((y_range[1] - y_range[0]) / resolution) + 1 + n_pts_z = int((z_range[1] - z_range[0]) / resolution) + 1 + + x = torch.linspace(x_range[0], x_range[1], n_pts_x) + y = torch.linspace(y_range[0], y_range[1], n_pts_y) + z = torch.linspace(z_range[0], z_range[1], n_pts_z) + + grid_x, grid_y, grid_z = torch.meshgrid(x, y, z, indexing='ij') + xyz = torch.stack((grid_x, grid_y, grid_z), dim=-1).reshape(-1, 3).unsqueeze(0).to(device) + + dino_full, invalid, sigma, seg = net(xyz, predict_segmentation=True, prediction_mode=prediction_mode) + + dino_full = dino_full.reshape(n_pts_x, n_pts_y, n_pts_z, -1) + sigma = sigma.reshape(n_pts_x, n_pts_y, n_pts_z) + + if seg is not None: + seg = seg.reshape(n_pts_x, n_pts_y, n_pts_z, -1).argmax(-1) + + return xyz, dino_full, sigma, seg + + +def get_fov_mask(proj_matrix, xyz): + proj_xyz = xyz @ proj_matrix.T + proj_xyz = proj_xyz / proj_xyz[..., 2:3] + + fov_mask = (proj_xyz[..., 0] > -0.99) & (proj_xyz[..., 0] < 0.99) & (proj_xyz[..., 1] > -0.99) & (proj_xyz[..., 1] < 0.99) + + return fov_mask + + + +def inference_rendered_2d( + net: BTSNet, + poses: Tensor, + projs: Tensor, + ray_sampler: ImageRaySampler, + renderer: NeRFRenderer, + prediction_mode: str = "stego_kmeans" +) -> tuple[Tensor, Tensor, Tensor]: + """ + Inference in 3D, rendered back into a 2D image, based on a provided camera pose and matrix. + + Args: + net (BTSNet): The SceneDINO network. + poses (Tensor): Camera pose. + projs (Tensor): Camera matrix. + ray_sampler (ImageRaySampler): Camera ray sampler for whole images. + renderer (NeRFRenderer): Volume rendering module. + + Returns: + dino_full (Tensor): SceneDINO features [H, W, 768]. + depth (Tensor): Ray termination depth [H, W]. + seg (Tensor): Predicted semantic classes [H, W]. + """ + all_rays, _ = ray_sampler.sample(None, poses[:, :], projs[:, :]) + render_dict = renderer(all_rays, want_weights=True, want_alphas=True) + render_dict = ray_sampler.reconstruct(render_dict) + + depth = render_dict["coarse"]["depth"].squeeze() + + dino_distilled = render_dict["coarse"]["dino_features"].squeeze() + dino_full = net.encoder.expand_dim(dino_distilled) + + if net.downstream_head is not None: + seg = net.downstream_head(dino_full, mode=prediction_mode) + else: + seg = None + + return dino_full, depth, seg + \ No newline at end of file diff --git a/download_checkpoint_hf.py b/download_checkpoint_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2ac25d439668d4ad908aac491cadbcce2348173e --- /dev/null +++ b/download_checkpoint_hf.py @@ -0,0 +1,70 @@ +import os +import sys +import argparse +from huggingface_hub import hf_hub_download +from pathlib import Path + +def download_scenedino_checkpoint(model_name): + print("----------------------- Downloading pretrained model -----------------------") + model_configs = { + "ssc-kitti-360-dino": { + "model-dir": "seg-best-dino" + }, + "ssc-kitti-360-dino-orb-slam": { + "model-dir": "seg-best-dino-orb-slam" + }, + "ssc-kitti-360-dinov2": { + "model-dir": "seg-best-dinov2" + } + } + + repo_id = "jev-aleks/SceneDINO" + checkpoint_filename = "checkpoint.pt" + config_filename = "training_config.yaml" + + if model_name not in model_configs: + raise ValueError(f"Unknown model: {model_name}. Possible options: {', '.join(model_configs.keys())}") + + config = model_configs[model_name] + + output_dir = Path("out/scenedino-pretrained") + output_dir.mkdir(parents=True, exist_ok=True) + + checkpoint_filename = Path(config["model-dir"]) / checkpoint_filename + config_filename = Path(config["model-dir"]) / config_filename + + checkpoint_path = output_dir / checkpoint_filename + config_path = output_dir / config_filename + + print(f"Operating in \"{os.getcwd()}\".") + print(f"Creating directories: {output_dir}") + + # Download checkpoint + print(f"Downloading checkpoint from HF repo \"{repo_id}\" to \"{checkpoint_path}\".") + hf_hub_download( + repo_id=repo_id, + filename=str(checkpoint_filename), + local_dir=str(output_dir), + ) + + # Download config + print(f"Downloading config from HF repo \"{repo_id}\" to \"{config_path}\".") + hf_hub_download( + repo_id=repo_id, + filename=str(config_filename), + local_dir=str(output_dir), + ) + + print("Download completed successfully!") + + +def main(): + parser = argparse.ArgumentParser(description="Download pretrained models from Hugging Face Hub") + parser.add_argument("model", help="Model name to download") + + args = parser.parse_args() + download_scenedino_checkpoint(args.model) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/environment.txt b/environment.txt new file mode 100644 index 0000000000000000000000000000000000000000..d009387f5de88f5f92f34c0e8fbafb2686a9a4c2 --- /dev/null +++ b/environment.txt @@ -0,0 +1,26 @@ +python==3.10 +torch==2.3.0 +torchvision==0.18.0 + +git+https://github.com/lucasb-eyer/pydensecrf.git +tensorflow-datasets +pytorch-ignite +hydra-core +omegaconf +lpips +dotmap +dotdict +pytorch-msssim +plyfile +tensorflow +opencv-python +matplotlib +numpy==1.26.4 +scipy +scikit-image +timm +kornia +pykeops +PuLP +optuna +gradio \ No newline at end of file diff --git a/scenedino/__init__.py b/scenedino/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/scenedino/common/array_operations.py b/scenedino/common/array_operations.py new file mode 100644 index 0000000000000000000000000000000000000000..3a21f8c330284567d73b8937df0c6c34e15f7131 --- /dev/null +++ b/scenedino/common/array_operations.py @@ -0,0 +1,81 @@ +from functools import partial + +import torch +import torch.nn.functional as F + + +unsqueezer = partial(torch.unsqueeze, dim=0) + + +def map_fn(batch, fn): + if isinstance(batch, dict): + for k in batch.keys(): + batch[k] = map_fn(batch[k], fn) + return batch + elif isinstance(batch, list): + return [map_fn(e, fn) for e in batch] + else: + return fn(batch) + + +def to(data, device, non_blocking=True): + if isinstance(data, dict): + return {k: to(data[k], device, non_blocking=non_blocking) for k in data.keys()} + elif isinstance(data, list): + return [to(v, device, non_blocking=non_blocking) for v in data] + else: + return data.to(device, non_blocking=non_blocking) + + +def set_requires_grad(nets, requires_grad=False): + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad + + +def mask_mean(t: torch.Tensor, m: torch.Tensor, dim=None, keepdim=False): + t = t.clone() + t[m] = 0 + els = 1 + if dim is None or len(dim)==0: + dim = list(range(len(t.shape))) + for d in dim: + els *= t.shape[d] + return torch.sum(t, dim=dim, keepdim=keepdim) / (els - torch.sum(m.to(torch.float), dim=dim, keepdim=keepdim)) + + +def apply_crop(array, crop): + return array[crop[0]:crop[0] + crop[2], crop[1]:crop[1] + crop[3]] + + +def shrink_mask(mask, shrink=3): + mask = F.avg_pool2d(mask.to(torch.float32), kernel_size=shrink, padding=shrink // 2, stride=1) + return (mask == 1.).to(torch.float32) + + +def get_mask(size, border=5, device=None): + mask = torch.ones(size, dtype=torch.float32) + mask = shrink_mask(mask, border) + if device is not None: + mask = mask.to(device) + return mask + + +def get_grid(H, W, normalize=True): + if normalize: + h_range = torch.linspace(-1,1,H) + w_range = torch.linspace(-1,1,W) + else: + h_range = torch.arange(0,H) + w_range = torch.arange(0,W) + grid = torch.stack(torch.meshgrid([h_range, w_range]), -1).flip(2).float() # flip h,w to x,y + return grid + + +def detach(t): + if isinstance(t, tuple): + return tuple(t_.detach() for t_ in t) + else: return t.detach() diff --git a/scenedino/common/augmentation.py b/scenedino/common/augmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..e3fe6ad7baf5f0bf3f6a95715db9ed6dfebb044e --- /dev/null +++ b/scenedino/common/augmentation.py @@ -0,0 +1,18 @@ +import torchvision.transforms.functional as F + +def get_color_aug_fn(params): + fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor = params + def color_aug_fn(img): + for fn_id in fn_idx: + if fn_id == 0 and brightness_factor is not None: + img = F.adjust_brightness(img, brightness_factor) + elif fn_id == 1 and contrast_factor is not None: + img = F.adjust_contrast(img, contrast_factor) + elif fn_id == 2 and saturation_factor is not None: + img = F.adjust_saturation(img, saturation_factor) + elif fn_id == 3 and hue_factor is not None: + img = F.adjust_hue(img, hue_factor) + + return img + + return color_aug_fn diff --git a/scenedino/common/cameras/fisheye.py b/scenedino/common/cameras/fisheye.py new file mode 100644 index 0000000000000000000000000000000000000000..29cbe3fad8a549f132bb4b6c3226d8e8470e021c --- /dev/null +++ b/scenedino/common/cameras/fisheye.py @@ -0,0 +1,136 @@ +import torch + + +# def project_to_image( +# pts: torch.Tensor, calib: dict[str, Any] +# ) -> tuple[torch.Tensor, torch.Tensor]: +# """Project pts in camera coordinates into image coordinates. + +# Args: +# pts (torch.Tensor): B, n_views, n_pts, 3 +# Ks (torch.Tensor): B, n_views, 3, 3 + +# Returns: +# tuple[torch.Tensor, torch.Tensor]: (B, n_views, n_pts, 2), (B, n_views, n_pts, 1) +# """ +# pts = pts / torch.norm(pts, dim=-1, keepdim=True) +# x = pts[:, 0] +# y = pts[:, 1] +# z = pts[:, 2] + +# xi_src = calib["mirror_parameters"]["xi"] +# x = x / (z + xi_src) +# y = y / (z + xi_src) + +# k1 = calib["distortion_parameters"]["k1"] +# k2 = calib["distortion_parameters"]["k2"] + +# r = x * x + y * y +# factor = 1 + k1 * r + k2 * r * r +# x = x * factor +# y = y * factor + +# gamma0 = calib["projection_parameters"]["gamma1"] +# gamma1 = calib["projection_parameters"]["gamma2"] +# u0 = calib["projection_parameters"]["u0"] +# v0 = calib["projection_parameters"]["v0"] + +# x = x * gamma0 + u0 +# y = y * gamma1 + v0 + +# return torch.stack([x, y], dim=-1), z +# TODO: lookup +EPS = 1.0e-6 + + +def normalize_calib(calib: torch.Tensor, img_sizes: torch.Tensor) -> torch.Tensor: + """Normalize the calibration matrices for fisheye cameras based on the image size + + Args: + calib (torch.Tensor): B, n_views, 7, [xi, k1, k2, gamma1, gamma2, u0, v0] + img_sizes (torch.Tensor): B, n_views, 2 + + Returns: + torch.Tensor: B, n_views 7 + """ + + calib[..., 3:5] = calib[..., 3:5] / img_sizes * 2.0 + calib[..., 5:7] = calib[..., 5:7] / img_sizes * 2.0 - 1.0 + + return calib + + +def unnormalize_calib(calib: torch.Tensor, img_sizes: torch.Tensor) -> torch.Tensor: + """Unnormalize the calibration matrices for fisheye cameras based on the image size + + Args: + calib (torch.Tensor): B, n_views, 7, [xi, k1, k2, gamma1, gamma2, u0, v0] + img_sizes (torch.Tensor): B, n_views, 2 + + Returns: + torch.Tensor: B, n_views 7 + """ + + calib[..., 3:5] = calib[..., 3:5] * img_sizes / 2.0 + calib[..., 5:7] = (calib[..., 5:7] + 1.0) * img_sizes / 2.0 + + return calib + + +def project_to_image( + pts: torch.Tensor, calib: torch.Tensor +) -> tuple[torch.Tensor, torch.Tensor]: + """Project pts in camera coordinates into image coordinates. + + Args: + pts (torch.Tensor): B, n_views, n_pts, 3 + Ks (torch.Tensor): B, n_views, 7 + + Returns: + tuple[torch.Tensor, torch.Tensor]: (B, n_views, n_pts, 2), (B, n_views, n_pts, 1) + """ + pts = pts / torch.norm(pts, dim=-1, keepdim=True) + xy = pts[..., 0:2] + z = pts[..., 2:3] + + xi_src = calib[..., 0:1].unsqueeze(-2) # B, n_views, 1, 1 + + xy = xy / (z + xi_src) + + r = torch.sum(torch.square(xy), dim=-1) + factor = 1 + calib[..., 1:2] * r + calib[..., 2:3] * torch.square(r) + + xy = xy * factor.unsqueeze(-1) + + xy = xy * calib[..., 3:5].unsqueeze(-2) + calib[..., 5:7].unsqueeze(-2) + + return xy, z + + +# TODO: define for fisheye +def outside_frustum( + xy: torch.Tensor, + z: torch.Tensor, + limits_x: tuple[float, float] | tuple[int, int] = (-1.0, 1.0), + limits_y: tuple[float, float] | tuple[int, int] = (-1.0, 1.0), + limit_z: float = EPS, +) -> torch.Tensor: + """_summary_ + + Args: + xy (torch.Tensor): _description_ + z (torch.Tensor): _description_ + limits_x (tuple[float, float] | tuple[int, int], optional): _description_. Defaults to (-1.0, 1.0). + limits_y (tuple[float, float] | tuple[int, int], optional): _description_. Defaults to (-1.0, 1.0). + limit_z (float, optional): _description_. Defaults to EPS. + + Returns: + torch.Tensor: _description_ + """ + return ( + (z <= limit_z) + | (xy[..., :1] < limits_x[0]) + | (xy[..., :1] > limits_x[1]) + | (xy[..., 1:2] < limits_y[0]) + | (xy[..., 1:2] > limits_y[1]) + ) diff --git a/scenedino/common/cameras/pinhole.py b/scenedino/common/cameras/pinhole.py new file mode 100644 index 0000000000000000000000000000000000000000..e41d184dc5d37c52c59ebd93bdcaf2abc79009fd --- /dev/null +++ b/scenedino/common/cameras/pinhole.py @@ -0,0 +1,112 @@ +import torch + +EPS = 1e-3 + + +def normalize_calib(K: torch.Tensor, img_sizes: torch.Tensor) -> torch.Tensor: + """Normalize the calibration matrices for fisheye cameras based on the image size + + Args: + calib (torch.Tensor): B, n_views, 3, 3 + img_sizes (torch.Tensor): B, n_views, 2 + + Returns: + torch.Tensor: B, n_views 7 + """ + + K[..., :2, :] = K[..., :2, :] / img_sizes.unsqueeze(-1) * 2.0 + K[..., :2, 2] = K[..., :2, 2] - 1.0 + + return K + + +def unnormalize_calib(K: torch.Tensor, img_sizes: torch.Tensor) -> torch.Tensor: + """Unnormalize the calibration matrices for fisheye cameras based on the image size + + Args: + calib (torch.Tensor): B, n_views, 3, 3 + img_sizes (torch.Tensor): B, n_views, 2 + + Returns: + torch.Tensor: B, n_views 7 + """ + + K[..., :2, 2] = K[..., :2, 2] + 1.0 + K[..., :2, :] = K[..., :2, :] * img_sizes.unsqueeze(-1) / 2.0 + + return K + + +def pts_into_camera(pts: torch.Tensor, poses_w2c: torch.Tensor) -> torch.Tensor: + """Project points from world coordinates into camera coordinate + + Args: + pts (torch.Tensor): B, n_pts, 3 + poses_w2c (torch.Tensor): B, n_view, 4, 4 + + Returns: + torch.Tensor: B, n_views, n_pts, 3 + """ + + # Add a singleton dimension to the input point cloud to match grid_f_poses_w2c shape + pts = pts.unsqueeze(1) # [B, 1, n_pts, 3] + ones = torch.ones_like( + pts[..., :1] + ) ## Create a tensor of ones to add a fourth dimension to the point cloud for homogeneous coordinates + pts = torch.cat( + (pts, ones), dim=-1 + ) ## Concatenate the tensor of ones with the point cloud to create homogeneous coordinates + return (poses_w2c[:, :, :3, :]) @ pts.permute(0, 1, 3, 2) + + +def project_to_image( + pts: torch.Tensor, Ks: torch.Tensor +) -> tuple[torch.Tensor, torch.Tensor]: + """Project pts in camera coordinates into image coordinates. + + Args: + pts (torch.Tensor): B, n_views, n_pts, 3 + Ks (torch.Tensor): B, n_views, 3, 3 + + Returns: + tuple[torch.Tensor, torch.Tensor]: (B, n_views, n_pts, 2), (B, n_views, n_pts, 1) + """ + pts = (Ks @ pts).permute( + 0, 1, 3, 2 + ) ## Apply the intrinsic camera parameters to the projected points to get pixel coordinates + xy = pts[ + :, :, :, :2 + ] ## Extract the x,y coordinates and depth value from the projected points + z_ = pts[:, :, :, 2:3] + + xy = xy / z_.clamp_min(EPS) + + return xy, z_ + + +def outside_frustum( + xy: torch.Tensor, + z: torch.Tensor, + limits_x: tuple[float, float] | tuple[int, int] = (-1.0, 1.0), + limits_y: tuple[float, float] | tuple[int, int] = (-1.0, 1.0), + limit_z: float = EPS, +) -> torch.Tensor: + """_summary_ + + Args: + xy (torch.Tensor): _description_ + z (torch.Tensor): _description_ + limits_x (tuple[float, float] | tuple[int, int], optional): _description_. Defaults to (-1.0, 1.0). + limits_y (tuple[float, float] | tuple[int, int], optional): _description_. Defaults to (-1.0, 1.0). + limit_z (float, optional): _description_. Defaults to EPS. + + Returns: + torch.Tensor: _description_ + """ + return ( + (z <= limit_z) + | (xy[..., :1] < limits_x[0]) + | (xy[..., :1] > limits_x[1]) + | (xy[..., 1:2] < limits_y[0]) + | (xy[..., 1:2] > limits_y[1]) + ) diff --git a/scenedino/common/errors.py b/scenedino/common/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..0aeba96c4987c6bbf24e1e8c260601b0c602a918 --- /dev/null +++ b/scenedino/common/errors.py @@ -0,0 +1,354 @@ +import math +import torch +import torch.nn.functional as F + +# TODO: check if the functions can be moved somewhere else +from scenedino.common.util import kl_div, normalized_entropy +from scenedino.models.prediction_heads.layers import ssim, geo + + +# TODO: have two signatures with override. One for mask, one without mask +# NOTE: what is the purpose of the mask. Ask Felix +def compute_l1ssim( + img0: torch.Tensor, img1: torch.Tensor, mask: torch.Tensor | None = None +) -> torch.Tensor: ## (img0 == pred, img1 == GT) + """Calculate the L1-SSIM error between two images. Use a mask if provided to ignore certain pixels. + + Args: + img0 (torch.Tensor): torch.Tensor of shape (B, c, h, w) containing the predicted images. + img1 (torch.Tensor): torch.Tensor of shape (B, c, h, w) containing the ground truth images. + mask (torch.Tensor | None, optional): torch.Tensor of shape (B, h, w). Defaults to None. + + Returns: + torch.Tensor: per patch error of shape (B, h, w) + """ + errors = 0.85 * torch.mean( + ssim(img0, img1, pad_reflection=False, gaussian_average=True, comp_mode=True), + dim=1, + ) + 0.15 * torch.mean(torch.abs(img0 - img1), dim=1) + # checking if a mask is provided. If a mask is provided, it is returned along with the errors. Otherwise, only the errors are returned. + # if mask is not None: + # return ( + # errors, + # mask, + # ) + return errors # (B, h, w) + + +def compute_normalized_l1( + flow0: torch.Tensor, flow1: torch.Tensor) -> torch.Tensor: + + errors = (flow0 - flow1).abs() / (flow0.detach().norm(dim=1, keepdim=True) + 1e-4) + + return errors + + +# TODO: integrate the mask +def compute_edge_aware_smoothness( + gt_img: torch.Tensor, input: torch.Tensor, mask: torch.Tensor | None = None, temperature: int = 1 +) -> torch.Tensor: + """Compute the edge aware smoothness loss of the depth prediction based on the gradient of the original image. + + Args: + gt_img (torch.Tensor): ground truth images of shape (B, c, h, w) + input (torch.Tensor): predicted tensor of shape (B, c, h, w) + mask (torch.Tensor | None, optional): Not used yet. Defaults to None. + + Returns: + torch.Tensor: per pixel edge aware smoothness loss of shape (B, h, w) + """ + _, _, h, w = gt_img.shape + + # TODO: check whether interpolation is necessary + # gt_img = F.interpolate(gt_img, (h, w)) + + input_dx = torch.mean( + torch.abs(input[:, :, :, :-1] - input[:, :, :, 1:]), 1, keepdim=True + ) # (B, 1, h, w-1) + input_dy = torch.mean( + torch.abs(input[:, :, :-1, :] - input[:, :, 1:, :]), 1, keepdim=True + ) # (B, 1, h-1, w) + + i_dx = torch.mean( + torch.abs(gt_img[:, :, :, :-1] - gt_img[:, :, :, 1:]), 1, keepdim=True + ) # (B, 1, h, w-1) + i_dy = torch.mean( + torch.abs(gt_img[:, :, :-1, :] - gt_img[:, :, 1:, :]), 1, keepdim=True + ) # (B, 1, h-1, w) + + input_dx *= torch.exp(-temperature * i_dx) # (B, 1, h, w-1) + input_dy *= torch.exp(-temperature * i_dy) # (B, 1, h-1, w) + + errors = F.pad(input_dx, pad=(0, 1), mode="constant", value=0) + F.pad( + input_dy, pad=(0, 0, 0, 1), mode="constant", value=0 + ) # (B, 1, h, w) + return errors[:, 0, :, :] # (B, h, w) + + +def compute_3d_smoothness( + feature_sample: torch.Tensor, sigma_sample: torch.Tensor +) -> torch.Tensor: + + return torch.var(feature_sample, dim=2) + + +def compute_occupancy_error( + teacher_field: torch.Tensor, + student_field: torch.Tensor, + mask: torch.Tensor | None = None, +) -> torch.Tensor: + """Compute the distillation error between the teacher and student density. + + Args: + teacher_density (torch.Tensor): teacher occpancy map of shape (B) + student_density (torch.Tensor): student occupancy map of shape (B) + mask (torch.Tensor | None, optional): Mask indicating bad occpancy values for student or teacher, e.g. invalid occupancies due to out of frustum. Defaults to None. + + Returns: + torch.Tensor: distillation error of shape (B) + """ + if mask is not None: + teacher_field = teacher_field[mask] + student_field = student_field[mask] + + return torch.nn.MSELoss(reduction="mean")(teacher_field, student_field) # (1) + + +def depth_regularization(depth: torch.Tensor) -> torch.Tensor: + """Compute the depth regularization loss. + + Args: + depth (torch.Tensor): depth map of shape (B, 1, h, w) + + Returns: + torch.Tensor: depth regularization loss of shape (B) + """ + depth_grad_x = depth[:, :, 1:, :] - depth[:, :, :-1, :] + depth_grad_y = depth[:, :, :, 1:] - depth[:, :, :, :-1] + depth_reg_loss = (depth_grad_x**2).mean() + (depth_grad_y**2).mean() + + return depth_reg_loss + + +def alpha_regularization( + alphas: torch.Tensor, invalids: torch.Tensor | None = None +) -> torch.Tensor: + # TODO: make configurable + alpha_reg_fraction = 1 / 8 + alpha_reg_reduction = "ray" + """Compute the alpha regularization loss. + + Args: + alphas (torch.Tensor): alpha map of shape (B, 1, h, w) + invalids (torch.Tensor | None, optional): Mask indicating bad alpha values, e.g. invalid alpha due to out of frustum. Defaults to None. + + Returns: + torch.Tensor: alpha regularization loss of shape (B) + """ + n_smps = alphas.shape[-1] + + alpha_sum = alphas[..., :-1].sum(-1) + min_cap = torch.ones_like(alpha_sum) * (n_smps * alpha_reg_fraction) + + if invalids is not None: + alpha_sum = alpha_sum * (1 - invalids.squeeze(-1).to(torch.float32)) + min_cap = min_cap * (1 - invalids.squeeze(-1).to(torch.float32)) + + match alpha_reg_reduction: + case "ray": + alpha_reg_loss = (alpha_sum - min_cap).clamp_min(0) + case "slice": + alpha_reg_loss = (alpha_sum.sum(dim=-1) - min_cap.sum(dim=-1)).clamp_min( + 0 + ) / alpha_sum.shape[-1] + case _: + raise ValueError(f"Invalid alpha_reg_reduction: {alpha_reg_reduction}") + + return alpha_reg_loss + + +def surfaceness_regularization( + alphas: torch.Tensor, invalids: torch.Tensor | None = None +) -> torch.Tensor: + p = -torch.log(torch.exp(-alphas.abs()) + torch.exp(-(1 - alphas).abs())) + p = p.mean(-1) + + if invalids is not None: + p = p * (1 - invalids.squeeze(-1).to(torch.float32)) + + surfaceness_reg_loss = p.mean() + return surfaceness_reg_loss + + +def depth_smoothness_regularization(depths: torch.Tensor) -> torch.Tensor: + depth_smoothness_loss = ((depths[..., :-1, :] - depths[..., 1:, :]) ** 2).mean() + ( + (depths[..., :, :-1] - depths[..., :, 1:]) ** 2 + ).mean() + + return depth_smoothness_loss + + +def sdf_eikonal_regularization(sdf: torch.Tensor) -> torch.Tensor: + grad_x = sdf[:, :1, :-1, :-1, 1:] - sdf[:, :1, :-1, :-1, :-1] + grad_y = sdf[:, :1, :-1, 1:, :-1] - sdf[:, :1, :-1, :-1, :-1] + grad_z = sdf[:, :1, 1:, :-1, :-1] - sdf[:, :1, :-1, :-1, :-1] + grad = (torch.cat((grad_x, grad_y, grad_z), dim=1) ** 2).sum(dim=1) ** 0.5 + + eikonal_loss = ((grad - 1) ** 2).mean(dim=(1, 2, 3)) + + return eikonal_loss + + +def weight_entropy_regularization( + weights: torch.Tensor, invalids: torch.Tensor | None = None +) -> torch.Tensor: + ignore_last = False + + weights = weights.clone() + + if ignore_last: + weights = weights[..., :-1] + weights = weights / weights.sum(dim=-1, keepdim=True) + + H_max = math.log2(weights.shape[-1]) + + # x log2 (x) -> 0 . Therefore, we can set log2 (x) to 0 if x is small enough. + # This should ensure numerical stability. + weights_too_small = weights < 2 ** (-16) + weights[weights_too_small] = 2 + + wlw = torch.log2(weights) * weights + + wlw[weights_too_small] = 0 + + # This is the formula for the normalised entropy + entropy = -wlw.sum(-1) / H_max + return entropy + + +def max_alpha_regularization(alphas: torch.Tensor, invalids: torch.Tensor | None = None): + alphas_max = alphas[..., :-1].max(dim=-1)[0] + alphas_reg = (1 - alphas_max).clamp(0, 1).mean() + return alphas_reg + + +def max_alpha_inputframe_regularization(alphas: torch.Tensor, ray_info, invalids: torch.Tensor | None = None): + mask = ray_info[..., 0] == 0 + alphas_max = alphas.max(dim=-1)[0] + alphas_reg = ((1 - alphas_max).clamp(0, 1) * mask.to(alphas_max.dtype)).mean() + return alphas_reg + + +def epipolar_line_regularization(data, rgb_gt, scale): + rgb = data["coarse"][scale]["rgb"] + rgb_samps = data["coarse"][scale]["rgb_samps"] + + b, pc, h, w, n_samps, nv, c = rgb_samps.shape + + rgb_gt = data["rgb_gt"].unsqueeze(-2).expand(rgb.shape) + + alphas = data["coarse"][scale]["alphas"] + + # TODO + + +def density_grid_regularization(density_grid, threshold): + density_grid = (density_grid.abs() - threshold).clamp_min(0) + + # Attempt to make it more numerically stable + max_v = density_grid.max().clamp_min(1).detach() + + # print(max_v.item()) + + error = (((density_grid / max_v)).mean() * max_v) + + error = torch.nan_to_num(error, 0, 0, 0) + + # Black magic to prevent error massages from anomaly detection when using AMP + if torch.all(error == 0): + error = error.detach() + + return error + + +def kl_prop(weights): + entropy = normalized_entropy(weights.detach()) + + kl_prop = entropy[..., 1:-1, 1:-1] * (entropy[..., 1:-1, 1:-1] - entropy[..., 2:, 1:-1]).clamp_min(0) * kl_div(weights[..., 2:, 1:-1, :].detach(), weights[..., 1:-1, 1:-1, :]) + kl_prop += entropy[..., 1:-1, 1:-1] * (entropy[..., 1:-1, 1:-1] - entropy[..., 0:-2, 1:-1]).clamp_min(0) * kl_div(weights[..., 0:-2, 1:-1, :].detach(), weights[..., 1:-1, 1:-1, :]) + kl_prop += entropy[..., 1:-1, 1:-1] * (entropy[..., 1:-1, 1:-1] - entropy[..., 1:-1, 2:]).clamp_min(0) * kl_div(weights[..., 1:-1, 2:, :].detach(), weights[..., 1:-1, 1:-1, :]) + kl_prop += entropy[..., 1:-1, 1:-1] * (entropy[..., 1:-1, 1:-1] - entropy[..., 1:-1, 0:-2]).clamp_min(0) * kl_div(weights[..., 1:-1, :-2, :].detach(), weights[..., 1:-1, 1:-1, :]) + + return kl_prop.mean() + + +def alpha_consistency(alphas, invalids, consistency_policy): + invalids = torch.all(invalids < .5, dim=-1) + + if consistency_policy == "max": + target = torch.max(alphas, dim=-1, keepdim=True)[0].detach() + elif consistency_policy == "min": + target = torch.max(alphas, dim=-1, keepdim=True)[0].detach() + elif consistency_policy == "median": + target = torch.median(alphas, dim=-1, keepdim=True)[0].detach() + elif consistency_policy == "mean": + target = torch.mean(alphas, dim=-1, keepdim=True).detach() + else: + raise NotImplementedError + + diff = (alphas - target).abs().mean(dim=-1) + + invalids = invalids.to(diff.dtype) + + diff = (diff * invalids) + + return diff.mean() + + +def alpha_consistency_uncert(alphas, invalids, uncert): + invalids = torch.all(invalids < .5, dim=-1) + alphas = alphas.detach() + nf = alphas.shape[-1] + + alphas_median = torch.median(alphas, dim=-1, keepdim=True)[0].detach() + + target = (alphas - alphas_median).abs().mean(dim=-1) * (nf / (nf-1)) + + diff = (uncert[..., None] - target).abs() + + invalids = invalids.to(diff.dtype) + + diff = (diff * invalids) + + return diff.mean() + + +def entropy_based_smoothness(weights, depth, invalids=None): + entropy = normalized_entropy(weights.detach()) + + error_fn = lambda d0, d1: (d0 - d1.detach()).abs() + + if invalids is None: + invalids = torch.zeros_like(depth) + + # up + kl_prop_up = entropy[..., :-1, :] * (entropy[..., :-1, :] - entropy[..., 1:, :]).clamp_min(0) * error_fn(depth[..., :-1, :], depth[..., 1:, :]) * (1 - invalids[..., :-1, :]) + # down + kl_prop_down = entropy[..., 1:, :] * (entropy[..., 1:, :] - entropy[..., :-1, :]).clamp_min(0) * error_fn(depth[..., 1:, :], depth[..., :-1, :]) * (1 - invalids[..., 1:, :]) + # left + kl_prop_left = entropy[..., :, :-1] * (entropy[..., :, :-1] - entropy[..., :, 1:]).clamp_min(0) * error_fn(depth[..., :, :-1], depth[..., :, 1:]) * (1 - invalids[..., :, :-1]) + # right + kl_prop_right = entropy[..., :, 1:] * (entropy[..., :, 1:] - entropy[..., :, :-1]).clamp_min(0) * error_fn(depth[..., :, 1:], depth[..., :, :-1]) * (1 - invalids[..., :, 1:]) + + kl_prop = kl_prop_up.mean() + kl_prop_down.mean() + kl_prop_left.mean() + kl_prop_right.mean() + + return kl_prop.mean() + + +def flow_regularization(flow, gt_flow, invalids=None): + flow_reg = (flow[..., 0, :] - gt_flow).abs().mean(dim=-1, keepdim=True) + + if invalids is not None: + flow_reg = flow_reg * (1 - invalids) + + return flow_reg.mean() diff --git a/scenedino/common/geometry.py b/scenedino/common/geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..1322800cb7097ca5cce34a14b99bc5ed56b063b1 --- /dev/null +++ b/scenedino/common/geometry.py @@ -0,0 +1,236 @@ +from math import sin, cos + +import torch +from torch.cuda.amp import autocast + + +def transform_pts(pts: torch.Tensor, rel_pose: torch.Tensor) -> torch.Tensor: + """Transform points by relative pose + + Args: + pts (torch.Tensor): B, n_pts, 3 + rel_pose (torch.Tensor): B, 4, 4 + + Returns: + torch.Tensor: B, n_pts, 3 + """ + pts = torch.cat((pts, torch.ones_like(pts[..., :1])), dim=-1) + return (pts @ rel_pose.transpose(-1, -2))[..., :3] + + +# TODO: unify +def distance_to_z(depths: torch.Tensor, projs: torch.Tensor): + n, nv, h, w = depths.shape + device = depths.device + + inv_K = torch.inverse(projs) + + grid_x = ( + torch.linspace(-1, 1, w, device=device).view(1, 1, 1, -1).expand(-1, -1, h, -1) + ) + grid_y = ( + torch.linspace(-1, 1, h, device=device).view(1, 1, -1, 1).expand(-1, -1, -1, w) + ) + img_points = torch.stack((grid_x, grid_y, torch.ones_like(grid_x)), dim=2).expand( + n, nv, -1, -1, -1 + ) + cam_points = (inv_K @ img_points.view(n, nv, 3, -1)).view(n, nv, 3, h, w) + factors = cam_points[:, :, 2, :, :] / torch.norm(cam_points, dim=2) + + return depths * factors + + +def z_to_distance(z: torch.Tensor, projs: torch.Tensor): + n, nv, h, w = z.shape + device = z.device + + inv_K = torch.inverse(projs) + + grid_x = ( + torch.linspace(-1, 1, w, device=device).view(1, 1, 1, -1).expand(-1, -1, h, -1) + ) + grid_y = ( + torch.linspace(-1, 1, h, device=device).view(1, 1, -1, 1).expand(-1, -1, -1, w) + ) + img_points = torch.stack((grid_x, grid_y, torch.ones_like(grid_x)), dim=2).expand( + n, nv, -1, -1, -1 + ) + cam_points = (inv_K @ img_points.view(n, nv, 3, -1)).view(n, nv, 3, h, w) + factors = cam_points[:, :, 2, :, :] / torch.norm(cam_points, dim=2) + + return z / factors + + +def azimuth_elevation_to_rotation(azimuth: float, elevation: float) -> torch.Tensor: + rot_z = torch.tensor( + [ + [cos(azimuth), -sin(azimuth), 0.0], + [sin(azimuth), cos(azimuth), 0.0], + [0.0, 0.0, 1.0], + ] + ) + rot_x = torch.tensor( + [ + [1.0, 0.0, 0.0], + [0.0, cos(azimuth), -sin(azimuth)], + [0.0, sin(azimuth), cos(azimuth)], + ] + ) + return rot_x @ rot_z + + +def estimate_frustum_overlap(proj_source: torch.Tensor, pose_source: torch.Tensor, proj_target: torch.Tensor, pose_target: torch.Tensor, dist_lim=50): + device = proj_source.device + dtype = proj_source.dtype + + # Check which camera has higher z value in target coordinate system + with autocast(enabled=False): + src2tgt = torch.inverse(pose_target) @ pose_source + + for i in range(len(src2tgt)): + if src2tgt[i, 2, 3] < 0: + print("SWAP", i) + proj_ = proj_target[i].clone() + pose_ = pose_target[i].clone() + proj_target[i] = proj_source[i] + pose_target[i] = pose_source[i] + proj_source[i] = proj_ + pose_source[i] = pose_ + + points = torch.tensor([[ + [-1, 1, 1, 1], + [1, 1, 1, 1], + [1, -1, 1, 1], + [-1, -1, 1, 1], + ]], device=device, dtype=dtype) + + with autocast(enabled=False): + K_src_inv = torch.inverse(proj_source) + K_tgt_inv = torch.inverse(proj_target) + + _ = K_src_inv.new_zeros(K_src_inv.shape[0], 4, 4) + _[:, 3, 3] = 1 + _[:, :3, :3] = K_src_inv + K_src_inv = _ + + _ = K_tgt_inv.new_zeros(K_tgt_inv.shape[0], 4, 4) + _[:, 3, 3] = 1 + _[:, :3, :3] = K_tgt_inv + K_tgt_inv = _ + + points_src = K_src_inv @ points.permute(0, 2, 1) + points_tgt = K_tgt_inv @ points.permute(0, 2, 1) + + normals_tgt = torch.cross(points_tgt[..., :3, :], torch.roll(points_tgt[..., :3, :], shifts=-1, dims=-2), dim=-2) + normals_tgt = normals_tgt / torch.norm(normals_tgt, dim=-2, keepdim=True) + + with autocast(enabled=False): + src2tgt = torch.inverse(pose_target) @ pose_source + + base = src2tgt[:, :3, 3, None] + points_src_tgt = src2tgt @ points_src + + dirs = points_src_tgt[..., :3, :] - base + # dirs = dirs / torch.norm(dirs, dim=-2) #dirs should have z length 1 + + dists = - (base[..., None] * normals_tgt[..., None, :]).sum(dim=-3) / (dirs[..., None] * normals_tgt[..., None, :]).sum(dim=-3).clamp_min(1e-4) + + # print(dists) + + # Ignore all non-positive + mask = (dists <= 0) | (dists > dist_lim) + dists[mask] = dist_lim + + # print(dists) + + dists = torch.min(dists, dim=-1)[0] + + mean_dist = dists.mean(dim=-1) + + # print(mean_dist, (torch.max(points_src[..., 0], dim=-1)[0] - torch.min(points_src[..., 0], dim=-1)[0]), (torch.max(points_src[..., 1], dim=-1)[0] - torch.min(points_src[..., 1], dim=-1)[0])) + + volume_estimate = \ + 1/3 * \ + (torch.max(points_src[..., 0], dim=-1)[0] - torch.min(points_src[..., 0], dim=-1)[0]) * mean_dist * \ + (torch.max(points_src[..., 1], dim=-1)[0] - torch.min(points_src[..., 1], dim=-1)[0]) * mean_dist * \ + mean_dist + + return volume_estimate + + +def estimate_frustum_overlap_2(proj_source: torch.Tensor, pose_source: torch.Tensor, proj_target: torch.Tensor, pose_target: torch.Tensor, z_range=(3, 40), res=(8, 8, 16)): + device = proj_source.device + dtype = proj_source.dtype + + with autocast(enabled=False): + K_src_inv = torch.inverse(proj_source) + + n = proj_source.shape[0] + w, h, d = res + + pixel_width = 2 / w + pixel_height = 2 / h + + x = torch.linspace(-1 + .5 * pixel_width, 1 - .5 * pixel_width, w, dtype=dtype, device=device).view(1, 1, 1, w).expand(n, d, h, w) + y = torch.linspace(-1 + .5 * pixel_height, 1 - .5 * pixel_height, h, dtype=dtype, device=device).view(1, 1, h, 1).expand(n, d, h, w) + z = torch.ones_like(x) + + xyz = torch.stack((x, y, z), dim=-1) + xyz = K_src_inv @ xyz.reshape(n, -1, 3).permute(0, 2, 1) + xyz = xyz.reshape(n, 3, d, h, w) + + # xyz = xyz * (1 / torch.linspace(1 / z_range[0], 1 / z_range[1], d, dtype=dtype, device=device).view(1, 1, d, 1, 1).expand(n, 1, d, h, w)) + xyz = xyz * torch.linspace(z_range[0], z_range[1], d, dtype=dtype, device=device).view(1, 1, d, 1, 1).expand(n, 1, d, h, w) + + xyz = torch.cat((xyz, torch.ones_like(xyz[:, :1])), dim=1) + + xyz = xyz.reshape(n, 4, -1) + + with autocast(enabled=False): + src2tgt = torch.inverse(pose_target) @ pose_source + + xyz = src2tgt @ xyz + + # print(xyz) + + xyz = proj_target @ xyz[:, :3, :] + + xyz[:, :2] = xyz[:, :2] / xyz[:, 2:3, :] + + # print(xyz) + + valid = (xyz[:, 0].abs() < 1) & (xyz[:, 1].abs() < 1) & (xyz[:, 2].abs() > z_range[0])# & (xyz[:, 2].abs() < z_range[1]) + + # print(valid) + + volume_estimate = valid.to(dtype).mean(-1) + + return volume_estimate + + +def compute_occlusions(flow0, flow1): + n, _, h, w = flow0.shape + device = flow0.device + x = torch.linspace(-1, 1, w, device=device).view(1, 1, w).expand(1, h, w) + y = torch.linspace(-1, 1, h, device=device).view(1, h, 1).expand(1, h, w) + xy = torch.cat((x, y), dim=0).view(1, 2, h, w).expand(n, 2, h, w) + flow0_r = torch.cat((flow0[:, 0:1, :, :] * 2 / w , flow0[:, 1:2, :, :] * 2 / h), dim=1) + flow1_r = torch.cat((flow1[:, 0:1, :, :] * 2 / w , flow1[:, 1:2, :, :] * 2 / h), dim=1) + + xy_0 = xy + flow0_r + xy_1 = xy + flow1_r + + xy_0 = xy_0.view(n, 2, -1) + xy_1 = xy_1.view(n, 2, -1) + + ns = torch.arange(n, device=device, dtype=xy_0.dtype) + nxy_0 = torch.cat((ns.view(n, 1, 1).expand(-1, 1, xy_0.shape[-1]), xy_0), dim=1) + nxy_1 = torch.cat((ns.view(n, 1, 1).expand(-1, 1, xy_1.shape[-1]), xy_1), dim=1) + + mask0 = torch.zeros_like(flow0[:, :1, :, :]) + mask0[nxy_1[:, 0, :].long(), 0, ((nxy_1[:, 2, :] * .5 + .5) * h).round().long().clamp(0, h-1), ((nxy_1[:, 1, :] * .5 + .5) * w).round().long().clamp(0, w-1)] = 1 + + mask1 = torch.zeros_like(flow1[:, :1, :, :]) + mask1[nxy_0[:, 0, :].long(), 0, ((nxy_0[:, 2, :] * .5 + .5) * h).round().long().clamp(0, h-1), ((nxy_0[:, 1, :] * .5 + .5) * w).round().long().clamp(0, w-1)] = 1 + + return mask0, mask1 \ No newline at end of file diff --git a/scenedino/common/image_processor.py b/scenedino/common/image_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..883567af58aeb6d96d5e5cddbf8e692119f03f46 --- /dev/null +++ b/scenedino/common/image_processor.py @@ -0,0 +1,282 @@ +from collections import namedtuple + +import torch +from torch import nn +import lpips +import torch.nn.functional as F + +from torchvision import transforms as tfs + +from torchvision.models.optical_flow import raft_large, Raft_Large_Weights + +from scenedino.common.geometry import compute_occlusions + + +def make_image_processor(config): + type = config.get("type", "RGB").lower() + if type == "rgb": + ip = RGBProcessor() + elif type == "perceptual": + ip = PerceptualProcessor(config.get("layers", 1)) + elif type == "patch": + ip = PatchProcessor(config.get("patch_size", 3)) + elif type == "raft": + ip = RaftExtractor() + elif type == "flow": + ip = FlowProcessor() + elif type == "flow_occlusion": + ip = FlowOcclusionProcessor() + else: + raise NotImplementedError(f"Unsupported image processor type: {type}") + return ip + + +class RGBProcessor(nn.Module): + def __init__(self): + super().__init__() + self.channels = 3 + + def forward(self, images): + images = images * .5 + .5 + return images + + +class PerceptualProcessor(nn.Module): + def __init__(self, layers=1) -> None: + super().__init__() + self.lpips_module = lpips.LPIPS(net="vgg") + self._layers = layers + self.channels = sum(self.lpips_module.chns[:self._layers]) + + def forward(self, images): + n, v, c, h, w = images.shape + images = images.view(n*v, c, h, w) + + in_input = self.lpips_module.scaling_layer(images) + + x = self.lpips_module.net.slice1(in_input) + h_relu1_2 = x + x = self.lpips_module.net.slice2(x) + h_relu2_2 = x + x = self.lpips_module.net.slice3(x) + h_relu3_3 = x + + vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3']) + outs = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3) + + feats = [] + + for kk in range(self._layers): + f = lpips.normalize_tensor(outs[kk]) + f = F.interpolate(f, (h, w)) + feats.append(f) + + feats = torch.cat(feats, dim=1) + + feats = feats.view(n, v, self.channels, h, w) + + return feats + + +class PatchProcessor(nn.Module): + def __init__(self, patch_size) -> None: + super().__init__() + self.patch_size = patch_size + self.channels = 3 * (patch_size ** 2) + + self._hps = self.patch_size // 2 + + def forward(self, images): + n, v, c, h, w = images.shape + images = images.view(n*v, c, h, w) * .5 + .5 + + images = F.pad(images, pad=(self.patch_size // 2,)*4, mode="replicate") + h_, w_ = images.shape[-2:] + + parts = [] + + for y in range(0, self.patch_size): + for x in range(0, self.patch_size): + parts.append(images[:, :, y:h_-(self.patch_size - y - 1), x:w_-(self.patch_size - x - 1)]) + + patch_images = torch.cat(parts, dim=1) + patch_images = patch_images.view(n, v, self.channels, h, w) + + return patch_images + + +class DinoExtractor(nn.Module): + def __init__(self, variant): + super().__init__() + + self.model = torch.hub.load('facebookresearch/dino:main', variant) + self.model.eval() + + def load_checkpoint(self, ckpt_file, checkpoint_key="model"): + state_dict = torch.load(ckpt_file, map_location="cpu") + if checkpoint_key is not None and checkpoint_key in state_dict: + print(f"Take key {checkpoint_key} in provided checkpoint dict") + state_dict = state_dict[checkpoint_key] + state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} + msg = self.model.load_state_dict(state_dict, strict=False) + print("Pretrained weights loaded with msg: {}".format(msg)) + + def forward( + self, img: torch.Tensor, transform=True, upsample=True + ): + n, c, h_in, w_in = img.shape + if transform: + img = self.transform(img, 256) # Nx3xHxW + with torch.no_grad(): + out = self.model.get_intermediate_layers(img.to(self.device), n=1)[0] + out = out[:, 1:, :] # we discard the [CLS] token + h, w = int(img.shape[2] / self.model.patch_embed.patch_size), int( + img.shape[3] / self.model.patch_embed.patch_size + ) + dim = out.shape[-1] + out = out.reshape(-1, h, w, dim).permute(0, 3, 1, 2) + if upsample: + out = torch.nn.functional.interpolate(out, (h_in, w_in), mode="bilinear") + return out + + @staticmethod + def transform(img, image_size): + + MEAN = [0.485, 0.456, 0.406] + STD = [0.229, 0.224, 0.225] + + transforms = tfs.Compose( + [ + tfs.Resize(image_size), + tfs.Normalize(MEAN, STD)] + ) + img = transforms(img) + return img + + @property + def device(self): + return next(self.parameters()).device + + +class RaftExtractor(nn.Module): + def __init__(self): + super().__init__() + + raft_weights = Raft_Large_Weights.DEFAULT + self.raft_transforms = raft_weights.transforms() + self.raft = raft_large(raft_weights) + self.raft.eval() + for param in self.raft.parameters(): + param.requires_grad = False + + @torch.no_grad() + def forward( + self, img: torch.Tensor, upsample=True + ): + n, v, c, h_in, w_in = img.shape + img = img.reshape(n * v, c, h_in, w_in) + img, _ = self.raft_transforms(img * .5 + .5, img * .5 + .5) + feats = self.raft.feature_encoder(img) + if upsample: + feats = F.interpolate(feats, (h_in, w_in), mode="bilinear") + feats = feats.view(n, v, -1, h_in, w_in) + else: + feats = feats.view(n, v, -1, feats.shape[-2], feats.shape[-1]) + return feats + + @property + def device(self): + return next(self.parameters()).device + + +class FlowProcessor(nn.Module): + def __init__(self): + super().__init__() + + raft_weights = Raft_Large_Weights.DEFAULT + self.raft_transforms = raft_weights.transforms() + self.raft = raft_large(raft_weights) + self.raft.eval() + for param in self.raft.parameters(): + param.requires_grad = False + + @torch.no_grad() + def forward( + self, img: torch.Tensor, upsample=True + ): + n, v, c, h, w = img.shape + img = img.reshape(n * v // 2, 2, c, h, w) + img0 = img[:, 0] + img1 = img[:, 1] + img0, img1 = self.raft_transforms(img0 * .5 + .5, img1 * .5 + .5) + flow_fwd = self.raft(img0, img1)[-1] + flow_bwd = self.raft(img1, img0)[-1] + flow0_r = torch.cat((flow_fwd[:, 0:1, :, :] * 2 / w , flow_fwd[:, 1:2, :, :] * 2 / h), dim=1) + flow1_r = torch.cat((flow_bwd[:, 0:1, :, :] * 2 / w , flow_bwd[:, 1:2, :, :] * 2 / h), dim=1) + flow = torch.stack((flow0_r, flow1_r), dim=1) + + img = torch.cat((img, flow), dim=2) + + img = img.reshape(n, v, -1, h, w) + + return img + + @property + def device(self): + return next(self.parameters()).device + + +class FlowOcclusionProcessor(nn.Module): + def __init__(self): + super().__init__() + + raft_weights = Raft_Large_Weights.DEFAULT + self.raft_transforms = raft_weights.transforms() + self.raft = raft_large(raft_weights) + self.raft.eval() + for param in self.raft.parameters(): + param.requires_grad = False + + @torch.no_grad() + def forward( + self, img: torch.Tensor, upsample=True + ): + n, v, c, h, w = img.shape + img = img.reshape(n * v // 2, 2, c, h, w) + img0 = img[:, 0] + img1 = img[:, 1] + img0, img1 = self.raft_transforms(img0 * .5 + .5, img1 * .5 + .5) + flow_fwd = self.raft(img0, img1)[-1] + flow_bwd = self.raft(img1, img0)[-1] + occ0, occ1 = compute_occlusions(flow_fwd, flow_bwd) + flow0_r = torch.cat((flow_fwd[:, 0:1, :, :] * 2 / w , flow_fwd[:, 1:2, :, :] * 2 / h), dim=1) + flow1_r = torch.cat((flow_bwd[:, 0:1, :, :] * 2 / w , flow_bwd[:, 1:2, :, :] * 2 / h), dim=1) + flow = torch.stack((flow0_r, flow1_r), dim=1) + occ = torch.stack((occ0, occ1), dim=1) + + img = torch.cat((img, flow, occ), dim=2) + + img = img.reshape(n, v, -1, h, w) + + return img + + @property + def device(self): + return next(self.parameters()).device + + +class AutoMaskingWrapper(nn.Module): + + # Adds the corresponding color from the input frame for reference + def __init__(self, image_processor): + super().__init__() + self.image_processor = image_processor + + self.channels = self.image_processor.channels + 1 + + def forward(self, images, threshold): + n, v, c, h, w = images.shape + processed_images = self.image_processor(images) + thresholds = threshold.view(n, 1, 1, h, w).expand(n, v, 1, h, w) + processed_images = torch.stack((processed_images, thresholds), dim=2) + return processed_images diff --git a/scenedino/common/io/configs.py b/scenedino/common/io/configs.py new file mode 100644 index 0000000000000000000000000000000000000000..7f14d18479bafbaabdc200e4be24086f185a03c7 --- /dev/null +++ b/scenedino/common/io/configs.py @@ -0,0 +1,23 @@ +from pathlib import Path + +import hydra +from omegaconf import OmegaConf + + +def load_model_config(output_path: Path, config): + # @hydra.main( + # version_base=None, config_path=str(output_path), config_name="training_config" + # ) + # def load_model_conf(cfg): + # return cfg + + # cfg = load_model_conf() + cfg = OmegaConf.load(output_path / "training_config.yaml") + if cfg: + config["model"] = cfg["model"] + + +def save_hydra_config(path: Path, config, force: bool = False): + # TODO: Save config as yaml. Check + if not path.is_file() and not force: + OmegaConf.save(config, path) diff --git a/scenedino/common/io/images.py b/scenedino/common/io/images.py new file mode 100644 index 0000000000000000000000000000000000000000..f9eec76245ac965889fadd8cbc9467a63f39a564 --- /dev/null +++ b/scenedino/common/io/images.py @@ -0,0 +1,63 @@ +from pathlib import Path + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from scenedino.common.cameras.fisheye import project_to_image + +from scenedino.common.geometry import azimuth_elevation_to_rotation + + +class FisheyeToPinholeSampler: + def __init__(self, K_target, image_size_target) -> None: + self.K_target = K_target + self.image_size_target = image_size_target + x = ( + torch.linspace(-1, 1, image_size_target[1]) + .view(1, -1) + .expand(image_size_target) + ) + y = ( + torch.linspace(-1, 1, image_size_target[0]) + .view(-1, 1) + .expand(image_size_target) + ) + z = torch.ones_like(x) + xyz = torch.stack((x, y, z), dim=-1).view(-1, 3) + self.pts = (torch.inverse(torch.tensor(K_target)) @ xyz.T).T + + def sample_pinhole( + self, img: torch.Tensor, calib, rotational_offset: torch.Tensor + ) -> torch.Tensor: + xyz_pinhole = (rotational_offset @ self.pts.T).T + + xy, _ = project_to_image(xyz_pinhole, calib) + + img = img.unsqueeze(0) + resampled_img = F.grid_sample( + img, xy.view(1, *self.image_size_target, 2), align_corners=True + ).squeeze(0) + return resampled_img + + +def load_image(path: Path) -> torch.Tensor: + return torch.from_numpy( + cv2.cvtColor( + cv2.imread(str(path)), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + + +def load_fisheye_image_as_pinhole( + path: Path, + resampler: FisheyeToPinholeSampler, + calib, + azimuth: float, + elevation: float, +) -> tuple[torch.Tensor, torch.Tensor]: + img = load_image(path) + rotational_offset = azimuth_elevation_to_rotation(azimuth, elevation) + return resampler.sample_pinhole(img, calib, rotational_offset), rotational_offset diff --git a/scenedino/common/io/model.py b/scenedino/common/io/model.py new file mode 100644 index 0000000000000000000000000000000000000000..b583a3c7394c09b93908ac64ce37a1110a82325f --- /dev/null +++ b/scenedino/common/io/model.py @@ -0,0 +1,15 @@ +from pathlib import Path +from typing import Any + +import torch +from ignite.handlers import Checkpoint + + +def load_checkpoint(ckpt_path: Path, to_save: dict[str, Any], strict: bool = False): + assert ckpt_path.exists(), f"__Checkpoint '{str(ckpt_path)}' is not found" + checkpoint = torch.load(str(ckpt_path), map_location="cpu") + + to_save = {"model": to_save["model"]} + checkpoint = {"model": checkpoint["model"]} + + Checkpoint.load_objects(to_load=to_save, checkpoint=checkpoint, strict=strict) diff --git a/scenedino/common/io/voxel_grids/__init__.py b/scenedino/common/io/voxel_grids/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5571d72f4c4a0db1ab308e3be7d144fc4d43c0cf --- /dev/null +++ b/scenedino/common/io/voxel_grids/__init__.py @@ -0,0 +1,21 @@ +from enum import Enum +from .semantic_kitti import save_semantic_kitti + + +# TODO: add more formats +class VoxelFormats(Enum): + """Enum for voxel grid formats.""" + + semantic_kitti = "semantic_kitti" + + +def save_voxel_grid(voxel_grid, path, format: VoxelFormats | str): + """Save a voxel grid to a bin file.""" + if isinstance(format, str): + format = VoxelFormats(format) + + match format: + case VoxelFormats.semantic_kitti: + save_semantic_kitti(voxel_grid, path, format) + case _: + raise NotImplementedError(f"Format {format} not implemented.") diff --git a/scenedino/common/io/voxel_grids/common.py b/scenedino/common/io/voxel_grids/common.py new file mode 100644 index 0000000000000000000000000000000000000000..e4d6af35fd172b9e9a77608ff5aa8101b7eb8830 --- /dev/null +++ b/scenedino/common/io/voxel_grids/common.py @@ -0,0 +1,14 @@ +import numpy as np + + +def remove_pts_ouside_frustum(voxel_grid: np.ndarray) -> np.ndarray: + """Remove points outside frustum. + + Args: + voxel_grid (np.ndarray): voxel grid of shape (B, C, H, W, D) + + Returns: + np.ndarray: voxel grid of shape (B, C, H, W, D) + """ + # TODO: implement, get additonal arguments + pass diff --git a/scenedino/common/io/voxel_grids/semantic_kitti.py b/scenedino/common/io/voxel_grids/semantic_kitti.py new file mode 100644 index 0000000000000000000000000000000000000000..a4b7240e6c3487eac83af471a177fc854ecf408a --- /dev/null +++ b/scenedino/common/io/voxel_grids/semantic_kitti.py @@ -0,0 +1,33 @@ +# code for saving voxel grid +import numpy as np + +# TODO: adapt to semantic voxel grid + + +def unpack(compressed): + """given a bit encoded voxel grid, make a normal voxel grid out of it.""" + uncompressed = np.zeros(compressed.shape[0] * 8, dtype=np.uint8) + uncompressed[::8] = compressed[:] >> 7 & 1 + uncompressed[1::8] = compressed[:] >> 6 & 1 + uncompressed[2::8] = compressed[:] >> 5 & 1 + uncompressed[3::8] = compressed[:] >> 4 & 1 + uncompressed[4::8] = compressed[:] >> 3 & 1 + uncompressed[5::8] = compressed[:] >> 2 & 1 + uncompressed[6::8] = compressed[:] >> 1 & 1 + uncompressed[7::8] = compressed[:] & 1 + + return uncompressed + + +def pack(uncompressed: np.ndarray) -> np.ndarray: + """convert a boolean array into a bitwise array.""" + uncompressed_r = uncompressed.reshape(-1, 8) + compressed = uncompressed_r.dot( + 1 << np.arange(uncompressed_r.shape[-1] - 1, -1, -1) + ) + return compressed + + +def save_semantic_kitti(voxel_grid, path, format): + """Save a voxel grid to a bin file.""" + pack(np.flip(voxel_grid, (0, 1, 2)).reshape(-1)).astype(np.uint8).tofile(path) diff --git a/scenedino/common/logging.py b/scenedino/common/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..e1cc58f2e1726a27b698fa398497ee7bb3b9460d --- /dev/null +++ b/scenedino/common/logging.py @@ -0,0 +1,85 @@ +from typing import Any + +import ignite +import ignite.distributed as idist +from ignite.engine import Engine, Events, EventsList +import torch +from omegaconf import OmegaConf + + +# TODO: move to utils or similar +def event_list_from_config(config) -> EventsList: + events = EventsList() + if isinstance(config, int): + events = events | Events.EPOCH_COMPLETED(every=config) | Events.COMPLETED + else: + for event in config: + if event["args"]: + events = events | Events[event["type"]](**event["args"]) + else: + events = events | Events[event["type"]] + + return events + + +def global_step_fn(trainer: Engine, config: dict[str, Any]): + match config.get("type", None): + case "trainer epoch": + return lambda engine, event_name: trainer.state.epoch + case "trainer iteration": + return lambda engine, event_name: trainer.state.iteration + case _: + raise ValueError(f"Unknown global step type: {config['type']}") + + # trainer iteration + gst = lambda engine, event_name: trainer.state.iteration + + # # iteration per epoch + # gst_it_epoch = ( + # lambda engine, event_name: (trainer.state.epoch - 1) + # * engine.state.epoch_length + # + engine.state.iteration + # - 1 + # ) + # gst_it_iters = ( + # lambda engine, event_name: ( + # ( + # (trainer.state.epoch - 1) * trainer.state.epoch_length + # + trainer.state.iteration + # ) + # // every + # ) + # * engine.state.epoch_length + # + engine.state.iteration + # - 1 + # ) + # gst_ep_iters = lambda engine, event_name: ( + # ( + # (trainer.state.epoch - 1) * trainer.state.epoch_length + # + trainer.state.iteration + # ) + # // every + # ) + + +def log_basic_info(logger, config): + logger.info(f"Run {config['name']}") + logger.info(f"PyTorch version: {torch.__version__}") + logger.info(f"Ignite version: {ignite.__version__}") + if torch.cuda.is_available(): + # explicitly import cudnn as + # torch.backends.cudnn can not be pickled with hvd spawning procs + from torch.backends import cudnn + + logger.info(f"GPU Device: {torch.cuda.get_device_name(idist.get_local_rank())}") + logger.info(f"CUDA version: {torch.version.cuda}") + logger.info(f"CUDNN version: {cudnn.version()}") + + if idist.get_world_size() > 1: + logger.info("\nDistributed setting:") + logger.info(f"\tbackend: {idist.backend()}") + logger.info(f"\tworld size: {idist.get_world_size()}") + logger.info("\n") + + logger.info("\n") + logger.info(f"Configuration: \n{OmegaConf.to_yaml(config)}") diff --git a/scenedino/common/metrics.py b/scenedino/common/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..1fb98a5bb2b47bea1b1b212c5c34ba8fc92b8fc5 --- /dev/null +++ b/scenedino/common/metrics.py @@ -0,0 +1,592 @@ +import sys +import math +from typing import Callable, Mapping + +import skimage.metrics as sk_metrics +import torch +import torch.nn.functional as F +from ignite.engine import Engine +from ignite.exceptions import NotComputableError +from ignite.metrics import Metric +from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce + +import pulp + + +def median_scaling( + depth_gt: torch.Tensor, + depth_pred: torch.Tensor, +): + # TODO: ensure this works for any batch size + mask = depth_gt > 0 + + depth_gt[mask] = torch.nan + depth_pred[mask] = torch.nan + scaling = torch.nanmedian(depth_gt.flatten(-2, -1), dim=-1) / torch.nanmedian( + depth_pred.flatten(-2, -1), dim=-1 + ) + depth_pred = scaling[..., None, None] * depth_pred + return depth_pred + + +def l2_scaling( + depth_gt: torch.Tensor, + depth_pred: torch.Tensor, +): + # TODO: ensure this works for any batch size + mask = depth_gt > 0 + depth_pred = depth_pred + depth_gt_ = depth_gt[mask] + depth_pred_ = depth_pred[mask] + depth_pred_ = torch.stack((depth_pred_, torch.ones_like(depth_pred_)), dim=-1) + x = torch.linalg.lstsq( + depth_pred_.to(torch.float32), depth_gt_.unsqueeze(-1).to(torch.float32) + ).solution.squeeze() + depth_pred = depth_pred * x[0] + x[1] + return depth_pred + + +def compute_depth_metrics( + depth_gt: torch.Tensor, + depth_pred: torch.Tensor, + scaling_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None, +): + # TODO: find out if dim -3 is dummy dimension or part of the batch + # TODO: Test if works for batches of images + if scaling_fn: + depth_pred = scaling_fn(depth_gt, depth_pred) + + depth_pred = torch.clamp(depth_pred, 1e-3, 80) + mask = depth_gt != 0 + + max_ratio = torch.maximum((depth_gt / depth_pred), (depth_pred / depth_gt)) + a_scores = {} + for name, thresh in {"a1": 1.25, "a2": 1.25**2, "a3": 1.25**3}.items(): + within_thresh = (max_ratio < thresh).to(torch.float) + within_thresh[~mask] = 0.0 + a_scores[name] = within_thresh.flatten(-2, -1).sum(dim=-1) / mask.to( + torch.float + ).flatten(-2, -1).sum(dim=-1) + + square_error = (depth_gt - depth_pred) ** 2 + square_error[~mask] = 0.0 + + log_square_error = (torch.log(depth_gt) - torch.log(depth_pred)) ** 2 + log_square_error[~mask] = 0.0 + + abs_error = torch.abs(depth_gt - depth_pred) + abs_error[~mask] = 0.0 + + rmse = ( + square_error.flatten(-2, -1).sum(dim=-1) + / mask.to(torch.float).flatten(-2, -1).sum(dim=-1) + ) ** 0.5 + + rmse_log = ( + log_square_error.flatten(-2, -1).sum(dim=-1) + / mask.to(torch.float).flatten(-2, -1).sum(dim=-1) + ) ** 0.5 + + abs_rel = abs_error / depth_gt + abs_rel[~mask] = 0.0 + abs_rel = ( + abs_rel.flatten(-2, -1).sum(dim=-1) + / mask.to(torch.float).flatten(-2, -1).sum(dim=-1) + ) ** 0.5 + + sq_rel = square_error / depth_gt + sq_rel[~mask] = 0.0 + sq_rel = ( + sq_rel.flatten(-2, -1).sum(dim=-1) + / mask.to(torch.float).flatten(-2, -1).sum(dim=-1) + ) ** 0.5 + + metrics_dict = { + "abs_rel": abs_rel, + "sq_rel": sq_rel, + "rmse": rmse, + "rmse_log": rmse_log, + "a1": a_scores["a1"], + "a2": a_scores["a2"], + "a3": a_scores["a3"], + } + return metrics_dict + + +def compute_occ_metrics( + occupancy_pred: torch.Tensor, occupancy_gt: torch.Tensor, is_visible: torch.Tensor +): + # Only not visible points can be occupied + occupancy_gt &= ~is_visible + + is_occupied_acc = (occupancy_pred == occupancy_gt).float().mean().item() + is_occupied_prec = occupancy_gt[occupancy_pred].float().mean().item() + is_occupied_rec = occupancy_pred[occupancy_gt].float().mean().item() + + not_occupied_not_visible_ratio = ( + ((~occupancy_gt) & (~is_visible)).float().mean().item() + ) + + total_ie = ((~occupancy_gt) & (~is_visible)).float().sum().item() + + ie_acc = (occupancy_pred == occupancy_gt)[(~is_visible)].float().mean().item() + ie_prec = (~occupancy_gt)[(~occupancy_pred) & (~is_visible)].float().mean() + ie_rec = (~occupancy_pred)[(~occupancy_gt) & (~is_visible)].float().mean() + total_no_nop_nv = ( + ((~occupancy_gt) & (~occupancy_pred))[(~is_visible) & (~occupancy_gt)] + .float() + .sum() + ) + + return { + "o_acc": is_occupied_acc, + "o_rec": is_occupied_rec, + "o_prec": is_occupied_prec, + "ie_acc": ie_acc, + "ie_rec": ie_rec, + "ie_prec": ie_prec, + "ie_r": not_occupied_not_visible_ratio, + "t_ie": total_ie, + "t_no_nop_nv": total_no_nop_nv, + } + + +def compute_nvs_metrics(data, lpips): + # TODO: This is only correct for batchsize 1! + # Following tucker et al. and others, we crop 5% on all sides + + # idx of stereo frame (the target frame is always the "stereo" frame). + sf_id = data["rgb_gt"].shape[1] // 2 + + imgs_gt = data["rgb_gt"][:1, sf_id : sf_id + 1] + imgs_pred = data["fine"][0]["rgb"][:1, sf_id : sf_id + 1] + + imgs_gt = imgs_gt.squeeze(0).permute(0, 3, 1, 2) + imgs_pred = imgs_pred.squeeze(0).squeeze(-2).permute(0, 3, 1, 2) + + n, c, h, w = imgs_gt.shape + y0 = int(math.ceil(0.05 * h)) + y1 = int(math.floor(0.95 * h)) + x0 = int(math.ceil(0.05 * w)) + x1 = int(math.floor(0.95 * w)) + + imgs_gt = imgs_gt[:, :, y0:y1, x0:x1] + imgs_pred = imgs_pred[:, :, y0:y1, x0:x1] + + imgs_gt_np = imgs_gt.detach().squeeze().permute(1, 2, 0).cpu().numpy() + imgs_pred_np = imgs_pred.detach().squeeze().permute(1, 2, 0).cpu().numpy() + + ssim_score = sk_metrics.structural_similarity( + imgs_pred_np, imgs_gt_np, multichannel=True, data_range=1, channel_axis=-1 + ) + psnr_score = sk_metrics.peak_signal_noise_ratio( + imgs_pred_np, imgs_gt_np, data_range=1 + ) + lpips_score = lpips(imgs_pred, imgs_gt, normalize=False).mean() + + metrics_dict = { + "ssim": torch.tensor([ssim_score], device=imgs_gt.device), + "psnr": torch.tensor([psnr_score], device=imgs_gt.device), + "lpips": torch.tensor([lpips_score], device=imgs_gt.device), + } + return metrics_dict + + +def compute_dino_metrics(data): + dino_gt = data["dino_gt"] + if "dino_features_downsampled" in data["coarse"][0]: + dino_pred = data["coarse"][0]["dino_features_downsampled"].squeeze(-2) + else: + dino_pred = data["coarse"][0]["dino_features"].squeeze(-2) + + l1_loss = F.l1_loss(dino_pred, dino_gt, reduction="none").mean(dim=(0, 2, 3, 4)) + l2_loss = F.mse_loss(dino_pred, dino_gt, reduction="none").mean(dim=(0, 2, 3, 4)) + cos_sim = F.cosine_similarity(dino_pred, dino_gt, dim=-1).mean(dim=(0, 2, 3)) + + metrics_dict = { + "l1": torch.tensor([l1_loss.mean()], device=dino_gt.device), + "l2": torch.tensor([l2_loss.mean()], device=dino_gt.device), + "cos_sim": torch.tensor([cos_sim.mean()], device=dino_gt.device) + } + for i in range(len(l1_loss)): + metrics_dict[f"l1_{i}"] = torch.tensor([l1_loss[i]], device=dino_gt.device) + metrics_dict[f"l2_{i}"] = torch.tensor([l2_loss[i]], device=dino_gt.device) + metrics_dict[f"cos_sim_{i}"] = torch.tensor([cos_sim[i]], device=dino_gt.device) + return metrics_dict + + +def compute_stego_metrics(data): + if "stego_corr" not in data["segmentation"]: + return {} + + metrics_dict = { + "stego_self_corr": data["segmentation"]["stego_corr"]["stego_self_corr"], + "stego_nn_corr": data["segmentation"]["stego_corr"]["stego_nn_corr"], + "stego_random_corr": data["segmentation"]["stego_corr"]["stego_random_corr"], + } + return metrics_dict + + +def compute_seg_metrics(data, n_classes, gt_classes): + segs_gt = data["segmentation"]["target"].flatten() + valid_mask = segs_gt >= 0 + segs_gt = segs_gt[valid_mask] + + metrics_dict = {} + for result_key, result in data["segmentation"]["results"].items(): + if "pseudo_segs_pred" in result: + segs_pred = result["pseudo_segs_pred"][:, 0].flatten() + else: + segs_pred = result["segs_pred"][:, 0].flatten() + + segs_pred = segs_pred[valid_mask] + confusion_matrix = torch.bincount(n_classes * segs_gt + segs_pred, + minlength=n_classes * gt_classes).reshape(gt_classes, n_classes) + metrics_dict[result_key] = confusion_matrix + + return metrics_dict + + +class MeanMetric(Metric): + def __init__(self, output_transform=lambda x: x["output"], device="cpu"): + super(MeanMetric, self).__init__( + output_transform=output_transform, device=device + ) + self._sum = torch.tensor(0, device=self._device, dtype=torch.float32) + self._num_examples = 0 + self.required_output_keys = () + + @reinit__is_reduced + def reset(self): + self._sum = torch.tensor(0, device=self._device, dtype=torch.float32) + self._num_examples = 0 + super(MeanMetric, self).reset() + + @reinit__is_reduced + def update(self, value): + if torch.any(torch.isnan(torch.tensor(value))): + raise ValueError("NaN values present in metric!") + self._sum += value + self._num_examples += 1 + + @sync_all_reduce("_num_examples:SUM", "_sum:SUM") + def compute(self): + if self._num_examples == 0: + raise NotComputableError( + "CustomAccuracy must have at least one example before it can be computed." + ) + return self._sum.item() / self._num_examples + + @torch.no_grad() + def iteration_completed(self, engine: Engine) -> None: + output = self._output_transform( + engine.state.output + ) ## engine.state.output.keys() == dict_keys(['output', 'loss_dict', 'timings_dict', 'metrics_dict']) + self.update(output) + + +class DictMeanMetric(Metric): + def __init__(self, name: str, output_transform=lambda x: x["output"], device="cpu"): + self._name = name + self._sums: dict[str, torch.Tensor] = {} + self._num_examples = 0 + self.required_output_keys = () + super(DictMeanMetric, self).__init__( + output_transform=output_transform, device=device + ) + + @reinit__is_reduced + def reset(self): + self._sums = {} + self._num_examples = 0 + super(DictMeanMetric, self).reset() + + @reinit__is_reduced + def update(self, value): + num_examples = None + for key, metric in value.items(): + if not key in self._sums: + self._sums[key] = torch.tensor( + 0, device=self._device, dtype=torch.float32 + ) + if torch.any(torch.isnan(metric)): + # TODO: integrate into logging + print(f"Warining: Metric {self._name}/{key} has a nan value") + continue + self._sums[key] += metric.sum().to(self._device) + # TODO: check if this works with batches + if num_examples is None: + num_examples = metric.shape[0] + self._num_examples += 1 + + @sync_all_reduce("_num_examples:SUM", "_sum:SUM") + def compute(self): + if self._num_examples == 0: + raise NotComputableError( + "CustomAccuracy must have at least one example before it can be computed." + ) + return { + f"{self._name}_{key}": metric.item() / self._num_examples + for key, metric in self._sums.items() + } + + @torch.no_grad() + def iteration_completed(self, engine: Engine) -> None: + output = self._output_transform(engine.state.output["output"]) + self.update(output) + + def completed(self, engine: Engine, name: str) -> None: + """Helper method to compute metric's value and put into the engine. It is automatically attached to the + `engine` with :meth:`~ignite.metrics.metric.Metric.attach`. If metrics' value is torch tensor, it is + explicitly sent to CPU device. + + Args: + engine: the engine to which the metric must be attached + name: the name of the metric used as key in dict `engine.state.metrics` + + .. changes from default implementation: + don't add whole result dict to engine state, but only the values + + """ + result = self.compute() + if isinstance(result, Mapping): + if name in result.keys(): + raise ValueError( + f"Argument name '{name}' is conflicting with mapping keys: {list(result.keys())}" + ) + + for key, value in result.items(): + engine.state.metrics[key] = value + else: + if isinstance(result, torch.Tensor): + if len(result.size()) == 0: + result = result.item() + elif "cpu" not in result.device.type: + result = result.cpu() + + engine.state.metrics[name] = result + + +class SegmentationMetric(DictMeanMetric): + def __init__(self, name: str, output_transform=lambda x: x["output"], device="cpu", assign_pseudo=True): + super(SegmentationMetric, self).__init__( + name, output_transform, device + ) + self.assign_pseudo = assign_pseudo + + # [road, sidewalk, building, wall, fence, pole, traffic light, traffic sign, vegetation, terrain, sky, person, rider, car, truck, bus, train, motorcycle, bicycle] + self.weights = torch.Tensor([4, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1]) + self.weights = self.weights / self.weights.mean() + + @reinit__is_reduced + def update(self, value): + for key, metric in value.items(): + if not key in self._sums: + self._sums[key] = torch.zeros(metric.shape, device=self._device, dtype=torch.int32) + if torch.any(torch.isnan(metric)): + print(f"Warining: Metric {self._name}/{key} has a nan value") + continue + self._sums[key] += metric.to(self._device) + self._num_examples += 1 + + @sync_all_reduce("_num_examples:SUM", "_sum:SUM") + def compute(self): + if self._num_examples == 0: + raise NotComputableError( + "CustomAccuracy must have at least one example before it can be computed." + ) + result = {} + for key, _sum in self._sums.items(): + if self.assign_pseudo: + assignment = self._calculate_pseudo_label_assignment(_sum) + gt_classes = _sum.size(0) + confusion_matrix = torch.zeros((gt_classes, gt_classes), dtype=_sum.dtype) + confusion_matrix.scatter_add_( + 1, + assignment.unsqueeze(0).expand(gt_classes, -1), + _sum + ) + result[key + "_assignment"] = assignment + else: + confusion_matrix = _sum + + # confusion_matrix axes: (actual, prediction) + + true_positives = confusion_matrix.diag() + false_negatives = torch.sum(confusion_matrix, dim=1) - true_positives + false_positives = torch.sum(confusion_matrix, dim=0) - true_positives + denominator = true_positives + false_positives + false_negatives + per_class_iou = torch.where(denominator > 0, true_positives / denominator, + torch.zeros_like(denominator)) + + result[key + "_per_class_iou"] = per_class_iou + result[key + "_miou"] = per_class_iou.mean().item() + result[key + "_weighted_miou"] = (per_class_iou * self.weights).mean().item() + result[key + "_acc"] = confusion_matrix.diag().sum().item() / confusion_matrix.sum().item() + + result[key + "_confusion_matrix"] = confusion_matrix + + return result + + def _calculate_pseudo_label_assignment(self, metric_matrix): + """Implemented this way to generalize to over-segmentation""" + gt_classes, n_classes = metric_matrix.size() + costs = metric_matrix.cpu().numpy() + + problem = pulp.LpProblem("CapacitatedAssignment", pulp.LpMaximize) + x = [[pulp.LpVariable(f"x_{i}_{j}", cat="Binary") for j in range(n_classes)] for i in + range(gt_classes)] + + problem += pulp.lpSum(costs[i][j] * x[i][j] for i in range(gt_classes) for j in range(n_classes)) + for j in range(n_classes): + problem += pulp.lpSum(x[i][j] for i in range(gt_classes)) == 1, f"AssignPseudoLabel_{j}" + + for i in range(gt_classes): + problem += pulp.lpSum(x[i][j] for j in range(n_classes)) >= 1, f"MinAssignActualLabel_{i}" + + problem.solve() + + print("Status:", pulp.LpStatus[problem.status]) + print("Objective:", pulp.value(problem.objective)) + + assignment = torch.zeros(n_classes, dtype=torch.int64) + for j in range(n_classes): + assignment[j] = next(i for i in range(gt_classes) if pulp.value(x[i][j]) == 1) + + return assignment + + +class ConcatenateMetric(DictMeanMetric): + @reinit__is_reduced + def update(self, value, every_nth=100): + n_bins = 50 + for key, metric in value.items(): + if not key in self._sums: + self._sums[key] = torch.zeros((n_bins,), device=self._device, dtype=torch.int32) + if torch.any(torch.isnan(metric)): + print(f"Warning: Metric {self._name}/{key} has a nan value") + continue + + metric_flat = metric.flatten().to(self._device)[::every_nth] + if key in self._sums: + self._sums[key] = torch.cat([self._sums[key], metric_flat]) + else: + self._sums[key] = metric_flat + + self._num_examples += 1 + + @sync_all_reduce("_num_examples:SUM", "_sum:SUM") + def compute(self): + return self._sums + + +class FG_ARI(Metric): + def __init__(self, output_transform=lambda x: x["output"], device="cpu"): + self._sum_fg_aris = torch.tensor(0, device=self._device, dtype=torch.float32) + self._num_examples = 0 + self.required_output_keys = () + super(FG_ARI, self).__init__(output_transform=output_transform, device=device) + + @reinit__is_reduced + def reset(self): + self._sum_fg_aris = torch.tensor(0, device=self._device, dtype=torch.float32) + self._num_examples = 0 + super(FG_ARI, self).reset() + + @reinit__is_reduced + def update(self, data): + true_masks = data["segs"] # fc [n, h, w] + pred_masks = data["slot_masks"] # n, fc, sc, h, w + + n, fc, sc, h, w = pred_masks.shape + + true_masks = [ + F.interpolate(tm.to(float).unsqueeze(1), (h, w), mode="nearest") + .squeeze(1) + .to(int) + for tm in true_masks + ] + + for i in range(n): + for f in range(fc): + true_mask = true_masks[f][i] + pred_mask = pred_masks[i, f] + + true_mask = true_mask.view(-1) + pred_mask = pred_mask.view(sc, -1) + + if torch.max(true_mask) == 0: + continue + + foreground = true_mask > 0 + true_mask = true_mask[foreground] + pred_mask = pred_mask[:, foreground].permute(1, 0) + + true_mask = F.one_hot(true_mask) + + # Filter out empty true groups + not_empty = torch.any(true_mask, dim=0) + true_mask = true_mask[:, not_empty] + + # Filter out empty predicted groups + not_empty = torch.any(pred_mask, dim=0) + pred_mask = pred_mask[:, not_empty] + + true_mask.unsqueeze_(0) + pred_mask.unsqueeze_(0) + + _, n_points, n_true_groups = true_mask.shape + n_pred_groups = pred_mask.shape[-1] + if n_points <= n_true_groups and n_points <= n_pred_groups: + print( + "adjusted_rand_index requires n_groups < n_points.", + file=sys.stderr, + ) + continue + + true_group_ids = torch.argmax(true_mask, -1) + pred_group_ids = torch.argmax(pred_mask, -1) + true_mask_oh = true_mask.to(torch.float32) + pred_mask_oh = F.one_hot(pred_group_ids, n_pred_groups).to( + torch.float32 + ) + + n_points = torch.sum(true_mask_oh, dim=[1, 2]).to(torch.float32) + + nij = torch.einsum("bji,bjk->bki", pred_mask_oh, true_mask_oh) + a = torch.sum(nij, dim=1) + b = torch.sum(nij, dim=2) + + rindex = torch.sum(nij * (nij - 1), dim=[1, 2]) + aindex = torch.sum(a * (a - 1), dim=1) + bindex = torch.sum(b * (b - 1), dim=1) + expected_rindex = aindex * bindex / (n_points * (n_points - 1)) + max_rindex = (aindex + bindex) / 2 + ari = (rindex - expected_rindex) / ( + max_rindex - expected_rindex + 0.000000000001 + ) + + _all_equal = lambda values: torch.all( + torch.eq(values, values[..., :1]), dim=-1 + ) + both_single_cluster = torch.logical_and( + _all_equal(true_group_ids), _all_equal(pred_group_ids) + ) + + self._sum_fg_aris += torch.where( + both_single_cluster, torch.ones_like(ari), ari + ).squeeze() + self._num_examples += 1 + + @sync_all_reduce("_num_examples:SUM", "_sum_fg_aris:SUM") + def compute(self): + if self._num_examples == 0: + raise NotComputableError( + "CustomAccuracy must have at least one example before it can be computed." + ) + return self._sum_fg_aris.item() / self._num_examples + + @torch.no_grad() + def iteration_completed(self, engine: Engine) -> None: + output = self._output_transform(engine.state.output) + self.update(output) diff --git a/scenedino/common/point_sampling.py b/scenedino/common/point_sampling.py new file mode 100644 index 0000000000000000000000000000000000000000..7aa076f2c8e900c1782e21fefface23b8103e76a --- /dev/null +++ b/scenedino/common/point_sampling.py @@ -0,0 +1,53 @@ +from typing import Iterable +import torch + +from scenedino.common.geometry import transform_pts + + +# TODO: cam_incl_adjust is not nesserarily needed in this function +def regular_grid( + x_range: tuple[int, int], + y_range: tuple[int, int], + z_range: tuple[int, int], + x_res: int, + y_res: int, + z_res: int, + cam_incl_adjust: torch.Tensor | None = None, +) -> torch.Tensor: + """Generate a regular grid of points. + + Args: + x_range (tuple[int, int]): x range + y_range (tuple[int, int]): y_range + z_range (tuple[int, int]): z_range + x_res (int): number of points in x direction + y_res (int): number of points in y direction + z_res (int): number of points in z direction + cam_incl_adjust (torch.Tensor | None, optional): Opional rigid body transformation. Defaults to None. + + Returns: + torch.Tensor: 3D grid of points of shape (y_res, z_res, x_res, 3) + """ + x = ( + torch.linspace(x_range[0], x_range[1], x_res) + .view(x_res, 1, 1) + .expand(-1, y_res, z_res) + ) + y = ( + torch.linspace(y_range[0], y_range[1], y_res) + .view(1, y_res, 1) + .expand(x_res, -1, z_res) + ) + z = ( + torch.linspace(z_range[0], z_range[1], z_res) + .view(1, 1, z_res) + .expand(x_res, y_res, -1) + ) + xyz = torch.stack((x, y, z), dim=-1) + + # The KITTI 360 cameras have a 5 degrees negative inclination. We need to account for that. + if cam_incl_adjust is not None: + xyz = transform_pts(xyz.view(1, -1, 3), cam_incl_adjust[None])[0] + xyz = xyz.view(x_res, y_res, z_res, 3) + + return xyz diff --git a/scenedino/common/positional_encoding.py b/scenedino/common/positional_encoding.py new file mode 100644 index 0000000000000000000000000000000000000000..63faa530f3b55bee89e9c707521dd2976c2cfd00 --- /dev/null +++ b/scenedino/common/positional_encoding.py @@ -0,0 +1,207 @@ +from typing import Callable +from numpy import pi +import torch +import torch.nn as nn +import numpy as np +import torch.autograd.profiler as profiler + + +# TODO: rethink encoding mode +def encoding_mode( + encoding_mode: str, d_min: float, d_max: float, inv_z: bool, EPS: float +) -> Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]: + def _z(xy: torch.Tensor, z: torch.Tensor, distance: torch.Tensor) -> torch.Tensor: + if inv_z: + z = (1 / z.clamp_min(EPS) - 1 / d_max) / (1 / d_min - 1 / d_max) + else: + z = (z - d_min) / (d_max - d_min) + z = 2 * z - 1 + return torch.cat( + (xy, z), dim=-1 + ) ## concatenates the normalized x, y, and z coordinates + + def _distance(xy: torch.Tensor, z: torch.Tensor, distance: torch.Tensor): + if inv_z: + distance = (1 / distance.clamp_min(EPS) - 1 / d_max) / ( + 1 / d_min - 1 / d_max + ) + else: + distance = (distance - d_min) / (d_max - d_min) + distance = 2 * distance - 1 + return torch.cat( + (xy, distance), dim=-1 + ) ## Apply the positional encoder to the concatenated xy and depth/distance coordinates (it enables the model to capture more complex spatial dependencies without a significant increase in model complexity or training data) + + match encoding_mode: + case "z": + return _z + case "distance": + return _distance + case _: + return _z + + +class PositionalEncoding(torch.nn.Module): + """ + Implement NeRF's positional encoding + """ + + def __init__(self, num_freqs=6, d_in=3, freq_factor=np.pi, include_input=True): + super().__init__() + self.num_freqs = num_freqs + self.d_in = d_in + self.freqs = freq_factor * 2.0 ** torch.arange(0, num_freqs) + self.d_out = self.num_freqs * 2 * d_in + self.include_input = include_input + if include_input: + self.d_out += d_in + # f1 f1 f2 f2 ... to multiply x by + self.register_buffer( + "_freqs", torch.repeat_interleave(self.freqs, 2).view(1, -1, 1) + ) + # 0 pi/2 0 pi/2 ... so that + # (sin(x + _phases[0]), sin(x + _phases[1]) ...) = (sin(x), cos(x)...) + _phases = torch.zeros(2 * self.num_freqs) + _phases[1::2] = np.pi * 0.5 + self.register_buffer("_phases", _phases.view(1, -1, 1)) + + def forward(self, x): + """ + Apply positional encoding (new implementation) + :param x (batch, self.d_in) + :return (batch, self.d_out) + """ + with profiler.record_function("positional_enc"): + embed = x.unsqueeze(1).repeat(1, self.num_freqs * 2, 1) + embed = torch.sin(torch.addcmul(self._phases, embed, self._freqs)) + embed = embed.view(x.shape[0], -1) + if self.include_input: + embed = torch.cat((x, embed), dim=-1) + return embed + + @classmethod + def from_conf(cls, conf, d_in=3): + # PyHocon construction + return cls( + conf.get("num_freqs", 6), + d_in, + conf.get("freq_factor", np.pi), + conf.get("include_input", True), + ) + + +def token_decoding(filter: nn.Module, pos_offset: float = 0.0): + def _decode(xyz: torch.Tensor, tokens: torch.Tensor): + """Decode tokens into density for given points + + Args: + x (torch.Tensor): points in xyz n_pts, 3 + tokens (torch.Tensor): tokens n_pts, n_tokens, d_in + 2 + """ + n_pts, n_tokens = tokens.shape + + with profiler.record_function("positional_enc"): + z = xyz[..., 3] + scale = tokens[..., 0] # n_pts, n_tokens + token_pos_offset = tokens[..., 1] # n_pts, n_tokens + weights = tokens[..., 2:] # n_pts, n_tokens, d_in + positions = ( + 2.0 + * (z.unsqueeze(1).unsqueeze(2).repeat(1, n_tokens) - token_pos_offset) + / scale + - 1.0 + ) # n_pts, n_tokens ((z - t_o) / s) * 2.0 - 1.0 t_o => -1.0 t_o + s => 1.0 + + individual_densities = filter(positions, weights) # n_pts, n_tokens + + densities = individual_densities.sum(-1) # n_pts + + return densities + + return _decode + + +class FourierFilter(nn.Module): + # TODO: add filter functions + def __init__( + self, + num_freqs=6, + d_in=3, + freq_factor=np.pi, + include_input=True, + filter_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None, + ): + super().__init__() + self.num_freqs = num_freqs + self.d_in = d_in + self.freqs = freq_factor * 2.0 ** torch.arange(0, num_freqs) + self.register_buffer( + "_freqs", torch.repeat_interleave(self.freqs, 2).view(1, -1, 1) + ) + # 0 pi/2 0 pi/2 ... so that + # (sin(x + _phases[0]), sin(x + _phases[1]) ...) = (sin(x), cos(x)...) + _phases = torch.zeros(2 * self.num_freqs) + _phases[1::2] = np.pi * 0.5 + self.register_buffer("_phases", _phases.view(1, -1, 1)) + self.filter_fn = filter_fn + + def forward(self, positions: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: + """Predict density for given normalized points using Fourier features + + Args: + positions (torch.Tensor): normalized positions between -1 and 1, (n_pts, n_tokens) + weights (torch.Tensor): weights for each point (n_pts, n_tokens, num_freqs * 2) + + Returns: + torch.Tensor: aggregated density for each point (n_pts) + """ + with profiler.record_function("positional_enc"): + positions = positions.unsqueeze(1).repeat( + 1, self.num_freqs * 2, 1 + ) # n_pts, num_freqs * 2, n_tokens + densities = weights.permute(0, 2, 1) * torch.sin( + torch.addcmul(self._phases, positions, self._freqs) + ) # n_pts, num_freqs * 2, n_tokens + + if self.filter_fn is not None: + densities = self.filter_fn(densities, positions) + + return densities.sum(-2) # n_pts, n_tokens + + @classmethod + def from_conf(cls, conf, d_in=3): + # PyHocon construction + return cls( + conf.get("num_freqs", 6), + d_in, + conf.get("freq_factor", np.pi), + ) + + +class LogisticFilter(nn.Module): + def __init__(self, slope: float) -> None: + super().__init__() + self.slope = slope + + def forward(self, positions: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: + """Predict the density as sum of weighted logistic functions + + Args: + positions (torch.Tensor): normalized positions between -1 and 1, (n_pts, n_tokens) + weights (torch.Tensor): weights for each point (n_pts, n_tokens, d_in) + + Returns: + torch.Tensor: density for each point (n_pts, n_tokens) + """ + with profiler.record_function("positional_enc"): + weights = weights.squeeze(-1) # n_pts, n_tokens + + sigmoid_pos = self.slope * positions + 1.0 + return ( + weights * torch.sigmoid(sigmoid_pos) * torch.sigmoid(-sigmoid_pos) + ) # n_pts, n_tokens + + @classmethod + def from_conf(cls, conf): + # PyHocon construction + return cls(conf.get("slope", 10.0)) diff --git a/scenedino/common/ray_sampler.py b/scenedino/common/ray_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..58b32f450bccd17134ce5217faa1953a9e60d4b9 --- /dev/null +++ b/scenedino/common/ray_sampler.py @@ -0,0 +1,719 @@ +from math import isqrt +import torch +import torch.nn.functional as F +from torch.cuda.amp import autocast +from omegaconf import ListConfig + +from scenedino.common import util +from scenedino.common.cameras.pinhole import outside_frustum, project_to_image, pts_into_camera + + +class RaySampler: + def __init__(self, z_near: float, z_far: float) -> None: + self.z_near = z_near + self.z_far = z_far + + def sample(self, images, poses, projs): + raise NotImplementedError + + def reconstruct(self, render_dict): + raise NotImplementedError + + +class RandomRaySampler(RaySampler): + def __init__( + self, z_near: float, z_far: float, ray_batch_size: int, channels: int = 3 + ) -> None: + super().__init__(z_near, z_far) + self.ray_batch_size = ray_batch_size + self.channels = channels + + def sample(self, images, poses, projs, image_ids=None): + n, v, c, h, w = images.shape + + all_rgb_gt = [] + all_rays = [] + + for n_ in range(n): + focals = projs[n_, :, [0, 1], [0, 1]] + centers = projs[n_, :, [0, 1], [2, 2]] + + rays, xy = util.gen_rays( + poses[n_].view(-1, 4, 4), + w, + h, + focal=focals, + c=centers, + z_near=self.z_near, + z_far=self.z_far, + ) + + # Append frame id to the ray + if image_ids is None: + ids = torch.arange(v, device=images.device, dtype=images.dtype) + else: + ids = torch.tensor(image_ids, device=images.device, dtype=images.dtype) + ids = ids.view(v, 1, 1, 1).expand(v, h, w, 1) + rays = torch.cat((rays, ids), dim=-1) + rays = torch.cat((rays, xy), dim=-1) + r_dim = rays.shape[-1] + rays = rays.view(-1, r_dim) + + rgb_gt = images[n_].view(-1, c, h, w) + rgb_gt = rgb_gt.permute(0, 2, 3, 1).contiguous().reshape(-1, c) + + pix_inds = torch.randint(0, v * h * w, (self.ray_batch_size,)) + + rgb_gt = rgb_gt[pix_inds] + rays = rays[pix_inds] + + all_rgb_gt.append(rgb_gt) + all_rays.append(rays) + + all_rgb_gt = torch.stack(all_rgb_gt) + all_rays = torch.stack(all_rays) + + return all_rays, all_rgb_gt + + def reconstruct(self, render_dict, channels=None): + for name, render_dict_part in render_dict.items(): + if not type(render_dict_part) == dict or not "rgb" in render_dict_part: + continue + + if channels is None: + channels = self.channels + + rgb = render_dict_part["rgb"] # n, n_pts, v * 3 + depth = render_dict_part["depth"] + invalid = render_dict_part["invalid"] + + rgb_gt = render_dict["rgb_gt"] + + n, n_pts, v_c = rgb.shape + v = v_c // channels + n_smps = invalid.shape[-2] + + render_dict_part["rgb"] = rgb.view(n, n_pts, v, channels) + render_dict_part["depth"] = depth.view(n, n_pts) + render_dict_part["invalid"] = invalid.view(n, n_pts, n_smps, v) + + if "invalid_features" in render_dict_part: + invalid_features = render_dict_part["invalid_features"] + render_dict_part["invalid_features"] = invalid_features.view(n, n_pts, n_smps, v) + + if "weights" in render_dict_part: + weights = render_dict_part["weights"] + render_dict_part["weights"] = weights.view(n, n_pts, n_smps) + + if "alphas" in render_dict_part: + alphas = render_dict_part["alphas"] + render_dict_part["alphas"] = alphas.view(n, n_pts, n_smps) + + if "z_samps" in render_dict_part: + z_samps = render_dict_part["z_samps"] + render_dict_part["z_samps"] = z_samps.view(n, n_pts, n_smps) + + if "rgb_samps" in render_dict_part: + rgb_samps = render_dict_part["rgb_samps"] + render_dict_part["rgb_samps"] = rgb_samps.view(n, n_pts, n_smps, v, channels) + + if "ray_info" in render_dict_part: + ri_shape = render_dict_part["ray_info"].shape[-1] + ray_info = render_dict_part["ray_info"] + render_dict_part["ray_info"] = ray_info.view(n, n_pts, ri_shape) + + if "extras" in render_dict_part: + extras_shape = render_dict_part["extras"].shape[-1] + extras = render_dict_part["extras"] + render_dict_part["extras"] = extras.view(n, n_pts, extras_shape) + + render_dict[name] = render_dict_part + render_dict["rgb_gt"] = rgb_gt.view(n, n_pts, channels) + + return render_dict + + +class PatchRaySampler(RaySampler): + def __init__( + self, + z_near: float, + z_far: float, + ray_batch_size: int, + patch_size: int, + channels: int = 3, + snap_to_grid: bool = False, + dino_upscaled: bool = False, + ) -> None: + super().__init__(z_near, z_far) + self.ray_batch_size = ray_batch_size + self.channels = channels + self.snap_to_grid = snap_to_grid + self.dino_upscaled = dino_upscaled + + if isinstance(patch_size, int): + self.patch_size_x, self.patch_size_y = patch_size, patch_size + elif ( + isinstance(patch_size, tuple) + or isinstance(patch_size, list) + or isinstance(patch_size, ListConfig) + ): + self.patch_size_y = patch_size[0] + self.patch_size_x = patch_size[1] + else: + raise ValueError(f"Invalid format for patch size") + assert (ray_batch_size % (self.patch_size_x * self.patch_size_y)) == 0 + self._patch_count = self.ray_batch_size // ( + self.patch_size_x * self.patch_size_y + ) + + def sample( + self, images, poses, projs, image_ids=None, dino_features=None, loss_feature_grid_shift=None, + ): ### dim(images) == nv (ids_loss nv randomly sampled) + n, v, c, h, w = images.shape + + self.channels = c + + if dino_features is not None: + _, _, dino_channels, _, _ = dino_features.shape + dino_features = dino_features.permute(0, 1, 3, 4, 2) + + device = images.device + + images = images.permute(0, 1, 3, 4, 2) + + all_rgb_gt, all_rays, all_dino_gt = [], [], [] + + for n_ in range(n): + focals = projs[n_, :, [0, 1], [0, 1]] + centers = projs[n_, :, [0, 1], [2, 2]] + + rays, xy = util.gen_rays( + poses[n_].view(-1, 4, 4), + w, + h, + focal=focals, + c=centers, + z_near=self.z_near, + z_far=self.z_far, + ) + + # Append frame id to the ray + if image_ids is None: + ids = torch.arange(v, device=images.device, dtype=images.dtype) + else: + ids = torch.tensor(image_ids, device=images.device, dtype=images.dtype) + ids = ids.view(v, 1, 1, 1).expand(v, h, w, 1) + rays = torch.cat((rays, ids), dim=-1) + rays = torch.cat((rays, xy), dim=-1) + r_dim = rays.shape[-1] + + patch_coords_v = torch.randint(0, v, (self._patch_count,)) + + if self.snap_to_grid: + if loss_feature_grid_shift is not None: + patch_coords_y = torch.randint(0, h // self.patch_size_y - 1, (self._patch_count,)) + patch_coords_x = torch.randint(0, w // self.patch_size_x - 1, (self._patch_count,)) + else: + patch_coords_y = torch.randint(0, h // self.patch_size_y, (self._patch_count,)) + patch_coords_x = torch.randint(0, w // self.patch_size_x, (self._patch_count,)) + else: + patch_coords_y = torch.randint(0, h - self.patch_size_y, (self._patch_count,)) + patch_coords_x = torch.randint(0, w - self.patch_size_x, (self._patch_count,)) + + sample_rgb_gt = [] + sample_rays = [] + sample_dino_gt = [] + + for v_, coord_y, coord_x in zip(patch_coords_v, patch_coords_y, patch_coords_x): + if self.snap_to_grid: + patch_y, patch_x = coord_y, coord_x + if loss_feature_grid_shift is not None: + y = (loss_feature_grid_shift[0] % self.patch_size_y) + self.patch_size_y * coord_y + x = (loss_feature_grid_shift[1] % self.patch_size_x) + self.patch_size_x * coord_x + if loss_feature_grid_shift[0] < 0: + patch_y += 1 + if loss_feature_grid_shift[1] < 0: + patch_x += 1 + else: + y = self.patch_size_y * coord_y + x = self.patch_size_x * coord_x + else: + raise NotImplementedError + + rgb_gt_patch = images[n_][ + v_, y : y + self.patch_size_y, x : x + self.patch_size_x, : + ].reshape(-1, self.channels) + rays_patch = rays[ + v_, y : y + self.patch_size_y, x : x + self.patch_size_x, : + ].reshape(-1, r_dim) + + sample_rgb_gt.append(rgb_gt_patch) + sample_rays.append(rays_patch) + + if dino_features is not None: + if self.dino_upscaled: + dino_gt_patch = dino_features[n_][ + v_, y: y + self.patch_size_y, x: x + self.patch_size_x, : + ].reshape(-1, dino_channels) + else: + dino_gt_patch = dino_features[n_][ + v_, patch_y, patch_x, : + ].reshape(-1, dino_channels) + sample_dino_gt.append(dino_gt_patch) + + sample_rgb_gt = torch.cat(sample_rgb_gt, dim=0) + sample_rays = torch.cat(sample_rays, dim=0) + all_rgb_gt.append(sample_rgb_gt) + all_rays.append(sample_rays) + + if dino_features is not None: + sample_dino_gt = torch.cat(sample_dino_gt, dim=0) + all_dino_gt.append(sample_dino_gt) + + all_rgb_gt = torch.stack(all_rgb_gt) + all_rays = torch.stack(all_rays) + + if dino_features is not None: + all_dino_gt = torch.stack(all_dino_gt) + return all_rays, all_rgb_gt, all_dino_gt + else: + return all_rays, all_rgb_gt + + def reconstruct(self, render_dict, channels=None, dino_channels=None): + + for name, render_dict_part in render_dict.items(): + if not type(render_dict_part) == dict or not "rgb" in render_dict_part: + continue + + if channels is None: + channels = self.channels + + rgb_gt = render_dict["rgb_gt"] + dino_gt = render_dict["dino_gt"] + + n, n_pts, v_c = render_dict_part["rgb"].shape + v = v_c // channels + n_smps = render_dict_part["weights"].shape[-1] + # (This can be a different v from the sample method) + + render_dict_part["rgb"] = render_dict_part["rgb"].view( + n, self._patch_count, self.patch_size_y, self.patch_size_x, v, channels + ) + render_dict_part["weights"] = render_dict_part["weights"].view( + n, self._patch_count, self.patch_size_y, self.patch_size_x, n_smps + ) + render_dict_part["depth"] = render_dict_part["depth"].view( + n, self._patch_count, self.patch_size_y, self.patch_size_x + ) + render_dict_part["invalid"] = render_dict_part["invalid"].view( + n, self._patch_count, self.patch_size_y, self.patch_size_x, n_smps, v + ) + + # TODO: Figure out DINO invalid policy + # if "invalid_features" in render_dict_part: + # render_dict_part["invalid_features"] = render_dict_part["invalid_features"].view( + # n, self._patch_count, self.patch_size_y, self.patch_size_x, n_smps, v + # ) + + if "alphas" in render_dict_part: + render_dict_part["alphas"] = render_dict_part["alphas"].view( + n, self._patch_count, self.patch_size_y, self.patch_size_x, n_smps + ) + + if "z_samps" in render_dict_part: + render_dict_part["z_samps"] = render_dict_part["z_samps"].view( + n, self._patch_count, self.patch_size_y, self.patch_size_x, n_smps + ) + + if "rgb_samps" in render_dict_part: + render_dict_part["rgb_samps"] = render_dict_part["rgb_samps"].view( + n, + self._patch_count, + self.patch_size_y, + self.patch_size_x, + n_smps, + v, + channels, + ) + + if "ray_info" in render_dict_part: + ri_shape = render_dict_part["ray_info"].shape[-1] + render_dict_part["ray_info"] = render_dict_part["ray_info"].view( + n, self._patch_count, self.patch_size_y, self.patch_size_x, ri_shape + ) + + if "extras" in render_dict_part: + extras_shape = render_dict_part["extras"].shape[-1] + render_dict_part["extras"] = render_dict_part["extras"].view( + n, self._patch_count, self.patch_size_y, self.patch_size_x, extras_shape + ) + + if "dino_features" in render_dict_part: + dino_shape = render_dict_part["dino_features"].shape[-1] + render_dict_part["dino_features"] = render_dict_part["dino_features"].view( + n, self._patch_count, self.patch_size_y, self.patch_size_x, 1, dino_shape + ) + + render_dict[name] = render_dict_part + + render_dict["rgb_gt"] = rgb_gt.view( + n, self._patch_count, self.patch_size_y, self.patch_size_x, channels + ) + dino_gt_shape = dino_gt.shape[-1] + if self.dino_upscaled: + render_dict["dino_gt"] = dino_gt.view( + n, self._patch_count, self.patch_size_y, self.patch_size_x, dino_gt_shape + ) + else: + render_dict["dino_gt"] = dino_gt.view( + n, self._patch_count, dino_gt_shape + ) + + if "dino_artifacts" in render_dict: + render_dict["dino_artifacts"] = render_dict["dino_artifacts"].view( + n, self._patch_count, dino_gt_shape + ) + + return render_dict + + +class PointBasedRaySampler(RandomRaySampler): + def sample( + self, images, poses, projs, xyz, image_ids=None + ): ### dim(images) == nv (ids_loss nv randomly sampled) + n, v, c, h, w = images.shape + + assert v == 1 + + with autocast(enabled=False): + poses_w2c = torch.inverse(poses) + inv_K = torch.inverse(projs[:, :, :3, :3]) + + B, n_pts, _ = xyz.shape + + xyz_projected = pts_into_camera(xyz, poses_w2c) + distance = torch.norm(xyz_projected, dim=-2, keepdim=True) + xy, z = project_to_image(xyz_projected, projs) + + xy = xy[:, 0] + z = z[:, 0] + distance = distance[:, 0, 0, :, None] + + # For numerical stability with AMP. Should not affect training outcome + xy = xy.clamp(-2, 2) + + # Build rays + cam_centers = poses[:, 0, None, :3, 3].expand(-1, n_pts, -1) + ray_dir = ((poses[:, 0, :3, :3] @ inv_K[:, 0]) @ torch.cat((xy, torch.ones_like(xy[..., :1])), dim=-1).permute(0, 2, 1)).permute(0, 2, 1) + + cam_nears = torch.ones_like(cam_centers[..., :1]) * self.z_near + cam_fars = torch.ones_like(cam_centers[..., :1]) * self.z_far + + ids = torch.zeros_like(cam_nears) + + rays = torch.cat((cam_centers, ray_dir, cam_nears, cam_fars, ids, xy, distance), dim=-1) + + rgb_gt = F.grid_sample(images[:, 0], xy.reshape(n, -1, 1, 2).to(images.dtype), padding_mode="border", align_corners=False)[..., 0].permute(0, 2, 1) + + return rays, rgb_gt + + +class ImageRaySampler(RaySampler): + def __init__( + self, + z_near: float, + z_far: float, + height: int | None = None, + width: int | None = None, + channels: int = 3, + norm_dir: bool = True, + dino_upscaled: bool = False, + ) -> None: + super().__init__(z_near, z_far) + self.height = height + self.width = width + self.channels = channels + self.norm_dir = norm_dir + self.dino_upscaled = dino_upscaled + + def sample(self, images, poses, projs, image_ids=None, dino_features=None, dino_artifacts=None): + n, v, _, _ = poses.shape + device = poses.device + dtype = poses.dtype + + if images is not None: + self.channels = images.shape[2] + + if self.height is None: + self.height, self.width = images.shape[-2:] + + if dino_features is not None: + _, _, dino_channels, _, _ = dino_features.shape + + h = self.height + w = self.width + + all_rgb_gt = [] + all_dino_gt = [] + all_rays = [] + + for n_ in range(n): + focals = projs[n_, :, [0, 1], [0, 1]] + centers = projs[n_, :, [0, 1], [2, 2]] + + rays, xy = util.gen_rays( + poses[n_].view(-1, 4, 4), + self.width, + self.height, + focal=focals, + c=centers, + z_near=self.z_near, + z_far=self.z_far, + norm_dir=self.norm_dir, + ) + + # Append frame id to the ray + if image_ids is None: + ids = torch.arange(v, device=device, dtype=dtype) + else: + ids = torch.tensor(image_ids, device=device, dtype=dtype) + ids = ids.view(v, 1, 1, 1).expand(v, h, w, 1) + rays = torch.cat((rays, ids), dim=-1) + rays = torch.cat((rays, xy), dim=-1) + r_dim = rays.shape[-1] + rays = rays.view(-1, r_dim) + + all_rays.append(rays) + + if images is not None: + rgb_gt = images[n_].view(-1, self.channels, self.height, self.width) + rgb_gt = ( + rgb_gt.permute(0, 2, 3, 1).contiguous().reshape(-1, self.channels) + ) + all_rgb_gt.append(rgb_gt) + + if dino_features is not None: + patch_h, patch_w = dino_features[n_].shape[-2], dino_features[n_].shape[-1] + dino_gt = dino_features[n_].view(-1, dino_channels, patch_h, patch_w) + dino_gt = ( + dino_gt.permute(0, 2, 3, 1).contiguous().reshape(-1, dino_channels) + ) + all_dino_gt.append(dino_gt) + + all_rays = torch.stack(all_rays) + if images is not None: + all_rgb_gt = torch.stack(all_rgb_gt) + else: + all_rgb_gt = None + + if dino_features is not None: + all_dino_gt = torch.stack(all_dino_gt) + return all_rays, all_rgb_gt, all_dino_gt + else: + return all_rays, all_rgb_gt + + def reconstruct(self, render_dict, channels=None, dino_channels=None): + for name, render_dict_part in render_dict.items(): + if not type(render_dict_part) == dict or not "rgb" in render_dict_part: + continue + + if channels is None: + channels = self.channels + + rgb = render_dict_part["rgb"] # n, n_pts, v * 3 + weights = render_dict_part["weights"] + depth = render_dict_part["depth"] + invalid = render_dict_part["invalid"] + + n, n_pts, v_c = rgb.shape + v_in = n_pts // (self.height * self.width) + v_render = v_c // channels + n_smps = weights.shape[-1] + # (This can be a different v from the sample method) + + render_dict_part["rgb"] = rgb.view(n, v_in, self.height, self.width, v_render, channels) + render_dict_part["weights"] = weights.view(n, v_in, self.height, self.width, n_smps) + render_dict_part["depth"] = depth.view(n, v_in, self.height, self.width) + render_dict_part["invalid"] = invalid.view( + n, v_in, self.height, self.width, n_smps, v_render + ) + + if "invalid_features" in render_dict_part: + invalid_features = render_dict_part["invalid_features"] + render_dict_part["invalid_features"] = invalid_features.view( + n, v_in, self.height, self.width, n_smps, v_render + ) + + if "alphas" in render_dict_part: + alphas = render_dict_part["alphas"] + render_dict_part["alphas"] = alphas.view(n, v_in, self.height, self.width, n_smps) + + if "z_samps" in render_dict_part: + z_samps = render_dict_part["z_samps"] + render_dict_part["z_samps"] = z_samps.view( + n, v_in, self.height, self.width, n_smps + ) + + if "rgb_samps" in render_dict_part: + rgb_samps = render_dict_part["rgb_samps"] + render_dict_part["rgb_samps"] = rgb_samps.view( + n, v_in, self.height, self.width, n_smps, v_render, channels + ) + + if "ray_info" in render_dict_part: + ri_shape = render_dict_part["ray_info"].shape[-1] + ray_info = render_dict_part["ray_info"] + render_dict_part["ray_info"] = ray_info.view(n, v_in, self.height, self.width, ri_shape) + + if "extras" in render_dict_part: + ex_shape = render_dict_part["extras"].shape[-1] + extras = render_dict_part["extras"] + render_dict_part["extras"] = extras.view(n, v_in, self.height, self.width, ex_shape) + + if "dino_features" in render_dict_part: + dino_shape = render_dict_part["dino_features"].shape[-1] + dino = render_dict_part["dino_features"] + render_dict_part["dino_features"] = dino.view(n, v_in, self.height, self.width, 1, dino_shape) + + render_dict[name] = render_dict_part + + if "rgb_gt" in render_dict: + rgb_gt = render_dict["rgb_gt"] + render_dict["rgb_gt"] = rgb_gt.view( + n, v_in, self.height, self.width, channels + ) + + if "dino_gt" in render_dict: + dino_gt = render_dict["dino_gt"] + dino_gt_shape = dino_gt.shape[-1] + if self.dino_upscaled: + render_dict["dino_gt"] = dino_gt.view( + n, v_in, self.height, self.width, dino_gt_shape + ) + else: + # TODO: patch size should not be inferred like this, but parameter + patch_size = isqrt((n * v_in * self.height * self.width * dino_gt_shape) // dino_gt.numel()) + render_dict["dino_gt"] = dino_gt.view( + n, v_in, self.height // patch_size, self.width // patch_size, dino_gt_shape + ) + if "dino_artifacts" in render_dict: + dino_artifacts = render_dict["dino_artifacts"] + # TODO: patch size should not be inferred like this, but parameter + patch_size = isqrt((n * v_in * self.height * self.width * dino_gt_shape) // dino_artifacts.numel()) + render_dict["dino_artifacts"] = dino_artifacts.view( + n, v_in, self.height // patch_size, self.width // patch_size, dino_gt_shape + ) + + return render_dict + + +class JitteredPatchRaySampler(PatchRaySampler): + def __init__( + self, + z_near: float, + z_far: float, + ray_batch_size: int, + patch_size: int, + jitter_strength: float, # In pixels, max [0, 1) + channels: int = 3, + ) -> None: + super().__init__(z_near, z_far, ray_batch_size, patch_size, channels) + + assert 0 <= jitter_strength < 1, "Jitter strength is invalid." + + self.jitter_strength = jitter_strength + + x = torch.arange(0, self.patch_size_x).view(1, 1, -1, 1).expand(-1, self.patch_size_y, -1, -1) + y = torch.arange(0, self.patch_size_y).view(1, -1, 1, 1).expand(-1, -1, self.patch_size_x, -1) + self._grid = torch.cat((x, y), dim=-1) + + def sample( + self, images, poses, projs, image_ids=None + ): ### dim(images) == nv (ids_loss nv randomly sampled) + n, v, c, h, w = images.shape + device = images.device + + all_rgb_gt, all_rays = [], [] + + xy_offset = ((torch.rand(2) - .5) * self.jitter_strength) + + for n_ in range(n): + focals = projs[n_, :, [0, 1], [0, 1]] + centers = projs[n_, :, [0, 1], [2, 2]] + + rays, xy = util.gen_rays( + poses[n_].view(-1, 4, 4), + w, + h, + focal=focals, + c=centers, + z_near=self.z_near, + z_far=self.z_far, + xy_offset=xy_offset, + ) + + # Append frame id to the ray + if image_ids is None: + ids = torch.arange(v, device=images.device, dtype=images.dtype) + else: + ids = torch.tensor(image_ids, device=images.device, dtype=images.dtype) + ids = ids.view(v, 1, 1, 1).expand(v, h, w, 1) + rays = torch.cat((rays, ids), dim=-1) + r_dim = rays.shape[-1] + + patch_coords_v = torch.randint(0, v, (self._patch_count,)) + patch_coords_y = torch.randint( + 0, h - self.patch_size_y, (self._patch_count,) + ) + patch_coords_x = torch.randint( + 0, w - self.patch_size_x, (self._patch_count,) + ) + + sample_rgb_gt = [] + sample_rays = [] + + for v_, y, x in zip(patch_coords_v, patch_coords_y, patch_coords_x): + xy_patch = torch.tensor((x, y)).view(1, 1, 1, 2) + patch_grid = self._grid + xy_patch + xy_offset.view(1, 1, 1, 2) + .5 + patch_grid = patch_grid.to(images.device) + patch_grid[..., 0] = (patch_grid[..., 0] / w) * 2 - 1 + patch_grid[..., 1] = (patch_grid[..., 1] / h) * 2 - 1 + + rgb_gt_patch = F.grid_sample(images[n_:n_+1, v_], patch_grid, padding_mode="border", align_corners=False) + rgb_gt_patch = rgb_gt_patch.permute(0, 2, 3, 1).reshape(-1, self.channels) + + rays_patch = rays[ + v_, y : y + self.patch_size_y, x : x + self.patch_size_x, : + ].reshape(-1, r_dim) + + sample_rgb_gt.append(rgb_gt_patch) + sample_rays.append(rays_patch) + + sample_rgb_gt = torch.cat(sample_rgb_gt, dim=0) + sample_rays = torch.cat(sample_rays, dim=0) + all_rgb_gt.append(sample_rgb_gt) + all_rays.append(sample_rays) + + all_rgb_gt = torch.stack(all_rgb_gt) + all_rays = torch.stack(all_rays) + + return all_rays, all_rgb_gt + + +def get_ray_sampler(config) -> RaySampler: + z_near = config["z_near"] + z_far = config["z_far"] + sample_mode = config.get("sample_mode", "random") + + # TODO: check channel size + match sample_mode: + case "random": + return RandomRaySampler(z_near, z_far, **config["args"]) + case "patch": + return PatchRaySampler(z_near, z_far, **config["args"]) + case "jitteredpatch": + return JitteredPatchRaySampler(z_near, z_far, **config["args"]) + case "image": + return ImageRaySampler(z_near, z_far) + case _: + raise NotImplementedError diff --git a/scenedino/common/sampling_strategies.py b/scenedino/common/sampling_strategies.py new file mode 100644 index 0000000000000000000000000000000000000000..0d90a947ecabfded8ca91604eb943c099d860bd4 --- /dev/null +++ b/scenedino/common/sampling_strategies.py @@ -0,0 +1,376 @@ +from random import shuffle +import random +from typing import Callable, Optional +import numpy as np + +import torch + + +EncoderSamplingStrategy = Callable[[int], list[int]] +LossSamplingStrategy = Callable[[int], tuple[list[int], list[int], Optional[list[list[bool]]]]] + + +# ============================================ ENCODING SAMPLING STRATEGIES ============================================ +def default_encoder_sampler() -> EncoderSamplingStrategy: + def _sampling_strategy(num_frames: int) -> list[int]: + return [0] + + return _sampling_strategy + + +def kitti_360_full_encoder_sampler( + num_encoder_frames: int, always_use_base_frame: bool = True +) -> EncoderSamplingStrategy: + def _sampling_strategy(num_frames: int) -> list[int]: + if always_use_base_frame: + encoder_perm = (torch.randperm(num_frames - 1) + 1)[ + : num_encoder_frames - 1 + ].tolist() + ids_encoder = [0] + ids_encoder.extend(encoder_perm) + else: + ids_encoder = (torch.randperm(num_frames - 1) + 1)[ + :num_encoder_frames + ].tolist() + return ids_encoder + + return _sampling_strategy + + +def kitti_360_stereo_encoder_sampler( + num_encoder_frames: int, num_stereo_frames: int, always_use_base_frame: bool = True +) -> EncoderSamplingStrategy: + def _sampling_strategy(num_frames: int) -> list[int]: + num_frames = min(num_frames, num_stereo_frames) + + if always_use_base_frame: + encoder_perm = (torch.randperm(num_frames - 1) + 1)[ + : num_encoder_frames - 1 + ].tolist() + ids_encoder = [0] + ids_encoder.extend(encoder_perm) + else: + ids_encoder = (torch.randperm(num_frames - 1) + 1)[ + :num_encoder_frames + ].tolist() + return ids_encoder + + return _sampling_strategy + + +def get_encoder_sampling(config) -> EncoderSamplingStrategy: + strategy = config.get("name", None) + match strategy: + case "kitti_360_full": + return kitti_360_full_encoder_sampler(**config["args"]) + case "kitti_360_stereo": + return kitti_360_stereo_encoder_sampler(**config["args"]) + case _: + return default_encoder_sampler() + + +# =============================================== LOSS SAMPLING STRATEGIES ============================================= +def single_view_loss_sampler( + shuffle_frames: bool = False, all_frames: bool = False +) -> LossSamplingStrategy: + if all_frames: + starting_frame = 0 + else: + starting_frame = 1 + + def _sampling_strategy(num_frames: int) -> tuple[list[int], list[int]]: + frames = [id for id in range(num_frames)] + if shuffle_frames: + shuffle(frames) + return frames[0:1], frames[starting_frame:], None + + return _sampling_strategy + + +def single_view_renderer_sampler( + shuffle_frames: bool = False, all_frames: bool = False +) -> LossSamplingStrategy: + def _sampling_strategy(num_frames: int) -> tuple[list[int], list[int]]: + frames = [id for id in range(num_frames)] + if shuffle_frames: + shuffle(frames) + if all_frames: + return frames, frames[0:1], None + else: + return frames[0:-1], frames[0:1], None + + return _sampling_strategy + + +def stereo_view_loss_sampler(shuffle_frames: bool = False) -> LossSamplingStrategy: + def _sampling_strategy(num_frames: int) -> tuple[list[int], list[int]]: + all_frames = [id for id in range(num_frames)] + if shuffle_frames: + shuffle(all_frames) + if all_frames[0] < num_frames // 2: + ids_loss = list(range(num_frames // 2)) + ids_renderer = list(range(num_frames // 2, num_frames)) + else: + ids_renderer = list(range(num_frames // 2)) + ids_loss = list(range(num_frames // 2, num_frames)) + + return ids_loss, ids_renderer, None + + return _sampling_strategy + + +def kitti_360_loss_sampler() -> LossSamplingStrategy: + def _sampling_strategy(num_frames: int) -> tuple[list[int], list[int]]: + ids_loss: list[int] = [] + ids_renderer: list[int] = [] + for cam_pair_base_id in range(0, num_frames, 2): + if random.randint(0, 2): + ids_loss.append(cam_pair_base_id) + ids_renderer.append(cam_pair_base_id + 1) + else: + ids_loss.append(cam_pair_base_id + 1) + ids_renderer.append(cam_pair_base_id) + + return ids_loss, ids_renderer, None + + return _sampling_strategy + + +def kitti_360_loss_sampler() -> LossSamplingStrategy: + def _sampling_strategy(num_frames: int) -> tuple[list[int], list[int]]: + ids_loss: list[int] = [] + ids_renderer: list[int] = [] + for cam_pair_base_id in range(0, num_frames, 2): + if random.randint(0, 2): + ids_loss.append(cam_pair_base_id) + ids_renderer.append(cam_pair_base_id + 1) + else: + ids_loss.append(cam_pair_base_id + 1) + ids_renderer.append(cam_pair_base_id) + + return ids_loss, ids_renderer, None + + return _sampling_strategy + + +def kitti_360_with_mapping_loss_sampler() -> LossSamplingStrategy: + def _sampling_strategy(num_frames: int) -> tuple[list[int], list[int]]: + ids_loss: list[int] = [] + ids_renderer: list[int] = [] + mapping = [] + for cam_pair_base_id in range(0, num_frames, 2): + if random.randint(0, 2): + ids_loss.append(cam_pair_base_id) + ids_renderer.append(cam_pair_base_id + 1) + mapping.append([len(ids_renderer) - 1]) + else: + ids_loss.append(cam_pair_base_id + 1) + ids_renderer.append(cam_pair_base_id) + mapping.append([len(ids_renderer) - 1]) + + mapping = np.array(mapping, dtype=np.int64) + + return ids_loss, ids_renderer, mapping + + return _sampling_strategy + + +def waymo_with_mapping_loss_sampler() -> LossSamplingStrategy: + def _sampling_strategy(num_frames: int) -> tuple[list[int], list[int]]: + ids_loss: list[int] = [] + ids_renderer: list[int] = [] + mapping = [] + for cam_pair_base_id in range(0, num_frames, 2): + if random.randint(0, 2): + ids_loss.append(cam_pair_base_id) + ids_renderer.append(cam_pair_base_id + 1) + mapping.extend([[len(ids_renderer) - 1], [len(ids_renderer) - 1]]) + else: + ids_loss.append(cam_pair_base_id + 1) + ids_renderer.append(cam_pair_base_id) + mapping.extend([[len(ids_renderer) - 1], [len(ids_renderer) - 1]]) + + mapping = np.array(mapping, dtype=np.int64) + + return ids_loss, ids_renderer, mapping + + return _sampling_strategy + + +def alternate_loss_sampler() -> LossSamplingStrategy: + def _sampling_strategy(num_frames: int) -> tuple[list[int], list[int]]: + frames = [id for id in range(num_frames)] + if random.randint(0, 2): + return list(range(0, num_frames, 2)), list(range(1, num_frames, 2)), None + else: + return list(range(1, num_frames, 2)), list(range(0, num_frames, 2)), None + + return _sampling_strategy + + +def get_loss_renderer_sampling(config) -> EncoderSamplingStrategy: + strategy = config.get("name", None) + match strategy: + case "single_loss": + return single_view_loss_sampler(**config.get("args", {})) + case "single_renderer": + return single_view_renderer_sampler(**config.get("args", {})) + case "stereo_loss": + return stereo_view_loss_sampler(**config.get("args", {})) + case "kitti_360": + return kitti_360_loss_sampler() + case "kitti_360_with_mapping": + return kitti_360_with_mapping_loss_sampler() + case "waymo_with_mapping": + return waymo_with_mapping_loss_sampler() + case "alternate": + return alternate_loss_sampler() + case _: + return single_view_loss_sampler(False) + + +# old sampling strategies + +# if self.training: +# frame_perm = torch.randperm(v) +# else: +# frame_perm = torch.arange(v) ## eval + +# if self.enc_style == "random": ## encoded views +# encoder_perm = (torch.randperm(v - 1) + 1)[ +# : self.nv_ - 1 +# ].tolist() ## nv-1 for mono [0] idx +# ids_encoder = [0] ## always starts sampling from mono cam +# ids_encoder.extend(encoder_perm) ## add more cam_views randomly incl. fe +# elif self.enc_style == "default": +# ids_encoder = [ +# v_ for v_ in range(self.nv_) +# ] ## iterating view(v_) over num_views(nv_) +# elif self.enc_style == "stereo": +# if self.training: +# # if v < 8: raise RuntimeError(f"__number of views should be more than 4 when excluding fisheye views") +# # if v < 8: raise RuntimeError(f"__number of views should be more than 4 when excluding fisheye views") +# encoder_perm = (torch.randperm(v - (1 + 4)) + 1)[ +# : self.nv_ - 1 +# ].tolist() +# ids_encoder = [0] +# ids_encoder.extend(encoder_perm) +# else: +# ids_encoder = [0] +# else: +# raise NotImplementedError(f"__unrecognized enc_style: {self.enc_style}") +# ## default: ids_encoder = [0,1,2,3] <=> front stereo for 1st + 2nd time stamps + +# if ( +# not self.training and self.ids_enc_viz_eval +# ): ## when eval in viz to be standardized with test: it's eval from line 354, base_trainer.py +# ids_encoder = self.ids_enc_viz_eval ## fixed during eval + +# ids_render = torch.sort( +# frame_perm[[i for i in self.frames_render if i < v]] +# ).values ## ? ### tensor([0, 4]) + +# combine_ids = None + +# if self.training: +# if self.frame_sample_mode == "only": +# ids_loss = [0] +# ids_render = ids_render[ids_render != 0] + +# elif self.frame_sample_mode == "not": +# frame_perm = torch.randperm(v - 1) + 1 +# ids_loss = torch.sort( +# frame_perm[[i for i in self.frames_render if i < v - 1]] +# ).values +# ids_render = [i for i in range(v) if i not in ids_loss] + +# elif self.frame_sample_mode == "stereo": +# if frame_perm[0] < v // 2: +# ids_loss = list(range(v // 2)) +# ids_render = list(range(v // 2, v)) +# else: +# ids_loss = list(range(v // 2, v)) +# ids_render = list(range(v // 2)) + +# elif self.frame_sample_mode == "mono": +# split_i = v // 2 +# if frame_perm[0] < v // 2: +# ids_loss = list(range(0, split_i, 2)) + list( +# range(split_i + 1, v, 2) +# ) +# ids_render = list(range(1, split_i, 2)) + list(range(split_i, v, 2)) +# else: +# ids_loss = list(range(1, split_i, 2)) + list(range(split_i, v, 2)) +# ids_render = list(range(0, split_i, 2)) + list( +# range(split_i + 1, v, 2) +# ) + +# elif self.frame_sample_mode == "kitti360-mono": +# steps = v // 4 +# start_from = 0 if frame_perm[0] < v // 2 else 1 + +# ids_loss, ids_render = [], [] + +# for cam in range( +# 4 +# ): ## stereo cam sampled for each time ## ! c.f. paper: N_{render}, N_{loss} +# ids_loss += [cam * steps + i for i in range(start_from, steps, 2)] +# ids_render += [ +# cam * steps + i for i in range(1 - start_from, steps, 2) +# ] +# start_from = 1 - start_from + +# if self.enc_style == "test": +# ids_encoder = ids_loss[: self.nv_] + +# elif self.frame_sample_mode.startswith("waymo"): +# num_views = int(self.frame_sample_mode.split("-")[-1]) +# steps = v // num_views +# split = steps // 2 + +# # Predict features from half-left, center, half-right +# ids_encoder = [0, steps, steps * 2] + +# # Combine all frames half-left, center, half-right for efficiency reasons +# combine_ids = [(i, steps + i, steps * 2 + i) for i in range(steps)] + +# if self.training: +# step_perm = torch.randperm(steps) +# else: +# step_perm = torch.arange(steps) ## eval +# step_perm = step_perm.tolist() + +# ids_loss = sum( +# [ +# [i + j * steps for j in range(num_views)] +# for i in step_perm[:split] +# ], +# [], +# ) +# ids_render = sum( +# [ +# [i + j * steps for j in range(num_views)] +# for i in step_perm[split:] +# ], +# [], +# ) + +# elif self.frame_sample_mode == "default": +# ids_loss = frame_perm[ +# [i for i in range(v) if frame_perm[i] not in ids_render] +# ] +# else: +# raise NotImplementedError + +# else: ## eval (!= self.training) +# ids_loss = torch.arange(v) +# ids_render = [0] + +# if self.frame_sample_mode.startswith("waymo"): +# num_views = int(self.frame_sample_mode.split("-")[-1]) +# steps = v // num_views +# split = steps // 2 +# # Predict features from half-left, center, half-right +# ids_encoder = [0, steps, steps * 2] +# ids_render = [0, steps, steps * 2] +# combine_ids = [(i, steps + i, steps * 2 + i) for i in range(steps)] diff --git a/scenedino/common/scheduler.py b/scenedino/common/scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..0baf925599a71b1c3a5f81d920976e3dd53e4daa --- /dev/null +++ b/scenedino/common/scheduler.py @@ -0,0 +1,29 @@ +from torch import optim +from torch.optim.lr_scheduler import StepLR + + +class FixLR(optim.lr_scheduler._LRScheduler): + def __init__(self, optimizer, last_epoch=-1, verbose=False): + super(FixLR, self).__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + return [base_lr for base_lr in self.base_lrs] + + def _get_closed_form_lr(self): + return [base_lr for base_lr in self.base_lrs] + + +def make_scheduler(config, optim): + type = config.get("type", "fix") + if type == "fix": + scheduler = FixLR(optim) + return scheduler + elif type == "step": + scheduler = StepLR( + optim, + config["step_size"], + config["gamma"] + ) + return scheduler + else: + raise NotImplementedError(f"Unknown learning rate scheduler type: {type}") diff --git a/scenedino/common/util.py b/scenedino/common/util.py new file mode 100644 index 0000000000000000000000000000000000000000..96ffd6f00349d4c02ba48e9c3efb32047652bbb1 --- /dev/null +++ b/scenedino/common/util.py @@ -0,0 +1,591 @@ +import cv2 +import numpy as np +import torch +from torchvision import transforms +from torch import nn +from torch.nn import init +import torch.nn.functional as F +import functools +import math +import warnings + + +def image_float_to_uint8(img): + """ + Convert a float image (0.0-1.0) to uint8 (0-255) + """ + vmin = np.min(img) + vmax = np.max(img) + if vmax - vmin < 1e-10: + vmax += 1e-10 + img = (img - vmin) / (vmax - vmin) + img *= 255.0 + return img.astype(np.uint8) + + +def cmap(img, color_map=cv2.COLORMAP_HOT): + """ + Apply 'HOT' color to a float image + """ + return cv2.applyColorMap(image_float_to_uint8(img), color_map) + + +def batched_index_select_nd(t, inds): + """ + Index select on dim 1 of a n-dimensional batched tensor. + :param t (batch, n, ...) + :param inds (batch, k) + :return (batch, k, ...) + """ + return t.gather( + 1, inds[(...,) + (None,) * (len(t.shape) - 2)].expand(-1, -1, *t.shape[2:]) + ) + + +def batched_index_select_nd_last(t, inds): + """ + Index select on dim -1 of a >=2D multi-batched tensor. inds assumed + to have all batch dimensions except one data dimension 'n' + :param t (batch..., n, m) + :param inds (batch..., k) + :return (batch..., n, k) + """ + dummy = inds.unsqueeze(-2).expand(*inds.shape[:-1], t.size(-2), inds.size(-1)) + out = t.gather(-1, dummy) + return out + + +def repeat_interleave(input, repeats, dim=0): + """ + Repeat interleave along axis 0 + torch.repeat_interleave is currently very slow + https://github.com/pytorch/pytorch/issues/31980 + """ + output = input.unsqueeze(1).expand(-1, repeats, *input.shape[1:]) + return output.reshape(-1, *input.shape[1:]) + + +def get_image_to_tensor_balanced(image_size=0): + ops = [] + if image_size > 0: + ops.append(transforms.Resize(image_size)) + ops.extend( + [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),] + ) + return transforms.Compose(ops) + + +def get_mask_to_tensor(): + return transforms.Compose( + [transforms.ToTensor(), transforms.Normalize((0.0,), (1.0,))] + ) + + +def homogeneous(points): + """ + Concat 1 to each point + :param points (..., 3) + :return (..., 4) + """ + return F.pad(points, (0, 1), "constant", 1.0) + + +def gen_grid(*args, ij_indexing=False): + """ + Generete len(args)-dimensional grid. + Each arg should be (lo, hi, sz) so that in that dimension points + are taken at linspace(lo, hi, sz). + Example: gen_grid((0,1,10), (-1,1,20)) + :return (prod_i args_i[2], len(args)), len(args)-dimensional grid points + """ + return torch.from_numpy( + np.vstack( + np.meshgrid( + *(np.linspace(lo, hi, sz, dtype=np.float32) for lo, hi, sz in args), + indexing="ij" if ij_indexing else "xy" + ) + ) + .reshape(len(args), -1) + .T + ) + + +def unproj_map(width, height, f, c=None, device="cpu", norm_dir=True, xy_offset=None): + """ + Get camera unprojection map for given image size. + [y,x] of output tensor will contain unit vector of camera ray of that pixel. + :param width image width + :param height image height + :param f focal length, either a number or tensor [fx, fy] + :param c principal point, optional, either None or tensor [fx, fy] + if not specified uses center of image + :return unproj map (height, width, 3) + """ + if c is None: + c = torch.tensor([[0.0, 0.0]], device=device) + elif isinstance(c, float): + c = torch.tensor([[c, c]], device=device) + elif len(c.shape) == 0: + c = c[None, None].expand(1, 2) + elif len(c.shape) == 1: + c = c.unsqueeze(-1).expand(1, 2) + + if isinstance(f, float): + f = torch.tensor([[f, f]], device=device) + elif len(f.shape) == 0: + f = f[None, None].expand(1, 2) + elif len(f.shape) == 1: + f = f.unsqueeze(-1).expand(1, 2) + n = f.shape[0] + + pixel_width = 2 / width + pixel_height = 2 / height + + x = torch.linspace(-1 + .5 * pixel_width, 1 - .5 * pixel_width, width, dtype=torch.float32, device=device).view(1, 1, width).expand(n, height, width) + y = torch.linspace(-1 + .5 * pixel_height, 1 - .5 * pixel_height, height, dtype=torch.float32, device=device).view(1, height, 1).expand(n, height, width) + + if xy_offset is not None: + x = x + xy_offset[0] * pixel_width + y = y + xy_offset[1] * pixel_height + + xy_img = torch.stack((x, y), dim=-1) + xy = (xy_img - c.view(n, 1, 1, 2)) / f.view(n, 1, 1, 2) + z = torch.ones_like(x).unsqueeze(-1) + unproj = torch.cat((xy, z), dim=-1) + + if norm_dir: + unproj /= torch.norm(unproj, dim=-1).unsqueeze(-1) + return unproj, xy_img + + +def coord_from_blender(dtype=torch.float32, device="cpu"): + """ + Blender to standard coordinate system transform. + Standard coordinate system is: x right y up z out (out=screen to face) + Blender coordinate system is: x right y in z up + :return (4, 4) + """ + return torch.tensor( + [[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]], + dtype=dtype, + device=device, + ) + + +def coord_to_blender(dtype=torch.float32, device="cpu"): + """ + Standard to Blender coordinate system transform. + Standard coordinate system is: x right y up z out (out=screen to face) + Blender coordinate system is: x right y in z up + :return (4, 4) + """ + return torch.tensor( + [[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]], + dtype=dtype, + device=device, + ) + + +def look_at(origin, target, world_up=np.array([0, 1, 0], dtype=np.float32)): + """ + Get 4x4 camera to world space matrix, for camera looking at target + """ + back = origin - target + back /= np.linalg.norm(back) + right = np.cross(world_up, back) + right /= np.linalg.norm(right) + up = np.cross(back, right) + + cam_to_world = np.empty((4, 4), dtype=np.float32) + cam_to_world[:3, 0] = right + cam_to_world[:3, 1] = up + cam_to_world[:3, 2] = back + cam_to_world[:3, 3] = origin + cam_to_world[3, :] = [0, 0, 0, 1] + return cam_to_world + + +def get_cuda(gpu_id): + """ + Get a torch.device for GPU gpu_id. If GPU not available, + returns CPU device. + """ + return ( + torch.device("cuda:%d" % gpu_id) + if torch.cuda.is_available() + else torch.device("cpu") + ) + + +def masked_sample(masks, num_pix, prop_inside, thresh=0.5): + """ + :return (num_pix, 3) + """ + num_inside = int(num_pix * prop_inside + 0.5) + num_outside = num_pix - num_inside + inside = (masks >= thresh).nonzero(as_tuple=False) + outside = (masks < thresh).nonzero(as_tuple=False) + + pix_inside = inside[torch.randint(0, inside.shape[0], (num_inside,))] + pix_outside = outside[torch.randint(0, outside.shape[0], (num_outside,))] + pix = torch.cat((pix_inside, pix_outside)) + return pix + + +def bbox_sample(bboxes, num_pix): + """ + :return (num_pix, 3) + """ + image_ids = torch.randint(0, bboxes.shape[0], (num_pix,)) + pix_bboxes = bboxes[image_ids] + x = ( + torch.rand(num_pix) * (pix_bboxes[:, 2] + 1 - pix_bboxes[:, 0]) + + pix_bboxes[:, 0] + ).long() + y = ( + torch.rand(num_pix) * (pix_bboxes[:, 3] + 1 - pix_bboxes[:, 1]) + + pix_bboxes[:, 1] + ).long() + pix = torch.stack((image_ids, y, x), dim=-1) + return pix + + +def gen_rays(poses, width, height, z_near, z_far, focal=None, c=None, norm_dir=True, xy_offset=None): + """ + Generate camera rays + :return (B, H, W, 8) + """ + num_images = poses.shape[0] + device = poses.device + + cam_unproj_map, xy = unproj_map(width, height, focal, c=c, device=device, norm_dir=norm_dir, xy_offset=xy_offset) + cam_unproj_map = cam_unproj_map.expand(num_images, -1, -1, -1) + xy = xy.expand(num_images, -1, -1, -1) + + cam_centers = poses[:, None, None, :3, 3].expand(-1, height, width, -1) + cam_raydir = torch.matmul( + poses[:, None, None, :3, :3], cam_unproj_map.unsqueeze(-1) + )[:, :, :, :, 0] + + cam_nears = ( + torch.tensor(z_near, device=device) + .view(1, 1, 1, 1) + .expand(num_images, height, width, -1) + ) + cam_fars = ( + torch.tensor(z_far, device=device) + .view(1, 1, 1, 1) + .expand(num_images, height, width, -1) + ) + + rays = torch.cat( + (cam_centers, cam_raydir, cam_nears, cam_fars), dim=-1 + ) + + return rays, xy + + +def trans_t(t): + return torch.tensor( + [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, t], [0, 0, 0, 1],], dtype=torch.float32, + ) + + +def rot_phi(phi): + return torch.tensor( + [ + [1, 0, 0, 0], + [0, np.cos(phi), -np.sin(phi), 0], + [0, np.sin(phi), np.cos(phi), 0], + [0, 0, 0, 1], + ], + dtype=torch.float32, + ) + + +def rot_theta(th): + return torch.tensor( + [ + [np.cos(th), 0, -np.sin(th), 0], + [0, 1, 0, 0], + [np.sin(th), 0, np.cos(th), 0], + [0, 0, 0, 1], + ], + dtype=torch.float32, + ) + + +def pose_spherical(theta, phi, radius): + """ + Spherical rendering poses, from NeRF + """ + c2w = trans_t(radius) + c2w = rot_phi(phi / 180.0 * np.pi) @ c2w + c2w = rot_theta(theta / 180.0 * np.pi) @ c2w + c2w = ( + torch.tensor( + [[-1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]], + dtype=torch.float32, + ) + @ c2w + ) + return c2w + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +def get_norm_layer(norm_type="instance", group_norm_groups=32): + """Return a normalization layer + Parameters: + norm_type (str) -- the name of the normalization layer: batch | instance | none + For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev). + For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics. + """ + if norm_type == "batch": + norm_layer = functools.partial( + nn.BatchNorm2d, affine=True, track_running_stats=True + ) + elif norm_type == "instance": + norm_layer = functools.partial( + nn.InstanceNorm2d, affine=False, track_running_stats=False + ) + elif norm_type == "group": + norm_layer = functools.partial(nn.GroupNorm, group_norm_groups) + elif norm_type == "none": + norm_layer = None + else: + raise NotImplementedError("normalization layer [%s] is not found" % norm_type) + return norm_layer + + +def make_conv_2d( + dim_in, + dim_out, + padding_type="reflect", + norm_layer=None, + activation=None, + kernel_size=3, + use_bias=False, + stride=1, + no_pad=False, + zero_init=False, +): + conv_block = [] + amt = kernel_size // 2 + if stride > 1 and not no_pad: + raise NotImplementedError( + "Padding with stride > 1 not supported, use same_pad_conv2d" + ) + + if amt > 0 and not no_pad: + if padding_type == "reflect": + conv_block += [nn.ReflectionPad2d(amt)] + elif padding_type == "replicate": + conv_block += [nn.ReplicationPad2d(amt)] + elif padding_type == "zero": + conv_block += [nn.ZeroPad2d(amt)] + else: + raise NotImplementedError("padding [%s] is not implemented" % padding_type) + + conv_block.append( + nn.Conv2d( + dim_in, dim_out, kernel_size=kernel_size, bias=use_bias, stride=stride + ) + ) + if zero_init: + nn.init.zeros_(conv_block[-1].weight) + # else: + # nn.init.kaiming_normal_(conv_block[-1].weight) + if norm_layer is not None: + conv_block.append(norm_layer(dim_out)) + + if activation is not None: + conv_block.append(activation) + return nn.Sequential(*conv_block) + + +def calc_same_pad_conv2d(t_shape, kernel_size=3, stride=1): + in_height, in_width = t_shape[-2:] + out_height = math.ceil(in_height / stride) + out_width = math.ceil(in_width / stride) + + pad_along_height = max((out_height - 1) * stride + kernel_size - in_height, 0) + pad_along_width = max((out_width - 1) * stride + kernel_size - in_width, 0) + pad_top = pad_along_height // 2 + pad_bottom = pad_along_height - pad_top + pad_left = pad_along_width // 2 + pad_right = pad_along_width - pad_left + return pad_left, pad_right, pad_top, pad_bottom + + +def same_pad_conv2d(t, padding_type="reflect", kernel_size=3, stride=1, layer=None): + """ + Perform SAME padding on tensor, given kernel size/stride of conv operator + assumes kernel/stride are equal in all dimensions. + Use before conv called. + Dilation not supported. + :param t image tensor input (B, C, H, W) + :param padding_type padding type constant | reflect | replicate | circular + constant is 0-pad. + :param kernel_size kernel size of conv + :param stride stride of conv + :param layer optionally, pass conv layer to automatically get kernel_size and stride + (overrides these) + """ + if layer is not None: + if isinstance(layer, nn.Sequential): + layer = next(layer.children()) + kernel_size = layer.kernel_size[0] + stride = layer.stride[0] + return F.pad( + t, calc_same_pad_conv2d(t.shape, kernel_size, stride), mode=padding_type + ) + + +def same_unpad_deconv2d(t, kernel_size=3, stride=1, layer=None): + """ + Perform SAME unpad on tensor, given kernel/stride of deconv operator. + Use after deconv called. + Dilation not supported. + """ + if layer is not None: + if isinstance(layer, nn.Sequential): + layer = next(layer.children()) + kernel_size = layer.kernel_size[0] + stride = layer.stride[0] + h_scaled = (t.shape[-2] - 1) * stride + w_scaled = (t.shape[-1] - 1) * stride + pad_left, pad_right, pad_top, pad_bottom = calc_same_pad_conv2d( + (h_scaled, w_scaled), kernel_size, stride + ) + if pad_right == 0: + pad_right = -10000 + if pad_bottom == 0: + pad_bottom = -10000 + return t[..., pad_top:-pad_bottom, pad_left:-pad_right] + + +def combine_interleaved(t, inner_dims=(1,), agg_type="average"): + if len(inner_dims) == 1 and inner_dims[0] == 1: + return t + t = t.reshape(-1, *inner_dims, *t.shape[1:]) + if agg_type == "average": + t = torch.mean(t, dim=1) + elif agg_type == "max": + t = torch.max(t, dim=1)[0] + else: + raise NotImplementedError("Unsupported combine type " + agg_type) + return t + + +def psnr(pred, target): + """ + Compute PSNR of two tensors in decibels. + pred/target should be of same size or broadcastable + """ + mse = ((pred - target) ** 2).mean() + psnr = -10 * math.log10(mse) + return psnr + + +def quat_to_rot(q): + """ + Quaternion to rotation matrix + """ + batch_size, _ = q.shape + q = F.normalize(q, dim=1) + R = torch.ones((batch_size, 3, 3), device=q.device) + qr = q[:, 0] + qi = q[:, 1] + qj = q[:, 2] + qk = q[:, 3] + R[:, 0, 0] = 1 - 2 * (qj ** 2 + qk ** 2) + R[:, 0, 1] = 2 * (qj * qi - qk * qr) + R[:, 0, 2] = 2 * (qi * qk + qr * qj) + R[:, 1, 0] = 2 * (qj * qi + qk * qr) + R[:, 1, 1] = 1 - 2 * (qi ** 2 + qk ** 2) + R[:, 1, 2] = 2 * (qj * qk - qi * qr) + R[:, 2, 0] = 2 * (qk * qi - qj * qr) + R[:, 2, 1] = 2 * (qj * qk + qi * qr) + R[:, 2, 2] = 1 - 2 * (qi ** 2 + qj ** 2) + return R + + +def rot_to_quat(R): + """ + Rotation matrix to quaternion + """ + batch_size, _, _ = R.shape + q = torch.ones((batch_size, 4), device=R.device) + + R00 = R[:, 0, 0] + R01 = R[:, 0, 1] + R02 = R[:, 0, 2] + R10 = R[:, 1, 0] + R11 = R[:, 1, 1] + R12 = R[:, 1, 2] + R20 = R[:, 2, 0] + R21 = R[:, 2, 1] + R22 = R[:, 2, 2] + + q[:, 0] = torch.sqrt(1.0 + R00 + R11 + R22) / 2 + q[:, 1] = (R21 - R12) / (4 * q[:, 0]) + q[:, 2] = (R02 - R20) / (4 * q[:, 0]) + q[:, 3] = (R10 - R01) / (4 * q[:, 0]) + return q + + +def get_module(net): + """ + Shorthand for either net.module (if net is instance of DataParallel) or net + """ + if isinstance(net, torch.nn.DataParallel): + return net.module + else: + return net + + +# Nan safe entropy +def normalized_entropy(p, dim=-1, eps=2 ** (-8)): + H_max = math.log2(p.shape[dim]) + + # x log2 (x) -> 0 . Therefore, we can set log2 (x) to 0 if x is small enough. + # This should ensure numerical stability. + p_too_small = p < eps + p = p.clone() + p[p_too_small] = 1 + + plp = torch.log2(p) * p + + plp[p_too_small] = 0 + + # This is the formula for the normalised entropy + entropy = -plp.sum(dim) / H_max + return entropy + + +def kl_div(p, q, dim=-1, eps=2 ** (-8)): + p_too_small = p < eps + q_too_small = q < eps + + too_small = p_too_small | q_too_small + + p = p.clone() + q = q.clone() + + p[too_small] = 0 + q[too_small] = 0 + + p = p / p.sum(dim, keepdims=True).detach() + q = q / q.sum(dim, keepdims=True).detach() + + p[too_small] = 1 + q[too_small] = 1 + + els = p * (p.log() - q.log()) + els[too_small] = 0 + + kl_div = els.sum(dim) + return kl_div diff --git a/scenedino/datasets/__init__.py b/scenedino/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6eb439ad604acab7e70d2cb640f3c4c5973f8715 --- /dev/null +++ b/scenedino/datasets/__init__.py @@ -0,0 +1,261 @@ +import os +from pathlib import Path + +from scenedino.datasets.kitti_360_v2 import KITTI360DatasetV2 +from datasets.cityscapes.cityscapes_dataset import CityscapesSeg +from datasets.bdd.bdd_dataset import BDDSeg + +from .base_dataset import BaseDataset +from .kitti_360 import KITTI360Dataset +from .old_kitti_360 import OldKITTI360Dataset +from .re10k_dataset import RealEstate10kDataset + +import torch + + +# TODO: make more generic -> no more two function +def make_datasets(config) -> tuple[BaseDataset, BaseDataset]: + dataset_type = config.get("type", "KITTI_360") + match dataset_type: + case "KITTI_360": + if config.get("split_path", None) is None: + train_split_path = None + test_split_path = None + else: + train_split_path = Path(config["split_path"]) / "train_files.txt" + test_split_path = Path(config["split_path"]) / "val_files.txt" + train_dataset = KITTI360Dataset( + data_path=Path(config["data_path"]), + pose_path=Path(config["pose_path"]), + split_path=train_split_path, + target_image_size=tuple(config.get("image_size", (192, 640))), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + frame_count=config.get("data_fc", 3), + return_depth=False, + return_segmentation=config.get("data_segmentation", False), + return_occupancy=False, + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offsets=config.get("fisheye_offset", [10]), + stereo_offsets=config.get("stereo_offset", [1]), + # color_aug=config.get("color_aug", False), + is_preprocessed=config.get("is_preprocessed", False), + ) + test_dataset = KITTI360Dataset( + data_path=Path(config["data_path"]), + pose_path=Path(config["pose_path"]), + split_path=test_split_path, + target_image_size=tuple(config.get("image_size", (192, 640))), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + frame_count=config.get("data_fc", 3), + return_depth=True, + return_segmentation=config.get("data_segmentation", False), + return_occupancy=config.get("occupancy", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offsets=[10], + stereo_offsets=[1], + is_preprocessed=config.get("is_preprocessed", False), + ) + return train_dataset, test_dataset + + case "old_KITTI_360": + if config.get("split_path", None) is None: + train_split_path = None + test_split_path = None + else: + train_split_path = Path(config["split_path"]) / "train_files.txt" + test_split_path = Path(config["split_path"]) / "test_files.txt" + train_dataset = OldKITTI360Dataset( + data_path=Path(config["data_path"]), + pose_path=Path(config["pose_path"]), + split_path=train_split_path, + target_image_size=tuple(config.get("image_size", (192, 640))), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + frame_count=config.get("data_fc", 3), + return_depth=False, + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=config.get("fisheye_offset", [10]), + # stereo_offsets=config.get("stereo_offset", [1]), + color_aug=config.get("color_aug", False), + is_preprocessed=config.get("is_preprocessed", False), + ) + test_dataset = OldKITTI360Dataset( + data_path=Path(config["data_path"]), + pose_path=Path(config["pose_path"]), + split_path=test_split_path, + target_image_size=tuple(config.get("image_size", (192, 640))), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + frame_count=config.get("data_fc", 3), + return_depth=True, + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=[10], + # stereo_offsets=[1], + is_preprocessed=config.get("is_preprocessed", False), + ) + return train_dataset, test_dataset + + case "KITTI_360_v2": + if config.get("split_path", None) is None: + train_split_path = None + test_split_path = None + else: + train_split_path = Path(config["split_path"]) / "train_files.txt" + test_split_path = Path(config["split_path"]) / "val_files.txt" + train_dataset = KITTI360DatasetV2( + data_path=Path(config["data_path"]), + pose_path=Path(config["pose_path"]), + split_path=train_split_path, + target_image_size=tuple(config.get("image_size", (192, 640))), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + frame_count=config.get("data_fc", 3), + return_depth=False, + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=config.get("fisheye_offset", [10]), + color_aug=config.get("color_aug", False), + is_preprocessed=config.get("is_preprocessed", False), + ) + test_dataset = KITTI360DatasetV2( + data_path=Path(config["data_path"]), + pose_path=Path(config["pose_path"]), + split_path=test_split_path, + target_image_size=tuple(config.get("image_size", (192, 640))), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + frame_count=config.get("data_fc", 3), + return_depth=True, + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=[10], + is_preprocessed=config.get("is_preprocessed", False), + ) + return train_dataset, test_dataset + + case "Cityscapes_seg": + train_dataset = CityscapesSeg( + root=config["data_path"], + image_set="train", + ) + test_dataset = CityscapesSeg( + root=config["data_path"], + image_set="val", + ) + return train_dataset, test_dataset + + case "RealEstate10K": + if config.get("split_path", None) is None: + train_split_path = None + test_split_path = None + else: + train_split_path = Path(config["split_path"]) / "train_files.txt" + test_split_path = Path(config["split_path"]) / "val_files.txt" + + train_dataset = RealEstate10kDataset( + data_path=config["data_path"], + split_path=train_split_path, + image_size=config["image_size"], + ) + test_dataset = RealEstate10kDataset( + data_path=config["data_path"], + split_path=test_split_path, + image_size=config["image_size"], + ) + return train_dataset, test_dataset + + case "BDD_seg": + train_dataset = BDDSeg( + root=config["data_path"], + image_set="train", + ) + test_dataset = BDDSeg( + root=config["data_path"], + image_set="val", + ) + return train_dataset, test_dataset + + case _: + raise NotImplementedError(f"Unsupported dataset type: {type}") + + +def make_test_dataset(config): + dataset_type = config.get("type", "KITTI_Raw") + match dataset_type: + case "KITTI_360": + test_dataset = OldKITTI360Dataset( + data_path=config["data_path"], + pose_path=config["pose_path"], + split_path=os.path.join( + config.get("split_path", None), "test_files.txt" + ), + target_image_size=tuple(config.get("image_size", (192, 640))), + frame_count=config.get("data_fc", 1), + return_stereo=config.get("data_stereo", False), + return_fisheye=config.get("data_fisheye", False), + return_3d_bboxes=config.get("data_3d_bboxes", False), + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=0, + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=config.get("fisheye_offset", 1), + dilation=config.get("dilation", 1), + is_preprocessed=config.get("is_preprocessed", False), + ) + return test_dataset + case "old_KITTI_360": + test_dataset = OldKITTI360Dataset( + data_path=Path(config["data_path"]), + pose_path=Path(config["pose_path"]), + split_path=os.path.join( + config.get("split_path", None), "test_files.txt" + ), + target_image_size=tuple(config.get("image_size", (192, 640))), + return_stereo=config.get("data_stereo", True), + return_fisheye=config.get("data_fisheye", True), + frame_count=config.get("data_fc", 3), + return_depth=True, + return_segmentation=config.get("data_segmentation", False), + keyframe_offset=config.get("keyframe_offset", 0), + dilation=config.get("dilation", 1), + fisheye_rotation=config.get("fisheye_rotation", 0), + fisheye_offset=config.get("fisheye_offset", 1), + # stereo_offsets=[1], + is_preprocessed=config.get("is_preprocessed", False), + ) + return test_dataset + case "Cityscapes_seg": + test_dataset = CityscapesSeg( + root=config["data_path"], + image_set="val", + ) + return test_dataset + case "RealEstate10K": + test_dataset = RealEstate10kDataset( + data_path=config["data_path"], + image_size=config["image_size"], + ) + return test_dataset + case "BDD_seg": + test_dataset = BDDSeg( + root=config["data_path"], + image_set="val", + ) + return test_dataset + case _: + raise NotImplementedError(f"Unsupported dataset type: {dataset_type}") diff --git a/scenedino/datasets/base_dataset.py b/scenedino/datasets/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..c613dd399ee8bf5eb202e7e72a532e756985f30a --- /dev/null +++ b/scenedino/datasets/base_dataset.py @@ -0,0 +1,87 @@ +from abc import abstractmethod +import time +from typing import Any +from pathlib import Path + +import numpy as np +from torch.utils.data import Dataset + + +class BaseDataset(Dataset): + def __init__(self) -> None: + super().__init__() + + # @abstractmethod + # def _get_img_indices(self, index) -> dict[str, list[Any]]: + # pass + + # @abstractmethod + # def _load_image(self, unique_id: Any) -> np.ndarray: + # pass + + # @abstractmethod + # def _load_depth_map(self, unique_id: Any) -> np.ndarray | None: + # pass + + # @abstractmethod + # def _get_pose(self, unique_id: Any) -> np.ndarray: + # pass + + # @abstractmethod + # def _get_calib(self, unique_id: Any) -> np.ndarray: + # pass + + # @abstractmethod + # def _load_occ(self, idx) -> np.ndarray | None: + # pass + + # TODO: Check if needs to return the values + @staticmethod + @abstractmethod + def _process_image( + img: np.ndarray, + proj: np.ndarray, + pose: np.ndarray, + depth: np.ndarray | None, + camera_type: str, + aug_fn: dict[str, Any], + ): + pass + + @abstractmethod + def _create_aug_fn(self) -> dict[str, Any]: + pass + + def __getitem__(self, index) -> dict[str, Any]: + _start_time = time.time() + + img_paths = self._get_img_indices(index) + occ = self._load_occ(index) + + aug_fn = self._create_aug_fn() + + frames = [] + for camera_type, unique_id in img_paths.items(): + img = self._load_image(unique_id) + proj = self._get_calib(unique_id) + pose = self._get_pose(unique_id) + depth = self._load_depth_map(unique_id) + + self._process_image(img, proj, pose, depth, camera_type, aug_fn) + + frames.append( + { + "model": camera_type, + "imgs": img, + "proj": proj, + "pose": pose, + "depth": depth, + } + ) + _proc_time = np.array(time.time() - _start_time) + + return { + "frames": frames, + "occ": occ, + "__t_get_item__": np.array([_proc_time]), + } diff --git a/scenedino/datasets/kitti_360.py b/scenedino/datasets/kitti_360.py new file mode 100644 index 0000000000000000000000000000000000000000..73bb63d383377d1ce7e8d4c19d873b099d2ea299 --- /dev/null +++ b/scenedino/datasets/kitti_360.py @@ -0,0 +1,618 @@ +from dataclasses import dataclass +import os +from random import shuffle +from typing import Any, Dict, List, Tuple +import yaml +from collections import Counter +from pathlib import Path + +import cv2 +import numpy as np +import torch +from scipy.spatial.transform import Rotation +from scenedino.common.io.images import FisheyeToPinholeSampler + +from .base_dataset import BaseDataset + + +class KITTI360Dataset(BaseDataset): + def __init__( + self, + data_path: Path, + pose_path: Path, + split_path: Path | None, + target_image_size: Tuple[int, int] = (192, 640), + return_stereo: bool = False, + return_fisheye: bool = True, + frame_count: int = 2, + return_depth: bool = False, + return_segmentation: bool = False, + return_occupancy: bool = False, + keyframe_offset: int = 0, + dilation: int = 1, + fisheye_rotation: int = 0, + fisheye_offsets: List[int] = [10], + stereo_offsets: List[int] = [1], + is_preprocessed: bool = False, + ) -> None: + super().__init__() + self.data_path = data_path + self.pose_path = pose_path + self.split_path = split_path + self.target_image_size = target_image_size + self.return_stereo = return_stereo + self.return_fisheye = return_fisheye + self.return_depth = return_depth + self.return_occupancy = return_occupancy + self.return_segmentation = return_segmentation + self.frame_count = frame_count + self.dilation = dilation + + self.fisheye_offsets = fisheye_offsets + self.stereo_offsets = stereo_offsets + + self.keyframe_offset = keyframe_offset + self._is_preprocessed = is_preprocessed + + # if isinstance(self.fisheye_rotation, float) or isinstance( + # self.fisheye_rotation, int + # ): + # self.fisheye_rotation = (0, self.fisheye_rotation) + # self.fisheye_rotation = tuple(self.fisheye_rotation) + + self._perspective_folder = ( + "data_rect" + if not self._is_preprocessed + else f"data_{self.target_image_size[0]}x{self.target_image_size[1]}" + ) + self._calibs = self._load_calibs(self.data_path) + + self._timestamps, self._sequences = self._load_sequences( + self.data_path, self.pose_path + ) + + self._datapoints = self._load_split( + self.split_path, self._timestamps, self._sequences + ) + + self._resampler = FisheyeToPinholeSampler( + self._calibs["K_00"], self.target_image_size + ) + + self.length = len(self._datapoints) + + @dataclass + class Datapoint: + sequence: str + id: int + pose: np.ndarray + is_split: bool + + @staticmethod + def _get_sequences(data_path: Path): + all_sequences = [] + + seqs_path = data_path / "data_2d_raw" + for seq in seqs_path.iterdir(): + if not seq.is_dir(): + continue + all_sequences.append(seq.name) + + return all_sequences + + @staticmethod + def _load_calibs(data_path: Path): + calib_folder = data_path / "calibration" + cam_to_pose_file = calib_folder / "calib_cam_to_pose.txt" + cam_to_velo_file = calib_folder / "calib_cam_to_velo.txt" + intrinsics_file = calib_folder / "perspective.txt" + fisheye_02_file = calib_folder / "image_02.yaml" + fisheye_03_file = calib_folder / "image_03.yaml" + + cam_to_pose_data = {} + with open(cam_to_pose_file, "r") as f: + for line in f.readlines(): + key, value = line.split(":", 1) + try: + cam_to_pose_data[key] = np.array( + [float(x) for x in value.split()], dtype=np.float32 + ) + except ValueError: + pass + + cam_to_velo_data = None + with open(cam_to_velo_file, "r") as f: + line = f.readline() + try: + cam_to_velo_data = np.array( + [float(x) for x in line.split()], dtype=np.float32 + ) + except ValueError: + pass + + intrinsics_data = {} + with open(intrinsics_file, "r") as f: + for line in f.readlines(): + key, value = line.split(":", 1) + try: + intrinsics_data[key] = np.array( + [float(x) for x in value.split()], dtype=np.float32 + ) + except ValueError: + pass + + with open(fisheye_02_file, "r") as f: + f.readline() # Skips first line that defines the YAML version + fisheye_02_data = yaml.safe_load(f) + + with open(fisheye_03_file, "r") as f: + f.readline() # Skips first line that defines the YAML version + fisheye_03_data = yaml.safe_load(f) + + im_size_rect = ( + int(intrinsics_data["S_rect_00"][1]), + int(intrinsics_data["S_rect_00"][0]), + ) + im_size_fish = (fisheye_02_data["image_height"], fisheye_02_data["image_width"]) + + # Projection matrices + # We use these projection matrices also when resampling the fisheye cameras. + # This makes downstream processing easier, but it could be done differently. + proj_rect_00 = np.reshape(intrinsics_data["P_rect_00"], (3, 4)) + proj_rect_01 = np.reshape(intrinsics_data["P_rect_01"], (3, 4)) + + # Rotation matrices from raw to rectified -> Needs to be inverted later + rotation_rect_00 = np.eye(4, dtype=np.float32) + rotation_rect_01 = np.eye(4, dtype=np.float32) + rotation_rect_00[:3, :3] = np.reshape(intrinsics_data["R_rect_00"], (3, 3)) + rotation_rect_01[:3, :3] = np.reshape(intrinsics_data["R_rect_01"], (3, 3)) + + # Rotation matrices from resampled fisheye to raw fisheye + # TODO: this is dummy + fisheye_rotation = [0, 0] + fisheye_rotation = np.array(fisheye_rotation).reshape((1, 2)) + R_02 = np.eye(4, dtype=np.float32) + R_03 = np.eye(4, dtype=np.float32) + R_02[:3, :3] = ( + Rotation.from_euler("xy", fisheye_rotation[:, [1, 0]], degrees=True) + .as_matrix() + .astype(np.float32) + ) + R_03[:3, :3] = ( + Rotation.from_euler( + "xy", fisheye_rotation[:, [1, 0]] * np.array([[1, -1]]), degrees=True + ) + .as_matrix() + .astype(np.float32) + ) + + # Load cam to pose transforms + T_00_to_pose = np.eye(4, dtype=np.float32) + T_01_to_pose = np.eye(4, dtype=np.float32) + T_02_to_pose = np.eye(4, dtype=np.float32) + T_03_to_pose = np.eye(4, dtype=np.float32) + T_00_to_velo = np.eye(4, dtype=np.float32) + + T_00_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_00"], (3, 4)) + T_01_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_01"], (3, 4)) + T_02_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_02"], (3, 4)) + T_03_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_03"], (3, 4)) + T_00_to_velo[:3, :] = np.reshape(cam_to_velo_data, (3, 4)) + + # Compute cam to pose transforms for rectified perspective cameras + T_rect_00_to_pose = T_00_to_pose @ np.linalg.inv(rotation_rect_00) + T_rect_01_to_pose = T_01_to_pose @ np.linalg.inv(rotation_rect_01) + + # Compute cam to pose transform for fisheye cameras + T_02_to_pose = T_02_to_pose @ R_02 + T_03_to_pose = T_03_to_pose @ R_03 + + # Compute velo to cameras and velo to pose transforms + T_velo_to_rect_00 = rotation_rect_00 @ np.linalg.inv(T_00_to_velo) + T_velo_to_pose = T_rect_00_to_pose @ T_velo_to_rect_00 + T_velo_to_rect_01 = np.linalg.inv(T_rect_01_to_pose) @ T_velo_to_pose + + # TODO: possibly normalize image coordinates + + calibs = { + "K_00": proj_rect_00[:3, :3], + "K_01": proj_rect_01[:3, :3], + "T_cam_to_pose": { + "00": T_rect_00_to_pose, + "01": T_rect_01_to_pose, + "02": T_02_to_pose, + "03": T_03_to_pose, + }, + "T_velo_to_cam": { + "00": T_velo_to_rect_00, + "01": T_velo_to_rect_01, + }, + "T_velo_to_pose": T_velo_to_pose, + "fisheye": { + "calib_02": fisheye_02_data, + "calib_03": fisheye_03_data, + "R_02": R_02[:3, :3], + "R_03": R_03[:3, :3], + }, + "im_size": im_size_rect, + } + + return calibs + + def _load_sequences( + self, data_path: Path, pose_path: Path + ) -> tuple[dict[str, list[Datapoint]], list[str]]: + sequences = self._get_sequences(data_path) + timestamps = {"pinhole": [], "fisheye": []} + for seq in sequences: + try: + pose_data = np.loadtxt(pose_path / seq / f"poses.txt") + except FileNotFoundError: + print(f"Ground truth poses are not avaialble for sequence {seq}.") + continue + ids_seq = pose_data[:, 0].astype(int) + poses_seq = pose_data[:, 1:].astype(np.float32).reshape((-1, 3, 4)) + poses_seq = np.concatenate( + (poses_seq, np.zeros_like(poses_seq[:, :1, :])), axis=1 + ) + poses_seq[:, 3, 3] = 1 + + for id, pose in zip(ids_seq, poses_seq): + file_name = f"{id:010d}.png" + + datapoint = self.Datapoint( + sequence=seq, id=id, pose=pose, is_split=False + ) + timestamps["pinhole"].append(datapoint) + + if self.return_fisheye: + datapoint = self.Datapoint( + sequence=seq, id=id, pose=pose, is_split=False + ) + timestamps["fisheye"].append(datapoint) + + return timestamps, sequences + + def _load_split( + self, + split_path: Path, + timestamps: Dict[str, List[Datapoint]], + sequences: List[str], + ): + timestamp_idx = {seq: {} for seq in sequences} + for idx, timestamp in enumerate(timestamps["pinhole"]): + timestamp_idx[timestamp.sequence][timestamp.id] = idx + + with open(split_path, "r") as f: + lines = f.readlines() + + def split_line(line): + segments = line.split(" ") + seq = segments[0] + id = int(segments[1]) + + return seq, id + + whole_split = list(map(split_line, lines)) + whole_split.sort() + + cut_split = [] + for idx, (seq, id) in enumerate(whole_split[: -self.frame_count]): + keep = True + for offset in range(1, self.frame_count): + if whole_split[idx + offset][0] != seq: + keep = False + break + if whole_split[idx + offset][1] != id + offset: + keep = False + break + t_idx = timestamp_idx[seq][id] + timestamps["pinhole"][t_idx].is_split = True + if self.return_fisheye: + timestamps["fisheye"][t_idx].is_split = True + + if keep: + cut_split.append((seq, id, timestamp_idx[seq][id])) + + return cut_split + + @staticmethod + def _load_poses(pose_path: Path, sequences: List[str]): + ids = {} + poses = {} + + for seq in sequences: + pose_file = pose_path / seq / f"poses.txt" + + try: + pose_data = np.loadtxt(pose_file) + except FileNotFoundError: + print(f"Ground truth poses are not avaialble for sequence {seq}.") + continue + + ids_seq = pose_data[:, 0].astype(int) + poses_seq = pose_data[:, 1:].astype(np.float32).reshape((-1, 3, 4)) + poses_seq = np.concatenate( + (poses_seq, np.zeros_like(poses_seq[:, :1, :])), axis=1 + ) + poses_seq[:, 3, 3] = 1 + + ids[seq] = ids_seq + poses[seq] = poses_seq + return ids, poses + + def get_img_id_from_id(self, sequence, id): + return self._img_ids[sequence][id] + + def _get_img_indices(self, index) -> Dict[str, List[Path]]: + sequence, id, is_right = self._datapoints[index] + seq_len = self._img_ids[sequence].shape[0] + + # TODO: reorganize the splits + load_left, load_right = ( + not is_right + ) or self.return_stereo, is_right or self.return_stereo + + shuffle(self.stereo_offsets) + ## randomly sample fisheye in the time steps where it can see the occlusion with the stereo + stereo_offsets = sorted(self.stereo_offsets[: self.frame_count - 1]) + + ids = [id] + [ + max(min(id + offset * self.dilation, seq_len - 1), 0) + for offset in stereo_offsets + ] + + img_ids = [self.get_img_id_from_id(sequence, id) for id in ids] + + pinhole_paths: List[Path] = [] + for idx in img_ids: + if load_left: + pinhole_paths.append( + self.data_path + / "data_2d_raw" + / sequence + / "image_00" + / self.perspective_folder + / f"{idx:010d}.png" + ) + if load_right: + pinhole_paths.append( + self.data_path + / "data_2d_raw" + / sequence + / "image_01" + / self.perspective_folder + / f"{idx:010d}.png" + ) + + fisheye_paths: List[Path] = [] + if self.return_fisheye: + shuffle(self.fisheye_offsets) + fisheye_offsets = sorted(self.fisheye_offsets[: self.frame_count]) + ids_fish = [ + max(min(id + fisheye_offsets * self.dilation, seq_len - 1), 0) + for offset in fisheye_offsets + ] + img_ids_fish = [self.get_img_id_from_id(sequence, id) for id in ids_fish] + + for idx in img_ids_fish: + if load_left: + pinhole_paths.append( + self.data_path + / "data_2d_raw" + / sequence + / "image_02" + / self._fisheye_folder + / f"{idx:010d}.png" + ) + if load_right: + pinhole_paths.append( + self.data_path + / "data_2d_raw" + / sequence + / "image_03" + / self._fisheye_folder + / f"{idx:010d}.png" + ) + + if self._is_preprocessed: + pinhole_paths.extend(fisheye_paths) + + if self.return_fisheye and not self._is_preprocessed: + return {"pinhole": pinhole_paths, "fisheye": fisheye_paths} + else: + return {"pinhole": pinhole_paths} + + def load_images(self, seq, img_ids, load_left, load_right, img_ids_fish=None): + imgs_p_left = [] + imgs_f_left = [] + imgs_p_right = [] + imgs_f_right = [] + + if img_ids_fish is None: + img_ids_fish = img_ids + + for id in img_ids: + if load_left: + img_perspective = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_00", + self._perspective_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_p_left += [img_perspective] + + if load_right: + img_perspective = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_01", + self._perspective_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_p_right += [img_perspective] + + for id in img_ids_fish: + if load_left: + img_fisheye = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_02", + self._fisheye_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_f_left += [img_fisheye] + if load_right: + img_fisheye = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_03", + self._fisheye_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_f_right += [img_fisheye] + + return imgs_p_left, imgs_f_left, imgs_p_right, imgs_f_right + + def process_img( + self, + img: np.ndarray, + color_aug_fn=None, + resampler: FisheyeToPinholeSampler | None = None, + ): + if resampler is not None and not self._is_preprocessed: + img = torch.tensor(img).permute(2, 0, 1) + img = resampler.resample(img) + else: + if self.target_image_size: + img = cv2.resize( + img, + (self.target_image_size[1], self.target_image_size[0]), + interpolation=cv2.INTER_LINEAR, + ) + img = np.transpose(img, (2, 0, 1)) + img = torch.tensor(img) + + if color_aug_fn is not None: + img = color_aug_fn(img) + + img = img * 2 - 1 + return img + + def load_depth(self, seq, img_id, is_right): + points = np.fromfile( + os.path.join( + self.data_path, + "data_3d_raw", + seq, + "velodyne_points", + "data", + f"{img_id:010d}.bin", + ), + dtype=np.float32, + ).reshape(-1, 4) + points[:, 3] = 1.0 + + T_velo_to_cam = self._calibs["T_velo_to_cam"]["00" if not is_right else "01"] + K = self._calibs["K_perspective"] + + # project the points to the camera + velo_pts_im = np.dot(K @ T_velo_to_cam[:3, :], points.T).T + velo_pts_im[:, :2] = velo_pts_im[:, :2] / velo_pts_im[:, 2][..., None] + + # the projection is normalized to [-1, 1] -> transform to [0, height-1] x [0, width-1] + velo_pts_im[:, 0] = np.round( + (velo_pts_im[:, 0] * 0.5 + 0.5) * self.target_image_size[1] + ) + velo_pts_im[:, 1] = np.round( + (velo_pts_im[:, 1] * 0.5 + 0.5) * self.target_image_size[0] + ) + + # check if in bounds + val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0) + val_inds = ( + val_inds + & (velo_pts_im[:, 0] < self.target_image_size[1]) + & (velo_pts_im[:, 1] < self.target_image_size[0]) + ) + velo_pts_im = velo_pts_im[val_inds, :] + + # project to image + depth = np.zeros(self.target_image_size) + depth[ + velo_pts_im[:, 1].astype(np.int32), velo_pts_im[:, 0].astype(np.int32) + ] = velo_pts_im[:, 2] + + # find the duplicate points and choose the closest depth + inds = ( + velo_pts_im[:, 1] * (self.target_image_size[1] - 1) + velo_pts_im[:, 0] - 1 + ) + dupe_inds = [item for item, count in Counter(inds).items() if count > 1] + for dd in dupe_inds: + pts = np.where(inds == dd)[0] + x_loc = int(velo_pts_im[pts[0], 0]) + y_loc = int(velo_pts_im[pts[0], 1]) + depth[y_loc, x_loc] = velo_pts_im[pts, 2].min() + depth[depth < 0] = 0 + + return depth[None, :, :] + + def _process_image( + self, + img: np.ndarray, + proj: np.ndarray, + pose: np.ndarray, + depth: np.ndarray | None, + camera_type: str, + aug_fn: dict[str, Any], + ): + return { + "model": camera_type, + "imgs": img, + "proj": proj, + "pose": pose, + "depth": depth, + } + + def _create_aug_fn(self) -> dict[str, Any]: + return {} + + def __len__(self) -> int: + return self.length diff --git a/scenedino/datasets/kitti_360_v2.py b/scenedino/datasets/kitti_360_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..9e06af98165dfa4d82521630898df082f25c77be --- /dev/null +++ b/scenedino/datasets/kitti_360_v2.py @@ -0,0 +1,341 @@ +import os +import random +import time +import xml.etree.ElementTree as ET +from collections import Counter, defaultdict +from pathlib import Path +from typing import Optional +from dotdict import dotdict +import yaml + +import cv2 +import numpy as np +import omegaconf +import torch +import torch.nn.functional as F +from scipy.spatial.transform import Rotation +from torch.utils.data import Dataset +from torchvision.transforms import ColorJitter +from scenedino.common.geometry import estimate_frustum_overlap_2 +from scenedino.common.point_sampling import regular_grid + +from scenedino.datasets.old_kitti_360 import FisheyeToPinholeSampler, OldKITTI360Dataset +from datasets.kitti_360.annotation import KITTI360Bbox3D +from scenedino.common.augmentation import get_color_aug_fn + + +def three_to_four(matrix): + new_matrix = torch.eye(4, dtype=matrix.dtype, device=matrix.device) + dims = len(matrix.shape) + new_matrix = new_matrix.view(*([1] * (dims-2)), 4, 4) + new_matrix[..., :3, :3] = matrix + return new_matrix + + +class FrameSamplingStrategy: + """Strategy that determines how frames should be sampled around a given base frame. + """ + def sample(self, index, nbr_samples): + raise NotImplementedError + + +class OverlapFrameSamplingStrategy(FrameSamplingStrategy): + def __init__(self, n_frames, cams=("00", "01"), **kwargs) -> None: + """Strategy to sample consecutive frames from the same cam. + + Args: + n_frames (int): How many frames should be sampled around the given frame + dilation (int, optional): By how much the returned frames should be separated. Defaults to 1. + """ + super().__init__() + + self.n_frames = n_frames + self.cams = cams + + self.max_samples = kwargs.get("max_samples", 128) + self.min_ratio = kwargs.get("min_ratio", .4) + self.max_steps = kwargs.get("max_steps", 5) + + self.ranges_00 = kwargs.get("ranges_00", { + "00": (-10, 20), + # "01": (10, 45), + "02": (10, 50), + "03": (10, 50), + }) + + self.ranges_01 = kwargs.get("ranges_01", { + # "00": self.ranges_00["01"], + "01": self.ranges_00["00"], + "02": self.ranges_00["02"], + "03": self.ranges_00["03"], + }) + + self.ranges = { + "00": self.ranges_00, + "01": self.ranges_01, + } + + def sample(self, index, nbr_samples, poses, calibs): + poses = torch.tensor(poses, dtype=torch.float32) + + ids = [] + + p_cam = random.random() + if p_cam < .5: + base_cam = "00" + else: + base_cam = "01" + + all_ranges = self.ranges[base_cam] + + # print(index, nbr_samples, len(poses), poses.shape, poses[index:index+1, :, :]) + + encoder_frame = (base_cam, index) + encoder_proj = torch.tensor(calibs["K_perspective"] if base_cam in ("00", "01") else calibs["K_fisheye"], dtype=torch.float32)[None, :, :] + encoder_pose = poses[index:index+1, :, :] @ calibs["T_cam_to_pose"][base_cam] # [None, :, :] + + off = 1 if random.random() > .5 else -1 + target_frame = (base_cam, encoder_frame[1] + off) + + ids += [encoder_frame, target_frame] + + for i in range(self.max_samples): + if len(ids) >= self.n_frames: + break + + c = random.choice(list(all_ranges.keys())) + index_offset = random.choice(range(all_ranges[c][0], all_ranges[c][1])) + off = 1 if random.random() >= .5 else -1 + + base_frame = (c, max(min(index + index_offset, nbr_samples-1), 0)) + target_frame = (c, max(min(base_frame[1] + off, nbr_samples-1), 0)) + + proj = torch.tensor(calibs["K_perspective"] if c in ("00", "01") else calibs["K_fisheye"], dtype=torch.float32)[None, :, :] + extr = poses[base_frame[1]:base_frame[1]+1, :, :] @ calibs["T_cam_to_pose"][c] #[None, :, :] + + # print(proj, extr, encoder_proj, encoder_pose) + # print(proj.shape, extr.shape, encoder_proj.shape, encoder_pose.shape) + + overlap = estimate_frustum_overlap_2(proj, extr, encoder_proj, encoder_pose) + + overlap = overlap.item() + + # print(overlap) + + # p_keep = (overlap - self.min_ratio) / (1 - self.min_ratio) + + # if p_keep < random.random() and (self.max_samples - i) * 2 > (self.n_frames - len(ids)): + # print("Skip frame:", base_frame) + # continue + + if overlap < self.min_ratio and (self.max_samples - i) * 2 > (self.n_frames - len(ids)): + continue + + ids += [base_frame, target_frame] + + ids = [(cam, max(min(id, nbr_samples-1), 0)) for cam, id in ids] + + # print(ids) + + return ids + + +class KITTI360DatasetV2(OldKITTI360Dataset): + def __init__( + self, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + + self._resamplers = { + "00": None, + "01": None, + "02": self._resampler_02, + "03": self._resampler_03, + } + + self.frame_sampling_strategy = OverlapFrameSamplingStrategy(n_frames=self.frame_count) + + def load_images(self, seq, img_ids): + imgs = [] + + for cam, id, img_id in img_ids: + path = os.path.join( + self.data_path, + "data_2d_raw", + seq, + f"image_{cam}", + self._perspective_folder if cam in ("00", "01") else self._fisheye_folder, + f"{img_id:010d}.png", + ) + + # print(path, os.path.exists(path)) + + img = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB,).astype(np.float32) / 255 + + imgs.append(img) + + return imgs + + def process_img( + self, + img: np.array, + color_aug_fn=None, + resampler: FisheyeToPinholeSampler = None, + ): + if resampler is not None and not self.is_preprocessed: + img = torch.tensor(img).permute(2, 0, 1) + img = resampler.resample(img) + else: + if self.target_image_size: + img = cv2.resize( + img, + (self.target_image_size[1], self.target_image_size[0]), + interpolation=cv2.INTER_LINEAR, + ) + img = np.transpose(img, (2, 0, 1)) + img = torch.tensor(img) + + if color_aug_fn is not None: + img = color_aug_fn(img) + + img = img * 2 - 1 + return img + + def load_depth(self, seq, img_id, cam): + assert cam in ("00", "01") + + points = np.fromfile( + os.path.join( + self.data_path, + "data_3d_raw", + seq, + "velodyne_points", + "data", + f"{img_id:010d}.bin", + ), + dtype=np.float32, + ).reshape(-1, 4) + points[:, 3] = 1.0 + + T_velo_to_cam = self._calibs["T_velo_to_cam"][cam] + K = self._calibs["K_perspective"] + + # project the points to the camera + velo_pts_im = np.dot(K @ T_velo_to_cam[:3, :], points.T).T + velo_pts_im[:, :2] = velo_pts_im[:, :2] / velo_pts_im[:, 2][..., None] + + # the projection is normalized to [-1, 1] -> transform to [0, height-1] x [0, width-1] + velo_pts_im[:, 0] = np.round( + (velo_pts_im[:, 0] * 0.5 + 0.5) * self.target_image_size[1] + ) + velo_pts_im[:, 1] = np.round( + (velo_pts_im[:, 1] * 0.5 + 0.5) * self.target_image_size[0] + ) + + # check if in bounds + val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0) + val_inds = ( + val_inds + & (velo_pts_im[:, 0] < self.target_image_size[1]) + & (velo_pts_im[:, 1] < self.target_image_size[0]) + ) + velo_pts_im = velo_pts_im[val_inds, :] + + # project to image + depth = np.zeros(self.target_image_size) + depth[ + velo_pts_im[:, 1].astype(np.int32), velo_pts_im[:, 0].astype(np.int32) + ] = velo_pts_im[:, 2] + + # find the duplicate points and choose the closest depth + inds = ( + velo_pts_im[:, 1] * (self.target_image_size[1] - 1) + velo_pts_im[:, 0] - 1 + ) + dupe_inds = [item for item, count in Counter(inds).items() if count > 1] + for dd in dupe_inds: + pts = np.where(inds == dd)[0] + x_loc = int(velo_pts_im[pts[0], 0]) + y_loc = int(velo_pts_im[pts[0], 1]) + depth[y_loc, x_loc] = velo_pts_im[pts, 2].min() + depth[depth < 0] = 0 + + return depth[None, :, :] + + def __getitem__(self, index: int): + _start_time = time.time() + + if index >= self.length: + raise IndexError() + + if self._skip != 0: + index += self._skip + + sequence, id, is_right = self._datapoints[index] + seq_len = self._img_ids[sequence].shape[0] + + samples = self.frame_sampling_strategy.sample(id, seq_len, self._poses[sequence], self._calibs) + + samples = [(cam, id, self.get_img_id_from_id(sequence, id)) for cam, id in samples] + + if self.color_aug: + color_aug_fn = get_color_aug_fn( + ColorJitter.get_params( + brightness=(0.8, 1.2), + contrast=(0.8, 1.2), + saturation=(0.8, 1.2), + hue=(-0.1, 0.1), + ) + ) + else: + color_aug_fn = None + + _start_time_loading = time.time() + imgs = self.load_images(sequence, samples) + _loading_time = np.array(time.time() - _start_time_loading) + + _start_time_processing = time.time() + imgs = [self.process_img(img, color_aug_fn=color_aug_fn, resampler=self._resamplers[cam]) for ((cam, id, img_id), img) in zip(samples, imgs)] + _processing_time = np.array(time.time() - _start_time_processing) + + # These poses are camera to world !! + poses = [self._poses[sequence][id, :, :] @ self._calibs["T_cam_to_pose"][cam] for cam, id, img_id in samples] + + projs = [self._calibs["K_perspective"] if cam in ("00", "01") else self._calibs["K_fisheye"] for cam, id, img_id in samples] + + ids = [id for cam, id, img_id in samples] + + if self.return_depth: + depths = [self.load_depth(sequence, samples[0][2], samples[0][1])] + else: + depths = [] + + if self.return_3d_bboxes: + bboxes_3d = [self.get_3d_bboxes(sequence, samples[0][2], poses[0], projs[0])] + else: + bboxes_3d = [] + + if self.return_segmentation: + segs = [self.load_segmentation(sequence, samples[0][2])] + else: + segs = [] + + _proc_time = np.array(time.time() - _start_time) + + data = { + "imgs": imgs, + "projs": projs, + "poses": poses, + "depths": depths, + "ts": ids, + "3d_bboxes": bboxes_3d, + "segs": segs, + "t__get_item__": np.array([_proc_time]), + "index": np.array([index]), + } + + return data + + def __len__(self) -> int: + return self.length diff --git a/scenedino/datasets/old_kitti_360.py b/scenedino/datasets/old_kitti_360.py new file mode 100644 index 0000000000000000000000000000000000000000..c399e802e647e3cab19348e00887408e67c96310 --- /dev/null +++ b/scenedino/datasets/old_kitti_360.py @@ -0,0 +1,1080 @@ +import os +import time +import xml.etree.ElementTree as ET +from collections import Counter, defaultdict +from pathlib import Path +from typing import Optional +from dotdict import dotdict +import yaml + +import cv2 +import numpy as np +import omegaconf +import torch +import torch.nn.functional as F +from scipy.spatial.transform import Rotation +from torch.utils.data import Dataset +from torchvision.transforms import ColorJitter +from scenedino.common.point_sampling import regular_grid + +from datasets.kitti_360.annotation import KITTI360Bbox3D +from scenedino.common.augmentation import get_color_aug_fn + + +class FisheyeToPinholeSampler: + def __init__(self, K_target, target_image_size, calibs, rotation=None): + self._compute_transform(K_target, target_image_size, calibs, rotation) + + def _compute_transform(self, K_target, target_image_size, calibs, rotation=None): + x = ( + torch.linspace(-1, 1, target_image_size[1]) + .view(1, -1) + .expand(target_image_size) + ) + y = ( + torch.linspace(-1, 1, target_image_size[0]) + .view(-1, 1) + .expand(target_image_size) + ) + z = torch.ones_like(x) + xyz = torch.stack((x, y, z), dim=-1).view(-1, 3) + + # Unproject + xyz = (torch.inverse(torch.tensor(K_target)) @ xyz.T).T + + if rotation is not None: + xyz = (torch.tensor(rotation) @ xyz.T).T + + # Backproject into fisheye + xyz = xyz / torch.norm(xyz, dim=-1, keepdim=True) + x = xyz[:, 0] + y = xyz[:, 1] + z = xyz[:, 2] + + xi_src = calibs["mirror_parameters"]["xi"] + x = x / (z + xi_src) + y = y / (z + xi_src) + + k1 = calibs["distortion_parameters"]["k1"] + k2 = calibs["distortion_parameters"]["k2"] + + r = x * x + y * y + factor = 1 + k1 * r + k2 * r * r + x = x * factor + y = y * factor + + gamma0 = calibs["projection_parameters"]["gamma1"] + gamma1 = calibs["projection_parameters"]["gamma2"] + u0 = calibs["projection_parameters"]["u0"] + v0 = calibs["projection_parameters"]["v0"] + + x = x * gamma0 + u0 + y = y * gamma1 + v0 + + xy = torch.stack((x, y), dim=-1).view(1, *target_image_size, 2) + self.sample_pts = xy + + def resample(self, img): + img = img.unsqueeze(0) + resampled_img = F.grid_sample(img, self.sample_pts, align_corners=True).squeeze( + 0 + ) + return resampled_img + + +class OldKITTI360Dataset(Dataset): + def __init__( + self, + data_path: str, + pose_path: str, + split_path: Optional[str], + target_image_size=(192, 640), + return_stereo=False, + return_depth=False, + return_fisheye=True, ## default: True + return_3d_bboxes=False, + return_segmentation=False, + frame_count=2, + keyframe_offset=0, + dilation=1, + fisheye_rotation=0, + fisheye_offset=0, + stereo_offset=0, + eigen_depth=True, + color_aug=False, + is_preprocessed=False, + **kwargs, + ): + self.data_path = data_path + self.pose_path = pose_path + self.split_path = split_path + self.target_image_size = target_image_size + self.return_stereo = return_stereo + self.return_fisheye = return_fisheye + self.return_depth = return_depth + self.return_3d_bboxes = return_3d_bboxes + self.return_segmentation = return_segmentation + self.frame_count = frame_count + self.dilation = dilation + self.fisheye_rotation = fisheye_rotation + self.fisheye_offset = fisheye_offset + self.stereo_offset = stereo_offset + self.keyframe_offset = keyframe_offset + self.eigen_depth = eigen_depth + self.color_aug = color_aug + self.is_preprocessed = is_preprocessed + + if isinstance(self.fisheye_rotation, float) or isinstance( + self.fisheye_rotation, int + ): + self.fisheye_rotation = (0, self.fisheye_rotation) + self.fisheye_rotation = tuple(self.fisheye_rotation) + + # if additional_random_front_offset and not self.random_fisheye_offset: + # raise ValueError("Random Fisheye Offset needs to be active for additional random front offset!") + # else: + # self.additional_random_front_offset = additional_random_front_offset + + # Support random fisheye offset + if type(self.fisheye_offset) == int: + self.random_fisheye_offset = False + self.fisheye_offset = (self.fisheye_offset,) + elif type(self.fisheye_offset) in [ + tuple, + list, + omegaconf.listconfig.ListConfig, + ]: + self.random_fisheye_offset = True + self.fisheye_offset = tuple(sorted(self.fisheye_offset)) + else: + raise ValueError( + f"Invalid datatype for fisheye offset: {type(self.fisheye_offset)}" + ) + + if type(self.stereo_offset) == int: + self.random_stereo_offset = False + self.stereo_offset = (self.stereo_offset,) + elif type(self.stereo_offset) in [tuple, list, omegaconf.listconfig.ListConfig]: + self.random_stereo_offset = True + self.stereo_offset = tuple(sorted(self.stereo_offset)) + else: + raise ValueError( + f"Invalid datatype for fisheye offset: {type(self.stereo_offset)}" + ) + + self._sequences = self._get_sequences(self.data_path) + + self._calibs = self._load_calibs(self.data_path, self.fisheye_rotation) + self._resampler_02, self._resampler_03 = self._get_resamplers( + self._calibs, self._calibs["K_fisheye"], self.target_image_size + ) + self._img_ids, self._poses = self._load_poses(self.pose_path, self._sequences) + self._left_offset = ( + (self.frame_count - 1) // 2 + self.keyframe_offset + ) * self.dilation + + self._perspective_folder = ( + "data_rect" + if not self.is_preprocessed + else f"data_{self.target_image_size[0]}x{self.target_image_size[1]}" + ) + self._fisheye_folder = ( + "data_rgb" + if not self.is_preprocessed + else f"data_{self.target_image_size[0]}x{self.target_image_size[1]}_{self.fisheye_rotation[0]}x{self.fisheye_rotation[1]}" + ) + + if self.split_path is not None: + self._datapoints = self._load_split(self.split_path, self._img_ids) + elif self.return_segmentation: + self._datapoints = self._semantics_split( + self._sequences, self.data_path, self._img_ids + ) + else: + self._datapoints = self._full_split( + self._sequences, self._img_ids, self.check_file_integrity + ) + + if self.return_3d_bboxes: + self._3d_bboxes = self._load_3d_bboxes( + Path(data_path) / "data_3d_bboxes" / "train_full", self._sequences + ) + + if self.return_segmentation: + # Segmentations are only provided for the left camera + self._datapoints = [dp for dp in self._datapoints if not dp[2]] + # make sure we can load all segmentation masks + self._datapoints = [dp for dp in self._datapoints if self.check_segmentation(dp)] + + seq_max_id = {seq: max([0] + [d[1] for d in self._datapoints if d[0] == seq]) for seq in self._sequences} + for seq in self._sequences: + self._poses[seq] = self._poses[seq][:seq_max_id[seq]+1] + self._img_ids[seq] = self._img_ids[seq][:seq_max_id[seq]+1] + + self._skip = 0 + self.length = len(self._datapoints) + + def check_segmentation(self, dp): + """Checks for a datapoint dp if we can load all the segmentation masks for all image_ids.""" + sequence, id, is_right = dp + seq_len = self._img_ids[sequence].shape[0] + + ids = [id] + [max(min(i, seq_len - 1), 0) for i in + range(id - self._left_offset, id - self._left_offset + self.frame_count * self.dilation, + self.dilation) if i != id] + img_ids = [self.get_img_id_from_id(sequence, id) for id in ids] + + for img_id in img_ids: + _p = os.path.join(self.data_path, "data_2d_semantics", "train", sequence, "image_00", "semantic", + f"{img_id:010d}.png") + + if not os.path.isfile(_p): + return False + + return True + + def get_points(self, pose: torch.Tensor) -> torch.Tensor: + """Get points from a pose. + + Args: + pose (torch.Tensor): Pose of shape (4, 4) + + Returns: + torch.Tensor: Points of shape (N, 3). NOTE: the points are in the world coordinate system. + """ + OUT_RES = dotdict( + X_RANGE=(-9, 9), + Y_RANGE=(0.0, 0.75), + Z_RANGE=(21, 3), + X_RES=256, + Y_RES=64, + Z_RES=256, + ) + + cam_incl_adjust = torch.tensor( + [ + [1.0000000, 0.0000000, 0.0000000, 0], + [0.0000000, 0.9961947, -0.0871557, 0], + [0.0000000, 0.0871557, 0.9961947, 0], + [0.0000000, 000000000, 0.0000000, 1], + ], + dtype=torch.float32, + ).view(4, 4) + + points = regular_grid( + OUT_RES.X_RANGE, + OUT_RES.Y_RANGE, + OUT_RES.Z_RANGE, + OUT_RES.X_RES, + OUT_RES.Y_RES, + OUT_RES.Z_RES, + cam_incl_adjust=cam_incl_adjust, + ) + return points + + def check_file_integrity(self, seq, id): + dp = Path(self.data_path) + image_00 = dp / "data_2d_raw" / seq / "image_00" / self._perspective_folder + image_01 = dp / "data_2d_raw" / seq / "image_01" / self._perspective_folder + image_02 = dp / "data_2d_raw" / seq / "image_02" / self._fisheye_folder + image_03 = dp / "data_2d_raw" / seq / "image_03" / self._fisheye_folder + + seq_len = self._img_ids[seq].shape[0] + + ids = [id] + [ + max(min(i, seq_len - 1), 0) + for i in range( + id - self._left_offset, + id - self._left_offset + self.frame_count * self.dilation, + self.dilation, + ) + if i != id + ] + ids_fish = [max(min(id + self.fisheye_offset, seq_len - 1), 0)] + [ + max(min(i, seq_len - 1), 0) + for i in range( + id + self.fisheye_offset - self._left_offset, + id + + self.fisheye_offset + - self._left_offset + + self.frame_count * self.dilation, + self.dilation, + ) + if i != id + self.fisheye_offset + ] + + img_ids = [self.get_img_id_from_id(seq, id) for id in ids] + img_ids_fish = [self.get_img_id_from_id(seq, id) for id in ids_fish] + + for img_id in img_ids: + if not ( + (image_00 / f"{img_id:010d}.png").exists() + and (image_01 / f"{img_id:010d}.png").exists() + ): + return False + if self.return_fisheye: + for img_id in img_ids_fish: + if not ( + (image_02 / f"{img_id:010d}.png").exists() + and (image_03 / f"{img_id:010d}.png").exists() + ): + return False + return True + + @staticmethod + def _get_sequences(data_path): + all_sequences = [] + + seqs_path = Path(data_path) / "data_2d_raw" + for seq in seqs_path.iterdir(): + if not seq.is_dir(): + continue + all_sequences.append(seq.name) + + return all_sequences + + @staticmethod + def _full_split(sequences, img_ids, check_integrity): + datapoints = [] + for seq in sorted(sequences): + ids = [id for id in range(len(img_ids[seq])) if check_integrity(seq, id)] + datapoints_seq = [(seq, id, False) for id in ids] + [ + (seq, id, True) for id in ids + ] + datapoints.extend(datapoints_seq) + return datapoints + + @staticmethod + def _semantics_split(sequences, data_path, img_ids): + datapoints = [] + for seq in sorted(sequences): + datapoints_seq = [(seq, id, False) for id in range(len(img_ids[seq]))] + datapoints_seq = [ + dp + for dp in datapoints_seq + if os.path.exists( + os.path.join( + data_path, + "data_2d_semantics", + "train", + seq, + "image_00", + "semantic_rgb", + f"{img_ids[seq][dp[1]]:010d}.png", + ) + ) + ] + datapoints.extend(datapoints_seq) + return datapoints + + @staticmethod + def _load_split(split_path, img_ids): + img_id2id = { + seq: {id: i for i, id in enumerate(ids)} for seq, ids in img_ids.items() + } + + with open(split_path, "r") as f: + lines = f.readlines() + + def split_line(l): + segments = l.split(" ") + seq = segments[0] + id = img_id2id[seq][int(segments[1])] + return seq, id, segments[2][0] == "r" + + return list(map(split_line, lines)) + + @staticmethod + def _load_calibs(data_path, fisheye_rotation=0): + data_path = Path(data_path) + + calib_folder = data_path / "calibration" + cam_to_pose_file = calib_folder / "calib_cam_to_pose.txt" + cam_to_velo_file = calib_folder / "calib_cam_to_velo.txt" + intrinsics_file = calib_folder / "perspective.txt" + fisheye_02_file = calib_folder / "image_02.yaml" + fisheye_03_file = calib_folder / "image_03.yaml" + + cam_to_pose_data = {} + with open(cam_to_pose_file, "r") as f: + for line in f.readlines(): + key, value = line.split(":", 1) + try: + cam_to_pose_data[key] = np.array( + [float(x) for x in value.split()], dtype=np.float32 + ) + except ValueError: + pass + + cam_to_velo_data = None + with open(cam_to_velo_file, "r") as f: + line = f.readline() + try: + cam_to_velo_data = np.array( + [float(x) for x in line.split()], dtype=np.float32 + ) + except ValueError: + pass + + intrinsics_data = {} + with open(intrinsics_file, "r") as f: + for line in f.readlines(): + key, value = line.split(":", 1) + try: + intrinsics_data[key] = np.array( + [float(x) for x in value.split()], dtype=np.float32 + ) + except ValueError: + pass + + with open(fisheye_02_file, "r") as f: + f.readline() # Skips first line that defines the YAML version + fisheye_02_data = yaml.safe_load(f) + + with open(fisheye_03_file, "r") as f: + f.readline() # Skips first line that defines the YAML version + fisheye_03_data = yaml.safe_load(f) + + im_size_rect = ( + int(intrinsics_data["S_rect_00"][1]), + int(intrinsics_data["S_rect_00"][0]), + ) + im_size_fish = (fisheye_02_data["image_height"], fisheye_02_data["image_width"]) + + # Projection matrices + # We use these projection matrices also when resampling the fisheye cameras. + # This makes downstream processing easier, but it could be done differently. + P_rect_00 = np.reshape(intrinsics_data["P_rect_00"], (3, 4)) + P_rect_01 = np.reshape(intrinsics_data["P_rect_01"], (3, 4)) + + # Rotation matrices from raw to rectified -> Needs to be inverted later + R_rect_00 = np.eye(4, dtype=np.float32) + R_rect_01 = np.eye(4, dtype=np.float32) + R_rect_00[:3, :3] = np.reshape(intrinsics_data["R_rect_00"], (3, 3)) + R_rect_01[:3, :3] = np.reshape(intrinsics_data["R_rect_01"], (3, 3)) + + # Rotation matrices from resampled fisheye to raw fisheye + fisheye_rotation = np.array(fisheye_rotation).reshape((1, 2)) + R_02 = np.eye(4, dtype=np.float32) + R_03 = np.eye(4, dtype=np.float32) + R_02[:3, :3] = ( + Rotation.from_euler("xy", fisheye_rotation[:, [1, 0]], degrees=True) + .as_matrix() + .astype(np.float32) + ) + R_03[:3, :3] = ( + Rotation.from_euler( + "xy", fisheye_rotation[:, [1, 0]] * np.array([[1, -1]]), degrees=True + ) + .as_matrix() + .astype(np.float32) + ) + + # Load cam to pose transforms + T_00_to_pose = np.eye(4, dtype=np.float32) + T_01_to_pose = np.eye(4, dtype=np.float32) + T_02_to_pose = np.eye(4, dtype=np.float32) + T_03_to_pose = np.eye(4, dtype=np.float32) + T_00_to_velo = np.eye(4, dtype=np.float32) + + T_00_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_00"], (3, 4)) + T_01_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_01"], (3, 4)) + T_02_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_02"], (3, 4)) + T_03_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_03"], (3, 4)) + T_00_to_velo[:3, :] = np.reshape(cam_to_velo_data, (3, 4)) + + # Compute cam to pose transforms for rectified perspective cameras + T_rect_00_to_pose = T_00_to_pose @ np.linalg.inv(R_rect_00) + T_rect_01_to_pose = T_01_to_pose @ np.linalg.inv(R_rect_01) + + # Compute cam to pose transform for fisheye cameras + T_02_to_pose = T_02_to_pose @ R_02 + T_03_to_pose = T_03_to_pose @ R_03 + + # Compute velo to cameras and velo to pose transforms + T_velo_to_rect_00 = R_rect_00 @ np.linalg.inv(T_00_to_velo) + T_velo_to_pose = T_rect_00_to_pose @ T_velo_to_rect_00 + T_velo_to_rect_01 = np.linalg.inv(T_rect_01_to_pose) @ T_velo_to_pose + + # Calibration matrix is the same for both perspective cameras + K = P_rect_00[:3, :3] + + # Normalize calibration + f_x = K[0, 0] / im_size_rect[1] + f_y = K[1, 1] / im_size_rect[0] + c_x = K[0, 2] / im_size_rect[1] + c_y = K[1, 2] / im_size_rect[0] + + # Change to image coordinates [-1, 1] + K[0, 0] = f_x * 2.0 + K[1, 1] = f_y * 2.0 + K[0, 2] = c_x * 2.0 - 1 + K[1, 2] = c_y * 2.0 - 1 + + # Convert fisheye calibration to [-1, 1] image dimensions + fisheye_02_data["projection_parameters"]["gamma1"] = ( + fisheye_02_data["projection_parameters"]["gamma1"] / im_size_fish[1] + ) * 2.0 + fisheye_02_data["projection_parameters"]["gamma2"] = ( + fisheye_02_data["projection_parameters"]["gamma2"] / im_size_fish[0] + ) * 2.0 + fisheye_02_data["projection_parameters"]["u0"] = ( + fisheye_02_data["projection_parameters"]["u0"] / im_size_fish[1] + ) * 2.0 - 1.0 + fisheye_02_data["projection_parameters"]["v0"] = ( + fisheye_02_data["projection_parameters"]["v0"] / im_size_fish[0] + ) * 2.0 - 1.0 + + fisheye_03_data["projection_parameters"]["gamma1"] = ( + fisheye_03_data["projection_parameters"]["gamma1"] / im_size_fish[1] + ) * 2.0 + fisheye_03_data["projection_parameters"]["gamma2"] = ( + fisheye_03_data["projection_parameters"]["gamma2"] / im_size_fish[0] + ) * 2.0 + fisheye_03_data["projection_parameters"]["u0"] = ( + fisheye_03_data["projection_parameters"]["u0"] / im_size_fish[1] + ) * 2.0 - 1.0 + fisheye_03_data["projection_parameters"]["v0"] = ( + fisheye_03_data["projection_parameters"]["v0"] / im_size_fish[0] + ) * 2.0 - 1.0 + + # Use same camera calibration as perspective cameras for resampling + # K_fisheye = np.eye(3, dtype=np.float32) + # K_fisheye[0, 0] = 2 + # K_fisheye[1, 1] = 2 + + K_fisheye = K + + calibs = { + "K_perspective": K, + "K_fisheye": K_fisheye, + "T_cam_to_pose": { + "00": T_rect_00_to_pose, + "01": T_rect_01_to_pose, + "02": T_02_to_pose, + "03": T_03_to_pose, + }, + "T_velo_to_cam": { + "00": T_velo_to_rect_00, + "01": T_velo_to_rect_01, + }, + "T_velo_to_pose": T_velo_to_pose, + "fisheye": { + "calib_02": fisheye_02_data, + "calib_03": fisheye_03_data, + "R_02": R_02[:3, :3], + "R_03": R_03[:3, :3], + }, + "im_size": im_size_rect, + } + + return calibs + + @staticmethod + def _get_resamplers(calibs, K_target, target_image_size): + resampler_02 = FisheyeToPinholeSampler( + K_target, + target_image_size, + calibs["fisheye"]["calib_02"], + calibs["fisheye"]["R_02"], + ) + resampler_03 = FisheyeToPinholeSampler( + K_target, + target_image_size, + calibs["fisheye"]["calib_03"], + calibs["fisheye"]["R_03"], + ) + + return resampler_02, resampler_03 + + @staticmethod + def _load_poses(pose_path, sequences): + ids = {} + poses = {} + + for seq in sequences: + pose_file = Path(pose_path) / seq / f"poses.txt" + + try: + pose_data = np.loadtxt(pose_file) + except FileNotFoundError: + print(f"Ground truth poses are not avaialble for sequence {seq}.") + + ids_seq = pose_data[:, 0].astype(int) + poses_seq = pose_data[:, 1:].astype(np.float32).reshape((-1, 3, 4)) + poses_seq = np.concatenate( + (poses_seq, np.zeros_like(poses_seq[:, :1, :])), axis=1 + ) + poses_seq[:, 3, 3] = 1 + + ids[seq] = ids_seq + poses[seq] = poses_seq + return ids, poses + + @staticmethod + def _load_3d_bboxes(bbox_path, sequences): + bboxes = {} + + for seq in sequences: + with open(Path(bbox_path) / f"{seq}.xml", "rb") as f: + tree = ET.parse(f) + root = tree.getroot() + + objects = defaultdict(list) + + num_bbox = 0 + + for child in root: + if child.find("transform") is None: + continue + obj = KITTI360Bbox3D() + if child.find("semanticId") is not None: + obj.parseBbox(child) + else: + obj.parseStuff(child) + # globalId = local2global(obj.semanticId, obj.instanceId) + # objects[globalId][obj.timestamp] = obj + objects[obj.timestamp].append(obj) + num_bbox += 1 + + # globalIds = np.asarray(list(objects.keys())) + # semanticIds, instanceIds = global2local(globalIds) + # for label in labels: + # if label.hasInstances: + # print(f'{label.name:<30}:\t {(semanticIds==label.id).sum()}') + # print(f'Loaded {len(globalIds)} instances') + # print(f'Loaded {num_bbox} boxes') + + bboxes[seq] = objects + + return bboxes + + def get_img_id_from_id(self, sequence, id): + return self._img_ids[sequence][id] + + def load_images(self, seq, img_ids, load_left, load_right, img_ids_fish=None): + imgs_p_left = [] + imgs_f_left = [] + imgs_p_right = [] + imgs_f_right = [] + + if img_ids_fish is None: + img_ids_fish = img_ids + + for id in img_ids: + if load_left: + img_perspective = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_00", + self._perspective_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_p_left += [img_perspective] + + if load_right: + img_perspective = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_01", + self._perspective_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_p_right += [img_perspective] + + for id in img_ids_fish: + if load_left: + img_fisheye = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_02", + self._fisheye_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_f_left += [img_fisheye] + if load_right: + img_fisheye = ( + cv2.cvtColor( + cv2.imread( + os.path.join( + self.data_path, + "data_2d_raw", + seq, + "image_03", + self._fisheye_folder, + f"{id:010d}.png", + ) + ), + cv2.COLOR_BGR2RGB, + ).astype(np.float32) + / 255 + ) + imgs_f_right += [img_fisheye] + + return imgs_p_left, imgs_f_left, imgs_p_right, imgs_f_right + + def process_img( + self, + img: np.array, + color_aug_fn=None, + resampler: FisheyeToPinholeSampler = None, + ): + if resampler is not None and not self.is_preprocessed: + img = torch.tensor(img).permute(2, 0, 1) + img = resampler.resample(img) + else: + if self.target_image_size: + img = cv2.resize( + img, + (self.target_image_size[1], self.target_image_size[0]), + interpolation=cv2.INTER_LINEAR, + ) + img = np.transpose(img, (2, 0, 1)) + img = torch.tensor(img) + + if color_aug_fn is not None: + img = color_aug_fn(img) + + img = img * 2 - 1 + return img + + def get_3d_bboxes(self, seq, img_id, pose, projs): + seq_3d_bboxes = self._3d_bboxes[seq] + pose_w2c = np.linalg.inv(pose) + + def filter_bbox(bbox): + verts = bbox.vertices + verts = (projs @ (pose_w2c[:3, :3] @ verts.T + pose_w2c[:3, 3, None])).T + verts[:, :2] /= verts[:, 2:3] + valid = ( + ((verts[:, 0] >= -1) & (verts[:, 0] <= 1)) + & ((verts[:, 1] >= -1) & (verts[:, 1] <= 1)) + & ((verts[:, 2] > 0) & (verts[:, 2] <= 80)) + ) + valid = np.any(valid, axis=-1) + return valid + + bboxes = seq_3d_bboxes[-1] + seq_3d_bboxes[img_id] + + bboxes = list(filter(filter_bbox, bboxes)) + + bboxes = [ + { + "vertices": bbox.vertices, + "faces": bbox.faces, + "semanticId": bbox.semanticId, + "instanceId": bbox.instanceId, + } + for i, bbox in enumerate(bboxes) + ] # if valid[i] + + return bboxes + + def load_segmentation(self, seq, img_id): + seg = cv2.imread( + os.path.join( + self.data_path, + "data_2d_semantics", + "train", + seq, + "image_00", + "semantic", + f"{img_id:010d}.png", + ), + cv2.IMREAD_UNCHANGED, + ) + seg = cv2.resize( + seg, + (self.target_image_size[1], self.target_image_size[0]), + interpolation=cv2.INTER_NEAREST, + ) + return seg + + def load_depth(self, seq, img_id, is_right): + points = np.fromfile( + os.path.join( + self.data_path, + "data_3d_raw", + seq, + "velodyne_points", + "data", + f"{img_id:010d}.bin", + ), + dtype=np.float32, + ).reshape(-1, 4) + points[:, 3] = 1.0 + + T_velo_to_cam = self._calibs["T_velo_to_cam"]["00" if not is_right else "01"] + K = self._calibs["K_perspective"] + + # project the points to the camera + velo_pts_im = np.dot(K @ T_velo_to_cam[:3, :], points.T).T + velo_pts_im[:, :2] = velo_pts_im[:, :2] / velo_pts_im[:, 2][..., None] + + # the projection is normalized to [-1, 1] -> transform to [0, height-1] x [0, width-1] + velo_pts_im[:, 0] = np.round( + (velo_pts_im[:, 0] * 0.5 + 0.5) * self.target_image_size[1] + ) + velo_pts_im[:, 1] = np.round( + (velo_pts_im[:, 1] * 0.5 + 0.5) * self.target_image_size[0] + ) + + # check if in bounds + val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0) + val_inds = ( + val_inds + & (velo_pts_im[:, 0] < self.target_image_size[1]) + & (velo_pts_im[:, 1] < self.target_image_size[0]) + ) + velo_pts_im = velo_pts_im[val_inds, :] + + # project to image + depth = np.zeros(self.target_image_size) + depth[ + velo_pts_im[:, 1].astype(np.int32), velo_pts_im[:, 0].astype(np.int32) + ] = velo_pts_im[:, 2] + + # find the duplicate points and choose the closest depth + inds = ( + velo_pts_im[:, 1] * (self.target_image_size[1] - 1) + velo_pts_im[:, 0] - 1 + ) + dupe_inds = [item for item, count in Counter(inds).items() if count > 1] + for dd in dupe_inds: + pts = np.where(inds == dd)[0] + x_loc = int(velo_pts_im[pts[0], 0]) + y_loc = int(velo_pts_im[pts[0], 1]) + depth[y_loc, x_loc] = velo_pts_im[pts, 2].min() + depth[depth < 0] = 0 + + return depth[None, :, :] + + def __getitem__(self, index: int): + _start_time = time.time() + + if index >= self.length: + raise IndexError() + + if self._skip != 0: + index += self._skip + + sequence, id, is_right = self._datapoints[index] + seq_len = self._img_ids[sequence].shape[0] + + load_left, load_right = ( + not is_right + ) or self.return_stereo, is_right or self.return_stereo + + ## randomly sample fisheye in the time steps where it can see the occlusion with the stereo + if self.random_fisheye_offset: + fisheye_offset = self.fisheye_offset[ + torch.randint(0, len(self.fisheye_offset), (1,)).item() + ] ## randomly select among the given list of fisheye_ids from config + else: + fisheye_offset = self.fisheye_offset[-1] + + if self.random_stereo_offset: + stereo_offset = self.stereo_offset[ + torch.randint(0, len(self.stereo_offset), (1,)).item() + ] + else: + stereo_offset = self.stereo_offset[-1] + + # ids = [id] + [max(min(i, seq_len-1), 0) for i in range(id - self._left_offset, id - self._left_offset + self.frame_count * self.dilation, self.dilation) if i != id] + # ids_fish = [max(min(id + self.fisheye_offset, seq_len-1), 0)] + [max(min(i, seq_len-1), 0) for i in range(id + self.fisheye_offset - self._left_offset, id + self.fisheye_offset - self._left_offset + self.frame_count * self.dilation, self.dilation) if i != id + self.fisheye_offset] + # img_ids = [self.get_img_id_from_id(sequence, id) for id in ids] + # img_ids_fish = [self.get_img_id_from_id(sequence, id) for id in ids_fish] + + id_st = ( + id + stereo_offset + ) ## TODO: find out how to deal with 3 steps ahead without -1 => as we sample scenes with the amount of stereo_offset + ids = [id] + [ + max(min(i, seq_len - 1), 0) + for i in range( + id_st - self._left_offset, + id_st - self._left_offset + self.frame_count * self.dilation, + self.dilation, + ) + if i != id_st + ] + ids_fish = [max(min(id + fisheye_offset, seq_len - 1), 0)] + [ + max(min(i, seq_len - 1), 0) + for i in range( + id + fisheye_offset - self._left_offset, + id + + fisheye_offset + - self._left_offset + + self.frame_count * self.dilation, + self.dilation, + ) + if i != id + fisheye_offset + ] + ## and now ids_fish is 5 steps ahead of ids with 2 fisheye scenes + img_ids = [self.get_img_id_from_id(sequence, id) for id in ids] + img_ids_fish = [self.get_img_id_from_id(sequence, id) for id in ids_fish] + + if not self.return_fisheye: + ids_fish, img_ids_fish = [], [] + + if self.color_aug: + color_aug_fn = get_color_aug_fn( + ColorJitter.get_params( + brightness=(0.8, 1.2), + contrast=(0.8, 1.2), + saturation=(0.8, 1.2), + hue=(-0.1, 0.1), + ) + ) + else: + color_aug_fn = None + + _start_time_loading = time.time() + imgs_p_left, imgs_f_left, imgs_p_right, imgs_f_right = self.load_images( + sequence, img_ids, load_left, load_right, img_ids_fish=img_ids_fish + ) + _loading_time = np.array(time.time() - _start_time_loading) + + _start_time_processing = time.time() + imgs_p_left = [ + self.process_img(img, color_aug_fn=color_aug_fn) for img in imgs_p_left + ] + imgs_f_left = [ + self.process_img( + img, color_aug_fn=color_aug_fn, resampler=self._resampler_02 + ) + for img in imgs_f_left + ] + imgs_p_right = [ + self.process_img(img, color_aug_fn=color_aug_fn) for img in imgs_p_right + ] + imgs_f_right = [ + self.process_img( + img, color_aug_fn=color_aug_fn, resampler=self._resampler_03 + ) + for img in imgs_f_right + ] + _processing_time = np.array(time.time() - _start_time_processing) + + # These poses are camera to world !! + poses_p_left = ( + [ + self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["00"] + for i in ids + ] + if load_left + else [] + ) + poses_f_left = ( + [ + self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["02"] + for i in ids_fish + ] + if load_left + else [] + ) + poses_p_right = ( + [ + self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["01"] + for i in ids + ] + if load_right + else [] + ) + poses_f_right = ( + [ + self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["03"] + for i in ids_fish + ] + if load_right + else [] + ) + + projs_p_left = [self._calibs["K_perspective"] for _ in ids] if load_left else [] + projs_f_left = ( + [self._calibs["K_fisheye"] for _ in ids_fish] if load_left else [] + ) + projs_p_right = ( + [self._calibs["K_perspective"] for _ in ids] if load_right else [] + ) + projs_f_right = ( + [self._calibs["K_fisheye"] for _ in ids_fish] if load_right else [] + ) + + imgs = ( + imgs_p_left + imgs_p_right + imgs_f_left + imgs_f_right + if not is_right + else imgs_p_right + imgs_p_left + imgs_f_right + imgs_f_left + ) + projs = ( + projs_p_left + projs_p_right + projs_f_left + projs_f_right + if not is_right + else projs_p_right + projs_p_left + projs_f_right + projs_f_left + ) + poses = ( + poses_p_left + poses_p_right + poses_f_left + poses_f_right + if not is_right + else poses_p_right + poses_p_left + poses_f_right + poses_f_left + ) + ids = np.array(ids + ids + ids_fish + ids_fish, dtype=np.int32) + + if self.return_depth: + depths = [self.load_depth(sequence, img_ids[0], is_right)] + else: + depths = [] + + if self.return_3d_bboxes: + bboxes_3d = [self.get_3d_bboxes(sequence, img_ids[0], poses[0], projs[0])] + else: + bboxes_3d = [] + + if self.return_segmentation: + segs = [self.load_segmentation(sequence, img_ids[0])] + else: + segs = [] + + _proc_time = np.array(time.time() - _start_time) + + # print(_loading_time, _processing_time, _proc_time) + + data = { + "imgs": imgs, + "projs": projs, + "poses": poses, + "depths": depths, + "ts": ids, + "3d_bboxes": bboxes_3d, + "segs": segs, + "t__get_item__": np.array([_proc_time]), + "index": np.array([index]), + } + + return data + + def __len__(self) -> int: + return self.length diff --git a/scenedino/datasets/re10k_dataset.py b/scenedino/datasets/re10k_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8ddd2fa2fbfa4969f0374331f221569106d50dd4 --- /dev/null +++ b/scenedino/datasets/re10k_dataset.py @@ -0,0 +1,230 @@ +import os +import pickle +import time +from pathlib import Path +from typing import Optional + +import cv2 +import numpy as np +import torch +from torch.utils.data import Dataset + +from .re10k_util import get_target_size_and_crop, process_flow, process_img, process_proj + + +class RealEstate10kDataset(Dataset): + NAME = "Re10K" + + def __init__( + self, + data_path: str, + split_path: Optional[str], + image_size: Optional[tuple] = None, + frame_count: int = 4, + keyframe_offset: int = 0, + dilation: int = 3, + return_depth: bool = False, + full_size_depth: bool = False, + return_flow: bool = False, + preprocessed_path: Optional[str] = None, + index_selector=None, + sequence_sampler=None, + ): + + self.data_path = os.path.dirname(data_path) + self.split = os.path.basename(data_path).split(".")[0] + self.split_path = split_path + self.image_size = image_size + + self.return_depth = return_depth + self.full_size_depth = full_size_depth + self.return_flow = return_flow + self.preprocessed_path = preprocessed_path + + self.frame_count = frame_count + self.keyframe_offset = keyframe_offset + self.dilation = dilation + + self._left_offset = ( + (self.frame_count - 1) // 2 + self.keyframe_offset + ) * self.dilation + + self._seq_data = self._get_sequences(data_path, self.data_path, self.split, has_split=split_path is not None) + self._seq_keys = list(self._seq_data.keys()) + + if self.split_path is not None: + self._datapoints = self._load_split(self.split_path) + else: + self._left_offset = 0 + self._datapoints = self._full_split(self._seq_data, self._left_offset, (self.frame_count - 1) * dilation, sequence_sampler) + + self.index_selector = index_selector + + self.length = len(self._datapoints) + + self._skip = 0 + + @staticmethod + def _get_sequences(data_path: str, data_root: str, split: str, has_split: bool = False): + with open(data_path, "rb") as f: + seq_data = pickle.load(f) + + seq_data = {k: v for k, v in seq_data.items() if os.path.exists(os.path.join(data_root, "frames_720", split, k))} + + if not has_split: + for k in seq_data.keys(): + seq_data[k]["timestamps"] = seq_data[k]["timestamps"][::10] + seq_data[k]["poses"] = seq_data[k]["poses"][::10] + seq_data[k]["intrinsics"] = seq_data[k]["intrinsics"][::10] + + return seq_data + + @staticmethod + def _full_split(seq_data, left_offset: int = 0, sub_seq_len: int = 2, sequence_sampler=None): + datapoints = [] + for k in seq_data.keys(): + seq_len = len(seq_data[k]["timestamps"]) + if sequence_sampler is not None: + datapoints.extend(sequence_sampler(k, seq_len, left_offset, sub_seq_len)) + else: + if seq_len < sub_seq_len: + continue + for i in range(seq_len - 1): # -1 because we need at least two frames + datapoints.append((k, i)) + return datapoints + + + def _get_id_from_timestamp(self, seq, timestamp): + data = self._seq_data[seq] + id = int(np.where(((data["timestamps"] / 1000).astype(np.int64)==int(timestamp)) | ((data["timestamps"]).astype(np.int64)==int(timestamp)))[0]) + return id + + def _load_split(self, split_path: str): + def get_key_id(s): + parts = s.split(" ") + key = parts[0] + t0 = parts[1] + t1 = parts[2] + id0 = self._get_id_from_timestamp(key, t0) + id1 = self._get_id_from_timestamp(key, t1) + return key, (id0, id1) + + with open(split_path, "r") as f: + lines = f.readlines() + datapoints = list(map(get_key_id, lines)) + return datapoints + + def __len__(self) -> int: + return self.length + + def load_images(self, seq: str, ids: list): + imgs = [] + + for id in ids: + timestamp = int(self._seq_data[seq]["timestamps"][id] / 1000) + img = cv2.cvtColor(cv2.imread(os.path.join(self.data_path, "frames_720", self.split, seq, f"{timestamp}.jpg")), cv2.COLOR_BGR2RGB).astype(np.float32) / 255 + imgs += [img] + + return imgs + + @staticmethod + def process_pose(pose): + pose = np.concatenate((pose.astype(np.float32), np.array([[0, 0, 0, 1]], dtype=np.float32)), axis=0) + pose = np.linalg.inv(pose) + return pose + + @staticmethod + def scale_projs(proj, original_size): + K = np.eye(3, dtype=np.float32) + K[0, 0] = proj[0] * original_size[1] + K[1, 1] = proj[1] * original_size[0] + K[0, 2] = proj[2] * original_size[1] + K[1, 2] = proj[3] * original_size[0] + return K + + def _index_to_seq_ids(self, index): + if index >= self.length: + raise IndexError() + + sequence, id = self._datapoints[index] + seq_len = len(self._seq_data[sequence]["timestamps"]) + + if type(id) != int: + ids = id + else: + if self.index_selector is not None: + ids = self.index_selector(id, self.frame_count, self.dilation, self._left_offset) + else: + ids = [id] + [i + for i in range( + id - self._left_offset, + id - self._left_offset + self.frame_count * self.dilation, + self.dilation, + ) + if i != id + ] + + ids = [max(min(i, seq_len - 1), 0) for i in ids] + + return sequence, ids + + def __getitem__(self, index: int): + sequence, ids = self._index_to_seq_ids(index) + + imgs = self.load_images(sequence, ids) + + original_size = imgs[0].shape[:2] + + target_size, crop = get_target_size_and_crop(self.image_size, original_size) + + if self.return_flow: + raise ValueError("Flow not implemented.") # flows_fwd, flows_bwd = self.load_flows(sequence, ids) + else: + flows_fwd = None + flows_bwd = None + + imgs = [process_img(img, target_size, crop) * 2.0 - 1.0 for img in imgs] + + if self.return_flow: + flows_fwd = np.stack([process_flow(flow, target_size, crop) for flow in flows_fwd]) + flows_bwd = np.stack([process_flow(flow, target_size, crop) for flow in flows_bwd]) + + # These poses are camera to world !! + poses = [self.process_pose(self._seq_data[sequence]["poses"][i, :, :]) for i in ids] + projs = [process_proj(self.scale_projs(self._seq_data[sequence]["intrinsics"][i, :], original_size), original_size, target_size, crop) for i in ids] + + depth = np.ones_like(imgs[0][:1, :, :]) + + # print(projs[0]) + # print(poses[0]) + + data = { + "imgs": imgs, + "projs": projs, + "poses": poses, + "ids": np.array(ids, dtype=np.int64), + "index": np.array([index]), + } + + if self.return_depth: + data["depths"] = depth[None, ...] + + if self.return_flow: + data["flows_fwd"] = flows_fwd + data["flows_bwd"] = flows_bwd + + return data + + def get_img_paths(self, index): + sequence, ids = self._index_to_seq_ids(index) + + img_paths = [ + os.path.join(self.data_path, "frames_720", self.split, sequence, f"{self._seq_data[sequence]['timestamps'][id]}.jpg") + for id in ids + ] + + return img_paths + + def get_sequence(self, index: int): + sequence, _ = self._index_to_seq_ids(index) + return sequence diff --git a/scenedino/datasets/re10k_util.py b/scenedino/datasets/re10k_util.py new file mode 100644 index 0000000000000000000000000000000000000000..6a64bf473f8d9768c18b45744525635d193f488e --- /dev/null +++ b/scenedino/datasets/re10k_util.py @@ -0,0 +1,203 @@ +from typing import Iterable + +import cv2 +import numpy as np + + +def get_target_size_and_crop(target_size, image_size): + if target_size is None: + return None, None + elif not isinstance(target_size, Iterable): # Resize the shorter side to target size and center crop to get a square + if image_size[0] <= image_size[1]: + aspect_ratio = image_size[0] / image_size[1] + resize_to = (target_size, int(round(target_size / aspect_ratio))) + + if resize_to[1] > target_size: + crop = ((resize_to[1] - target_size) // 2, 0, target_size, target_size) + else: + crop = None + + else: + aspect_ratio = image_size[1] / image_size[0] + resize_to = (int(round(target_size / aspect_ratio)), target_size) + if resize_to[0] > target_size: + crop = (0, (resize_to[0] - target_size) // 2, target_size, target_size) + else: + crop = None + elif isinstance(target_size, Iterable) and len(target_size) == 2: # Resize to target size and center crop + target_aspect_ratio = target_size[1] / target_size[0] + image_aspect_ratio = image_size[1] / image_size[0] + + if target_aspect_ratio > image_aspect_ratio: + resize_to = (int(round(target_size[1] / image_aspect_ratio)), target_size[1]) + crop = (0, (resize_to[0] - target_size[0]) // 2, target_size[1], target_size[0]) + else: + resize_to = (target_size[0], int(round(target_size[0] * image_aspect_ratio))) + crop = ((resize_to[1] - target_size[1]) // 2, 0, target_size[1], target_size[0]) + + # print(f"Resizing to {resize_to} and cropping to {crop}") + + return resize_to, crop + + +def process_img( + img: np.array, target_size=None, crop=None +): + if target_size is not None and (target_size[0] != img.shape[0] or target_size[1] != img.shape[1]): + img = cv2.resize( + img, + (target_size[1], target_size[0]), + interpolation=cv2.INTER_LINEAR, + ) + + if crop is not None: + img = img[crop[1]:crop[1]+crop[3], crop[0]:crop[0]+crop[2], :] + + img = np.transpose(img, (2, 0, 1)) + + return img + + +def process_depth(depth: np.array, target_size=None, crop=None): + if target_size is not None and (target_size[0] != depth.shape[0] or target_size[1] != depth.shape[1]): + depth = cv2.resize( + depth, + (target_size[1], target_size[0]), + interpolation=cv2.INTER_NEAREST, + ) + depth = depth[..., None] + + if crop is not None: + depth = depth[crop[1]:crop[1]+crop[3], crop[0]:crop[0]+crop[2], :] + + depth = np.transpose(depth, (2, 0, 1)) + + return depth + + +def process_flow(flow: np.array, target_size=None, crop=None): + flow = flow.transpose(1, 2, 0) + + if target_size is not None and (target_size[0] != flow.shape[0] or target_size[1] != flow.shape[1]): + x_scale = target_size[1] / flow.shape[1] + y_scale = target_size[0] / flow.shape[0] + + flow = cv2.resize( + flow, + (target_size[1], target_size[0]), + interpolation=cv2.INTER_NEAREST, + ) + + flow = flow * np.array([x_scale, y_scale], dtype=np.float32).reshape(1, 1, 2) + + if crop is not None: + flow = flow[crop[1]:crop[1]+crop[3], crop[0]:crop[0]+crop[2], :] + + flow = np.transpose(flow, (2, 0, 1)) + + return flow + + +def process_proj(proj: np.array, original_size, target_size=None, crop=None): + proj = proj.copy() + + proj[0, :] *= 2.0 / original_size[1] + proj[1, :] *= 2.0 / original_size[0] + + proj[0, 2] = proj[0, 2] - 1.0 + proj[1, 2] = proj[1, 2] - 1.0 + + #if crop is not None: + # raise NotImplementedError() + + #if target_size is not None and (target_size[0] != original_size[0] or target_size[1] != original_size[1]): + # x_scale = target_size[1] / original_size[1] + # y_scale = target_size[0] / original_size[0] + + # proj[0, :] *= x_scale + # proj[1, :] *= y_scale + + #if crop is not None: + # proj[0, 2] -= crop[0] + # proj[1, 2] -= crop[1] + + return proj + + +def flow_selector_2(ids): + return ((ids[0], True),), ((ids[1], False),) + + +def flow_selector_3(ids): + return ((ids[0], False), (ids[0], True)), ((ids[1], True), (ids[2], False)) + + +def flow_selector_seq(ids): + fwd = [(i, True) for i in ids[:-1]] + bwd = [(i, False) for i in ids[1:]] + return fwd, bwd + + +def get_flow_selector(frame_count, is_sequential=False): + if not is_sequential: + if frame_count == 2: + flow_selector = flow_selector_2 + elif frame_count == 3: + flow_selector = flow_selector_3 + else: + raise ValueError(f"Unknown frame count: {frame_count}") + else: + flow_selector = flow_selector_seq + return flow_selector + + +def index_selector_pair(id, frame_count, dilation, left_offset): + ids = [i + for i in range( + id - left_offset, + id - left_offset + frame_count * dilation, + dilation, + ) + if i != id + ] + return ids + + +def index_selector_seq(id, frame_count, dilation, left_offset): + ids = [id + i * dilation - left_offset for i in range(frame_count)] + return ids + + +def get_index_selector(is_sequential=False): + if not is_sequential: + index_selector = index_selector_pair + else: + index_selector = index_selector_seq + return index_selector + + +def sequence_sampler_crop(seq, seq_len, left_offset, sub_seq_len): + if seq_len < sub_seq_len: + return [] + + datapoints = [(seq, i + left_offset) for i in range(seq_len - sub_seq_len)] + return datapoints + + +def sequence_sampler_full(seq, seq_len, left_offset, sub_seq_len): + datapoints = [(seq, i) for i in range(seq_len - 1)] + return datapoints + + +def get_sequence_sampler(crop=True): + if crop: + sequence_sampler = sequence_sampler_crop + else: + sequence_sampler = sequence_sampler_full + return sequence_sampler + +def get_ids_for_sequence(dataset, sequence): + seq_id_datapoints = [(i, dp) for i, dp in enumerate(dataset._datapoints) if dp[0] == sequence] + seq_ids = [i for i, _ in seq_id_datapoints] + seq_datapoints = [dp for _, dp in seq_id_datapoints] + return seq_ids, seq_datapoints diff --git a/scenedino/downstream_head/__init__.py b/scenedino/downstream_head/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5bb554107c31ad15288622f9c9f60863f367128b --- /dev/null +++ b/scenedino/downstream_head/__init__.py @@ -0,0 +1,9 @@ +from .semantic_head import SemanticHead + + +def make_downstream_head(conf): + head_type = conf.get("type", None) + if head_type == "segmentation": + return SemanticHead.from_conf(conf) + else: + raise NotImplementedError(f"Downstream head type '{head_type}' is not implemented.") diff --git a/scenedino/downstream_head/crf.py b/scenedino/downstream_head/crf.py new file mode 100644 index 0000000000000000000000000000000000000000..e7ffc6f0a1f78fa938afee4dbc32b9f67a08794c --- /dev/null +++ b/scenedino/downstream_head/crf.py @@ -0,0 +1,43 @@ +# +# Authors: Wouter Van Gansbeke & Simon Vandenhende +# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/) + +import numpy as np +import pydensecrf.densecrf as dcrf +import pydensecrf.utils as utils +import torch +import torch.nn.functional as F +import torchvision.transforms.functional as VF + +MAX_ITER = 10 +POS_W = 3 +POS_XY_STD = 0.3 # 1 +Bi_W = 4 +Bi_XY_STD = 20 # 67 +Bi_RGB_STD = 3 + + +def dense_crf(image_tensor: torch.FloatTensor, output_logits: torch.FloatTensor): + image = np.array(VF.to_pil_image(image_tensor))[:, :, ::-1] + H, W = image.shape[:2] + image = np.ascontiguousarray(image) + + output_logits = F.interpolate(output_logits.unsqueeze(0), size=(H, W), mode="bilinear", + align_corners=False).squeeze() + output_probs = F.softmax(output_logits, dim=0).cpu().numpy() + + c = output_probs.shape[0] + h = output_probs.shape[1] + w = output_probs.shape[2] + + U = utils.unary_from_softmax(output_probs) + U = np.ascontiguousarray(U) + + d = dcrf.DenseCRF2D(w, h, c) + d.setUnaryEnergy(U) + d.addPairwiseGaussian(sxy=POS_XY_STD, compat=POS_W) + d.addPairwiseBilateral(sxy=Bi_XY_STD, srgb=Bi_RGB_STD, rgbim=image, compat=Bi_W) + + Q = d.inference(MAX_ITER) + Q = np.array(Q).reshape((c, h, w)) + return Q diff --git a/scenedino/downstream_head/semantic_head.py b/scenedino/downstream_head/semantic_head.py new file mode 100644 index 0000000000000000000000000000000000000000..5ae137baa8daa285e559919cf6bdb50cc9858290 --- /dev/null +++ b/scenedino/downstream_head/semantic_head.py @@ -0,0 +1,501 @@ +from itertools import chain +import torch +from torch import nn +import torch.nn.functional as F + +from datasets.kitti_360.labels import labels as kitti_labels +from datasets.kitti_360.labels import trainId2label + +# from pykeops.torch import LazyTensor + +from multiprocessing import Pool +from .crf import dense_crf + + +def _five_crop(features, sample_factor=1): + _, _, h, w, _, _ = features.shape + assert h % (4*sample_factor) == 0 and w % (4*sample_factor) == 0 + + center_shift = sample_factor // 2 + crop_length = min(h, w) // 4 + crop_centers = [ + (h//2, w//2), + (3*h//4, w//4), + (3*h//4, 3*w//4), + (h//4, w//4), + (h//4, 3*w//4), + ] + result = torch.cat([ + features[:, :, + crop_center[0]-crop_length+center_shift : crop_center[0]+crop_length+center_shift : sample_factor, + crop_center[1]-crop_length+center_shift : crop_center[1]+crop_length+center_shift : sample_factor] + for crop_center in crop_centers + ]) + return result + + +def _norm(x): + return F.normalize(x, dim=-1, eps=1e-10) + + +class SemanticHead(nn.Module): + def __init__(self, + n_classes, + gt_classes, + input_dim, + code_dim, + buffer_size, + patch_sample_size, + knn_neighbors, + mode, + mlp_head, + apply_crf, + ): + + super().__init__() + self.n_classes = n_classes + self.gt_classes = gt_classes + + self.input_dim = input_dim + self.code_dim = code_dim + self.knn_neighbors = knn_neighbors + self.mode = mode + + self.apply_crf = apply_crf + + self.buffer_size = buffer_size + self.buffer_idx = 0 + self.buffer_filled = 1 + + self.dino_patch_buffer = torch.zeros((buffer_size, patch_sample_size, input_dim), device="cuda") + self.dino_gap_buffer = torch.zeros((buffer_size, input_dim), device="cuda") + + self.direct_cluster_head = KMeansParamHead(n_classes, gt_classes, input_dim) + self.stego_head = StegoClusterHead(input_dim, code_dim) + self.stego_cluster_head = KMeansParamHead(n_classes, gt_classes, code_dim) + + if mlp_head: + self.direct_linear_head = MLPHead(input_dim, gt_classes) + self.stego_linear_head = MLPHead(code_dim, gt_classes) + else: + self.direct_linear_head = LinearHead(input_dim, gt_classes) + self.stego_linear_head = LinearHead(code_dim, gt_classes) + + self.label_colors = [torch.Tensor(trainId2label[train_id].color) for train_id in range(gt_classes)] + self.label_colors.append(torch.Tensor([0, 0, 0])) + self.label_colors = torch.stack(self.label_colors, dim=0).to("cuda") / 255.0 + + self.dropout = nn.Dropout2d(p=.1) + self.dropout1d = nn.Dropout1d(p=.1) + + + @classmethod + def from_conf(cls, config): + return SemanticHead( + n_classes=config.n_classes, + gt_classes=config.gt_classes, + input_dim=config.input_dim, + code_dim=config.code_dim, + buffer_size=config.buffer_size, + patch_sample_size=config.patch_sample_size, + knn_neighbors=config.knn_neighbors, + mode=config.get("mode", "2d"), + mlp_head=config.get("mlp_head", False), + apply_crf=config.get("apply_crf", False), + ) + + def forward(self, features, mode="stego_kmeans"): + features = _norm(features) + if mode == "stego_kmeans": + stego_features = self.stego_head(features) + return self.stego_cluster_head(stego_features)["segs_pred"] + elif mode == "stego_linear": + stego_features = self.stego_head(features) + return self.stego_linear_head(stego_features)["segs_pred"] + elif mode == "direct_kmeans": + return self.direct_cluster_head(features)["segs_pred"] + elif mode == "direct_linear": + return self.direct_linear_head(features)["segs_pred"] + else: + raise NotImplementedError(f"Mode '{mode}' is not known!") + + def forward_training(self, data, visualize=False, sample_factor=4): # TODO: visualization + rgb_image = data["coarse"][0]["rgb"].detach() + dino_features = data["coarse"][0]["dino_features"].detach() # [n, v, h, w, 1, c] + dino_features = _norm(dino_features) + + n, v, h, w, _, c = dino_features.shape + reshaped_dino_features = dino_features.squeeze(-2).flatten(0, 1) # [n*v, h, w, c] + stego_features = self.stego_head(reshaped_dino_features).reshape(n, v, h, w, 1, -1) + + dino_features = self.dropout(dino_features.reshape(n*v, h, w, c).permute(0, 3, 1, 2)) + dino_features = dino_features.permute(0, 2, 3, 1).reshape(n, v, h, w, 1, c) + dino_features = dino_features.reshape(n, v, h, w, 1, c) + + data["segmentation"] = {} + if data["sample_surface_sigma"] is not None: + if self.mode == "3d": + cropped_dino_features = data["sample_surface_dino_features"].detach().squeeze(0) + cropped_dino_features = _norm(cropped_dino_features) + + cropped_dino_features = self.dropout1d(cropped_dino_features.swapaxes(-2, -1)).swapaxes(-2, -1) + stego_self_features = self.stego_head(cropped_dino_features.unsqueeze(1)).squeeze(1) + + elif self.mode == "2d": + dino_features = dino_features[:, :1] + stego_features = stego_features[:, :1] + + # Single view + cropped_dino_features = _five_crop(dino_features, sample_factor).flatten(0, 1).flatten(1, 2).squeeze(-2) + stego_self_features = _five_crop(stego_features, sample_factor).flatten(0, 1).flatten(1, 2).squeeze(-2) + + dino_feature_gap = cropped_dino_features.mean(dim=-2) + dino_feature_gap = _norm(dino_feature_gap) + + + # Just in training: update knn buffer + if self.training: + new_idx = self._update_buffer(self.dino_patch_buffer, cropped_dino_features) + assert new_idx == self._update_buffer(self.dino_gap_buffer, dino_feature_gap) + + if new_idx < self.buffer_idx: + self.buffer_filled = self.buffer_size + else: + self.buffer_filled = max(new_idx, self.buffer_filled) + self.buffer_idx = new_idx + + + # Calculate from buffer - "kNN", "random" + pairwise_cos_sims = torch.einsum("nf,mf->nm", dino_feature_gap, self.dino_gap_buffer) + topk_indices = torch.topk(pairwise_cos_sims, self.knn_neighbors+1, dim=1)[1][:, 1:] # (n, k_nn) + + n = cropped_dino_features.size(0) + random_nn_indices = topk_indices[torch.arange(n), torch.randint(self.knn_neighbors, (n,))] + dino_nn_features = self.dino_patch_buffer[random_nn_indices].detach() + stego_nn_features = self.stego_head(dino_nn_features) + + random_indices = torch.randint(self.buffer_filled, (n,)) + dino_random_features = self.dino_patch_buffer[random_indices].detach() + stego_random_features = self.stego_head(dino_random_features) + + stego_corr = { + "dino_self_corr": self._compute_stego_correlation(cropped_dino_features, cropped_dino_features), + "stego_self_corr": self._compute_stego_correlation(stego_self_features, stego_self_features), + "dino_nn_corr": self._compute_stego_correlation(cropped_dino_features, dino_nn_features), + "stego_nn_corr": self._compute_stego_correlation(stego_self_features, stego_nn_features), + "dino_random_corr": self._compute_stego_correlation(cropped_dino_features, dino_random_features), + "stego_random_corr": self._compute_stego_correlation(stego_self_features, stego_random_features), + } + + data["segmentation"]["stego_corr"] = stego_corr + + else: + data["sample_surface_sigma"] = torch.Tensor([0.0]) + data["sample_surface_dino_features"] = torch.Tensor([0.0]) + + + # IMPORTANT, train heads after detaching features! + dino_features = dino_features.detach() + stego_features = stego_features.detach() + + direct_cluster_result = self.direct_cluster_head(dino_features) + stego_cluster_result = self.stego_cluster_head(stego_features) + + data["segmentation"]["results"] = { + "direct_cluster": direct_cluster_result, + "stego_cluster": stego_cluster_result, + } + + data["segmentation"]["visualization"] = { + "direct_cluster": self.visualize(direct_cluster_result["segs_pred"]), + "stego_cluster": self.visualize(stego_cluster_result["segs_pred"]) + } + + if "segs" in data: + seg_target = self.map_kitti_id_to_train_id(data["segs"][0]).to(stego_features.device) + direct_linear_result = self.direct_linear_head(dino_features, seg_target) + stego_linear_result = self.stego_linear_head(stego_features, seg_target) + + data["segmentation"]["target"] = seg_target + data["segmentation"]["results"]["direct_linear"] = direct_linear_result + data["segmentation"]["results"]["stego_linear"] = stego_linear_result + data["segmentation"]["visualization"]["target"] = self.visualize(seg_target) + + if self.apply_crf: + result_names = list(data["segmentation"]["results"].keys()) + for result_name in result_names: + pred_no_crf = data["segmentation"]["results"][result_name]["segs_pred"] + pred_crf = self.forward_crf(pred_no_crf, rgb_image) + + data["segmentation"]["results"][result_name + "_crf"] = {"segs_pred": pred_crf} + + for result_name, result in data["segmentation"]["results"].items(): + data["segmentation"]["visualization"][result_name] = self.visualize(result["segs_pred"]) + + return data + + def forward_crf(self, pred_no_crf, rgb_image): + pred_no_crf_logits = F.one_hot(pred_no_crf.squeeze()).permute(2,0,1).float() + pred_crf = torch.Tensor(dense_crf(rgb_image.squeeze().permute(2,0,1), pred_no_crf_logits)).to(pred_no_crf.device) + pred_crf = pred_crf.argmax(dim=0).reshape(pred_no_crf.shape) + return pred_crf + + def update_model_eval(self, metrics): + self.direct_cluster_head.pseudo_assignment[:] = metrics["direct_cluster_assignment"] + self.stego_cluster_head.pseudo_assignment[:] = metrics["stego_cluster_assignment"] + + def map_kitti_id_to_train_id(self, labels): + result = torch.zeros(labels.shape).long() + for kitti_label in kitti_labels: + result[labels == kitti_label.id] = kitti_label.trainId + + result[result == 255] = -1 + return result + + def visualize(self, labels): + label_map = self.label_colors[labels.long()] + return label_map + + def parameters_lr(self): + return [ + (1.0, self.stego_head.parameters()), + (10.0, self.direct_cluster_head.parameters()), + (10.0, self.stego_cluster_head.parameters()), + (10.0, self.direct_linear_head.parameters()), + (10.0, self.stego_linear_head.parameters()), + ] + + def _compute_stego_correlation(self, tensor1, tensor2): + corr = torch.einsum("npf,nqf->npq", _norm(tensor1), _norm(tensor2)) + return corr + + def _update_buffer(self, buffer, x): + n = x.size(0) + if n >= self.buffer_size: + buffer[:] = x[-self.buffer_size:] + new_buffer_idx = 0 # Reset write index + else: + indices = (torch.arange(n) + self.buffer_idx) % self.buffer_size + buffer[indices] = x + new_buffer_idx = (self.buffer_idx + n) % self.buffer_size + + return new_buffer_idx + + +class StegoClusterHead(nn.Module): + def __init__(self, in_channels, out_channels, mid_channels=None): + super().__init__() + if mid_channels is None: + mid_channels = in_channels + + self.linear_path = nn.Sequential( + nn.Conv2d(in_channels, out_channels, (1, 1)), + nn.Dropout2d(p=.1), + ) + self.nonlinear_path = nn.Sequential( + nn.Conv2d(in_channels, mid_channels, (1, 1)), + nn.ReLU(), + nn.Conv2d(mid_channels, out_channels, (1, 1)), + nn.Dropout2d(p=.1), + ) + + def forward(self, x): + x = x.swapaxes(-1, -3) + result = self.linear_path(x) + self.nonlinear_path(x) + return _norm(result.swapaxes(-1, -3)).to(x.dtype) + + +class KMeansParamHead(nn.Module): + def __init__(self, + n_classes: int, + gt_classes: int, + dim: int, + ): + super().__init__() + self.n_classes = n_classes + self.dim = dim + self.init_type = "random" + self.cluster_centers = torch.nn.Parameter(torch.randn(self.n_classes, self.dim)) + self.centroids_initialized = False + self.register_buffer("pseudo_assignment", torch.arange(0, n_classes).remainder(gt_classes)) + + def forward(self, features, weight=None): + features_flat = features.flatten(0, -2) # (n, d) + if weight is not None: + weight_flat = weight.flatten() + else: + weight_flat = torch.ones(features_flat.size(0), device=features.device) + + # K-means++ init + if not self.centroids_initialized and self.training: + if self.init_type == "kmeans++": + cluster_centers = torch.empty(self.n_classes, self.dim, device=features.device) + first_idx = torch.randint(0, features_flat.size(0), (1,)) + cluster_centers[0] = features_flat[first_idx] + for k in range(1, self.n_classes): + current_centroids = cluster_centers[:k] # (k, d) + similarity = (current_centroids @ features_flat.transpose(1, 0)) # (k, n) + max_similarity = similarity.max(dim=0).values # Closest centroid per point + distances = 1 - max_similarity + probabilities = distances ** 2 + probabilities /= probabilities.sum() + next_idx = torch.multinomial(probabilities, 1) + cluster_centers[k] = features_flat[next_idx] + self.cluster_centers.data = cluster_centers + else: + self.cluster_centers.data = torch.randn(self.n_classes, self.dim, device=self.cluster_centers.device) + self.centroids_initialized = True + + class_labels, cluster_loss, _ = self._kmeans_cosine(features_flat) + pseudo_segs_pred = class_labels.view(*features.shape[:-1]) + result = { + "pseudo_segs_pred": pseudo_segs_pred, + "segs_pred": self._assign_pseudo_labels(pseudo_segs_pred), + "loss": torch.mean(cluster_loss * weight_flat) + } + return result + + def _assign_pseudo_labels(self, pseudo_labels): + return self.pseudo_assignment[pseudo_labels.cpu()].long() + + def _kmeans_cosine(self, features): + normed_clusters = F.normalize(self.cluster_centers, dim=1) + normed_features = F.normalize(features, dim=1) + inner_products = normed_features.matmul(normed_clusters.t()) + + class_labels = torch.argmax(inner_products, dim=1) + + # cluster_probs = F.softmax(inner_products, dim=1) + cluster_probs = F.one_hot(class_labels, normed_clusters.shape[0]).to(torch.float32) + cluster_loss = -(cluster_probs * inner_products).sum(1) + + # return nn.functional.log_softmax(inner_products * alpha, dim=1) + return class_labels, cluster_loss, cluster_probs + + +class KMeansIterHead(nn.Module): + def __init__(self, + n_classes: int, + gt_classes: int, + dim: int, + reassignment_threshold: int = 5000, + kmeans_update_factor: float = 1.0, + training_chunk: int = 100000, + ): + super().__init__() + self.n_classes = n_classes + self.dim = dim + + self.reassignment_threshold = reassignment_threshold + self.kmeans_update_factor = kmeans_update_factor + self.training_chunk = training_chunk + + self.centroids_initialized = False + self.register_buffer("cluster_centers", torch.empty(self.n_classes, self.dim, device="cuda")) + self.register_buffer("pseudo_assignment", torch.arange(0, n_classes).remainder(gt_classes)) + + def forward(self, features): + features_flat = features.flatten(0, -2) # (n, d) + + # K-means++ init + if not self.centroids_initialized and self.training: + first_idx = torch.randint(0, features_flat.size(0), (1,)) + self.cluster_centers[0] = features_flat[first_idx] + for k in range(1, self.n_classes): + current_centroids = self.cluster_centers[:k] # (k, d) + similarity = (current_centroids @ features_flat.transpose(1, 0)) # (k, n) + max_similarity = similarity.max(dim=0).values # Closest centroid per point + distances = 1 - max_similarity + probabilities = distances ** 2 + probabilities /= probabilities.sum() + next_idx = torch.multinomial(probabilities, 1) + self.cluster_centers[k] = features_flat[next_idx] + self.centroids_initialized = True + + class_labels = self._kmeans_cosine(features_flat) + pseudo_segs_pred = class_labels.view(*features.shape[:-1]) + result = { + "pseudo_segs_pred": pseudo_segs_pred, + "segs_pred": self._assign_pseudo_labels(pseudo_segs_pred), + } + return result + + def _assign_pseudo_labels(self, pseudo_labels): + return self.pseudo_assignment[pseudo_labels.cpu()].long() + + def _kmeans_cosine(self, features): + """Implements Lloyd's algorithm for the Cosine similarity metric.""" + features = F.normalize(features, dim=1, p=2) + n, d = features.shape + + x_i = LazyTensor(features.view(n, 1, d).contiguous()) # (n, 1, d) samples + c_j = LazyTensor(self.cluster_centers.view(1, self.n_classes, d).contiguous()) # (1, n_classes, d) centroids + + s_ij = x_i | c_j # (N, K) symbolic Gram matrix of dot products + class_labels = s_ij.argmax(dim=1).long().view(-1) # Points -> Nearest cluster + + if self.training: + class_labels_count = class_labels.bincount(minlength=self.n_classes) + cluster_center_update = torch.zeros_like(self.cluster_centers) + + if self.training_chunk: + for i in range(0, n, self.training_chunk): + if i + self.training_chunk < n: + cluster_center_update.scatter_add_(0, class_labels[i:i+self.training_chunk, None].repeat(1, d), features) + else: + cluster_center_update.scatter_add_(0, class_labels[i:, None].repeat(1, d), features) + else: + cluster_center_update.scatter_add_(0, class_labels[:, None].repeat(1, d), features) + + cluster_center_update = F.normalize(cluster_center_update) + + update_factor = self.kmeans_update_factor * (class_labels_count > self.reassignment_threshold) + update_factor = update_factor.unsqueeze(-1) + + self.cluster_centers[:] = F.normalize(cluster_center_update * update_factor + self.cluster_centers * (1-update_factor)) + + return class_labels + + +class LinearHead(nn.Module): + def __init__(self, + dim: int, + gt_classes: int + ): + super().__init__() + self.linear = torch.nn.Linear(dim, gt_classes) + + def forward(self, features, target=None): + logit = self.linear(features).float() + result = { + "segs_pred": logit.argmax(-1), + } + if target is not None: + target = target.long().to(logit.device) + result["loss"] = F.cross_entropy(logit[:, 0].movedim(-1, 1).squeeze(-1), target, ignore_index=-1) + + return result + + +class MLPHead(nn.Module): + def __init__(self, + dim: int, + gt_classes: int + ): + super().__init__() + self.linear1 = torch.nn.Linear(dim, 2*dim) + self.linear2 = torch.nn.Linear(2*dim, gt_classes) + self.activation = torch.nn.ReLU() + + def forward(self, features, target=None): + features = self.linear1(features) + features = self.activation(features) + logit = self.linear2(features).float() + result = { + "segs_pred": logit.argmax(-1), + } + if target is not None: + target = target.long().to(logit.device) + result["loss"] = F.cross_entropy(logit[:, 0].movedim(-1, 1).squeeze(-1), target, ignore_index=-1) + + return result diff --git a/scenedino/evaluation/base_evaluator.py b/scenedino/evaluation/base_evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..64ead1e2bc6faf94e48237cf6d6a3b83fda59ee3 --- /dev/null +++ b/scenedino/evaluation/base_evaluator.py @@ -0,0 +1,213 @@ +from datetime import datetime +from pathlib import Path + +import ignite.distributed as idist +import torch +from ignite.contrib.engines import common +from ignite.engine import Engine, Events +from ignite.utils import manual_seed, setup_logger +from torch.cuda.amp import autocast +from scenedino.common.logging import log_basic_info + +from scenedino.common.array_operations import to + +# from ignite.contrib.handlers.tensorboard_logger import * +from ignite.contrib.handlers import TensorboardLogger + +from scenedino.common.metrics import DictMeanMetric, SegmentationMetric, ConcatenateMetric +from scenedino.training.handlers import VisualizationHandler +from scenedino.visualization.vis_2d import tb_visualize + +from .wrapper import make_eval_fn + + +def base_evaluation( + local_rank, + config, + get_dataflow, + initialize, +): + rank = idist.get_rank() + if "eval_seed" in config: + manual_seed(config["eval_seed"] + rank) + else: + manual_seed(config["seed"] + rank) + device = idist.device() + + model_name = config["name"] + logger = setup_logger( + name=model_name, format="%(levelname)s: %(message)s" + ) ## default + + output_path = config["output"]["path"] + if rank == 0: + unique_id = config["output"].get( + "unique_id", datetime.now().strftime("%Y%m%d-%H%M%S") + ) + folder_name = unique_id + + output_path = Path(output_path) / folder_name + if not output_path.exists(): + output_path.mkdir(parents=True) + + config["output"]["path"] = output_path.as_posix() + logger.info(f"Output path: {config['output']['path']}") + + if "cuda" in device.type: + config["cuda device name"] = torch.cuda.get_device_name(local_rank) + tb_logger = TensorboardLogger(log_dir=output_path) + + log_basic_info(logger, config) + + # Setup dataflow, model, optimizer, criterion + test_loader = get_dataflow(config) ## default + + if hasattr(test_loader, "dataset"): + logger.info(f"Dataset length: Test: {len(test_loader.dataset)}") + + config["dataset"]["steps_per_epoch"] = len(test_loader) + + # ===================================================== MODEL ===================================================== + model = initialize(config) + + cp_path = config.get("checkpoint", None) + + if cp_path is not None: + if not cp_path.endswith(".pt"): + cp_path = Path(cp_path) + cp_path = next(cp_path.glob("training*.pt")) + checkpoint = torch.load(cp_path, map_location=device) + logger.info(f"Loading checkpoint from path: {cp_path}") + if "model" in checkpoint: + model.load_state_dict(checkpoint["model"], strict=False) + else: + model.load_state_dict(checkpoint, strict=False) + else: + logger.warning("Careful, no model is loaded") + model.to(device) + + logger.info(f"Model parameters: {sum(p.numel() for p in model.parameters())}") + logger.info(f"Trainable model parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}") + + # Let's now setup evaluator engine to perform model's validation and compute metrics + evaluator = create_evaluator(model, config=config, logger=logger, vis_logger=tb_logger) + + # evaluator.add_event_handler( + # Events.ITERATION_COMPLETED(every=config["log_every"]), + # log_metrics_current(logger, metrics), + # ) + + try: + state = evaluator.run(test_loader, max_epochs=1) + log_metrics(logger, state.times["COMPLETED"], "Test", state.metrics) + logger.info(f"Checkpoint: {str(cp_path)}") + except Exception as e: + logger.exception("") + raise e + + +# def log_metrics_current(logger, metrics): +# def f(engine): +# out_str = "\n" + "\t".join( +# [ +# f"{v.compute():.3f}".ljust(8) +# for v in metrics.values() +# if v._num_examples != 0 +# ] +# ) +# out_str += "\n" + "\t".join([f"{k}".ljust(8) for k in metrics.keys()]) +# logger.info(out_str) + +# return f + + +def log_metrics(logger, elapsed, tag, metrics): + metrics_output = "\n".join([f"\t{k}: {v}" for k, v in metrics.items()]) + logger.info( + f"\nEvaluation time (seconds): {elapsed:.2f} - {tag} metrics:\n {metrics_output}" + ) + + +# def create_evaluator(model, metrics, config, tag="val"): +def create_evaluator(model, config, logger=None, vis_logger=None, tag="val"): + with_amp = config["with_amp"] + device = idist.device() + + metrics = {} + for eval_config in config["evaluations"]: + agg_type = eval_config.get("agg_type", None) + if agg_type == "unsup_seg": + metrics[eval_config["type"]] = SegmentationMetric( + eval_config["type"], make_eval_fn(model, eval_config), assign_pseudo=True + ) + elif agg_type == "sup_seg": + metrics[eval_config["type"]] = SegmentationMetric( + eval_config["type"], make_eval_fn(model, eval_config), assign_pseudo=False + ) + elif agg_type == "concat": + metrics[eval_config["type"]] = ConcatenateMetric( + eval_config["type"], make_eval_fn(model, eval_config) + ) + else: + metrics[eval_config["type"]] = DictMeanMetric( + eval_config["type"], make_eval_fn(model, eval_config) + ) + + @torch.no_grad() + def evaluate_step(engine: Engine, data): + # if not engine.state_dict["iteration"] % 10 == 0: ## to prevent iterating whole testset for viz purpose + model.eval() + if "t__get_item__" in data: + timing = {"t__get_item__": torch.mean(data["t__get_item__"]).item()} + else: + timing = {} + + data = to(data, device) + + with autocast(enabled=with_amp): + data = model(data) ## ! This is where the occupancy prediction is made. + + loss_metrics = {} + + return { + "output": data, + "loss_dict": loss_metrics, + "timings_dict": timing, + "metrics_dict": {}, + } + + evaluator = Engine(evaluate_step) + evaluator.logger = logger ## + + for name, metric in metrics.items(): + metric.attach(evaluator, name) + + eval_visualize = config.get("eval_visualize", []) + if eval_visualize and vis_logger is not None: + for name, vis_config in config["validation"].items(): + if "visualize" in vis_config: + visualize = tb_visualize( + (model.renderer.net if hasattr(model, "renderer") else model.module.renderer.net), + None, + vis_config["visualize"], + ) + def vis_wrapper(*args, **kwargs): + with autocast(enabled=with_amp): + return visualize(*args, **kwargs) + + def custom_vis_filter(engine, event): + return engine.state.iteration-1 in eval_visualize + + vis_logger.attach( + evaluator, + VisualizationHandler( + tag=tag, + visualizer=vis_wrapper, + ), + Events.ITERATION_COMPLETED(event_filter=custom_vis_filter), + ) + + if idist.get_rank() == 0 and (not config.get("with_clearml", False)): + common.ProgressBar(desc=f"Evaluation ({tag})", persist=False).attach(evaluator) + + return evaluator diff --git a/scenedino/evaluation/unified_evaluator.py b/scenedino/evaluation/unified_evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..9cc7f79c38aacb69c8514dd532b93e1a1ea1e158 --- /dev/null +++ b/scenedino/evaluation/unified_evaluator.py @@ -0,0 +1,150 @@ +import logging +from pathlib import Path +import torch +from torch import nn +from torch.utils.data import DataLoader, Subset +from scenedino.common.io.configs import load_model_config +from scenedino.models import make_model + + +from scenedino.datasets import make_test_dataset +from scenedino.common.geometry import distance_to_z +from scenedino.renderer import NeRFRenderer +from scenedino.common.ray_sampler import ImageRaySampler, get_ray_sampler +from scenedino.evaluation.base_evaluator import base_evaluation + +from scenedino.training.trainer_downstream import BTSDownstreamWrapper + +IDX = 0 + +logger = logging.getLogger("evaluation") + + +class BTSWrapper(nn.Module): + def __init__( + self, + renderer, + config, + # evaluation_fns + ) -> None: + super().__init__() + + self.renderer = renderer + + # TODO: have a consitent sampling range + self.z_near = config.get("z_near", 3.0) + self.z_far = config.get("z_far", 80.0) + self.sampler = ImageRaySampler(self.z_near, self.z_far) + + # self.evaluation_fns = evaluation_fns + + @staticmethod + def get_loss_metric_names(): + return ["loss", "loss_l2", "loss_mask", "loss_temporal"] + + def forward(self, data): + data = dict(data) + images = torch.stack(data["imgs"], dim=1) # n, v, c, h, w + poses = torch.stack(data["poses"], dim=1) # n, v, 4, 4 w2c + projs = torch.stack(data["projs"], dim=1) # n, v, 4, 4 (-1, 1) + + B, n_frames, c, h, w = images.shape + device = images.device + + # Use first frame as keyframe + to_base_pose = torch.inverse(poses[:, :1, :, :]) + poses = to_base_pose.expand(-1, n_frames, -1, -1) @ poses + + # TODO: make configurable + ids_encoder = [0] + + self.renderer.net.compute_grid_transforms( + projs[:, ids_encoder], poses[:, ids_encoder] + ) + self.renderer.net.encode( + images, + projs, + poses, + ids_encoder=ids_encoder, + ids_render=ids_encoder, + images_alt=images * 0.5 + 0.5, + ) + + all_rays, all_rgb_gt = self.sampler.sample(images * 0.5 + 0.5, poses, projs) + + data["fine"] = [] + data["coarse"] = [] + + self.renderer.net.set_scale(0) + render_dict = self.renderer(all_rays, want_weights=True, want_alphas=True) + + if "fine" not in render_dict: + render_dict["fine"] = dict(render_dict["coarse"]) + + render_dict["rgb_gt"] = all_rgb_gt + render_dict["rays"] = all_rays + + # TODO: check if distance to z is needed + render_dict = self.sampler.reconstruct(render_dict) + render_dict["coarse"]["depth"] = distance_to_z( + render_dict["coarse"]["depth"], projs + ) + render_dict["fine"]["depth"] = distance_to_z( + render_dict["fine"]["depth"], projs + ) + + data["fine"].append(render_dict["fine"]) + data["coarse"].append(render_dict["coarse"]) + data["rgb_gt"] = render_dict["rgb_gt"] + data["rays"] = render_dict["rays"] + + data["z_near"] = torch.tensor(self.z_near, device=images.device) + data["z_far"] = torch.tensor(self.z_far, device=images.device) + + # for eval_fn in self.evaluation_fns: + # data["metrics"].update(eval_fn(data, model=self.renderer.net)) + + return data + + +def evaluation(local_rank, config): + return base_evaluation(local_rank, config, get_dataflow, initialize) + + +def get_dataflow(config): + test_dataset = make_test_dataset(config["dataset"]) + test_loader = DataLoader( + test_dataset, # Subset(test_dataset, torch.randperm(test_dataset.length)[:1000]), + batch_size=config.get("batch_size", 1), + num_workers=config["num_workers"], + shuffle=False, + drop_last=False, + ) + + return test_loader + + +def initialize(config: dict): + checkpoint = Path(config["checkpoint"]) + logger.info(f"Loading model config from {checkpoint.parent}") + load_model_config(checkpoint.parent, config) + + net = make_model(config["model"], config["downstream"]) + # net = make_model(config["model"]) + + renderer = NeRFRenderer.from_conf(config["renderer"]) + renderer = renderer.bind_parallel(net, gpus=None).eval() + + # TODO: attach evaluation functions rather that add them to the wrapper + # eval_fns = [] + # for eval_conf in config["evaluations"]: + # eval_fn = make_eval_fn(eval_conf) + # if eval_fn is not None: + # eval_fns.append(eval_fn) + + ray_sampler = get_ray_sampler(config["training"]["ray_sampler"]) + model = BTSDownstreamWrapper(renderer, ray_sampler, config["model"]) + # model = BTSWrapper(renderer, config["model"]) + # model = BTSWrapper(renderer, config["model"], eval_fns) + + return model diff --git a/scenedino/evaluation/wrapper.py b/scenedino/evaluation/wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..5befe0b8c05554050415d4b26ee2956e7008ad46 --- /dev/null +++ b/scenedino/evaluation/wrapper.py @@ -0,0 +1,199 @@ +from typing import Callable +import lpips +import torch +import torch.nn as nn +import torch.nn.functional as F +import ignite.distributed as idst + +from scenedino.common.geometry import distance_to_z +import scenedino.common.metrics as metrics + + +def create_depth_eval( + model: nn.Module, + scaling_function: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] + | None = None, +): + def _compute_depth_metrics( + data, + # TODO: maybe integrate model + # model: nn.Module, + ): + return metrics.compute_depth_metrics( + data["depths"][0], data["coarse"][0]["depth"][:, :1], scaling_function + ) + + return _compute_depth_metrics + + +def create_nvs_eval(model: nn.Module): + lpips_fn = lpips.LPIPS().to(idst.device()) + + def _compute_nvs_metrics( + data, + # model: nn.Module, + ): + return metrics.compute_nvs_metrics(data, lpips_fn) + + return _compute_nvs_metrics + + +def create_dino_eval(model: nn.Module): + def _compute_dino_metrics( + data, + ): + return metrics.compute_dino_metrics(data) + + return _compute_dino_metrics + + +def create_seg_eval(model: nn.Module, n_classes: int, gt_classes: int): + def _compute_seg_metrics( + data, + ): + return metrics.compute_seg_metrics(data, n_classes, gt_classes) # Why is this necessary? + + return _compute_seg_metrics + + +def create_stego_eval(model: nn.Module): + def _compute_stego_metrics( + data, + ): + return metrics.compute_stego_metrics(data) # Why is this necessary? + + return _compute_stego_metrics + + +# code for saving voxel grid +# def pack(uncompressed): +# """convert a boolean array into a bitwise array.""" +# uncompressed_r = uncompressed.reshape(-1, 8) +# compressed = uncompressed_r.dot( +# 1 << np.arange(uncompressed_r.shape[-1] - 1, -1, -1) +# ) +# return compressed + +# if self.save_bin_path: +# # base_file = "/storage/user/hank/methods_test/semantic-kitti-api/bts_test/sequences/00/voxels" +# outside_frustum = ( +# ( +# (cam_pts[:, 0] < -1.0) +# | (cam_pts[:, 0] > 1.0) +# | (cam_pts[:, 1] < -1.0) +# | (cam_pts[:, 0] > 1.0) +# ) +# .reshape(q_pts_shape) +# .permute(1, 2, 0) +# .detach() +# .cpu() +# .numpy() +# ) +# is_occupied_numpy = ( +# is_occupied_pred.reshape(q_pts_shape) +# .permute(1, 2, 0) +# .detach() +# .cpu() +# .numpy() +# .astype(np.float32) +# ) +# is_occupied_numpy[outside_frustum] = 0.0 +# ## carving out the invisible regions out of view-frustum +# # for i_ in range( +# # (is_occupied_numpy.shape[0]) // 2 +# # ): ## left | right half of the space +# # for j_ in range(i_ + 1): +# # is_occupied_numpy[i_, j_] = 0 + +# pack(np.flip(is_occupied_numpy, (0, 1, 2)).reshape(-1)).astype( +# np.uint8 +# ).tofile( +# # f"{base_file}/{self.counter:0>6}.bin" +# f"{self.save_bin_path}/{self.counter:0>6}.bin" +# ) +# # for idx_i, image in enumerate(images[0]): +# # torchvision.utils.save_image( +# # image, f"{self.save_bin_path}/{self.counter:0>6}_{idx_i}.png" +# # ) + + +def project_into_cam(pts, proj, pose): + pts = torch.cat((pts, torch.ones_like(pts[:, :1])), dim=-1) + cam_pts = (proj @ (torch.inverse(pose).squeeze()[:3, :] @ pts.T)).T + cam_pts[:, :2] /= cam_pts[:, 2:3] + dist = cam_pts[:, 2] + return cam_pts, dist + + +def create_occ_eval( + model: nn.Module, + occ_threshold: float, + query_batch_size: int, +): + # TODO: deal with other models such as IBRnet + + def _compute_occ_metrics( + data, + ): + projs = torch.stack(data["projs"], dim=1) + images = torch.stack(data["imgs"], dim=1) + _, _, _, h, w = images.shape + poses = torch.stack(data["poses"], dim=1) + device = poses.device + # TODO: get occ points and occupation from dataset + occ_pts = data["occ_pts"].permute(0, 2, 1, 3).contiguous() + occ_pts = occ_pts.to(device).view(-1, 3) + + pred_depth = distance_to_z(data["coarse"]["depth"], projs[:1, :1]) + + # is visible? Check whether point is closer than the computed pseudo depth + cam_pts, dists = project_into_cam(occ_pts, projs[0, 0], poses[0, 0]) + pred_dist = F.grid_sample( + pred_depth.view(1, 1, h, w), + cam_pts[:, :2].view(1, 1, -1, 2), + mode="nearest", + padding_mode="border", + align_corners=True, + ).view(-1) + is_visible_pred = dists <= pred_dist + + depth_plus4meters = False + if depth_plus4meters: + mask = (dists >= pred_dist) & (dists < pred_dist + 4) + densities = torch.zeros_like(occ_pts[..., 0]) + densities[mask] = 1.0 + is_occupied_pred = densities > occ_threshold + else: + # Query the density of the query points from the density field + densities = [] + for i_from in range(0, len(occ_pts), query_batch_size): + i_to = min(i_from + query_batch_size, len(occ_pts)) + q_pts_ = occ_pts[i_from:i_to] + _, _, densities_, _ = model( + q_pts_.unsqueeze(0), only_density=True + ) ## ! occupancy estimation + densities.append(densities_.squeeze(0)) + densities = torch.cat(densities, dim=0).squeeze() + is_occupied_pred = densities > occ_threshold + + is_occupied = data["is_occupied"] + is_visible = data["is_visible"] + + return metrics.compute_occ_metrics(is_occupied_pred, is_occupied, is_visible) + + return _compute_occ_metrics + + +def make_eval_fn( + model: nn.Module, + conf, +): + eval_type = conf["type"] + eval_fn = globals().get(f"create_{eval_type}_eval", None) + if eval_fn: + if conf.get("args", None): + return eval_fn(model, **conf["args"]) + else: + return eval_fn(model) + else: + return None diff --git a/scenedino/losses/__init__.py b/scenedino/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4a5b03356f8a8c16cac8d5a8c04f1721340725c5 --- /dev/null +++ b/scenedino/losses/__init__.py @@ -0,0 +1,14 @@ +from .base_loss import BaseLoss +from .reconstruction_loss import ReconstructionLoss +from .stego_loss import StegoLoss + + +def make_loss(config) -> BaseLoss: + loss_type = config["type"] + match loss_type: + case "reconstruction": + return ReconstructionLoss(config) + case "stego": + return StegoLoss(config) + case _: + raise ValueError(f"Unknown loss type {loss_type}") diff --git a/scenedino/losses/base_loss.py b/scenedino/losses/base_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..64e273588690b33c511cda06d163ee99943f2dc2 --- /dev/null +++ b/scenedino/losses/base_loss.py @@ -0,0 +1,16 @@ +from abc import ABC, abstractmethod + +import torch + + +class BaseLoss(ABC): + def __init__(self, config) -> None: + super().__init__() + + @abstractmethod + def get_loss_metric_names(self) -> list[str]: + ... + + @abstractmethod + def __call__(self, data) -> dict[str, torch.Tensor]: + ... diff --git a/scenedino/losses/reconstruction_loss.py b/scenedino/losses/reconstruction_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..7886656fed61458c5b4a0b8ca5fe419032644c35 --- /dev/null +++ b/scenedino/losses/reconstruction_loss.py @@ -0,0 +1,356 @@ +import math +from typing import Any, Callable, Protocol + +import torch +import kornia +from torch import profiler + +import torch.nn.functional as F + +from scenedino.common.util import kl_div, normalized_entropy +from scenedino.losses.base_loss import BaseLoss +from scenedino.common.errors import ( + alpha_consistency_uncert, + compute_l1ssim, + compute_edge_aware_smoothness, + compute_3d_smoothness, + compute_normalized_l1, + depth_smoothness_regularization, + depth_regularization, + alpha_regularization, + flow_regularization, + kl_prop, + max_alpha_inputframe_regularization, + surfaceness_regularization, + sdf_eikonal_regularization, + weight_entropy_regularization, + max_alpha_regularization, + density_grid_regularization, + alpha_consistency, + entropy_based_smoothness, +) + + +EPS = 1e-5 + + +# TODO: need wrappers around the different losses as an interface to the data variable +def make_reconstruction_error( + criterion: str, +) -> Callable[[torch.Tensor, torch.Tensor], torch.Tensor]: + match criterion: + case "l1": + return lambda a, b: torch.mean(torch.nn.L1Loss(reduction="none")(a, b), dim=1) + case "l1+ssim": + return compute_l1ssim + case "l2": + return lambda a, b: torch.mean(torch.nn.MSELoss(reduction="none")(a, b) / 2, dim=1) + case "cosine": + return lambda a, b: 1 - torch.nn.CosineSimilarity(dim=1)(a, b) + case _: + raise ValueError(f"Unknown reconstruction error: {criterion}") + + +def make_regularization( + config, ignore_invalid: bool +) -> Callable[[Any, int], torch.Tensor]: + """Make a regularization function from the config. + + Args: + config (dict): config dict + + Returns: + Callable[[torch.Tensor], torch.Tensor]: regularization function + """ + match config["type"]: + case "edge_aware_smoothness": + + def _wrapper(data, scale): + gt_img = data["rgb_gt"][..., :3] + depth = data["coarse"][scale]["depth"].permute(1, 0, 2, 3) + _, _, h, w = depth.shape + gt_img = ( + gt_img.unsqueeze(-2).permute(0, 1, 4, 5, 2, 3).reshape(-1, 3, h, w) + ) + depth_input = 1 / depth.reshape(-1, 1, h, w).clamp(1e-3, 80) + depth_input = depth_input / torch.mean(depth_input, dim=[2, 3], keepdim=True) + + return compute_edge_aware_smoothness( + gt_img, depth_input, temperature=1 + ).mean() + + return _wrapper + + case "dino_edge_aware_smoothness": + + def _wrapper(data, scale): + gt_img = data["rgb_gt"][..., :3] + dino = data["coarse"][scale]["dino_features"] + + _, _, h, w, _, c_dino = dino.shape + gt_img = gt_img.unsqueeze(-2).permute(0, 1, 4, 5, 2, 3).reshape(-1, 3, h, w) + dino_input = dino.permute(0, 1, 4, 5, 2, 3).reshape(-1, c_dino, h, w) + + return compute_edge_aware_smoothness( + gt_img, dino_input, temperature=25 + ).mean() + + return _wrapper + + case _: + raise ValueError(f"Unknown regularization type: {config['type']}") + + +class PolicyCallable(Protocol): + def __call__(self, invalids: torch.Tensor, **kwargs) -> torch.Tensor: + ... + + +def strict_policy(invalids: torch.Tensor, **kwargs: Any) -> torch.Tensor: + invalid = torch.all(torch.any(invalids > 0.5, dim=-2), dim=-1).unsqueeze(-1) + return invalid + + +def weight_guided_policy(invalids: torch.Tensor, **kwargs) -> torch.Tensor: + weights = kwargs["weights"] + invalid = torch.all( + (invalids.to(weights.dtype) * weights.unsqueeze(-1)).sum(-2) > 0.9, + dim=-1, + keepdim=True, + ) + return invalid + + +def occ_and_weight_guided_policy(invalids: torch.Tensor, **kwargs) -> torch.Tensor: + weight_guided_invalid = weight_guided_policy(invalids, **kwargs) + + # occs = 1 indicates that there can be a valid reprojection. Therefore, we have to negate it + occ = kwargs["occ"] + + invalid = weight_guided_invalid | (~(occ.to(kwargs["weights"].dtype) > 0.5)) + + return invalid + + +def weight_guided_diverse_policy(invalids: torch.Tensor, **kwargs) -> torch.Tensor: + rgb_samps = kwargs["rgb_samps"] + ray_std = torch.std(rgb_samps, dim=-3).mean(-1) + weights = kwargs["weights"] + invalid = torch.all( + ((invalids.to(torch.float32) * weights.unsqueeze(-1)).sum(-2) > 0.9) + | (ray_std < 0.01), + dim=-1, + keepdim=True, + ) + return invalid + + +def no_policy(invalids: torch.Tensor, **kwargs) -> torch.Tensor: + invalid = torch.zeros_like( + torch.all(torch.any(invalids > 0.5, dim=-2), dim=-1).unsqueeze(-1), + dtype=torch.bool, + ) + return invalid + + +def invalid_policy( + invalid_policy: str, +) -> PolicyCallable: + match invalid_policy: + case "strict": + return strict_policy + case "weight_guided": + return weight_guided_policy + case "weight_guided_diverse": + return weight_guided_diverse_policy + case "occ_weight_guided": + return occ_and_weight_guided_policy + case None | "none": + return no_policy + case _: + raise ValueError(f"Unknown invalid policy: {invalid_policy}") + + +# TODO: scale all of them with a lambda factor +class ReconstructionLoss(BaseLoss): + def __init__(self, config, use_automasking: bool = False) -> None: + super().__init__(config) + if config.get("fine", None) is None: + self.rgb_fine_crit = None + else: + self.rgb_fine_crit = make_reconstruction_error( + config["fine"].get("criterion", "l2") + ) + self.dino_fine_crit = make_reconstruction_error( + config["fine"].get("dino_criterion", "l2") + ) + self.lambda_fine = config["fine"].get("lambda", 1) + if config.get("coarse", None) is None: + self.rgb_coarse_crit = None + else: + self.rgb_coarse_crit = make_reconstruction_error( + config["coarse"].get("criterion", "l2") + ) + self.dino_coarse_crit = make_reconstruction_error( + config["coarse"].get("dino_criterion", "l2") + ) + self.lambda_coarse = config["coarse"].get("lambda", 1) + self.invalid_policy = invalid_policy(config.get("invalid_policy", "strict")) + self.ignore_invalid = self.invalid_policy is not no_policy + + self.regularizations: list[tuple] = [] + for regularization_config in config["regularizations"]: + reg_fn = make_regularization(regularization_config, self.ignore_invalid) + self.regularizations.append( + (regularization_config["type"], reg_fn, regularization_config["lambda"]) + ) + + self.median_thresholding = config.get("median_thresholding", False) + + self.reconstruct_dino = config.get("reconstruct_dino", False) + self.lambda_dino_coarse = config.get("lambda_dino_coarse", 1) + self.lambda_dino_fine = config.get("lambda_dino_fine", 1) + self.temperature_dino = config.get("temperature_dino", 1) + + def get_loss_metric_names(self) -> list[str]: + loss_metric_names = ["rec_loss"] + if self.rgb_fine_crit is not None: + loss_metric_names.append("loss_rgb_fine") + if self.reconstruct_dino: + loss_metric_names.append("loss_dino_fine") + if self.rgb_coarse_crit is not None: + loss_metric_names.append("loss_rgb_coarse") + if self.reconstruct_dino: + loss_metric_names.append("loss_dino_coarse") + for regularization in self.regularizations: + loss_metric_names.append(regularization[0]) + return loss_metric_names + + def __call__(self, data) -> dict[str, torch.Tensor]: + # print(data["dino_gt"].shape) + # print(data["coarse"][0]["dino_features"].shape) + with profiler.record_function("loss_computation"): + n_scales = len(data["coarse"]) + + if self.rgb_coarse_crit is not None: + invalid_coarse = self.invalid_policy( + data["coarse"][0]["invalid"], + weights=data["coarse"][0]["weights"], + # rgb_samps=data["coarse"][0]["rgb_samps"], + ) + loss_device = invalid_coarse.device + + if self.rgb_fine_crit is not None: + invalid_fine = self.invalid_policy( + data["fine"][0]["invalid"], + weights=data["fine"][0]["weights"], + # rgb_samps=data["fine"][0]["rgb_samps"], + ) + loss_device = invalid_fine.device + + losses = { + name: torch.tensor(0.0, device=loss_device) + for name in self.get_loss_metric_names() + } + + for scale in range(n_scales): + if self.rgb_coarse_crit is not None: + coarse = data["coarse"][scale] + rgb_coarse = coarse["rgb"] + if "dino_features_downsampled" in coarse: + dino_coarse = coarse["dino_features_downsampled"] + else: + dino_coarse = coarse["dino_features"] + + if self.rgb_fine_crit is not None: + fine = data["fine"][scale] + rgb_fine = fine["rgb"] + if "dino_features_downsampled" in fine: + dino_fine = fine["dino_features_downsampled"] + else: + dino_fine = fine["dino_features"] + + if "dino_artifacts" in data: + dino_artifacts = data["dino_artifacts"].unsqueeze(-2).expand(dino_coarse.shape) + dino_coarse = dino_coarse + dino_artifacts + + rgb_gt = data["rgb_gt"].unsqueeze(-2).expand(rgb_coarse.shape) + dino_gt = data["dino_gt"].unsqueeze(-2).expand(dino_coarse.shape) + + def rgb_loss(pred, gt, invalid, criterion): + # TODO: move the reshaping and selection to the wrapper, maybe other functions as well + b, pc, h, w, num_views, channels = pred.shape + loss = ( + criterion( + pred.permute(0, 1, 4, 5, 2, 3).reshape(-1, channels, h, w), + gt.permute(0, 1, 4, 5, 2, 3).reshape(-1, channels, h, w), + ) + .view(b, pc, num_views, h, w) + .permute(0, 1, 3, 4, 2) + .unsqueeze(-1) + ) + loss = loss.amin(-2) + + if self.ignore_invalid and invalid is not None: + loss = loss * (1 - invalid.to(torch.float32)) + + if self.median_thresholding: + threshold = torch.median(loss.view(b, -1), dim=-1)[0].view( + -1, 1, 1, 1, 1 + ) + loss = loss[loss <= threshold] + + return loss.mean() + + def dino_loss(pred, gt, invalid, criterion): + # TODO: move the reshaping and selection to the wrapper, maybe other functions as well + channels = pred.shape[-1] + loss = ( + criterion( + pred.reshape(-1, channels), + gt.reshape(-1, channels), + ) + ) + # TODO: invalid feature handling + return loss.nanmean() + + if self.rgb_coarse_crit is not None: + loss_coarse = rgb_loss( + rgb_coarse, rgb_gt, invalid_coarse, self.rgb_coarse_crit + ) + losses["loss_rgb_coarse"] += loss_coarse.item() + losses["rec_loss"] += loss_coarse * self.lambda_coarse + + if self.reconstruct_dino: + loss_coarse = dino_loss( + self.temperature_dino * dino_coarse, self.temperature_dino * dino_gt, + None, self.dino_coarse_crit + ) + losses["loss_dino_coarse"] += loss_coarse.item() + losses["rec_loss"] += loss_coarse * self.lambda_coarse * self.lambda_dino_coarse + + if self.rgb_fine_crit is not None: + loss_fine = rgb_loss( + rgb_fine, rgb_gt, invalid_fine, self.rgb_fine_crit + ) + losses["loss_rgb_fine"] += loss_fine.item() + losses["rec_loss"] += loss_fine * self.lambda_fine + + if self.reconstruct_dino: + loss_fine = dino_loss( + dino_fine, dino_gt, invalid_fine.unsqueeze(-1), self.dino_fine_crit + ) + losses["loss_dino_fine"] += loss_fine.item() + losses["rec_loss"] += loss_fine * self.lambda_fine * self.lambda_dino_fine + + for regularization in self.regularizations: + # TODO: make it properly work with the different scales + reg_loss = regularization[1](data, scale) + + if reg_loss: + losses[regularization[0]] += reg_loss.item() + losses["rec_loss"] += reg_loss * regularization[2] + + losses = {name: value / n_scales for name, value in losses.items()} + + return losses diff --git a/scenedino/losses/stego_loss.py b/scenedino/losses/stego_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..7040f9818f19fb17bfe26afcf2c3a43d3c93762e --- /dev/null +++ b/scenedino/losses/stego_loss.py @@ -0,0 +1,79 @@ +from scenedino.losses.base_loss import BaseLoss +import torch +import torch.nn.functional as F + + +class StegoLoss(BaseLoss): + def __init__(self, config) -> None: + super().__init__(config) + self.random_weight = config.get("random_weight", 1.0) + self.knn_weight = config.get("knn_weight", 1.0) + self.self_weight = config.get("self_weight", 1.0) + + self.random_shift = config.get("random_shift", 0.0) + self.knn_shift = config.get("knn_shift", 0.0) + self.self_shift = config.get("self_shift", 0.0) + + self.pointwise = config.get("pointwise", True) + + def get_loss_metric_names(self) -> list[str]: + return [ + "total_loss", + "self_loss", "knn_loss", "random_loss", + "direct_cluster_loss", "direct_linear_loss", "stego_cluster_loss", "stego_linear_loss" + ] + + def __call__(self, data) -> dict[str, torch.Tensor]: + + if "stego_corr" not in data["segmentation"]: + self_loss, knn_loss, random_loss, total_loss = 0, 0, 0, 0 + else: + dino_self_corr = data["segmentation"]["stego_corr"]["dino_self_corr"] + stego_self_corr = data["segmentation"]["stego_corr"]["stego_self_corr"] + + dino_nn_corr = data["segmentation"]["stego_corr"]["dino_nn_corr"] + stego_nn_corr = data["segmentation"]["stego_corr"]["stego_nn_corr"] + + dino_random_corr = data["segmentation"]["stego_corr"]["dino_random_corr"] + stego_random_corr = data["segmentation"]["stego_corr"]["stego_random_corr"] + + self_loss = self._compute_stego_loss(dino_self_corr, stego_self_corr, + self.self_weight, self.self_shift) + knn_loss = self._compute_stego_loss(dino_nn_corr, stego_nn_corr, + self.knn_weight, self.knn_shift) + random_loss = self._compute_stego_loss(dino_random_corr, stego_random_corr, + self.random_weight, self.random_shift) + total_loss = self_loss + knn_loss + random_loss + + direct_cluster_loss = data["segmentation"]["results"]["direct_cluster"].get("loss", 0.0) + stego_cluster_loss = data["segmentation"]["results"]["stego_cluster"].get("loss", 0.0) + + # If linear heads present + direct_linear_loss = data["segmentation"]["results"].get("direct_linear", {}).get("loss", 0.0) + stego_linear_loss = data["segmentation"]["results"].get("stego_linear", {}).get("loss", 0.0) + + total_loss += direct_cluster_loss + direct_linear_loss + stego_cluster_loss + stego_linear_loss + + losses = { + "total_loss": total_loss, + + "self_loss": self_loss, + "knn_loss": knn_loss, + "random_loss": random_loss, + + "direct_cluster_loss": direct_cluster_loss, + "direct_linear_loss": direct_linear_loss, + "stego_cluster_loss": stego_cluster_loss, + "stego_linear_loss": stego_linear_loss, + } + return losses + + def _compute_stego_loss(self, dino_corr, stego_corr, weight, shift): + if self.pointwise: + old_mean = dino_corr.mean() + dino_corr -= dino_corr.mean(dim=-1, keepdim=True) + dino_corr = dino_corr - dino_corr.mean() + old_mean + + loss = -weight * stego_corr.clamp(0) * (dino_corr - shift) + + return loss.mean() diff --git a/scenedino/models/__init__.py b/scenedino/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7da91b29e27f9984f464c6d246d5e5ae2075a2e1 --- /dev/null +++ b/scenedino/models/__init__.py @@ -0,0 +1,63 @@ +from scenedino.common.positional_encoding import PositionalEncoding +from .backbones import make_backbone +from .prediction_heads import make_head +from .bts import BTSNet + +from scenedino.downstream_head import make_downstream_head + + +def make_model(config, downstream_config=None): + arch = config.get("arch", "BTSNet") + + sample_color = config.get("sample_color", True) + predict_dino = config.get("predict_dino", False) + dino_dims = config.get("dino_dims", 16) + if sample_color and predict_dino: + d_out = 1 + dino_dims + elif sample_color: + d_out = 1 + else: + d_out = 4 + + uncertainty_predictor_conf = config.get("uncertainty_predictor", None) + if uncertainty_predictor_conf is not None: + uncertainty_predictor = make_backbone(uncertainty_predictor_conf) + else: + uncertainty_predictor = None + + match arch: + case "BTSNet": + code_xyz = PositionalEncoding.from_conf(config["code"], d_in=3) + encoder = make_backbone(config["encoder"]) + d_in = encoder.latent_size + code_xyz.d_out + + split_dino_heads = config.get("split_dino_heads", False) + if split_dino_heads: + heads = { + head_conf["name"]: make_head(head_conf, d_in, 1 if head_conf["name"] == "normal_head" else dino_dims) + for head_conf in config["decoder_heads"] + } + else: + heads = { + head_conf["name"]: make_head(head_conf, d_in, d_out) + for head_conf in config["decoder_heads"] + } + + if downstream_config is not None: + downstream_head = make_downstream_head(downstream_config) + else: + downstream_head = None + + # TODO: check ren_nc + return BTSNet( + config, + encoder, + code_xyz, + heads, + config.get("final_pred_head", None), + uncertainty_predictor=uncertainty_predictor, + ren_nc=None, + downstream_head=downstream_head + ) + case _: + raise NotImplementedError("Model architecture was not implemented yet") diff --git a/scenedino/models/backbones/__init__.py b/scenedino/models/backbones/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..254cb9e1ae7fc3c4a5bc1afefb2816abd3200119 --- /dev/null +++ b/scenedino/models/backbones/__init__.py @@ -0,0 +1 @@ +from .backbone_util import * \ No newline at end of file diff --git a/scenedino/models/backbones/backbone_util.py b/scenedino/models/backbones/backbone_util.py new file mode 100644 index 0000000000000000000000000000000000000000..782bb996e1cda3cfa2e793441effde53e9ad8e91 --- /dev/null +++ b/scenedino/models/backbones/backbone_util.py @@ -0,0 +1,19 @@ +from .image_encoder import ImageEncoder +from .monodepth2 import Monodepth2 +from .spatial_encoder import SpatialEncoder +from scenedino.models.backbones.dino.dinov2_module import DINOv2Module + + +def make_backbone(conf, **kwargs): + enc_type = conf.get("type", "monodepth2") # spatial | global + if enc_type == "monodepth2": + net = Monodepth2.from_conf(conf, **kwargs) + elif enc_type == "spatial": + net = SpatialEncoder.from_conf(conf, **kwargs) + elif enc_type == "global": + net = ImageEncoder.from_conf(conf, **kwargs) + elif enc_type == "dinov2": + net = DINOv2Module.from_conf(conf, **kwargs) + else: + raise NotImplementedError(f"Unsupported encoder type: {enc_type}") + return net diff --git a/scenedino/models/backbones/dino/decoder.py b/scenedino/models/backbones/dino/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..7a52d1372fbd42d0a797713e0a2b8c98cf8038eb --- /dev/null +++ b/scenedino/models/backbones/dino/decoder.py @@ -0,0 +1,73 @@ +import torch +import torchvision +from torch import nn + +from scenedino.models.backbones.monodepth2 import Decoder + + +class NoDecoder(nn.Module): + def __init__(self, image_size, interpolation, normalize_features): + super().__init__() + + match interpolation: + case 'nearest': + inter_mode = torchvision.transforms.InterpolationMode.NEAREST + case 'bilinear': + inter_mode = torchvision.transforms.InterpolationMode.BILINEAR + case 'bicubic': + inter_mode = torchvision.transforms.InterpolationMode.BICUBIC + case _: + raise NotImplementedError(f"Interpolation mode \"{interpolation}\" not implemented!") + + self.image_size = image_size + self.resize_tf = torchvision.transforms.Resize(size=image_size, interpolation=inter_mode) + self.normalize_features = normalize_features + + def forward(self, x): + features = x[-1] + resized_features = self.resize_tf(features) + + if self.normalize_features: + resized_features = resized_features / torch.linalg.norm(resized_features, dim=1, keepdim=True) + + return [resized_features] + + +class SimpleFeaturePyramidDecoder(nn.Module): + def __init__(self, + latent_size, + num_ch_enc, + num_ch_dec, + d_out, + scales, + use_skips, + device): + super().__init__() + + self.scales = scales + self.resize_layers = [ + nn.ConvTranspose2d(in_channels=latent_size, out_channels=num_ch_enc[0], kernel_size=8, stride=8, padding=0, device=device), + nn.ConvTranspose2d(in_channels=latent_size, out_channels=num_ch_enc[1], kernel_size=4, stride=4, padding=0, device=device), + nn.ConvTranspose2d(in_channels=latent_size, out_channels=num_ch_enc[2], kernel_size=2, stride=2, padding=0, device=device), + nn.Conv2d(in_channels=latent_size, out_channels=num_ch_enc[3], kernel_size=3, stride=1, padding=1, device=device), + nn.Conv2d(in_channels=latent_size, out_channels=num_ch_enc[4], kernel_size=3, stride=2, padding=1, device=device), + ] + + num_ch_dec = [max(d_out, chns) for chns in num_ch_dec] + self.decoder = Decoder( + num_ch_enc=num_ch_enc, + num_ch_dec=num_ch_dec, + d_out=d_out, + scales=scales, + use_skips=use_skips, + extra_outs=0, + ) + + def forward(self, x): + dino_features = x[-1] + features = [] + for resize_layer in self.resize_layers: + features.append(resize_layer(dino_features)) + + outputs = self.decoder(features) + return [outputs[("disp", i)] for i in self.scales] \ No newline at end of file diff --git a/scenedino/models/backbones/dino/dim_reduction.py b/scenedino/models/backbones/dino/dim_reduction.py new file mode 100644 index 0000000000000000000000000000000000000000..08282c9e5bc406fa19b3be0ffd01d42bfa2edd1f --- /dev/null +++ b/scenedino/models/backbones/dino/dim_reduction.py @@ -0,0 +1,36 @@ +import torch +from torch import nn +import torch.nn.functional as F + + +class NoDimReduction(nn.Module): + def __init__(self, full_channels, reduced_channels): + super().__init__() + assert full_channels == reduced_channels + + def forward(self, features): + return features + + +class MlpDimReduction(nn.Module): + def __init__(self, full_channels, reduced_channels, latent_channels): + super().__init__() + self.linear_in = nn.Linear(reduced_channels, latent_channels) + self.linear_out = nn.Linear(latent_channels, full_channels) + self.relu = nn.ReLU() + + def transform_expand(self, features): + latent = self.relu(self.linear_in(features)) + output = self.linear_out(latent) + return F.normalize(output, dim=-1) + + +class OrthogonalLinearDimReduction(nn.Module): + def __init__(self, full_channels, reduced_channels): + super().__init__() + self.bias = torch.nn.Parameter(torch.zeros(full_channels)) + self.weights = torch.nn.Parameter(torch.eye(full_channels, reduced_channels)) + + def transform_expand(self, features): + output = features @ self.weights.transpose(0, 1) + self.bias + return F.normalize(output, dim=-1) diff --git a/scenedino/models/backbones/dino/dinov2_module.py b/scenedino/models/backbones/dino/dinov2_module.py new file mode 100644 index 0000000000000000000000000000000000000000..273ad7ca8f475fbab186493b4beeea99f49574df --- /dev/null +++ b/scenedino/models/backbones/dino/dinov2_module.py @@ -0,0 +1,339 @@ +import numpy as np +from typing import Optional, Tuple, List + +import torch +import torchvision +from torch import nn +import torch.nn.functional as F + +from .vit import dino_small8, dino_base8, dino_small, dino_base, dinov2_small, dinov2_base, dino_reg_small, dino_reg_base + +from .decoder import NoDecoder, SimpleFeaturePyramidDecoder +from .dpt_head import DPTHead +from .downsampler import PatchSalienceDownsampler, BilinearDownsampler +from .upsampler import InterpolatedGT, MultiScaleCropGT, MultiScaleCropGT_kornia +from .dim_reduction import OrthogonalLinearDimReduction, MlpDimReduction, NoDimReduction +from .visualization import VisualizationModule + + +def build_encoder(backbone: str, image_size: Tuple[int, int], intermediate_features: List[int], key_features: bool, version: str): + match backbone: + case "vit-s" | "vit-b" | "fit3d-s": + return DINOv2Encoder(backbone, + image_size, + intermediate_features=intermediate_features, + key_features=key_features, + version=version) + case _: + raise NotImplementedError + + +def build_decoder(decoder_arch: str, patch_size: int, image_size: Tuple[int, int], latent_size: int, num_ch_enc: List[int], decoder_out_dim: int): + match decoder_arch: + case "nearest" | "bilinear" | "bicubic": + return NoDecoder(image_size, + interpolation=decoder_arch, + normalize_features=True) + case "spf": + # TODO: SPF with patch size 8 is not implemented yet + num_ch_dec = np.array([128, 128, 256, 256, 512]) + scales = range(4) + return SimpleFeaturePyramidDecoder(latent_size=latent_size, + num_ch_enc=num_ch_enc, + num_ch_dec=num_ch_dec, + d_out=decoder_out_dim, + scales=scales, + use_skips=True, + device="cuda") + case "dpt": + return DPTHead(embed_dims=latent_size, + post_process_channels=num_ch_enc, + readout_type="ignore", + patch_size=patch_size, + d_out=decoder_out_dim, + expand_channels=False) + case _: + raise NotImplementedError + + +def build_downsampler(arch: str, dim: int, patch_size: int): + match arch: + case "featup": + return PatchSalienceDownsampler(dim, patch_size=patch_size, normalize_features=True) + case "bilinear": + return BilinearDownsampler(patch_size=patch_size) + case _: + raise NotImplementedError + + +def build_gt_upsampling_wrapper(arch: str, gt_encoder: nn.Module, image_size: Tuple[int, int]): + match arch: + case "nearest" | "bilinear" | "bicubic": + return InterpolatedGT(arch, gt_encoder, image_size) + case "multiscale-crop": + return MultiScaleCropGT_kornia(gt_encoder, num_views=4, image_size=image_size) + case _: + raise NotImplementedError + + +def build_dim_reduction(arch: str, full_channels: int, reduced_channels: int): + match arch: + case "none": + return NoDimReduction(full_channels, reduced_channels) + case "mlp": + return MlpDimReduction(full_channels, reduced_channels, latent_channels=128) + case "orthogonal-linear": + return OrthogonalLinearDimReduction(full_channels, reduced_channels) + case _: + raise NotImplementedError + + +class DINOv2Module(nn.Module): + def __init__(self, + mode: str, # downsample-prediction, upsample-gt + decoder_arch: str, # nearest, bilinear, sfp, dpt + upsampler_arch: Optional[str], # nearest, bilinear, multiscale-crop + downsampler_arch: Optional[str], # sample-center, featup + encoder_arch: str, # vit-s, vit-b + encoder_freeze: bool, + flip_avg_gt: bool, + dim_reduction_arch: str, # orthogonal-linear, mlp + num_ch_enc: np.array, + intermediate_features: List[int], + decoder_out_dim: int, + dino_pca_dim: int, + image_size: Tuple[int, int], + key_features: bool, + dino_version: str, # v1, v2, reg, fit3d + separate_gt_version: Optional[str], # v1, v2, reg, fit3d, None (reuses encoder) + ): + + super().__init__() + + self.encoder = build_encoder(encoder_arch, image_size, intermediate_features, key_features, dino_version) + self.flip_avg_gt = flip_avg_gt + + if encoder_freeze or separate_gt_version is None: + self.encoder_frozen = True + for p in self.encoder.parameters(True): + p.requires_grad = False + else: + self.encoder_frozen = False + + self.decoder = build_decoder(decoder_arch, + self.encoder.patch_size, + image_size, + self.encoder.latent_size, + num_ch_enc, + decoder_out_dim) + + if separate_gt_version is None: + self.gt_encoder = self.encoder + else: + self.gt_encoder = build_encoder(encoder_arch, image_size, [], key_features, separate_gt_version) + for p in self.gt_encoder.parameters(True): + p.requires_grad = False + + # General way of creating loss + if mode == "downsample-prediction": + assert upsampler_arch is None + self.downsampler = build_downsampler(downsampler_arch, self.gt_encoder.latent_size, self.gt_encoder.patch_size) + self.gt_wrapper = None + + elif mode == "upsample-gt": + assert downsampler_arch is None + self.downsampler = None + self.gt_wrapper = build_gt_upsampling_wrapper(upsampler_arch, self.gt_encoder, image_size) + + else: + raise NotImplementedError + + self.extra_outs = 0 + self.latent_size = decoder_out_dim + + self.dino_pca_dim = dino_pca_dim + self.dim_reduction = build_dim_reduction(dim_reduction_arch, self.encoder.latent_size, dino_pca_dim) + self.visualization = VisualizationModule(self.encoder.latent_size) + + def forward(self, x, ground_truth=False): + if ground_truth: + with torch.no_grad(): + if self.gt_wrapper is not None: + gt_0 = self.gt_wrapper(x) + if self.flip_avg_gt: + gt_flipped = self.gt_wrapper(x.flip([-1])) + gt_avg = [F.normalize(gt_flipped[i].flip([-1]) + gt_0[i], dim=1) for i in range(len(gt_0))] + return gt_avg + else: + return gt_0 + else: + gt_0 = self.gt_encoder(x)[-1] + if self.flip_avg_gt: + gt_flipped = self.gt_encoder(x.flip([-1]))[-1] + gt_avg = F.normalize(gt_flipped.flip([-1]) + gt_0, dim=1) + return [gt_avg] + else: + return [gt_0] + else: + if self.encoder_frozen: + with torch.no_grad(): + patch_features = self.encoder(x) + else: + patch_features = self.encoder(x) + return self.decoder(patch_features) + + def downsample(self, x, mode="patch"): + if self.downsampler is None: + return None + else: + return self.downsampler(x, mode) + + def expand_dim(self, features): + return self.dim_reduction.transform_expand(features) + + def fit_visualization(self, features, refit=True): + return self.visualization.fit_pca(features, refit) + + def transform_visualization(self, features, norm=False, from_dim=0): + return self.visualization.transform_pca(features, norm, from_dim) + + def fit_transform_kmeans_visualization(self, features): + return self.visualization.fit_transform_kmeans_batch(features) + + @classmethod + def from_conf(cls, conf): + return cls( + mode=conf.mode, + decoder_arch=conf.decoder_arch, + upsampler_arch=conf.get("upsampler_arch", None), + downsampler_arch=conf.get("downsampler_arch", None), + encoder_arch=conf.encoder_arch, + encoder_freeze=conf.encoder_freeze, + flip_avg_gt=conf.get("flip_avg_gt", False), + dim_reduction_arch=conf.dim_reduction_arch, + num_ch_enc=conf.get("num_ch_enc", None), + intermediate_features=conf.get("intermediate_features", []), + decoder_out_dim=conf.decoder_out_dim, + dino_pca_dim=conf.dino_pca_dim, + image_size=conf.image_size, + key_features=conf.key_features, + dino_version=conf.get("version", "reg"), + separate_gt_version=conf.get("separate_gt_version", None) + ) + + +def _normalize_input(x): + norm_tf = torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) + return norm_tf(x / 2 + 0.5) + + +class DINOv2Encoder(nn.Module): + def __init__(self, backbone, image_size, intermediate_features, key_features, version): + super().__init__() + + self.image_size = image_size + if version in ["fit3d", "v2", "reg"]: + # "Internal" patch size 14 is resized to "External" patch size 16 for decoder! + self.patch_size = 16 + adjusted_image_size = (image_size[0] * 14 // self.patch_size, image_size[1] * 14 // self.patch_size) + self.resize_tf = torchvision.transforms.Resize(size=adjusted_image_size, + interpolation=torchvision.transforms.InterpolationMode.BILINEAR) + elif version == "v1": + self.patch_size = 8 + adjusted_image_size = self.image_size + self.resize_tf = None + elif version == "v1_16": + self.patch_size = 16 + adjusted_image_size = self.image_size + self.resize_tf = None + else: + raise NotImplementedError() + + self.key_features = key_features + self.backbone = backbone + self.version = version + + self.model, self.latent_size = self.load_model(backbone, version, adjusted_image_size, intermediate_features) + + def forward(self, x): + x = _normalize_input(x) + if self.resize_tf: + x = self.resize_tf(x) + + output_dict = self.model(x) + if self.version == "fit3d": + output_dict = self.model.output_dict + + inter_keys = [output_key for output_key in output_dict if output_key.startswith("intermediate_features.")] + result = [] + + for inter_key in sorted(inter_keys): + output = output_dict[inter_key].transpose(-1, -2) # (L, B, C_dino, H*W) + output_grid = output.view(*output.size()[:-1], + x.size(-2) // self.model.patch_size, + x.size(-1) // self.model.patch_size) + result.append(output_grid) + + if self.key_features: + output = output_dict['key_features'].transpose(-1, -2).flatten(1, 2) + output = F.normalize(output, dim=1) + else: + output = output_dict['features_normalized'].transpose(-1, -2) + output = F.normalize(output, dim=1) + + output_grid = output.view(*output.size()[:-1], + x.size(-2) // self.model.patch_size, + x.size(-1) // self.model.patch_size) + result.append(output_grid) + return result + + def load_model(self, backbone, version, image_size, intermediate_features): + if version == "fit3d": + if backbone == "vit-s": + model_name = "dinov2_reg_small_fine" + elif backbone == "vit-b": + model_name = "dinov2_reg_base_fine" + else: + raise NotImplementedError() + + def get_features(model, key): + def hook(blk, input, output): + model.output_dict[key] = output[:, 5:] + return hook + + model = torch.hub.load("ywyue/FiT3D", model_name).to("cuda") + model.norm.register_forward_hook(get_features(model, f"features_normalized")) + for i, _blk in enumerate(model.blocks): + if i in intermediate_features: + _blk.register_forward_hook(get_features(model, f"intermediate_features.{i}")) + model.output_dict = {} + model.patch_size = 14 + + elif version == "v1" and backbone == "vit-s": + model = dino_small8(image_size=image_size, intermediate_features=intermediate_features) + elif version == "v1" and backbone == "vit-b": + model = dino_base8(image_size=image_size, intermediate_features=intermediate_features) + + elif version == "v1_16" and backbone == "vit-s": + model = dino_small(image_size=image_size, intermediate_features=intermediate_features) + elif version == "v1_16" and backbone == "vit-b": + model = dino_base(image_size=image_size, intermediate_features=intermediate_features) + + elif version == "v2" and backbone == "vit-s": + model = dinov2_small(image_size=image_size, intermediate_features=intermediate_features) + elif version == "v2" and backbone == "vit-b": + model = dinov2_base(image_size=image_size, intermediate_features=intermediate_features) + + elif version == "reg" and backbone == "vit-s": + model = dino_reg_small(image_size=image_size, intermediate_features=intermediate_features) + elif version == "reg" and backbone == "vit-b": + model = dino_reg_base(image_size=image_size, intermediate_features=intermediate_features) + else: + raise NotImplementedError() + + if backbone == "vit-s": + latent_size = 384 + elif backbone == "vit-b": + latent_size = 768 + + return model, latent_size diff --git a/scenedino/models/backbones/dino/downsampler.py b/scenedino/models/backbones/dino/downsampler.py new file mode 100644 index 0000000000000000000000000000000000000000..e02ef21a02d23dc0377c4a5ddbe8da9d504c7958 --- /dev/null +++ b/scenedino/models/backbones/dino/downsampler.py @@ -0,0 +1,98 @@ +import torch +import torch.nn.functional as F +import torchvision + + +class BilinearDownsampler(torch.nn.Module): + def __init__( + self, + patch_size, + ): + super().__init__() + if isinstance(patch_size, int): + self.patch_size = (patch_size, patch_size) + elif isinstance(patch_size, tuple): + self.patch_size = patch_size + + def forward(self, x, mode): + n, v, h, w, _, c = x.shape + + assert h % self.patch_size[0] == 0 + target_h = h // self.patch_size[0] + assert w % self.patch_size[1] == 0 + target_w = w // self.patch_size[1] + + x = x.permute(0, 1, 4, 5, 2, 3).flatten(0, 2) + x = F.interpolate(x, size=(target_h, target_w), mode="bilinear") + x = x.reshape(n, v, -1, c, target_h, target_w).permute(0, 1, 4, 5, 2, 3) + return x.squeeze(2, 3) + + +class PatchSalienceDownsampler(torch.nn.Module): + def __init__( + self, + channels, + patch_size, + normalize_features, + ): + super().__init__() + + if isinstance(patch_size, int): + self.patch_size = (patch_size, patch_size) + elif isinstance(patch_size, tuple): + self.patch_size = patch_size + + self.conv = torch.nn.Conv2d(channels, 1, kernel_size=1) + self.patch_weight = torch.nn.Parameter(torch.ones(self.patch_size)) + self.patch_bias = torch.nn.Parameter(torch.zeros(self.patch_size)) + + self.normalize_features = normalize_features + + torch.nn.init.kaiming_normal_(self.conv.weight, a=0, mode="fan_in") + torch.nn.init.zeros_(self.conv.bias) + torch.nn.init.normal_(self.patch_weight, mean=1.0, std=0.01) + torch.nn.init.normal_(self.patch_bias, mean=0.0, std=0.01) + + def forward(self, x, mode): + if mode == "patch": + return self.forward_patches(x) + + elif mode == "image": + n, v, h, w, _, c = x.shape + patch_h, patch_w = self.patch_size[0], self.patch_size[1] + no_patches_h, no_patches_w = h // patch_h, w // patch_w + + patches = x.reshape(n, v, no_patches_h, patch_h, no_patches_w, patch_w, 1, c) + patches = patches.swapaxes(3, 4).flatten(1, 3) + + patched_result, salience_map, weight_map, patch_weight_bias = self.forward_patches(patches) + patched_result = patched_result.reshape(n, v, no_patches_h, no_patches_w, 1, c) + + salience_map = salience_map.reshape(n, v, no_patches_h, no_patches_w, patch_h, patch_w, 1, 1) + salience_map = salience_map.swapaxes(3, 4).reshape(n, v, h, w, 1, 1) + + weight_map = weight_map.reshape(n, v, no_patches_h, no_patches_w, patch_h, patch_w, 1, 1) + weight_map = weight_map.swapaxes(3, 4).reshape(n, v, h, w, 1, 1) + + return patched_result, salience_map, weight_map, patch_weight_bias + else: + return None + + def forward_patches(self, x): + n, p, patch_h, patch_w, _, c = x.shape + x_flat = x.reshape(-1, patch_h, patch_w, c).permute(0, 3, 1, 2) + + salience_map = self.conv(x_flat).squeeze(1) + + weight_map = salience_map * self.patch_weight + self.patch_bias + weight_map = torch.nn.functional.softmax(weight_map.reshape(-1, patch_h * patch_w), dim=1) + weight_map = weight_map.reshape(n, p, patch_h, patch_w, 1, 1) + + patched_features = torch.sum(weight_map * x, dim=(2, 3)) + if self.normalize_features: + patched_features = patched_features / torch.linalg.norm(patched_features, dim=-1, keepdim=True) + + return (patched_features, + salience_map.reshape(n, p, patch_h, patch_w, 1, 1), + weight_map, + torch.cat([self.patch_weight, self.patch_bias], dim=1)) diff --git a/scenedino/models/backbones/dino/dpt_head.py b/scenedino/models/backbones/dino/dpt_head.py new file mode 100644 index 0000000000000000000000000000000000000000..22f4ea0d34f6df21b0c90569b07775eaf87e6e60 --- /dev/null +++ b/scenedino/models/backbones/dino/dpt_head.py @@ -0,0 +1,236 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +import torch.nn as nn +import torch.nn.functional as F + + +class Interpolate(nn.Module): # TODO: Not sure about normalization generally (norm_cfg in original code) + def __init__(self, scale_factor, mode, align_corners=False): + super().__init__() + self.interp = nn.functional.interpolate + self.scale_factor = scale_factor + self.mode = mode + self.align_corners = align_corners + + def forward(self, x): + x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners) + return x + + +class ReassembleBlocks(nn.Module): + """ViTPostProcessBlock, process cls_token in ViT backbone output and + rearrange the feature vector to feature map. + Args: + in_channels (int): ViT feature channels. Default: 768. + out_channels (List): output channels of each stage. + Default: [96, 192, 384, 768]. + readout_type (str): Type of readout operation. Default: 'ignore'. + patch_size (int): The patch size. Default: 16. + """ + def __init__( + self, in_channels=768, out_channels=None, readout_type="ignore", patch_size=16 + ): + super().__init__() + + if out_channels is None: + out_channels = [96, 192, 384, 384] + + assert readout_type in ["ignore"] # ["ignore", "add", "project"] + self.readout_type = readout_type + self.patch_size = patch_size + + self.projects = nn.ModuleList( + [ + nn.Conv2d(in_channels, out_channel, kernel_size=1) for out_channel in out_channels + ] + ) + + self.resize_layers = nn.ModuleList( + [ + nn.ConvTranspose2d( + in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0 + ), + nn.ConvTranspose2d( + in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0 + ), + nn.Identity(), + nn.Conv2d( + in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1 + ) + ] + ) + + def forward(self, inputs): + out = [] + for i, x in enumerate(inputs): + x = self.projects[i](x) + x = self.resize_layers[i](x) + out.append(x) + return out + + +class PreActResidualConvUnit(nn.Module): + """ResidualConvUnit, pre-activate residual unit. + Args: + in_channels (int): number of channels in the input feature map. + stride (int): stride of the first block. Default: 1 + dilation (int): dilation rate for convs layers. Default: 1. + """ + def __init__(self, in_channels, stride=1, dilation=1, bn=False): + super().__init__() + + self.bn = bn + self.act = nn.ReLU() + self.conv1 = nn.Conv2d( + in_channels, + in_channels, + 3, + stride=stride, + padding=dilation, + dilation=dilation, + bias=not self.bn, + ) + self.conv2 = nn.Conv2d( + in_channels, + in_channels, + 3, + padding=1, + bias=not self.bn, + ) + if self.bn: + self.bn1 = nn.BatchNorm2d(in_channels) + self.bn2 = nn.BatchNorm2d(in_channels) + + def forward(self, inputs): + inputs_ = inputs.clone() + x = self.act(inputs) + x = self.conv1(x) + if self.bn: + x = self.bn1(x) + + x = self.act(x) + x = self.conv2(x) + if self.bn: + x = self.bn2(x) + + return x + inputs_ + + +class FeatureFusionBlock(nn.Module): + """FeatureFusionBlock, merge feature map from different stages. + Args: + in_channels (int): Input channels. + expand (bool): Whether expand the channels in post process block. + Default: False. + align_corners (bool): align_corner setting for bilinear upsample. + Default: True. + """ + def __init__(self, in_channels, expand=False, align_corners=True): + super().__init__() + + self.in_channels = in_channels + self.expand = expand + self.align_corners = align_corners + + self.out_channels = in_channels + if self.expand: + self.out_channels = in_channels // 2 + + self.project = nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1) + + self.res_conv_unit1 = PreActResidualConvUnit(in_channels=self.in_channels) + self.res_conv_unit2 = PreActResidualConvUnit(in_channels=self.in_channels) + + def forward(self, *inputs): + x = inputs[0] + if len(inputs) == 2: + if x.shape != inputs[1].shape: + res = F.interpolate(inputs[1], size=(x.shape[2], x.shape[3]), mode="bilinear", align_corners=False) + else: + res = inputs[1] + x = x + self.res_conv_unit1(res) + x = self.res_conv_unit2(x) + x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners) + x = self.project(x) + return x + + +class OutputHead(nn.Module): + def __init__(self, latent_size=768): + super().__init__() + # TODO: Not sure about structure + self.head_modules = nn.ModuleList( + [ + nn.Conv2d(in_channels=latent_size, out_channels=latent_size, kernel_size=3, stride=1, padding=1), + nn.ConvTranspose2d(in_channels=latent_size, out_channels=latent_size, kernel_size=2, stride=2, padding=0), + nn.Conv2d(in_channels=latent_size, out_channels=latent_size, kernel_size=3, stride=1, padding=1), + ] + ) + + def forward(self, x): + for module in self.head_modules: + x = module(x) + return x + + +class DPTHead(nn.Module): + """Vision Transformers for Dense Prediction. + This head is implemented of `DPT `_. + Args: + embed_dims (int): The embed dimension of the ViT backbone. + Default: 768. + post_process_channels (List): Out channels of post process conv + layers. Default: [96, 192, 384, 768]. + readout_type (str): Type of readout operation. Default: 'ignore'. + patch_size (int): The patch size. Default: 16. + expand_channels (bool): Whether expand the channels in post process + block. Default: False. + """ + def __init__( + self, + embed_dims=768, + post_process_channels=None, + readout_type="ignore", + patch_size=16, + d_out=384, + expand_channels=False, + ): + super().__init__() + + if not post_process_channels: + self.post_process_channels = [96, 192, 384, 768] + self.post_process_channels = [min(d_out, channel) for channel in post_process_channels] + + self.d_out = d_out + self.expand_channels = expand_channels + self.reassemble_blocks = ReassembleBlocks(embed_dims, self.post_process_channels, readout_type, patch_size) + self.convs = nn.ModuleList() + for channel in self.post_process_channels: + self.convs.append(nn.Conv2d(channel, self.d_out, kernel_size=3, padding=1, bias=False)) + self.fusion_blocks = nn.ModuleList() + for _ in range(len(self.convs)): + self.fusion_blocks.append(FeatureFusionBlock(self.d_out)) + self.fusion_blocks[0].res_conv_unit1 = None + self.project = nn.Conv2d(self.d_out, self.d_out, kernel_size=3, padding=1) + self.output_head = OutputHead(d_out) + + self.num_fusion_blocks = len(self.fusion_blocks) + self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers) + self.num_post_process_channels = len(self.post_process_channels) + assert self.num_fusion_blocks == self.num_reassemble_blocks + assert self.num_reassemble_blocks == self.num_post_process_channels + + def forward(self, inputs): + assert len(inputs) == self.num_reassemble_blocks + x = [inp for inp in inputs] + x = self.reassemble_blocks(x) + x = [self.convs[i](feature) for i, feature in enumerate(x)] + out = self.fusion_blocks[0](x[-1]) + for i in range(1, len(self.fusion_blocks)): + out = self.fusion_blocks[i](out, x[-(i + 1)]) + out = self.project(out) + out = self.output_head(out) + return [out] # list for BTS diff --git a/scenedino/models/backbones/dino/upsampler.py b/scenedino/models/backbones/dino/upsampler.py new file mode 100644 index 0000000000000000000000000000000000000000..e89442bb05164934cb1ab2706af9cf07db664b83 --- /dev/null +++ b/scenedino/models/backbones/dino/upsampler.py @@ -0,0 +1,322 @@ +import math +import random +from typing import Tuple, Optional + +import kornia +import torch +from torch import nn, Tensor +import torch.nn.functional as F +import torchvision.transforms as tf + +from scenedino.models.backbones.dino.decoder import NoDecoder + +import logging +logger = logging.getLogger("training") + + +class MultiScaleCropGT_kornia(nn.Module): + """This class implements multi-scale-crop augmentation for DINO features.""" + def __init__( + self, + gt_encoder: nn.Module, + num_views: int = 8, + image_size: Tuple[int, int] = (192, 640), + feature_stride: int = 16, + ) -> None: + """Constructor method. + + Args: + num_views (int): Number of view per image. Default 8. + augmentations (Tuple[AugmentationBase2D, ...]): Geometric augmentations to be applied. + feature_stride (int): Stride of the features. Default 16. + """ + # Call super constructor + super(MultiScaleCropGT_kornia, self).__init__() + + # GT encoder + self.gt_encoder = gt_encoder + + # Save parameters + self.augmentations_per_sample: int = num_views + self.feature_stride: int = feature_stride + # Init augmentations + + image_ratio = image_size[0] / image_size[1] + augmentations = ( + kornia.augmentation.RandomHorizontalFlip(p=0.5), + #kornia.augmentation.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2), + kornia.augmentation.RandomResizedCrop( + scale=(0.5, 1.0), size=tuple(image_size), ratio=(image_ratio/1.2, image_ratio*1.2), p=1.0 + # Here you need to set your resolution + ), + ) + self.augmentations: nn.Module = kornia.augmentation.VideoSequential(*augmentations, same_on_frame=True) + + @staticmethod + def _affine_transform_valid_pixels(transform: Tensor, mask: Tensor) -> Tensor: + """Applies affine transform to a mask of ones to estimate valid pixels. + + Args: + transform (Tensor): Affine transform of the shape [B, 3, 3] + mask (Tensor): Mask of the shape [B, 1, H, W]. + + Returns: + valid_pixels (Tensor): Mask of valid pixels of the shape [B, 1, H, W]. + """ + # Get shape + H, W = mask.shape[2:] # type: int, int + # Resample mask map + valid_pixels: Tensor = kornia.geometry.warp_perspective( + mask, + transform, + (H, W), + mode="nearest", + ) + # Threshold mask + valid_pixels = torch.where( # type: ignore + valid_pixels > 0.999, torch.ones_like(valid_pixels), torch.zeros_like(valid_pixels) + ) + return valid_pixels + + def _accumulate_predictions(self, features: Tensor, transforms: Tensor) -> Tensor: + """Accumulates features over multiple predictions. + + Args: + features (Tensor): Feature predictions of the shape [B, num_views, H, W]. + transforms (Tensor): Affine transformations of the shape [B, num_views, 3, 3]. + + Returns: + optical_flow_predictions_accumulated (Tensor): Accumulated optical flow of the shape [B, 2, H, W]. + """ + # Get shape + B, N, C, H, W = features.shape # type: int, int, int, int, int + # Get base and augmented views + features_base = features[:, -2:] + features_augmented = features[:, :-2] + # Combine batch dimension and view dimension + features_augmented = features_augmented.flatten(0, 1) + transforms = transforms.flatten(0, 1) + # Rescale transformation + transforms[:, 0, -1] = transforms[:, 0, -1] #/ float(self.feature_stride) + transforms[:, 1, -1] = transforms[:, 1, -1] #/ float(self.feature_stride) + # Invert transformations + transforms_inv: Tensor = torch.inverse(transforms) + # Resample optical flow map + features_resampled: Tensor = kornia.geometry.warp_perspective( + features_augmented, + transforms_inv, + (H, W), + mode="bilinear", + ) + # Separate batch and view dimension again + features_resampled = features_resampled.reshape(B, -1, C, H, W) + # Add base views + features_resampled = torch.cat((features_resampled, features_base), dim=1) + # Reverse flip + features_resampled[:, -2] = features_resampled[:, -2].flip(dims=(-1,)) + # Compute valid pixels + mask: Tensor = torch.ones( + B, N - 2, 1, H, W, dtype=features_resampled.dtype, device=features_resampled.device + ) + mask = mask.flatten(0, 1) + valid_pixels: Tensor = self._affine_transform_valid_pixels(transforms_inv, mask) + valid_pixels = valid_pixels.reshape(B, N - 2, 1, H, W) + valid_pixels = F.pad(valid_pixels, (0, 0, 0, 0, 0, 0, 0, 2), value=1) + # Set invalid flow vectors to zero + features_resampled[valid_pixels.repeat(1, 1, C, 1, 1) == 0.0] = torch.nan + # Average optical flow over different views given the sum valid pixels for the specific pixel + + # logger.info(features_resampled.shape) + return features_resampled.nanmean(dim=1) + + def _get_augmentations(self, images: Tensor) -> Tuple[Tensor, Tensor]: + """Forward pass generates different augmentations of the input images. + + Args: + images (Tensor): Images of the shape [B, 3, H, W] + + Returns: + images_augmented (Tensor): Augmented images of the shape [B, N, H, W]. + transforms (Tensor): Transformations of the shape [B, N, 3, 3]. + """ + # Add dummy dimension shape is [B, num_views, 3, H, W] + images = images[:, None] + # Init tensor to store transformations + transformations: Tensor = torch.empty( + images.shape[0], self.augmentations_per_sample - 2, 3, 3, dtype=torch.float32, device=images.device + ) + # Init tensor to store augmented images + images_augmented: Tensor = torch.empty_like(images) + images_augmented = images_augmented[:, None].repeat_interleave(self.augmentations_per_sample, dim=1) + # Save original and flipped images + images_augmented[:, -1] = images.clone() + images_augmented[:, -2] = images.clone().flip(dims=(-1,)) + # Apply geometric augmentations + for index in range(images.shape[0]): + images_repeated: Tensor = images[index][None].repeat_interleave(self.augmentations_per_sample - 2, dim=0) + images_augmented[index, :-2] = self.augmentations(images_repeated) + transformations[index] = self.augmentations.get_transformation_matrix( + images_repeated, self.augmentations._params + ) + return images_augmented[:, :, 0], transformations + + def forward_chunk(self, images): + batch_size, _, h, w = images.shape + + # Perform augmentation + images_aug, transformations = self._get_augmentations(images) + # Get representations + features = self.gt_encoder(images_aug.flatten(0, 1))[-1] + features = F.interpolate(features, size=(h, w), mode="bilinear") + # features = features.repeat_interleave(self.feature_stride, -1).repeat_interleave(self.feature_stride, -2) + + _, dino_dim, _, _ = features.shape + features = features.view(batch_size, -1, dino_dim, h, w) + + chunks = torch.chunk(features, chunks=4, dim=2) # Split into 4 parts along dim=3 + chunks = [self._accumulate_predictions(chunk, transformations) for chunk in chunks] + features_accumulated = torch.cat(chunks, dim=1) + + # features_accumulated = self._accumulate_predictions(features, transformations) + return features_accumulated / torch.linalg.norm(features_accumulated, dim=1, keepdim=True) + + def forward(self, images): + max_chunk = 16 + aug_no_images = images.shape[0] * self.augmentations_per_sample + + if aug_no_images > max_chunk: + no_chunks = aug_no_images // max_chunk + images = torch.chunk(images, no_chunks) + features = [self.forward_chunk(image) for image in images] + features = torch.cat(features, dim=0) + return [features] + else: + return [self.forward_chunk(images)] + + +class InterpolatedGT(nn.Module): + def __init__(self, arch: str, gt_encoder: nn.Module, image_size: Tuple[int, int]): + super().__init__() + self.upsampler = NoDecoder(image_size, arch, normalize_features=False) + self.gt_encoder = gt_encoder + + def forward(self, x): + gt_patches = self.gt_encoder(x) + return self.upsampler(gt_patches) + + +def _get_affine(params, crop_size, batch_size): + # construct affine operator + affine = torch.zeros(batch_size, 2, 3) + + aspect_ratio = float(crop_size[0]) / float(crop_size[1]) + for i, (dy, dx, alpha, scale, flip) in enumerate(params): + # R inverse + sin = math.sin(alpha * math.pi / 180.) + cos = math.cos(alpha * math.pi / 180.) + + # inverse, note how flipping is incorporated + affine[i, 0, 0], affine[i, 0, 1] = flip * cos, sin * aspect_ratio + affine[i, 1, 0], affine[i, 1, 1] = -sin / aspect_ratio, cos + + # T inverse Rinv * t == R^T * t + affine[i, 0, 2] = -1. * (cos * dx + sin * dy) + affine[i, 1, 2] = -1. * (-sin * dx + cos * dy) + + # T + affine[i, 0, 2] /= float(crop_size[1] // 2) + affine[i, 1, 2] /= float(crop_size[0] // 2) + + # scaling + affine[i] *= scale + + return affine + + +class MultiScaleCropGT(nn.Module): + def __init__(self, + gt_encoder: nn.Module, + num_views: int, + scale_from: float = 0.4, + grid_sample_batch: Optional[int] = 96): + + super().__init__() + self.gt_encoder = gt_encoder + self.num_views = num_views + self.augmentation = MaskRandScaleCrop(scale_from) + self.grid_sample_batch = grid_sample_batch + + def forward(self, x): + result = None + count = 0 + batch_size, _, h, w = x.shape + + for i in range(self.num_views): + if i > 0: + x, params = self.augmentation(x) + else: + params = [[0., 0., 0., 1., 1.] for _ in range(x.shape[0])] + gt_patches = self.gt_encoder(x)[-1] + affine = _get_affine(params, (h, w), batch_size).cuda() + affine_grid_gt = F.affine_grid(affine, x.size(), align_corners=False) + if self.grid_sample_batch: + d = gt_patches.shape[1] + assert d % self.grid_sample_batch == 0 + for idx in range(0, d, self.grid_sample_batch): + gt_aligned_batch = F.grid_sample(gt_patches[:, idx:idx+self.grid_sample_batch], affine_grid_gt, + mode="bilinear", align_corners=False) + if result is None: + result = torch.zeros(batch_size, d, h, w, device="cuda") + result[:, idx:idx+self.grid_sample_batch] += gt_aligned_batch + else: + gt_aligned = F.grid_sample(gt_patches, affine_grid_gt, mode="bilinear", align_corners=False) + if result is None: + result = 0 + result += gt_aligned + + within_bounds_x = (affine_grid_gt[..., 0] >= -1) & (affine_grid_gt[..., 0] <= 1) + within_bounds_y = (affine_grid_gt[..., 1] >= -1) & (affine_grid_gt[..., 1] <= 1) + not_padded_mask = within_bounds_x & within_bounds_y + count += not_padded_mask.unsqueeze(1) + + count[count == 0] = 1 + return [result.div_(count)] + + +class MaskRandScaleCrop(object): + def __init__(self, scale_from): + self.scale_from = scale_from + + def get_params(self, h, w): + new_scale = random.uniform(self.scale_from, 1) + new_h = int(new_scale * h) + new_w = int(new_scale * w) + i = random.randint(0, h - new_h) + j = random.randint(0, w - new_w) + flip = 1 if random.random() > 0.5 else -1 + return i, j, new_h, new_w, new_scale, flip + + def __call__(self, images, affine=None): + if affine is None: + affine = [[0., 0., 0., 1., 1.] for _ in range(len(images))] + _, H, W = images[0].shape + i2 = H / 2 + j2 = W / 2 + for k, image in enumerate(images): + ii, jj, h, w, s, flip = self.get_params(H, W) + if s == 1.: + continue # no change in scale + # displacement of the centre + dy = ii + h / 2 - i2 + dx = jj + w / 2 - j2 + affine[k][0] = dy + affine[k][1] = dx + affine[k][3] = 1 / s + # affine[k][4] = flip + + assert ii >= 0 and jj >= 0 + + image_crop = tf.functional.crop(image, ii, jj, h, w) + images[k] = tf.functional.resize(image_crop, (H, W), tf.InterpolationMode.BILINEAR) + + return images, affine diff --git a/scenedino/models/backbones/dino/visualization.py b/scenedino/models/backbones/dino/visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..0e35af0fa2a3bd5244cfc2742b81ede085335f59 --- /dev/null +++ b/scenedino/models/backbones/dino/visualization.py @@ -0,0 +1,153 @@ +# from pykeops.torch import LazyTensor +from typing import Tuple + +import matplotlib.pyplot as plt +import torch +from torch import nn, Tensor + + +class VisualizationModule(nn.Module): + def __init__(self, in_channels, reduce_images=3): + super().__init__() + self.batch_rgb_mean = torch.zeros(in_channels) + self.batch_rgb_comp = torch.eye(in_channels, 3) + self.reduce_images = reduce_images + self.fitted_pca = False + + self.n_kmeans_clusters = 8 + self.kmeans_cluster_centers = torch.zeros(self.n_kmeans_clusters, in_channels) + self.cmap_kmeans = plt.get_cmap("tab10") + + def fit_pca(self, batch_features, refit): + if batch_features.dim() > 2: + raise ValueError(f"Wrong dims for PCA: {batch_features.shape}") + if not self.fitted_pca or refit: + # filter nan values + batch_features = batch_features[~torch.isnan(batch_features).any(dim=1)] + self._pca_fast(batch_features, num_components=3*self.reduce_images) + self.fitted_pca = True + + def transform_pca(self, features, norm, from_dim): + features = features - self.batch_rgb_mean + if norm: + features = features / torch.linalg.norm(features, dim=-1, keepdim=True) + return features @ self.batch_rgb_comp[..., from_dim:from_dim+3] + + def _pca_fast(self, data: Tensor, num_components: int = 3) -> Tuple[Tensor, Tensor]: + """Function implements PCA using PyTorch fast low-rank approximation. + + Args: + data (Tensor): Data matrix of the shape [N, C] or [B, N, C]. + num_components (int): Number of principal components to be used. + + Returns: + data_pca (Tensor): Transformed low-dimensional data of the shape [N, num_components] or [B, N, num_components]. + pca_components (Tensor): Principal components of the shape [num_components, C] or [B, num_components, C]. + """ + # Normalize data + data_mean = data.mean(dim=-2, keepdim=True) + data_normalize = (data - data_mean) / (data.std(dim=-2, keepdim=True) + 1e-08) + # Perform fast low-rank PCA + u, _, v = torch.pca_lowrank(data_normalize, q=max(num_components, 6), niter=2, center=True) + v = v.transpose(-1, -2) + # Perform SVD flip + u, v = self._svd_flip(u, v) # type: Tensor, Tensor + # Transpose PCA components to match scikit-learn + if data_normalize.ndim == 2: + pca_components = v[:num_components] + else: + pca_components = v[:, :num_components] + + self.batch_rgb_mean = data_mean + self.batch_rgb_comp = pca_components.transpose(-1, -2) + + def _svd_flip(self, u: Tensor, v: Tensor) -> Tuple[Tensor, Tensor]: + """Perform SVD flip to solve sign issue of SVD. + + Args: + u (Tensor): u matrix of the shape [N, C] or [B, N, C]. + v (Tensor): v matrix of the shape [C, C] or [B, C, C]. + + Returns: + u (Tensor): Fixed u matrix of the shape [N, C] or [B, N, C]. + v (Tensor): Fixed v matrix of the shape [C, C] or [B, C, C]. + """ + max_abs: Tensor = torch.abs(u).argmax(dim=-2) + indexes: Tensor = torch.arange(u.shape[-1], device=u.device) + if u.ndim == 2: + signs: Tensor = torch.sign(u[max_abs, indexes]) + u = u * signs + v = v * signs.unsqueeze(dim=-1) + else: + # Maybe fix looping the future... + signs = torch.stack( + [torch.sign(u[batch_index, max_abs[batch_index], indexes]) for batch_index in range(u.shape[0])], dim=0 + ) + u = u * signs.unsqueeze(dim=1) + v = v * signs.unsqueeze(dim=-1) + return u, v + + def old_fit_transform_kmeans_batch(self, batch_features, subsample_size=20000): + feats_map_flattened = batch_features.flatten(1, -2) + from torch_kmeans import KMeans, CosineSimilarity + kmeans_engine = KMeans(n_clusters=self.n_kmeans_clusters, distance=CosineSimilarity) + + n = feats_map_flattened.size(1) + if subsample_size is not None and subsample_size < n: + indices = torch.randperm(n)[:subsample_size] + feats_map_subsampled = feats_map_flattened[:, indices] + kmeans_engine.fit(feats_map_subsampled) + else: + kmeans_engine.fit(feats_map_flattened) + + labels = kmeans_engine.predict(feats_map_flattened) + labels = labels.reshape(batch_features.shape[:-1]).float().cpu().numpy() + + label_map = self.cmap_kmeans(labels / (self.n_kmeans_clusters - 1))[..., :3] + label_map = torch.Tensor(label_map).squeeze(-2) + + return label_map + + def fit_transform_kmeans_batch(self, batch_features): + feats_map_flattened = batch_features.flatten(0, -2) + + with torch.no_grad(): + cl, c = self._KMeans_cosine(feats_map_flattened.float(), K=self.n_kmeans_clusters) + self.kmeans_cluster_centers = c + + labels = cl.reshape(batch_features.shape[:-1]).float().cpu().numpy() + + label_map = self.cmap_kmeans(labels / (self.n_kmeans_clusters - 1))[..., :3] + label_map = torch.Tensor(label_map).squeeze(-2) + + return label_map + + def _KMeans_cosine(self, x, K=19, Niter=100): + """Implements Lloyd's algorithm for the Cosine similarity metric.""" + N, D = x.shape # Number of samples, dimension of the ambient space + + c = x[:K, :].clone() # Simplistic initialization for the centroids + # Normalize the centroids for the cosine similarity: + c[:] = torch.nn.functional.normalize(c, dim=1, p=2) + + x_i = LazyTensor(x.view(N, 1, D)) # (N, 1, D) samples + c_j = LazyTensor(c.view(1, K, D)) # (1, K, D) centroids + + # K-means loop: + # - x is the (N, D) point cloud, + # - cl is the (N,) vector of class labels + # - c is the (K, D) cloud of cluster centroids + for i in range(Niter): + # E step: assign points to the closest cluster ------------------------- + S_ij = x_i | c_j # (N, K) symbolic Gram matrix of dot products + cl = S_ij.argmax(dim=1).long().view(-1) # Points -> Nearest cluster + + # M step: update the centroids to the normalized cluster average: ------ + # Compute the sum of points per cluster: + c.zero_() + c.scatter_add_(0, cl[:, None].repeat(1, D), x) + + # Normalize the centroids, in place: + c[:] = torch.nn.functional.normalize(c, dim=1, p=2) + + return cl, c \ No newline at end of file diff --git a/scenedino/models/backbones/dino/vit.py b/scenedino/models/backbones/dino/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..45dc192624ac83b95b0ca1ac5ff7694cef57505a --- /dev/null +++ b/scenedino/models/backbones/dino/vit.py @@ -0,0 +1,590 @@ +import os +import pathlib +from typing import Dict, Tuple, List + +import timm +import torch +import torch.nn as nn +import torch.nn.functional as F +from timm.models.vision_transformer import VisionTransformer +from torch import Tensor +from torch.fx import GraphModule +from torchvision.models.feature_extraction import create_feature_extractor + +# from ups.utils import normalize + +__all__: Tuple[str, ...] = ( + "dino_small", + "dino_base", + "dinov2_small", + "dinov2_base", + "dino_reg_small", + "dino_reg_base", + "i_jepa_huge", + "mae_base", + "self_patch_small", + "synclr_base", + "mocov3_base", + "msn_base", + "vmae_large", +) + + +def _disable_fused_attention(model: nn.Module) -> None: + """Function disables the use of fused attention in Timm's ViT models. (Don't use anywhere else!) + + Args: + model (nn.Module): Timm ViT model. + """ + # Get ViT depth + depth: int = len(model.blocks) # type: ignore + # Disable fused attention for last block + for name, module in model.named_modules(): + if "Attention" in str(type(module)): + if str(depth - 1) in name: + module.fused_attn = False # type: ignore + + +def _load_vit(name: str, image_size: Tuple[int, int] = (224, 224), depth: int = 12) -> nn.Module: + """Function to load ViT models from Timm. + + Args: + name (str): Timm name of the model. + depth (int): Depth of the model. + + Returns: + model (nn.Module): ViT model as a nn.Module. + """ + # Load model + model: nn.Module = timm.create_model(name, pretrained=True, img_size=image_size, num_classes=0) + # Force not to use fused attention to access attention maps + # _disable_fused_attention(model) + return model + + +def _interpolate_positional_embeddings( + positional_embeddings: Tensor, + original_image_size: Tuple[int, int], + target_image_size: Tuple[int, int], + patch_size: int, + num_additional_tokens: int = 1, +) -> Tensor: + """Function interpolates positional embeddings to a different image size. + + Args: + positional_embeddings (Tensor): Positional embeddings of the sape [1, N, C]. + original_image_size (Tuple[int, int]): Original image size as a tuple. + target_image_size (Tuple[int, int]): Target image size as a tuple. + patch_size (int): Utilize patch size. + num_additional_tokens (int): Number of additional tokens used. Default 1 (class token). + + Returns: + positional_embeddings_interpolated (Tensor): Interpolated positional embeddings [1, N_new, C]. + """ + # Get positional embeddings for image + if num_additional_tokens > 0: + positional_embeddings_add_tokens: Tensor = positional_embeddings[:, :num_additional_tokens] + positional_embeddings_image: Tensor = positional_embeddings[:, num_additional_tokens:] + else: + positional_embeddings_image = positional_embeddings + # Reshape embeddings to 2D + positional_embeddings_image = positional_embeddings_image.view( + 1, original_image_size[0] // patch_size, original_image_size[1] // patch_size, -1 + ) + # Interpolate positional embeddings + positional_embeddings_image = F.interpolate( + positional_embeddings_image.permute(0, 3, 1, 2), + size=(target_image_size[0] // patch_size, target_image_size[1] // patch_size), + mode="bicubic", + align_corners=False, + antialias=False, + ).permute(0, 2, 3, 1) + # Stack positional embeddings again + if num_additional_tokens > 0: + positional_embeddings_interpolated: Tensor = torch.cat( + (positional_embeddings_add_tokens, positional_embeddings_image.flatten(1, 2)), dim=1 + ) + else: + positional_embeddings_interpolated = positional_embeddings_image.flatten(1, 2) + return positional_embeddings_interpolated + + +class _ViT(nn.Module): + """This class wraps Timm's ViT's and always ensures eval mode.""" + + def __init__( + self, + vit: nn.Module, + patch_size: int, + registers: bool = False, + class_token: bool = False, + intermediate_features: List[int] = None, + ) -> None: + """Constructor method. + + Args: + vit (nn.Module): Timm ViT model. + patch_size (int): Patch size utilized. + registers (bool): Set to true if registers are use. Default False. + class_token (bool): Set true if class token is use. Default False. + """ + # Call super constructor + super(_ViT, self).__init__() + # Save parameter + self.patch_size: int = patch_size + self.registers: bool = registers + self.class_token: bool = class_token + # Get ViT depth + depth: int = len(vit.blocks) # type: ignore + + return_nodes = { + #f"blocks.{depth - 1}.attn.softmax": "attention_maps", + "norm": "features_normalized", + f"blocks.{depth - 1}.attn.getitem_4": "key_features", + } + if intermediate_features is not None: + for idx, feat in enumerate(intermediate_features): + return_nodes[f"blocks.{feat}"] = f"intermediate_features.{idx}" + + # Make FX graph module for feature extraction + self.vit: GraphModule = create_feature_extractor(vit, return_nodes) + + def forward(self, images: Tensor) -> Dict[str, Tensor]: + """Forward pass. + + Notes: + attention_maps have the shape of [B, num heads, N, N]. + features have the shape of [B, N, C] + Class token and register tokens omitted! + + Args: + images (Tensor): Images (w/ pix. range of [0, 1]) of the shape [B, 3, H, W]. + + Returns: + output_dict (Dict[str, Tensor]): Dict of features ("attention_maps" and "features"). + """ + # Ensure model is in eval mode + self.vit.eval() + # Normalize images + images_normalized: Tensor = images # normalize(images) + # Perform forward pass + output_dict: Dict[str, Tensor] = self.vit(images_normalized) + # Omit class token (and registers) from attention maps and features + if self.registers: + output_dict["features_normalized"] = output_dict["features_normalized"][:, 5:] + #output_dict["attention_maps"] = output_dict["attention_maps"][..., 5:, 5:] + output_dict["key_features"] = output_dict["key_features"][:, :, 5:] + for output_key in output_dict: + if output_key.startswith('intermediate_features'): + output_dict[output_key] = output_dict[output_key][:, 5:] + elif self.class_token: + output_dict["features_normalized"] = output_dict["features_normalized"][:, 1:] + #output_dict["attention_maps"] = output_dict["attention_maps"][..., 1:, 1:] + output_dict["key_features"] = output_dict["key_features"][:, :, 1:] + for output_key in output_dict: + if output_key.startswith('intermediate_features'): + output_dict[output_key] = output_dict[output_key][:, 1:] + # Normalize features + output_dict["features_normalized"] = F.normalize(output_dict["features_normalized"], p=2, dim=2) # [B, N, C] + return output_dict + + +def mae_base(image_size: Tuple[int, int] = (224, 224)) -> nn.Module: + """Builds the pre-trained MAE base model (patch size is 16 x 16). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): ViT MAE model as a nn.Module. + """ + return _ViT( + _load_vit(name="vit_base_patch16_224.mae", image_size=image_size), + patch_size=16, + class_token=True, + ) + + +def vmae_large(image_size: Tuple[int, int] = (224, 224)) -> nn.Module: + """Builds the pre-trained video MAE large model (patch size is 16 x 16). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): ViT MAE model as a nn.Module. + """ + # Init model + model = VisionTransformer( + img_size=image_size, patch_size=16, num_classes=0, qkv_bias=True, embed_dim=1024, depth=24, num_heads=16 + ) + # Force not to use fused attention to access attention maps + _disable_fused_attention(model) + # Load and adapt checkpoint + checkpoint: Dict[str, Tensor] = torch.load( + os.path.join(pathlib.Path(__file__).parent.resolve(), "checkpoints/mae_pretrain_vit_large_k700.pth"), + map_location="cpu", + )["model_state"] + checkpoint["pos_embed"] = checkpoint["pos_embed_spatial"] + checkpoint["pos_embed_temporal"].mean( + dim=1, keepdim=True + ) + checkpoint["pos_embed"] = torch.cat((checkpoint["pos_embed_class"], checkpoint["pos_embed"]), dim=1) + checkpoint["patch_embed.proj.weight"] = checkpoint["patch_embed.proj.weight"][:, :, 0] + for layer in range(24): + checkpoint[f"blocks.{layer}.attn.qkv.weight"] = torch.cat( + ( + checkpoint[f"blocks.{layer}.attn.q.weight"], + checkpoint[f"blocks.{layer}.attn.k.weight"], + checkpoint[f"blocks.{layer}.attn.v.weight"], + ), + dim=0, + ) + checkpoint[f"blocks.{layer}.attn.qkv.bias"] = torch.cat( + ( + checkpoint[f"blocks.{layer}.attn.q.bias"], + checkpoint[f"blocks.{layer}.attn.k.bias"], + checkpoint[f"blocks.{layer}.attn.v.bias"], + ), + dim=0, + ) + checkpoint = {key: value for key, value in checkpoint.items() if key in model.state_dict().keys()} + # Interpolated positional embeddings + checkpoint["pos_embed"] = _interpolate_positional_embeddings( + checkpoint["pos_embed"], + original_image_size=(224, 224), + target_image_size=image_size, + patch_size=16, + num_additional_tokens=1, + ) + # Load checkpoint + model.load_state_dict(checkpoint) + return _ViT(model, patch_size=16, class_token=True) + + +def dino_small(image_size: Tuple[int, int] = (224, 224), intermediate_features: List[int] = None) -> nn.Module: + """Builds the pre-trained ViT DINO small model (patch size is 16 x 16). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): ViT Dino model as a nn.Module. + """ + return _ViT( + _load_vit(name="vit_small_patch16_224.dino", image_size=image_size), + patch_size=16, + class_token=True, + intermediate_features=intermediate_features, + ) + + +def dino_small8(image_size: Tuple[int, int] = (224, 224), intermediate_features: List[int] = None) -> nn.Module: + """Builds the pre-trained ViT DINO small model (patch size is 8 x 8). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): ViT Dino model as a nn.Module. + """ + return _ViT( + _load_vit(name="vit_small_patch8_224.dino", image_size=image_size), + patch_size=8, + class_token=True, + intermediate_features=intermediate_features, + ) + + +def dino_base(image_size: Tuple[int, int] = (224, 224), intermediate_features: List[int] = None) -> nn.Module: + """Builds the pre-trained ViT DINO base model (patch size is 16 x 16). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): ViT Dino model as a nn.Module. + """ + return _ViT( + _load_vit(name="vit_base_patch16_224.dino", image_size=image_size), + patch_size=16, + class_token=True, + intermediate_features=intermediate_features, + ) + + +def dino_base8(image_size: Tuple[int, int] = (224, 224), intermediate_features: List[int] = None) -> nn.Module: + """Builds the pre-trained ViT DINO base model (patch size is 16 x 16). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): ViT Dino model as a nn.Module. + """ + return _ViT( + _load_vit(name="vit_base_patch8_224.dino", image_size=image_size), + patch_size=8, + class_token=True, + intermediate_features=intermediate_features, + ) + + +def dinov2_small(image_size: Tuple[int, int] = (224, 224), intermediate_features: List[int] = None) -> nn.Module: + """Builds the pre-trained ViT DINO V2 small model (patch size is 14 x 14). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + intermediate_features (List[int]): Index of intermediate layer features to return. + + Returns: + model (nn.Module): ViT Dino model as a nn.Module. + """ + return _ViT( + _load_vit(name="vit_small_patch14_dinov2.lvd142m", image_size=image_size), + patch_size=14, + class_token=True, + intermediate_features=intermediate_features, + ) + + +def dinov2_base(image_size: Tuple[int, int] = (224, 224), intermediate_features: List[int] = None) -> nn.Module: + """Builds the pre-trained ViT DINO V2 base model (patch size is 14 x 14). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + intermediate_features (List[int]): Index of intermediate layer features to return. + + Returns: + model (nn.Module): ViT Dino model as a nn.Module. + """ + return _ViT( + _load_vit(name="vit_base_patch14_dinov2.lvd142m", image_size=image_size), + patch_size=14, + class_token=True, + intermediate_features=intermediate_features, + ) + + +def dino_reg_small(image_size: Tuple[int, int] = (224, 224), intermediate_features: List[int] = None) -> nn.Module: + """Builds the pre-trained ViT DINO (w/ registers) small model (patch size is 14 x 14). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + intermediate_features (List[int]): Index of intermediate layer features to return. + + Returns: + model (nn.Module): ViT Dino model as a nn.Module. + """ + return _ViT( + _load_vit(name="vit_small_patch14_reg4_dinov2.lvd142m", image_size=image_size), + patch_size=14, + registers=True, + intermediate_features=intermediate_features, + ) + + +def dino_reg_base(image_size: Tuple[int, int] = (224, 224), intermediate_features: List[int] = None) -> nn.Module: + """Builds the pre-trained ViT DINO (w/ registers) base model (patch size is 14 x 14). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + intermediate_features (List[int]): Index of intermediate layer features to return. + + Returns: + model (nn.Module): ViT Dino model as a nn.Module. + """ + return _ViT( + _load_vit(name="vit_base_patch14_reg4_dinov2.lvd142m", image_size=image_size), + patch_size=14, + registers=True, + intermediate_features=intermediate_features, + + ) + + +def synclr_base(image_size: Tuple[int, int] = (224, 224)) -> nn.Module: + """Builds the pre-trained SynCLR ViT base model (patch size is 16 x 16). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): SynCLR ViT model as a nn.Module + """ + # Init model + model = _load_vit(name="vit_base_patch16_224", image_size=image_size) + # Load and adapt checkpoint + checkpoint: Dict[str, Tensor] = torch.load( + os.path.join(pathlib.Path(__file__).parent.resolve(), "checkpoints/synclr_vit_b_16.pth") + )["model"] + checkpoint = {key.replace("module.visual.", ""): value for key, value in checkpoint.items()} + # Interpolated positional embeddings + if image_size != (224, 224): + checkpoint["pos_embed"] = _interpolate_positional_embeddings( + checkpoint["pos_embed"], + original_image_size=(224, 224), + target_image_size=image_size, + patch_size=16, + num_additional_tokens=1, + ) + # Load checkpoint + model.load_state_dict(checkpoint) + return _ViT(model, patch_size=16, class_token=True) + + +def mocov3_base(image_size: Tuple[int, int] = (224, 224)) -> nn.Module: + """Builds the pre-trained MoCo-V3 ViT base model (patch size is 16 x 16). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): MoCo-V3 ViT model as a nn.Module + """ + # Init model + model = _load_vit(name="vit_base_patch16_224", image_size=image_size) + # Load and adapt checkpoint + checkpoint: Dict[str, Tensor] = torch.load( + os.path.join(pathlib.Path(__file__).parent.resolve(), "checkpoints/vit-b-300ep.pth.tar") + )["state_dict"] + checkpoint = { + key.replace("module.momentum_encoder.", ""): value + for key, value in checkpoint.items() + if ("module.momentum_encoder." in key) and ("head." not in key) + } + # Interpolated positional embeddings + if image_size != (224, 224): + checkpoint["pos_embed"] = _interpolate_positional_embeddings( + checkpoint["pos_embed"], + original_image_size=(224, 224), + target_image_size=image_size, + patch_size=16, + num_additional_tokens=1, + ) + # Load checkpoint + model.load_state_dict(checkpoint) + return _ViT(model, patch_size=16, class_token=True) + + +def msn_base(image_size: Tuple[int, int] = (224, 224)) -> nn.Module: + """Builds the pre-trained MSN ViT base model (patch size is 16 x 16). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): MSN ViT model as a nn.Module + """ + # Init model + model = _load_vit(name="vit_base_patch16_224", image_size=image_size) + # Load and adapt checkpoint + checkpoint: Dict[str, Tensor] = torch.load( + os.path.join(pathlib.Path(__file__).parent.resolve(), "checkpoints/vitb16_600ep.pth.tar") + )["target_encoder"] + checkpoint = { + key.replace("module.", ""): value + for key, value in checkpoint.items() + if key.replace("module.", "") in model.state_dict().keys() + } + # Interpolated positional embeddings + if image_size != (224, 224): + checkpoint["pos_embed"] = _interpolate_positional_embeddings( + checkpoint["pos_embed"], + original_image_size=(224, 224), + target_image_size=image_size, + patch_size=16, + num_additional_tokens=1, + ) + # Load checkpoint + model.load_state_dict(checkpoint) + return _ViT(model, patch_size=16, class_token=True) + + +def self_patch_small(image_size: Tuple[int, int] = (224, 224)) -> nn.Module: + """Builds the pre-trained Self-Patch ViT small model (patch size is 16 x 16). + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): Self-Patch ViT model as a nn.Module + """ + # Init model + model = VisionTransformer( + img_size=image_size, + patch_size=16, + num_classes=0, + class_token=False, + qkv_bias=True, + global_pool="avg", + embed_dim=384, + depth=12, + num_heads=6, + ) + # Force not to use fused attention to access attention maps + _disable_fused_attention(model) + # Load and adapt checkpoint + checkpoint: Dict[str, Tensor] = torch.load( + os.path.join(pathlib.Path(__file__).parent.resolve(), "checkpoints/dino_selfpatch.pth") + ) + checkpoint = { + key.replace("module.", ""): value for key, value in checkpoint.items() if key in model.state_dict().keys() + } + # Interpolated positional embeddings + if image_size != (224, 224): + checkpoint["pos_embed"] = _interpolate_positional_embeddings( + checkpoint["pos_embed"], + original_image_size=(224, 224), + target_image_size=image_size, + patch_size=16, + num_additional_tokens=0, + ) + # Load checkpoint + model.load_state_dict(checkpoint, strict=False) + return _ViT(model, patch_size=16, class_token=False, registers=False) + + +def i_jepa_huge(image_size: Tuple[int, int] = (224, 224)) -> nn.Module: + """Builds the pre-trained I-JEPA ViT huge model (patch size is 14 x 14). + + Notes: + ViT huge is very large... + + Args: + image_size (Tuple[int, int]): Image size to be used. Default is (224, 224). + + Returns: + model (nn.Module): SynCLR ViT model as a nn.Module + """ + # Init model + model = VisionTransformer( + img_size=image_size, + patch_size=14, + num_classes=0, + class_token=False, + global_pool="avg", + qkv_bias=True, + embed_dim=1280, + depth=32, + num_heads=16, + ) + # Force not to use fused attention to access attention maps + _disable_fused_attention(model) + # Load and adapt checkpoint + checkpoint: Dict[str, Tensor] = torch.load( + os.path.join(pathlib.Path(__file__).parent.resolve(), "checkpoints/IN22k-vit.h.14-900e.pth.tar"), + map_location="cpu", + )["encoder"] + checkpoint = {key.replace("module.", ""): value for key, value in checkpoint.items()} + # Interpolated positional embeddings + if image_size != (224, 224): + checkpoint["pos_embed"] = _interpolate_positional_embeddings( + checkpoint["pos_embed"], + original_image_size=(224, 224), + target_image_size=image_size, + patch_size=14, + num_additional_tokens=0, + ) + # Load checkpoint + model.load_state_dict(checkpoint, strict=False) + return _ViT(model, patch_size=14, class_token=False, registers=False) diff --git a/scenedino/models/backbones/ibrnet.py b/scenedino/models/backbones/ibrnet.py new file mode 100644 index 0000000000000000000000000000000000000000..8fe6fb7b9e52cd7fbf4124c5037a65ceeeaf9443 --- /dev/null +++ b/scenedino/models/backbones/ibrnet.py @@ -0,0 +1,403 @@ +import numpy as np +import torch.nn.functional as F +import torch.nn as nn +import torch + +import models.common.model.multi_view_head as DFT + + +class ScaledDotProductAttention(nn.Module): + """Scaled Dot-Product Attention""" + + def __init__(self, temperature, attn_dropout=0.1): + super().__init__() + self.temperature = temperature + # self.dropout = nn.Dropout(attn_dropout) + + def forward(self, q, k, v, mask=None): + attn = torch.matmul(q / self.temperature, k.transpose(2, 3)) + + if mask is not None: + attn = attn.masked_fill(mask == 0, -1e9) + # attn = attn * mask + + attn = F.softmax(attn, dim=-1) + # attn = self.dropout(F.softmax(attn, dim=-1)) + output = torch.matmul(attn, v) + + return output, attn + + +class PositionwiseFeedForward(nn.Module): + """A two-feed-forward-layer module""" + + def __init__(self, d_in, d_hid, dropout=0.1): + super().__init__() + self.w_1 = nn.Linear(d_in, d_hid) # position-wise + self.w_2 = nn.Linear(d_hid, d_in) # position-wise + self.layer_norm = nn.LayerNorm(d_in, eps=1e-6) + # self.dropout = nn.Dropout(dropout) + + def forward(self, x): + residual = x + + x = self.w_2(F.relu(self.w_1(x))) + # x = self.dropout(x) + x += residual + + x = self.layer_norm(x) + + return x + + +class MultiHeadAttention(nn.Module): + """Multi-Head Attention module""" + + def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): + super().__init__() + + self.n_head = n_head + self.d_k = d_k + self.d_v = d_v + + self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False) + self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False) + self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False) + self.fc = nn.Linear(n_head * d_v, d_model, bias=False) + + self.attention = ScaledDotProductAttention(temperature=d_k**0.5) + + # self.dropout = nn.Dropout(dropout) + self.layer_norm = nn.LayerNorm(d_model, eps=1e-6) + + def forward(self, q, k, v, mask=None): + d_k, d_v, n_head = self.d_k, self.d_v, self.n_head + sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1) + + residual = q + + # Pass through the pre-attention projection: b x lq x (n*dv) + # Separate different heads: b x lq x n x dv + q = self.w_qs(q).view(sz_b, len_q, n_head, d_k) + k = self.w_ks(k).view(sz_b, len_k, n_head, d_k) + v = self.w_vs(v).view(sz_b, len_v, n_head, d_v) + + # Transpose for attention dot product: b x n x lq x dv + q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) + + if mask is not None: + mask = mask.unsqueeze(1) # For head axis broadcasting. + + q, attn = self.attention(q, k, v, mask=mask) + + # Transpose to move the head dimension back: b x lq x n x dv + # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv) + q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1) + # q = self.dropout(self.fc(q)) + q = self.fc(q) + q += residual + + q = self.layer_norm(q) + + return q, attn + + +# default tensorflow initialization of linear layers +def weights_init(m): + if isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight.data) + if m.bias is not None: + nn.init.zeros_(m.bias.data) + + +@torch.jit.script +def fused_mean_variance(x, weight): + mean = torch.sum(x * weight, dim=2, keepdim=True) + var = torch.sum(weight * (x - mean) ** 2, dim=2, keepdim=True) + return mean, var + + + +# class IBRNet(nn.Module): +# def __init__(self, in_feat_ch=32, n_samples=64, **kwargs): +# super(IBRNet, self).__init__() +# # self.args = args +# self.anti_alias_pooling = False +# if self.anti_alias_pooling: +# self.s = nn.Parameter(torch.tensor(0.2), requires_grad=True) +# activation_func = nn.ELU(inplace=True) +# self.n_samples = n_samples +# self.ray_dir_fc = nn.Sequential(nn.Linear(4, 16), +# activation_func, +# nn.Linear(16, in_feat_ch + 3), +# activation_func) + +# self.base_fc = nn.Sequential(nn.Linear((in_feat_ch+3)*3, 64), +# activation_func, +# nn.Linear(64, 32), +# activation_func) + +# self.vis_fc = nn.Sequential(nn.Linear(32, 32), +# activation_func, +# nn.Linear(32, 33), +# activation_func, +# ) + +# self.vis_fc2 = nn.Sequential(nn.Linear(32, 32), +# activation_func, +# nn.Linear(32, 1), +# nn.Sigmoid() +# ) + +# self.geometry_fc = nn.Sequential(nn.Linear(32*2+1, 64), +# activation_func, +# nn.Linear(64, 16), +# activation_func) + +# self.ray_attention = MultiHeadAttention(4, 16, 4, 4) +# self.out_geometry_fc = nn.Sequential(nn.Linear(16, 16), +# activation_func, +# nn.Linear(16, 1), +# nn.ReLU()) + +# self.rgb_fc = nn.Sequential(nn.Linear(32+1+4, 16), +# activation_func, +# nn.Linear(16, 8), +# activation_func, +# nn.Linear(8, 1)) + +# self.pos_encoding = self.posenc(d_hid=16, n_samples=self.n_samples) + +# self.base_fc.apply(weights_init) +# self.vis_fc2.apply(weights_init) +# self.vis_fc.apply(weights_init) +# self.geometry_fc.apply(weights_init) +# self.rgb_fc.apply(weights_init) + +# def posenc(self, d_hid, n_samples): + +# def get_position_angle_vec(position): +# return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] + +# sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_samples)]) +# sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i +# sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 +# sinusoid_table = torch.from_numpy(sinusoid_table).to("cuda:{}".format(self.args.local_rank)).float().unsqueeze(0) +# return sinusoid_table + +# def forward(self, rgb_feat, ray_diff, mask): +# ''' +# :param rgb_feat: featumre map from encoder from BTS. rgbs and image features [n_rays, n_samples, n_views, n_feat] +# :param ray_diff: ray direction difference [n_rays, n_samples, n_views, 4], first 3 channels are directions, +# last channel is inner product +# :param mask: mask for whether each projection is valid or not. [n_rays, n_samples, n_views, 1] +# :return: rgb and density output, [n_rays, n_samples, 4] +# ''' + +# num_views = rgb_feat.shape[2] +# direction_feat = self.ray_dir_fc(ray_diff) +# rgb_in = rgb_feat[..., :3] +# rgb_feat = rgb_feat + direction_feat ## element-wise summation +# if self.anti_alias_pooling: +# _, dot_prod = torch.split(ray_diff, [3, 1], dim=-1) +# exp_dot_prod = torch.exp(torch.abs(self.s) * (dot_prod - 1)) +# weight = (exp_dot_prod - torch.min(exp_dot_prod, dim=2, keepdim=True)[0]) * mask +# weight = weight / (torch.sum(weight, dim=2, keepdim=True) + 1e-8) # means it will trust the one more with more consistent view point +# else: ## from view dirs +# weight = mask / (torch.sum(mask, dim=2, keepdim=True) + 1e-8) + +# # compute mean and variance across different views for each point +# mean, var = fused_mean_variance(rgb_feat, weight) # [n_rays, n_samples, 1, n_feat] +# globalfeat = torch.cat([mean, var], dim=-1) # [n_rays, n_samples, 1, 2*n_feat] + +# x = torch.cat([globalfeat.expand(-1, -1, num_views, -1), rgb_feat], dim=-1) # [n_rays, n_samples, n_views, 3*n_feat] +# x = self.base_fc(x) + +# x_vis = self.vis_fc(x * weight) +# x_res, vis = torch.split(x_vis, [x_vis.shape[-1]-1, 1], dim=-1) +# vis = F.sigmoid(vis) * mask +# x = x + x_res +# vis = self.vis_fc2(x * vis) * mask +# weight = vis / (torch.sum(vis, dim=2, keepdim=True) + 1e-8) ## weight vector + +# mean, var = fused_mean_variance(x, weight) +# globalfeat = torch.cat([mean.squeeze(2), var.squeeze(2), weight.mean(dim=2)], dim=-1) # [n_rays, n_samples, 32*2+1] +# globalfeat = self.geometry_fc(globalfeat) # [n_rays, n_samples, 16] +# num_valid_obs = torch.sum(mask, dim=2) +# globalfeat = globalfeat + self.pos_encoding +# globalfeat, _ = self.ray_attention(globalfeat, globalfeat, globalfeat, +# mask=(num_valid_obs > 1).float()) # [n_rays, n_samples, 16] +# sigma = self.out_geometry_fc(globalfeat) # [n_rays, n_samples, 1] +# sigma_out = sigma.masked_fill(num_valid_obs < 1, 0.) # set the sigma of invalid point to zero + +# # rgb computation +# x = torch.cat([x, vis, ray_diff], dim=-1) +# x = self.rgb_fc(x) +# x = x.masked_fill(mask == 0, -1e9) +# blending_weights_valid = F.softmax(x, dim=2) # color blending +# rgb_out = torch.sum(rgb_in*blending_weights_valid, dim=2) +# out = torch.cat([rgb_out, sigma_out], dim=-1) +# return out + + +class IBRNetWithNeuRay(nn.Module): + def __init__( + self, neuray_in_dim=32, in_feat_ch=32, n_samples=64, att_feat=16, d_model=103, rbs=2048, nhead=4, **kwargs + ): + super().__init__() + # self.args = args + self.anti_alias_pooling = False + if self.anti_alias_pooling: + self.s = nn.Parameter(torch.tensor(0.2), requires_grad=True) + activation_func = nn.ELU( + inplace=True + ) ## (+): Mean Outputs Closer to Zero: want activations with mean outputs closer to zero. ## nn.LeakyReLU: (+): faster convergence, When the distribution of the negative values in your dataset is meaningful and shouldn't be discarded. + self.n_samples = n_samples + self.ray_dir_fc = nn.Sequential( + nn.Linear(4, 16), ## defualt: 4 + activation_func, + nn.Linear(16, in_feat_ch), ## default: in_feat_ch + 3 + activation_func, + ) + + self.base_fc = nn.Sequential( + nn.Linear((in_feat_ch) * 5 + neuray_in_dim, 64), ## default: ((in_feat_ch+3)*5+neuray_in_dim, 64) + activation_func, + nn.Linear(64, 32), + activation_func, + ) + + self.vis_fc = nn.Sequential( + nn.Linear(32, 32), + activation_func, + nn.Linear(32, 33), + activation_func, + ) + + self.vis_fc2 = nn.Sequential(nn.Linear(32, 32), activation_func, nn.Linear(32, 1), nn.Sigmoid()) + + self.geometry_fc = nn.Sequential( + nn.Linear(32 * 2 + 1, att_feat * 2), ## default: (32*2+1, 64) + activation_func, + nn.Linear(att_feat * 2, att_feat), + activation_func, + ) + + # self.ray_attention = MultiHeadAttention(nhead, att_feat, 4, 4) ## default: (4, 16, 4, 4) + self.out_geometry_fc = nn.Sequential(nn.Linear(16, 16), activation_func, nn.Linear(16, 1), nn.ReLU()) + + self.rgb_fc = nn.Sequential( + nn.Linear(32 + 1 + 4, 16), activation_func, nn.Linear(16, 8), activation_func, nn.Linear(8, 1) + ) + + self.neuray_fc = nn.Sequential( + nn.Linear( + neuray_in_dim, + 8, + ), + activation_func, + nn.Linear(8, 1), + ) + + self.img_feat2low = nn.Sequential( + nn.Linear(rbs, rbs // 4), ## TODO: replace this hard coded with the flexible + activation_func, + nn.Linear(rbs // 4, d_model), + ) + + # self.pos_encoding = self.posenc(d_hid=16, n_samples=self.n_samples) + + self.base_fc.apply(weights_init) + self.vis_fc2.apply(weights_init) + self.vis_fc.apply(weights_init) + # self.geometry_fc.apply(weights_init) + self.rgb_fc.apply(weights_init) + self.neuray_fc.apply(weights_init) + + # def change_pos_encoding(self,n_samples): + # self.pos_encoding = self.posenc(16, n_samples=n_samples) + + # def posenc(self, d_hid, n_samples): + # def get_position_angle_vec(position): + # return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] + # + # sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_samples)]) + # sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + # sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + # sinusoid_table = torch.from_numpy(sinusoid_table).to("cuda:{}".format(0)).float().unsqueeze(0) + # return sinusoid_table + + def forward(self, rgb_feat, neuray_feat, ray_diff, mask): + """ibrnet dim e.g. [6, 64, 8, 35] + :param rgb_feat: rgbs and image features [n_rays, n_samples, n_views, n_feat] == img_feat + :param neuray_feat: rgbs and image features [n_rays, n_samples, n_views, n_feat] == viz_feat + :param ray_diff: ray direction difference [n_rays, n_samples, n_views, 4], first 3 channels are directions, ## tensor encodes information about how rays in the novel view differ from rays in the source views + last channel is inner product + :param mask: mask for whether each projection is valid or not. [n_rays, n_samples, n_views, 1] + :return: rgb and density output, [n_rays, n_samples, 4] + """ + + ## Assumption: rgb_feat already contains image feature + dir_feat / this can be implemented further + num_views = rgb_feat.shape[2] + direction_feat = self.ray_dir_fc(ray_diff) + # rgb_in = rgb_feat[..., :3] ## no used in both original code and necessary code here + rgb_feat = self.img_feat2low(rgb_feat) + rgb_feat = rgb_feat + direction_feat + + if self.anti_alias_pooling: + _, dot_prod = torch.split(ray_diff, [3, 1], dim=-1) + exp_dot_prod = torch.exp(torch.abs(self.s) * (dot_prod - 1)) + weight = (exp_dot_prod - torch.min(exp_dot_prod, dim=2, keepdim=True)[0]) * mask + weight = weight / ( + torch.sum(weight, dim=2, keepdim=True) + 1e-8 + ) # means it will trust the one more with more consistent view point + else: + weight = mask / (torch.sum(mask, dim=2, keepdim=True) + 1e-8) + + # neuray layer 0 ## == feature aggregation networks (M) above pipeline from fig. 19 + weight0 = torch.sigmoid(self.neuray_fc(neuray_feat)) * weight # [rn,dn,rfn,f] + mean0, var0 = fused_mean_variance(rgb_feat, weight0) # [n_rays, n_samples, 1, n_feat] ## 2nd one + mean1, var1 = fused_mean_variance(rgb_feat, weight) # [n_rays, n_samples, 1, n_feat] ## 1st one + globalfeat = torch.cat([mean0, var0, mean1, var1], dim=-1) # [n_rays, n_samples, 1, 2*n_feat] + + x = torch.cat( + [globalfeat.expand(-1, -1, num_views, -1), rgb_feat, neuray_feat], dim=-1 + ) # [n_rays, n_samples, n_views, 3*n_feat] + x = self.base_fc(x) ## after concat it gives input for net A + + x_vis = self.vis_fc(x * weight) + x_res, vis = torch.split(x_vis, [x_vis.shape[-1] - 1, 1], dim=-1) + vis = F.sigmoid(vis) * mask + x = x + x_res + vis = self.vis_fc2(x * vis) * mask ## above one from Network A from Fig. 19 + weight = vis / ( + torch.sum(vis, dim=2, keepdim=True) + 1e-8 + ) ## normalized: weighed mean and var ## weight == buttom from net A [N, K, 32] + + mean, var = fused_mean_variance(x, weight) + globalfeat = torch.cat( + [mean.squeeze(2), var.squeeze(2), weight.mean(dim=2)], dim=-1 + ) # [n_rays, n_samples, 32*2+1] + globalfeat = self.geometry_fc(globalfeat) # [n_rays, n_samples, att_feat] ## MLP for input transformer + + num_valid_obs = torch.sum(mask, dim=2) + num_valid_obs = num_valid_obs > torch.mean(num_valid_obs, dtype=float) ## making boolean + + # globalfeat = globalfeat + self.pos_encoding + # globalfeat, _ = self.ray_attention(globalfeat, globalfeat, globalfeat, ## This should be replaced by DFT + # mask=(num_valid_obs > 1).float()) # [n_rays, n_samples, 16] + + # sigma = self.out_geometry_fc(globalfeat) # [n_rays, n_samples, 1] + # sigma_out = sigma.masked_fill(num_valid_obs < 1, 0.) # set the sigma of invalid point to zero + # return sigma + + return globalfeat, num_valid_obs ### [M, 64, att_feat], [M, 64, 1] ## Note: gfeat is already masked out above + + # rgb computation + # x = torch.cat([x, vis, ray_diff], dim=-1) + # x = self.rgb_fc(x) + # x = x.masked_fill(mask == 0, -1e9) + # blending_weights_valid = F.softmax(x, dim=2) # color blending + # rgb_out = torch.sum(rgb_in*blending_weights_valid, dim=2) + # out = torch.cat([rgb_out, sigma_out], dim=-1) + # return out diff --git a/scenedino/models/backbones/image_encoder.py b/scenedino/models/backbones/image_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..1c39487904e46c2f9eee7974b10c9bc0990748c1 --- /dev/null +++ b/scenedino/models/backbones/image_encoder.py @@ -0,0 +1,67 @@ +import torch +import torchvision +from torch import nn + + +class ImageEncoder(nn.Module): + """ + Global image encoder + """ + + def __init__(self, backbone="resnet34", pretrained=True, latent_size=128): + """ + :param backbone Backbone network. Assumes it is resnet* + e.g. resnet34 | resnet50 + :param num_layers number of resnet layers to use, 1-5 + :param pretrained Whether to use model pretrained on ImageNet + """ + super().__init__() + self.model = getattr(torchvision.models, backbone)(pretrained=pretrained) + self.model.fc = nn.Sequential() + self.register_buffer("latent", torch.empty(1, 1), persistent=False) + # self.latent (B, L) + self.latent_size = latent_size + if latent_size != 512: + self.fc = nn.Linear(512, latent_size) + + def index(self, uv, cam_z=None, image_size=(), z_bounds=()): + """ + Params ignored (compatibility) + :param uv (B, N, 2) only used for shape + :return latent vector (B, L, N) + """ + return self.latent.unsqueeze(-1).expand(-1, -1, uv.shape[1]) + + def forward(self, x): + """ + For extracting ResNet's features. + :param x image (B, C, H, W) + :return latent (B, latent_size) + """ + x = x.to(device=self.latent.device) + x = self.model.conv1(x) + x = self.model.bn1(x) + x = self.model.relu(x) + + x = self.model.maxpool(x) + x = self.model.layer1(x) + x = self.model.layer2(x) + x = self.model.layer3(x) + x = self.model.layer4(x) + + x = self.model.avgpool(x) + x = torch.flatten(x, 1) + + if self.latent_size != 512: + x = self.fc(x) + + self.latent = x # (B, latent_size) + return self.latent + + @classmethod + def from_conf(cls, conf): + return cls( + conf.get_string("backbone"), + pretrained=conf.get_bool("pretrained", True), + latent_size=conf.get_int("latent_size", 128), + ) diff --git a/scenedino/models/backbones/monodepth2.py b/scenedino/models/backbones/monodepth2.py new file mode 100644 index 0000000000000000000000000000000000000000..3765d2a2bc8776f7c6593d1677174ae81e5b9b05 --- /dev/null +++ b/scenedino/models/backbones/monodepth2.py @@ -0,0 +1,388 @@ +""" +Implements image encoders +""" + +from collections import OrderedDict + +from torch import profiler + +from scenedino.models.prediction_heads.layers import * + +import numpy as np +import torch +import torch.nn as nn + +import torchvision.models as models +import torch.utils.model_zoo as model_zoo + + +# Code taken from https://github.com/nianticlabs/monodepth2 +# +# Godard, Clément, et al. +# "Digging into self-supervised monocular depth estimation." +# Proceedings of the IEEE/CVF international conference on computer vision. +# 2019. + + +class ResNetMultiImageInput(models.ResNet): + """Constructs a resnet model with varying number of input images. + Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py + """ + + def __init__(self, block, layers, num_classes=1000, num_input_images=1): + super(ResNetMultiImageInput, self).__init__(block, layers) + self.inplanes = 64 + self.conv1 = nn.Conv2d( + num_input_images * 3, 64, kernel_size=7, stride=2, padding=3, bias=False + ) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +def resnet_multiimage_input(num_layers, pretrained=False, num_input_images=1): + """Constructs a ResNet model. + Args: + num_layers (int): Number of resnet layers. Must be 18 or 50 + pretrained (bool): If True, returns a model pre-trained on ImageNet + num_input_images (int): Number of frames stacked as input + """ + assert num_layers in [18, 50], "Can only run with 18 or 50 layer resnet" + blocks = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers] + block_type = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[ + num_layers + ] + model = ResNetMultiImageInput(block_type, blocks, num_input_images=num_input_images) + + if pretrained: + loaded = model_zoo.load_url( + models.resnet.model_urls["resnet{}".format(num_layers)] + ) + loaded["conv1.weight"] = ( + torch.cat([loaded["conv1.weight"]] * num_input_images, 1) / num_input_images + ) + model.load_state_dict(loaded) + return model + + +class ResnetEncoder(nn.Module): + """Pytorch module for a resnet encoder""" + + def __init__(self, num_layers, pretrained, num_input_images=1): + super(ResnetEncoder, self).__init__() + + self.num_ch_enc = np.array([64, 64, 128, 256, 512]) + + resnets = { + 18: models.resnet18, + 34: models.resnet34, + 50: models.resnet50, + 101: models.resnet101, + 152: models.resnet152, + } + + weights = { + 18: models.resnet.ResNet18_Weights.IMAGENET1K_V1, + 34: models.resnet.ResNet34_Weights.IMAGENET1K_V1, + 50: models.resnet.ResNet50_Weights.IMAGENET1K_V1, + 101: models.resnet.ResNet101_Weights.IMAGENET1K_V1, + 152: models.resnet.ResNet152_Weights.IMAGENET1K_V1, + } + + if num_layers not in resnets: + raise ValueError( + "{} is not a valid number of resnet layers".format(num_layers) + ) + + if num_input_images > 1: + self.encoder = resnet_multiimage_input( + num_layers, pretrained, num_input_images + ) + else: + # TODO: currently hardcoded make adaptable + + self.encoder = resnets[num_layers]( + weights=weights[num_layers] + ) + + if num_layers > 34: + self.num_ch_enc[1:] *= 4 + + def forward(self, input_image): + self.features = [] + x = (input_image - 0.45) / 0.225 + x = self.encoder.conv1(x) + x = self.encoder.bn1(x) + self.features.append(self.encoder.relu(x)) + self.features.append( + self.encoder.layer1(self.encoder.maxpool(self.features[-1])) + ) + self.features.append(self.encoder.layer2(self.features[-1])) + self.features.append(self.encoder.layer3(self.features[-1])) + self.features.append(self.encoder.layer4(self.features[-1])) + + return self.features + + +class DepthDecoder(nn.Module): + def __init__( + self, num_ch_enc, scales=range(4), num_output_channels=1, use_skips=True + ): + super(DepthDecoder, self).__init__() + + self.num_output_channels = num_output_channels + self.use_skips = use_skips + self.upsample_mode = "nearest" + self.scales = scales + + self.num_ch_enc = num_ch_enc + self.num_ch_dec = np.array([16, 32, 64, 128, 256]) + + # decoder + self.convs = OrderedDict() + for i in range(4, -1, -1): + # upconv_0 + num_ch_in = self.num_ch_enc[-1] if i == 4 else self.num_ch_dec[i + 1] + num_ch_out = self.num_ch_dec[i] + self.convs[("upconv", i, 0)] = ConvBlock(num_ch_in, num_ch_out) + + # upconv_1 + num_ch_in = self.num_ch_dec[i] + if self.use_skips and i > 0: + num_ch_in += self.num_ch_enc[i - 1] + num_ch_out = self.num_ch_dec[i] + self.convs[("upconv", i, 1)] = ConvBlock(num_ch_in, num_ch_out) + + for s in self.scales: + self.convs[("dispconv", s)] = Conv3x3( + self.num_ch_dec[s], self.num_output_channels + ) + + self.decoder_keys = {k: i for i, k in enumerate(self.convs.keys())} + self.decoder = nn.ModuleList(list(self.convs.values())) + self.sigmoid = nn.Sigmoid() + + def forward(self, input_features): + self.outputs = {} + + # decoder + x = input_features[-1] + for i in range(4, -1, -1): + # x = self.convs[("upconv", i, 0)](x) + x = self.decoder[self.decoder_keys[("upconv", i, 0)]](x) + x = [upsample(x)] + if self.use_skips and i > 0: + if x[0].shape[2] > input_features[i - 1].shape[2]: + x[0] = x[0][:, :, : input_features[i - 1].shape[2], :] + if x[0].shape[3] > input_features[i - 1].shape[3]: + x[0] = x[0][:, :, :, : input_features[i - 1].shape[3]] + x += [input_features[i - 1]] + x = torch.cat(x, 1) + # x = self.convs[("upconv", i, 1)](x) + x = self.decoder[self.decoder_keys[("upconv", i, 1)]](x) + + self.outputs[("features", i)] = x + + if i in self.scales: + # self.outputs[("disp", i)] = self.sigmoid(self.convs[("dispconv", i)](x)) + self.outputs[("disp", i)] = self.sigmoid( + self.decoder[self.decoder_keys[("dispconv", i)]](x) + ) + + return self.outputs + + +class Decoder(nn.Module): + def __init__( + self, num_ch_enc, num_ch_dec=None, d_out=1, scales=range(4), use_skips=True, extra_outs=0 + ): + super(Decoder, self).__init__() + + self.use_skips = use_skips + self.upsample_mode = "nearest" + + self.num_ch_enc = num_ch_enc + if num_ch_dec is None: + self.num_ch_dec = np.array([128, 128, 256, 256, 512]) + else: + self.num_ch_dec = num_ch_dec + self.d_out = d_out + self.scales = scales + self.extra_outs = extra_outs + + self.num_ch_dec = [max(self.d_out, chns) for chns in self.num_ch_dec] + + # decoder + self.convs = OrderedDict() + for i in range(4, -1, -1): + # upconv_0 + num_ch_in = self.num_ch_enc[-1] if i == 4 else self.num_ch_dec[i + 1] + num_ch_out = self.num_ch_dec[i] + self.convs[("upconv", i, 0)] = ConvBlock(num_ch_in, num_ch_out) + + # upconv_1 + num_ch_in = self.num_ch_dec[i] + if self.use_skips and i > 0: + num_ch_in += self.num_ch_enc[i - 1] + num_ch_out = self.num_ch_dec[i] + self.convs[("upconv", i, 1)] = ConvBlock(num_ch_in, num_ch_out) + + for s in self.scales: + self.convs[("dispconv", s)] = Conv3x3(self.num_ch_dec[s], self.d_out) + + if self.extra_outs > 0: + for s in self.scales: + self.convs[("extra_outs", s)] = Conv3x3(self.num_ch_dec[s], self.extra_outs) + + self.d_out += self.extra_outs + + self.decoder_keys = {k: i for i, k in enumerate(self.convs.keys())} + self.decoder = nn.ModuleList(list(self.convs.values())) + self.sigmoid = nn.Sigmoid() + + def forward(self, input_features): + with profiler.record_function("encoder_forward"): + self.outputs = {} + + # decoder + x = input_features[-1] + for i in range(4, -1, -1): + x = self.decoder[self.decoder_keys[("upconv", i, 0)]](x) + + x = [F.interpolate(x, scale_factor=(2, 2), mode="nearest")] + + if self.use_skips and i > 0: + feats = input_features[i - 1] + + if x[0].shape[2] > feats.shape[2]: + x[0] = x[0][:, :, : feats.shape[2], :] + if x[0].shape[3] > feats.shape[3]: + x[0] = x[0][:, :, :, : feats.shape[3]] + x += [feats] + x = torch.cat(x, 1) + + x = self.decoder[self.decoder_keys[("upconv", i, 1)]](x) + + self.outputs[("features", i)] = x + + if i in self.scales: + self.outputs[("disp", i)] = self.decoder[ + self.decoder_keys[("dispconv", i)] + ](x) + + if self.extra_outs > 0: + self.outputs[("extra_outs", i)] = self.decoder[ + self.decoder_keys[("extra_outs", i)] + ](x) + + return self.outputs + + +class Monodepth2(nn.Module): + """ + 2D (Spatial/Pixel-aligned/local) image encoder + """ + + def __init__( + self, + resnet_layers=18, + cp_location=None, + freeze=False, + num_ch_dec=None, + d_out=128, + scales=range(4), + pointwise_convs=None, + extra_outs=0 + ): + super().__init__() + + self.encoder = ResnetEncoder(resnet_layers, True, 1) + self.num_ch_enc = self.encoder.num_ch_enc + + self.upsample_mode = "nearest" + self.d_out = d_out + self.extra_outs = extra_outs + self.scales = scales + + if pointwise_convs is None: + decoder_out = self.d_out + else: + decoder_out = pointwise_convs[0] + + # decoder + self.decoder = Decoder( + num_ch_enc=self.num_ch_enc, + d_out=decoder_out, + num_ch_dec=num_ch_dec, + scales=self.scales, + extra_outs=extra_outs, + ) + self.num_ch_dec = self.decoder.num_ch_dec + + # pointwise_convs + if pointwise_convs is None: + self.pointwise_convs = None + else: + pointwise_convs.append(self.d_out) + self.pointwise_convs = nn.Sequential( + *[nn.Sequential( + nn.ReLU(), + nn.Conv2d(pointwise_convs[i], pointwise_convs[i+1], 1, 1, 0), + ) for i in range(len(pointwise_convs)-1)] + ) + + self.latent_size = self.d_out + + if cp_location is not None: + cp = torch.load(cp_location) + self.load_state_dict(cp["model"]) + + if freeze: + for p in self.parameters(True): + p.requires_grad = False + + def forward(self, x): + """ + For extracting ResNet's features. + :param x image (B, C, H, W) + :return latent (B, latent_size, H, W) + """ + with profiler.record_function("encoder_forward"): + x = torch.cat([x * 0.5 + 0.5], dim=1) + image_features = self.encoder(x) ## output encoder from Monodepth2 + outputs = self.decoder(image_features) + + x = [outputs[("disp", i)] for i in self.scales] + + if self.decoder.extra_outs > 0: + x = [torch.cat((x[i], outputs[("extra_outs", i)]), dim=1) for i in self.scales] + + if self.pointwise_convs is not None: + x = [ + self.pointwise_convs(x_) for x_ in x + ] + + return x ## default: x ## note: we need img_feat for feeding into NeuRay + + @classmethod + def from_conf(cls, conf): + return cls( + cp_location=conf.get("cp_location", None), + freeze=conf.get("freeze", False), + num_ch_dec=conf.get("num_ch_dec", None), + d_out=conf.get("d_out", 128), + resnet_layers=conf.get("resnet_layers", 18), + pointwise_convs=conf.get("pointwise_convs", None), + extra_outs=conf.get("extra_outs", 0), + ) diff --git a/scenedino/models/backbones/spatial_encoder.py b/scenedino/models/backbones/spatial_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..deda22d600ff784ee400ab482f37dee493366142 --- /dev/null +++ b/scenedino/models/backbones/spatial_encoder.py @@ -0,0 +1,169 @@ +import torch +import torch.autograd.profiler as profiler +import torch.nn.functional as F +import torchvision +from torch import nn + +from scenedino.common import util + + +class SpatialEncoder(nn.Module): + """ + 2D (Spatial/Pixel-aligned/local) image encoder + """ + + def __init__( + self, + backbone="resnet34", + pretrained=True, + num_layers=4, + index_interp="bilinear", + index_padding="border", + upsample_interp="bilinear", + feature_scale=1.0, + use_first_pool=True, + norm_type="batch", + ): + """ + :param backbone Backbone network. Either custom, in which case + model.custom_encoder.ConvEncoder is used OR resnet18/resnet34, in which case the relevant + model from torchvision is used + :param num_layers number of resnet layers to use, 1-5 + :param pretrained Whether to use model weights pretrained on ImageNet + :param index_interp Interpolation to use for indexing + :param index_padding Padding mode to use for indexing, border | zeros | reflection + :param upsample_interp Interpolation to use for upscaling latent code + :param feature_scale factor to scale all latent by. Useful (<1) if image + is extremely large, to fit in memory. + :param use_first_pool if false, skips first maxpool layer to avoid downscaling image + features too much (ResNet only) + :param norm_type norm type to applied; pretrained model must use batch + """ + super().__init__() + + if norm_type != "batch": + assert not pretrained + + self.use_custom_resnet = backbone == "custom" + self.feature_scale = feature_scale + self.use_first_pool = use_first_pool + norm_layer = util.get_norm_layer(norm_type) + + print("Using torchvision", backbone, "encoder") + self.model = getattr(torchvision.models, backbone)( + pretrained=pretrained, norm_layer=norm_layer + ) + # Following 2 lines need to be uncommented for older configs + self.model.fc = nn.Sequential() + self.model.avgpool = nn.Sequential() + self.latent_size = [0, 64, 128, 256, 512, 1024][num_layers] + + self.num_layers = num_layers + self.index_interp = index_interp + self.index_padding = index_padding + self.upsample_interp = upsample_interp + self.register_buffer("latent", torch.empty(1, 1, 1, 1), persistent=False) + self.register_buffer( + "latent_scaling", torch.empty(2, dtype=torch.float32), persistent=False + ) + self.scales = [0] + # self.latent (B, L, H, W) + + def index(self, uv, cam_z=None, image_size=(), z_bounds=None): + """ + Get pixel-aligned image features at 2D image coordinates + :param uv (B, N, 2) image points (x,y) + :param cam_z ignored (for compatibility) + :param image_size image size, either (width, height) or single int. + if not specified, assumes coords are in [-1, 1] + :param z_bounds ignored (for compatibility) + :return (B, L, N) L is latent size + """ + with profiler.record_function("encoder_index"): + if uv.shape[0] == 1 and self.latent.shape[0] > 1: + uv = uv.expand(self.latent.shape[0], -1, -1) + + with profiler.record_function("encoder_index_pre"): + if len(image_size) > 0: + if len(image_size) == 1: + image_size = (image_size, image_size) + scale = self.latent_scaling / image_size + uv = uv * scale - 1.0 + + uv = uv.unsqueeze(2) # (B, N, 1, 2) + samples = F.grid_sample( + self.latent, + uv, + align_corners=True, + mode=self.index_interp, + padding_mode=self.index_padding, + ) + return samples[:, :, :, 0] # (B, C, N) + + def forward(self, x): + """ + For extracting ResNet's features. + :param x image (B, C, H, W) + :return latent (B, latent_size, H, W) + """ + if self.feature_scale != 1.0: + x = F.interpolate( + x, + scale_factor=self.feature_scale, + mode="bilinear" if self.feature_scale > 1.0 else "area", + align_corners=True if self.feature_scale > 1.0 else None, + recompute_scale_factor=True, + ) + x = x.to(device=self.latent.device) + + if self.use_custom_resnet: + self.latent = self.model(x) + else: + x = self.model.conv1(x) + x = self.model.bn1(x) + x = self.model.relu(x) + + latents = [x] + if self.num_layers > 1: + if self.use_first_pool: + x = self.model.maxpool(x) + x = self.model.layer1(x) + latents.append(x) + if self.num_layers > 2: + x = self.model.layer2(x) + latents.append(x) + if self.num_layers > 3: + x = self.model.layer3(x) + latents.append(x) + if self.num_layers > 4: + x = self.model.layer4(x) + latents.append(x) + + self.latents = latents + align_corners = None if self.index_interp == "nearest " else True + latent_sz = latents[0].shape[-2:] + for i in range(len(latents)): + latents[i] = F.interpolate( + latents[i], + latent_sz, + mode=self.upsample_interp, + align_corners=align_corners, + ) + self.latent = torch.cat(latents, dim=1) + self.latent_scaling[0] = self.latent.shape[-1] + self.latent_scaling[1] = self.latent.shape[-2] + self.latent_scaling = self.latent_scaling / (self.latent_scaling - 1) * 2.0 + return [self.latent] + + @classmethod + def from_conf(cls, conf): + return cls( + conf.get("backbone"), + pretrained=conf.get("pretrained", True), + num_layers=conf.get("num_layers", 4), + index_interp=conf.get("index_interp", "bilinear"), + index_padding=conf.get("index_padding", "border"), + upsample_interp=conf.get("upsample_interp", "bilinear"), + feature_scale=conf.get("feature_scale", 1.0), + use_first_pool=conf.get("use_first_pool", True), + ) diff --git a/scenedino/models/base_model.py b/scenedino/models/base_model.py new file mode 100644 index 0000000000000000000000000000000000000000..ae91d5cc186b572aa094937a6b4a8d650c63e7a4 --- /dev/null +++ b/scenedino/models/base_model.py @@ -0,0 +1,15 @@ +from abc import ABC, abstractmethod +from typing import Any +import torch +import torch.nn as nn + + +class BaseModel(ABC, nn.Module): + def __init__(self) -> None: + super().__init__() + + @abstractmethod + def forward( + self, xyz: torch.Tensor, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, dict[str, Any]]: + pass diff --git a/scenedino/models/bts.py b/scenedino/models/bts.py new file mode 100644 index 0000000000000000000000000000000000000000..f27265e8d64505129c5373316600a82617ec226d --- /dev/null +++ b/scenedino/models/bts.py @@ -0,0 +1,595 @@ +import torch +import torch.nn as nn +import torch.autograd.profiler as profiler +import torch.nn.functional as F +from torch.cuda.amp import autocast + +from torchvision import transforms + +from scenedino.common.cameras.pinhole import ( + outside_frustum, + project_to_image, + pts_into_camera, +) +from scenedino.common.cameras.pinhole import EPS +from scenedino.common.positional_encoding import encoding_mode +from scenedino.models.base_model import BaseModel + + +torch.inverse(torch.ones((1, 1), device="cuda:0")) + + +class BTSNet(BaseModel): + def __init__( + self, + conf, + encoder: nn.Module, + code_xyz, + heads: dict[str, nn.Module], + final_pred_head: str | None = None, + uncertainty_predictor: nn.Module | None = None, + ren_nc=None, + downstream_head: nn.Module | None = None + ): + super().__init__() + self.encoder = encoder + self.code_xyz = code_xyz + self.heads = nn.ModuleDict(heads) + self.uncertainty_predictor = uncertainty_predictor + + self.extra_outs = self.encoder.extra_outs + + if final_pred_head: + self.final_pred_head = final_pred_head + else: + self.final_pred_head = list(self.heads.keys())[0] + + self.requires_bottleneck_feats = False + + # for _, head in self.heads.items(): + # if hasattr(head, "require_bottleneck_feats"): + # if head.require_bottleneck_feats and ( + # head.independent_token_net.__class__.__name__ + # == "NeuRayIndependentToken" + # ): ## For read out token type: "NeuRayIndependentToken" + # self.requires_bottleneck_feats = True + # break + self.use_viewdirs = conf.get("use_viewdirs", False) + + # TODO: figure out how to pass z_near and z_far to the model, probably outside with the positional encoding + self.d_min, self.d_max = conf.get("z_near", 3), conf.get("z_far", 80) + self.learn_empty, self.empty_empty, self.inv_z = ( + conf.get("learn_empty", True), + conf.get("empty_empty", False), + conf.get("inv_z", True), + ) + self.color_interpolation = conf.get("color_interpolation", "bilinear") + + # TODO: rethink encoding mode + self.encoding_mode = encoding_mode( + conf.get("code_mode", "z"), self.d_min, self.d_max, self.inv_z, EPS + ) + + self.flip_augmentation = conf.get("flip_augmentation", False) + self.return_sample_depth = conf.get("return_sample_depth", False) + self.sample_color = conf.get("sample_color", True) + self.predict_dino = conf.get("predict_dino", False) + + # TODO: manage _d_out in another way + d_in = self.encoder.latent_size + self.code_xyz.d_out ### 64 + 39 + if self.sample_color and self.predict_dino: + dino_dims = conf.get("dino_dims", 16) + d_out = 1 + dino_dims + elif self.sample_color: + d_out = 1 + else: + d_out = 4 + + self._d_in, self._d_out = d_in, d_out + + if self.learn_empty: + self.empty_feature = nn.Parameter( + torch.randn((self.encoder.latent_size,), requires_grad=True) + ) + self._scale = 0 ## set spatial resolution size accoridng to the scale of output feature map from the encoder + + self.downstream_head = downstream_head + if downstream_head is not None: + self.gt_classes = downstream_head.gt_classes + else: + self.gt_classes = None + + + def set_scale(self, scale): + self._scale = scale + + def get_scale(self): + return self._scale + + def compute_grid_transforms(self, *args, **kwargs): + pass + + def encode( + self, + images, + Ks, + poses_c2w, + ids_encoder=None, + ids_render=None, + ids_loss=None, + images_alt=None, + combine_ids=None, + color_frame_filter=None, + loss_feature_grid_shift=None, + ): + with autocast(enabled=False): + poses_w2c = torch.inverse(poses_c2w.float()) + + if ids_encoder is None: + images_encoder = images + Ks_encoder = Ks + poses_w2c_encoder = poses_w2c + ids_encoder = list(range(len(images))) + else: + images_encoder = images[:, ids_encoder] + Ks_encoder = Ks[:, ids_encoder] + poses_w2c_encoder = poses_w2c[:, ids_encoder] + + if ids_loss is None: + images_loss = images + ids_loss = list(range(len(images))) + else: + images_loss = images[:, ids_loss] + + # TODO: Why? + if images_alt is not None: + images = images_alt + else: + images = images * 0.5 + 0.5 + + if ids_render is None: + images_render = images + Ks_render = Ks + poses_w2c_render = poses_w2c + ids_render = list(range(len(images))) + else: + images_render = images[:, ids_render] + Ks_render = Ks[:, ids_render] + poses_w2c_render = poses_w2c[:, ids_render] + + if combine_ids is not None: + combine_ids = list(list(group) for group in combine_ids) + get_combined = set(sum(combine_ids, [])) + for i in range(images.shape[1]): + if i not in get_combined: + combine_ids.append((i,)) + remap_encoder = {v: i for i, v in enumerate(ids_encoder)} + remap_render = {v: i for i, v in enumerate(ids_render)} + comb_encoder = [ + [remap_encoder[i] for i in group if i in ids_encoder] + for group in combine_ids + ] + comb_render = [ + [remap_render[i] for i in group if i in ids_render] + for group in combine_ids + ] + comb_encoder = [group for group in comb_encoder if len(group) > 0] + comb_render = [group for group in comb_render if len(group) > 0] + else: + comb_encoder = None + comb_render = None + ## Note: This is yet to be feature map before passing img to encoder + n_, nv_, c_, h_, w_ = images_encoder.shape ### [n_, nv_, 3:=RGB, 192, 640] + n_loss_, nv_loss_, _, _, _ = images_loss.shape + + if self.flip_augmentation and self.training: ## data augmentation for color + do_flip = (torch.rand(1) > 0.5).item() + else: + do_flip = False + + if do_flip: + images_encoder = torch.flip(images_encoder, dims=(-1,)) + # images_loss = torch.flip(images_loss, dims=(-1,)) + + image_latents_ms = self.encoder(images_encoder.view(n_ * nv_, c_, h_, w_)) + + # TODO: figure out patch shift + if loss_feature_grid_shift is not None and loss_feature_grid_shift != (0, 0): + i_shift = 8 + loss_feature_grid_shift[0] + j_shift = 8 + loss_feature_grid_shift[1] + + n, v, _, _, _ = images_loss.shape + images_loss = images_loss.flatten(0, 1) + images_loss = transforms.Pad(8, padding_mode="edge")(images_loss) + images_loss = transforms.functional.crop(images_loss, i_shift, j_shift, h_, w_) + images_loss = images_loss.unflatten(0, (n, v)) + + image_loss_latents_ms = self.encoder(images_loss.view(n_loss_ * nv_loss_, c_, h_, w_), + ground_truth=True) + + if do_flip: + image_latents_ms = [torch.flip(il, dims=(-1,)) for il in image_latents_ms] + # image_loss_latents_ms = [torch.flip(il, dims=(-1,)) for il in image_loss_latents_ms] + + _, _, h_, w_ = image_latents_ms[ + 0 + ].shape ## get spatial resol from 1st layer out of 4 from feature maps generated by Enc + image_latents_ms = [ + F.interpolate(image_latents, size=(h_, w_)).view( + n_, nv_, -1, h_, w_ + ) + for image_latents in image_latents_ms + ] ## upsampling the feature maps from down-sampled 4 layers to the same spatial resolution of 1st layer + # img_feat_ms = [F.interpolate(feat_latents, size=(h_, w_)).view(n_, nv_, img_feat_ms[-1].shape[1], h_, w_) for feat_latents in img_feat_ms] ## upsampling the feature maps from down-sampled 4 layers to the same spatial resolution of 1st layer + + _, _, h_, w_ = image_loss_latents_ms[ + 0 + ].shape ## get spatial resol from 1st layer out of 4 from feature maps generated by Enc + image_loss_latents_ms = [ + image_loss_latents.view( + n_loss_, nv_loss_, -1, h_, w_ + ) + for image_loss_latents in image_loss_latents_ms + ] + + if self.extra_outs > 0: + self.grid_f_extra = [ + il_ms[:, :, -self.extra_outs:, :, :] for il_ms in image_latents_ms + ] + image_latents_ms = [ + il_ms[:, :, :-self.extra_outs, :, :] for il_ms in image_latents_ms + ] + else: + self.grid_f_extra = None + + ## feature + self.grid_f_features = image_latents_ms + self.grid_f_Ks = Ks_encoder + self.grid_f_poses_w2c = poses_w2c_encoder + self.grid_f_combine = comb_encoder + + ## color + self.grid_c_imgs = images_render.detach() + self.grid_c_Ks = Ks_render + self.grid_c_poses_w2c = poses_w2c_render + self.grid_c_combine = comb_render + + self.grid_l_loss_features = image_loss_latents_ms + + self.color_frame_filter = color_frame_filter + + def pad_zeros(self, x, padding): + shape = list(x.shape) + shape[-2] += 2 * padding + shape[-1] += 2 * padding + + padded_x = torch.zeros(shape, dtype=x.dtype, device=x.device) + padded_x[..., padding:-padding, padding:-padding] = x + + return padded_x + + def sample_features( + self, + xyz, + # use_single_featuremap=True + ): + ## Get the shape of the input point cloud and the feature grid (n, pts, spatial_coordinate == 3) + B, n_pts, _ = xyz.shape + B, n_views, c_, h_, w_ = self.grid_f_features[ + self._scale + ].shape # [B, n_views, C, H, W] + + with autocast(enabled=False): + xyz_projected = pts_into_camera( + xyz, self.grid_f_poses_w2c + ) # [B, n_views, n_pts, 3] + distance = torch.norm(xyz_projected, dim=-2, keepdim=True) + xy, z = project_to_image(xyz_projected, self.grid_f_Ks) + invalid = outside_frustum(xy, z) + + # For numerical stability with AMP. Should not affect training outcome + xy = xy.clamp(-2, 2) + + """given a vector p = (x, y, z) this is the difference of normalizing either:z ||p|| = sqrt(x^2 + y^2 + z^2). So you either give the network (x, y, z_normalized) or (x, y, ||p||_normalized) as input. It is just different parameterizations of the same point.""" + xyz_code = self.code_xyz( + self.encoding_mode(xy, z, distance).view(B * n_views * n_pts, -1) + ).view(B, n_views, n_pts, -1) + + # These samples are from different scales + sampled_features = ( + F.grid_sample( + self.grid_f_features[self._scale].view(B * n_views, c_, h_, w_), + xy.view(B * n_views, 1, -1, 2), + mode="bilinear", + padding_mode="border", + align_corners=False, + ) + .view(B, n_views, c_, n_pts) + .permute(0, 1, 3, 2) + ) ## set x,y coordinates as grid feature + + if self.learn_empty: + ## "empty space" can refer to areas in a scene where there is no object, or it could also refer to areas that are not observed or are beyond the range of the sensor. This allows the model to have a distinct learned representation for "empty" space, which can be beneficial in tasks like 3D reconstruction where understanding both the objects in a scene and the empty space between them is important. + ## Replace invalid features in the sampled features tensor with the corresponding features from the expanded empty feature + empty_feature_expanded = self.empty_feature.view(1, 1, 1, c_).expand( + B, n_views, n_pts, c_ + ) ## trainable parameter, initialized with random features + sampled_features[invalid.expand(-1, -1, -1, c_)] = empty_feature_expanded[ + invalid.expand(-1, -1, -1, c_) + ] ## broadcasting and make it fit to feature map + + sampled_features = torch.cat( + (sampled_features, xyz_code), dim=-1 + ) # [B, n_views, n_pts, C+C_pos_emb] + + return ( + sampled_features.permute(0, 2, 1, 3), + invalid[..., 0].permute(0, 2, 1), + ) + + def sample_colors(self, xyz, **kwargs): + n_, n_pts, _ = xyz.shape ## n := batch size, n_pts := #_points in world coord. + n_, nv_, c_, h_, w_ = self.grid_c_imgs.shape ## nv_ := #_views + ray_info = kwargs.get("ray_info", None) + render_flow = kwargs.get("render_flow", False) + + xyz_projected = pts_into_camera( + xyz, self.grid_c_poses_w2c + ) # [B, n_views, n_pts, 3] + distance = torch.norm(xyz_projected, dim=-2, keepdim=True) + + xy, z = project_to_image(xyz_projected, self.grid_c_Ks) + + # For numerical stability with AMP. Should not affect training outcome. + xy = xy.clamp(-2, 2) + + invalid = outside_frustum(xy, z) + + sampled_colors = ( + F.grid_sample( + self.grid_c_imgs.view(n_ * nv_, c_, h_, w_), + xy.view(n_ * nv_, 1, -1, 2), + mode=self.color_interpolation, + padding_mode="border", + align_corners=False, + ) + .view(n_, nv_, c_, n_pts) + .permute(0, 1, 3, 2) + ) ## Sample colors from the grid using the projected world coordinates. + + assert not torch.any( + torch.isnan(sampled_colors) + ) ## Check that there are no NaN values in the sampled colors tensor. + + if ( + self.grid_c_combine is not None + ): ## If self.grid_c_combine is not None, combine colors from multiple points in the same group. + invalid_groups, sampled_colors_groups = [], [] + + for ( + group + ) in ( + self.grid_c_combine + ): ## group:=list of indices that correspond to a subset of the total set of points in the point cloud. These subsets are combined to create a single image of the entire point cloud from multiple views. + if ( + len(group) == 1 + ): ## If the group contains only one point, append the corresponding invalid tensor and sampled colors tensor to the respective lists. + invalid_groups.append(invalid[:, group]) + sampled_colors_groups.append(sampled_colors[:, group]) + continue + + invalid_to_combine = invalid[ + :, group + ] ## Otherwise, combine colors from the group by picking the color of the first valid point in the group. + colors_to_combine = sampled_colors[:, group] + + indices = torch.min(invalid_to_combine, dim=1, keepdim=True)[ + 1 + ] ## Get the index of the first valid point in the group. + invalid_picked = torch.gather( + invalid_to_combine, dim=1, index=indices + ) ## Pick the invalid tensor and sampled colors tensor corresponding to the first valid point in the group. + colors_picked = torch.gather( + colors_to_combine, + dim=1, + index=indices.expand(-1, -1, -1, colors_to_combine.shape[-1]), + ) + + invalid_groups.append( + invalid_picked + ) ## Append the picked invalid tensor and sampled colors tensor to the respective lists. + sampled_colors_groups.append(colors_picked) + + invalid = torch.cat( + invalid_groups, dim=1 + ) ## Concatenate the invalid tensors and sampled colors tensors along the second dimension. + sampled_colors = torch.cat(sampled_colors_groups, dim=1) + + if (self.color_frame_filter is not None) and (ray_info is not None): + source_frame = ray_info[..., 0].to(torch.int64) + + # colors are in shape (n, nv, n_pts, c) + # we aim to collaps nv + + frame_mask = self.color_frame_filter[source_frame, :] + frame_mask = frame_mask.permute(0, 2, 1).unsqueeze(-1).expand(-1, -1, -1, sampled_colors.shape[-1]) + + sampled_colors = torch.gather(sampled_colors, dim=1, index=frame_mask) + invalid = torch.gather(invalid, dim=1, index=frame_mask[..., :1]) + + nv_ = sampled_colors.shape[1] + else: + frame_mask = None + + if render_flow and ray_info.shape[-1] > 1: + xy_origin = ray_info[..., 1:3].unsqueeze(1) + + if frame_mask is not None: + xy = torch.gather(xy, dim=1, index=frame_mask[..., :2]) + + flow = xy - xy_origin + + if sampled_colors.shape[-1] >= 5: + sampled_colors[..., 3:5] = flow + else: + sampled_colors = torch.cat((sampled_colors, flow), dim=-1) + + + return ( + sampled_colors, + invalid, + ) ## Return the sampled colors tensor and the invalid tensor. + + def sample_extras(self, xyz, **kwargs): + if self.grid_f_extra is None: + return None + + B, n_pts, _ = xyz.shape + B, n_views, c_, h_, w_ = self.grid_f_extra[ + self._scale + ].shape # [B, n_views, C, H, W] + + xyz_projected = pts_into_camera( + xyz, self.grid_f_poses_w2c + ) # [B, n_views, n_pts, 3] + distance = torch.norm(xyz_projected, dim=-2, keepdim=True) + xy, z = project_to_image(xyz_projected, self.grid_f_Ks) + invalid = outside_frustum(xy, z) + + # For numerical stability with AMP. Should not affect training outcome + xy = xy.clamp(-2, 2) + + sampled_extras = ( + F.grid_sample( + self.grid_f_extra[self._scale].view(B * n_views, c_, h_, w_), + xy.view(B * n_views, 1, -1, 2), + mode="bilinear", + padding_mode="border", + align_corners=False, + ) + .view(B, n_views, c_, n_pts) + .permute(0, 1, 3, 2) + ) ## set x,y coordinates as grid feature + + return sampled_extras.permute(0, 2, 1, 3) + + def forward(self, xyz: torch.Tensor, **kwargs): + # context manager that helps to measure the execution time of the code block inside it. i.e. used to profile the execution time of the forward pass of the model during inference for performance analysis and optimization purposes. ## to analyze the performance of the code block, helping developers identify bottlenecks and optimize their code. + with profiler.record_function( + "model_inference" + ): ## create object with the name "model_inference". ## stop the timer when exiting the block + only_density = kwargs.get("only_density", False) + ray_info = kwargs.get("ray_info", None) + render_flow = kwargs.get("render_flow", False) + predict_segmentation = kwargs.get("predict_segmentation", False) + prediction_mode = kwargs.get("prediction_mode", "stego_kmeans") + n_, n_pts, _ = xyz.shape ## n_ := Batch_size, n_pts == M + nv_ = self.grid_c_imgs.shape[1] ## 4 == (stereo 2 + side fish eye cam 2) + + if self.grid_c_combine is not None: + nv_ = len(self.grid_c_combine) + + ( + sampled_features, + invalid_features, + ) = self.sample_features( + xyz, + # use_single_featuremap=False, + ) + + extras = self.sample_extras(xyz) + + mlp_input = sampled_features.flatten(0, 1) # (B * n_pts, n_views, C) + + # Camera frustum culling stuff, currently disabled + combine_index, dim_size = None, None + + kwargs = { + "invalid_features": invalid_features.flatten( + 0, 1 + ), # (B* n_pts, n_views) + "combine_inner_dims": (n_pts,), + "combine_index": combine_index, + "dim_size": dim_size, + } + + head_outputs = { + name: head(mlp_input, **{**kwargs, "head_name": name}).reshape( + n_, -1, head.d_out + ) + for name, head in self.heads.items() + } + + if "normal_head" in head_outputs and "dino_head" in head_outputs: + mlp_output = torch.cat([head_outputs["normal_head"], head_outputs["dino_head"]], dim=-1) + else: + mlp_output = head_outputs[self.final_pred_head] + + if predict_segmentation: + sigma = mlp_output[..., :1] + sigma = F.softplus(sigma) + nv_ = 1 + dino = mlp_output[..., 1:] # tanh? + invalid = None + else: + if self.sample_color: + if self.predict_dino: + sigma = mlp_output[..., :1] + sigma = F.softplus(sigma) + rgb, invalid_colors = self.sample_colors(xyz, ray_info=ray_info, render_flow=render_flow) # (n, nv_, pts, 3) + nv_ = rgb.shape[1] # RGB shape can change due to color frame filtering. + dino = mlp_output[..., 1:] # tanh? + # dino = dino / torch.linalg.norm(dino, keepdim=True) + else: + sigma = mlp_output[..., :1] + sigma = F.softplus(sigma) + rgb, invalid_colors = self.sample_colors(xyz, ray_info=ray_info, render_flow=render_flow) # (n, nv_, pts, 3) + nv_ = rgb.shape[1] # RGB shape can change due to color frame filtering. + else: ## RGB colors and invalid colors are computed directly from the mlp_output tensor. i.e. w/o calling sample_colors(xyz) + sigma = mlp_output[..., :1] + sigma = F.relu(sigma) + rgb = mlp_output[..., 1:4].reshape(n_, 1, n_pts, 3) + rgb = F.sigmoid(rgb) + invalid_colors = invalid_features.unsqueeze(-2) + nv_ = 1 + + """Combine RGB colors and invalid colors""" + if not only_density: + _, _, _, c_ = rgb.shape + rgb = rgb.permute(0, 2, 1, 3).reshape( + n_, n_pts, nv_ * c_ + ) # (n, pts, nv * 3) + invalid_colors = invalid_colors.permute(0, 2, 1, 3).reshape( + n_, n_pts, nv_ + ) + + invalid = ( + invalid_colors | torch.all(invalid_features, dim=-1)[..., None] + ) + invalid = invalid.to(rgb.dtype) + else: + rgb = torch.zeros((n_, n_pts, nv_ * 3), device=sigma.device) + invalid = invalid_features.to(sigma.dtype) + + if extras is not None: + extras = F.softplus(extras) + extras = extras.permute(0, 2, 1, 3).reshape(n_, n_pts, -1) + + state_dict = { + "invalid_features": invalid_features.flatten(0, 1)[None], + # TODO: figure out state dict fusion, probably collate fn + "dino_features": dino, + } + + if predict_segmentation: + dino_full = self.encoder.expand_dim(dino) + if self.downstream_head is not None: + seg = self.downstream_head(dino_full, mode=prediction_mode) + seg = F.one_hot(seg, self.gt_classes) # TODO: one hot + else: + # No downstream head linked! + seg = None + return dino_full, invalid, sigma, seg + + else: + return rgb, invalid, sigma, extras, state_dict diff --git a/scenedino/models/downsampler/__init__.py b/scenedino/models/downsampler/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3d46ae657da6a5cf4d23dcfdf1b12ece0b43c117 --- /dev/null +++ b/scenedino/models/downsampler/__init__.py @@ -0,0 +1 @@ +from .downsampler import * diff --git a/scenedino/models/prediction_heads/__init__.py b/scenedino/models/prediction_heads/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..094c3b44440f70ff574bd82f400f65af1eabb036 --- /dev/null +++ b/scenedino/models/prediction_heads/__init__.py @@ -0,0 +1,47 @@ +from .multi_view_head import ( + MultiViewHead, + SimpleMultiViewHead, + MultiViewHead2, + MultiViewHead3, + make_attn_layers, +) + +from .resnetfc import ResnetFC +from .mlp import ImplicitNet, make_embedding_encoder + + +# TODO: reorganize this directory +def make_head(conf, d_in: int, d_out: int): + head_type = conf.get("type", "resnet") + + if head_type == "mlp": + head = ImplicitNet.from_conf(conf["args"], d_in, d_out) + elif head_type == "resnet": + head = ResnetFC.from_conf(conf["args"], d_in, d_out) + elif head_type == "MultiViewHead": + head = MultiViewHead.from_conf(conf["args"], d_in, d_out) + elif head_type == "SimpleMultiViewHead": + head = SimpleMultiViewHead.from_conf(conf["args"], d_in, d_out) + elif head_type == "MultiViewHead2": + head = MultiViewHead2.from_conf(conf["args"], d_in, d_out) + elif head_type == "MultiViewHead3": + head = MultiViewHead3.from_conf(conf["args"], d_in, d_out) + ## For baseline comparison + # elif head_type == "IBRNet": + # head = MultiViewHead.from_conf(conf["args"], d_in, d_out) + # elif head_type == "NeuRay": + # head = MultiViewHead.from_conf(conf["args"], d_in, d_out) + # elif head_type == "GeoNeRF": + # head = MultiViewHead.from_conf(conf["args"], d_in, d_out) + # elif head_type == "PixelNeRF": + # head = MultiViewHead.from_conf(conf["args"], d_in, d_out) + + else: + raise NotImplementedError("Unsupported Head type") + if conf.get("freeze", False): + for param in head.parameters(): + param.requires_grad = False + print(f"__freezing the {conf['name']} for knowledge distillation.") + # else: + # print("__No freezing heads during training.") + return head diff --git a/scenedino/models/prediction_heads/independent_tokens.py b/scenedino/models/prediction_heads/independent_tokens.py new file mode 100644 index 0000000000000000000000000000000000000000..0424827d6a2cc4d48592f8c2c62e0e76dc9becc9 --- /dev/null +++ b/scenedino/models/prediction_heads/independent_tokens.py @@ -0,0 +1,375 @@ +from abc import abstractmethod + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def make_independent_token(conf, attn_feat): + token_type = conf.get("type", "FixedViewIndependentToken") + if token_type == "FixedViewIndependentToken": + return FixedViewIndependentToken(attn_feat) + elif token_type == "DataViewIndependentToken": + return DataViewIndependentToken(attn_feat) + elif token_type == "NeuRayIndependentToken": + return NeuRayIndependentToken(att_feat=attn_feat, **conf["args"]) + else: + raise NotImplementedError("Unsupported Token type") + + +class BaseIndependentToken(nn.Module): + def __init__(self, attn_feat: int) -> None: + super().__init__() + + self.attn_feat = attn_feat + self.require_bottleneck_feats = False + + @abstractmethod + def forward(self, view_dependent_tokens: torch.Tensor, **kwargs) -> torch.Tensor: + pass + + +class FixedViewIndependentToken(BaseIndependentToken): + def __init__(self, attn_feat: int) -> None: + super().__init__(attn_feat) + self.require_bottleneck_feats = False + + self.readout_token = nn.Parameter(torch.rand(1, 1, attn_feat), requires_grad=True) + + def forward(self, view_dependent_tokens: torch.Tensor, **kwargs) -> torch.Tensor: + return self.readout_token.expand(view_dependent_tokens.shape[0], -1, -1) ### (n_pts, 1, 16) + + +def weights_init(m): + if isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight.data) + if m.bias is not None: + nn.init.zeros_(m.bias.data) + + +@torch.jit.script +def fused_mean_variance(x, weight): + mean = torch.sum(x * weight, dim=-2, keepdim=True) + var = torch.sum(weight * (x - mean) ** 2, dim=-2, keepdim=True) + return mean, var + + +class DataViewIndependentToken(BaseIndependentToken): + def __init__(self, attn_feat: int) -> None: + super().__init__(attn_feat) + self.require_bottleneck_feats = False + + self.eps = 1.0e-9 + self.layer = nn.Linear(2 * attn_feat, attn_feat, bias=True) + + # def forward(self, view_dependent_tokens: torch.Tensor, invalid_mask: torch.Tensor) -> torch.Tensor: + def forward(self, view_dependent_tokens: torch.Tensor, **kwargs) -> torch.Tensor: + mask = 1 - kwargs["invalid_features"].float() + # mask = 1 - invalid_mask + weights = mask / (torch.sum(mask, dim=-1, keepdim=True) + 1e-8) + mean, var = fused_mean_variance(view_dependent_tokens, weights.unsqueeze(-1)) + # num_valid_tokens = torch.sum((1 - invalid_mask), dim=-1, keepdim=True) + self.eps + # mean = torch.sum(view_dependent_tokens * (1 - invalid_mask).unsqueeze(-1), dim=-2) / num_valid_tokens + # var = torch.sum((view_dependent_tokens - mean)**2 * (1 - invalid_mask).unsqueeze(-1), dim=-2) / num_valid_tokens + return nn.ELU()(self.layer(torch.cat([mean, var], dim=-1))) + + +class NeuRayIndependentToken(BaseIndependentToken): + def __init__( + self, + n_points_per_ray: int, + # neuray_in_dim: int = 32, + in_feat_ch: int = 32, + n_samples: int = 64, + att_feat: int = 16, + d_model: int = 103, + rbs: int = 2048, + **kwargs + ): + super().__init__(att_feat) + + self.n_points_per_ray = n_points_per_ray + self.require_bottleneck_feats = True + # self.args = args + self.anti_alias_pooling = False + if self.anti_alias_pooling: + self.s = nn.Parameter(torch.tensor(0.2), requires_grad=True) + activation_func = nn.ELU( + inplace=True + ) ## (+): Mean Outputs Closer to Zero: want activations with mean outputs closer to zero. ## nn.LeakyReLU: (+): faster convergence, When the distribution of the negative values in your dataset is meaningful and shouldn't be discarded. + self.n_samples = n_samples + self.ray_dir_fc = nn.Sequential( + nn.Linear(4, 16), ## defualt: 4 + activation_func, + nn.Linear(16, in_feat_ch), ## default: in_feat_ch + 3 + activation_func, + ) + + self.base_fc = nn.Sequential( + nn.Linear((in_feat_ch) * 5 + att_feat, 64), ## default: ((in_feat_ch+3)*5+neuray_in_dim, 64) + activation_func, + nn.Linear(64, 32), + activation_func, + ) + + self.vis_fc = nn.Sequential( + nn.Linear(32, 32), + activation_func, + nn.Linear(32, 33), + activation_func, + ) + + self.vis_fc2 = nn.Sequential(nn.Linear(32, 32), activation_func, nn.Linear(32, 1), nn.Sigmoid()) + + self.geometry_fc = nn.Sequential( + nn.Linear(32 * 2 + 1, att_feat * 2), ## default: (32*2+1, 64) + activation_func, + nn.Linear(att_feat * 2, att_feat), + activation_func, + ) + + # self.ray_attention = MultiHeadAttention(nhead, att_feat, 4, 4) ## default: (4, 16, 4, 4) + self.out_geometry_fc = nn.Sequential(nn.Linear(16, 16), activation_func, nn.Linear(16, 1), nn.ReLU()) + + self.rgb_fc = nn.Sequential( + nn.Linear(32 + 1 + 4, 16), activation_func, nn.Linear(16, 8), activation_func, nn.Linear(8, 1) + ) + + self.neuray_fc = nn.Sequential( + nn.Linear( + att_feat, + 8, + ), + activation_func, + nn.Linear(8, 1), + ) + + self.img_feat2low = nn.Sequential( + nn.Linear(rbs, rbs // 4), ## TODO: replace this hard coded with the flexible + activation_func, + # nn.Linear(rbs // 4, d_model), + nn.Linear(rbs // 4, in_feat_ch), + ) + + # self.pos_encoding = self.posenc(d_hid=16, n_samples=self.n_samples) + + self.base_fc.apply(weights_init) + self.vis_fc2.apply(weights_init) + self.vis_fc.apply(weights_init) + # self.geometry_fc.apply(weights_init) + self.rgb_fc.apply(weights_init) + self.neuray_fc.apply(weights_init) + + def forward(self, view_dependent_tokens, bottleneck_feats, ray_diff, invalid_features, **kwargs): + """ibrnet dim e.g. [6, 64, 8, 35] + :param rgb_feat: rgbs and image features [n_rays, n_samples, n_views, n_feat] == img_feat + :param neuray_feat: rgbs and image features [n_rays, n_samples, n_views, n_feat] == viz_feat + :param ray_diff: ray direction difference [n_rays, n_samples, n_views, 4], first 3 channels are directions, ## tensor encodes information about how rays in the novel view differ from rays in the source views + last channel is inner product + :param mask: mask for whether each projection is valid or not. [n_rays, n_samples, n_views, 1] + :return: rgb and density output, [n_rays, n_samples, 4] + """ + """ibrnet dim e.g. [6, 64, 8, 35] + :param view_dependent_tokens: (B*n_pts, n_views, C) = (B*num_rays*point_per_ray, n_views, C) + :param bottleneck_features: (B*n_pts, n_views, C_bottleneck) = (B*num_rays*point_per_ray, n_views, C) + :param ray_diff: (B*n_pts, n_views, 4) = (B*num_rays*point_per_ray, n_views, 4) + :param invalid_features: (B*n_pts, n_views) = (B*num_rays*point_per_ray, n_views) + :return: rgb and density output, [n_rays, n_samples, 4] + """ + + view_dependent_tokens = view_dependent_tokens.reshape( + (-1, self.n_points_per_ray) + view_dependent_tokens.shape[-2:] + ) # (B*num_rays, point_per_ray, n_views, C) + bottleneck_feats = bottleneck_feats.reshape( + (-1, self.n_points_per_ray) + bottleneck_feats.shape[-2:] + ) # (B*num_rays, point_per_ray, n_views, C_bottleneck) + ray_diff = ray_diff.reshape( + (-1, self.n_points_per_ray) + ray_diff.shape[-2:] + ) # (B*num_rays, point_per_ray, n_views, 4) + invalid_features = invalid_features.reshape( + (-1, self.n_points_per_ray) + invalid_features.shape[-1:] + ) # (B*num_rays, point_per_ray, n_views) + + ## Assumption: rgb_feat already contains image feature + dir_feat / this can be implemented further + mask = ~invalid_features.unsqueeze(-1) + num_views = bottleneck_feats.shape[2] + direction_feat = self.ray_dir_fc(ray_diff) + # rgb_in = rgb_feat[..., :3] ## no used in both original code and necessary code here + bottleneck_feats = self.img_feat2low(bottleneck_feats) + bottleneck_feats = bottleneck_feats + direction_feat + + if self.anti_alias_pooling: + _, dot_prod = torch.split(ray_diff, [3, 1], dim=-1) + exp_dot_prod = torch.exp(torch.abs(self.s) * (dot_prod - 1)) + weight = (exp_dot_prod - torch.min(exp_dot_prod, dim=2, keepdim=True)[0]) * mask + weight = weight / ( + torch.sum(weight, dim=2, keepdim=True) + 1e-8 + ) # means it will trust the one more with more consistent view point + else: + weight = mask / (torch.sum(mask, dim=2, keepdim=True) + 1e-8) + + # neuray layer 0 ## == feature aggregation networks (M) above pipeline from fig. 19 + weight0 = torch.sigmoid(self.neuray_fc(view_dependent_tokens)) * weight # [rn,dn,rfn,f] + mean0, var0 = fused_mean_variance(bottleneck_feats, weight0) # [n_rays, n_samples, 1, n_feat] ## 2nd one + mean1, var1 = fused_mean_variance(bottleneck_feats, weight) # [n_rays, n_samples, 1, n_feat] ## 1st one + globalfeat = torch.cat([mean0, var0, mean1, var1], dim=-1) # [n_rays, n_samples, 1, 2*n_feat] + + x = torch.cat( + [globalfeat.expand(-1, -1, num_views, -1), bottleneck_feats, view_dependent_tokens], dim=-1 + ) # [n_rays, n_samples, n_views, 3*n_feat] + x = self.base_fc(x) ## after concat it gives input for net A + + x_vis = self.vis_fc(x * weight) + x_res, vis = torch.split(x_vis, [x_vis.shape[-1] - 1, 1], dim=-1) + vis = F.sigmoid(vis) * mask + x = x + x_res + vis = self.vis_fc2(x * vis) * mask ## above one from Network A from Fig. 19 + weight = vis / ( + torch.sum(vis, dim=2, keepdim=True) + 1e-8 + ) ## normalized: weighed mean and var ## weight == buttom from net A [N, K, 32] + + mean, var = fused_mean_variance(x, weight) + globalfeat = torch.cat( + [mean.squeeze(2), var.squeeze(2), weight.mean(dim=2)], dim=-1 + ) # [n_rays, n_samples, 32*2+1] + globalfeat = self.geometry_fc(globalfeat) # [n_rays, n_samples, att_feat] ## MLP for input transformer + + # num_valid_obs = torch.sum(mask, dim=2) + # num_valid_obs = num_valid_obs > torch.mean(num_valid_obs, dtype=float) ## making boolean + + return globalfeat.flatten(0, 1).unsqueeze(-2) # (B*num_rays*point_per_ray, 1, C) + # return globalfeat, num_valid_obs + + +# class IBRNetWithNeuRay(nn.Module): +# def __init__( +# self, neuray_in_dim=32, in_feat_ch=32, n_samples=64, att_feat=16, d_model=103, rbs=2048, nhead=4, **kwargs +# ): +# super().__init__() +# # self.args = args +# self.anti_alias_pooling = False +# if self.anti_alias_pooling: +# self.s = nn.Parameter(torch.tensor(0.2), requires_grad=True) +# activation_func = nn.ELU( +# inplace=True +# ) ## (+): Mean Outputs Closer to Zero: want activations with mean outputs closer to zero. ## nn.LeakyReLU: (+): faster convergence, When the distribution of the negative values in your dataset is meaningful and shouldn't be discarded. +# self.n_samples = n_samples +# self.ray_dir_fc = nn.Sequential( +# nn.Linear(4, 16), ## defualt: 4 +# activation_func, +# nn.Linear(16, in_feat_ch), ## default: in_feat_ch + 3 +# activation_func, +# ) + +# self.base_fc = nn.Sequential( +# nn.Linear((in_feat_ch) * 5 + neuray_in_dim, 64), ## default: ((in_feat_ch+3)*5+neuray_in_dim, 64) +# activation_func, +# nn.Linear(64, 32), +# activation_func, +# ) + +# self.vis_fc = nn.Sequential( +# nn.Linear(32, 32), +# activation_func, +# nn.Linear(32, 33), +# activation_func, +# ) + +# self.vis_fc2 = nn.Sequential(nn.Linear(32, 32), activation_func, nn.Linear(32, 1), nn.Sigmoid()) + +# self.geometry_fc = nn.Sequential( +# nn.Linear(32 * 2 + 1, att_feat * 2), ## default: (32*2+1, 64) +# activation_func, +# nn.Linear(att_feat * 2, att_feat), +# activation_func, +# ) + +# # self.ray_attention = MultiHeadAttention(nhead, att_feat, 4, 4) ## default: (4, 16, 4, 4) +# self.out_geometry_fc = nn.Sequential(nn.Linear(16, 16), activation_func, nn.Linear(16, 1), nn.ReLU()) + +# self.rgb_fc = nn.Sequential( +# nn.Linear(32 + 1 + 4, 16), activation_func, nn.Linear(16, 8), activation_func, nn.Linear(8, 1) +# ) + +# self.neuray_fc = nn.Sequential( +# nn.Linear( +# neuray_in_dim, +# 8, +# ), +# activation_func, +# nn.Linear(8, 1), +# ) + +# self.img_feat2low = nn.Sequential( +# nn.Linear(rbs, rbs // 4), ## TODO: replace this hard coded with the flexible +# activation_func, +# nn.Linear(rbs // 4, d_model), +# ) + +# # self.pos_encoding = self.posenc(d_hid=16, n_samples=self.n_samples) + +# self.base_fc.apply(weights_init) +# self.vis_fc2.apply(weights_init) +# self.vis_fc.apply(weights_init) +# # self.geometry_fc.apply(weights_init) +# self.rgb_fc.apply(weights_init) +# self.neuray_fc.apply(weights_init) + +# def forward(self, rgb_feat, neuray_feat, ray_diff, mask): +# """ibrnet dim e.g. [6, 64, 8, 35] +# :param rgb_feat: rgbs and image features [n_rays, n_samples, n_views, n_feat] == img_feat +# :param neuray_feat: rgbs and image features [n_rays, n_samples, n_views, n_feat] == viz_feat +# :param ray_diff: ray direction difference [n_rays, n_samples, n_views, 4], first 3 channels are directions, ## tensor encodes information about how rays in the novel view differ from rays in the source views +# last channel is inner product +# :param mask: mask for whether each projection is valid or not. [n_rays, n_samples, n_views, 1] +# :return: rgb and density output, [n_rays, n_samples, 4] +# """ + +# ## Assumption: rgb_feat already contains image feature + dir_feat / this can be implemented further +# num_views = rgb_feat.shape[2] +# direction_feat = self.ray_dir_fc(ray_diff) +# # rgb_in = rgb_feat[..., :3] ## no used in both original code and necessary code here +# rgb_feat = self.img_feat2low(rgb_feat) +# rgb_feat = rgb_feat + direction_feat + +# if self.anti_alias_pooling: +# _, dot_prod = torch.split(ray_diff, [3, 1], dim=-1) +# exp_dot_prod = torch.exp(torch.abs(self.s) * (dot_prod - 1)) +# weight = (exp_dot_prod - torch.min(exp_dot_prod, dim=2, keepdim=True)[0]) * mask +# weight = weight / ( +# torch.sum(weight, dim=2, keepdim=True) + 1e-8 +# ) # means it will trust the one more with more consistent view point +# else: +# weight = mask / (torch.sum(mask, dim=2, keepdim=True) + 1e-8) + +# # neuray layer 0 ## == feature aggregation networks (M) above pipeline from fig. 19 +# weight0 = torch.sigmoid(self.neuray_fc(neuray_feat)) * weight # [rn,dn,rfn,f] +# mean0, var0 = fused_mean_variance(rgb_feat, weight0) # [n_rays, n_samples, 1, n_feat] ## 2nd one +# mean1, var1 = fused_mean_variance(rgb_feat, weight) # [n_rays, n_samples, 1, n_feat] ## 1st one +# globalfeat = torch.cat([mean0, var0, mean1, var1], dim=-1) # [n_rays, n_samples, 1, 2*n_feat] + +# x = torch.cat( +# [globalfeat.expand(-1, -1, num_views, -1), rgb_feat, neuray_feat], dim=-1 +# ) # [n_rays, n_samples, n_views, 3*n_feat] +# x = self.base_fc(x) ## after concat it gives input for net A + +# x_vis = self.vis_fc(x * weight) +# x_res, vis = torch.split(x_vis, [x_vis.shape[-1] - 1, 1], dim=-1) +# vis = F.sigmoid(vis) * mask +# x = x + x_res +# vis = self.vis_fc2(x * vis) * mask ## above one from Network A from Fig. 19 +# weight = vis / ( +# torch.sum(vis, dim=2, keepdim=True) + 1e-8 +# ) ## normalized: weighed mean and var ## weight == buttom from net A [N, K, 32] + +# mean, var = fused_mean_variance(x, weight) +# globalfeat = torch.cat( +# [mean.squeeze(2), var.squeeze(2), weight.mean(dim=2)], dim=-1 +# ) # [n_rays, n_samples, 32*2+1] +# globalfeat = self.geometry_fc(globalfeat) # [n_rays, n_samples, att_feat] ## MLP for input transformer + +# # num_valid_obs = torch.sum(mask, dim=2) +# # num_valid_obs = num_valid_obs > torch.mean(num_valid_obs, dtype=float) ## making boolean + +# return globalfeat +# # return globalfeat, num_valid_obs diff --git a/scenedino/models/prediction_heads/layers.py b/scenedino/models/prediction_heads/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..df14f8826855466fb17ed9728a810afa6e460679 --- /dev/null +++ b/scenedino/models/prediction_heads/layers.py @@ -0,0 +1,604 @@ +from __future__ import absolute_import, division, print_function + +import math + +import numpy as np +import torch +import torch.nn as nn +from torch.nn import ( + functional as F, + Conv2d, + LeakyReLU, + Upsample, + Sigmoid, + ConvTranspose2d, +) + + +class ConvBlock(nn.Module): + """Layer to perform a convolution followed by ELU""" + + def __init__(self, in_channels, out_channels): + super(ConvBlock, self).__init__() + + self.conv = Conv3x3(in_channels, out_channels) + self.nonlin = nn.ELU(inplace=True) + + def forward(self, x): + out = self.conv(x) + out = self.nonlin(out) + return out + + + + +class Conv3x3(nn.Module): + """Layer to pad and convolve input""" + + def __init__(self, in_channels, out_channels, use_refl=True): + super(Conv3x3, self).__init__() + + if use_refl: + self.pad = nn.ReflectionPad2d(1) + else: + self.pad = nn.ZeroPad2d(1) + self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3) + + def forward(self, x): + out = self.pad(x) + out = self.conv(out) + return out + + +class Backprojection(nn.Module): + def __init__(self, height, width): + super(Backprojection, self).__init__() + + self.H, self.W = height, width + + yy, xx = torch.meshgrid( + [torch.linspace(-1.0, 1.0, self.H), torch.linspace(-1.0, 1.0, self.W)] + ) + yy = yy.contiguous().view(-1) + xx = xx.contiguous().view(-1) + self.ones = nn.Parameter( + torch.ones(1, 1, self.H * self.W, dtype=torch.float), requires_grad=False + ) + self.coord = torch.unsqueeze(torch.stack([xx, yy], 0), 0) + self.coord = nn.Parameter( + torch.cat([self.coord, self.ones], 1), requires_grad=False + ) + + def forward(self, depth, inv_K): + n = depth.shape[0] + cam_p_norm = torch.matmul(inv_K[:, :3, :3], self.coord.expand(n, -1, -1)) + cam_p_euc = depth.view(n, 1, -1) * cam_p_norm + cam_p_h = torch.cat([cam_p_euc, self.ones.expand(n,e -1, -1)], 1) + return cam_p_h + + +def point_projection(points3D, batch_size, height, width, K, T): + N, H, W = batch_size, height, width + cam_coord = torch.matmul(torch.matmul(K, T[:, :3, :]), points3D) + img_coord = cam_coord[:, :2, :] / (cam_coord[:, 2:3, :] + 1e-7) + img_coord = img_coord.view(N, 2, H, W).permute(0, 2, 3, 1) + return img_coord, cam_coord[:, 2, :] + + +def upsample(x): + """Upsample input tensor by a factor of 2""" + return F.interpolate(x, scale_factor=2, mode="nearest") + + +class GaussianAverage(nn.Module): + def __init__(self) -> None: + super().__init__() + self.window = torch.Tensor( + [ + [0.0947, 0.1183, 0.0947], + [0.1183, 0.1478, 0.1183], + [0.0947, 0.1183, 0.0947], + ] + ) + + def forward(self, x): + kernel = self.window.to(x.device).to(x.dtype).repeat(x.shape[1], 1, 1, 1) + return F.conv2d(x, kernel, padding=0, groups=x.shape[1]) + + +class SSIM(nn.Module): + """Layer to compute the SSIM loss between a pair of images""" + + def __init__( + self, + pad_reflection=True, + gaussian_average=False, + comp_mode=False, + eval_mode=False, + ): + super(SSIM, self).__init__() + self.comp_mode = comp_mode + self.eval_mode = eval_mode + + if not gaussian_average: + self.mu_x_pool = nn.AvgPool2d(3, 1) + self.mu_y_pool = nn.AvgPool2d(3, 1) + self.sig_x_pool = nn.AvgPool2d(3, 1) + self.sig_y_pool = nn.AvgPool2d(3, 1) + self.sig_xy_pool = nn.AvgPool2d(3, 1) + else: + self.mu_x_pool = GaussianAverage() + self.mu_y_pool = GaussianAverage() + self.sig_x_pool = GaussianAverage() + self.sig_y_pool = GaussianAverage() + self.sig_xy_pool = GaussianAverage() + + if pad_reflection: + self.pad = nn.ReflectionPad2d(1) + else: + self.pad = nn.ZeroPad2d(1) + + self.C1 = 0.01**2 + self.C2 = 0.03**2 + + def forward(self, x, y, pad=True): + if pad: + x = self.pad(x) + y = self.pad(y) + ## average of pixels in x and y, average pooling or Gaussian averaging, based on the initialization + mu_x = self.mu_x_pool(x) + mu_y = self.mu_y_pool(y) + mu_x_sq = ( + mu_x**2 + ) ## squares of the averages and the product of the averages, respectively. + mu_y_sq = mu_y**2 + mu_x_y = mu_x * mu_y + ## variances and covariance: + sigma_x = self.sig_x_pool(x**2) - mu_x_sq + sigma_y = self.sig_y_pool(y**2) - mu_y_sq + sigma_xy = self.sig_xy_pool(x * y) - mu_x_y + + SSIM_n = (2 * mu_x_y + self.C1) * (2 * sigma_xy + self.C2) + SSIM_d = (mu_x_sq + mu_y_sq + self.C1) * (sigma_x + sigma_y + self.C2) + + if ( + not self.eval_mode + ): ## determines how to handle the output of the SSIM calculation + if ( + not self.comp_mode + ): ## error (1 - SSIM index), used as a loss function during training + return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1) + else: ## return the SSIM index itself. + return torch.clamp((1 - SSIM_n / SSIM_d), 0, 1) / 2 + else: + return ( + SSIM_n / SSIM_d + ) ## returned error is scaled to range between 0 and 1 for easier interpretation and usage in loss calculations + ## (2*mu_x*mu_y + C1)*(2*sigma_xy + C2) / ((mu_x^2 + mu_y^2 + C1)*(sigma_x + sigma_y + C2)). + ## SSIM index ranges from -1 to 1, where 1 means the images are identical, -1 means the images are totally different, and 0 means the images are not correlated + + +class GEO(nn.Module): + """Layer to compute the pseudo label, L_{geo}, loss between a pair of images""" + + def __init__( + self, + pad_reflection=True, + gaussian_average=False, + comp_mode=False, + eval_mode=False, + ): + super(GEO, self).__init__() + self.comp_mode = comp_mode + self.eval_mode = eval_mode + + if not gaussian_average: + self.mu_x_pool = nn.AvgPool2d(3, 1) + self.mu_y_pool = nn.AvgPool2d(3, 1) + self.sig_x_pool = nn.AvgPool2d(3, 1) + self.sig_y_pool = nn.AvgPool2d(3, 1) + self.sig_xy_pool = nn.AvgPool2d(3, 1) + else: + self.mu_x_pool = GaussianAverage() + self.mu_y_pool = GaussianAverage() + self.sig_x_pool = GaussianAverage() + self.sig_y_pool = GaussianAverage() + self.sig_xy_pool = GaussianAverage() + + if pad_reflection: + self.pad = nn.ReflectionPad2d(1) + else: + self.pad = nn.ZeroPad2d(1) + + self.C1 = 0.01**2 + self.C2 = 0.03**2 + + def forward(self, x, y, pad=True): + if pad: + x = self.pad(x) + y = self.pad(y) + ## average of pixels in x and y, average pooling or Gaussian averaging, based on the initialization + mu_x = self.mu_x_pool(x) + mu_y = self.mu_y_pool(y) + mu_x_sq = ( + mu_x**2 + ) ## squares of the averages and the product of the averages, respectively. + mu_y_sq = mu_y**2 + mu_x_y = mu_x * mu_y + ## variances and covariance: + sigma_x = self.sig_x_pool(x**2) - mu_x_sq + sigma_y = self.sig_y_pool(y**2) - mu_y_sq + sigma_xy = self.sig_xy_pool(x * y) - mu_x_y + + SSIM_n = (2 * mu_x_y + self.C1) * (2 * sigma_xy + self.C2) + SSIM_d = (mu_x_sq + mu_y_sq + self.C1) * (sigma_x + sigma_y + self.C2) + + if ( + not self.eval_mode + ): ## determines how to handle the output of the SSIM calculation + if ( + not self.comp_mode + ): ## error (1 - SSIM index), used as a loss function during training + return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1) + else: ## return the SSIM index itself. + return torch.clamp((1 - SSIM_n / SSIM_d), 0, 1) / 2 + else: + return ( + SSIM_n / SSIM_d + ) ## returned error is scaled to range between 0 and 1 for easier interpretation and usage in loss calculations + ## (2*mu_x*mu_y + C1)*(2*sigma_xy + C2) / ((mu_x^2 + mu_y^2 + C1)*(sigma_x + sigma_y + C2)). + ## SSIM index ranges from -1 to 1, where 1 means the images are identical, -1 means the images are totally different, and 0 means the images are not correlated + + +def ssim( + x, + y, + pad_reflection=True, + gaussian_average=False, + comp_mode=False, + eval_mode=False, + pad=True, +): + ssim_ = SSIM(pad_reflection, gaussian_average, comp_mode, eval_mode) + return ssim_(x, y, pad=pad) + + +def geo( + x, + y, + pad_reflection=True, + gaussian_average=False, + comp_mode=False, + eval_mode=False, + pad=True, +): + geo_ = GEO(pad_reflection, gaussian_average, comp_mode, eval_mode) + return geo_(x, y, pad=pad) + + +# NOTE: is the following used? Ask Felix +class ResidualImage(nn.Module): + def __init__(self): + super().__init__() + self.residual_image = ResidualImageModule() + + def forward( + self, + keyframe: torch.Tensor, + keyframe_pose: torch.Tensor, + keyframe_intrinsics: torch.Tensor, + depths: torch.Tensor, + frames: list, + poses: list, + intrinsics: list, + ): + data_dict = { + "keyframe": keyframe, + "keyframe_pose": keyframe_pose, + "keyframe_intrinsics": keyframe_intrinsics, + "predicted_inverse_depths": [depths], + "frames": frames, + "poses": poses, + "list": list, + "intrinsics": intrinsics, + "inv_depth_max": 0, + "inv_depth_min": 1, + } + data_dict = self.residual_image(data_dict) + return data_dict["residual_image"] + + +class ResidualImageModule(nn.Module): + def __init__(self, use_mono=True, use_stereo=False): + super().__init__() + self.use_mono = use_mono + self.use_stereo = use_stereo + self.ssim = SSIM() + + def forward(self, data_dict): + keyframe = data_dict["keyframe"] + keyframe_intrinsics = data_dict["keyframe_intrinsics"] + keyframe_pose = data_dict["keyframe_pose"] + depths = (1 - data_dict["predicted_inverse_depths"][0]) * data_dict[ + "inv_depth_max" + ] + data_dict["predicted_inverse_depths"][0] * data_dict["inv_depth_min"] + + frames = [] + intrinsics = [] + poses = [] + + if self.use_mono: + frames += data_dict["frames"] + intrinsics += data_dict["intrinsics"] + poses += data_dict["poses"] + if self.use_stereo: + frames += [data_dict["stereoframe"]] + intrinsics += [data_dict["stereoframe_intrinsics"]] + poses += [data_dict["stereoframe_pose"]] + + n, c, h, w = keyframe.shape + + backproject_depth = Backprojection(n, h, w) + backproject_depth.to(keyframe.device) + + inv_k = torch.inverse(keyframe_intrinsics) + cam_points = inv_k[:, :3, :3] @ backproject_depth.pix_coords + cam_points = cam_points / depths.view(n, 1, -1) + cam_points = torch.cat([cam_points, backproject_depth.ones], 1) + + masks = [] + residuals = [] + + for i, image in enumerate(frames): + t = torch.inverse(poses[i]) @ keyframe_pose + pix_coords = point_projection(cam_points, n, h, w, intrinsics[i], t) + warped_image = F.grid_sample(image + 1, pix_coords) + mask = torch.any(warped_image == 0, dim=1, keepdim=True) + warped_image -= 0.5 + residual = self.ssim(warped_image, keyframe + 0.5) + masks.append(mask) + residuals.append(residual) + + masks = torch.stack(masks, dim=1) + residuals = torch.stack(residuals, dim=1) + residuals[masks.expand(-1, -1, c, -1, -1)] = float("inf") + + residual_image = torch.min(torch.mean(residuals, dim=2, keepdim=True), dim=1)[0] + residual_image[torch.min(masks, dim=1)[0]] = 0 + data_dict["residual_image"] = residual_image + return data_dict + + +class PadSameConv2d(torch.nn.Module): + def __init__(self, kernel_size, stride=1): + """ + Imitates padding_mode="same" from tensorflow. + :param kernel_size: Kernelsize of the convolution, int or tuple/list + :param stride: Stride of the convolution, int or tuple/list + """ + super().__init__() + if isinstance(kernel_size, (tuple, list)): + self.kernel_size_y = kernel_size[0] + self.kernel_size_x = kernel_size[1] + else: + self.kernel_size_y = kernel_size + self.kernel_size_x = kernel_size + if isinstance(stride, (tuple, list)): + self.stride_y = stride[0] + self.stride_x = stride[1] + else: + self.stride_y = stride + self.stride_x = stride + + def forward(self, x: torch.Tensor): + _, _, height, width = x.shape + + # For the convolution we want to achieve a output size of (n_h, n_w) = (math.ceil(h / s_y), math.ceil(w / s_y)). + # Therefore we need to apply n_h convolution kernels with stride s_y. We will have n_h - 1 offsets of size s_y. + # Additionally, we need to add the size of our kernel. This is the height we require to get n_h. We need to pad + # the read difference between this and the old height. We will pad math.floor(pad_y / 2) on the left and + # math-ceil(pad_y / 2) on the right. Same for pad_x respectively. + padding_y = ( + self.stride_y * (math.ceil(height / self.stride_y) - 1) + + self.kernel_size_y + - height + ) / 2 + padding_x = ( + self.stride_x * (math.ceil(width / self.stride_x) - 1) + + self.kernel_size_x + - width + ) / 2 + padding = [ + math.floor(padding_x), + math.ceil(padding_x), + math.floor(padding_y), + math.ceil(padding_y), + ] + return F.pad(input=x, pad=padding) + + +class PadSameConv2dTransposed(torch.nn.Module): + def __init__(self, stride): + """ + Imitates padding_mode="same" from tensorflow. + :param stride: Stride of the convolution_transposed, int or tuple/list + """ + super().__init__() + if isinstance(stride, (tuple, list)): + self.stride_y = stride[0] + self.stride_x = stride[1] + else: + self.stride_y = stride + self.stride_x = stride + + def forward(self, x: torch.Tensor, orig_shape: torch.Tensor): + target_shape = x.new_tensor(list(orig_shape)) + target_shape[-2] *= self.stride_y + target_shape[-1] *= self.stride_x + oversize = target_shape[-2:] - x.new_tensor(x.shape)[-2:] + if oversize[0] > 0 and oversize[1] > 0: + x = F.pad( + x, + [ + math.floor(oversize[1] / 2), + math.ceil(oversize[1] / 2), + math.floor(oversize[0] / 2), + math.ceil(oversize[0] / 2), + ], + ) + elif oversize[0] > 0 >= oversize[1]: + x = F.pad( + x, [0, 0, math.floor(oversize[0] / 2), math.ceil(oversize[0] / 2)] + ) + x = x[:, :, :, math.floor(-oversize[1] / 2) : -math.ceil(-oversize[1] / 2)] + elif oversize[0] <= 0 < oversize[1]: + x = F.pad(x, [math.floor(oversize[1] / 2), math.ceil(oversize[1] / 2)]) + x = x[:, :, math.floor(-oversize[0] / 2) : -math.ceil(-oversize[0] / 2), :] + else: + x = x[ + :, + :, + math.floor(-oversize[0] / 2) : -math.ceil(-oversize[0] / 2), + math.floor(-oversize[1] / 2) : -math.ceil(-oversize[1] / 2), + ] + return x + + +class ConvReLU2(torch.nn.Module): + def __init__( + self, in_channels, out_channels, kernel_size, stride=1, leaky_relu_neg_slope=0.1 + ): + """ + Performs two convolutions and a leaky relu. The first operation only convolves in y direction, the second one + only in x direction. + :param in_channels: Number of input channels + :param out_channels: Number of output channels + :param kernel_size: Kernel size for the convolutions, first in y direction, then in x direction + :param stride: Stride for the convolutions, first in y direction, then in x direction + """ + super().__init__() + self.pad_0 = PadSameConv2d(kernel_size=(kernel_size, 1), stride=(stride, 1)) + self.conv_y = Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=(kernel_size, 1), + stride=(stride, 1), + ) + self.leaky_relu = LeakyReLU(negative_slope=leaky_relu_neg_slope) + self.pad_1 = PadSameConv2d(kernel_size=(1, kernel_size), stride=(1, stride)) + self.conv_x = Conv2d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=(1, kernel_size), + stride=(1, stride), + ) + + def forward(self, x: torch.Tensor): + t = self.pad_0(x) + t = self.conv_y(t) + t = self.leaky_relu(t) + t = self.pad_1(t) + t = self.conv_x(t) + return self.leaky_relu(t) + + +class ConvReLU(torch.nn.Module): + def __init__( + self, in_channels, out_channels, kernel_size, stride=1, leaky_relu_neg_slope=0.1 + ): + """ + Performs two convolutions and a leaky relu. The first operation only convolves in y direction, the second one + only in x direction. + :param in_channels: Number of input channels + :param out_channels: Number of output channels + :param kernel_size: Kernel size for the convolutions, first in y direction, then in x direction + :param stride: Stride for the convolutions, first in y direction, then in x direction + """ + super().__init__() + self.pad = PadSameConv2d(kernel_size=kernel_size, stride=stride) + self.conv = Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + ) + self.leaky_relu = LeakyReLU(negative_slope=leaky_relu_neg_slope) + + def forward(self, x: torch.Tensor): + t = self.pad(x) + t = self.conv(t) + return self.leaky_relu(t) + + +class Upconv(torch.nn.Module): + def __init__(self, in_channels, out_channels): + """ + Performs two convolutions and a leaky relu. The first operation only convolves in y direction, the second one + only in x direction. + :param in_channels: Number of input channels + :param out_channels: Number of output channels + :param kernel_size: Kernel size for the convolutions, first in y direction, then in x direction + :param stride: Stride for the convolutions, first in y direction, then in x direction + """ + super().__init__() + self.upsample = Upsample(scale_factor=2) + self.pad = PadSameConv2d(kernel_size=2) + self.conv = Conv2d( + in_channels=in_channels, out_channels=out_channels, kernel_size=2, stride=1 + ) + + def forward(self, x: torch.Tensor): + t = self.upsample(x) + t = self.pad(t) + return self.conv(t) + + +class ConvSig(torch.nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride=1): + """ + Performs two convolutions and a leaky relu. The first operation only convolves in y direction, the second one + only in x direction. + :param in_channels: Number of input channels + :param out_channels: Number of output channels + :param kernel_size: Kernel size for the convolutions, first in y direction, then in x direction + :param stride: Stride for the convolutions, first in y direction, then in x direction + """ + super().__init__() + self.pad = PadSameConv2d(kernel_size=kernel_size, stride=stride) + self.conv = Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + ) + self.sig = Sigmoid() + + def forward(self, x: torch.Tensor): + t = self.pad(x) + t = self.conv(t) + return self.sig(t) + + +class Refine(torch.nn.Module): + def __init__(self, in_channels, out_channels, leaky_relu_neg_slope=0.1): + """ + Performs a transposed conv2d with padding that imitates tensorflow same behaviour. The transposed conv2d has + parameters kernel_size=4 and stride=2. + :param in_channels: Channels that go into the conv2d_transposed + :param out_channels: Channels that come out of the conv2d_transposed + """ + super().__init__() + self.conv2d_t = ConvTranspose2d( + in_channels=in_channels, out_channels=out_channels, kernel_size=4, stride=2 + ) + self.pad = PadSameConv2dTransposed(stride=2) + self.leaky_relu = LeakyReLU(negative_slope=leaky_relu_neg_slope) + + def forward(self, x: torch.Tensor, features_direct=None): + orig_shape = x.shape + x = self.conv2d_t(x) + x = self.leaky_relu(x) + x = self.pad(x, orig_shape) + if features_direct is not None: + x = torch.cat([x, features_direct], dim=1) + return x diff --git a/scenedino/models/prediction_heads/mlp.py b/scenedino/models/prediction_heads/mlp.py new file mode 100644 index 0000000000000000000000000000000000000000..c4f266f44929a93ee926fe492ee7e3ea5f01f5e4 --- /dev/null +++ b/scenedino/models/prediction_heads/mlp.py @@ -0,0 +1,778 @@ +from typing import Optional + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from scenedino.common import util + + +class ImplicitNet(nn.Module): + """ + Represents a MLP; + Original code from IGR + """ + + def __init__( + self, + d_in, + dims, + skip_in=(), + d_out=4, + geometric_init=True, + radius_init=0.3, + beta=0.0, + output_init_gain=2.0, + num_position_inputs=3, + sdf_scale=1.0, + dim_excludes_skip=False, + combine_layer=1000, + combine_type="average", + ): + """ + :param d_in input size + :param dims dimensions of hidden layers. Num hidden layers == len(dims) + :param skip_in layers with skip connections from input (residual) + :param d_out output size + :param geometric_init if true, uses geometric initialization + (to SDF of sphere) + :param radius_init if geometric_init, then SDF sphere will have + this radius + :param beta softplus beta, 100 is reasonable; if <=0 uses ReLU activations instead + :param output_init_gain output layer normal std, only used for + output dimension >= 1, when d_out >= 1 + :param dim_excludes_skip if true, dimension sizes do not include skip + connections + """ + super().__init__() + + dims = [d_in] + dims + [d_out] + if dim_excludes_skip: + for i in range(1, len(dims) - 1): + if i in skip_in: + dims[i] += d_in + + self.num_layers = len(dims) + self.skip_in = skip_in + self.dims = dims + self.combine_layer = combine_layer + self.combine_type = combine_type + + for layer in range(0, self.num_layers - 1): + if layer + 1 in skip_in: + out_dim = dims[layer + 1] - d_in + else: + out_dim = dims[layer + 1] + lin = nn.Linear(dims[layer], out_dim) + + # if true preform geometric initialization + if geometric_init: + if layer == self.num_layers - 2: + # Note our geometric init is negated (compared to IDR) + # since we are using the opposite SDF convention: + # inside is + + nn.init.normal_( + lin.weight[0], + mean=-np.sqrt(np.pi) / np.sqrt(dims[layer]) * sdf_scale, + std=0.00001, + ) + nn.init.constant_(lin.bias[0], radius_init) + if d_out > 1: + # More than SDF output + nn.init.normal_(lin.weight[1:], mean=0.0, std=output_init_gain) + nn.init.constant_(lin.bias[1:], 0.0) + else: + nn.init.constant_(lin.bias, 0.0) + nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim)) + if d_in > num_position_inputs and (layer == 0 or layer in skip_in): + # Special handling for input to allow positional encoding + nn.init.constant_(lin.weight[:, -d_in + num_position_inputs :], 0.0) + else: + nn.init.constant_(lin.bias, 0.0) + nn.init.kaiming_normal_(lin.weight, a=0, mode="fan_in") + + setattr(self, "lin" + str(layer), lin) + + if beta > 0: + self.activation = nn.Softplus(beta=beta) + else: + # Vanilla ReLU + self.activation = nn.ReLU() + + def forward(self, x, combine_inner_dims=(1,)): + """ + :param x (..., d_in) + :param combine_inner_dims Combining dimensions for use with multiview inputs. + Tensor will be reshaped to (-1, combine_inner_dims, ...) and reduced using combine_type + on dim 1, at combine_layer + """ + x_init = x + for layer in range(0, self.num_layers - 1): + lin = getattr(self, "lin" + str(layer)) + + if layer == self.combine_layer: + x = util.combine_interleaved(x, combine_inner_dims, self.combine_type) + x_init = util.combine_interleaved( + x_init, combine_inner_dims, self.combine_type + ) + + if layer < self.combine_layer and layer in self.skip_in: + x = torch.cat([x, x_init], -1) / np.sqrt(2) + + x = lin(x) + if layer < self.num_layers - 2: + x = self.activation(x) + + return x + + @classmethod + def from_conf(cls, conf, d_in, d_out): + return cls(d_in=d_in, d_out=d_out, **conf) + + # @classmethod + # def from_conf(cls, conf, d_in, **kwargs): + # # PyHocon construction + # return cls( + # d_in, + # conf.get_list("dims"), + # skip_in=conf.get_list("skip_in"), + # beta=conf.get_float("beta", 0.0), + # dim_excludes_skip=conf.get_bool("dim_excludes_skip", False), + # combine_layer=conf.get_int("combine_layer", 1000), + # combine_type=conf.get_string("combine_type", "average"), # average | max + # **kwargs, + # ) + + +""" +GeoNeRF +https://github.com/idiap/GeoNeRF/blob/e6249fdae5672853c6bbbd4ba380c4c166d02c95/model/self_attn_renderer.py#L60 +""" + + +# Custom TransposeLayer to perform transpose operation +class TransposeLayer(nn.Module): + def __init__(self): + super(TransposeLayer, self).__init__() + + def forward(self, x): + print("x_shape before transpose: ", x.shape) + return x.transpose(1, 2) + + +# +# class CNN2AE(nn.Module): +# def __init__(self, num_channels, num_features, desired_spatial_output): ## reduced mapping: num_points |-> num_features +# super(CNN2AE, self).__init__() +# self.conv1 = nn.Conv1d(num_channels, num_channels*2, kernel_size=3, stride=1, padding=1) +# self.conv2 = nn.Conv1d(num_channels*2, num_channels*4, kernel_size=3, stride=1, padding=1) +# self.conv3 = nn.Conv1d(num_channels*4, num_channels*8, kernel_size=3, stride=1, padding=1) +# self.pool = nn.AvgPool1d(kernel_size=2, stride=2) +# self.desired_spatial_output = desired_spatial_output +# # self.fc = nn.Linear(num_channels*4 * num_features, num_features) # Fully connected layer to further reduce dimension +# # self.fc = nn.Linear(num_channels*4 * (num_features // 4), num_channels) # Fully connected layer to reduce dimension +# +# def forward(self, x): ## input_tensor's shape: (batch_size=1, C=num_channels, M=num_points) +# _, num_channels, num_features = x.shape +# x = self.pool(nn.functional.relu(self.conv1(x))) +# x = self.pool(nn.functional.relu(self.conv2(x))) +# x = self.pool(nn.functional.relu(self.conv3(x))) +# x = x.view(x.size(0), num_channels, self.desired_spatial_output) # Reshape to (batch_size, num_channels, reduced_features) +# return x + +device = torch.device( + "cuda" if torch.cuda.is_available() else "cpu" +) # Use GPU if available, else CPU + + +class CNN2AE( + nn.Module +): ## convolute density sampled features along a ray from end of cam's frustum to the end. ( n_coarse==16 x att_feat==32 x (8x8) ) + def __init__(self, num_channels: int = 32, num_features: int = 64): + super(CNN2AE, self).__init__() + self.n_coarse = num_features + self.conv1 = nn.Conv1d( + num_channels, num_channels, kernel_size=3, stride=1, padding=1 + ) + # self.conv2 = nn.Conv1d(num_channels*2, num_channels*4, kernel_size=3, stride=1, padding=1) + self.pool = nn.AvgPool1d(kernel_size=2, stride=2) + # self.fc = nn.Linear(num_channels * num_features, num_features) # Fully connected layer to further reduce dimension + # self.fc = None # We will initialize this later + + def forward(self, x): ## , desired_spatial_output): + assert ( + x.size(0) % self.n_coarse + ) == 0, f"__given points should be dividable by n_coarse: {self.n_coarse},but points given: {x.size(0)}" + # x = x.to(device) # Move the input data to the device + # B_, C_, M_ = x.shape # Get the new number of channels and points + x = self.pool(F.relu(self.conv1(x))) # Apply first conv layer and pool + x = self.pool(F.relu(self.conv1(x))) # Apply second conv layer and pool + + # if self.fc is None: + # # Initialize the fully connected layer now that we know the input size + # self.fc = nn.Linear(C_ * M_, C_ * desired_spatial_output).to(device) + + # x = x.view(B_, C_ * M_) # Reshape to (batch_size, C * M) + # x = self.fc(x) # Apply fully connected layer + # x = x.view(B_, C_, desired_spatial_output) # Reshape to (batch_size, num_channels, desired_spatial_output) + return x + + +## Auto-encoder network +class ConvAutoEncoder(nn.Module): ## purpose: to enforce the geometric generalization + def __init__( + self, num_ch: int = 32, S_: int = 64 + ): ## S:= Sequence length of the input tensor. i.e. nb_samples_per_ray + super(ConvAutoEncoder, self).__init__() + # Encoder + self.conv1 = nn.Sequential( + nn.Conv1d(num_ch, num_ch * 2, 3, stride=1, padding=1), + # TransposeLayer(), # Use the custom TransposeLayer to transpose the output + nn.LayerNorm( + S_, elementwise_affine=False + ), ## RuntimeError: Given normalized_shape=[64], expected input with shape [*, 64], but got input of size[1, 64, 100000] + nn.ELU(alpha=1.0, inplace=True), + # TransposeLayer(), # Use the custom TransposeLayer to transpose the output + nn.MaxPool1d(2), + ) + self.conv2 = nn.Sequential( + nn.Conv1d(num_ch * 2, num_ch * 4, 3, stride=1, padding=1), + # TransposeLayer(), # Use the custom TransposeLayer to transpose the output + nn.LayerNorm(S_ // 2, elementwise_affine=False), + nn.ELU(alpha=1.0, inplace=True), + # TransposeLayer(), # Use the custom TransposeLayer to transpose the output + nn.MaxPool1d(2), + ) + self.conv3 = nn.Sequential( + nn.Conv1d(num_ch * 4, num_ch * 4, 3, stride=1, padding=1), + # TransposeLayer(), # Use the custom TransposeLayer to transpose the output + nn.LayerNorm(S_ // 4, elementwise_affine=False), + nn.ELU(alpha=1.0, inplace=True), + # TransposeLayer(), # Use the custom TransposeLayer to transpose the output + nn.MaxPool1d(2), + ) + + # Decoder + self.t_conv1 = nn.Sequential( + nn.ConvTranspose1d(num_ch * 4, num_ch * 4, 4, stride=2, padding=1), + nn.LayerNorm(S_ // 4, elementwise_affine=False), + nn.ELU(alpha=1.0, inplace=True), + ) + self.t_conv2 = nn.Sequential( + nn.ConvTranspose1d(num_ch * 8, num_ch * 2, 4, stride=2, padding=1), + nn.LayerNorm(S_ // 2, elementwise_affine=False), + nn.ELU(alpha=1.0, inplace=True), + ) + self.t_conv3 = nn.Sequential( + nn.ConvTranspose1d(num_ch * 4, num_ch, 4, stride=2, padding=1), + nn.LayerNorm(S_, elementwise_affine=False), + nn.ELU(alpha=1.0, inplace=True), + ) + # Output + self.conv_out = nn.Sequential( + nn.Conv1d(num_ch * 2, num_ch, 3, stride=1, padding=1), + nn.LayerNorm(S_, elementwise_affine=False), + nn.ELU(alpha=1.0, inplace=True), + ) + + def forward(self, x): + input = x + x = self.conv1(x) + conv1_out = x + x = self.conv2(x) + conv2_out = x + x = self.conv3(x) + + x = self.t_conv1(x) + x = self.t_conv2(torch.cat([x, conv2_out], dim=1)) + x = self.t_conv3(torch.cat([x, conv1_out], dim=1)) + + x = self.conv_out(torch.cat([x, input], dim=1)) + + return x + + +""" +Transformer encoder part from IBRNet network +https://github.com/googleinterns/IBRNet/blob/master/ibrnet/mlp_network.py +""" + + +class ScaledDotProductAttention(nn.Module): + """Scaled Dot-Product Attention""" + + def __init__(self, temperature, attn_dropout=0.1): + super().__init__() + self.temperature = temperature + # self.dropout = nn.Dropout(attn_dropout) + + def forward(self, q, k, v, mask=None): + attn = torch.matmul( + q / self.temperature, k.transpose(2, 3) + ) ### ?? [32768, 4, 7, 7] + + if mask is not None: ### [32768, 1, 7] + mask = mask.unsqueeze(-1) ## + mask = mask.expand( + -1, attn.shape[1], -1, attn.shape[-1] + ) ## TODO: matrix should be investiated to validate the operator + mask = 1.0 - ( + (1.0 - mask) * (1.0 - mask.transpose(-2, -1)) + ) ### As being symmetric of the mask matrix => the info of masked info won't give result: 2 problems: 1) computation bottleneck demand, eval_batch_size=25000 decreasing (setup pipeline using smaller pipeline nerf.py) + attn = attn.masked_fill( + mask == 1, -1e9 + ) ## masking should be done when the value of invalidity as boolean is 1 by making the value of element zero (numerical stability) + # attn = attn * mask + """ + def masked_fill(self, mask, value): + result = self.clone() # Start with a copy of the original data + result[mask] = value # Replace values where the mask is true + return result + """ + + attn = F.softmax(attn, dim=-1) + # attn = self.dropout(F.softmax(attn, dim=-1)) + output = torch.matmul(attn, v) + + return output, attn + + +class PositionwiseFeedForward(nn.Module): + """A two-feed-forward-layer module""" + + def __init__(self, d_in, d_hid, dropout=0.1): + super().__init__() + self.w_1 = nn.Linear(d_in, d_hid) # position-wise + self.w_2 = nn.Linear(d_hid, d_in) # position-wise + self.layer_norm = nn.LayerNorm(d_in, eps=1e-6) + # self.dropout = nn.Dropout(dropout) + + def forward(self, x): + residual = x + + x = self.w_2(F.relu(self.w_1(x))) + # x = self.dropout(x) + x += residual + + x = self.layer_norm(x) + + return x + + +class PoswiseFF_emb4enc(nn.Module): + """A two-feed-forward-layer module (tailored to encoder for DFT model's input) inspired code from Transformer's encoder""" + + def __init__(self, d_in, d_hid, d_out, dropout=0.1): + super().__init__() + self.w_1 = nn.Linear(d_in, d_hid) # position-wise + self.w_2 = nn.Linear(d_hid, d_out) # position-wise + self.w_match = nn.Linear(d_in, d_out) # position-wise + # self.post_layer_norm = nn.LayerNorm(d_out, eps=1e-6) + self.pre_layer_norm = nn.LayerNorm(d_in, eps=1e-6) + # self.dropout = nn.Dropout(dropout) + + def forward(self, x): + # embedding for residual input + emb_residual = self.w_match(x) + + # Pre-layer normalization + x = self.pre_layer_norm(x) + + # Transform the (normalized) input + x = self.w_2( + F.elu(self.w_1(x)) + ) ## default: ReLU | or F.leaky_relu, LeakyReLU used to handle dying gradients, espeically when dense outputs are expected, so that it wouldn't lose expressiveness for Transformer due to lack of info + # x = self.dropout(x) + + # Post-layer normaliation + # x = self.post_layer_norm(x) + + # Residual connection + x += emb_residual + + return x + + +class PreLNPositionwiseFeedForward(nn.Module): + """A two-feed-forward-layer module""" + + def __init__(self, d_in, d_hid, dropout=0.1): + super().__init__() + self.w_1 = nn.Linear(d_in, d_hid) # position-wise + self.w_2 = nn.Linear(d_hid, d_in) # position-wise + self.layer_norm = nn.LayerNorm(d_in, eps=1e-6) + # self.dropout = nn.Dropout(dropout) + + def forward(self, x): + residual = x + + x = self.layer_norm(x) + + x = self.w_2(F.leaky_relu(self.w_1(x))) ## default: F.relu + # x = self.dropout(x) + x += residual + + return x + + +def make_embedding_encoder( + config, input_channels: int, output_channels: int +) -> Optional[nn.Module]: + emb_enc_type = config.get("type", "none") + non_linearity = nn.ELU() # make configurable + if emb_enc_type == "none": + return None + elif emb_enc_type == "pwf": + return PoswiseFF_emb4enc(input_channels, 2 * output_channels, output_channels) + elif emb_enc_type == "ff": + return nn.Sequential( + nn.Linear(input_channels, 2 * output_channels, bias=True), + non_linearity, + nn.Linear(2 * output_channels, output_channels, bias=True), + ) ## default: ReLU | nn.LeakyReLU() + elif emb_enc_type == "ffh": + return nn.Sequential( + nn.Linear(input_channels, output_channels, bias=True) + ) ## default: ReLU | nn.LeakyReLU() + elif emb_enc_type == "hpwf": + return nn.Sequential( ## == mlp.PositionwiseFeedForward + nn.Linear(input_channels, 2 * output_channels, bias=True), + non_linearity, + nn.LayerNorm(2 * output_channels, eps=1e-6), + nn.Linear(2 * output_channels, output_channels, bias=True), + ) + else: + raise NotImplementedError( + "__unrecognized input for emb_enc, not using an embedding encoder." + ) + return None + + +class MultiHeadAttention(nn.Module): + """Multi-Head Attention module""" + + def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): + super().__init__() + + self.n_head = n_head + self.d_k = d_k + self.d_v = d_v + + self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False) + self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False) + self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False) + self.fc = nn.Linear(n_head * d_v, d_model, bias=False) + + self.attention = ScaledDotProductAttention(temperature=d_k**0.5) + + # self.dropout = nn.Dropout(dropout) + self.layer_norm = nn.LayerNorm(d_model, eps=1e-6) + + def forward(self, q, k, v, mask=None): + d_k, d_v, n_head = self.d_k, self.d_v, self.n_head + sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1) + + residual = q + + # Pass through the pre-attention projection: b x lq x (n*dv) + # Separate different heads: b x lq x n x dv + q = self.w_qs(q).view(sz_b, len_q, n_head, d_k) + k = self.w_ks(k).view(sz_b, len_k, n_head, d_k) + v = self.w_vs(v).view(sz_b, len_v, n_head, d_v) + + # Transpose for attention dot product: b x n x lq x dv + q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) + + if mask is not None: + mask = mask.unsqueeze(1) # For head axis broadcasting. + + q, attn = self.attention(q, k, v, mask=mask) + + # Transpose to move the head dimension back: b x lq x n x dv + # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv) + q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1) + # q = self.dropout(self.fc(q)) + q = self.fc(q) + q += residual + + q = self.layer_norm(q) + + return q, attn + + +class PreLNMultiHeadAttention(nn.Module): + """Multi-Head Attention module""" + + def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): + super().__init__() + + self.n_head = n_head + self.d_k = d_k + self.d_v = d_v + + self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False) + self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False) + self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False) + self.fc = nn.Linear(n_head * d_v, d_model, bias=False) + + self.attention = ScaledDotProductAttention(temperature=d_k**0.5) + + # self.dropout = nn.Dropout(dropout) + self.layer_norm = nn.LayerNorm(d_model, eps=1e-6) + + def forward(self, q, k, v, mask=None): + d_k, d_v, n_head = self.d_k, self.d_v, self.n_head + sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1) + + residual = q + + q = self.layer_norm(q) + # Pass through the pre-attention projection: b x lq x (n*dv) + # Separate different heads: b x lq x n x dv + q = self.w_qs(q).view(sz_b, len_q, n_head, d_k) + k = self.w_ks(k).view(sz_b, len_k, n_head, d_k) + v = self.w_vs(v).view(sz_b, len_v, n_head, d_v) + + # Transpose for attention dot product: b x n x lq x dv + q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) + + if mask is not None: + mask = mask.unsqueeze(1) # For head axis broadcasting. + + q, attn = self.attention(q, k, v, mask=mask) + + # Transpose to move the head dimension back: b x lq x n x dv + # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv) + q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1) + # q = self.dropout(self.fc(q)) + q = self.fc(q) + q += residual + + return q, attn + + +class EncoderLayer(nn.Module): + """Compose with two layers""" + + def __init__( + self, d_model, d_inner, n_head, d_k, d_v, dropout=0, pre_ln: bool = False + ): + super(EncoderLayer, self).__init__() + if pre_ln: + self.slf_attn = PreLNMultiHeadAttention( + n_head, d_model, d_k, d_v, dropout=dropout + ) + self.pos_ffn = PreLNPositionwiseFeedForward( + d_model, d_inner, dropout=dropout + ) + else: + self.slf_attn = MultiHeadAttention( + n_head, d_model, d_k, d_v, dropout=dropout + ) + self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout) + + def forward(self, enc_input, slf_attn_mask=None): + enc_output, enc_slf_attn = self.slf_attn( + enc_input, enc_input, enc_input, mask=slf_attn_mask + ) + enc_output = self.pos_ffn(enc_output) + return enc_output, enc_slf_attn + + +"""(modified) Transformer arch from Pytorch library +to be compatible with nn.TransformerEncoder() as input arg""" + + +class TrEnLayer(nn.Module): + r""" + Args: + encoder_layer: an instance of the TransformerEncoderLayer() class (required). + num_layers: the number of sub-encoder-layers in the encoder (required). + norm: the layer normalization component (optional). + enable_nested_tensor: if True, input will automatically convert to nested tensor + (and convert back on output). This will improve the overall performance of + TransformerEncoder when padding rate is high. Default: ``True`` (enabled). + """ + + def __init__( + self, + encoder_layer, + num_layers, + norm=None, + enable_nested_tensor=True, + mask_check=True, + ): + super(TrEnLayer, self).__init__() + # self.layers = nn.ModuleList([deepcopy(encoder_layer) for _ in range(num_layers)]) + self.layers = TTF._get_clones(encoder_layer, num_layers) ## deep copy + self.num_layers = num_layers + self.norm = norm + self.enable_nested_tensor = enable_nested_tensor + self.mask_check = mask_check + + def forward( + self, + src: torch.Tensor, + mask: Optional[torch.Tensor] = None, + src_key_padding_mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + r"""Pass the input through the encoder layers in turn. + + Args: + src: the sequence to the encoder (required). + mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + see the docs in Transformer class. + """ + if src_key_padding_mask is not None: + _skpm_dtype = src_key_padding_mask.dtype + if _skpm_dtype != torch.bool and not torch.is_floating_point( + src_key_padding_mask + ): + raise AssertionError( + "only bool and floating types of key_padding_mask are supported" + ) + output = src + convert_to_nested = False + first_layer = self.layers[0] + src_key_padding_mask_for_layers = src_key_padding_mask + why_not_sparsity_fast_path = "" + str_first_layer = "self.layers[0]" + + # if not isinstance(first_layer, EncoderLayer): + # why_not_sparsity_fast_path = f"{str_first_layer} was not IBR EncoderLayer" + # elif first_layer.norm_first : + # why_not_sparsity_fast_path = f"{str_first_layer}.norm_first was True" + # elif first_layer.training: + # why_not_sparsity_fast_path = f"{str_first_layer} was in training mode" + # elif not first_layer.self_attn.batch_first: + # why_not_sparsity_fast_path = f" {str_first_layer}.self_attn.batch_first was not True" + # elif not first_layer.self_attn._qkv_same_embed_dim: + # why_not_sparsity_fast_path = f"{str_first_layer}.self_attn._qkv_same_embed_dim was not True" + # elif not first_layer.activation_relu_or_gelu: + # why_not_sparsity_fast_path = f" {str_first_layer}.activation_relu_or_gelu was not True" + # elif not (first_layer.norm1.eps == first_layer.norm2.eps) : + # why_not_sparsity_fast_path = f"{str_first_layer}.norm1.eps was not equal to {str_first_layer}.norm2.eps" + # elif not src.dim() == 3: + # why_not_sparsity_fast_path = f"input not batched; expected src.dim() of 3 but got {src.dim()}" + # elif not self.enable_nested_tensor: + # why_not_sparsity_fast_path = "enable_nested_tensor was not True" + # elif src_key_padding_mask is None: + # why_not_sparsity_fast_path = "src_key_padding_mask was None" + # elif (((not hasattr(self, "mask_check")) or self.mask_check) + # and not torch._nested_tensor_from_mask_left_aligned(src, src_key_padding_mask.logical_not())): + # why_not_sparsity_fast_path = "mask_check enabled, and src and src_key_padding_mask was not left aligned" + # elif output.is_nested: + # why_not_sparsity_fast_path = "NestedTensor input is not supported" + # elif mask is not None: + # why_not_sparsity_fast_path = "src_key_padding_mask and mask were both supplied" + # elif first_layer.self_attn.num_heads % 2 == 1: + # why_not_sparsity_fast_path = "num_head is odd" + # elif torch.is_autocast_enabled(): + # why_not_sparsity_fast_path = "autocast is enabled" + # + # if not why_not_sparsity_fast_path: + # tensor_args = ( + # src, + # first_layer.self_attn.in_proj_weight, + # first_layer.self_attn.in_proj_bias, + # first_layer.self_attn.out_proj.weight, + # first_layer.self_attn.out_proj.bias, + # first_layer.norm1.weight, + # first_layer.norm1.bias, + # first_layer.norm2.weight, + # first_layer.norm2.bias, + # first_layer.linear1.weight, + # first_layer.linear1.bias, + # first_layer.linear2.weight, + # first_layer.linear2.bias, + # ) + # + # if torch.overrides.has_torch_function(tensor_args): + # why_not_sparsity_fast_path = "some Tensor argument has_torch_function" + # elif not (src.is_cuda or 'cpu' in str(src.device)): + # why_not_sparsity_fast_path = "src is neither CUDA nor CPU" + # elif torch.is_grad_enabled() and any(x.requires_grad for x in tensor_args): + # why_not_sparsity_fast_path = ("grad is enabled and at least one of query or the " + # "input/output projection weights or biases requires_grad") + # + # if (not why_not_sparsity_fast_path) and (src_key_padding_mask is not None): + # convert_to_nested = True + # output = torch._nested_tensor_from_mask(output, src_key_padding_mask.logical_not(), mask_check=False) + # src_key_padding_mask_for_layers = None + + for mod in self.layers: + # output = mod(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask_for_layers) + output = mod(output, slf_attn_mask=src_key_padding_mask_for_layers)[0] + + if convert_to_nested: + output = output.to_padded_tensor(0.0) + + if self.norm is not None: + output = self.norm(output) + + return output + + +# class TrEnLayer(torch.nn.Module): +# def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, +# activation="relu", batch_first=True, norm_first=False, +# activation_relu_or_gelu=True): +# super(TransformerEncoderLayer, self).__init__() +# self.self_attn = torch.nn.MultiheadAttention(d_model, nhead, dropout=dropout) +# # Implementation of Feedforward model +# self.linear1 = torch.nn.Linear(d_model, dim_feedforward) +# self.dropout = torch.nn.Dropout(dropout) +# self.linear2 = torch.nn.Linear(dim_feedforward, d_model) +# +# self.norm1 = torch.nn.LayerNorm(d_model) +# self.norm2 = torch.nn.LayerNorm(d_model) +# self.dropout1 = torch.nn.Dropout(dropout) +# self.dropout2 = torch.nn.Dropout(dropout) +# +# # Legacy string support for activation function. +# if isinstance(activation, str): +# self.activation = _get_activation_fn(activation) +# else: +# self.activation = activation +# +# self.pos_ffn = PositionwiseFeedForward(d_model, dim_feedforward, dropout) +# +# self.self_attn.batch_first = batch_first +# self.self_attn._qkv_same_embed_dim = True # assuming d_model is the same for query, key, value +# self.norm_first = norm_first +# self.activation_relu_or_gelu = activation_relu_or_gelu +# +# def forward(self, src, src_mask=None, src_key_padding_mask=None): +# src2 = self.self_attn(src, src, src, attn_mask=src_mask, +# key_padding_mask=src_key_padding_mask)[0] +# if self.norm_first: +# src = src + self.dropout1(src2) +# src = self.norm1(src) +# src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) +# src = src + self.dropout2(src2) +# src = self.norm2(src) +# else: +# src = self.norm1(src) +# src = src + self.dropout1(src2) +# src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) +# src = self.norm2(src) +# src = src + self.dropout2(src2) +# return src + +# ''' +# c.f. nn.transformer.py +# ''' +# def _get_activation_fn(activation: str) -> Callable[[Tensor], Tensor]: +# if activation == "relu": +# return F.relu +# elif activation == "gelu": +# return F.gelu +# +# raise RuntimeError("activation should be relu/gelu, not {}".format(activation)) +# +# def _get_clones(module, N): +# return ModuleList([copy.deepcopy(module) for i in range(N)]) diff --git a/scenedino/models/prediction_heads/mlp_util.py b/scenedino/models/prediction_heads/mlp_util.py new file mode 100644 index 0000000000000000000000000000000000000000..1c986da7413102e80606f0dd04c7f2fbe1d2d058 --- /dev/null +++ b/scenedino/models/prediction_heads/mlp_util.py @@ -0,0 +1,17 @@ +from .mlp import ImplicitNet +from .resnetfc import ResnetFC + + +def make_mlp(conf, d_in, d_latent=0, allow_empty=False, **kwargs): + mlp_type = conf.get("type", "mlp") # mlp | resnet + if mlp_type == "mlp": + net = ImplicitNet.from_conf(conf, d_in + d_latent, **kwargs) + elif mlp_type == "resnet": + net = ResnetFC.from_conf(conf, d_in, d_latent=d_latent, **kwargs) + elif mlp_type == "resnet2": + net = ResnetFC.from_conf2(conf, d_in, d_latent=d_latent, **kwargs) + elif mlp_type == "empty" and allow_empty: + net = None + else: + raise NotImplementedError("Unsupported MLP type") + return net diff --git a/scenedino/models/prediction_heads/multi_view_head.py b/scenedino/models/prediction_heads/multi_view_head.py new file mode 100644 index 0000000000000000000000000000000000000000..74701c602b5eba164d1cbf9bd3bfa82574224c86 --- /dev/null +++ b/scenedino/models/prediction_heads/multi_view_head.py @@ -0,0 +1,490 @@ +import torch +import torch.nn as nn +from torch.nn import TransformerEncoder, TransformerEncoderLayer +from . import mlp +from .resnetfc import ResnetFC +from .independent_tokens import BaseIndependentToken, make_independent_token + +""" +# BTS model: The BTS model is used to predict the density field for each view of the input images. +The predicted density fields are stored in a list. + +# Stacking density fields: The predicted density fields from the BTS model are stacked along a new dimension, +creating a tensor of shape (batch_size, num_views, height, width). + +# Flattening and embedding: The density fields tensor is reshaped to (batch_size, num_views, height * width), e.g. features are stacked along the row, +and then passed through an embedding layer that converts the density field values into a suitable format for the Transformer. +The embedding layer is a linear layer that maps the input features to the desired dimension d_model. + +# Transformer encoder: The embedded features are processed by a Transformer encoder, which consists of multiple layers + of multi-head self-attention and feedforward sublayers. The Transformer encoder is designed to learn and capture + relationships between the multiple views by attending to the most relevant parts of the input features. (density field as geometric consistency) + The output of the Transformer encoder has the same shape as the input, (batch_size, num_views, d_model). + +# Density field prediction: The transformed features are passed through a density field prediction layer, +which is a sequential model containing a linear layer followed by a ReLU activation function. This layer predicts +the accumulated density field for each pixel. The output shape is (batch_size, num_views, 1). + +# Reshaping: The accumulated density field tensor is reshaped back to its original spatial dimensions (batch_size, height, width). +""" + + +def make_attn_layers(config, ndim: int) -> nn.Module: + num_layers = config.get("n_layers", 3) + n_heads = config.get("n_heads", 4) + use_built_in = config.get("IBRAttn", False) + if use_built_in: + transformer_enlayer = mlp.EncoderLayer(ndim, ndim, n_heads, ndim, ndim) + return mlp.TrEnLayer( + transformer_enlayer, num_layers + ) ## TODO: replace MHA module with IBRNet network and complete integretable encoder part of transformer + elif not use_built_in: + transformer_enlayer = TransformerEncoderLayer( + ndim, n_heads, dim_feedforward=ndim, batch_first=True + ) + return TransformerEncoder(transformer_enlayer, num_layers) + else: + raise NotImplementedError(f"__unrecognized use_built_in: {use_built_in}") + + +class MultiViewHead(nn.Module): + def __init__( + self, + emb_encoder: nn.Module | None, + independent_token_net: BaseIndependentToken, + attn_layers: nn.Module, + density_head: nn.Module, + do_: float = 0.0, + do_mvh: bool = False, + ): + """Attention based feature aggregation module for multi-view density prediction. + + Args: + emb_encoder (nn.Module, optional): small network to compress the per view feature vectors to a lower dimensional representation. Defaults to Optional[nn.Module]. + independent_feature_net (nn.Module, optional): module to generate the view independent token from the view dependent tokens. Defaults to nn.Module. + attn_layers (nn.Module, optional): attention layers of the module responsible for information sharing between the views. Defaults to nn.Module. + density_head (nn.Module, optional): final network layers to predict the density from the view independent token. Defaults to nn.Module. + do_ (float, optional): probability of dropping out a single view for training. Defaults to 0.0. + do_mvh (bool, optional): to decide whether the first view feature map should be droppout due to pgt_loss computation. Defaults to 0.0. + """ + + super(MultiViewHead, self).__init__() + self.emb_encoder = emb_encoder + + self.independent_token_net = independent_token_net + self.require_bottleneck_feats = ( + self.independent_token_net.require_bottleneck_feats + ) + self.attn_layers = attn_layers + + self.dropout = nn.Dropout(do_) + self.do_mvh = do_mvh + + self.density_head = density_head + + def forward( + self, sampled_features, **kwargs + ): ### [n_, nv_, M, C1+C_pos_emb], [nv_==2, M==100000, C==1] + ## invalid_features: invalid features to mask the features to let model learn without occluded points in the camera's view + invalid_features = kwargs.get("invalid_features", None) + assert isinstance( + invalid_features, torch.Tensor + ), f"__The {invalid_features} is not a torch.Tensor." + assert ( + invalid_features.dtype == torch.bool + ), f"The elements of the {invalid_features} are not boolean." + # invalid_features = (invalid_features > 0.5) ## round the each of values of 3D points simply by step function within the range of std_var [0,1] + + if ( + self.dropout.p != 0 and self.do_mvh + ): ## dropping out except first view feature map due to pgt_loss computation + invalid_features = torch.concat( + [ + invalid_features[:, :1], + 1 - self.dropout((1 - invalid_features[:, 1:].float())), + ], + dim=1, + ) + elif self.dropout.p != 0 and not self.do_mvh: + invalid_features = 1 - self.dropout( + (1 - invalid_features.float()) + ) ## Note: after dropping out NeuRay, the values of elements are 2. ## randomly zero out the valid sampled_features' matrix. i.e. (1-invalid_features) + elif self.dropout.p == 0 and not self.do_mvh: + pass + else: + raise NotImplementedError( + f"__unrecognized self.dropout: {self.dropout}, self.do_mvh: {self.do_mvh} condition" + ) + + if self.emb_encoder is not None: + encoded_features = self.emb_encoder( + sampled_features.flatten(0, -2) + ).reshape( + sampled_features.shape[:-1] + (-1,) + ) ### [M*n==100000, nv_==6, 32] ## Embedding to Transformer arch. + else: + encoded_features = sampled_features.flatten(0, -2).reshape( + sampled_features.shape[:-1] + (-1,) + ) + + ## Process the embedded features with the Transformer + view_independent_feature = self.independent_token_net( + encoded_features, **kwargs + ).to(encoded_features.device) + + # padding + padded_features = torch.concat( + [view_independent_feature, encoded_features], dim=1 + ) ### (B*n_pts, nv_+1, 103) == ([100000, 2+1, 103]): padding along the num_token dim. B*n_pts:=Batch size or number of data points being processed. + padded_invalid = torch.concat( ## Note: view_independent_feature is 1st index in Tensor (:,0,:) + [ + torch.zeros(invalid_features.shape[0], 1, device="cuda"), + invalid_features, + ], + dim=1, + ) + + transformed_features = self.attn_layers( + src=padded_features, src_key_padding_mask=padded_invalid + )[ + :, 0, : + ] # [n_pts, C] ##Note: remember the tensor shape is batch-first mode, sequence length is determined by the size of the first dimension of the input tensor + ## ## first token refers to the readout token where it stores the feature information accumulated from the layers + ## TODO: GeoNeRF: Identify readout token belongs to single ray: M should be divisable by nhead, so that it can feed into AE, Note: make sure sampled points are in valid in the mask. (camera frustum) + ## !TODO: Q K^T V each element of which is a density field prediction for a corresponding 3D point. + density_field = self.density_head(transformed_features) + + return density_field + + @classmethod + def from_conf(cls, conf, d_in, d_out): + d_enc = conf["embedding_encoder"].get("d_out", d_in) + embedding_encoder = mlp.make_embedding_encoder( + conf["embedding_encoder"], d_in, d_enc + ) + attn_layers = make_attn_layers(conf["attn_layers"], d_enc) + independent_token = make_independent_token(conf["independent_token"], d_enc) + probing_layer = nn.Sequential( + nn.Linear(d_enc, d_enc // 2), nn.ELU(), nn.Linear(d_enc // 2, d_out) + ) ## This FFNet is how the final density field scalar element is inferred. + return cls( + embedding_encoder, + independent_token, + attn_layers, + probing_layer, + conf.get("dropout_views_rate", 0.0), + conf.get("dropout_multiviewhead", False), + ) + + +class SimpleMultiViewHead(nn.Module): + def __init__( + self, + mlp: nn.Module, + do_: float = 0.0, + do_mvh: bool = True, + ): + """Attention based feature aggregation module for multi-view density prediction. + + Args: + emb_encoder (nn.Module, optional): small network to compress the per view feature vectors to a lower dimensional representation. Defaults to Optional[nn.Module]. + independent_feature_net (nn.Module, optional): module to generate the view independent token from the view dependent tokens. Defaults to nn.Module. + attn_layers (nn.Module, optional): attention layers of the module responsible for information sharing between the views. Defaults to nn.Module. + density_head (nn.Module, optional): final network layers to predict the density from the view independent token. Defaults to nn.Module. + do_ (float, optional): probability of dropping out a single view for training. Defaults to 0.0. + do_mvh (bool, optional): to decide whether the first view feature map should be droppout due to pgt_loss computation. Defaults to 0.0. + """ + + super(SimpleMultiViewHead, self).__init__() + + self.dropout = nn.Dropout(do_) + self.do_mvh = do_mvh + + self.mlp = mlp + + def forward( + self, sampled_features, **kwargs + ): ### [n_, nv_, M, C1+C_pos_emb], [nv_==2, M==100000, C==1] + ## invalid_features: invalid features to mask the features to let model learn without occluded points in the camera's view + invalid_features = kwargs.get("invalid_features", None) + assert isinstance( + invalid_features, torch.Tensor + ), f"__The {invalid_features} is not a torch.Tensor." + assert ( + invalid_features.dtype == torch.bool + ), f"The elements of the {invalid_features} are not boolean." + # invalid_features = (invalid_features > 0.5) ## round the each of values of 3D points simply by step function within the range of std_var [0,1] + + if ( + self.dropout.p != 0 and self.do_mvh + ): ## dropping out except first view feature map due to pgt_loss computation + invalid_features = torch.concat( + [ + invalid_features[:, :1], + 1 - self.dropout((1 - invalid_features[:, 1:].float())), + ], + dim=1, + ) + elif self.dropout.p != 0 and not self.do_mvh: + invalid_features = 1 - self.dropout( + (1 - invalid_features.float()) + ) ## Note: after dropping out NeuRay, the values of elements are 2. ## randomly zero out the valid sampled_features' matrix. i.e. (1-invalid_features) + elif self.dropout.p == 0 and not self.do_mvh: + pass + else: + raise NotImplementedError( + f"__unrecognized self.dropout: {self.dropout}, self.do_mvh: {self.do_mvh} condition" + ) + + output = self.mlp(sampled_features) + + weights = torch.nn.functional.softmax( + output[..., 0].masked_fill(invalid_features == 1, -1e9), dim=-1 + ) + + density_field = torch.sum(output[..., 1:] * weights.unsqueeze(-1), dim=-2) + + return density_field + + @classmethod + def from_conf(cls, conf, d_in, d_out): + mlp = ResnetFC.from_conf(conf["mlp"]["args"], d_in, d_out + 1) + return cls( + mlp, + conf.get("dropout_views_rate", 0.0), + conf.get("dropout_multiviewhead", False), + ) + + +class MultiViewHead2(nn.Module): + def __init__( + self, + mlp: nn.Module, + do_: float = 0.0, + do_mvh: bool = True, + attn_layers: nn.Module | None = None, + independent_token_net: BaseIndependentToken | None = None, + mlp2: nn.Module | None = None, + ): + """Attention based feature aggregation module for multi-view density prediction. + + Args: + emb_encoder (nn.Module, optional): small network to compress the per view feature vectors to a lower dimensional representation. Defaults to Optional[nn.Module]. + independent_feature_net (nn.Module, optional): module to generate the view independent token from the view dependent tokens. Defaults to nn.Module. + attn_layers (nn.Module, optional): attention layers of the module responsible for information sharing between the views. Defaults to nn.Module. + density_head (nn.Module, optional): final network layers to predict the density from the view independent token. Defaults to nn.Module. + do_ (float, optional): probability of dropping out a single view for training. Defaults to 0.0. + do_mvh (bool, optional): to decide whether the first view feature map should be droppout due to pgt_loss computation. Defaults to 0.0. + """ + + super(MultiViewHead2, self).__init__() + + self.dropout = nn.Dropout(do_) + self.do_mvh = do_mvh + + self.mlp = mlp + + self.attn_layers = attn_layers + self.independent_token = independent_token_net + self.mlp2 = mlp2 + + def forward( + self, sampled_features, **kwargs + ): ### [n_, nv_, M, C1+C_pos_emb], [nv_==2, M==100000, C==1] + ## invalid_features: invalid features to mask the features to let model learn without occluded points in the camera's view + invalid_features = kwargs.get("invalid_features", None) + assert isinstance( + invalid_features, torch.Tensor + ), f"__The {invalid_features} is not a torch.Tensor." + assert ( + invalid_features.dtype == torch.bool + ), f"The elements of the {invalid_features} are not boolean." + # invalid_features = (invalid_features > 0.5) ## round the each of values of 3D points simply by step function within the range of std_var [0,1] + + if ( + self.dropout.p != 0 and self.do_mvh + ): ## dropping out except first view feature map due to pgt_loss computation + invalid_features = torch.concat( + [ + invalid_features[:, :1], + 1 - self.dropout((1 - invalid_features[:, 1:].float())), + ], + dim=1, + ) + elif self.dropout.p != 0 and not self.do_mvh: + invalid_features = 1 - self.dropout( + (1 - invalid_features.float()) + ) ## Note: after dropping out NeuRay, the values of elements are 2. ## randomly zero out the valid sampled_features' matrix. i.e. (1-invalid_features) + elif self.dropout.p == 0 and not self.do_mvh: + pass + else: + raise NotImplementedError( + f"__unrecognized self.dropout: {self.dropout}, self.do_mvh: {self.do_mvh} condition" + ) + + encoded_features = self.mlp(sampled_features) + + if self.independent_token is not None: + view_independent_feature = self.independent_token( + encoded_features, **kwargs + ).to(encoded_features.device) + + # padding + encoded_features = torch.concat( + [view_independent_feature, encoded_features], dim=1 + ) ### (B*n_pts, nv_+1, 103) == ([100000, 2+1, 103]): padding along the num_token dim. B*n_pts:=Batch size or number of data points being processed. + invalid_features = torch.concat( ## Note: view_independent_feature is 1st index in Tensor (:,0,:) + [ + torch.zeros(invalid_features.shape[0], 1, device="cuda"), + invalid_features, + ], + dim=1, + ) + + if self.attn_layers is not None: + encoded_features = self.attn_layers( + encoded_features, src_key_padding_mask=invalid_features + ) + + if self.independent_token is not None: + if self.mlp2 is not None: + return self.mlp2(encoded_features[..., 0, :]) + else: + return encoded_features[..., 0, 1:] + else: + if self.mlp2 is not None: + encoded_features = self.mlp2(encoded_features) + + weights = torch.nn.functional.softmax( + encoded_features[..., 0].masked_fill(invalid_features == 1, -1e9), + dim=-1, + ) + return torch.sum(encoded_features[..., 1:] * weights.unsqueeze(-1), dim=-2) + + # return density_field + + @classmethod + def from_conf(cls, conf, d_in, d_out): + if conf["mlp2"] is not None: + d_out_mlp = conf["mlp2"]["d_in"] + else: + d_out_mlp = d_out + 1 + mlp = ResnetFC.from_conf(conf["mlp"]["args"], d_in, d_out_mlp) + + if conf["attn_layers"] is not None: + attn_layers = make_attn_layers(conf["attn_layers"], d_out_mlp) + else: + attn_layers = None + + if conf["independent_token"] is not None: + independent_token = make_independent_token( + conf["independent_token"], d_out_mlp + ) + else: + independent_token = None + + if conf["mlp2"] is not None: + if conf["independent_token"] is not None: + d_out_mlp2 = d_out + else: + d_out_mlp2 = d_out + 1 + mlp2 = ResnetFC.from_conf(conf["mlp2"]["args"], d_out_mlp, d_out_mlp2) + else: + mlp2 = None + + return cls( + mlp, + conf.get("dropout_views_rate", 0.0), + conf.get("dropout_multiviewhead", False), + attn_layers, + independent_token, + mlp2, + ) + + +class MultiViewHead3(nn.Module): + def __init__( + self, + mlp: nn.Module, + mlp2: nn.Module, + do_: float = 0.0, + do_mvh: bool = True, + ): + """Attention based feature aggregation module for multi-view density prediction. + + Args: + emb_encoder (nn.Module, optional): small network to compress the per view feature vectors to a lower dimensional representation. Defaults to Optional[nn.Module]. + independent_feature_net (nn.Module, optional): module to generate the view independent token from the view dependent tokens. Defaults to nn.Module. + attn_layers (nn.Module, optional): attention layers of the module responsible for information sharing between the views. Defaults to nn.Module. + density_head (nn.Module, optional): final network layers to predict the density from the view independent token. Defaults to nn.Module. + do_ (float, optional): probability of dropping out a single view for training. Defaults to 0.0. + do_mvh (bool, optional): to decide whether the first view feature map should be droppout due to pgt_loss computation. Defaults to 0.0. + """ + + super(MultiViewHead3, self).__init__() + + self.dropout = nn.Dropout(do_) + self.do_mvh = do_mvh + + self.mlp = mlp + + self.mlp2 = mlp2 + + def forward( + self, sampled_features, **kwargs + ): ### [n_, nv_, M, C1+C_pos_emb], [nv_==2, M==100000, C==1] + ## invalid_features: invalid features to mask the features to let model learn without occluded points in the camera's view + invalid_features = kwargs.get("invalid_features", None) + assert isinstance( + invalid_features, torch.Tensor + ), f"__The {invalid_features} is not a torch.Tensor." + assert ( + invalid_features.dtype == torch.bool + ), f"The elements of the {invalid_features} are not boolean." + # invalid_features = (invalid_features > 0.5) ## round the each of values of 3D points simply by step function within the range of std_var [0,1] + + if ( + self.dropout.p != 0 and self.do_mvh + ): ## dropping out except first view feature map due to pgt_loss computation + invalid_features = torch.concat( + [ + invalid_features[:, :1], + 1 - self.dropout((1 - invalid_features[:, 1:].float())), + ], + dim=1, + ) + elif self.dropout.p != 0 and not self.do_mvh: + invalid_features = 1 - self.dropout( + (1 - invalid_features.float()) + ) ## Note: after dropping out NeuRay, the values of elements are 2. ## randomly zero out the valid sampled_features' matrix. i.e. (1-invalid_features) + elif self.dropout.p == 0 and not self.do_mvh: + pass + else: + raise NotImplementedError( + f"__unrecognized self.dropout: {self.dropout}, self.do_mvh: {self.do_mvh} condition" + ) + + encoded_features = self.mlp(sampled_features) + + weights = torch.nn.functional.softmax( + encoded_features[..., 0].masked_fill(invalid_features == 1, -1e9), dim=-1 + ) + + density_feature = torch.sum( + encoded_features[..., 1:] * weights.unsqueeze(-1), dim=-2 + ) + + return self.mlp2(density_feature) + + @classmethod + def from_conf(cls, conf, d_in, d_out): + mlp = ResnetFC.from_conf(conf["mlp"]["args"], d_in, conf["mlp2"]["d_in"] + 1) + + mlp2 = ResnetFC.from_conf(conf["mlp2"]["args"], conf["mlp2"]["d_in"], d_out) + + return cls( + mlp, + mlp2, + conf.get("dropout_views_rate", 0.0), + conf.get("dropout_multiviewhead", False), + ) diff --git a/scenedino/models/prediction_heads/resnetfc.py b/scenedino/models/prediction_heads/resnetfc.py new file mode 100644 index 0000000000000000000000000000000000000000..696cc6ca74844e4ec3acb9168d70944a28f3942d --- /dev/null +++ b/scenedino/models/prediction_heads/resnetfc.py @@ -0,0 +1,237 @@ +from typing import Optional +from torch import nn +import torch + +# import torch_scatter +import torch.autograd.profiler as profiler +from scenedino.common import util + + +# Resnet Blocks +class ResnetBlockFC(nn.Module): + """ + Fully connected ResNet Block class. + Taken from DVR code. + :param size_in (int): input dimension + :param size_out (int): output dimension + :param size_h (int): hidden dimension + """ + + def __init__(self, size_in, size_out=None, size_h=None, beta=0.0): + super().__init__() + # Attributes + if size_out is None: + size_out = size_in + + if size_h is None: + size_h = min(size_in, size_out) + + self.size_in = size_in + self.size_h = size_h + self.size_out = size_out + # Submodules + self.fc_0 = nn.Linear(size_in, size_h) + self.fc_1 = nn.Linear(size_h, size_out) + + # Init + nn.init.constant_(self.fc_0.bias, 0.0) + nn.init.kaiming_normal_(self.fc_0.weight, a=0, mode="fan_in") + nn.init.constant_(self.fc_1.bias, 0.0) + nn.init.zeros_(self.fc_1.weight) + + if beta > 0: + self.activation = nn.Softplus(beta=beta) + else: + self.activation = nn.ReLU() + + if size_in == size_out: + self.shortcut = None + else: + self.shortcut = nn.Linear(size_in, size_out, bias=False) + nn.init.constant_(self.shortcut.bias, 0.0) + nn.init.kaiming_normal_(self.shortcut.weight, a=0, mode="fan_in") + + def forward(self, x): + with profiler.record_function("resblock"): + net = self.fc_0(self.activation(x)) + dx = self.fc_1(self.activation(net)) + + if self.shortcut is not None: + x_s = self.shortcut(x) + else: + x_s = x + return x_s + dx + + +class ResnetFC(nn.Module): + def __init__( + self, + d_in, + view_number: Optional[int] = None, + d_out=4, + n_blocks=5, + d_latent=0, + d_hidden=128, + beta=0.0, + combine_layer=1000, + combine_type="average", + use_spade=False, + ): + """ + :param d_in input size + :param d_out output size + :param n_blocks number of Resnet blocks + :param d_latent latent size, added in each resnet block (0 = disable) + :param d_hidden hiddent dimension throughout network + :param beta softplus beta, 100 is reasonable; if <=0 uses ReLU activations instead + """ + super().__init__() + if d_in > 0: + self.lin_in = nn.Linear(d_in, d_hidden) + nn.init.constant_(self.lin_in.bias, 0.0) + nn.init.kaiming_normal_(self.lin_in.weight, a=0, mode="fan_in") + + self.lin_out = nn.Linear(d_hidden, d_out) + nn.init.constant_(self.lin_out.bias, 0.0) + nn.init.kaiming_normal_(self.lin_out.weight, a=0, mode="fan_in") + + self.n_blocks = n_blocks + self.d_latent = d_latent + self.d_in = d_in + self.view_number = view_number + self.d_out = d_out + self.d_hidden = d_hidden + + self.combine_layer = combine_layer + self.combine_type = combine_type + self.use_spade = use_spade + + self.blocks = nn.ModuleList( + [ResnetBlockFC(d_hidden, beta=beta) for i in range(n_blocks)] + ) + + if d_latent != 0: + n_lin_z = min(combine_layer, n_blocks) + self.lin_z = nn.ModuleList( + [nn.Linear(d_latent, d_hidden) for i in range(n_lin_z)] + ) + for i in range(n_lin_z): + nn.init.constant_(self.lin_z[i].bias, 0.0) + nn.init.kaiming_normal_(self.lin_z[i].weight, a=0, mode="fan_in") + + if self.use_spade: + self.scale_z = nn.ModuleList( + [nn.Linear(d_latent, d_hidden) for _ in range(n_lin_z)] + ) + for i in range(n_lin_z): + nn.init.constant_(self.scale_z[i].bias, 0.0) + nn.init.kaiming_normal_(self.scale_z[i].weight, a=0, mode="fan_in") + + if beta > 0: + self.activation = nn.Softplus(beta=beta) + else: + self.activation = nn.ReLU() + + def forward( + self, + sampled_features, + combine_inner_dims=(1,), + combine_index=None, + dim_size=None, + **kwargs + ): + """ + :param zx (..., d_latent + d_in) + :param combine_inner_dims Combining dimensions for use with multiview inputs. + Tensor will be reshaped to (-1, combine_inner_dims, ...) and reduced using combine_type + on dim 1, at combine_layer + """ + with profiler.record_function("resnetfc_infer"): + if self.view_number is not None: + zx = sampled_features[..., self.view_number, :] + else: + zx = sampled_features + + assert zx.size(-1) == self.d_latent + self.d_in + + if self.d_latent > 0: + z = zx[..., : self.d_latent] + x = zx[..., self.d_latent :] + else: + x = zx + if self.d_in > 0: + x = self.lin_in(x) + else: + x = torch.zeros(self.d_hidden, device=zx.device) + + for blkid in range(self.n_blocks): + if blkid == self.combine_layer: + # The following implements camera frustum culling, requires torch_scatter + # if combine_index is not None: + # combine_type = ( + # "mean" + # if self.combine_type == "average" + # else self.combine_type + # ) + # if dim_size is not None: + # assert isinstance(dim_size, int) + # x = torch_scatter.scatter( + # x, + # combine_index, + # dim=0, + # dim_size=dim_size, + # reduce=combine_type, + # ) + # else: + x = util.combine_interleaved( + x, combine_inner_dims, self.combine_type + ) + + if self.d_latent > 0 and blkid < self.combine_layer: + tz = self.lin_z[blkid](z) + if self.use_spade: + sz = self.scale_z[blkid](z) + x = sz * x + tz + else: + x = x + tz + + x = self.blocks[blkid](x) + out = self.lin_out(self.activation(x)) + + # if kwargs["head_name"] == "singleviewhead": ## To recognize resnerfc.py that it only creates singleview feature map for pgt loss + # return out[:,0,:] ## Take 1st feature map as viz frame as evluation purpose mono camera + return out + + @classmethod ## For foward_hook arguments matching: For multi view BTS model + # def from_conf(cls, conf, d_in, d_out): ## default + def from_conf(cls, conf, d_in, d_out, d_latent=0): + return cls(d_in=d_in, d_out=d_out, **conf) + + @classmethod ## default for original resnetfc.py + def from_conf2(cls, conf, d_in, **kwargs): + # PyHocon construction + return cls( + d_in, + n_blocks=conf.get("n_blocks", 5), + d_hidden=conf.get("d_hidden", 128), + beta=conf.get("beta", 0.0), + combine_layer=conf.get("combine_layer", 1000), + combine_type=conf.get("combine_type", "average"), # average | max + use_spade=conf.get("use_spade", False), + **kwargs + ) + + # @classmethod ## For both multi and single view BTS model (integrated from both classmethod) + # def from_conf(cls, conf, d_in, d_out, **kwargs): + # # PyHocon construction + # return cls( + # d_out = d_out, + # d_in = d_in, + # n_blocks = conf.get("n_blocks", 5), + # d_hidden = conf.get("d_hidden", 128), + # beta = conf.get("beta", 0.0), + # combine_layer = conf.get("combine_layer", 1000), + # combine_type = conf.get("combine_type", "average"), # average | max + # use_spade = conf.get("use_spade", False), + # **kwargs + # ) diff --git a/scenedino/renderer/__init__.py b/scenedino/renderer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..384c8b189f47c41b304b4cd77abdfd67d5f77086 --- /dev/null +++ b/scenedino/renderer/__init__.py @@ -0,0 +1 @@ +from .nerf import NeRFRenderer diff --git a/scenedino/renderer/nerf.py b/scenedino/renderer/nerf.py new file mode 100644 index 0000000000000000000000000000000000000000..f44a9f378ce8f976e4dc61351d8c422fef255d57 --- /dev/null +++ b/scenedino/renderer/nerf.py @@ -0,0 +1,658 @@ +""" +NeRF differentiable renderer. +References: +https://github.com/bmild/nerf +https://github.com/kwea123/nerf_pl +""" +import torch +import torch.autograd.profiler as profiler +from dotmap import DotMap + + +class _RenderWrapper(torch.nn.Module): + def __init__(self, net, renderer, simple_output): + super().__init__() + self.net = net + self.renderer = renderer + self.simple_output = simple_output + + def forward( + self, + rays, + want_weights=False, + want_alphas=False, + want_z_samps=False, + want_rgb_samps=False, + sample_from_dist=None, + ): + if rays.shape[0] == 0: + return ( + torch.zeros(0, 3, device=rays.device), + torch.zeros(0, device=rays.device), + ) + + outputs = self.renderer( + self.net, + rays, + want_weights=want_weights and not self.simple_output, + want_alphas=want_alphas and not self.simple_output, + want_z_samps=want_z_samps and not self.simple_output, + want_rgb_samps=want_rgb_samps and not self.simple_output, + sample_from_dist=sample_from_dist, + ) + if self.simple_output: + if self.renderer.using_fine: + rgb = outputs.fine.rgb + depth = outputs.fine.depth + else: + rgb = outputs.coarse.rgb + depth = outputs.coarse.depth + return rgb, depth + else: + # Make DotMap to dict to support DataParallel + return outputs.toDict() + + +class NeRFRenderer(torch.nn.Module): + """ + NeRF differentiable renderer + :param n_coarse number of coarse (binned uniform) samples + :param n_fine number of fine (importance) samples + :param n_fine_depth number of expected depth samples + :param noise_std noise to add to sigma. We do not use it + :param depth_std noise for depth samples + :param eval_batch_size ray batch size for evaluation + :param white_bkgd if true, background color is white; else black + :param lindisp if to use samples linear in disparity instead of distance + :param sched ray sampling schedule. list containing 3 lists of equal length. + sched[0] is list of iteration numbers, + sched[1] is list of coarse sample numbers, + sched[2] is list of fine sample numbers + """ + + def __init__( + self, + n_coarse=128, + n_fine=0, + n_fine_depth=0, + noise_std=0.0, + depth_std=0.01, + eval_batch_size=100000, + white_bkgd=False, + lindisp=False, + sched=None, # ray sampling schedule for coarse and fine rays + hard_alpha_cap=False, + render_mode="volumetric", + surface_sigmoid_scale=.1, + render_flow=False, + normalize_dino=False, + ): + super().__init__() + self.n_coarse, self.n_fine = n_coarse, n_fine + self.n_fine_depth = n_fine_depth + + self.noise_std = noise_std + self.depth_std = depth_std + + self.eval_batch_size = eval_batch_size + self.white_bkgd = white_bkgd + self.lindisp = lindisp + if lindisp: + print("Using linear displacement rays") + self.using_fine = n_fine > 0 + self.sched = sched + if sched is not None and len(sched) == 0: + self.sched = None + self.register_buffer( + "iter_idx", torch.tensor(0, dtype=torch.long), persistent=True + ) + self.register_buffer( + "last_sched", torch.tensor(0, dtype=torch.long), persistent=True + ) + self.hard_alpha_cap = hard_alpha_cap + + assert render_mode in ("volumetric", "surface", "neus") + self.render_mode = render_mode + self.only_surface_color = (self.render_mode == "surface") + self.surface_sigmoid_scale = surface_sigmoid_scale + self.render_flow = render_flow + self.normalize_dino = normalize_dino + + def sample_coarse(self, rays): + """ + Stratified sampling. Note this is different from original NeRF slightly. + :param rays ray [origins (3), directions (3), near (1), far (1)] (B, 8) + :return (B, Kc) + """ + device = rays.device + near, far = rays[:, 6:7], rays[:, 7:8] # (B, 1) + + step = 1.0 / self.n_coarse + B = rays.shape[0] + z_steps = torch.linspace(0, 1 - step, self.n_coarse, device=device) # (Kc) + z_steps = z_steps.unsqueeze(0).repeat(B, 1) # (B, Kc) + z_steps += torch.rand_like(z_steps) * step + if not self.lindisp: # Use linear sampling in depth space + return near * (1 - z_steps) + far * z_steps # (B, Kf) + else: # Use linear sampling in disparity space + return 1 / (1 / near * (1 - z_steps) + 1 / far * z_steps) # (B, Kf) + + # Use linear sampling in depth space + return near * (1 - z_steps) + far * z_steps # (B, Kc) + + def sample_coarse_from_dist(self, rays, weights, z_samp): + device = rays.device + B = rays.shape[0] + + num_bins = weights.shape[-1] + num_samples = self.n_coarse + + weights = weights.detach() + 1e-5 # Prevent division by zero + pdf = weights / torch.sum(weights, -1, keepdim=True) # (B, Kc) + cdf = torch.cumsum(pdf, -1) # (B, Kc) + cdf = torch.cat([torch.zeros_like(cdf[:, :1]), cdf], -1) # (B, Kc+1) + + u = torch.rand(B, num_samples, dtype=torch.float32, device=device) # (B, Kf) + interval_ids = torch.searchsorted(cdf, u, right=True) - 1 # (B, Kf) + interval_ids = torch.clamp(interval_ids, 0, num_samples - 1) + interval_interp = torch.rand_like(interval_ids, dtype=torch.float32) + + # z_samps describe the centers of the respective histogram bins. Therefore, we have to extend them to the left and right + if self.lindisp: + z_samp = 1 / z_samp + + centers = 0.5 * (z_samp[:, 1:] + z_samp[:, :-1]) + interval_borders = torch.cat((z_samp[:, :1], centers, z_samp[:, -1:]), dim=-1) + + left_border = torch.gather(interval_borders, dim=-1, index=interval_ids) + right_border = torch.gather(interval_borders, dim=-1, index=interval_ids + 1) + + z_samp_new = ( + left_border * (1 - interval_interp) + right_border * interval_interp + ) + + if self.lindisp: + z_samp_new = 1 / z_samp_new + + assert not torch.any(torch.isnan(z_samp_new)) + + return z_samp_new + + def sample_fine(self, rays, weights): + """min + Weighted stratified (importance) sample + :param rays ray [origins (3), directions (3), near (1), far (1)] (B, 8) + :param weights (B, Kc) + :return (B, Kf-Kfd) + """ + device = rays.device + B = rays.shape[0] + + weights = weights.detach() + 1e-5 # Prevent division by zero + pdf = weights / torch.sum(weights, -1, keepdim=True) # (B, Kc) + cdf = torch.cumsum(pdf, -1) # (B, Kc) + cdf = torch.cat([torch.zeros_like(cdf[:, :1]), cdf], -1) # (B, Kc+1) + + u = torch.rand( + B, self.n_fine - self.n_fine_depth, dtype=torch.float32, device=device + ) # (B, Kf) + inds = torch.searchsorted(cdf, u, right=True).float() - 1.0 # (B, Kf) + inds = torch.clamp_min(inds, 0.0) + + z_steps = (inds + torch.rand_like(inds)) / self.n_coarse # (B, Kf) + + near, far = rays[:, 6:7], rays[:, 7:8] # (B, 1) + if not self.lindisp: # Use linear sampling in depth space + z_samp = near * (1 - z_steps) + far * z_steps # (B, Kf) + else: # Use linear sampling in disparity space + z_samp = 1 / (1 / near * (1 - z_steps) + 1 / far * z_steps) # (B, Kf) + + assert not torch.any(torch.isnan(z_samp)) + + return z_samp + + def sample_fine_depth(self, rays, depth): + """ + Sample around specified depth + :param rays ray [origins (3), directions (3), near (1), far (1)] (B, 8) + :param depth (B) + :return (B, Kfd) + """ + z_samp = depth.unsqueeze(1).repeat((1, self.n_fine_depth)) + z_samp += torch.randn_like(z_samp) * self.depth_std + # Clamp does not support tensor bounds + z_samp = torch.max(torch.min(z_samp, rays[:, 7:8]), rays[:, 6:7]) + + assert not torch.any(torch.isnan(z_samp)) + + return z_samp + + def composite(self, model, rays, z_samp, coarse=True, sb=0): + """ + Render RGB and depth for each ray using NeRF alpha-compositing formula, + given sampled positions along each ray (see sample_*) + :param model should return (B, (r, g, b, sigma)) when called with (B, (x, y, z)) + should also support 'coarse' boolean argument + :param rays ray [origins (3), directions (3), near (1), far (1)] (B, 8) + :param z_samp z positions sampled for each ray (B, K) + :param coarse whether to evaluate using coarse NeRF + :param sb super-batch dimension; 0 = disable + :return weights (B, K), rgb (B, 3), depth (B) + """ + with profiler.record_function("renderer_composite"): + B, K = z_samp.shape + r_dim = rays.shape[-1] + + deltas = z_samp[:, 1:] - z_samp[:, :-1] # (B, K-1) + delta_inf = 1e10 * torch.ones_like(deltas[:, :1]) # infty (B, 1) + # delta_inf = rays[:, -1:] - z_samp[:, -1:] + deltas = torch.cat([deltas, delta_inf], -1) # (B, K) + + # (B, K, 3) + points = rays[:, None, :3] + z_samp.unsqueeze(2) * rays[:, None, 3:6] + points = points.reshape(-1, 3) # (B*K, 3) + + if r_dim > 8: + ray_info = rays[:, None, 8:].expand(-1, K, -1) + else: + ray_info = None + + if hasattr(model, "use_viewdirs"): + use_viewdirs = model.use_viewdirs + else: + use_viewdirs = None + + viewdirs_all = [] + rgbs_all, invalid_all, sigmas_all, extras_all, state_dicts_all = [], [], [], [], [] + + if sb > 0: + points = points.reshape( + sb, -1, 3 + ) # (SB, B'*K, 3) B' is real ray batch size + + if ray_info is not None: + ray_info = ray_info.reshape(sb, -1, ray_info.shape[-1]) + + eval_batch_dim = 1 + eval_batch_size = (self.eval_batch_size - 1) // sb + 1 + else: + eval_batch_size = self.eval_batch_size + eval_batch_dim = 0 + + split_points = torch.split(points, eval_batch_size, dim=eval_batch_dim) + + if ray_info is not None: + split_ray_infos = torch.split(ray_info, eval_batch_size, dim=eval_batch_dim) + else: + split_ray_infos = [None for _ in split_points] + + if use_viewdirs: + dim1 = K + viewdirs = rays[:, None, 3:6].expand(-1, dim1, -1) + if sb > 0: + viewdirs = viewdirs.reshape(sb, -1, 3) # (SB, B'*K, 3) + else: + viewdirs = viewdirs.reshape(-1, 3) # (B*K, 3) + split_viewdirs = torch.split( + viewdirs, eval_batch_size, dim=eval_batch_dim + ) + + for i, pnts in enumerate(split_points): + dirs = split_viewdirs[i] + infos = split_ray_infos[i] + + rgbs, invalid, sigmas, extras, state_dict = model( + pnts, coarse=coarse, viewdirs=dirs, only_density=self.only_surface_color, ray_info=ray_info, render_flow=self.render_flow + ) + rgbs_all.append(rgbs) + invalid_all.append(invalid) + sigmas_all.append(sigmas) + extras_all.append(extras) + + viewdirs_all.append(dirs) + if state_dict is not None: + state_dicts_all.append(state_dict) + else: + for i, pnts in enumerate(split_points): + infos = split_ray_infos[i] + + rgbs, invalid, sigmas, extras, state_dict = model(pnts, coarse=coarse, only_density=self.only_surface_color, ray_info=infos, render_flow=self.render_flow) + rgbs_all.append(rgbs) + invalid_all.append(invalid) + sigmas_all.append(sigmas) + extras_all.append(extras) + + if state_dict is not None: + state_dicts_all.append(state_dict) + + points, viewdirs = None, None + + # (B*K, 4) OR (SB, B'*K, 4) + if not self.only_surface_color: + rgbs = torch.cat(rgbs_all, dim=eval_batch_dim) + else: + rgbs = None + invalid = torch.cat(invalid_all, dim=eval_batch_dim) + sigmas = torch.cat(sigmas_all, dim=eval_batch_dim) + + if not extras_all[0] is None: + extras = torch.cat(extras_all, dim=eval_batch_dim) + else: + extras = None + + deltas = deltas.float() + sigmas = sigmas.float() + + if ( + state_dicts_all is not None and len(state_dicts_all) != 0 + ): ## not empty in a list + state_dicts = { + key: torch.cat( + [state_dicts[key] for state_dicts in state_dicts_all], + dim=eval_batch_dim, + ) + for key in state_dicts_all[0].keys() + } + else: + state_dicts = None + + if rgbs is not None: + rgbs = rgbs.reshape(B, K, -1) # (B, K, 4 or 5) + invalid = invalid.reshape(B, K, -1) + sigmas = sigmas.reshape(B, K) + + if extras is not None: + extras = extras.reshape(B, K, -1) + + if state_dicts is not None: + state_dicts = { + key: value.reshape(B, K, *value.shape[2:]) + for key, value in state_dicts.items() + } # BxKx... (BxKxn_viewsx...) + + if self.training and self.noise_std > 0.0: + sigmas = sigmas + torch.randn_like(sigmas) * self.noise_std + + alphas = 1 - torch.exp( + -deltas.abs() * torch.relu(sigmas) + ) # (B, K) (delta should be positive anyways) + + if self.hard_alpha_cap: + alphas[:, -1] = 1 + + deltas, sigmas = None, None + + alphas_shifted = torch.cat( + [torch.ones_like(alphas[:, :1]), 1 - alphas + 1e-10], -1 + ) # (B, K+1) = [1, a1, a2, ...] + T = torch.cumprod(alphas_shifted, -1) # (B) + weights = alphas * T[:, :-1] # (B, K) + # alphas = None + alphas_shifted = None + + depth_final = torch.sum(weights * z_samp, -1) # (B) + state_dicts["dino_features"] = torch.sum(state_dicts["dino_features"].mul_(weights.unsqueeze(-1)), -2) + + if self.render_mode == "neus": + # dist_from_surf = z_samp - depth_final[..., None] + indices = torch.arange(0, weights.shape[-1], device=weights.device, dtype=weights.dtype).unsqueeze(0) + surface_index = torch.sum(weights * indices, dim=-1, keepdim=True) + dist_from_surf = surface_index - indices + weights = torch.exp(-.5 * (dist_from_surf * self.surface_sigmoid_scale) ** 2) + weights = weights / torch.sum(weights, dim=-1, keepdim=True) + + if not self.only_surface_color: + rgb_final = torch.sum(weights.unsqueeze(-1) * rgbs, -2) # (B, 3) + else: + surface_points = rays[:, None, :3] + depth_final[:, None, None] * rays[:, None, 3:6] + surface_points = surface_points.reshape(sb, -1, 3) + + if ray_info is not None: + ray_info = ray_info.reshape(sb, -1, K, ray_info.shape[-1])[:, :, 0, :] + + rgb_final, invalid_colors = model.sample_colors(surface_points, ray_info=ray_info, render_flow=self.render_flow) + rgb_final = rgb_final.permute(0, 2, 1, 3).reshape(B, -1) + invalid_colors = invalid_colors.permute(0, 2, 1, 3).reshape(B, 1, -1) + invalid = ((invalid > .5) | invalid_colors).float() + + if self.white_bkgd: + # White background + pix_alpha = weights.sum(dim=1) # (B), pixel alpha + rgb_final = rgb_final + 1 - pix_alpha.unsqueeze(-1) # (B, 3) + + if extras is not None: + extras_final = torch.sum(weights.unsqueeze(-1) * extras, -2) # (B, extras) + else: + extras_final = None + + for name, x in [("weights", weights), ("rgb_final", rgb_final), ("depth_final", depth_final), ("alphas", alphas), ("invalid", invalid), ("z_samp", z_samp)]: + if torch.any(torch.isnan(x)): + print(f"Detected NaN in {name} ({x.dtype}):") + print(x) + exit() + + if ray_info is not None: + ray_info = rays[:, None, 8:] + + # return (weights, rgb_final, depth_final, alphas, invalid, z_samp, rgbs, viewdirs) + return ( + weights, + rgb_final, + depth_final, + alphas, + invalid, + z_samp, + rgbs, + ray_info, + extras_final, + state_dicts, + ) + + def forward( + self, + model, + rays, + want_weights=False, + want_alphas=False, + want_z_samps=False, + want_rgb_samps=False, + sample_from_dist=None, + ): + """ + :model nerf model, should return (SB, B, (r, g, b, sigma)) + when called with (SB, B, (x, y, z)), for multi-object: + SB = 'super-batch' = size of object batch, + B = size of per-object ray batch. + Should also support 'coarse' boolean argument for coarse NeRF. + :param rays ray spec [origins (3), directions (3), near (1), far (1)] (SB, B, 8) + :param want_weights if true, returns compositing weights (SB, B, K) + :return render dict + """ + with profiler.record_function("renderer_forward"): + if self.sched is not None and self.last_sched.item() > 0: + self.n_coarse = self.sched[1][self.last_sched.item() - 1] + self.n_fine = self.sched[2][self.last_sched.item() - 1] + + assert len(rays.shape) == 3 + superbatch_size = rays.shape[0] + r_dim = rays.shape[-1] + + rays = rays.reshape(-1, r_dim) # (SB * B, 8) + + if sample_from_dist is None: + z_coarse = self.sample_coarse(rays) # (B, Kc) + else: + prop_weights, prop_z_samp = sample_from_dist + n_samples = prop_weights.shape[-1] + prop_weights = prop_weights.reshape(-1, n_samples) + prop_z_samp = prop_z_samp.reshape(-1, n_samples) + z_coarse = self.sample_coarse_from_dist(rays, prop_weights, prop_z_samp) + z_coarse, _ = torch.sort(z_coarse, dim=-1) + coarse_composite = self.composite( + model, + rays, + z_coarse, + coarse=True, + sb=superbatch_size, + ) + + outputs = DotMap( + coarse=self._format_outputs( + coarse_composite, + superbatch_size, + want_weights=want_weights, + want_alphas=want_alphas, + want_z_samps=want_z_samps, + want_rgb_samps=want_rgb_samps, + ), + ) + outputs.state_dict = coarse_composite[-1] + + if self.using_fine: + all_samps = [z_coarse] + if self.n_fine - self.n_fine_depth > 0: + all_samps.append( + self.sample_fine(rays, coarse_composite[0].detach()) + ) # (B, Kf - Kfd) + if self.n_fine_depth > 0: + all_samps.append( + self.sample_fine_depth(rays, coarse_composite[2]) + ) # (B, Kfd) + z_combine = torch.cat(all_samps, dim=-1) # (B, Kc + Kf) + z_combine_sorted, argsort = torch.sort(z_combine, dim=-1) + fine_composite = self.composite( + model, + rays, + z_combine_sorted, + coarse=False, + sb=superbatch_size, + ) + outputs.fine = self._format_outputs( + fine_composite, + superbatch_size, + want_weights=want_weights, + want_alphas=want_alphas, + want_z_samps=want_z_samps, + want_rgb_samps=want_rgb_samps, + ) + + return outputs + + def _format_outputs( + self, + rendered_outputs, + superbatch_size, + want_weights=False, + want_alphas=False, + want_z_samps=False, + want_rgb_samps=False, + ): + ( + weights, + rgb_final, + depth, + alphas, + invalid, + z_samps, + rgb_samps, + ray_info, + extras, + state_dict, + ) = rendered_outputs + n_smps = weights.shape[-1] + out_d_rgb = rgb_final.shape[-1] + out_d_i = invalid.shape[-1] + out_d_dino = state_dict["dino_features"].shape[-1] + + if superbatch_size > 0: + rgb_final = rgb_final.reshape(superbatch_size, -1, out_d_rgb) + depth = depth.reshape(superbatch_size, -1) + invalid = invalid.reshape(superbatch_size, -1, n_smps, out_d_i) + ret_dict = DotMap(rgb=rgb_final, depth=depth, invalid=invalid) + if ray_info is not None: + ri_shape = ray_info.shape[-1] + ray_info = ray_info.reshape(superbatch_size, -1, ri_shape) + ret_dict.ray_info = ray_info + if extras is not None: + extras_shape = extras.shape[-1] + extras = extras.reshape(superbatch_size, -1, extras_shape) + ret_dict.extras = extras + if want_weights: + weights = weights.reshape(superbatch_size, -1, n_smps) + ret_dict.weights = weights + if want_alphas: + alphas = alphas.reshape(superbatch_size, -1, n_smps) + ret_dict.alphas = alphas + if want_z_samps: + z_samps = z_samps.reshape(superbatch_size, -1, n_smps) + ret_dict.z_samps = z_samps + if want_rgb_samps: + rgb_samps = rgb_samps.reshape(superbatch_size, -1, n_smps, out_d_rgb) + ret_dict.rgb_samps = rgb_samps + if "dino_features" in state_dict: + dino_features = state_dict["dino_features"].reshape(superbatch_size, -1, out_d_dino) + ret_dict.dino_features = dino_features + if "invalid_features" in state_dict: + invalid_features = state_dict["invalid_features"].reshape(superbatch_size, -1, n_smps, out_d_i) + ret_dict.invalid_features = invalid_features + return ret_dict + + def sched_step(self, steps=1): + """ + Called each training iteration to update sample numbers + according to schedule + """ + if self.sched is None: + return + self.iter_idx += steps + while ( + self.last_sched.item() < len(self.sched[0]) + and self.iter_idx.item() >= self.sched[0][self.last_sched.item()] + ): + self.n_coarse = self.sched[1][self.last_sched.item()] + self.n_fine = self.sched[2][self.last_sched.item()] + print( + "INFO: NeRF sampling resolution changed on schedule ==> c", + self.n_coarse, + "f", + self.n_fine, + ) + self.last_sched += 1 + + @classmethod + def from_conf(cls, conf, white_bkgd=False, eval_batch_size=100000): + return cls( + conf.get("n_coarse", 128), + conf.get("n_fine", 0), + n_fine_depth=conf.get("n_fine_depth", 0), + noise_std=conf.get("noise_std", 0.0), + depth_std=conf.get("depth_std", 0.01), + white_bkgd=conf.get("white_bkgd", white_bkgd), + lindisp=conf.get("lindisp", True), + eval_batch_size=conf.get("eval_batch_size", eval_batch_size), + sched=conf.get("sched", None), + hard_alpha_cap=conf.get("hard_alpha_cap", False), + render_mode=conf.get("render_mode", "volumetric"), + surface_sigmoid_scale=conf.get("surface_sigmoid_scale", 1), + render_flow=conf.get("render_flow", False), + normalize_dino=conf.get("normalize_dino", False), + ) + + def bind_parallel(self, net, gpus=None, simple_output=False): + """ + Returns a wrapper module compatible with DataParallel. + Specifically, it renders rays with this renderer + but always using the given network instance. + Specify a list of GPU ids in 'gpus' to apply DataParallel automatically. + :param net A PixelNeRF network + :param gpus list of GPU ids to parallize to. If length is 1, + does not parallelize + :param simple_output only returns rendered (rgb, depth) instead of the + full render output map. Saves data tranfer cost. + :return torch module + """ + wrapped = _RenderWrapper(net, self, simple_output=simple_output) + if gpus is not None and len(gpus) > 1: + print("Using multi-GPU", gpus) + wrapped = torch.nn.DataParallel(wrapped, gpus, dim=1) + return wrapped diff --git a/scenedino/training/base_trainer.py b/scenedino/training/base_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..1b9d229623f5ccd0b5c2add3992629e22e38a46b --- /dev/null +++ b/scenedino/training/base_trainer.py @@ -0,0 +1,486 @@ +import time +from datetime import datetime +from pathlib import Path +from typing import Any + +from omegaconf import OmegaConf + +import ignite.distributed as idist +import numpy as np +import torch +from torch.utils.data import DataLoader +from ignite.contrib.engines import common +from ignite.contrib.handlers import TensorboardLogger +from ignite.engine import Engine, Events, EventsList +from ignite.handlers import Checkpoint, DiskSaver, global_step_from_engine +from ignite.utils import manual_seed, setup_logger +from torch.cuda.amp import autocast, GradScaler + +from scenedino.common.logging import event_list_from_config, global_step_fn, log_basic_info +from scenedino.common.io.configs import save_hydra_config +from scenedino.common.io.model import load_checkpoint +from scenedino.evaluation.wrapper import make_eval_fn +from scenedino.losses.base_loss import BaseLoss +from scenedino.training.handlers import ( + MetricLoggingHandler, + VisualizationHandler, + add_time_handlers, +) + +from scenedino.common.array_operations import to +from scenedino.common.metrics import DictMeanMetric, MeanMetric, SegmentationMetric, ConcatenateMetric +from scenedino.visualization.vis_2d import tb_visualize + +import optuna + + +def base_training(local_rank, config, get_dataflow, initialize, sweep_trial=None): + # ============================================ LOGGING AND OUTPUT SETUP ============================================ + # TODO: figure out rank + rank = ( + idist.get_rank() + ) ## rank of the current process within a group of processes: each process could handle a unique subset of the data, based on its rank + manual_seed(config["seed"] + rank) + device = idist.device() + + model_name = config["name"] + logger = setup_logger( + name=model_name, format="%(levelname)s: %(message)s" + ) ## default + + output_path = config["output"]["path"] + if rank == 0: + unique_id = config["output"].get( + "unique_id", datetime.now().strftime("%Y%m%d-%H%M%S") + ) + folder_name = unique_id + # folder_name = f"{model_name}_backend-{idist.backend()}-{idist.get_world_size()}_{unique_id}" + + output_path = Path(output_path) / folder_name + if not output_path.exists(): + output_path.mkdir(parents=True) + + config["output"]["path"] = output_path.as_posix() + logger.info(f"Output path: {config['output']['path']}") + + if "cuda" in device.type: + config["cuda device name"] = torch.cuda.get_device_name(local_rank) + log_basic_info(logger, config) + tb_logger = TensorboardLogger(log_dir=output_path) + + # ================================================== DATASET SETUP ================================================= + # TODO: think about moving the dataset setup to the create validators and create trainer functions + train_loader, val_loaders = get_dataflow(config) + + if isinstance(train_loader, tuple): + train_loader = train_loader[0] + + if hasattr(train_loader, "dataset"): + val_loader_lengths = "\n".join( + [ + f"{name}: {len(val_loader.dataset)}" + for name, val_loader in val_loaders.items() + if hasattr(val_loader, "dataset") + ] + ) + logger.info( + f"Dataset lengths:\nTrain: {len(train_loader.dataset)}\n{val_loader_lengths}" + ) + config["dataset"]["steps_per_epoch"] = len(train_loader) + + # ============================================= MODEL AND OPTIMIZATION ============================================= + model, optimizer, criterion, lr_scheduler = initialize(config) + logger.info(f"Model parameters: {sum(p.numel() for p in model.parameters())}") + logger.info(f"Trainable model parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}") + + # Create trainer for current task + trainer = create_trainer( + model, + optimizer, + criterion, + lr_scheduler, + train_loader.sampler if hasattr(train_loader, "sampler") else None, + config, + logger, + metrics={}, + ) + if rank == 0: + tb_logger.attach( + trainer, + MetricLoggingHandler("train", optimizer), + Events.ITERATION_COMPLETED(every=config.get("log_every_iters", 1)), + ) + + # ========================================= EVALUTATION, AND VISUALIZATION ========================================= + validators: dict[str, tuple[Engine, EventsList]] = create_validators( + config, + model, + val_loaders, + criterion, + tb_logger, + trainer, + ) + + # NOTE: not super elegant as val_loaders has to have the same name but should work + def run_validation(name: str, validator: Engine): + def _run(engine: Engine): + epoch = trainer.state.epoch + state = validator.run(val_loaders[name]) + log_metrics(logger, epoch, state.times["COMPLETED"], name, state.metrics) + + if sweep_trial is not None and name == "validation": + sweep_trial.report(trainer.state.best_metric, trainer.state.iteration) + if sweep_trial.should_prune(): + raise optuna.TrialPruned() + + return _run + + for name, validator in validators.items(): + trainer.add_event_handler(validator[1], run_validation(name, validator[0])) + + # ================================================ SAVE FINAL CONFIG =============================================== + if rank == 0: + # Plot config to tensorboard + config_yaml = OmegaConf.to_yaml(config) + config_yaml = "".join("\t" + line for line in config_yaml.splitlines(True)) + tb_logger.writer.add_text("config", text_string=config_yaml, global_step=0) + save_hydra_config(output_path / "training_config.yaml", config, force=False) + + # ================================================= TRAINING LOOP ================================================== + # In order to check training resuming we can stop training on a given iteration + if config.get("stop_iteration", None): + + @trainer.on(Events.ITERATION_STARTED(once=config["stop_iteration"])) + def _(): + logger.info(f"Stop training on {trainer.state.iteration} iteration") + trainer.terminate() + + try: ## train_loader == models.bts.trainer_overfit.DataloaderDummy object + trainer.run(train_loader, + max_epochs=config["training"]["num_epochs"], + epoch_length=config["training"].get("epoch_length", None)) + except Exception as e: + logger.exception("") + raise e + + if rank == 0: + tb_logger.close() + + return trainer.state.best_metric + + +def log_metrics(logger, epoch, elapsed, tag, metrics): + metrics_output = "\n".join([f"\t{k}: {v}" for k, v in metrics.items()]) + logger.info( + f"\nEpoch {epoch} - Evaluation time (seconds): {elapsed:.2f} - {tag} metrics:\n {metrics_output}" + ) + + +def create_trainer( + model: torch.nn.Module, + optimizer: torch.optim.Optimizer, + criterions: list[Any], + lr_scheduler, + train_sampler, + config, + logger, + metrics={}, +): + device = idist.device() + model = model.to(device) + + # Setup Ignite trainer: + # - let's define training step + # - add other common handlers: + # - TerminateOnNan, + # - handler to setup learning rate scheduling, + # - ModelCheckpoint + # - RunningAverage` on `train_step` output + # - Two progress bars on epochs and optionally on iterations + + with_amp = config["with_amp"] + gradient_accum_factor = config.get("gradient_accum_factor", 1) + + scaler = GradScaler(enabled=with_amp) + + def train_step(engine, data: dict): + if "t__get_item__" in data: + timing = {"t__get_item__": torch.mean(data["t__get_item__"]).item()} + else: + timing = {} + + _start_time = time.time() + + data = to(data, device) + + timing["t_to_gpu"] = time.time() - _start_time + + model.train() + model.validation_tag = None + + _start_time = time.time() + + with autocast(enabled=with_amp): + data = model(data) + + timing["t_forward"] = time.time() - _start_time + + loss_metrics = {} + if optimizer is not None: + _start_time = time.time() + overall_loss = torch.tensor(0.0, device=device) + for criterion in criterions: + losses = criterion(data) + names = criterion.get_loss_metric_names() + + overall_loss += losses[names[0]] + loss_metrics.update({name: loss for name, loss in losses.items()}) + + timing["t_loss"] = time.time() - _start_time + + ## make same scale for gradients. Note: it's not ignite built-in func. (c.f. https://wandb.ai/wandb_fc/tips/reports/How-To-Use-GradScaler-in-PyTorch--VmlldzoyMTY5MDA5) + _start_time = time.time() + + # optimizer.zero_grad() + # scaler.scale(overall_loss).backward() + # scaler.step(optimizer) + # scaler.update() + + # Gradient accumulation + overall_loss = overall_loss / gradient_accum_factor + scaler.scale(overall_loss).backward() + if engine.state.iteration % gradient_accum_factor == 0: + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + timing["t_backward"] = time.time() - _start_time + + return { + "output": data, + "loss_dict": loss_metrics, + "timings_dict": timing, + "metrics_dict": {}, + } + + trainer = Engine(train_step) + trainer.logger = logger + + for name, metric in metrics.items(): + metric.attach(trainer, name) + + # TODO: maybe save only the network not the whole wrapper + # TODO: Make adaptable + to_save = { + "trainer": trainer, + "model": model, + # "optimizer": optimizer, + # "lr_scheduler": lr_scheduler, + } + + common.setup_common_training_handlers( + trainer=trainer, + train_sampler=train_sampler, + to_save=to_save, + save_every_iters=config["training"]["checkpoint_every"], + save_handler=DiskSaver(config["output"]["path"], require_empty=False), + lr_scheduler=lr_scheduler, + output_names=None, + with_pbars=False, + clear_cuda_cache=False, + log_every_iters=config.get("log_every_iters", 100), + n_saved=1, + ) + + # NOTE: don't move to initialization, as to save is also needed here + if config["training"].get("resume_from", None): + ckpt_path = Path(config["training"]["resume_from"]) + logger.info(f"Resuming from checkpoint: {str(ckpt_path)}") + + load_checkpoint(ckpt_path, to_save, strict=False) + + if config["training"].get("from_pretrained", None): + ckpt_path = Path(config["training"]["from_pretrained"]) + logger.info(f"Pretrained from checkpoint: {str(ckpt_path)}") + + to_save = {"model": to_save["model"]} + + load_checkpoint(ckpt_path, to_save, strict=False) + + if idist.get_rank() == 0: + common.ProgressBar(desc=f"Training", persist=False).attach(trainer) + + return trainer + + +def create_validators( + config, + model: torch.nn.Module, + dataloaders: dict[str, DataLoader], + criterions: list[BaseLoss], + tb_logger: TensorboardLogger, + trainer: Engine, +) -> dict[str, tuple[Engine, EventsList]]: + # TODO: change model object to evaluator object that has a different ray sampler + with_amp = config["with_amp"] + device = idist.device() + + def _create_validator( + tag: str, + validation_config, + ) -> tuple[Engine, EventsList]: + # TODO: make eval functions configurable from config + metrics = {} + for metric_config in validation_config["metrics"]: + agg_type = metric_config.get("agg_type", None) + if agg_type == "unsup_seg": + metrics[metric_config["type"]] = SegmentationMetric( + metric_config["type"], make_eval_fn(model, metric_config), assign_pseudo=True + ) + elif agg_type == "sup_seg": + metrics[metric_config["type"]] = SegmentationMetric( + metric_config["type"], make_eval_fn(model, metric_config), assign_pseudo=False + ) + elif agg_type == "concat": + metrics[metric_config["type"]] = ConcatenateMetric( + metric_config["type"], make_eval_fn(model, metric_config) + ) + else: + metrics[metric_config["type"]] = DictMeanMetric( + metric_config["type"], make_eval_fn(model, metric_config) + ) + + loss_during_validation = validation_config.get("log_loss", True) + if loss_during_validation: + metrics_loss = {} + for criterion in criterions: + metrics_loss.update( + { + k: MeanMetric((lambda y: lambda x: x["loss_dict"][y])(k)) + for k in criterion.get_loss_metric_names() + } + ) + eval_metrics = {**metrics, **metrics_loss} + else: + eval_metrics = metrics + + @torch.no_grad() + def validation_step(engine: Engine, data): + model.eval() + model.validation_tag = tag + if "t__get_item__" in data: + timing = {"t__get_item__": torch.mean(data["t__get_item__"]).item()} + else: + timing = {} + + data = to(data, device) + + with autocast(enabled=with_amp): + data = model(data) + + overall_loss = torch.tensor(0.0, device=device) + loss_metrics = {} + if loss_during_validation: + for criterion in criterions: + losses = criterion(data) + names = criterion.get_loss_metric_names() + + overall_loss += losses[names[0]] + loss_metrics.update({name: loss for name, loss in losses.items()}) + else: + loss_metrics = {} + + return { + "output": data, + "loss_dict": loss_metrics, + "timings_dict": timing, + "metrics_dict": {}, + } + + validator = Engine(validation_step) + + add_time_handlers(validator) + + # ADD METRICS + for name, metric in eval_metrics.items(): + metric.attach(validator, name) + + # ADD LOGGING HANDLER + # TODO: split up handlers + tb_logger.attach( + validator, + MetricLoggingHandler( + tag, + log_loss=False, + global_step_transform=global_step_fn( + trainer, validation_config["global_step"] + ), + ), + Events.EPOCH_COMPLETED, + ) + + # ADD VISUALIZATION HANDLER + if validation_config.get("visualize", None): + visualize = tb_visualize( + (model.renderer.net if hasattr(model, "renderer") else model.module.renderer.net), + dataloaders[tag].dataset.dataset, + validation_config["visualize"], + ) + + def vis_wrapper(*args, **kwargs): + with autocast(enabled=with_amp): + return visualize(*args, **kwargs) + + tb_logger.attach( + validator, + VisualizationHandler( + tag=tag, + visualizer=vis_wrapper, + global_step_transform=global_step_fn( + trainer, validation_config["global_step"] + ), + ), + Events.ITERATION_COMPLETED(every=1), + ) + + if "save_best" in validation_config: + save_best_config = validation_config["save_best"] + metric_name = save_best_config["metric"] + sign = save_best_config.get("sign", 1.0) + update_model = save_best_config.get("update_model", False) + dry_run = save_best_config.get("dry_run", False) + + best_model_handler = Checkpoint( + {"model": model}, + # NOTE: fixes a problem with log_dir or logdir + DiskSaver(Path(config["output"]["path"]), require_empty=False), + # DiskSaver(tb_logger.writer.log_dir, require_empty=False), + filename_prefix=f"{metric_name}_best", + n_saved=1, + global_step_transform=global_step_from_engine(trainer), + score_name=metric_name, + score_function=Checkpoint.get_default_score_fn( + metric_name, score_sign=sign + ), + ) + + def event_handler(engine): + if update_model: + model.update_model_eval(engine.state.metrics) + if not dry_run: + best_model_handler(engine) + trainer.state.best_metric = best_model_handler._saved[0].priority + + validator.add_event_handler(Events.COMPLETED, event_handler) + + if idist.get_rank() == 0 and (not validation_config.get("with_clearml", False)): + common.ProgressBar(desc=f"Evaluation ({tag})", persist=False).attach( + validator + ) + + return validator, event_list_from_config(validation_config["events"]) + + return { + name: _create_validator(name, config) + for name, config in config["validation"].items() + } diff --git a/scenedino/training/handlers.py b/scenedino/training/handlers.py new file mode 100644 index 0000000000000000000000000000000000000000..4b9de0512e2b2c89fd625beaca9998431e663cee --- /dev/null +++ b/scenedino/training/handlers.py @@ -0,0 +1,181 @@ +import time +from typing import Mapping, Union + +from ignite.contrib.handlers import TensorboardLogger +from ignite.handlers import global_step_from_engine +from ignite.contrib.handlers.base_logger import BaseHandler +from ignite.engine import Engine, EventEnum, Events + +import torch + + +def add_time_handlers(engine: Engine): + iteration_time_handler = TimeHandler("iter", freq=True, period=True) + batch_time_handler = TimeHandler("get_batch", freq=False, period=True) + engine.add_event_handler( + Events.ITERATION_STARTED, iteration_time_handler.start_timing + ) + engine.add_event_handler( + Events.ITERATION_COMPLETED, iteration_time_handler.end_timing + ) + engine.add_event_handler(Events.GET_BATCH_STARTED, batch_time_handler.start_timing) + engine.add_event_handler(Events.GET_BATCH_COMPLETED, batch_time_handler.end_timing) + + +class MetricLoggingHandler(BaseHandler): + def __init__( + self, + tag, + optimizer=None, + log_loss=True, + log_metrics=True, + log_timings=True, + global_step_transform=None, + ): + self.tag = tag + self.optimizer = optimizer + self.log_loss = log_loss + self.log_metrics = log_metrics + self.log_timings = log_timings + self.gst = global_step_transform + super(MetricLoggingHandler, self).__init__() + + def __call__( + self, + engine: Engine, + logger: TensorboardLogger, + event_name: Union[str, EventEnum], + ): + if not isinstance(logger, TensorboardLogger): + raise RuntimeError( + "Handler 'MetricLoggingHandler' works only with TensorboardLogger" + ) + + if self.gst is None: + gst = global_step_from_engine(engine) + else: + gst = self.gst + global_step = gst(engine, event_name) # type: ignore[misc] + + if not isinstance(global_step, int): + raise TypeError( + f"global_step must be int, got {type(global_step)}." + " Please check the output of global_step_transform." + ) + + writer = logger.writer + + # Optimizer parameters + if self.optimizer is not None: + params = { + k: float(param_group["lr"]) + for k, param_group in enumerate(self.optimizer.param_groups) + } + + for k, param in params.items(): + writer.add_scalar(f"lr-{self.tag}/{k}", param, global_step) + + if self.log_loss: + # Plot losses + loss_dict = engine.state.output["loss_dict"] + for k, v in loss_dict.items(): + # TODO: is this needed? + # if not isinstance(v, (float, int)): + # print(f"{k}: {type(v)}") + writer.add_scalar(f"loss-{self.tag}/{k}", v, global_step) + + if self.log_metrics: + # Plot metrics + metrics_dict = engine.state.metrics + metrics_dict_custom = engine.state.output["metrics_dict"] + + for k, v in metrics_dict.items(): + # Avoid dictionaries because of weird ignite handling of Mapping metrics + if isinstance(v, Mapping) or k.endswith("assignment"): # TODO: Remove hard-coded assignment + continue + if isinstance(v, torch.Tensor) and v.ndim > 0: + writer.add_histogram(f"metrics-{self.tag}/{k}", v, global_step) + else: + writer.add_scalar(f"metrics-{self.tag}/{k}", v, global_step) + + for k, v in metrics_dict_custom.items(): + if isinstance(v, Mapping): + continue + if isinstance(v, torch.Tensor) and v.ndim > 0: + writer.add_histogram(f"metrics-{self.tag}/{k}", v, global_step) + else: + writer.add_scalar(f"metrics-{self.tag}/{k}", v, global_step) + + if self.log_timings: + # Plot timings + timings_dict = engine.state.times + timings_dict_custom = engine.state.output["timings_dict"] + for k, v in timings_dict.items(): + if k == "COMPLETED": + continue + writer.add_scalar(f"timing-{self.tag}/{k}", v, global_step) + for k, v in timings_dict_custom.items(): + writer.add_scalar(f"timing-{self.tag}/{k}", v, global_step) + + engine.state.output = None # For memory efficiency, val results do not need to stay in the state + + +class TimeHandler: + def __init__(self, name: str, freq: bool = False, period: bool = False) -> None: + self.name = name + self.freq = freq + self.period = period + if not self.period and not self.freq: + print(f"Warning: No timings logged for {name}") + self._start_time = None + + def start_timing(self, engine): + self._start_time = time.time() + + def end_timing(self, engine): + if self._start_time is None: + period = 0 + freq = 0 + else: + period = max(time.time() - self._start_time, 1e-6) + freq = 1 / period + if not hasattr(engine.state, "times"): + engine.state.times = {} + else: + if self.period: + engine.state.times[f"secs_per_{self.name}"] = period + if self.freq: + engine.state.times[f"num_{self.name}_per_sec"] = freq + + +class VisualizationHandler(BaseHandler): + def __init__(self, tag, visualizer, global_step_transform=None): + self.tag = tag + self.visualizer = visualizer + self.gst = global_step_transform + super(VisualizationHandler, self).__init__() + + def __call__( + self, + engine: Engine, + logger: TensorboardLogger, + event_name: Union[str, EventEnum], + ) -> None: + if not isinstance(logger, TensorboardLogger): + raise RuntimeError( + "Handler 'VisualizationHandler' works only with TensorboardLogger" + ) + + if self.gst is None: + gst = global_step_from_engine(engine) + else: + gst = self.gst + global_step = gst(engine, event_name) # type: ignore[misc] + + if not isinstance(global_step, int): + raise TypeError( + f"global_step must be int, got {type(global_step)}." + " Please check the output of global_step_transform." + ) + + self.visualizer(engine, logger, global_step, self.tag) diff --git a/scenedino/training/trainer.py b/scenedino/training/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..0c4f3dc6af06c47be222f747001684081f323cc1 --- /dev/null +++ b/scenedino/training/trainer.py @@ -0,0 +1,589 @@ +from copy import copy +import logging +from pathlib import Path + +import ignite.distributed as idist +import torch +from torch import nn +from torch import optim +from torch.nn import functional as F +from torch.utils.data import Subset +from torch import profiler + +import lpips + +from scenedino.datasets import make_datasets +from scenedino.losses import make_loss +from scenedino.common.image_processor import make_image_processor, RGBProcessor +from scenedino.common.ray_sampler import ( + ImageRaySampler, + PointBasedRaySampler, + RandomRaySampler, + RaySampler, + get_ray_sampler, +) +from scenedino.common.io.configs import load_model_config +from scenedino.common.sampling_strategies import ( + get_encoder_sampling, + get_loss_renderer_sampling, +) +from scenedino.models import make_model +from scenedino.models.backbones.dino.dinov2_module import OrthogonalLinearDimReduction + +# TODO: change +from scenedino.training.base_trainer import base_training +from scenedino.common.scheduler import make_scheduler +from scenedino.renderer import NeRFRenderer + +from torch.cuda.amp import autocast +from scenedino.common import util + + +logger = logging.getLogger("training") + + +class BTSWrapper(nn.Module): + def __init__( + self, renderer: NeRFRenderer, ray_sampler: RaySampler, config, eval_nvs=False, dino_channels=None + ) -> None: + super().__init__() + self.renderer = renderer + + self.loss_from_single_img = config.get("loss_from_single_img", False) + + self.use_automasking = config.get("use_automasking", False) + + self.prediction_mode = config.get("prediction_mode", "multiscale") + + self.alternating_ratio = config.get("alternating_ratio", None) + + self.encoder_sampling = get_encoder_sampling(config["encoding_strategy"]) + self.eval_encoder_sampling = get_encoder_sampling( + config["eval_encoding_strategy"] + ) + self.loss_renderer_sampling = get_loss_renderer_sampling( + config["loss_renderer_strategy"] + ) + self.eval_loss_renderer_sampling = get_loss_renderer_sampling( + config["eval_loss_renderer_strategy"] + ) + + cfg_ip = config.get("image_processor", {}) + self.train_image_processor = make_image_processor(cfg_ip) + self.val_image_processor = RGBProcessor() if not self.renderer.renderer.render_flow else make_image_processor({"type": "flow_occlusion"}) + + self.ray_sampler = ray_sampler + + if self.use_automasking: + self.train_sampler.channels += 1 + + self.val_sampler = ImageRaySampler( + self.ray_sampler.z_near, self.ray_sampler.z_far, dino_upscaled=self.ray_sampler.dino_upscaled + ) + + self.predict_uncertainty = config.get("predict_uncertainty", False) + self.uncertainty_predictor_res = config.get("uncertainty_predictor_res", 0) + + self.predict_consistency = config.get("predict_consistency", False) + + + if self.predict_consistency: + z_near = self.ray_sampler.z_near + z_far = self.ray_sampler.z_far + consistency_rays = config.get("consistency_rays", 512) + + self.random_ray_sampler = RandomRaySampler(z_near, z_far, consistency_rays) + self.point_ray_sampler = PointBasedRaySampler(z_near, z_far, consistency_rays) + + if self.predict_uncertainty: + assert self.renderer.net.uncertainty_predictor is not None + + self.eval_nvs = eval_nvs + if self.eval_nvs: + self.lpips = lpips.LPIPS(net="alex") + + self._counter = 0 + + self.compensate_artifacts = config.get("compensate_artifacts", True) + if self.compensate_artifacts: + patch_size = renderer.net.encoder.gt_encoder.patch_size + image_size = renderer.net.encoder.gt_encoder.image_size + latent_size = renderer.net.encoder.gt_encoder.latent_size + + self.artifact_field = nn.Parameter(torch.zeros(latent_size, image_size[0]//patch_size, image_size[1]//patch_size)) + nn.init.normal_(self.artifact_field, mean=0.0, std=0.001) + else: + self.artifact_field = None + + @staticmethod + def get_loss_metric_names(): + return [ + "loss", + "loss_l2", + "loss_mask", + "loss_temporal", + "loss_pgt", + ] + + def forward(self, data): + data = dict(data) + images = torch.stack(data["imgs"], dim=1) # B, n_framnes, c, h, w + poses = torch.stack(data["poses"], dim=1) # B, n_framnes, 4, 4 w2c + projs = torch.stack(data["projs"], dim=1) # B, n_frames, 4, 4 (-1, 1) + data_index = data["index"] + + n, n_frames, c, h, w = images.shape + device = images.device + + with autocast(enabled=False): + to_base_pose = torch.inverse(poses[:, :1, :, :]) + poses = to_base_pose.expand(-1, n_frames, -1, -1) @ poses + + if self.training and self.alternating_ratio is not None: + step = self._counter % (self.alternating_ratio + 1) + if step < self.alternating_ratio: + for params in self.renderer.net.encoder.parameters(True): + params.requires_grad_(True) + for params in self.renderer.net.mlp_coarse.parameters(True): + params.requires_grad_(False) + else: + for params in self.renderer.net.encoder.parameters(True): + params.requires_grad_(False) + for params in self.renderer.net.mlp_coarse.parameters(True): + params.requires_grad_(True) + + if self.training: + ids_encoder = self.encoder_sampling(n_frames) + ids_loss, ids_renderer, color_frame_filter = self.loss_renderer_sampling(n_frames) + else: + ids_encoder = self.eval_encoder_sampling(n_frames) + ids_loss, ids_renderer, color_frame_filter = self.eval_loss_renderer_sampling(n_frames) + + combine_ids = None + + if self.loss_from_single_img: + ids_loss = ids_loss[:1] + + if color_frame_filter is not None: + color_frame_filter = torch.tensor(color_frame_filter, device=images.device) + + ip = self.train_image_processor if self.training else self.val_image_processor + images_ip = ip(images) + + if self.predict_uncertainty: + images_uncert = images.reshape(-1, c, h, w) + uncertainties = self.renderer.net.uncertainty_predictor(images_uncert) + uncertainties = F.interpolate(uncertainties[self.uncertainty_predictor_res], (h, w), mode="bilinear", align_corners=False) + uncertainties = F.softplus(uncertainties).reshape(n, -1, 1, h, w) + images_ip = torch.cat((images_ip, uncertainties), dim=2) + + with profiler.record_function( + "trainer_encode-grid" + ): + self.renderer.net.compute_grid_transforms( + projs[:, ids_encoder], poses[:, ids_encoder] + ) + shift = self.renderer.net.encoder.encoder.patch_size // 2 + loss_feature_grid_shift = torch.randint(-shift, shift, (2,)) if self.training else None + self.renderer.net.encode( + images, + projs, + poses, + ids_encoder=ids_encoder, + ids_render=ids_renderer, + ids_loss=ids_loss, + images_alt=images_ip, + combine_ids=combine_ids, + color_frame_filter=color_frame_filter, + loss_feature_grid_shift=loss_feature_grid_shift, + ) + + sampler = self.ray_sampler if self.training else self.val_sampler + + with autocast(enabled=False), profiler.record_function("trainer_sample-rays"): + renderer_scale = self.renderer.net._scale + dino_features = self.renderer.net.grid_l_loss_features[renderer_scale] + + if self.artifact_field is not None: + dino_features = torch.cat(torch.broadcast_tensors(dino_features, self.artifact_field), dim=2) + + if loss_feature_grid_shift is not None: + all_rays, all_rgb_gt, all_dino_gt = sampler.sample( + images_ip[:, ids_loss], poses[:, ids_loss], projs[:, ids_loss], image_ids=ids_loss, + dino_features=dino_features, loss_feature_grid_shift=loss_feature_grid_shift + ) + else: + all_rays, all_rgb_gt, all_dino_gt = sampler.sample( + images_ip[:, ids_loss], poses[:, ids_loss], projs[:, ids_loss], image_ids=ids_loss, + dino_features=dino_features + ) + + if self.artifact_field is not None: + all_dino_artifacts = all_dino_gt[:, :, self.artifact_field.shape[0]:] + all_dino_gt = all_dino_gt[:, :, :self.artifact_field.shape[0]] + else: + all_dino_artifacts = None + + data["fine"], data["coarse"] = [], [] + + scales = list( + self.renderer.net.encoder.scales + if self.prediction_mode == "multiscale" + else [self.renderer.net.get_scale()] + ) + + for scale in scales: + self.renderer.net.set_scale(scale) + + using_fine = self.renderer.renderer.using_fine + + if scale == 0: + with profiler.record_function("trainer_render"): + render_dict = self.renderer( + all_rays, + want_weights=True, + want_alphas=True, + want_rgb_samps=True, + ) + else: + using_fine = self.renderer.renderer.using_fine + self.renderer.renderer.using_fine = False + render_dict = self.renderer( + all_rays, + want_weights=True, + want_alphas=True, + want_rgb_samps=False, + ) + self.renderer.renderer.using_fine = using_fine + + # if "fine" not in render_dict: + # render_dict["fine"] = dict(render_dict["coarse"]) + + render_dict["rgb_gt"] = all_rgb_gt + render_dict["rays"] = all_rays + render_dict["dino_gt"] = all_dino_gt.float() + + if all_dino_artifacts is not None: + render_dict["dino_artifacts"] = all_dino_artifacts.float() + + render_dict = sampler.reconstruct(render_dict, + channels=images_ip.shape[2], + dino_channels=self.renderer.net.encoder.dino_pca_dim) + + if "fine" in render_dict: + data["fine"].append(render_dict["fine"]) + data["coarse"].append(render_dict["coarse"]) + data["rgb_gt"] = render_dict["rgb_gt"] + data["dino_gt"] = render_dict["dino_gt"] + if "dino_artifacts" in render_dict: + data["dino_artifacts"] = render_dict["dino_artifacts"] + data["rays"] = render_dict["rays"] + + dino_module = self.renderer.net.encoder + if isinstance(dino_module.dim_reduction, OrthogonalLinearDimReduction): + data["reduction_matrix"] = dino_module.dim_reduction.weights + + downsampling_mode = "patch" if self.training else "image" + for _data_coarse in data["coarse"]: + _data_coarse["dino_features"] = dino_module.expand_dim(_data_coarse["dino_features"]) + downsampling_result = dino_module.downsample(_data_coarse["dino_features"], downsampling_mode) + if isinstance(downsampling_result, tuple): + (_data_coarse["dino_features_downsampled"], + _data_coarse["dino_features_salience_map"], + _data_coarse["dino_features_weight_map"], + _data_coarse["dino_features_per_patch_weight"]) = downsampling_result + elif downsampling_result is not None: + _data_coarse["dino_features_downsampled"] = downsampling_result + + if not self.training and self.validation_tag == "visualization": + logger.info("Visualizing a batch...") + with torch.amp.autocast(render_dict["dino_gt"].device.type, enabled=False): + dino_module.fit_visualization(render_dict["dino_gt"].flatten(0, -2)) + data["vis_batch_dino_gt"] = [ + dino_module.transform_visualization(data["dino_gt"], norm=True, from_dim=0), + dino_module.transform_visualization(data["dino_gt"], norm=True, from_dim=3), + dino_module.transform_visualization(data["dino_gt"], norm=True, from_dim=6), + ] + #data["vis_batch_dino_gt_kmeans"] = dino_module.fit_transform_kmeans_visualization(data["dino_gt"]) + for _data_coarse in data["coarse"]: + with torch.amp.autocast(_data_coarse["dino_features"].device.type, enabled=False): + dino_module.fit_visualization(_data_coarse["dino_features"].flatten(0, -2)) + _data_coarse["vis_batch_dino_features"] = [ + dino_module.transform_visualization(_data_coarse["dino_features"], norm=True, from_dim=0), + dino_module.transform_visualization(_data_coarse["dino_features"], norm=True, from_dim=3), + dino_module.transform_visualization(_data_coarse["dino_features"], norm=True, from_dim=6), + ] + #_data_coarse["vis_batch_dino_features_kmeans"] = dino_module.fit_transform_kmeans_visualization(_data_coarse["dino_features"]) + if "dino_features_downsampled" in _data_coarse: + _data_coarse["vis_batch_dino_features_downsampled"] = [ + dino_module.transform_visualization(_data_coarse["dino_features_downsampled"], norm=True, from_dim=0), + dino_module.transform_visualization(_data_coarse["dino_features_downsampled"], norm=True, from_dim=3), + dino_module.transform_visualization(_data_coarse["dino_features_downsampled"], norm=True, from_dim=6), + ] + + if "dino_artifacts" in data: + with torch.amp.autocast(render_dict["dino_gt"].device.type, enabled=False): + dino_module.fit_visualization(render_dict["dino_artifacts"].flatten(0, -2)) + data["vis_batch_dino_artifacts"] = [ + dino_module.transform_visualization(data["dino_artifacts"], norm=True, from_dim=0), + dino_module.transform_visualization(data["dino_artifacts"], norm=True, from_dim=3), + dino_module.transform_visualization(data["dino_artifacts"], norm=True, from_dim=6), + ] + + + if self.training: + data["feature_volume"] = self.renderer.net.grid_f_features[0] + + if self.predict_consistency and self.training: + cf = 1 + + data["consistency"] = [] + + rays_0, rgb_gt_0 = self.random_ray_sampler.sample( + images_ip[:, :1], poses[:, :1], projs[:, :1] + ) + + render_dict_0 = self.renderer( + rays_0, + want_weights=False, + want_alphas=False, + want_rgb_samps=False, + ) + + render_dict_0["rgb_gt"] = rgb_gt_0 + render_dict_0["rays"] = rays_0 + + render_dict_0 = self.random_ray_sampler.reconstruct(render_dict_0, channels=images_ip.shape[2]) + + xyz = rays_0[..., :3] + rays_0[..., 3:6] / torch.norm(rays_0[..., 3:6], keepdim=True, dim=-1) * render_dict_0["coarse"]["depth"][..., None] + + rays_1, rgb_gt_1 = self.point_ray_sampler.sample( + images_ip[:, cf:cf+1], poses[:, cf:cf+1], projs[:, cf:cf+1], xyz + ) + + self.renderer.net.encode( + images[:, cf:cf+1], + projs[:, cf:cf+1], + poses[:, cf:cf+1], + images_alt=images_ip[:, cf:cf+1], + ) + + render_dict_1 = self.renderer( + rays_1, + want_weights=True, + want_alphas=False, + want_rgb_samps=False, + ) + + render_dict_1["rgb_gt"] = rgb_gt_1 + render_dict_1["rays"] = rays_1 + + render_dict_1 = self.point_ray_sampler.reconstruct(render_dict_1, channels=images_ip.shape[2]) + + data["consistency"] = { + "render_dict_0": render_dict_0, + "render_dict_1": render_dict_1, + } + + data["z_near"] = torch.tensor(self.ray_sampler.z_near, device=images.device) + data["z_far"] = torch.tensor(self.ray_sampler.z_far, device=images.device) + + surface_sample = self.sample_from_3d(poses, projs, data["coarse"][0]["depth"]) + if surface_sample is not None: + data["sample_surface_dino_features"], data["sample_surface_sigma"] = surface_sample + + if self.training: + self._counter += 1 + + return data + + + def sample_from_3d(self, poses, projs, depth, z_near=2, z_far=50, n_crops=5, n_samples=576, sample_radius=0.1): + positions_samples = [] + n = projs.size(0) + for n_ in range(n): + focals = projs[n_, :1, [0, 1], [0, 1]] + centers = projs[n_, :1, [0, 1], [2, 2]] + + _, _, height, width = depth.shape + rays, _ = util.gen_rays( + poses[n_, :1].view(-1, 4, 4), + width, + height, + focal=focals, + c=centers, + z_near=0, + z_far=0, + norm_dir=True, + ) + current_depth = depth[n_, 0] # [h, w] + + valid_positions = torch.nonzero((current_depth > z_near) & (current_depth < z_far), as_tuple=False) + if valid_positions.size(0) < n_crops: # Not enough samples in depth range (z_near, z_far) + return None + sampled_positions = valid_positions[torch.randperm(valid_positions.size(0))[:n_crops]] + + cam_centers = rays[0, :, :, :3] # [h, w, 3] + cam_raydir = rays[0, :, :, 3:6] # [h, w, 3] + + depth_crop = current_depth[sampled_positions[:, 0], sampled_positions[:, 1]] # [n_crops] + cam_centers_crop = cam_centers[sampled_positions[:, 0], sampled_positions[:, 1]] # [n_crops, 3] + cam_raydir_crop = cam_raydir[sampled_positions[:, 0], sampled_positions[:, 1]] # [n_crops, 3] + + positions_crop = cam_centers_crop + cam_raydir_crop * depth_crop.unsqueeze(-1) # [n_crops, 3] + random_shifts = sample_radius * torch.randn(n_crops, n_samples, 3, device=positions_crop.device) # [n_crops, n_samples, 3] + # random_shifts = random_shifts * depth_crop[:, None, None] / 5.0 + + positions_samples.append(positions_crop.unsqueeze(1) + random_shifts) # [n_crops, n_samples, 3] + + positions_samples = torch.stack(positions_samples, dim=0) # [n, n_crops, n_samples, 3] + + _, _, sigma, _, state_dict = self.renderer.net(positions_samples.flatten(1, -2)) # [n, n_crops*n_samples, ...] + sigma = sigma.view(n, n_crops, n_samples, -1) + dino = state_dict["dino_features"].view(n, n_crops, n_samples, -1) + + return self.renderer.net.encoder.expand_dim(dino), 1 - torch.exp(-sigma) + + +def training(local_rank, config): + return base_training( + local_rank, + config, + get_dataflow, + initialize, + ) + + +def get_subset(config, len_dataset: int): + subset_type = config.get("type", None) + match subset_type: + case "random": + return torch.sort( + torch.randperm(len_dataset)[: config["args"]["size"]] + )[0].tolist() + case "range": + return list( + range( + config["args"].get("start", 0), + config["args"].get("end", len_dataset), + ) + ) + case _: + return list(range(len_dataset)) + + +# NOTE: type hints are difficult but should be tuple[DataLoader, dict[str, DataLoader]] +def get_dataflow(config): + # TODO: change to reflect evaluation + # - Get train/test datasets + if idist.get_local_rank() > 0: + # Ensure that only local rank 0 download the dataset + # Thus each node will download a copy of the dataset + idist.barrier() + + # REMOVE: ? + mode = config.get("mode", "depth") + + train_dataset, test_dataset = make_datasets(config["dataset"]) + train_loader = idist.auto_dataloader( + train_dataset, + batch_size=config["batch_size"], + num_workers=config["num_workers"], + shuffle=True, + drop_last=True, + ) + + validation_loaders = {} + for name, validation_config in config["validation"].items(): + dataset = copy(test_dataset) + # TODO: check the following configs + # dataset.frame_count = ( + # 1 + # if isinstance(train_dataset, KittiRawDataset) + # or isinstance(train_dataset, KittiOdometryDataset) + # else 2 + # ) + dataset._left_offset = 0 + dataset.return_stereo = True + dataset.return_depth = True + + subset = Subset(dataset, get_subset(validation_config["subset"], len(dataset))) + validation_loaders[name] = idist.auto_dataloader( + subset, + batch_size=validation_config.get("batch_size", 1), + num_workers=0, # Find issue here + shuffle=False, + ) + + if idist.get_local_rank() == 0: + # Ensure that only local rank 0 download the dataset + idist.barrier() + + return train_loader, validation_loaders + + +def initialize(config: dict): + # Continue if checkpoint already exists + if config["training"].get("continue", False): + prefix = "training_checkpoint_" + ckpts = Path(config["output"]["path"]).glob(f"{prefix}*.pt") + # TODO: probably correct logic but please check + training_steps = [int(ckpt.stem.split(prefix)[1]) for ckpt in ckpts] + if training_steps: + config["training"]["resume_from"] = ( + Path(config["output"]["path"]) / f"{prefix}{max(training_steps)}.pt" + ) + + # TODO: think about this again + if config["training"].get("continue", False) and config["training"].get( + "resume_from", None + ): + config_path = Path(config["output"]["path"]) + logger.info(f"Loading model config from {config_path}") + load_model_config(config_path, config) + + net = make_model(config["model"]) + + renderer = NeRFRenderer.from_conf(config["renderer"]) + renderer = renderer.bind_parallel(net, gpus=None).eval() + + mode = config.get("mode", "depth") + + ray_sampler = get_ray_sampler(config["training"]["ray_sampler"]) + + model = BTSWrapper(renderer, ray_sampler, config["model"], mode == "nvs") + + model = idist.auto_model(model) + + dino_decoder_optim_args = config["training"]["optimizer"]["args"].copy() + dino_decoder_optim_args["lr"] = dino_decoder_optim_args["lr"] + + dino_encoder_optim_args = config["training"]["optimizer"]["args"].copy() + dino_encoder_optim_args["lr"] = dino_encoder_optim_args["lr"] / 10 # for fine-tuning + + # TODO: make optimizer itself configurable configurable + optimizer = optim.Adam( + [ + {"params": (p for n, p in model.named_parameters() if not (n.startswith('renderer.net.encoder.encoder.') or n.startswith('renderer.net.encoder.decoder.'))), + **config["training"]["optimizer"]["args"]}, + {"params": model.renderer.net.encoder.decoder.parameters(), + **dino_decoder_optim_args}, + {"params": model.renderer.net.encoder.encoder.parameters(), + **dino_encoder_optim_args}, + ] + ) + optimizer = idist.auto_optim(optimizer) + + lr_scheduler = make_scheduler(config["training"].get("scheduler", {}), optimizer) + + # TODO: change to reflect all the losses together with the config + # TODO: integrate lambda for all losses + criterion = [ + make_loss(config_loss) + for config_loss in config["training"]["loss"] + # ReconstructionLoss( + # config["training"]["loss"], config["model"].get("use_automasking", False) + # ) + ] + + return model, optimizer, criterion, lr_scheduler diff --git a/scenedino/training/trainer_downstream.py b/scenedino/training/trainer_downstream.py new file mode 100644 index 0000000000000000000000000000000000000000..fbedb9e2257ab203a01a1bed817705042170fe19 --- /dev/null +++ b/scenedino/training/trainer_downstream.py @@ -0,0 +1,373 @@ +import logging +from pathlib import Path + +import ignite.distributed as idist +import torch +from torch import optim + +from scenedino.losses import make_loss +from scenedino.common.ray_sampler import ( + RaySampler, + get_ray_sampler, +) +from scenedino.common.io.configs import load_model_config + +from scenedino.models import make_model +from scenedino.training.trainer import BTSWrapper, get_dataflow + +from scenedino.training.base_trainer import base_training +from scenedino.common.scheduler import make_scheduler +from scenedino.renderer import NeRFRenderer +from scenedino.common import util + +from torch.cuda.amp import autocast + +logger = logging.getLogger("training") + + +class BTSDownstreamWrapper(BTSWrapper): + def __init__( + self, renderer: NeRFRenderer, ray_sampler: RaySampler, config, eval_nvs=False, dino_channels=None + ) -> None: + super().__init__(renderer, ray_sampler, config, eval_nvs, dino_channels) + for param in super().parameters(True): + param.requires_grad_(False) + for param in renderer.net.downstream_head.parameters(True): + param.requires_grad_(True) + + self.sample_radius_3d = config.get("sample_radius_3d", 0.5) + + def forward(self, data): + with torch.no_grad(): + # TODO: CLEAN THIS UP + if self.renderer.net.downstream_head.training and len(data["imgs"]) > 1 and torch.rand(1).item() < 0.5: + # side view + encode_id = torch.randint(low=4, high=8, size=(1,)).item() + # Segmentation only present in front view + data.pop("segs") + else: + encode_id = 0 + + data["imgs"] = [data["imgs"][encode_id]] + data["projs"] = [data["projs"][encode_id]] + data["poses"] = [data["poses"][encode_id]] + + data = self.forward_downstream(data, id_encoder=0) + if not self.renderer.net.downstream_head.training and hasattr(self, "validation_tag") and self.validation_tag == "visualization_seg": + dino_module = self.renderer.net.encoder + dino_module.visualization.n_kmeans_clusters = 19 + for _data_coarse in data["coarse"]: + with torch.amp.autocast(_data_coarse["dino_features"].device.type, enabled=False): + dino_module.fit_visualization(_data_coarse["dino_features"].float().flatten(0, -2)) + _data_coarse["vis_batch_dino_features"] = [ + dino_module.transform_visualization(_data_coarse["dino_features"], norm=True, from_dim=0), + dino_module.transform_visualization(_data_coarse["dino_features"], norm=True, from_dim=3), + dino_module.transform_visualization(_data_coarse["dino_features"], norm=True, from_dim=6), + ] + #_data_coarse["vis_batch_dino_features_kmeans"] = dino_module.fit_transform_kmeans_visualization(_data_coarse["dino_features"]) + + data = self.renderer.net.downstream_head.forward_training(data, visualize=not self.training and hasattr(self, "validation_tag") and self.validation_tag == "visualization_seg") + return data + + def forward_downstream(self, data, id_encoder): + data = dict(data) + images = torch.stack(data["imgs"], dim=1) # B, n_framnes, c, h, w + poses = torch.stack(data["poses"], dim=1) # B, n_framnes, 4, 4 w2c + projs = torch.stack(data["projs"], dim=1) # B, n_frames, 4, 4 (-1, 1) + + n, n_frames, c, h, w = images.shape + + with autocast(enabled=False): + to_base_pose = torch.inverse(poses[:, :1, :, :]) + poses = to_base_pose.expand(-1, n_frames, -1, -1) @ poses + + ids_encoder = [id_encoder] + ids_loss = ids_encoder + ids_renderer = ids_encoder + + ip = self.train_image_processor if self.training else self.val_image_processor + images_ip = ip(images) + + self.renderer.net.compute_grid_transforms( + projs[:, ids_encoder], poses[:, ids_encoder] + ) + self.renderer.net.encode( + images, + projs, + poses, + ids_encoder=ids_encoder, + ids_render=ids_renderer, + ids_loss=ids_loss, + images_alt=images_ip, + combine_ids=None, + color_frame_filter=None, + ) + + sampler = self.ray_sampler if self.training else self.val_sampler + + renderer_scale = self.renderer.net._scale + dino_features = self.renderer.net.grid_l_loss_features[renderer_scale] + + if self.artifact_field is not None: + dino_features = torch.cat(torch.broadcast_tensors(dino_features, self.artifact_field), dim=2) + + all_rays, all_rgb_gt, all_dino_gt = sampler.sample( + images_ip[:, ids_loss], poses[:, ids_loss], projs[:, ids_loss], image_ids=ids_loss, + dino_features=dino_features + ) + + if self.artifact_field is not None: + all_dino_artifacts = all_dino_gt[:, :, self.artifact_field.shape[0]:] + all_dino_gt = all_dino_gt[:, :, :self.artifact_field.shape[0]] + else: + all_dino_artifacts = None + + data["fine"], data["coarse"] = [], [] + + scales = list( + self.renderer.net.encoder.scales + if self.prediction_mode == "multiscale" + else [self.renderer.net.get_scale()] + ) + + for scale in scales: + self.renderer.net.set_scale(scale) + + using_fine = self.renderer.renderer.using_fine + + if scale == 0: + render_dict = self.renderer( + all_rays, + want_weights=True, + want_alphas=True, + want_rgb_samps=True, + ) + else: + using_fine = self.renderer.renderer.using_fine + self.renderer.renderer.using_fine = False + render_dict = self.renderer( + all_rays, + want_weights=True, + want_alphas=True, + want_rgb_samps=False, + ) + self.renderer.renderer.using_fine = using_fine + + render_dict["rgb_gt"] = all_rgb_gt + render_dict["rays"] = all_rays + render_dict["dino_gt"] = all_dino_gt.float() + + if all_dino_artifacts is not None: + render_dict["dino_artifacts"] = all_dino_artifacts.float() + + render_dict = sampler.reconstruct(render_dict, + channels=images_ip.shape[2], + dino_channels=self.renderer.net.encoder.dino_pca_dim) + + if "fine" in render_dict: + data["fine"].append(render_dict["fine"]) + data["coarse"].append(render_dict["coarse"]) + data["rgb_gt"] = render_dict["rgb_gt"] + data["dino_gt"] = render_dict["dino_gt"] + if "dino_artifacts" in render_dict: + data["dino_artifacts"] = render_dict["dino_artifacts"] + data["rays"] = render_dict["rays"] + + dino_module = self.renderer.net.encoder + downsampling_mode = "patch" if self.training else "image" + for _data_coarse in data["coarse"]: + _data_coarse["dino_features"] = dino_module.expand_dim(_data_coarse["dino_features"]) + downsampling_result = dino_module.downsample(_data_coarse["dino_features"], downsampling_mode) + if isinstance(downsampling_result, tuple): + (_data_coarse["dino_features_downsampled"], + _data_coarse["dino_features_salience_map"], + _data_coarse["dino_features_weight_map"], + _data_coarse["dino_features_per_patch_weight"]) = downsampling_result + elif downsampling_result is not None: + _data_coarse["dino_features_downsampled"] = downsampling_result + + if not self.training and hasattr(self, "validation_tag") and self.validation_tag == "visualization": + for _data_coarse in data["coarse"]: + with torch.amp.autocast(_data_coarse["dino_features"].device.type, enabled=False): + dino_module.fit_visualization(_data_coarse["dino_features"].flatten(0, -2)) + _data_coarse["vis_batch_dino_features"] = [ + dino_module.transform_visualization(_data_coarse["dino_features"], norm=True, from_dim=0), + dino_module.transform_visualization(_data_coarse["dino_features"], norm=True, from_dim=3), + dino_module.transform_visualization(_data_coarse["dino_features"], norm=True, from_dim=6), + ] + #_data_coarse["vis_batch_dino_features_kmeans"] = dino_module.fit_transform_kmeans_visualization(_data_coarse["dino_features"]) + + + if self.training: + data["feature_volume"] = self.renderer.net.grid_f_features[0] + + data["z_near"] = torch.tensor(self.ray_sampler.z_near, device=images.device) + data["z_far"] = torch.tensor(self.ray_sampler.z_far, device=images.device) + + surface_sample = self.sample_3d_crop(poses, projs, data["coarse"][0]["depth"], sample_radius=self.sample_radius_3d) + if surface_sample is not None: + data["sample_surface_dino_features"], data["sample_surface_sigma"] = surface_sample + + if self.training: + self._counter += 1 + + return data + + def sample_3d_crop(self, poses, projs, depth, z_far=100, n_crops=5, n_samples=576, sample_radius=0.5, sigma_threshold=0.5): + positions_samples = [] + n = projs.size(0) + + oversampling = 4 + for n_ in range(n): + focals = projs[n_, :1, [0, 1], [0, 1]] + centers = projs[n_, :1, [0, 1], [2, 2]] + + _, _, height, width = depth.shape + rays, _ = util.gen_rays( + poses[n_, :1].view(-1, 4, 4), + width, + height, + focal=focals, + c=centers, + z_near=0, + z_far=0, + norm_dir=True, + ) + current_depth = depth[n_, 0] # [h, w] + limits = torch.quantile(current_depth[current_depth < z_far], torch.range(0, 1, 1/n_crops).cuda()) + + sampled_positions = [] + for i in range(n_crops): + valid_positions = torch.nonzero((current_depth > limits[i]) & (current_depth < limits[i+1]), as_tuple=False) + if valid_positions.size(0) > 0: # Not enough samples in depth range + sampled_positions.append(valid_positions[torch.randint(valid_positions.size(0), (1,)).item()]) + + n_crops = len(sampled_positions) + if n_crops > 0: + sampled_positions = torch.stack(sampled_positions, dim=0) + + cam_centers = rays[0, :, :, :3] # [h, w, 3] + cam_raydir = rays[0, :, :, 3:6] # [h, w, 3] + + depth_crop = current_depth[sampled_positions[:, 0], sampled_positions[:, 1]] # [n_crops] + cam_centers_crop = cam_centers[sampled_positions[:, 0], sampled_positions[:, 1]] # [n_crops, 3] + cam_raydir_crop = cam_raydir[sampled_positions[:, 0], sampled_positions[:, 1]] # [n_crops, 3] + + positions_crop = cam_centers_crop + cam_raydir_crop * depth_crop.unsqueeze(-1) # [n_crops, 3] + + # Sample in unit sphere + unit_vecs = torch.randn(n_crops, oversampling*n_samples, 3, device=positions_crop.device) # [n_crops, n_samples, 3] + unit_vecs /= torch.norm(unit_vecs, dim=2, keepdim=True) + radii = sample_radius * torch.rand(n_crops, oversampling*n_samples, 1).cuda() ** (1/3) + + # Scale radius in view space + # radii = radii * depth_crop[:, None, None] / 20.0 + + random_shifts = unit_vecs * radii + positions_samples.append(positions_crop.unsqueeze(1) + random_shifts) # [n_crops, n_samples, 3] + + if not positions_samples: + return None, None + + positions_samples = torch.stack(positions_samples, dim=0) # [n, n_crops, n_samples, 3] + + _, _, sigma, _, state_dict = self.renderer.net(positions_samples.flatten(1, -2)) # [n, n_crops*n_samples, ...] + sigma = sigma.view(n * n_crops, oversampling*n_samples) + dino = state_dict["dino_features"].view(n * n_crops, oversampling * n_samples, -1) + + valid_samples = sigma > sigma_threshold + valid_crop = valid_samples.sum(-1) > n_samples + + if valid_crop.sum() == 0: + return None, None + + # Keep only crops with enough valid samples + sigma = sigma[valid_crop] + dino = dino[valid_crop] + + # For each crop, take the first n_samples valid samples + sigma = torch.stack([s[mask][:n_samples] for s, mask in zip(sigma, valid_samples[valid_crop])]).unsqueeze(0).unsqueeze(-1) + dino = torch.stack([d[mask][:n_samples] for d, mask in zip(dino, valid_samples[valid_crop])]).unsqueeze(0) + + return self.renderer.net.encoder.expand_dim(dino), 1 - torch.exp(-sigma) + + def train(self, mode=True): + super().train(False) + self.renderer.net.downstream_head.train(mode) + + def parameters(self, recurse=True): + return self.renderer.net.downstream_head.parameters(recurse) + + def parameters_lr(self): + return self.renderer.net.downstream_head.parameters_lr() + + def update_model_eval(self, metrics): + self.renderer.net.downstream_head.update_model_eval(metrics) + + +def training(local_rank, config, sweep_trial=None): + return base_training( + local_rank, + config, + get_dataflow, + initialize, + sweep_trial, + ) + + +def initialize(config: dict): + # Continue if checkpoint already exists + if config["training"].get("continue", False): + prefix = "training_checkpoint_" + ckpts = Path(config["output"]["path"]).glob(f"{prefix}*.pt") + # TODO: probably correct logic but please check + training_steps = [int(ckpt.stem.split(prefix)[1]) for ckpt in ckpts] + if training_steps: + config["training"]["resume_from"] = ( + Path(config["output"]["path"]) / f"{prefix}{max(training_steps)}.pt" + ) + + if config["training"].get("continue", False) and config["training"].get( + "resume_from", None + ): + config_path = Path(config["output"]["path"]) + logger.info(f"Loading model config from {config_path}") + load_model_config(config_path, config) + + net = make_model(config["model"], config["downstream"]) + renderer = NeRFRenderer.from_conf(config["renderer"]) + renderer = renderer.bind_parallel(net, gpus=None).eval() + + mode = config.get("mode", "depth") + + ray_sampler = get_ray_sampler(config["training"]["ray_sampler"]) + + model = BTSDownstreamWrapper(renderer, ray_sampler, config["model"], mode == "nvs") + model = idist.auto_model(model) + + # TODO: make optimizer itself configurable configurable + if config["training"].get("optimizer", None): + optim_args = config["training"]["optimizer"]["args"].copy() + optim_lr = optim_args.pop("lr") + optimizer = optim.Adam( + [ + {"params": params, "lr": lr_factor * optim_lr} + for lr_factor, params in model.parameters_lr() + ], + **optim_args + ) + optimizer = idist.auto_optim(optimizer) + else: + optimizer = None + + if config["training"].get("scheduler", None): + lr_scheduler = make_scheduler(config["training"].get("scheduler", {}), optimizer) + else: + lr_scheduler = None + + criterion = [ + make_loss(config_loss) + for config_loss in config["training"].get("loss", []) + ] + + return model, optimizer, criterion, lr_scheduler diff --git a/scenedino/training/trainer_overfit.py b/scenedino/training/trainer_overfit.py new file mode 100644 index 0000000000000000000000000000000000000000..530463935663e7fd3fc74815f6065f35ceb57325 --- /dev/null +++ b/scenedino/training/trainer_overfit.py @@ -0,0 +1,218 @@ +from copy import copy + +import ignite.distributed as idist +from torch import optim +from torch.utils.data import DataLoader, Subset + +from scenedino.training.base_trainer import base_training + +# TODO: change dataset +from scenedino.datasets import make_datasets +from scenedino.common.scheduler import make_scheduler +from scenedino.renderer import NeRFRenderer +from scenedino.models.backbones.dino.dinov2_module import * +from scenedino.training.trainer import BTSWrapper +from scenedino.models import make_model +from scenedino.common.ray_sampler import get_ray_sampler +from scenedino.losses import make_loss + + + +class EncoderDummy(nn.Module): + def __init__(self, size, feat_dim, num_views=1) -> None: + super().__init__() ## initializes this feature map as a random tensor of a specified size + self.feats = nn.Parameter(torch.randn(num_views, feat_dim, *size)) + self.latent_size = feat_dim + + def forward(self, x): + n = x.shape[0] + return [self.feats.expand(n, -1, -1, -1)] + + +class EncoderDinoDummy(nn.Module): + def __init__(self, + mode: str, # downsample-prediction, upsample-gt + decoder_arch: str, # nearest, bilinear, sfp, dpt + upsampler_arch: Optional[str], # nearest, bilinear, multiscale-crop + downsampler_arch: Optional[str], # sample-center, featup + encoder_arch: str, # vit-s, vit-b, fit3d-s + separate_gt_encoder_arch: Optional[str], # vit-s, vit-b, fit3d-s, None (reuses encoder) + encoder_freeze: bool, + dim_reduction_arch: str, # orthogonal-linear, mlp + num_ch_enc: np.array, + intermediate_features: List[int], + decoder_out_dim: int, + dino_pca_dim: int, + image_size: Tuple[int, int], + key_features: bool, + ): + + super().__init__() + + self.feats = nn.Parameter(torch.randn(1, decoder_out_dim, *image_size)) + self.latent_size = decoder_out_dim + + if separate_gt_encoder_arch is None: + self.gt_encoder = build_encoder(encoder_arch, image_size, [], key_features) # ONLY IN OVERFIT DUMMY! + else: + self.gt_encoder = build_encoder(separate_gt_encoder_arch, image_size, [], key_features) + + for p in self.gt_encoder.parameters(True): + p.requires_grad = False + + # General way of creating loss + if mode == "downsample-prediction": + assert upsampler_arch is None + self.downsampler = build_downsampler(downsampler_arch, self.gt_encoder.latent_size) + self.gt_wrapper = self.gt_encoder + + elif mode == "upsample-gt": + assert downsampler_arch is None + self.downsampler = None + self.gt_wrapper = build_gt_upsampling_wrapper(upsampler_arch, self.gt_encoder, image_size) + + else: + raise NotImplementedError + + self.extra_outs = 0 + self.latent_size = decoder_out_dim + + self.dino_pca_dim = dino_pca_dim + self.dim_reduction = build_dim_reduction(dim_reduction_arch, self.gt_encoder.latent_size, dino_pca_dim) + self.visualization = VisualizationModule(self.gt_encoder.latent_size) + + def forward(self, x, ground_truth=False): + if ground_truth: + return self.gt_wrapper(x) + + return [self.feats.expand(x.shape[0], -1, -1, -1)] + + def downsample(self, x, mode="patch"): + if self.downsampler is None: + return None + else: + return self.downsampler(x, mode) + + def expand_dim(self, features): + return self.dim_reduction.transform_expand(features) + + def fit_visualization(self, features, refit=True): + return self.visualization.fit_pca(features, refit) + + def transform_visualization(self, features, norm=False, from_dim=0): + return self.visualization.transform_pca(features, norm, from_dim) + + def fit_transform_kmeans_visualization(self, features): + return self.visualization.fit_transform_kmeans_batch(features) + + @classmethod + def from_conf(cls, conf): + return cls( + mode=conf.mode, + decoder_arch=conf.decoder_arch, + upsampler_arch=conf.get("upsampler_arch", None), + downsampler_arch=conf.get("downsampler_arch", None), + encoder_arch=conf.encoder_arch, + separate_gt_encoder_arch=conf.get("separate_gt_encoder_arch", None), + encoder_freeze=conf.encoder_freeze, + dim_reduction_arch=conf.dim_reduction_arch, + num_ch_enc=conf.get("num_ch_enc", None), + intermediate_features=conf.get("intermediate_features", []), + decoder_out_dim=conf.decoder_out_dim, + dino_pca_dim=conf.dino_pca_dim, + image_size=conf.image_size, + key_features=conf.key_features, + ) + + +class BTSWrapperOverfit(BTSWrapper): + def __init__(self, renderer, ray_sampler, config, eval_nvs=False, size=None) -> None: + super().__init__(renderer, ray_sampler, config, eval_nvs) + + if config["predict_dino"]: + encoder_dummy = EncoderDinoDummy.from_conf(config["encoder"]) + else: + encoder_dummy = EncoderDummy( + size, + config["encoder"]["d_out"], + ) + + self.renderer.net.encoder = encoder_dummy + + +def training(local_rank, config): + return base_training( + local_rank, + config, + get_dataflow, + initialize, + ) + + +def get_dataflow(config): + # - Get train/test datasets + if idist.get_local_rank() > 0: + # Ensure that only local rank 0 download the dataset + # Thus each node will download a copy of the datasetMVBTSNet + idist.barrier() + + train_dataset_full = make_datasets(config["dataset"])[0] + train_dataset = Subset( + train_dataset_full, + [config.get("example", config["dataset"].get("skip", 0))], + ) + + train_dataset.dataset._skip = config["dataset"].get("skip", 0) + + validation_datasets = {} + for name, validation_config in config["validation"].items(): + dataset = copy(train_dataset) + dataset.dataset.return_depth = True + validation_datasets[name] = dataset + + if idist.get_local_rank() == 0: + # Ensure that only local rank 0 download the dataset + idist.barrier() ## Once the dataset has been downloaded, the barrier is invoked, and only then are the other processes allowed to proceed. + ## By using this method, you can control the order of execution in a distributed setting and ensure that certain + ## steps are not performed multiple times by different processes. This can be very useful when working with shared + ## resources or when coordination is required between different processes. + + # Setup data loader also adapted to distributed config: nccl, gloo, xla-tpu + train_loader_full = DataLoader(train_dataset_full) + train_loader = DataLoader(train_dataset) + + validation_loaders = {} + for name, dataset in validation_datasets.items(): + validation_loaders[name] = DataLoader(dataset) + + return (train_loader, train_loader_full), validation_loaders + + +def initialize(config: dict): + net = make_model(config["model"]) + renderer = NeRFRenderer.from_conf(config["renderer"]) + renderer = renderer.bind_parallel(net, gpus=None).eval() + + mode = config.get("mode", "depth") + ray_sampler = get_ray_sampler(config["training"]["ray_sampler"]) + + model = BTSWrapperOverfit( + renderer, + ray_sampler, + config["model"], + mode == "nvs", + size=config["dataset"].get("image_size", (192, 640)), + ) + + model = idist.auto_model(model) + optimizer = optim.Adam(model.parameters(), **config["training"]["optimizer"]["args"]) + optimizer = idist.auto_optim(optimizer) + + lr_scheduler = make_scheduler(config["training"].get("scheduler", {}), optimizer) + + criterion = [ + make_loss(config_loss) + for config_loss in config["training"]["loss"] + ] + + return model, optimizer, criterion, lr_scheduler diff --git a/scenedino/visualization/common.py b/scenedino/visualization/common.py new file mode 100644 index 0000000000000000000000000000000000000000..e1f2bef7141580046eb8a65b87407e4f8d3f7e07 --- /dev/null +++ b/scenedino/visualization/common.py @@ -0,0 +1,66 @@ +import numpy as np +import torch + +import matplotlib.pyplot as plt + + +def draw_bbox(im, size): + b, c, h, w = im.shape + h2, w2 = (h - size) // 2, (w - size) // 2 + marker = np.tile(np.array([[1.0], [0.0], [0.0]]), (1, size)) + marker = torch.FloatTensor(marker) + im[:, :, h2, w2 : w2 + size] = marker + im[:, :, h2 + size, w2 : w2 + size] = marker + im[:, :, h2 : h2 + size, w2] = marker + im[:, :, h2 : h2 + size, w2 + size] = marker + return im + + +def plot_image_grid( + images, rows, cols, directions=None, imsize=(2, 2), title=None, show=True +): + fig, axs = plt.subplots( + rows, + cols, + gridspec_kw={"wspace": 0, "hspace": 0}, + squeeze=True, + figsize=(rows * imsize[0], cols * imsize[1]), + ) + for i, image in enumerate(images): + axs[i % rows][i // rows].axis("off") + if directions is not None: + axs[i % rows][i // rows].arrow( + 32, + 32, + directions[i][0] * 16, + directions[i][1] * 16, + color="red", + length_includes_head=True, + head_width=2.0, + head_length=1.0, + ) + axs[i % rows][i // rows].imshow(image, aspect="auto") + plt.subplots_adjust(hspace=0, wspace=0) + if title is not None: + fig.suptitle(title, fontsize=12) + if show: + plt.show() + return fig + + +def show_save(save_path, show=True, save=False): + if show: + plt.show() + if save: + plt.savefig(save_path) + + +def color_tensor(tensor: torch.Tensor, cmap, norm=False): + if norm: + tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min()) + map = plt.cm.get_cmap(cmap) + # tensor = torch.tensor(map(tensor.cpu().numpy()), device=tensor.device)[..., :3] ## default + tensor = torch.tensor(map(tensor.cpu().numpy()))[ + ..., :3 + ] ## This is when the input tensor is numpy array already + return tensor diff --git a/scenedino/visualization/vis_2d.py b/scenedino/visualization/vis_2d.py new file mode 100644 index 0000000000000000000000000000000000000000..e326f851d37d5bbf1155fd509db914bdc71390a7 --- /dev/null +++ b/scenedino/visualization/vis_2d.py @@ -0,0 +1,515 @@ +import logging +import math +from typing import Any, Callable +from dotdict import dotdict +import ignite.distributed as idist + +import torch +from ignite.contrib.handlers import TensorboardLogger +from ignite.engine import Engine +from torchvision.utils import make_grid +from scenedino.models.bts import BTSNet +from torchvision.utils import flow_to_image + + +from scenedino.visualization.common import color_tensor + +# TODO: configure logger somewhere else +logger = logging.getLogger("Visualization") + + +def render_profile( + model: BTSNet, + points: torch.Tensor, + viewdirs: torch.Tensor | None = None, + dim: int = 1, +): + """Render a profile of the scene. + + Args: + model (BTSNet): model + points (torch.Tensor): points to render in world coordinates. Shape (res_x, res_y, res_z, 3) + viewdirs (torch.Tensor | None, optional): view directions. Defaults to None. + + Returns: + torch.Tensor: profile of the scene + """ + + res_x, res_y, res_z = points.shape[:-1] + device = idist.device() + points = points.to(device).view(1, -1, 3) + batch_size = 65536 + split_points = torch.split(points, batch_size, dim=1) + sigmas, invalid = [], [] + for pnts in split_points: + _, invalid_, sigmas_, _, _ = model.forward(pnts, viewdirs=viewdirs) + invalid.append(invalid_) + sigmas.append(sigmas_) + sigmas = torch.cat(sigmas, dim=0) + invalid = torch.cat(invalid, dim=0) + sigmas[invalid.bool()] = 1.0 + + sigmas = sigmas.view(res_x, res_y, res_z) + invalid = invalid.view(res_x, res_y, res_z) + + sigmas_sum = torch.cumsum(sigmas, dim=dim) + profile = (sigmas_sum <= 1).float().sum(dim=dim) / sigmas.shape[dim] + + return profile + + +def get_profiles(data) -> torch.Tensor | None: + # TODO: check for permutation + if "profiles" in data: + # profiles = data["profiles"] + profiles = torch.stack([data["profiles"]], dim=0).transpose(-1, -2) + return color_tensor(profiles, cmap="magma", norm=True).permute(0, 3, 1, 2) + logger.warning( + "No profiles found in model output. Not creating a profile visualization." + ) + return None + + +def get_input_imgs(data) -> torch.Tensor | None: + if "imgs" in data: + return torch.stack(data["imgs"], dim=1).detach()[0] * 0.5 + 0.5 + logger.warning( + "No images found in model output. Not creating a input image visualization." + ) + return None + + +def get_reconstructed_imgs(data) -> torch.Tensor | None: + if "rgb" in data["coarse"][0] and "imgs" in data: + images = torch.stack(data["imgs"], dim=1).detach()[0] + + _, c, h, w = images.shape + recon_imgs = data["coarse"][0]["rgb"].detach()[0][..., :3] * .5 + .5 + nv = recon_imgs.shape[0] + recon_imgs = recon_imgs.view(nv, h, w, -1, c) + + # Aggregate recon_imgs by taking the mean + return recon_imgs.mean(dim=-2).permute(0, 3, 1, 2) + logger.warning( + "No reconstructed images found in model output. Not creating a recontructed image visualization." + ) + return None + + +def get_reconstruction_rmse(data) -> torch.Tensor | None: + if "rgb" in data["coarse"][0] and "imgs" in data: + images = torch.stack(data["imgs"], dim=1).detach()[0] + recon_imgs = data["coarse"][0]["rgb"].detach()[0][..., :3] + + _, c, h, w = images.shape + nv = recon_imgs.shape[0] + + images = images * 0.5 + 0.5 + + recon_imgs = recon_imgs.view(nv, h, w, -1, c) + # Aggregate recon_imgs by taking the mean + recon_imgs = recon_imgs.mean(dim=-2).permute(0, 3, 1, 2) + + recon_mse = (((images - recon_imgs) ** 2) / 2).mean(dim=1).clamp(0, 1) + return color_tensor(recon_mse, cmap="plasma").permute(0, 3, 1, 2) + logger.warning( + "No reconstructed images found in model output. Not creating a recontructed image visualization." + ) + return None + + +def get_dino_cos_sim_downsampled(data) -> torch.Tensor | None: + if "dino_features_downsampled" in data["coarse"][0] and "imgs" in data: + dino_gt = data["dino_gt"].detach() + recon_dino = data["coarse"][0]["dino_features_downsampled"].detach().squeeze(-2) + + cos_sim = torch.nn.CosineSimilarity(dim=-1)(dino_gt, recon_dino).squeeze(0) + return color_tensor(cos_sim, cmap="plasma").permute(0, 3, 1, 2) + logger.warning( + "No downsampled dino cos-sim found in model output. Not creating a visualization." + ) + return None + + +def get_dino_gt(data) -> torch.Tensor | None: + if "vis_dino_gt" in data: + vis_dino_gt = data["vis_dino_gt"][0].permute(0, -1, 1, 2) / 2 + 0.5 + return torch.clamp(vis_dino_gt, min=0, max=1) + logger.warning( + "No dino GT found in model output. Not creating a dino GT visualization." + ) + return None + + +def get_reconstructed_dino(data) -> torch.Tensor | None: + if "vis_dino_features" in data["coarse"][0]: + vis_dino_features = data["coarse"][0]["vis_dino_features"][0, :, :, :, 0, :].permute(0, -1, 1, 2) / 2 + 0.5 + return torch.clamp(vis_dino_features, min=0, max=1) + logger.warning( + "No reconstructed dino features found in model output. Not creating a reconstructed dino visualization." + ) + return None + + +def get_reconstructed_dino_downsampled(data) -> torch.Tensor | None: + if "vis_dino_features_downsampled" in data["coarse"][0]: + vis_dino_features = data["coarse"][0]["vis_dino_features_downsampled"][0, :, :, :, 0, :].permute(0, -1, 1, 2) / 2 + 0.5 + return torch.clamp(vis_dino_features, min=0, max=1) + logger.warning( + "No downsampled reconstructed dino features found in model output. Not creating a reconstructed dino visualization." + ) + return None + +def get_batch_dino_gt(data) -> torch.Tensor | None: + if "vis_batch_dino_gt" in data: + vis_batch_dino_gt = [v[0].permute(0, -1, 1, 2) / 2 + 0.5 for v in data["vis_batch_dino_gt"]] + return [torch.clamp(v, min=0, max=1) for v in vis_batch_dino_gt] + logger.warning( + "No dino GT (batch vis) found in model output. Not creating a dino GT visualization." + ) + return None + +def get_batch_dino_artifacts(data) -> torch.Tensor | None: + if "vis_batch_dino_artifacts" in data: + vis_batch_dino_artifacts = [v[0].permute(0, -1, 1, 2) / 2 + 0.5 for v in data["vis_batch_dino_artifacts"]] + return [torch.clamp(v, min=0, max=1) for v in vis_batch_dino_artifacts] + logger.warning( + "No dino artifacts (batch vis) found in model output. Not creating a dino GT visualization." + ) + return None + +def get_batch_dino_features_kmeans(data) -> torch.Tensor | None: + if "vis_batch_dino_features_kmeans" in data["coarse"][0]: + vis_batch_dino_features_kmeans = data["coarse"][0]["vis_batch_dino_features_kmeans"][0].permute(0, -1, 1, 2) + return torch.clamp(vis_batch_dino_features_kmeans, min=0, max=1) + logger.warning( + "No dino kmeans segmentation (batch vis) found in model output. Not creating a visualization." + ) + return None + +def get_batch_dino_gt_kmeans(data) -> torch.Tensor | None: + if "vis_batch_dino_gt_kmeans" in data: + vis_batch_dino_gt_kmeans = data["vis_batch_dino_gt_kmeans"][0].permute(0, -1, 1, 2) + return torch.clamp(vis_batch_dino_gt_kmeans, min=0, max=1) + logger.warning( + "No dino kmeans segmentation (batch vis) found in model output. Not creating a visualization." + ) + return None + +def get_segs_gt(data) -> torch.Tensor | None: + if "segmentation" in data: + vis_segs_gt = data["segmentation"]["visualization"]["target"].movedim(-1, -3) + return torch.clamp(vis_segs_gt, min=0, max=1) + logger.warning( + "No Segmentation target (batch vis) found in model output. Not creating a visualization." + ) + return None + +def get_segs_pred(data) -> torch.Tensor | None: + if "segmentation" in data: + vis_segs_pred = torch.cat( + [data["segmentation"]["visualization"][result_name][:, 0].squeeze(-2) + for result_name in data["segmentation"]["visualization"] if result_name != "target"], + dim=0 + ).movedim(-1, -3) + + return torch.clamp(vis_segs_pred, min=0, max=1) + logger.warning( + "No Segmentation (batch vis) found in model output. Not creating a visualization." + ) + return None + +def get_batch_reconstructed_dino(data) -> torch.Tensor | None: + if "vis_batch_dino_features" in data["coarse"][0]: + vis_batch_dino_features = [v[0, :, :, :, 0, :].permute(0, -1, 1, 2) / 2 + 0.5 for v in + data["coarse"][0]["vis_batch_dino_features"]] + return [torch.clamp(v, min=0, max=1) for v in vis_batch_dino_features] + logger.warning( + "No reconstructed dino features (batch vis) found in model output. Not creating a reconstructed dino visualization." + ) + return None + + +def get_batch_reconstructed_dino_downsampled(data) -> torch.Tensor | None: + if "vis_batch_dino_features_downsampled" in data["coarse"][0]: + vis_batch_dino_features_downsampled = [v[0, :, :, :, 0, :].permute(0, -1, 1, 2) / 2 + 0.5 for v in + data["coarse"][0]["vis_batch_dino_features_downsampled"]] + return [torch.clamp(v, min=0, max=1) for v in vis_batch_dino_features_downsampled] + logger.warning( + "No downsampled reconstructed dino features (batch vis) found in model output. Not creating a reconstructed dino visualization." + ) + return None + + +def get_dino_downsampling_weight(data) -> torch.Tensor | None: + if "dino_features_weight_map" in data["coarse"][0]: + vis_dino_weight = data["coarse"][0]["dino_features_weight_map"][0, :, :, :, 0, :].permute(0, -1, 1, 2) + return vis_dino_weight / vis_dino_weight.max() + logger.warning( + "No vis_dino_weight found in model output. Not creating a reconstructed dino visualization." + ) + return None + + +def get_dino_downsampling_salience(data) -> torch.Tensor | None: + if "dino_features_salience_map" in data["coarse"][0]: + vis_dino_salience = data["coarse"][0]["dino_features_salience_map"][0, :, :, :, 0, :].permute(0, -1, 1, 2) + return (vis_dino_salience - vis_dino_salience.min()) / (vis_dino_salience.max() - vis_dino_salience.min()) + logger.warning( + "No vis_dino_salience found in model output. Not creating a reconstructed dino visualization." + ) + return None + + +def get_dino_downsampling_per_patch_weight(data) -> torch.Tensor | None: + if "dino_features_per_patch_weight" in data["coarse"][0]: + vis_dino_patch_weight = data["coarse"][0]["dino_features_per_patch_weight"] + return (vis_dino_patch_weight - vis_dino_patch_weight.min()) / (vis_dino_patch_weight.max() - vis_dino_patch_weight.min()) + logger.warning( + "No vis_dino_patch_weight found in model output. Not creating a reconstructed dino visualization." + ) + return None + + +def get_depth(data) -> torch.Tensor | None: + if "depth" in data["coarse"][0] and "imgs" in data: + z_near = data["z_near"] + z_far = data["z_far"] + recon_depth = data["coarse"][0]["depth"].detach()[0] + recon_depth = (1 / recon_depth - 1 / z_far) / (1 / z_near - 1 / z_far) + return color_tensor(recon_depth.squeeze(1).clamp(0, 1), cmap="plasma").permute( + 0, 3, 1, 2 + ) + logger.warning( + "No reconstructed depth found in model output. Not creating a depth visualization." + ) + return None + + +def get_depth_profile(data) -> torch.Tensor | None: + if "alphas" in data["coarse"][0] and "imgs" in data: + images = torch.stack(data["imgs"], dim=1).detach()[0] + _, _, h, w = images.shape + depth_profile = data["coarse"][0]["alphas"].detach()[0] + depth_profile = ( + depth_profile[:, [h // 4, h // 2, 3 * h // 4], :, :] + .view(depth_profile.shape[0] * 3, w, -1) + .permute(0, 2, 1) + ) + depth_profile = depth_profile.clamp_min(0) / depth_profile.max() + return color_tensor(depth_profile, cmap="plasma").permute(0, 3, 1, 2) + logger.warning( + "No alphas found in model output. Not creating a depth profile visualization." + ) + return None + + +def get_invalids(data) -> torch.Tensor | None: + if "invalid" in data["coarse"][0]: + invalids = data["coarse"][0]["invalid"].detach()[0] + invalids = invalids + invalids = invalids.mean(-2).mean(-1) + return color_tensor(invalids, cmap="plasma").permute(0, 3, 1, 2) + logger.warning( + "No invalids found in model output. Not creating a invalid visualization." + ) + return None + + +def get_ray_entropy(data) -> torch.Tensor | None: + if "alphas" in data["coarse"][0]: + alphas = data["coarse"][0]["alphas"].detach()[0] + alphas += 1e-5 + + ray_density = alphas / alphas.sum(dim=-1, keepdim=True) + ray_entropy = -(ray_density * torch.log(ray_density)).sum(-1) / ( + math.log2(alphas.shape[-1]) + ) + return color_tensor(ray_entropy, cmap="plasma").permute(0, 3, 1, 2) + logger.warning( + "No alphas found in model output. Not creating a ray entropy visualization." + ) + return None + + +def get_ray_entropy_weights(data) -> torch.Tensor | None: + if "weights" in data["coarse"][0]: + weights = data["coarse"][0]["weights"].detach()[0] + weights += 1e-5 + + ray_density = weights / weights.sum(dim=-1, keepdim=True) + ray_entropy = -(ray_density * torch.log(ray_density)).sum(-1) / ( + math.log2(weights.shape[-1]) + ) + return color_tensor(ray_entropy, cmap="plasma").permute(0, 3, 1, 2) + logger.warning( + "No alphas found in model output. Not creating a ray entropy for weights visualization." + ) + return None + + +def get_alpha_sum(data) -> torch.Tensor | None: + if "alphas" in data["coarse"][0]: + alphas = data["coarse"][0]["alphas"].detach()[0] + alphas += 1e-5 + + alpha_sum = (alphas.sum(dim=-1) / alphas.shape[-1]).clamp(-1) + return color_tensor(alpha_sum, cmap="plasma").permute(0, 3, 1, 2) + logger.warning( + "No alphas found in model output. Not creating a alpha sum visualization." + ) + return None + + +def get_uncertainty(data) -> torch.Tensor | None: + if data["rgb_gt"].shape[-1] >= 7: + uncert = data["rgb_gt"][0][:, :, :, 6].detach() + + return color_tensor(uncert, cmap="plasma", norm=True).permute(0, 3, 1, 2) + elif "extras" in data["coarse"][0]: + uncert = data["coarse"][0]["extras"][0, :, :, :, 0].detach() + + return color_tensor(uncert, cmap="plasma", norm=True).permute(0, 3, 1, 2) + logger.warning( + "No uncertainty found in model output. Not creating a uncertainty visualization." + ) + return None + + +def get_rendered_flow(data) -> torch.Tensor | None: + if data["coarse"][0]["rgb"].shape[-1] >= 5: + flow = data["coarse"][0]["rgb"][0][:, :, :, 0, 3:5].detach() + + images = torch.stack(data["imgs"], dim=1).detach()[0] + + _, c, h, w = images.shape + nv = flow.shape[0] + + flow = flow.view(nv, h, w, 2) + + flow = torch.cat((flow[:, :, :, 0:1] / 2 * w , flow[:, :, :, 1:2] / 2 * h), dim=-1).permute(0, 3, 1, 2) + + flow_imgs = [] + for i in range(nv): + flow_imgs.append(flow_to_image(flow[i].cpu().squeeze()).float() / 255) + + flow_imgs = torch.stack(flow_imgs, dim=0) + return flow_imgs + logger.warning( + "No rendered flows found in model output. Not creating a rendered_flow visualization." + ) + return None + + +def get_predicted_occlusions(data) -> torch.Tensor | None: + if data["rgb_gt"].shape[-1] >= 6: + occs = data["rgb_gt"][0][:, :, :, 5].detach() + + return color_tensor(occs, cmap="plasma", norm=True).permute(0, 3, 1, 2) + logger.warning( + "No predicted occlusions found in model output. Not creating a predicted occlusions visualization." + ) + return None + + +def get_depth_direct(data) -> torch.Tensor | None: + if "depths" in data: + depths = 1 / data["depths"][0:2, 0].detach() + + return color_tensor(depths, cmap="plasma", norm=True).permute(0, 3, 1, 2) + logger.warning( + "No alphas found in model output. Not creating a alpha sum visualization." + ) + return None + + +def get_occlusions(data) -> torch.Tensor | None: + if "occs_fwd" in data and "occs_bwd" in data: + occs_fwd = data["occs_fwd"][0:1, 0].detach() + occs_bwd = data["occs_bwd"][0:1, 0].detach() + + occs = torch.cat((occs_fwd, occs_bwd), dim=-1) + + return color_tensor(occs, cmap="plasma", norm=True).permute(0, 3, 1, 2) + logger.warning( + "No alphas found in model output. Not creating a alpha sum visualization." + ) + return None + + +def get_flow(data) -> torch.Tensor | None: + if "flows_fwd" in data and "occs_bwd" in data: + flows_fwd = data["flows_fwd"][0:1].detach() + flows_bwd = data["flows_bwd"][0:1].detach() + + flows_fwd = flow_to_image(flows_fwd.cpu().squeeze()) + flows_bwd = flow_to_image(flows_bwd.cpu().squeeze()) + + flows = torch.cat((flows_fwd, flows_bwd), dim=-1) + + return flows[None, :] + logger.warning( + "No alphas found in model output. Not creating a alpha sum visualization." + ) + return None + + +def tb_visualize(model: BTSNet, dataset, config: dict[str, Any] | None = None): + if config is None: + vis_fns: dict[str, Callable[[Any], torch.Tensor | None]] = { + "input_imgs": get_input_imgs, + "reconstructed_imgs": get_reconstructed_imgs, + "reconstruction_rmse": get_reconstruction_rmse, + "get_dino_gt": get_dino_gt, + "get_reconstructed_dino": get_reconstructed_dino, + "get_reconstructed_dino_downsampled": get_reconstructed_dino_downsampled, + "get_batch_dino_gt": get_batch_dino_gt, + "get_batch_reconstructed_dino": get_batch_reconstructed_dino, + "get_batch_reconstructed_dino_downsampled": get_batch_reconstructed_dino_downsampled, + "get_dino_downsampling_weight": get_dino_downsampling_weight, + "get_dino_cos_sim_downsampled": get_dino_cos_sim, + "profiles": get_profiles, + "depth": get_depth, + "depth_profile": get_depth_profile, + "alpha_sum": get_alpha_sum, + "ray_entropy": get_ray_entropy, + "ray_entropy_weights": get_ray_entropy_weights, + "invalids": get_invalids, + "rendered_flow": get_rendered_flow, + "predicted_occlusions": get_predicted_occlusions, + "uncertainty": get_uncertainty, + "depth_direct": get_depth_direct, + "occlusions": get_occlusions, + "flow": get_flow, + } + else: + # TODO: inform user about not found functions + vis_fns = { + name: globals()[f"get_{name}"] + for name, _ in config.items() + if [globals().get(f"get_{name}", None)] + } + + def _visualize(engine: Engine, tb_logger: TensorboardLogger, step: int, tag: str): + data = engine.state.output["output"] + if "profiles" in vis_fns.keys(): + # TODO: choose between data["poses"][0][0] and model.grid_f_poses_w2c[0] + points = dataset.get_points(model.grid_f_poses_w2c[0]) + # points = dataset.get_points(data["poses"][0][0]) + data["profiles"] = render_profile(model, points) + + writer = tb_logger.writer + for name, vis_fn in vis_fns.items(): + output = vis_fn(data) + if output is not None: + if name == "profiles": + grid = make_grid(output, padding=0) + elif isinstance(output, list): + nrow = len(output) + output = torch.stack(output, dim=1).flatten(0, 1) + grid = make_grid(output, nrow=nrow, padding=0) + else: + grid = make_grid(output, nrow=int(math.sqrt(output.shape[0])), padding=0) + writer.add_image(f"{tag}/{name}", grid.cpu(), global_step=step) + + return _visualize diff --git a/scenedino/visualization/vis_3d.py b/scenedino/visualization/vis_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/sscbench/augmentation.py b/sscbench/augmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..e3fe6ad7baf5f0bf3f6a95715db9ed6dfebb044e --- /dev/null +++ b/sscbench/augmentation.py @@ -0,0 +1,18 @@ +import torchvision.transforms.functional as F + +def get_color_aug_fn(params): + fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor = params + def color_aug_fn(img): + for fn_id in fn_idx: + if fn_id == 0 and brightness_factor is not None: + img = F.adjust_brightness(img, brightness_factor) + elif fn_id == 1 and contrast_factor is not None: + img = F.adjust_contrast(img, contrast_factor) + elif fn_id == 2 and saturation_factor is not None: + img = F.adjust_saturation(img, saturation_factor) + elif fn_id == 3 and hue_factor is not None: + img = F.adjust_hue(img, hue_factor) + + return img + + return color_aug_fn diff --git a/sscbench/dataset.pkl b/sscbench/dataset.pkl new file mode 100644 index 0000000000000000000000000000000000000000..92240a67fe38009747743908bdea70913d3920cd --- /dev/null +++ b/sscbench/dataset.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2f3bc0851ade944c4229bca758756066f84affa9ef508742000789d8a3519ea5 +size 976921 diff --git a/sscbench/evaluate_model_sscbench.py b/sscbench/evaluate_model_sscbench.py new file mode 100644 index 0000000000000000000000000000000000000000..2b8dce659c2862aa1f00f1f3d5f45182dd18b9bb --- /dev/null +++ b/sscbench/evaluate_model_sscbench.py @@ -0,0 +1,930 @@ +import argparse +import sys +import random +import time + +from omegaconf import open_dict + +import matplotlib.pyplot as plt + +sys.path.extend([".", ".."]) + +from generate_ply_sequence import get_cam_k +from point_utils import read_calib, generate_point_grid, get_fov_mask +from gen_voxelgrid_npy import save_as_voxel_ply, remove_invisible + +import logging + +from pathlib import Path +import subprocess +import yaml + +import cv2 +import os +import numpy as np +from tqdm import tqdm +import pickle +import torch +from torch import nn +import torch.nn.functional as F +from hydra import compose, initialize + +import matplotlib.pyplot as plt + +from sscbench_dataset import SSCBenchDataset +from pathlib import Path + +from scipy.optimize import linear_sum_assignment +import torchvision + + +RELOAD_DATASET = True +DATASET_LENGTH = 10 +FULL_EVAL = True +SAMPLE_EVERY = None +SAMPLE_OFFSET = 2 +SAMPLE_RANGE = None + +SIZE = 51.2 # Can be: 51.2, 25.6, 12.8 +SIZES = (12.8, 25.6, 51.2) +VOXEL_SIZE = 0.2 # Needs: 0.2 % VOXEL_SIZE == 0 + +USE_ADDITIONAL_INVALIDS = True + +TEST_ALPHA_CUTOFFS = False +SEARCH_VALUES = [10e-1, 10e-2, 10e-3, 10e-4, 10e-5, 10e-6, 10e-7] + +SIGMA_CUTOFF = 0.2 + +USE_ALPHA_WEIGHTING = True +USE_GROW = True + +CREATE_SIGMA_TRADEOFF_PLOT = True +SIGMA_VALUES = [1, 0.5, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005, 0.0025, 0.001] + +PLOT_ALL_IMAGES = False + +GENERATE_PLY_FILES = False +PLY_ONLY_FOV = True +PLY_IDS = [300, 400, 470] +OUTPUT_PATH = Path("") +PLY_SIZES = [25.6, 51.2] + +GENERATE_STATISTICS = False + +# For ply generation: +# USE_ADDITIONAL_INVALIDS = False +# USE_GROW = False +# GENERATE_PLY_FILES = True + +os.system("nvidia-smi") + +device = f'cuda:0' + +# DO NOT TOUCH OR YOU WILL BREAK RUNS (should be None) +gpu_id = None + +if gpu_id is not None: + print("GPU ID: " + str(gpu_id)) + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id) +if torch.cuda.is_available(): + torch.backends.cudnn.enabled = True + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.deterministic = True + +logging.basicConfig(level=logging.INFO) + + +def main(): + parser = argparse.ArgumentParser("SSCBenchmark Output generation") + parser.add_argument("--sscbench_data_root", "-ssc", type=str) + parser.add_argument("--voxel_gt_path", "-vgt", type=str) + parser.add_argument("--resolution", "-r", default=(192, 640)) + parser.add_argument("--checkpoint", "-cp", type=str, required=True) + parser.add_argument("--full", "-f", action="store_true") + parser.add_argument("--mode", "-m", default="s4c") + parser.add_argument("--ply_checkname", "-p", default="none") + + args = parser.parse_args() + + sscbench_data_root = args.sscbench_data_root + voxel_gt_path = args.voxel_gt_path + resolution = args.resolution + cp_path = args.checkpoint + full_evaluation = args.full + mode = args.mode + ply_checkname = args.ply_checkname + + if FULL_EVAL: + full_evaluation = True + + if GENERATE_PLY_FILES: + assert (not USE_GROW) and (not USE_ADDITIONAL_INVALIDS) # and VOXEL_SIZE == 0.1 + + # make the necessary dirs + for size in PLY_SIZES: + if not os.path.exists(OUTPUT_PATH / ply_checkname / str(int(size))): + os.makedirs(OUTPUT_PATH / ply_checkname / str(int(size))) + + if not os.path.exists(OUTPUT_PATH / ply_checkname): + os.makedirs(OUTPUT_PATH / ply_checkname) + + logging.info(f"Using a sigma cutoff of {SIGMA_CUTOFF}") + logging.info("Setting up dataset") + + with open("label_maps.yaml", "r") as f: + label_maps = yaml.safe_load(f) + + # pickle the dataset so we don't have to wait all the time + if os.path.isfile("dataset.pkl") and not RELOAD_DATASET: + logging.info("Loading dataset from dataset.pkl file.") + with open("dataset.pkl", "rb") as f: + dataset = pickle.load(f) + else: + logging.info("Generating the dataset and dumping it to dataset.pkl") + dataset = SSCBenchDataset( + data_path=sscbench_data_root, + voxel_gt_path=voxel_gt_path, + sequences=(9,), + target_image_size=resolution, + return_stereo=False, + frame_count=1, + color_aug=False, + load_fisheye=True, + fisheye_offset=10, + ) + if DATASET_LENGTH and not full_evaluation: + dataset.length = DATASET_LENGTH + + with open("dataset.pkl", 'wb') as f: + pickle.dump(dataset, f) + + logging.info("Setting up the model...") + + config_path = "exp_kitti_360" + + cp_path = Path(cp_path) + if cp_path.suffix == ".pt": + cp_root_path = cp_path.parent + else: + cp_root_path = cp_path + cp_path = next(cp_root_path.glob("training*.pt")) + + bts_dino_config_path = "training_config.yaml" + + PRODUCE_FEAT_VIS = GENERATE_PLY_FILES and mode.startswith("scenedino") + prediction_mode = None + if mode == "s4c": + from models.bts.model import BTSNet + from models.common.render import NeRFRenderer + + initialize(version_base=None, config_path="../../../configs", job_name="gen_sscbench_outputs") + config = compose(config_name=config_path, overrides=[]) + + logging.info('Loading checkpoint') + cp = torch.load(cp_path, map_location=device) + + with open_dict(config): + config["renderer"]["hard_alpha_cap"] = True + config["model_conf"]["code_mode"] = "z" + # config["model_conf"]["z_near"] = 8 + config["model_conf"]["mlp_coarse"]["n_blocks"] = 0 + config["model_conf"]["mlp_coarse"]["d_hidden"] = 64 + config["model_conf"]["encoder"]["d_out"] = 64 + config["model_conf"]["encoder"]["type"] = "monodepth2" + config["model_conf"]["grid_learn_empty"] = False + config["model_conf"]["sample_color"] = True + + # stuff for segmentation + config["model_conf"]["segmentation_mode"] = "panoptic_deeplab" + + net = BTSNet(config["model_conf"]) + net.sample_color = False + renderer = NeRFRenderer.from_conf(config["renderer"]) + renderer = renderer.bind_parallel(net, gpus=None).eval() + renderer.renderer.n_coarse = 64 + renderer.renderer.lindisp = True + + class _Wrapper(nn.Module): + def __init__(self): + super().__init__() + self.renderer = renderer + + _wrapper = _Wrapper() + + _wrapper.load_state_dict(cp["model"], strict=False) + renderer.to(device) + renderer.eval() + + elif mode.startswith("scenedino"): + from scenedino.models import make_model as dino_bts_make_model + from scenedino.renderer.nerf import NeRFRenderer as dino_bts_NeRFRenderer + from scenedino.common.ray_sampler import ImageRaySampler as dino_bts_ImageRaySampler + + bts_dino_parent_relative = Path("../../../../") + bts_dino_parent_absolute = str(bts_dino_parent_relative.resolve()) + initialize(version_base=None, + config_path=str(bts_dino_parent_relative / cp_root_path.relative_to(bts_dino_parent_absolute)), + job_name="gen_sscbench_outputs") + config = compose(config_name=bts_dino_config_path, overrides=[]) + + logging.info('Loading checkpoint') + cp = torch.load(cp_path, map_location=device) + + net = dino_bts_make_model(config["model"], config["downstream"]) + renderer = dino_bts_NeRFRenderer.from_conf(config["renderer"]) + renderer.hard_alpha_cap = False + renderer = renderer.bind_parallel(net, gpus=None).eval() + + class _Wrapper(nn.Module): + def __init__(self): + super().__init__() + self.renderer = renderer + + _wrapper = _Wrapper() + _wrapper.load_state_dict(cp, strict=False) # _wrapper.load_state_dict(cp["model"], strict=False) + renderer.to(device) + renderer.eval() + + height, width = config["dataset"]["image_size"] + ray_sampler = dino_bts_ImageRaySampler(z_near=3, z_far=80, width=width, height=height) + + if mode == "scenedino_linear": + prediction_mode = "direct_linear" + elif mode == "scenedino_direct_cluster": + prediction_mode = "direct_kmeans" + else: + prediction_mode = "stego_kmeans" + + else: + raise NotImplementedError() + + logging.info("Loading the Lidar to Camera matrices...") + + calib = read_calib() + T_velo_2_cam = calib["Tr"] + + logging.info("Generating the point cloud...") + + pts, _ = generate_point_grid(vox_origin=np.array([0, -25.6, -2]), + scene_size=(51.2, 51.2, 6.4), + voxel_size=VOXEL_SIZE, + cam_E=T_velo_2_cam, + cam_k=get_cam_k()) + + fov_mask = get_fov_mask() + + pts = torch.tensor(pts).to(device).reshape(1, -1, 3).float() + fov_mask = fov_mask.reshape(256, 256, 32) + + logging.info("Setting up folders...") + + downsample_factor = int(0.2 // VOXEL_SIZE) + + results = {} + for size in SIZES: + results[size] = { + "tp": 0, + "fp": 0, + "tn": 0, + "fn": 0, + "tp_seg": np.zeros(15), + "fp_seg": np.zeros(15), + "tn_seg": np.zeros(15), + "fn_seg": np.zeros(15), + "confusion_seg": np.zeros((16, 16)), + "tp_recall_seg": np.zeros(15), + "sum_recall_seg": np.zeros(15), + } + + # for the sigma tradeoff plots + trade_off_values = np.zeros([len(SIGMA_VALUES), 4]) + + cutoff_results = {i: {sv: {"tp":0, "fp": 0, "tn": 0, "fn": 0} for sv in SEARCH_VALUES} for i in range(1, 16)} + + pbar = tqdm(range(len(dataset))) + + # Randomly select indices without replacement + # dataset_size = len(dataset) + # subset_size = dataset_size // 10 + # subset_indices = random.sample(range(dataset_size), subset_size) + # pbar = tqdm(subset_indices) + + images = {"ids": [], "images": []} + + ids = [125, 280, 960, 1000, 1150, 1325, 2300, 3175, 3750, 4300, 5155, 5475, 5750, 6475, 6525, 6670, 6775, 7500, 7860, 8000, 8350, 9000, 9350, 10975] + + ids = [60, 250, 455, 690, 835, 2235, 2385, 2495, 3385, 4235, 4360, 4550, 4875, 5550, 6035, 7010, 7110, 8575, 9010, 9410, 11260, 11460, 11885] + + # for our statistics + tframeIds = [] + tinval = [] + ttp = [] + tfp = [] + ttn = [] + tfn = [] + + # plot_image_at_frame_id(dataset, 952) + for i in pbar: + if SAMPLE_EVERY: + if (i - SAMPLE_OFFSET) % SAMPLE_EVERY != 0: + continue + + sequence, id, is_right = dataset._datapoints[i] + + if SAMPLE_RANGE: + if id not in SAMPLE_RANGE: + continue + + if GENERATE_PLY_FILES and id not in PLY_IDS: + continue + + if GENERATE_STATISTICS: + tframeIds.append(id) + + data = dataset[i] + + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + torch.cuda.synchronize() + start_time = time.time() + + # downsample the sigmas + sigmas, segs, dino = downsample_and_predict(data, net, pts, downsample_factor, prediction_mode, vis=GENERATE_PLY_FILES, feat_vis=PRODUCE_FEAT_VIS) + + torch.cuda.synchronize() + inference_time = time.time() - start_time + memory_used = torch.cuda.max_memory_allocated(device) / 1024**2 # in MB + num_params = sum(p.numel() for key, p in net.named_parameters() if not key.startswith("encoder.gt_encoder")) + + #print(f"Inference time: {inference_time:.6f} seconds") + #print(f"Memory used: {memory_used:.2f} MB") + #print(f"Number of parameters: {num_params:,}") + + # convert both to the right format + segs = convert_voxels(segs, label_maps["cityscapes_to_label"]) + target = convert_voxels(data["voxel_gt"][0].astype(int), label_maps["sscbench_to_label"]) + + is_occupied_seg = torch.Tensor(sigmas > SIGMA_CUTOFF).to(torch.bool) + is_occupied_seg = remove_invisible(is_occupied_seg) + #raise ValueError(is_occupied_seg, segs) + is_occupied_seg[segs==0] = False + + images = torch.stack([torch.Tensor(_img) for _img in data["imgs"]], dim=0).cuda() + if PRODUCE_FEAT_VIS: + dino = calculate_pca(dino, is_occupied_seg, net) + dino = (255*dino).astype(int) + + poses = torch.stack([torch.Tensor(_pose) for _pose in data["poses"]], dim=0).unsqueeze(0).cuda() + projs = torch.stack([torch.Tensor(_proj) for _proj in data["projs"]], dim=0).unsqueeze(0).cuda() + + poses = torch.inverse(poses[:, :1]) @ poses + + all_rays, _ = ray_sampler.sample(None, poses, projs) + render_dict = renderer(all_rays[:, :], want_weights=True, want_alphas=True) + render_dict = ray_sampler.reconstruct(render_dict) + dino_features = net.encoder.expand_dim(render_dict["coarse"]["dino_features"]).squeeze() + + dino_gt = net.encoder.gt_encoder(images / 2 + 0.5)[-1].permute(0, 2, 3, 1) + dino_gt = F.normalize(dino_gt, dim=-1) + + dino_rgb_vis = torch.clamp(net.encoder.transform_visualization(dino_features.cpu()), min=-0.5, max=0.5) + 0.5 + dino_rgb_vis_gt = torch.clamp(net.encoder.transform_visualization(dino_gt.cpu()), min=-0.5, max=0.5) + 0.5 + dino_rgb_vis_gt = dino_rgb_vis_gt.repeat_interleave(8, 1).repeat_interleave(8, 2) + + if PLOT_ALL_IMAGES: + images["ids"].append(id) + images["images"].append(((data["imgs"][0] + 1) / 2).permute(1, 2, 0)) + + if len(images["ids"]) == 6: + plot_images(images) + images = {"images": [], "ids": []} + + # print(f"Image_Id: {id}") + # + # plt.imshow(((data["imgs"][0] + 1) / 2).permute(1, 2, 0)) + # plt.show() + # + # out_dict = {"sigmas": sigmas, "segs": segs.copy(), "gt": target, "fov_mask": fov_mask} + # + # with open(f'plots10_40/{id:06d}.pkl', 'wb') as f: + # pickle.dump(out_dict, f) + + if GENERATE_PLY_FILES: + _segs = segs.copy() + _target = target.copy() + if PRODUCE_FEAT_VIS: + _dino = dino.copy() + + mask = target != 255 + if PLY_ONLY_FOV: + mask = mask & fov_mask + + seg_mask = mask.copy() + for dim in range(seg_mask.ndim): + seg_mask = np.repeat(seg_mask, downsample_factor, axis=dim) + + # _segs[~seg_mask] = 0 + # _dino[~seg_mask] = 0 + _target[~mask] = 0 + + is_occupied_seg = is_occupied_seg.logical_and(torch.Tensor(fov_mask)) + # is_occupied_seg = torch.tensor(_segs > 0) + is_occupied_gt = torch.tensor(_target > 0) + + full_num_voxels = int(SIZE // VOXEL_SIZE) + + for idx in range(images.size(0)): + torchvision.utils.save_image(((images[idx] + 1) / 2), OUTPUT_PATH / ply_checkname / str(int(size)) / f"{id:06d}_image_{idx}.png") + if PRODUCE_FEAT_VIS: + torchvision.utils.save_image(dino_rgb_vis[idx].permute(2, 0, 1), OUTPUT_PATH / ply_checkname / str(int(size)) / f"{id:06d}_features_{idx}.png") + torchvision.utils.save_image(dino_rgb_vis_gt[idx].permute(2, 0, 1), OUTPUT_PATH / ply_checkname / str(int(size)) / f"{id:06d}_features_gt_{idx}.png") + images = None + + for size in PLY_SIZES: + num_voxels = int(size // 0.2) + save_as_voxel_ply(OUTPUT_PATH / ply_checkname / str(int(size)) / f"{id:06d}_gt.ply", + is_occupied_gt[: num_voxels, (128 - num_voxels // 2): (128 + num_voxels // 2),:], + voxel_size=0.2, + classes=torch.tensor(_target[: num_voxels, (128 - num_voxels // 2): (128 + num_voxels // 2),:])) + num_voxels = int(size // VOXEL_SIZE) + save_as_voxel_ply(OUTPUT_PATH / ply_checkname / str(int(size)) / f"{id:06d}.ply", + is_occupied_seg[: num_voxels, (full_num_voxels // 2 - num_voxels // 2): (full_num_voxels // 2 + num_voxels // 2),:], + size=(num_voxels, num_voxels, num_voxels//8), + voxel_size=VOXEL_SIZE, + classes=torch.tensor(_segs[: num_voxels, (full_num_voxels // 2 - num_voxels // 2): (full_num_voxels // 2 + num_voxels // 2),:])) + if PRODUCE_FEAT_VIS: + save_as_voxel_ply(OUTPUT_PATH / ply_checkname / str(int(size)) / f"{id:06d}_feat.ply", + is_occupied_seg[: num_voxels, (full_num_voxels // 2 - num_voxels // 2): (full_num_voxels // 2 + num_voxels // 2),:], + size=(num_voxels, num_voxels, num_voxels//8), + voxel_size=VOXEL_SIZE, + colors=torch.tensor(_dino[: num_voxels, (full_num_voxels // 2 - num_voxels // 2): (full_num_voxels // 2 + num_voxels // 2),:])) + continue + + if USE_ADDITIONAL_INVALIDS: + invalids = identify_additional_invalids(target) + # logging.info(np.mean(invalids)) + target[invalids == 1] = 255 + + if GENERATE_STATISTICS: + tinval.append(np.mean(invalids)) + + # test and summarize different alpha cutoffs + if TEST_ALPHA_CUTOFFS: + for i in range(1, 16): + for search_value in SEARCH_VALUES: + _tmp = segs.copy() + _tmp[np.logical_and(segs == i, sigmas < search_value)] = 0 + _tp_seg, _fp_seg, _tn_seg, _fn_seg = compute_occupancy_numbers_segmentation( + y_pred=_tmp, y_true=target, fov_mask=fov_mask, labels=label_maps["labels"]) + cutoff_results[i][search_value]["tp"] += _tp_seg[i-1] + cutoff_results[i][search_value]["fp"] += _fp_seg[i-1] + cutoff_results[i][search_value]["tn"] += _tn_seg[i-1] + cutoff_results[i][search_value]["fn"] += _fn_seg[i-1] + + if CREATE_SIGMA_TRADEOFF_PLOT: + for i, val in enumerate(SIGMA_VALUES): + _tmp = segs.copy() + _tmp[sigmas < val] = 0 + _tp, _fp, _tn, _fn = compute_occupancy_numbers(y_pred=_tmp, y_true=target, fov_mask=fov_mask) + trade_off_values[i] += np.array([_tp, _fp, _tn, _fn]) + + segs[sigmas < SIGMA_CUTOFF] = 0 + + for size in SIZES: + num_voxels = int(size // 0.2) + + # resize to right scene size + _segs = segs[:num_voxels, (128 - num_voxels//2):(128 + num_voxels//2), :] + _target = target[:num_voxels, (128 - num_voxels//2):(128 + num_voxels//2), :] + _fov_mask = fov_mask[:num_voxels, (128 - num_voxels // 2):(128 + num_voxels // 2), :] + + _tp, _fp, _tn, _fn = compute_occupancy_numbers(y_pred=_segs, y_true=_target, fov_mask=_fov_mask) + _tp_seg, _fp_seg, _tn_seg, _fn_seg, _confusion_seg = compute_occupancy_numbers_segmentation( + y_pred=_segs, y_true=_target, fov_mask=_fov_mask, labels=label_maps["labels"]) + _tp_rec_seg, _sum_rec_seg = compute_occupancy_recall_segmentation( + y_pred=_segs, y_true=_target, fov_mask=_fov_mask, labels=label_maps["labels"]) + + if size == 51.2 and GENERATE_STATISTICS: + ttp += [_tp] + tfp += [_fp] + ttn += [_fn] + tfn += [_fn] + + results[size]["tp"] += _tp + results[size]["fp"] += _fp + results[size]["tn"] += _tn + results[size]["fn"] += _fn + + results[size]["tp_seg"] += _tp_seg + results[size]["fp_seg"] += _fp_seg + results[size]["tn_seg"] += _tn_seg + results[size]["fn_seg"] += _fn_seg + + results[size]["confusion_seg"] += _confusion_seg + + results[size]["tp_recall_seg"] += _tp_rec_seg + results[size]["sum_recall_seg"] += _sum_rec_seg + + recall = results[size]["tp"] / (results[size]["tp"] + results[size]["fn"]) + precision = results[size]["tp"] / (results[size]["tp"] + results[size]["fp"]) + iou = results[size]["tp"] / (results[size]["tp"] + results[size]["fp"] + results[size]["fn"]) + + pbar.set_postfix_str(f"IoU: {iou*100:.2f} Prec: {precision*100:.2f} Rec: {recall*100:.2f}") + + result_str = "" + for mode in ["direct", "hungarian"]: + results_table = np.zeros((19, 3), dtype=np.float32) + + if mode == "hungarian": + assignments = linear_sum_assignment(results[51.2]["confusion_seg"], maximize=True) # Hungarian matching on full range + + # Here we compute all the metrics + for size_i, size in enumerate(SIZES): + recall = results[size]["tp"] / (results[size]["tp"] + results[size]["fn"]) + precision = results[size]["tp"] / (results[size]["tp"] + results[size]["fp"]) + iou = results[size]["tp"] / (results[size]["tp"] + results[size]["fp"] + results[size]["fn"]) + + results_table[0, size_i] = iou + results_table[1, size_i] = precision + results_table[2, size_i] = recall + + # logging.info(f"#" * 50) + # logging.info(f"Results for size {size}. ") + # logging.info(f"#" * 50) + + # logging.info("Occupancy metrics") + # logging.info(f"Recall: {recall*100:.2f}%") + # logging.info(f"Precision: {precision*100:.2f}%") + # logging.info(f"IoU: {iou*100:.2f}") + + # recall_seg = results[size]["tp_seg"] / (results[size]["tp_seg"] + results[size]["fn_seg"]) + # precision_seg = results[size]["tp_seg"] / (results[size]["tp_seg"] + results[size]["fp_seg"]) + # iou_seg = results[size]["tp_seg"] / (results[size]["tp_seg"] + results[size]["fp_seg"] + results[size]["fn_seg"]) + # mean_iou = np.mean(np.nan_to_num(iou_seg)) + + # Calculate hungarian matching + confusion_matrix = results[size]["confusion_seg"] + if mode == "hungarian": + confusion_matrix = confusion_matrix[np.argsort(assignments[1]), :] + + confusion_matrix_tp = np.diag(confusion_matrix) + confusion_matrix_denom = confusion_matrix.sum(0) + confusion_matrix.sum(1) - confusion_matrix_tp + confusion_matrix_per_class_iou = confusion_matrix_tp[1:] / confusion_matrix_denom[1:] + confusion_matrix_miou = np.mean(np.nan_to_num(confusion_matrix_per_class_iou)) + + # occupancy_recall_seg = results[size]["tp_recall_seg"] / results[size]["sum_recall_seg"] + + weights = label_maps["weights"] + weights_val = np.array(list(weights.values())) + weighted_mean_iou = np.sum(weights_val * np.nan_to_num(confusion_matrix_per_class_iou)) / np.sum(weights_val) + + results_table[3, size_i] = confusion_matrix_miou + results_table[4:, size_i] = confusion_matrix_per_class_iou + + row_labels = [ + "IoU", "Precision", "Recall", + "mIoU", "car", "bicycle", "motorcycle", "truck", "other-vehicle", "person", + "road", "sidewalk", "building", "fence", "vegetation", "terrain", "pole", + "traffic-sign", "other-object" + ] + column_headers = ["12.8m", "25.6m", "51.2m"] + + result_str += f"\n# Benchmark Results for '{ply_checkname}' / Mode: {mode}\n" + + result_str += "\n| | " + " | ".join(column_headers) + " |\n" + result_str += "|---------------|-------|-------|-------|\n" + for i in range(len(row_labels)): + row_values = results_table[i] + row_str = f"| {row_labels[i]:<13} | " + " | ".join(f"{v * 100:5.2f}" for v in row_values) + " |\n" + result_str += row_str + if i == 2: + result_str += "|---------------|-------|-------|-------|\n" + + result_str += "\n" + if mode == "hungarian": + result_str += f"Reassignment: {np.argsort(assignments[1])}\n" + result_str += f"Mean IoU: {confusion_matrix_miou * 100:.2f}\n" + result_str += f"Weighted Mean IoU: {weighted_mean_iou * 100:.2f}\n\n" + + print(result_str) + if not GENERATE_PLY_FILES: + with open(OUTPUT_PATH / ply_checkname / "results.md", "w") as file: + file.write(result_str) + + if TEST_ALPHA_CUTOFFS: + cutoff_metrics = \ + {i: {sv: {"precision": np.nan_to_num(100*cutoff_results[i][sv]["tp"] / (cutoff_results[i][sv]["tp"] + cutoff_results[i][sv]["fp"])), + "recall": np.nan_to_num(100*cutoff_results[i][sv]["tp"] / (cutoff_results[i][sv]["tp"] + cutoff_results[i][sv]["fn"])), + "IoU": np.nan_to_num(100*cutoff_results[i][sv]["tp"] / (cutoff_results[i][sv]["tp"] + cutoff_results[i][sv]["fn"] + cutoff_results[i][sv]["fp"]))} + for sv in SEARCH_VALUES} for i in range(1, 16)} + + best_values = {i: SEARCH_VALUES[torch.argmax(torch.tensor([cutoff_metrics[i][sv]["IoU"] for sv in SEARCH_VALUES]))] for i in range(1, 16)} + + print(best_values) + + if CREATE_SIGMA_TRADEOFF_PLOT: + plt.figure(figsize=(10, 8)) + plt.xlabel("Precision") + plt.ylabel("Recall") + plt.xlim([10, 70]) + # plt.ylim([0, 100]) + + for i, val in enumerate(SIGMA_VALUES): + tp, fp, tn, fn = trade_off_values[i] + pres = 100*tp / (tp + fp) + recall = 100*tp/ (tp + fn) + plt.scatter(pres, recall) + plt.annotate(f"Sigma: {val}; IoU: {100*tp / (tp + fp + fn):.2f}", (pres, recall)) + + identifier = os.path.basename(cp_path) + if FULL_EVAL: + path = f"figures/inv{str(USE_ADDITIONAL_INVALIDS)}_{VOXEL_SIZE:.1f}_mp{str(USE_GROW)}_{identifier}.png" + else: + path = f"figures/inv{str(USE_ADDITIONAL_INVALIDS)}_{DATASET_LENGTH}_{VOXEL_SIZE:.1f}_mp{str(USE_GROW)}_{identifier}.png" + + if os.path.isfile(path): + os.remove(path) + plt.savefig(path) + + plt.show() + + if GENERATE_STATISTICS: + statistics_raw = {"frameId": tframeIds, "TP": ttp, "FP": tfp, "TN": ttn, "FN": tfn, "invalids": tinval} + with open("stats.pkl", "wb") as f: + pickle.dump(statistics_raw, f) + logging.info("Saved the statistics for further analysis.") + + +def downsample_and_predict(data, net, pts, factor, prediction_mode, vis=False, feat_vis=False): + pts = pts.reshape(256*factor, 256*factor, 32*factor, 3) + + if vis: + sigmas = torch.zeros(256*factor, 256*factor, 32*factor).numpy() + segs = torch.zeros(256*factor, 256*factor, 32*factor).numpy() + if feat_vis: + dino = torch.zeros(256*factor, 256*factor, 32*factor, 768).numpy() + else: + dino = None + else: + sigmas = torch.zeros(256, 256, 32).numpy() + segs = torch.zeros(256, 256, 32).numpy() + dino = None + + chunk_size_x = chunk_size_y = 128 + chunk_size_z = 32 + + n_chunks_x = int(256*factor / chunk_size_x) + n_chunks_y = int(256*factor / chunk_size_y) + n_chunks_z = int(32*factor / chunk_size_z) + + if vis: + factor = 1 + + b_x = chunk_size_x // factor # size of the mini blocks + b_y = chunk_size_y // factor + b_z = chunk_size_z // factor + + # Changed for efficiency + images = torch.stack(data["imgs"], dim=0).unsqueeze(0).to(device).float() + poses = torch.tensor(np.stack(data["poses"], 0)).unsqueeze(0).to(device).float() + projs = torch.tensor(np.stack(data["projs"], 0)).unsqueeze(0).to(device).float() + + poses = torch.inverse(poses[:, :1]) @ poses + + extra_args = {"images_alt": images * 0.5 + 0.5} + + net.compute_grid_transforms(projs, poses) + + torch.cuda.synchronize() + encoding_start_time = time.time() + + net.encode(images, projs, poses, ids_encoder=[0], ids_render=[0], **extra_args) + + torch.cuda.synchronize() + encoding_time = time.time() - encoding_start_time + #print(f" - Encoding time: {encoding_time:.6f} seconds") + + net.set_scale(0) + + for i in range(n_chunks_x): + for j in range(n_chunks_y): + for k in range(n_chunks_z): + pts_block = pts[i * chunk_size_x:(i + 1) * chunk_size_x, j * chunk_size_y:(j + 1) * chunk_size_y, k * chunk_size_z:(k + 1) * chunk_size_z] + + #with torch.autograd.profiler.profile([torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA], use_cuda=True) as prof: + sigmas_block, segs_block, dino_feat_block = predict_grid(data, net, pts_block, prediction_mode) + #print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=-1)) + #raise ValueError("Profiling done.") + + sigmas_block = sigmas_block.reshape(chunk_size_x, chunk_size_y, chunk_size_z) + segs_block = segs_block.reshape(chunk_size_x, chunk_size_y, chunk_size_z, 19) + + if feat_vis: + dino_feat_block = dino_feat_block.reshape(chunk_size_x, chunk_size_y, chunk_size_z, dino_feat_block.size(-1)) + + if USE_ALPHA_WEIGHTING: + alphas = 1 - torch.exp(- VOXEL_SIZE * sigmas_block) + segs_block = (alphas.unsqueeze(-1) * segs_block).unsqueeze(0) + else: + segs_block = (sigmas_block.unsqueeze(-1) * segs_block).unsqueeze(0) + + if vis: + sigmas_block = sigmas_block.detach().cpu().numpy() + segs_pool = torch.argmax(segs_block, dim=-1).detach().cpu().numpy() + if feat_vis: + dino_feat_block = dino_feat_block.detach().cpu().numpy() + else: + segs_pool_list = [F.avg_pool3d(segs_block[..., i], kernel_size=factor, stride=factor, padding=0) for i in + range(segs_block.shape[-1])] + segs_pool = torch.stack(segs_pool_list, dim=-1).unsqueeze(0) + segs_pool = torch.argmax(segs_pool, dim=-1).detach().cpu().numpy() + + # pool the observations + sigmas_block = F.max_pool3d(sigmas_block.unsqueeze(0), kernel_size=factor, stride=factor, padding=0).squeeze(0).detach().cpu().numpy() + + sigmas[i * b_x:(i + 1) * b_x, j * b_y: (j + 1) * b_y, b_z * k:b_z * (k + 1)] = sigmas_block + segs[i * b_x:(i + 1) * b_x, j * b_y: (j + 1) * b_y, b_z * k:b_z * (k + 1)] = segs_pool + + if feat_vis: + dino[i * b_x:(i + 1) * b_x, j * b_y: (j + 1) * b_y, b_z * k:b_z * (k + 1), :] = dino_feat_block + + torch.cuda.empty_cache() + + if USE_GROW: + sigmas = F.max_pool3d(torch.tensor(sigmas).unsqueeze(0), kernel_size=3, stride=1, padding=1).squeeze(0).numpy() + + return sigmas, segs, dino + + +def calculate_pca(dino, is_occupied_seg, net): + dino = torch.Tensor(dino) + visible_dino = dino[is_occupied_seg] + + # print(net.encoder.visualization.batch_rgb_mean, net.encoder.visualization.batch_rgb_comp) + net.encoder.fit_visualization(visible_dino.flatten(0, -2), refit=True) + return torch.clamp(net.encoder.transform_visualization(dino), min=-0.5, max=0.5).cpu().numpy() + 0.5 + + +def use_custom_maxpool(_sigmas): + sigmas = torch.zeros(258, 258, 34) + sigmas[1:257, 1:257, 1:33] = torch.tensor(_sigmas) + sigmas_pooled = torch.zeros(256, 256, 32) + + for i in range(256): + for j in range(256): + for k in range(32): + sigmas_pooled[i, j, k] = max(sigmas[i+1, j+1, k+1], + sigmas[i, j+1, k+1], sigmas[i+1, j, k+1],sigmas[i+1, j+1, k], + sigmas[i+2, j+1, k+1], sigmas[i+1, j+2, k+1],sigmas[i+1, j+1, k+2]) + return sigmas_pooled + +def plot_images(images_dict): + """The images dict should include six images and six corresponding ids""" + images = images_dict["images"] + ids = images_dict["ids"] + + fig, axes = plt.subplots(3, 2, figsize=(10, 6)) + + axes = axes.flatten() + + for i, img in enumerate(images): + axes[i].imshow(images[i]) + axes[i].axis("off") + axes[i].set_title(f"FrameId: {ids[i]}") + + plt.subplots_adjust(wspace=0.01, hspace=0.01) + plt.show() + +def plot_image_at_frame_id(dataset, frame_id): + + for i in range(len(dataset)): + sequence, id, is_right = dataset._datapoints[i] + if id == frame_id: + data = dataset[i] + plt.figure(figsize=(10, 4)) + plt.imshow(((data["imgs"][0] + 1) / 2).permute(1, 2, 0)) + plt.gca().set_axis_off() + plt.show() + return + + + +def identify_additional_invalids(target): + # Note: The Numpy implementation is a bit faster (about 0.1 seconds per iteration) + + _t = np.concatenate([np.zeros([256, 256, 1]), target], axis=2) + invalids = np.cumsum(np.logical_and(_t != 255, _t != 0), axis=2)[:, :, :32] == 0 + # _t = torch.cat([torch.zeros([256, 256, 1], device=device, dtype=torch.int32), torch.tensor(target, dtype=torch.int32).to(device)], dim=2) + # invalids = torch.cumsum((_t != 255) & (_t != 0), axis=2)[:,:, :32] == 0 + # height cut-off (z > 6 ==> no invalid) + invalids[: , :, 7:] = 0 + # only empty voxels matter + invalids[target != 0] = 0 + + # return invalids.cpu().numpy() + return invalids + +def predict_grid(data_batch, net, points, prediction_mode): + # Removed for efficiency + # images = torch.stack(data_batch["imgs"], dim=0).unsqueeze(0).to(device).float() + # poses = torch.tensor(np.stack(data_batch["poses"], 0)).unsqueeze(0).to(device).float() + # projs = torch.tensor(np.stack(data_batch["projs"], 0)).unsqueeze(0).to(device).float() + + # poses = torch.inverse(poses[:, :1]) @ poses + + # extra_args = {"images_alt": images * 0.5 + 0.5} + + # net.compute_grid_transforms(projs, poses) + # net.encode(images, projs, poses, ids_encoder=[0], ids_render=[0], **extra_args) + + # net.set_scale(0) + + # q_pts = get_pts(X_RANGE, Y_RANGE, Z_RANGE, p_res[1], p_res_y, p_res[0]) + # q_pts = q_pts.to(device).reshape(1, -1, 3) + # # _, invalid, sigmas = net.forward(q_pts) + # + points = points.reshape(1, -1, 3) + if prediction_mode is not None: + dino_feat, invalid, sigmas, segs = net.forward(points, predict_segmentation=True, prediction_mode=prediction_mode) + else: + dino_feat, invalid, sigmas, segs = net.forward(points, predict_segmentation=True) + + return sigmas, segs, dino_feat + + +def convert_voxels(arr, map_dict): + f = np.vectorize(map_dict.__getitem__) + return f(arr) + + +def compute_occupancy_numbers_segmentation(y_pred, y_true, fov_mask, labels): + label_ids = list(labels.keys())[1:] + mask = y_true != 255 + mask = np.logical_and(mask, fov_mask) + mask = mask.flatten() + + y_pred = y_pred.flatten()[mask] + y_true = y_true.flatten()[mask] + + tp = np.zeros(len(label_ids)) + fp = np.zeros(len(label_ids)) + fn = np.zeros(len(label_ids)) + tn = np.zeros(len(label_ids)) + + for label_id in label_ids: + tp[label_id - 1] = np.sum(np.logical_and(y_true == label_id, y_pred == label_id)) + fp[label_id - 1] = np.sum(np.logical_and(y_true != label_id, y_pred == label_id)) + fn[label_id - 1] = np.sum(np.logical_and(y_true == label_id, y_pred != label_id)) + tn[label_id - 1] = np.sum(np.logical_and(y_true != label_id, y_pred != label_id)) + + dim_conf = len(label_ids) + 1 + bincount_values = dim_conf * y_true + y_pred + confusion_matrix = np.bincount(bincount_values, minlength=dim_conf*dim_conf).reshape(dim_conf, dim_conf) + + return tp, fp, tn, fn, confusion_matrix + + +def compute_occupancy_recall_segmentation(y_pred, y_true, fov_mask, labels): + label_ids = list(labels.keys())[1:] + mask = y_true != 255 + mask = np.logical_and(mask, fov_mask) + mask = mask.flatten() + + y_pred = y_pred.flatten()[mask] + y_true = y_true.flatten()[mask] + + tp = np.zeros(len(label_ids)) + sum = np.zeros(len(label_ids)) + + for label_id in label_ids: + tp[label_id - 1] = np.sum(np.logical_and(y_true == label_id, y_pred > 0)) + sum[label_id - 1] = np.sum(y_true == label_id) + + return tp, sum + + +def compute_occupancy_numbers(y_pred, y_true, fov_mask): + mask = y_true != 255 + mask = np.logical_and(mask, fov_mask) + mask = mask.flatten() + + y_pred = y_pred.flatten() + y_true = y_true.flatten() + + occ_true = y_true[mask] > 0 + occ_pred = y_pred[mask] > 0 + + tp = np.sum(np.logical_and(occ_true == 1, occ_pred == 1)) + fp = np.sum(np.logical_and(occ_true == 0, occ_pred == 1)) + fn = np.sum(np.logical_and(occ_true == 1, occ_pred == 0)) + tn = np.sum(np.logical_and(occ_true == 0, occ_pred == 0)) + + return tp, fp, tn, fn + +if __name__ == "__main__": + + #with torch.cuda.amp.autocast(dtype=torch.float16): + with torch.no_grad(): + main() diff --git a/sscbench/fusion.py b/sscbench/fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..3eaf1f277ae11acdbfbe3272c856f0591f86ed18 --- /dev/null +++ b/sscbench/fusion.py @@ -0,0 +1,508 @@ +# code taken from MonoScene/SSCBench +""" +Most of the code is taken from https://github.com/andyzeng/tsdf-fusion-python/blob/master/fusion.py + +@inproceedings{zeng20163dmatch, + title={3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions}, + author={Zeng, Andy and Song, Shuran and Nie{\ss}ner, Matthias and Fisher, Matthew and Xiao, Jianxiong and Funkhouser, Thomas}, + booktitle={CVPR}, + year={2017} +} +""" + +import numpy as np + +from numba import njit, prange +from skimage import measure + +FUSION_GPU_MODE = 0 + + +class TSDFVolume: + """Volumetric TSDF Fusion of RGB-D Images.""" + + def __init__(self, vol_bnds, voxel_size, use_gpu=True): + """Constructor. + + Args: + vol_bnds (ndarray): An ndarray of shape (3, 2). Specifies the + xyz bounds (min/max) in meters. + voxel_size (float): The volume discretization in meters. + """ + vol_bnds = np.asarray(vol_bnds) + assert vol_bnds.shape == (3, 2), "[!] `vol_bnds` should be of shape (3, 2)." + + # Define voxel volume parameters + self._vol_bnds = vol_bnds + self._voxel_size = float(voxel_size) + self._trunc_margin = 5 * self._voxel_size # truncation on SDF + # self._trunc_margin = 10 # truncation on SDF + self._color_const = 256 * 256 + + # Adjust volume bounds and ensure C-order contiguous + self._vol_dim = ( + np.ceil((self._vol_bnds[:, 1] - self._vol_bnds[:, 0]) / self._voxel_size) + .copy(order="C") + .astype(int) + ) + self._vol_bnds[:, 1] = self._vol_bnds[:, 0] + self._vol_dim * self._voxel_size + self._vol_origin = self._vol_bnds[:, 0].copy(order="C").astype(np.float32) + + print( + "Voxel volume size: {} x {} x {} - # points: {:,}".format( + self._vol_dim[0], + self._vol_dim[1], + self._vol_dim[2], + self._vol_dim[0] * self._vol_dim[1] * self._vol_dim[2], + ) + ) + + # Initialize pointers to voxel volume in CPU memory + self._tsdf_vol_cpu = np.zeros(self._vol_dim).astype(np.float32) + # for computing the cumulative moving average of observations per voxel + self._weight_vol_cpu = np.zeros(self._vol_dim).astype(np.float32) + self._color_vol_cpu = np.zeros(self._vol_dim).astype(np.float32) + + self.gpu_mode = use_gpu and FUSION_GPU_MODE + + # Copy voxel volumes to GPU + if self.gpu_mode: + self._tsdf_vol_gpu = cuda.mem_alloc(self._tsdf_vol_cpu.nbytes) + cuda.memcpy_htod(self._tsdf_vol_gpu, self._tsdf_vol_cpu) + self._weight_vol_gpu = cuda.mem_alloc(self._weight_vol_cpu.nbytes) + cuda.memcpy_htod(self._weight_vol_gpu, self._weight_vol_cpu) + self._color_vol_gpu = cuda.mem_alloc(self._color_vol_cpu.nbytes) + cuda.memcpy_htod(self._color_vol_gpu, self._color_vol_cpu) + + # Cuda kernel function (C++) + self._cuda_src_mod = SourceModule( + """ + __global__ void integrate(float * tsdf_vol, + float * weight_vol, + float * color_vol, + float * vol_dim, + float * vol_origin, + float * cam_intr, + float * cam_pose, + float * other_params, + float * color_im, + float * depth_im) { + // Get voxel index + int gpu_loop_idx = (int) other_params[0]; + int max_threads_per_block = blockDim.x; + int block_idx = blockIdx.z*gridDim.y*gridDim.x+blockIdx.y*gridDim.x+blockIdx.x; + int voxel_idx = gpu_loop_idx*gridDim.x*gridDim.y*gridDim.z*max_threads_per_block+block_idx*max_threads_per_block+threadIdx.x; + int vol_dim_x = (int) vol_dim[0]; + int vol_dim_y = (int) vol_dim[1]; + int vol_dim_z = (int) vol_dim[2]; + if (voxel_idx > vol_dim_x*vol_dim_y*vol_dim_z) + return; + // Get voxel grid coordinates (note: be careful when casting) + float voxel_x = floorf(((float)voxel_idx)/((float)(vol_dim_y*vol_dim_z))); + float voxel_y = floorf(((float)(voxel_idx-((int)voxel_x)*vol_dim_y*vol_dim_z))/((float)vol_dim_z)); + float voxel_z = (float)(voxel_idx-((int)voxel_x)*vol_dim_y*vol_dim_z-((int)voxel_y)*vol_dim_z); + // Voxel grid coordinates to world coordinates + float voxel_size = other_params[1]; + float pt_x = vol_origin[0]+voxel_x*voxel_size; + float pt_y = vol_origin[1]+voxel_y*voxel_size; + float pt_z = vol_origin[2]+voxel_z*voxel_size; + // World coordinates to camera coordinates + float tmp_pt_x = pt_x-cam_pose[0*4+3]; + float tmp_pt_y = pt_y-cam_pose[1*4+3]; + float tmp_pt_z = pt_z-cam_pose[2*4+3]; + float cam_pt_x = cam_pose[0*4+0]*tmp_pt_x+cam_pose[1*4+0]*tmp_pt_y+cam_pose[2*4+0]*tmp_pt_z; + float cam_pt_y = cam_pose[0*4+1]*tmp_pt_x+cam_pose[1*4+1]*tmp_pt_y+cam_pose[2*4+1]*tmp_pt_z; + float cam_pt_z = cam_pose[0*4+2]*tmp_pt_x+cam_pose[1*4+2]*tmp_pt_y+cam_pose[2*4+2]*tmp_pt_z; + // Camera coordinates to image pixels + int pixel_x = (int) roundf(cam_intr[0*3+0]*(cam_pt_x/cam_pt_z)+cam_intr[0*3+2]); + int pixel_y = (int) roundf(cam_intr[1*3+1]*(cam_pt_y/cam_pt_z)+cam_intr[1*3+2]); + // Skip if outside view frustum + int im_h = (int) other_params[2]; + int im_w = (int) other_params[3]; + if (pixel_x < 0 || pixel_x >= im_w || pixel_y < 0 || pixel_y >= im_h || cam_pt_z<0) + return; + // Skip invalid depth + float depth_value = depth_im[pixel_y*im_w+pixel_x]; + if (depth_value == 0) + return; + // Integrate TSDF + float trunc_margin = other_params[4]; + float depth_diff = depth_value-cam_pt_z; + if (depth_diff < -trunc_margin) + return; + float dist = fmin(1.0f,depth_diff/trunc_margin); + float w_old = weight_vol[voxel_idx]; + float obs_weight = other_params[5]; + float w_new = w_old + obs_weight; + weight_vol[voxel_idx] = w_new; + tsdf_vol[voxel_idx] = (tsdf_vol[voxel_idx]*w_old+obs_weight*dist)/w_new; + // Integrate color + float old_color = color_vol[voxel_idx]; + float old_b = floorf(old_color/(256*256)); + float old_g = floorf((old_color-old_b*256*256)/256); + float old_r = old_color-old_b*256*256-old_g*256; + float new_color = color_im[pixel_y*im_w+pixel_x]; + float new_b = floorf(new_color/(256*256)); + float new_g = floorf((new_color-new_b*256*256)/256); + float new_r = new_color-new_b*256*256-new_g*256; + new_b = fmin(roundf((old_b*w_old+obs_weight*new_b)/w_new),255.0f); + new_g = fmin(roundf((old_g*w_old+obs_weight*new_g)/w_new),255.0f); + new_r = fmin(roundf((old_r*w_old+obs_weight*new_r)/w_new),255.0f); + color_vol[voxel_idx] = new_b*256*256+new_g*256+new_r; + }""" + ) + + self._cuda_integrate = self._cuda_src_mod.get_function("integrate") + + # Determine block/grid size on GPU + gpu_dev = cuda.Device(0) + self._max_gpu_threads_per_block = gpu_dev.MAX_THREADS_PER_BLOCK + n_blocks = int( + np.ceil( + float(np.prod(self._vol_dim)) + / float(self._max_gpu_threads_per_block) + ) + ) + grid_dim_x = min(gpu_dev.MAX_GRID_DIM_X, int(np.floor(np.cbrt(n_blocks)))) + grid_dim_y = min( + gpu_dev.MAX_GRID_DIM_Y, int(np.floor(np.sqrt(n_blocks / grid_dim_x))) + ) + grid_dim_z = min( + gpu_dev.MAX_GRID_DIM_Z, + int(np.ceil(float(n_blocks) / float(grid_dim_x * grid_dim_y))), + ) + self._max_gpu_grid_dim = np.array( + [grid_dim_x, grid_dim_y, grid_dim_z] + ).astype(int) + self._n_gpu_loops = int( + np.ceil( + float(np.prod(self._vol_dim)) + / float( + np.prod(self._max_gpu_grid_dim) + * self._max_gpu_threads_per_block + ) + ) + ) + + else: + # Get voxel grid coordinates + xv, yv, zv = np.meshgrid( + range(self._vol_dim[0]), + range(self._vol_dim[1]), + range(self._vol_dim[2]), + indexing="ij", + ) + self.vox_coords = ( + np.concatenate( + [xv.reshape(1, -1), yv.reshape(1, -1), zv.reshape(1, -1)], axis=0 + ) + .astype(int) + .T + ) + + @staticmethod + @njit(parallel=True) + def vox2world(vol_origin, vox_coords, vox_size, offsets=(0.5, 0.5, 0.5)): + """Convert voxel grid coordinates to world coordinates.""" + vol_origin = vol_origin.astype(np.float32) + vox_coords = vox_coords.astype(np.float32) + # print(np.min(vox_coords)) + cam_pts = np.empty_like(vox_coords, dtype=np.float32) + + for i in prange(vox_coords.shape[0]): + for j in range(3): + cam_pts[i, j] = ( + vol_origin[j] + + (vox_size * vox_coords[i, j]) + + vox_size * offsets[j] + ) + return cam_pts + + @staticmethod + @njit(parallel=True) + def cam2pix(cam_pts, intr): + """Convert camera coordinates to pixel coordinates.""" + intr = intr.astype(np.float32) + fx, fy = intr[0, 0], intr[1, 1] + cx, cy = intr[0, 2], intr[1, 2] + pix = np.empty((cam_pts.shape[0], 2), dtype=np.int64) + for i in prange(cam_pts.shape[0]): + pix[i, 0] = int(np.round((cam_pts[i, 0] * fx / cam_pts[i, 2]) + cx)) + pix[i, 1] = int(np.round((cam_pts[i, 1] * fy / cam_pts[i, 2]) + cy)) + return pix + + @staticmethod + @njit(parallel=True) + def integrate_tsdf(tsdf_vol, dist, w_old, obs_weight): + """Integrate the TSDF volume.""" + tsdf_vol_int = np.empty_like(tsdf_vol, dtype=np.float32) + # print(tsdf_vol.shape) + w_new = np.empty_like(w_old, dtype=np.float32) + for i in prange(len(tsdf_vol)): + w_new[i] = w_old[i] + obs_weight + tsdf_vol_int[i] = (w_old[i] * tsdf_vol[i] + obs_weight * dist[i]) / w_new[i] + return tsdf_vol_int, w_new + + def integrate(self, color_im, depth_im, cam_intr, cam_pose, obs_weight=1.0): + """Integrate an RGB-D frame into the TSDF volume. + + Args: + color_im (ndarray): An RGB image of shape (H, W, 3). + depth_im (ndarray): A depth image of shape (H, W). + cam_intr (ndarray): The camera intrinsics matrix of shape (3, 3). + cam_pose (ndarray): The camera pose (i.e. extrinsics) of shape (4, 4). + obs_weight (float): The weight to assign for the current observation. A higher + value + """ + im_h, im_w = depth_im.shape + + # Fold RGB color image into a single channel image + color_im = color_im.astype(np.float32) + color_im = np.floor( + color_im[..., 2] * self._color_const + + color_im[..., 1] * 256 + + color_im[..., 0] + ) + + if self.gpu_mode: # GPU mode: integrate voxel volume (calls CUDA kernel) + for gpu_loop_idx in range(self._n_gpu_loops): + self._cuda_integrate( + self._tsdf_vol_gpu, + self._weight_vol_gpu, + self._color_vol_gpu, + cuda.InOut(self._vol_dim.astype(np.float32)), + cuda.InOut(self._vol_origin.astype(np.float32)), + cuda.InOut(cam_intr.reshape(-1).astype(np.float32)), + cuda.InOut(cam_pose.reshape(-1).astype(np.float32)), + cuda.InOut( + np.asarray( + [ + gpu_loop_idx, + self._voxel_size, + im_h, + im_w, + self._trunc_margin, + obs_weight, + ], + np.float32, + ) + ), + cuda.InOut(color_im.reshape(-1).astype(np.float32)), + cuda.InOut(depth_im.reshape(-1).astype(np.float32)), + block=(self._max_gpu_threads_per_block, 1, 1), + grid=( + int(self._max_gpu_grid_dim[0]), + int(self._max_gpu_grid_dim[1]), + int(self._max_gpu_grid_dim[2]), + ), + ) + else: # CPU mode: integrate voxel volume (vectorized implementation) + # Convert voxel grid coordinates to pixel coordinates + cam_pts = self.vox2world( + self._vol_origin, self.vox_coords, self._voxel_size + ) + cam_pts = rigid_transform(cam_pts, np.linalg.inv(cam_pose)) + pix_z = cam_pts[:, 2] + pix = self.cam2pix(cam_pts, cam_intr) + pix_x, pix_y = pix[:, 0], pix[:, 1] + + # Eliminate pixels outside view frustum + valid_pix = np.logical_and( + pix_x >= 0, + np.logical_and( + pix_x < im_w, + np.logical_and(pix_y >= 0, np.logical_and(pix_y < im_h, pix_z > 0)), + ), + ) + depth_val = np.zeros(pix_x.shape) + depth_val[valid_pix] = depth_im[pix_y[valid_pix], pix_x[valid_pix]] + + # Integrate TSDF + depth_diff = depth_val - pix_z + + valid_pts = np.logical_and(depth_val > 0, depth_diff >= -10) + dist = depth_diff + + valid_vox_x = self.vox_coords[valid_pts, 0] + valid_vox_y = self.vox_coords[valid_pts, 1] + valid_vox_z = self.vox_coords[valid_pts, 2] + w_old = self._weight_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] + tsdf_vals = self._tsdf_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] + valid_dist = dist[valid_pts] + tsdf_vol_new, w_new = self.integrate_tsdf( + tsdf_vals, valid_dist, w_old, obs_weight + ) + self._weight_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] = w_new + self._tsdf_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] = tsdf_vol_new + + # Integrate color + old_color = self._color_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] + old_b = np.floor(old_color / self._color_const) + old_g = np.floor((old_color - old_b * self._color_const) / 256) + old_r = old_color - old_b * self._color_const - old_g * 256 + new_color = color_im[pix_y[valid_pts], pix_x[valid_pts]] + new_b = np.floor(new_color / self._color_const) + new_g = np.floor((new_color - new_b * self._color_const) / 256) + new_r = new_color - new_b * self._color_const - new_g * 256 + new_b = np.minimum( + 255.0, np.round((w_old * old_b + obs_weight * new_b) / w_new) + ) + new_g = np.minimum( + 255.0, np.round((w_old * old_g + obs_weight * new_g) / w_new) + ) + new_r = np.minimum( + 255.0, np.round((w_old * old_r + obs_weight * new_r) / w_new) + ) + self._color_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] = ( + new_b * self._color_const + new_g * 256 + new_r + ) + + def get_volume(self): + if self.gpu_mode: + cuda.memcpy_dtoh(self._tsdf_vol_cpu, self._tsdf_vol_gpu) + cuda.memcpy_dtoh(self._color_vol_cpu, self._color_vol_gpu) + return self._tsdf_vol_cpu, self._color_vol_cpu + + def get_point_cloud(self): + """Extract a point cloud from the voxel volume.""" + tsdf_vol, color_vol = self.get_volume() + + # Marching cubes + verts = measure.marching_cubes_lewiner(tsdf_vol, level=0)[0] + verts_ind = np.round(verts).astype(int) + verts = verts * self._voxel_size + self._vol_origin + + # Get vertex colors + rgb_vals = color_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]] + colors_b = np.floor(rgb_vals / self._color_const) + colors_g = np.floor((rgb_vals - colors_b * self._color_const) / 256) + colors_r = rgb_vals - colors_b * self._color_const - colors_g * 256 + colors = np.floor(np.asarray([colors_r, colors_g, colors_b])).T + colors = colors.astype(np.uint8) + + pc = np.hstack([verts, colors]) + return pc + + def get_mesh(self): + """Compute a mesh from the voxel volume using marching cubes.""" + tsdf_vol, color_vol = self.get_volume() + + # Marching cubes + verts, faces, norms, vals = measure.marching_cubes_lewiner(tsdf_vol, level=0) + verts_ind = np.round(verts).astype(int) + verts = ( + verts * self._voxel_size + self._vol_origin + ) # voxel grid coordinates to world coordinates + + # Get vertex colors + rgb_vals = color_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]] + colors_b = np.floor(rgb_vals / self._color_const) + colors_g = np.floor((rgb_vals - colors_b * self._color_const) / 256) + colors_r = rgb_vals - colors_b * self._color_const - colors_g * 256 + colors = np.floor(np.asarray([colors_r, colors_g, colors_b])).T + colors = colors.astype(np.uint8) + return verts, faces, norms, colors + + +def rigid_transform(xyz, transform): + """Applies a rigid transform to an (N, 3) pointcloud.""" + xyz_h = np.hstack([xyz, np.ones((len(xyz), 1), dtype=np.float32)]) + xyz_t_h = np.dot(transform, xyz_h.T).T + return xyz_t_h[:, :3] + + +def get_view_frustum(depth_im, cam_intr, cam_pose): + """Get corners of 3D camera view frustum of depth image""" + im_h = depth_im.shape[0] + im_w = depth_im.shape[1] + max_depth = np.max(depth_im) + view_frust_pts = np.array( + [ + (np.array([0, 0, 0, im_w, im_w]) - cam_intr[0, 2]) + * np.array([0, max_depth, max_depth, max_depth, max_depth]) + / cam_intr[0, 0], + (np.array([0, 0, im_h, 0, im_h]) - cam_intr[1, 2]) + * np.array([0, max_depth, max_depth, max_depth, max_depth]) + / cam_intr[1, 1], + np.array([0, max_depth, max_depth, max_depth, max_depth]), + ] + ) + view_frust_pts = rigid_transform(view_frust_pts.T, cam_pose).T + return view_frust_pts + + +def meshwrite(filename, verts, faces, norms, colors): + """Save a 3D mesh to a polygon .ply file.""" + # Write header + ply_file = open(filename, "w") + ply_file.write("ply\n") + ply_file.write("format ascii 1.0\n") + ply_file.write("element vertex %d\n" % (verts.shape[0])) + ply_file.write("property float x\n") + ply_file.write("property float y\n") + ply_file.write("property float z\n") + ply_file.write("property float nx\n") + ply_file.write("property float ny\n") + ply_file.write("property float nz\n") + ply_file.write("property uchar red\n") + ply_file.write("property uchar green\n") + ply_file.write("property uchar blue\n") + ply_file.write("element face %d\n" % (faces.shape[0])) + ply_file.write("property list uchar int vertex_index\n") + ply_file.write("end_header\n") + + # Write vertex list + for i in range(verts.shape[0]): + ply_file.write( + "%f %f %f %f %f %f %d %d %d\n" + % ( + verts[i, 0], + verts[i, 1], + verts[i, 2], + norms[i, 0], + norms[i, 1], + norms[i, 2], + colors[i, 0], + colors[i, 1], + colors[i, 2], + ) + ) + + # Write face list + for i in range(faces.shape[0]): + ply_file.write("3 %d %d %d\n" % (faces[i, 0], faces[i, 1], faces[i, 2])) + + ply_file.close() + + +def pcwrite(filename, xyzrgb): + """Save a point cloud to a polygon .ply file.""" + xyz = xyzrgb[:, :3] + rgb = xyzrgb[:, 3:].astype(np.uint8) + + # Write header + ply_file = open(filename, "w") + ply_file.write("ply\n") + ply_file.write("format ascii 1.0\n") + ply_file.write("element vertex %d\n" % (xyz.shape[0])) + ply_file.write("property float x\n") + ply_file.write("property float y\n") + ply_file.write("property float z\n") + ply_file.write("property uchar red\n") + ply_file.write("property uchar green\n") + ply_file.write("property uchar blue\n") + ply_file.write("end_header\n") + + # Write vertex list + for i in range(xyz.shape[0]): + ply_file.write( + "%f %f %f %d %d %d\n" + % ( + xyz[i, 0], + xyz[i, 1], + xyz[i, 2], + rgb[i, 0], + rgb[i, 1], + rgb[i, 2], + ) + ) diff --git a/sscbench/gen_voxelgrid_npy.py b/sscbench/gen_voxelgrid_npy.py new file mode 100644 index 0000000000000000000000000000000000000000..0d3f9b0c157fd318f872c05e038cd0a87ca5c185 --- /dev/null +++ b/sscbench/gen_voxelgrid_npy.py @@ -0,0 +1,346 @@ +import os +import os +import sys +from pathlib import Path + +sys.path.append(".") + +from point_utils import get_fov_mask + +import cv2 +import hydra as hydra +import torch.nn.functional as F +import yaml +from matplotlib import pyplot as plt +from omegaconf import open_dict +from torch import nn +from tqdm import tqdm +import glob + + +import numpy as np +import torch +from plyfile import PlyData, PlyElement + + +os.system("nvidia-smi") + +in_path = Path("") +TARGET_PATH = Path("") +# out_path = Path("media/voxel/npy/") +out_path = Path("") +# out_path = Path("/storage/slurm/hayler/bts/voxel_outputs/sscnet") +# out_path.mkdir(exist_ok=True, parents=True) + +fov_mask = get_fov_mask() + +X_RANGE = (25.6, -25.6) +Y_RANGE = (51.2, 0) +Z_RANGE = (0, 6.4) +# +# gpu_id = 1 +# +# device = f'cpu' +# if gpu_id is not None: +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +# os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id) +# if torch.cuda.is_available(): +# torch.backends.cudnn.enabled = True +# torch.backends.cudnn.benchmark = True +# torch.backends.cudnn.deterministic = True + +# classes_to_colors = torch.tensor( +# [ +# [255, 255, 255], +# [100, 150, 245], # 1 +# [255, 0, 0], +# [255, 0, 255], +# [255, 150, 255], +# [75, 0, 75], +# [175, 0, 75], # 6 +# [255, 200, 0], +# [150, 150, 150], +# [30, 60, 150], +# [80, 30, 180], +# [8, 97, 0], # 11 +# [184, 56, 2], +# [255, 143, 46], +# [112, 255, 50], +# [194, 0, 0], +# [135, 60, 0], +# [150, 240, 80], +# [255, 240, 150], +# [255, 0, 0], +# ] +# ) + +classes_to_colors = torch.tensor( + [ + [70, 130, 180], # sky for unlabeled (?) + [0, 0, 142], # 1 + [119, 11, 32], + [0, 0, 230], + [0, 0, 70], + [0, 60,100], + [220, 20, 60], # 6 + [128, 64,128], + [244, 35,232], + [90, 90, 90], + [190,153,153], + [107,142, 35], # 11 + [152,251,152], + [153,153,153], + [220,220, 0], + [250,170, 30], + [135, 60, 0], + [150, 240, 80], + [255, 240, 150], + [255, 0, 0], + ] +) + +with open("sscbench/label_maps.yaml", "r") as f: + label_maps = yaml.safe_load(f) + +device = "cpu" + +r, c, = 0, 0 +n_rows, n_cols = 3, 3 + +def plot(img, fig, axs, i=None): + global r, c + if r == 0 and c == 0: + plt.show() + fig, axs = plt.subplots(n_rows, n_cols, figsize=(n_cols * 4, n_rows * 2)) + axs[r][c].imshow(img, interpolation="none") + if i is not None: + axs[r][c].title.set_text(f"{i}") + c += 1 + r += c // n_cols + c %= n_cols + r %= n_rows + return fig, axs + + +def save_plot(img, file_name=None, grey=False, mask=None): + if mask is not None: + if mask.shape[-1] != img.shape[-1]: + mask = np.broadcast_to(np.expand_dims(mask, -1), img.shape) + img = np.array(img) + img[~mask] = 0 + if dry_run: + plt.imshow(img) + plt.title(file_name) + plt.show() + else: + cv2.imwrite(file_name, cv2.cvtColor((img * 255).clip(max=255).astype(np.uint8), cv2.COLOR_RGB2BGR) if not grey else (img * 255).clip(max=255).astype(np.uint8)) + +faces = [[0, 1, 2, 3], [0, 3, 7, 4], [2, 6, 7, 3], [5, 6, 2, 1], [4, 5, 1, 0], [7, 6, 5, 4]] +faces_t = torch.tensor(faces, device=device) + + + +def build_voxel(i, j, k, x_res, y_res, z_res, xyz, offset): + ids = [[i+1, j+1, k], [i+1, j, k], + [i, j, k], [i, j+1, k], + [i+1, j+1, k+1], [i+1, j, k+1], + [i, j, k+1], [i, j+1, k+1]] + + faces_off = [[v+offset for v in f] for f in faces] + + ids_flat = list(map(lambda ijk: ijk[0]*y_res*z_res + ijk[1]*z_res + ijk[2], ids)) + + verts = xyz[:, ids_flat].cpu().numpy().T + + colors = np.tile(np.array(plt.cm.get_cmap("magma")(1 - (verts[..., 1].mean().item() - Y_RANGE[0]) / (Y_RANGE[1] - Y_RANGE[0]))[:3]).reshape((1, 3)), ((len(faces_off), 1))) + colors = (colors * 255).astype(np.uint8) + + return verts, faces_off, colors + + +ids_offset = torch.tensor( + [[1, 1, 0], [1, 0, 0], + [0, 0, 0], [0, 1, 0], + [1, 1, 1], [1, 0, 1], + [0, 0, 1], [0, 1, 1]], + dtype=torch.int32, + device=device +) # (8, 3) + + +def remove_invisible(volume): + kernel = torch.tensor([[[0, 0, 0], + [0, 1, 0], + [0, 0, 0]], + [[0, 1, 0], + [1, 0, 1], + [0, 1, 0]], + [[0, 0, 0], + [0, 1, 0], + [0, 0, 0]]], dtype=torch.float32, device=volume.device).view(1, 1, 3, 3, 3) + + neighbors = F.conv3d(volume.to(torch.float32).view(1, 1, *volume.shape), kernel, stride=1, padding=1)[0, 0, :, :, :] + is_hidden = neighbors >= 6 + volume = volume & (~is_hidden) + return volume + + +def check_neighbors(volume): + kernel = torch.zeros((6, 3, 3, 3), device=volume.device, dtype=torch.float32) + kernel[0, 1, 1, 0] = 1 + kernel[1, 1, 2, 1] = 1 + kernel[2, 0, 1, 1] = 1 + kernel[3, 1, 0, 1] = 1 + kernel[4, 2, 1, 1] = 1 + kernel[5, 1, 1, 2] = 1 + + kernel = kernel.unsqueeze(1) + + neighbors = F.conv3d(volume.to(torch.float32).view(1, 1, *volume.shape), kernel, stride=1, padding=1)[0, :, :, :, :] + neighbors = neighbors >= 1 + return neighbors + + +def build_voxels(ijks, x_res, y_res, z_res, xyz, neighbors=None, colors=None, classes=None): + # ijks (N, 3) + + ids = ijks.view(-1, 1, 3) + ids_offset.view(1, -1, 3) + + ids_flat = ids[..., 0] * y_res * z_res + ids[..., 1] * z_res + ids[..., 2] + + verts = xyz[:, ids_flat.reshape(-1)] + + faces_off = torch.arange(0, ijks.shape[0] * 8, 8, device=device) + faces_off = faces_off.view(-1, 1, 1) + faces_t.view(-1, 6, 4) + + if classes is not None: + index_classes = classes[ijks[:, 0], ijks[:, 1], ijks[:, 2]].to(int) + + colors = classes_to_colors[index_classes].view(-1, 1, 3).expand(-1, 8, -1) + elif colors is None: + z_steps = (1 - (torch.linspace(0, 1 - 1 / z_res, z_res) + 1 / (2 * z_res))).tolist() + cmap = plt.cm.get_cmap("magma") + z_to_color = (torch.tensor(list(map(cmap, z_steps)), device=device)[:, :3] * 255).to(torch.uint8) + + colors = z_to_color[ijks[:, 2], :].view(-1, 1, 3).expand(-1, 8, -1) + else: + colors = colors[ijks[:, 0], ijks[:, 1], ijks[:, 2]].view(-1, 1, 3).expand(-1, 8, -1) + + if neighbors is not None: + faces_off = faces_off.reshape(-1, 4)[~neighbors.reshape(-1), :] + + return verts.cpu().numpy().T, faces_off.reshape(-1, 4).cpu().numpy(), colors.reshape(-1, 3).cpu().numpy() + +def get_pts(x_range, y_range, z_range, x_res, y_res, z_res): + x = torch.linspace(x_range[0], x_range[1], x_res).view(x_res, 1, 1).expand(-1, y_res, z_res) + y = torch.linspace(y_range[0], y_range[1], y_res).view(1, y_res, 1).expand(x_res, -1, z_res) + z = torch.linspace(z_range[0], z_range[1], z_res).view(1, 1, z_res).expand(x_res, y_res, -1) + xyz = torch.stack((x, y, z), dim=-1) # (x, y, z) + + # The KITTI 360 cameras have a 5 degrees negative inclination. We need to account for that tan(5°) = 0.0874886635 + return xyz + + +def save_as_voxel_ply(path, is_occupied, voxel_size=0.2, size=(256, 256, 32), classes=None, colors=None, fov_mask=None): + is_occupied = remove_invisible(is_occupied) + if fov_mask is not None: + is_occupied &= fov_mask.to(is_occupied.device) + + is_occupied[0] = 0 + is_occupied[-1] = 0 + is_occupied[:, 0] = 0 + is_occupied[:, -1] = 0 + is_occupied[:, :, 0] = 0 + is_occupied[:, :, -1] = 0 + + res = (size[0] + 1, size[1] + 1, size[2] + 1) + x_range = (size[0] * voxel_size * .5, -size[0] * voxel_size * .5) + y_range = (size[1] * voxel_size, 0) + z_range = (0, size[2] * voxel_size) + + neighbors = check_neighbors(is_occupied) + neighbors = neighbors.view(6, -1)[:, is_occupied.reshape(-1)].T + + q_pts = get_pts(x_range, y_range, z_range, *res) + q_pts = q_pts.to(device).reshape(1, -1, 3) + verts, faces, colors = build_voxels(is_occupied.nonzero(), *res, q_pts.squeeze(0).T, neighbors, classes=classes, colors=colors) + + verts = list(map(tuple, verts)) + colors = list(map(tuple, colors)) + verts_colors = [v + c for v, c in zip(verts, colors)] + verts_data = np.array(verts_colors, dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]) + + face_data = np.array(faces, dtype='i4') + ply_faces = np.empty(len(faces), dtype=[('vertex_indices', 'i4', (4,))]) + ply_faces['vertex_indices'] = face_data + + verts_el = PlyElement.describe(verts_data, "vertex") + faces_el = PlyElement.describe(ply_faces, "face") + PlyData([verts_el, faces_el]).write(str(path)) + +def convert_voxels(arr, map_dict): + f = np.vectorize(map_dict.__getitem__) + return f(arr) + +def main(): + print('Loading file') + + is_occupied = torch.tensor(np.load(in_path), dtype=torch.bool, device=device) > 0 + save_as_voxel_ply(out_path / f"{in_path.stem}.ply", is_occupied) + +def safe_filepath_segementation(path, suffix="", gt=None, sizes=None, use_fov_mask=False): + path = Path(path) + segmentations = convert_voxels(np.load(path).astype(int), label_maps["sscbench_to_label"]) + if gt is not None: + segmentations[gt == 255] = 0 + segmentations[segmentations == 255] = 0 + is_occupied = torch.tensor(segmentations > 0) + + if use_fov_mask: + is_occupied[~fov_mask] = 0 + + if sizes: + for size in sizes: + if suffix != "": + fp = out_path / str(int(size)) / f"{id:06d}.ply" + else: + fp = out_path / str(int(size)) / f"{path.stem}.ply" + num_voxels = int(size // 0.2) + save_as_voxel_ply(fp, + is_occupied[: num_voxels, (128 - num_voxels // 2): (128 + num_voxels // 2), :], + classes=torch.tensor( + segmentations[: num_voxels, (128 - num_voxels // 2): (128 + num_voxels // 2), :])) + else: + if suffix != "": + save_as_voxel_ply(out_path / f"{path.stem}_{suffix}.ply", is_occupied, classes=torch.tensor(segmentations)) + else: + save_as_voxel_ply(out_path / f"{path.stem}.ply", is_occupied, classes=torch.tensor(segmentations)) + + +def safe_folder_segmentation(path:str, suffix="", ids=None, sizes=None, use_fov_mask=False): + if sizes: + for size in sizes: + if not os.path.exists(out_path / str(int(size))): + os.makedirs(out_path / str(int(size))) + + for file in tqdm(sorted(glob.glob(path + "/*"))): + frameId = int(file.split('/')[-1].split(".")[0]) + + gt = np.load(TARGET_PATH / f"{frameId:06d}_1_1.npy") + + if ids and frameId not in ids: + continue + safe_filepath_segementation(file, suffix, gt=gt, sizes=sizes, use_fov_mask=use_fov_mask) + +def main_segmentation(): + segmentations = convert_voxels(np.load(in_path).astype(int), label_maps["sscbench_to_label"]) + segmentations[segmentations == 255] = 0 + is_occupied = torch.tensor(segmentations > 0) + save_as_voxel_ply(out_path / f"{in_path.stem}.ply", is_occupied, classes=torch.tensor(segmentations)) + +if __name__ == '__main__': + # safe_folder_segmentation("/storage/slurm/hayler/sscbench/3data/monoscene/paper", "monoscene") + safe_folder_segmentation("/storage/slurm/hayler/sscbench/outputs/lmscnet", suffix="", sizes=[12.8, 25.6, 51.2], use_fov_mask=True) + # safe_folder_segmentation("/storage/slurm/hayler/sscbench/outputs/sscnet", suffix="", sizes=[12.8, 25.6, 51.2], use_fov_mask=True) + # safe_folder_segmentation("/storage/slurm/hayler/sscbench/outputs/sscnet", suffix="sscnet", + # ids=[125, 5475, 6670, 6775, 7860, 8000]) diff --git a/sscbench/generate_ply_sequence.py b/sscbench/generate_ply_sequence.py new file mode 100644 index 0000000000000000000000000000000000000000..51ac0d409623aa47c78dd44723baa25ae986b277 --- /dev/null +++ b/sscbench/generate_ply_sequence.py @@ -0,0 +1,424 @@ +import argparse +import sys + +from omegaconf import open_dict + +import matplotlib.pyplot as plt + +sys.path.append(".") + +from gen_voxelgrid_npy import save_as_voxel_ply + +import logging + +from pathlib import Path +import subprocess +import yaml + +import cv2 +import os +import numpy as np +from tqdm import tqdm +import pickle +import torch +from torch import nn +import torch.nn.functional as F +from hydra import compose, initialize + +import matplotlib.pyplot as plt + +# from datasets.kitti_360.kitti_360_dataset import Kitti360Dataset + +from fusion import TSDFVolume, rigid_transform + +from sscbench_dataset import SSCBenchDataset +from pathlib import Path + +RELOAD_DATASET = True +DATASET_LENGTH = 100 +FULL_EVAL = True +SAMPLE_EVERY = None +SAMPLE_OFFSET = 2 +# SAMPLE_RANGE = list(range(1000, 1600)) +SAMPLE_RANGE = None + +import time + + +SIZE = 51.2 # Can be: 51.2, 25.6, 12.8 +SIZES = (12.8, 25.6, 51.2) +VOXEL_SIZE = 0.1 # Needs: 0.2 % VOXEL_SIZE == 0 + +USE_CUSTOM_CUTOFFS = False +SIGMA_CUTOFF = 0.25 + +USE_ALPHA_WEIGHTING = True +USE_MAXPOOLING = False + +GENERATE_PLY_FILES = True +PLY_ONLY_FOV = True +# PLY_IDS = [2235, 2495, 2385, 3385, 4360, 6035, 8575, 9010, 11260] # 10:40 +# PLY_IDS = [2495, 6035, 8575, 9010, 11260] # 10 +#PLY_IDS = [125, 5475, 6035, 6670, 6775, 7860, 8000] +# PLY_IDS = list(range(1000, 1600)) +PLY_IDS = None +# PLY_PATH = Path("/usr/stud/hayler/dev/BehindTheScenes/scripts/benchmarks/sscbench/ply10_fov") +PLY_PATH = Path("") +PLY_SIZES = [12.8, 25.6, 51.2] + +if GENERATE_PLY_FILES: + assert (not USE_MAXPOOLING) and VOXEL_SIZE == 0.1 + + # make the necessary dirs + for size in PLY_SIZES: + if not os.path.exists(PLY_PATH / str(int(size))): + os.makedirs(PLY_PATH / str(int(size))) + + +# Setup of CUDA device and logging + +os.system("nvidia-smi") + +device = f'cuda:0' + +# DO NOT TOUCH OR YOU WILL BREAK RUNS (should be None) +gpu_id = None + +if gpu_id is not None: + print("GPU ID: " + str(gpu_id)) + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id) +if torch.cuda.is_available(): + torch.backends.cudnn.enabled = True + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.deterministic = True + +logging.basicConfig(level=logging.INFO) + +times = [] + + +def downsample_and_predict(data, net, pts, factor): + pts = pts.reshape(256*factor, 256*factor, 32*factor, 3) + + sigmas = torch.zeros(256, 256, 32).numpy() + segs = torch.zeros(256, 256, 32).numpy() + + chunk_size_x = chunk_size_y = 256 + chunk_size_z = 32 + + n_chunks_x = int(256*factor / chunk_size_x) + n_chunks_y = int(256*factor / chunk_size_y) + n_chunks_z = int(32*factor / chunk_size_z) + + + b_x = chunk_size_x // factor # size of the mini blocks + b_y = chunk_size_y // factor + b_z = chunk_size_z // factor + + + for i in range(n_chunks_x): + for j in range(n_chunks_y): + for k in range(n_chunks_z): + pts_block = pts[i * chunk_size_x:(i + 1) * chunk_size_x, j * chunk_size_y:(j + 1) * chunk_size_y, k * chunk_size_z:(k + 1) * chunk_size_z] + sigmas_block, segs_block = predict_grid(data, net, pts_block) + sigmas_block = sigmas_block.reshape(chunk_size_x, chunk_size_y, chunk_size_z) + segs_block = segs_block.reshape(chunk_size_x, chunk_size_y, chunk_size_z, 19) + + if USE_ALPHA_WEIGHTING: + alphas = 1 - torch.exp(- VOXEL_SIZE * sigmas_block) + segs_block = (alphas.unsqueeze(-1) * segs_block).unsqueeze(0) + else: + segs_block = (sigmas_block.unsqueeze(-1) * segs_block).unsqueeze(0) + + segs_pool_list = [F.avg_pool3d(segs_block[..., i], kernel_size=factor, stride=factor, padding=0) for i in + range(segs_block.shape[-1])] + segs_pool = torch.stack(segs_pool_list, dim=-1).unsqueeze(0) + segs_pool = torch.argmax(segs_pool, dim=-1).detach().cpu().numpy() + + # pool the observations + sigmas_block = F.max_pool3d(sigmas_block.unsqueeze(0), kernel_size=factor, stride=factor, padding=0).squeeze(0).detach().cpu().numpy() + + sigmas[i * b_x:(i + 1) * b_x, j * b_y: (j + 1) * b_y, b_z * k:b_z * (k + 1)] = sigmas_block + segs[i * b_x:(i + 1) * b_x, j * b_y: (j + 1) * b_y, b_z * k:b_z * (k + 1)] = segs_pool + + torch.cuda.empty_cache() + + if USE_MAXPOOLING: + sigmas = F.max_pool3d(torch.tensor(sigmas).unsqueeze(0), kernel_size=3, stride=1, padding=1).squeeze(0).numpy() + + return sigmas, segs + +def use_custom_maxpool(_sigmas): + sigmas = torch.zeros(258, 258, 34) + sigmas[1:257, 1:257, 1:33] = torch.tensor(_sigmas) + sigmas_pooled = torch.zeros(256, 256, 32) + + for i in range(256): + for j in range(256): + for k in range(32): + sigmas_pooled[i, j, k] = max(sigmas[i+1, j+1, k+1], + sigmas[i, j+1, k+1], sigmas[i+1, j, k+1],sigmas[i+1, j+1, k], + sigmas[i+2, j+1, k+1], sigmas[i+1, j+2, k+1],sigmas[i+1, j+1, k+2]) + return sigmas_pooled + +def plot_images(images_dict): + """The images dict should include six images and six corresponding ids""" + images = images_dict["images"] + ids = images_dict["ids"] + + fig, axes = plt.subplots(3, 2, figsize=(10, 6)) + + axes = axes.flatten() + + for i, img in enumerate(images): + axes[i].imshow(images[i]) + axes[i].axis("off") + axes[i].set_title(f"FrameId: {ids[i]}") + + plt.subplots_adjust(wspace=0.01, hspace=0.01) + plt.show() + +def plot_image_at_frame_id(dataset, frame_id): + + for i in range(len(dataset)): + sequence, id, is_right = dataset._datapoints[i] + if id == frame_id: + data = dataset[i] + plt.figure(figsize=(10, 4)) + plt.imshow(((data["imgs"][0] + 1) / 2).permute(1, 2, 0)) + plt.gca().set_axis_off() + plt.show() + return + + + +def identify_additional_invalids(target): + # Note: The Numpy implementation is a bit faster (about 0.1 seconds per iteration) + + _t = np.concatenate([np.zeros([256, 256, 1]), target], axis=2) + invalids = np.cumsum(np.logical_and(_t != 255, _t != 0), axis=2)[:, :, :32] == 0 + # _t = torch.cat([torch.zeros([256, 256, 1], device=device, dtype=torch.int32), torch.tensor(target, dtype=torch.int32).to(device)], dim=2) + # invalids = torch.cumsum((_t != 255) & (_t != 0), axis=2)[:,:, :32] == 0 + # height cut-off (z > 6 ==> no invalid) + invalids[: , :, 7:] = 0 + # only empty voxels matter + invalids[target != 0] = 0 + + # return invalids.cpu().numpy() + return invalids + + +def generate_point_grid(cam_E, vox_origin, voxel_size, scene_size, cam_k, img_W=1408, img_H=376): + """ + compute the 2D projection of voxels centroids + + Parameters: + ---------- + cam_E: 4x4 + =camera pose in case of NYUv2 dataset + =Transformation from camera to lidar coordinate in case of SemKITTI + cam_k: 3x3 + camera intrinsics + vox_origin: (3,) + world(NYU)/lidar(SemKITTI) cooridnates of the voxel at index (0, 0, 0) + img_W: int + image width + img_H: int + image height + scene_size: (3,) + scene size in meter: (51.2, 51.2, 6.4) for SemKITTI and (4.8, 4.8, 2.88) for NYUv2 + + Returns + ------- + projected_pix: (N, 2) + Projected 2D positions of voxels + fov_mask: (N,) + Voxels mask indice voxels inside image's FOV + pix_z: (N,) + Voxels'distance to the sensor in meter + """ + # Compute the x, y, z bounding of the scene in meter + vol_bnds = np.zeros((3, 2)) + vol_bnds[:, 0] = vox_origin + vol_bnds[:, 1] = vox_origin + np.array(scene_size) + + # Compute the voxels centroids in lidar cooridnates + vol_dim = np.ceil((vol_bnds[:, 1] - vol_bnds[:, 0]) / voxel_size).copy(order='C').astype(int) + xv, yv, zv = np.meshgrid( + range(vol_dim[0]), + range(vol_dim[1]), + range(vol_dim[2]), + indexing='ij' + ) + vox_coords = np.concatenate([ + xv.reshape(1, -1), + yv.reshape(1, -1), + zv.reshape(1, -1) + ], axis=0).astype(int).T + + # Project voxels'centroid from lidar coordinates to camera coordinates + cam_pts = TSDFVolume.vox2world(vox_origin, vox_coords, voxel_size) + cam_pts = rigid_transform(cam_pts, cam_E) + + # Project camera coordinates to pixel positions + projected_pix = TSDFVolume.cam2pix(cam_pts, cam_k) + pix_x, pix_y = projected_pix[:, 0], projected_pix[:, 1] + + # Eliminate pixels outside view frustum + pix_z = cam_pts[:, 2] + fov_mask = np.logical_and(pix_x >= 0, + np.logical_and(pix_x < img_W, + np.logical_and(pix_y >= 0, + np.logical_and(pix_y < img_H, + pix_z > 0)))) + + return cam_pts, fov_mask + + +def predict_grid(data_batch, net, points): + images = torch.stack(data_batch["imgs"], dim=0).unsqueeze(0).to(device).float() + poses = torch.tensor(np.stack(data_batch["poses"], 0)).unsqueeze(0).to(device).float() + projs = torch.tensor(np.stack(data_batch["projs"], 0)).unsqueeze(0).to(device).float() + + poses = torch.inverse(poses[:, :1]) @ poses + + n, nv, c, h, w = images.shape + + net.compute_grid_transforms(projs, poses) + net.encode(images, projs, poses, ids_encoder=[0], ids_render=[0]) + + net.set_scale(0) + + # q_pts = get_pts(X_RANGE, Y_RANGE, Z_RANGE, p_res[1], p_res_y, p_res[0]) + # q_pts = q_pts.to(device).reshape(1, -1, 3) + # # _, invalid, sigmas = net.forward(q_pts) + # + points = points.reshape(1, -1, 3) + _, invalid, sigmas, segs = net.forward(points, predict_segmentation=True) + + return sigmas, segs + + +def convert_voxels(arr, map_dict): + f = np.vectorize(map_dict.__getitem__) + return f(arr) + + +def compute_occupancy_numbers_segmentation(y_pred, y_true, fov_mask, labels): + label_ids = list(labels.keys())[1:] + mask = y_true != 255 + mask = np.logical_and(mask, fov_mask) + mask = mask.flatten() + + y_pred = y_pred.flatten()[mask] + y_true = y_true.flatten()[mask] + + tp = np.zeros(len(label_ids)) + fp = np.zeros(len(label_ids)) + fn = np.zeros(len(label_ids)) + tn = np.zeros(len(label_ids)) + + for label_id in label_ids: + tp[label_id - 1] = np.sum(np.logical_and(y_true == label_id, y_pred == label_id)) + fp[label_id - 1] = np.sum(np.logical_and(y_true != label_id, y_pred == label_id)) + fn[label_id - 1] = np.sum(np.logical_and(y_true == label_id, y_pred != label_id)) + tn[label_id - 1] = np.sum(np.logical_and(y_true != label_id, y_pred != label_id)) + + return tp, fp, tn, fn + + +def compute_occupancy_numbers(y_pred, y_true, fov_mask): + mask = y_true != 255 + mask = np.logical_and(mask, fov_mask) + mask = mask.flatten() + + y_pred = y_pred.flatten() + y_true = y_true.flatten() + + occ_true = y_true[mask] > 0 + occ_pred = y_pred[mask] > 0 + + tp = np.sum(np.logical_and(occ_true == 1, occ_pred == 1)) + fp = np.sum(np.logical_and(occ_true == 0, occ_pred == 1)) + fn = np.sum(np.logical_and(occ_true == 1, occ_pred == 0)) + tn = np.sum(np.logical_and(occ_true == 0, occ_pred == 0)) + + return tp, fp, tn, fn + + +def read_calib(): + """ + :param calib_path: Path to a calibration text file. + :return: dict with calibration matrices. + """ + P = np.array( + [ + 552.554261, + 0.000000, + 682.049453, + 0.000000, + 0.000000, + 552.554261, + 238.769549, + 0.000000, + 0.000000, + 0.000000, + 1.000000, + 0.000000, + ] + ).reshape(3, 4) + + cam2velo = np.array( + [ + 0.04307104361, + -0.08829286498, + 0.995162929, + 0.8043914418, + -0.999004371, + 0.007784614041, + 0.04392796942, + 0.2993489574, + -0.01162548558, + -0.9960641394, + -0.08786966659, + -0.1770225824, + ] + ).reshape(3, 4) + C2V = np.concatenate( + [cam2velo, np.array([0, 0, 0, 1]).reshape(1, 4)], axis=0 + ) + # print("C2V: ", C2V) + V2C = np.linalg.inv(C2V) + # print("V2C: ", V2C) + V2C = V2C[:3, :] + # print("V2C: ", V2C) + + # reshape matrices + calib_out = {} + # 3x4 projection matrix for left camera + calib_out["P2"] = P + calib_out["Tr"] = np.identity(4) # 4x4 matrix + calib_out["Tr"][:3, :4] = V2C + return calib_out + + +def get_cam_k(): + cam_k = np.array( + [ + 552.554261, + 0.000000, + 682.049453, + 0.000000, + 0.000000, + 552.554261, + 238.769549, + 0.000000, + 0.000000, + 0.000000, + 1.000000, + 0.000000, + ] + ).reshape(3, 4) + return cam_k[:3, :3] + diff --git a/sscbench/kitti_360_preprocess.yaml b/sscbench/kitti_360_preprocess.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f5d9e09c53d56a94df0582ac79de0fd16e26dc25 --- /dev/null +++ b/sscbench/kitti_360_preprocess.yaml @@ -0,0 +1,198 @@ +# This file is covered by the LICENSE file in the root of this project. +nbr_classes: 19 +grid_dims: [256, 32, 256] # (W, H, D) +labels: + 0 : "unlabeled" + 1 : "outlier" + 10: "car" + 11: "bicycle" + 13: "bus" + 15: "motorcycle" + 16: "on-rails" + 18: "truck" + 20: "other-vehicle" + 30: "person" + 31: "bicyclist" + 32: "motorcyclist" + 40: "road" + 44: "parking" + 48: "sidewalk" + 49: "other-ground" + 50: "building" + 51: "fence" + 52: "other-structure" + 60: "lane-marking" + 70: "vegetation" + 71: "trunk" + 72: "terrain" + 80: "pole" + 81: "traffic-sign" + 99: "other-object" + 252: "moving-car" + 253: "moving-bicyclist" + 254: "moving-person" + 255: "moving-motorcyclist" + 256: "moving-on-rails" + 257: "moving-bus" + 258: "moving-truck" + 259: "moving-other-vehicle" +color_map: # bgr + 0 : [0, 0, 0] + 1 : [0, 0, 255] + 10: [245, 150, 100] + 11: [245, 230, 100] + 13: [250, 80, 100] + 15: [150, 60, 30] + 16: [255, 0, 0] + 18: [180, 30, 80] + 20: [255, 0, 0] + 30: [30, 30, 255] + 31: [200, 40, 255] + 32: [90, 30, 150] + 40: [255, 0, 255] + 44: [255, 150, 255] + 48: [75, 0, 75] + 49: [75, 0, 175] + 50: [0, 200, 255] + 51: [50, 120, 255] + 52: [0, 150, 255] + 60: [170, 255, 150] + 70: [0, 175, 0] + 71: [0, 60, 135] + 72: [80, 240, 150] + 80: [150, 240, 255] + 81: [0, 0, 255] + 99: [255, 255, 50] + 252: [245, 150, 100] + 256: [255, 0, 0] + 253: [200, 40, 255] + 254: [30, 30, 255] + 255: [90, 30, 150] + 257: [250, 80, 100] + 258: [180, 30, 80] + 259: [255, 0, 0] +content: # as a ratio with the total number of points + 0: 0.2848417452056936 + 1: 0.0 + 10: 0.01983749599411903 + 11: 9.719043492190106e-05 + 13: 8.812172901085409e-05 + 15: 0.00010387247418585811 + 16: 0.003498548779509923 + 18: 0.0022174305343550995 + 20: 0.0012293279317961631 + 30: 0.00019936933118689005 + 31: 8.34775248250103e-05 + 32: 0.0 + 40: 0.18301380891811173 + 44: 0.022222770320975044 + 48: 0.10658338271700511 + 49: 0.013312019108448078 + 50: 0.08266303552442433 + 51: 0.008460835861454078 + 52: 0.033257320133170704 + 60: 0.0 + 70: 0.16860900009553803 + 71: 0.0 + 72: 0.04732251443431639 + 80: 0.00032178137333487255 + 81: 8.959468958873611e-05 + 99: 0.0015188036331984448 + 252: 0.0 + 253: 0.0 + 254: 0.0 + 255: 0.0 + 256: 0.0 + 257: 0.0 + 258: 0.0 + 259: 0.0 +# classes that are indistinguishable from single scan or inconsistent in +# ground truth are mapped to their closest equivalent +learning_map: + 0 : 0 # "unlabeled" + 1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped + 10: 1 # "car" + 11: 2 # "bicycle" + 13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped + 15: 3 # "motorcycle" + 16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped + 18: 4 # "truck" + 20: 5 # "other-vehicle" + 30: 6 # "person" + 31: 0 # "bicyclist" mapped to "unlabeled" --------------------mapped + 32: 0 # "motorcyclist" mapped to "unlabeled" -----------------mapped + 40: 7 # "road" + 44: 8 # "parking" + 48: 9 # "sidewalk" + 49: 10 # "other-ground" + 50: 11 # "building" + 51: 12 # "fence" + 52: 17 # "other-structure" + 60: 7 # "lane-marking" to "road" ---------------------------------mapped + 70: 13 # "vegetation" + 71: 0 # "trunk" + 72: 14 # "terrain" + 80: 15 # "pole" + 81: 16 # "traffic-sign" + 99: 18 # "other-object" + 252: 1 # "moving-car" to "car" ------------------------------------mapped + 253: 0 # "moving-bicyclist" to "unlabeled" ------------------------mapped + 254: 6 # "moving-person" to "person" ------------------------------mapped + 255: 0 # "moving-motorcyclist" to "unlabeled" ------------------mapped + 256: 5 # "moving-on-rails" mapped to "other-vehicle" --------------mapped + 257: 5 # "moving-bus" mapped to "other-vehicle" -------------------mapped + 258: 4 # "moving-truck" to "truck" --------------------------------mapped + 259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------mapped +learning_map_inv: # inverse of previous map + 0: 0 # "unlabeled", and others ignored + 1: 10 # "car" + 2: 11 # "bicycle" + 3: 15 # "motorcycle" + 4: 18 # "truck" + 5: 20 # "other-vehicle" + 6: 30 # "person" + 7: 40 # "road" + 8: 44 # "parking" + 9: 48 # "sidewalk" + 10: 49 # "other-ground" + 11: 50 # "building" + 12: 51 # "fence" + 13: 70 # "vegetation" + 14: 72 # "terrain" + 15: 80 # "pole" + 16: 81 # "traffic-sign" + 17: 52 # "other-structure" + 18: 99 # "other-object" +learning_ignore: # Ignore classes + 0: True # "unlabeled", and others ignored + 1: False # "car" + 2: False # "bicycle" + 3: False # "motorcycle" + 4: False # "truck" + 5: False # "other-vehicle" + 6: False # "person" + 7: False # "road" + 8: False # "parking" + 9: False # "sidewalk" + 10: False # "other-ground" + 11: False # "building" + 12: False # "fence" + 13: False # "vegetation" + 14: False # "terrain" + 15: False # "pole" + 16: False # "traffic-sign" + 17: False # "other-structure" + 18: False # "other-object" +split: # sequence numbers + train: + - 0 + - 2 + - 3 + - 4 + - 5 + - 7 + - 10 + valid: + - 6 + test: + - 9 diff --git a/sscbench/label_maps.yaml b/sscbench/label_maps.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e903ec2857b79ddda4d53e8ab9f879400ac60a77 --- /dev/null +++ b/sscbench/label_maps.yaml @@ -0,0 +1,79 @@ +sscbench_to_label: + 0 : 0 # unlabeled; to be ignored during eval + 1 : 1 # car + 2 : 2 # bicycle + 3: 3 # motorcycle + 4: 4 # truck + 5: 5 # other-vehicle + 6: 6 # person + 7: 7 # road + 8: 8 # parking --> sidewalk #TODO: find the right mapping for parking (in cityscapes we map to unlabeled) + 9: 8 # sidewalk + 10: 12 # other-ground --> terrain + 11: 9 # building + 12: 10 # fence + 13: 11 # vegetation + 14: 12 # terrain + 15: 13 # pole + 16: 14 # traffic-sign + 17: 9 # other-structure --> building + 18: 15 #other-object + 19: 0 # weird behavior of the voxformer model + 255: 255 # invalid mask + + +cityscapes_to_label: + 0: 7 # road + 1: 8 # sidewalk + 2: 9 # building + 3: 9 # wall --> building #TODO + 4: 10 # fence + 5: 13 # pole + 6: 15 # traffic-light --> other-object + 7: 14 # traffic-sign + 8: 11 # vegetation + 9: 12 # terrain + 10: 0 # sky --> unlabeled + 11: 6 # person + 12: 0 # rider --> unlabeled + 13: 1 # car + 14: 4 # truck + 15: 5 # bus --> other-vehicle + 16: 5 # train --> other-vehicle + 17: 3 # motorcycle + 18: 2 # bicycle + +labels: + 0: "unlabeled" + 1: "car" + 2: "bicycle" + 3: "motorcycle" + 4: "truck" + 5: "other-vehicle" + 6: "person" + 7: "road" + 8: "sidewalk" + 9: "building" + 10: "fence" + 11: "vegetation" + 12: "terrain" + 13: "pole" + 14: "traffic-sign" + 15: "other-object" + +weights: # approx. in percent; weights from the SSCBenchPaper; Checksum: 102.87 + 1: 2.85 # "car" + 2: 0.01 # "bicycle" + 3: 0.01 # "motorcycle" + 4: 0.16 # "truck" + 5: 5.75 # "other-vehicle" + 6: 0.02 # "person" + 7: 14.98 # "road" + 8: 6.43 # "sidewalk" + "sidewalk"; 6.43 + 2.31 = 8.74 + 9: 20.00 # "building" + "other struct"; 15.67 + 4.33 = 20.00 + 10: 0.96 # "fence" + 11: 41.99 # "vegetation + 12: 9.15 # "terrain" + "other-ground"; 7.10 + 2.05 = 9.15 + 13: 0.22 # pole + 14: 0.06 # traffic-sign + 15: 0.28 # other-object \ No newline at end of file diff --git a/sscbench/point_utils.py b/sscbench/point_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..009564b4810e4f1083aeb619ace6b076d1b09295 --- /dev/null +++ b/sscbench/point_utils.py @@ -0,0 +1,157 @@ + +import numpy as np +from fusion import TSDFVolume, rigid_transform + + +def get_fov_mask(): + calib = read_calib() + T_velo_2_cam = calib["Tr"] + _, fov_mask = generate_point_grid(vox_origin=np.array([0, -25.6, -2]), + scene_size=(51.2, 51.2, 6.4), + voxel_size=0.2, + cam_E=T_velo_2_cam, + cam_k=get_cam_k()) + + return fov_mask.reshape(256, 256, 32) + +def generate_point_grid(cam_E, vox_origin, voxel_size, scene_size, cam_k, img_W=1408, img_H=376): + """ + compute the 2D projection of voxels centroids + + Parameters: + ---------- + cam_E: 4x4 + =camera pose in case of NYUv2 dataset + =Transformation from camera to lidar coordinate in case of SemKITTI + cam_k: 3x3 + camera intrinsics + vox_origin: (3,) + world(NYU)/lidar(SemKITTI) cooridnates of the voxel at index (0, 0, 0) + img_W: int + image width + img_H: int + image height + scene_size: (3,) + scene size in meter: (51.2, 51.2, 6.4) for SemKITTI and (4.8, 4.8, 2.88) for NYUv2 + + Returns + ------- + projected_pix: (N, 2) + Projected 2D positions of voxels + fov_mask: (N,) + Voxels mask indice voxels inside image's FOV + pix_z: (N,) + Voxels'distance to the sensor in meter + """ + # Compute the x, y, z bounding of the scene in meter + vol_bnds = np.zeros((3, 2)) + vol_bnds[:, 0] = vox_origin + vol_bnds[:, 1] = vox_origin + np.array(scene_size) + + # Compute the voxels centroids in lidar cooridnates + vol_dim = np.ceil((vol_bnds[:, 1] - vol_bnds[:, 0]) / voxel_size).copy(order='C').astype(int) + xv, yv, zv = np.meshgrid( + range(vol_dim[0]), + range(vol_dim[1]), + range(vol_dim[2]), + indexing='ij' + ) + print(xv.shape, yv.shape, zv.shape) + vox_coords = np.concatenate([ + xv.reshape(1, -1), + yv.reshape(1, -1), + zv.reshape(1, -1) + ], axis=0).astype(int).T + + # Project voxels'centroid from lidar coordinates to camera coordinates + cam_pts = TSDFVolume.vox2world(vox_origin, vox_coords, voxel_size) + cam_pts = rigid_transform(cam_pts, cam_E) + + # Project camera coordinates to pixel positions + projected_pix = TSDFVolume.cam2pix(cam_pts, cam_k) + pix_x, pix_y = projected_pix[:, 0], projected_pix[:, 1] + + # Eliminate pixels outside view frustum + pix_z = cam_pts[:, 2] + fov_mask = np.logical_and(pix_x >= 0, + np.logical_and(pix_x < img_W, + np.logical_and(pix_y >= 0, + np.logical_and(pix_y < img_H, + pix_z > 0)))) + + return cam_pts, fov_mask + +def read_calib(): + """ + :param calib_path: Path to a calibration text file. + :return: dict with calibration matrices. + """ + P = np.array( + [ + 552.554261, + 0.000000, + 682.049453, + 0.000000, + 0.000000, + 552.554261, + 238.769549, + 0.000000, + 0.000000, + 0.000000, + 1.000000, + 0.000000, + ] + ).reshape(3, 4) + + cam2velo = np.array( + [ + 0.04307104361, + -0.08829286498, + 0.995162929, + 0.8043914418, + -0.999004371, + 0.007784614041, + 0.04392796942, + 0.2993489574, + -0.01162548558, + -0.9960641394, + -0.08786966659, + -0.1770225824, + ] + ).reshape(3, 4) + C2V = np.concatenate( + [cam2velo, np.array([0, 0, 0, 1]).reshape(1, 4)], axis=0 + ) + # print("C2V: ", C2V) + V2C = np.linalg.inv(C2V) + # print("V2C: ", V2C) + V2C = V2C[:3, :] + # print("V2C: ", V2C) + + # reshape matrices + calib_out = {} + # 3x4 projection matrix for left camera + calib_out["P2"] = P + calib_out["Tr"] = np.identity(4) # 4x4 matrix + calib_out["Tr"][:3, :4] = V2C + return calib_out + + +def get_cam_k(): + cam_k = np.array( + [ + 552.554261, + 0.000000, + 682.049453, + 0.000000, + 0.000000, + 552.554261, + 238.769549, + 0.000000, + 0.000000, + 0.000000, + 1.000000, + 0.000000, + ] + ).reshape(3, 4) + return cam_k[:3, :3] \ No newline at end of file diff --git a/sscbench/sscbench_dataset.py b/sscbench/sscbench_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..f3fffc906500e07685a24d03c7aafefb7bc344f2 --- /dev/null +++ b/sscbench/sscbench_dataset.py @@ -0,0 +1,511 @@ +import os +import time +import xml.etree.ElementTree as ET +from collections import Counter, defaultdict +from pathlib import Path +from typing import Optional + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +import yaml +from scipy.spatial.transform import Rotation +from torch.utils.data import Dataset +from torchvision.transforms import ColorJitter + +from datasets.kitti_360.annotation import KITTI360Bbox3D +from datasets.kitti_360.labels import labels + +from augmentation import get_color_aug_fn + + +name2label = {label.name: label for label in labels} +id2ProposedId = {label.id: label.trainId for label in labels} + +PropsedId2TrainId = dict(enumerate(list(set(id2ProposedId.values())))) +PropsedId2TrainId = {v : k for k, v in PropsedId2TrainId.items()} +id2TrainId = {k : PropsedId2TrainId[v] for k, v in id2ProposedId.items()} + + +class FisheyeToPinholeSampler: + def __init__(self, K_target, target_image_size, calibs, rotation=None): + self._compute_transform(K_target, target_image_size, calibs, rotation) + + def _compute_transform(self, K_target, target_image_size, calibs, rotation=None): + x = torch.linspace(-1, 1, target_image_size[1]).view(1, -1).expand(target_image_size) + y = torch.linspace(-1, 1, target_image_size[0]).view(-1, 1).expand(target_image_size) + z = torch.ones_like(x) + xyz = torch.stack((x, y, z), dim=-1).view(-1, 3) + + # Unproject + xyz = (torch.inverse(torch.tensor(K_target)) @ xyz.T).T + + if rotation is not None: + xyz = (torch.tensor(rotation) @ xyz.T).T + + # Backproject into fisheye + xyz = xyz / torch.norm(xyz, dim=-1, keepdim=True) + x = xyz[:, 0] + y = xyz[:, 1] + z = xyz[:, 2] + + xi_src = calibs["mirror_parameters"]["xi"] + x = x / (z + xi_src) + y = y / (z + xi_src) + + k1 = calibs["distortion_parameters"]["k1"] + k2 = calibs["distortion_parameters"]["k2"] + + r = x*x + y*y + factor = (1 + k1 * r + k2 * r * r) + x = x * factor + y = y * factor + + gamma0 = calibs["projection_parameters"]["gamma1"] + gamma1 = calibs["projection_parameters"]["gamma2"] + u0 = calibs["projection_parameters"]["u0"] + v0 = calibs["projection_parameters"]["v0"] + + x = x * gamma0 + u0 + y = y * gamma1 + v0 + + xy = torch.stack((x, y), dim=-1).view(1, *target_image_size, 2) + self.sample_pts = xy + + def resample(self, img): + img = img.unsqueeze(0) + resampled_img = F.grid_sample(img, self.sample_pts, align_corners=True).squeeze(0) + return resampled_img + + +class SSCBenchDataset(Dataset): + def __init__(self, + data_path: str, + voxel_gt_path: str, + sequences: Optional[tuple], + target_image_size=(192, 640), + return_stereo=False, + return_depth=False, + data_segmentation_path=None, + frame_count=2, + keyframe_offset=0, + dilation=1, + eigen_depth=True, + color_aug=False, + load_kitti_360_segmentation_gt=False, + load_all=False, + load_fisheye=False, + fisheye_offset=0, + ): + self.data_path = Path(data_path) + self.voxel_gt_path = Path(voxel_gt_path) + self.data_segmentation_path = data_segmentation_path + self.pose_path = os.path.join("") + self.target_image_size = target_image_size + self.return_stereo = return_stereo + self.return_depth = return_depth + + self.frame_count = frame_count + self.dilation = dilation + self.keyframe_offset = keyframe_offset + self.eigen_depth = eigen_depth + self.color_aug = color_aug + self.load_kitti_360_segmentation_gt = load_kitti_360_segmentation_gt + self.load_all = load_all + self.load_fisheye = load_fisheye + self.fisheye_offset = fisheye_offset + + if sequences is None: + self._sequences = self._get_sequences(self.data_path) + else: + self._sequences = [f"2013_05_28_drive_00{s:02d}_sync" for s in sequences] + + self._calibs = self._load_calibs(self.data_path) + self._left_offset = ((self.frame_count - 1) // 2 + self.keyframe_offset) * self.dilation + + self._img_ids, self._poses = self._load_poses(self.pose_path, self._sequences) + self._perspective_folder = "data_rect" + self._segmentation_perspective_folder = "data_192x640" + self._segmentation_fisheye_folder = "data_192x640_0x-15" + + if self.load_all: + self._datapoints = self._load_all_datapoints(self.data_path, self._sequences) + else: + self._datapoints = self._load_datapoints(self.voxel_gt_path, self._sequences) + + self._skip = 0 + self.length = len(self._datapoints) + + @staticmethod + def _get_sequences(data_path): + all_sequences = [] + + seqs_path = Path(data_path) / "data_2d_raw" + for seq in seqs_path.iterdir(): + if not seq.is_dir(): + continue + all_sequences.append(seq.name) + + return all_sequences + + @staticmethod + def _load_calibs(data_path, fisheye_rotation=(0, 0)): + data_path = Path(data_path) + + calib_folder = data_path / "calibration" + cam_to_pose_file = calib_folder / "calib_cam_to_pose.txt" + cam_to_velo_file = calib_folder / "calib_cam_to_velo.txt" + intrinsics_file = calib_folder / "perspective.txt" + fisheye_02_file = calib_folder / "image_02.yaml" + fisheye_03_file = calib_folder / "image_03.yaml" + + cam_to_pose_data = {} + with open(cam_to_pose_file, 'r') as f: + for line in f.readlines(): + key, value = line.split(':', 1) + try: + cam_to_pose_data[key] = np.array([float(x) for x in value.split()], dtype=np.float32) + except ValueError: + pass + + cam_to_velo_data = None + with open(cam_to_velo_file, 'r') as f: + line = f.readline() + try: + cam_to_velo_data = np.array([float(x) for x in line.split()], dtype=np.float32) + except ValueError: + pass + + intrinsics_data = {} + with open(intrinsics_file, 'r') as f: + for line in f.readlines(): + key, value = line.split(':', 1) + try: + intrinsics_data[key] = np.array([float(x) for x in value.split()], dtype=np.float32) + except ValueError: + pass + + with open(fisheye_02_file, 'r') as f: + f.readline() # Skips first line that defines the YAML version + fisheye_02_data = yaml.safe_load(f) + + with open(fisheye_03_file, 'r') as f: + f.readline() # Skips first line that defines the YAML version + fisheye_03_data = yaml.safe_load(f) + + im_size_rect = (int(intrinsics_data["S_rect_00"][1]), int(intrinsics_data["S_rect_00"][0])) + im_size_fish = (fisheye_02_data["image_height"], fisheye_02_data["image_width"]) + + # Projection matrices + # We use these projection matrices also when resampling the fisheye cameras. + # This makes downstream processing easier, but it could be done differently. + P_rect_00 = np.reshape(intrinsics_data['P_rect_00'], (3, 4)) + P_rect_01 = np.reshape(intrinsics_data['P_rect_01'], (3, 4)) + + # Rotation matrices from raw to rectified -> Needs to be inverted later + R_rect_00 = np.eye(4, dtype=np.float32) + R_rect_01 = np.eye(4, dtype=np.float32) + R_rect_00[:3, :3] = np.reshape(intrinsics_data['R_rect_00'], (3, 3)) + R_rect_01[:3, :3] = np.reshape(intrinsics_data['R_rect_01'], (3, 3)) + + # Rotation matrices from resampled fisheye to raw fisheye + fisheye_rotation = np.array(fisheye_rotation).reshape((1, 2)) + R_02 = np.eye(4, dtype=np.float32) + R_03 = np.eye(4, dtype=np.float32) + R_02[:3, :3] = Rotation.from_euler("xy", fisheye_rotation[:, [1, 0]], degrees=True).as_matrix().astype(np.float32) + R_03[:3, :3] = Rotation.from_euler("xy", fisheye_rotation[:, [1, 0]] * np.array([[1, -1]]), degrees=True).as_matrix().astype(np.float32) + + # Load cam to pose transforms + T_00_to_pose = np.eye(4, dtype=np.float32) + T_01_to_pose = np.eye(4, dtype=np.float32) + T_02_to_pose = np.eye(4, dtype=np.float32) + T_03_to_pose = np.eye(4, dtype=np.float32) + T_00_to_velo = np.eye(4, dtype=np.float32) + + T_00_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_00"], (3, 4)) + T_01_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_01"], (3, 4)) + T_02_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_02"], (3, 4)) + T_03_to_pose[:3, :] = np.reshape(cam_to_pose_data["image_03"], (3, 4)) + T_00_to_velo[:3, :] = np.reshape(cam_to_velo_data, (3, 4)) + + # Compute cam to pose transforms for rectified perspective cameras + T_rect_00_to_pose = T_00_to_pose @ np.linalg.inv(R_rect_00) + T_rect_01_to_pose = T_01_to_pose @ np.linalg.inv(R_rect_01) + + # Compute cam to pose transform for fisheye cameras + T_02_to_pose = T_02_to_pose @ R_02 + T_03_to_pose = T_03_to_pose @ R_03 + + # Compute velo to cameras and velo to pose transforms + T_velo_to_rect_00 = R_rect_00 @ np.linalg.inv(T_00_to_velo) + T_velo_to_pose = T_rect_00_to_pose @ T_velo_to_rect_00 + T_velo_to_rect_01 = np.linalg.inv(T_rect_01_to_pose) @ T_velo_to_pose + + # Calibration matrix is the same for both perspective cameras + K = P_rect_00[:3, :3] + + # Normalize calibration + f_x = K[0, 0] / im_size_rect[1] + f_y = K[1, 1] / im_size_rect[0] + c_x = K[0, 2] / im_size_rect[1] + c_y = K[1, 2] / im_size_rect[0] + + # Change to image coordinates [-1, 1] + K[0, 0] = f_x * 2. + K[1, 1] = f_y * 2. + K[0, 2] = c_x * 2. - 1 + K[1, 2] = c_y * 2. - 1 + + # Convert fisheye calibration to [-1, 1] image dimensions + fisheye_02_data["projection_parameters"]["gamma1"] = (fisheye_02_data["projection_parameters"]["gamma1"] / im_size_fish[1]) * 2. + fisheye_02_data["projection_parameters"]["gamma2"] = (fisheye_02_data["projection_parameters"]["gamma2"] / im_size_fish[0]) * 2. + fisheye_02_data["projection_parameters"]["u0"] = (fisheye_02_data["projection_parameters"]["u0"] / im_size_fish[1]) * 2. - 1. + fisheye_02_data["projection_parameters"]["v0"] = (fisheye_02_data["projection_parameters"]["v0"] / im_size_fish[0]) * 2. - 1. + + fisheye_03_data["projection_parameters"]["gamma1"] = (fisheye_03_data["projection_parameters"]["gamma1"] / im_size_fish[1]) * 2. + fisheye_03_data["projection_parameters"]["gamma2"] = (fisheye_03_data["projection_parameters"]["gamma2"] / im_size_fish[0]) * 2. + fisheye_03_data["projection_parameters"]["u0"] = (fisheye_03_data["projection_parameters"]["u0"] / im_size_fish[1]) * 2. - 1. + fisheye_03_data["projection_parameters"]["v0"] = (fisheye_03_data["projection_parameters"]["v0"] / im_size_fish[0]) * 2. - 1. + + # Use same camera calibration as perspective cameras for resampling + # K_fisheye = np.eye(3, dtype=np.float32) + # K_fisheye[0, 0] = 2 + # K_fisheye[1, 1] = 2 + + K_fisheye = K + + calibs = { + "K_perspective": K, + "K_fisheye": K_fisheye, + "T_cam_to_pose": { + "00": T_rect_00_to_pose, + "01": T_rect_01_to_pose, + "02": T_02_to_pose, + "03": T_03_to_pose, + }, + "T_velo_to_cam": { + "00": T_velo_to_rect_00, + "01": T_velo_to_rect_01, + }, + "T_velo_to_pose": T_velo_to_pose, + "fisheye": { + "calib_02": fisheye_02_data, + "calib_03": fisheye_03_data, + "R_02": R_02[:3, :3], + "R_03": R_03[:3, :3] + }, + "im_size": im_size_rect + } + + return calibs + + @staticmethod + def _load_poses(pose_path, sequences): + ids = {} + poses = {} + + for seq in sequences: + pose_file = Path(pose_path) / seq / f"poses.txt" + + try: + pose_data = np.loadtxt(pose_file) + except FileNotFoundError: + print(f'Ground truth poses are not avaialble for sequence {seq}, {pose_file}.') + + ids_seq = pose_data[:, 0].astype(int) + poses_seq = pose_data[:, 1:].astype(np.float32).reshape((-1, 3, 4)) + poses_seq = np.concatenate((poses_seq, np.zeros_like(poses_seq[:, :1, :])), axis=1) + poses_seq[:, 3, 3] = 1 + + ids[seq] = ids_seq + poses[seq] = poses_seq + return ids, poses + + @staticmethod + def _get_resamplers(calibs, K_target, target_image_size): + resampler_02 = FisheyeToPinholeSampler(K_target, target_image_size, calibs["fisheye"]["calib_02"], calibs["fisheye"]["R_02"]) + resampler_03 = FisheyeToPinholeSampler(K_target, target_image_size, calibs["fisheye"]["calib_03"], calibs["fisheye"]["R_03"]) + + return resampler_02, resampler_03 + + @staticmethod + def _load_datapoints(voxel_gt_path, sequences): + datapoints = [] + for seq in sorted(sequences): + ids = [int(file.name[:6]) for file in sorted((voxel_gt_path / seq).glob("*_1_1.npy"))] + datapoints_seq = [(seq, id, False) for id in ids] + datapoints.extend(datapoints_seq) + return datapoints + + @staticmethod + def _load_all_datapoints(voxel_gt_path, sequences): + datapoints = [] + for seq in sorted(sequences): + ids = [int(file.name[:6]) for file in sorted((voxel_gt_path / 'data_2d_raw' / seq / 'image_00' / 'data_rect').glob("*.png"))] + datapoints_seq = [(seq, id, False) for id in ids] + datapoints.extend(datapoints_seq) + return datapoints + + def load_images(self, seq, img_ids): + imgs_p_left = [] + + for id in img_ids: + # id = self._img_ids[seq][id] + img_perspective = cv2.cvtColor(cv2.imread(os.path.join(self.data_path, "data_2d_raw", seq, "image_00", self._perspective_folder, f"{id:06d}.png")), cv2.COLOR_BGR2RGB).astype(np.float32) / 255 + + imgs_p_left += [img_perspective] + + return imgs_p_left + + def load_fisheye_images(self, seq, img_ids): + imgs_f_left, imgs_f_right = [], [] + + for id in img_ids: + #img_fisheye = cv2.cvtColor(cv2.imread(os.path.join(self.data_path, "data_2d_raw", seq, "image_02", self._segmentation_fisheye_folder, f"{id:010d}.png")), cv2.COLOR_BGR2RGB).astype(np.float32) / 255 + #img_fisheye = cv2.cvtColor(cv2.imread(os.path.join(self.data_path, "data_2d_raw", seq, "image_03", self._segmentation_fisheye_folder, f"{id:010d}.png")), cv2.COLOR_BGR2RGB).astype(np.float32) / 255 + + id = self._img_ids[seq][id] + + img_fisheye_left = cv2.cvtColor(cv2.imread(os.path.join("", "data_2d_raw", seq, "image_02", self._segmentation_fisheye_folder, f"{id:010d}.png")), cv2.COLOR_BGR2RGB).astype(np.float32) / 255 + img_fisheye_right = cv2.cvtColor(cv2.imread(os.path.join("", "data_2d_raw", seq, "image_03", self._segmentation_fisheye_folder, f"{id:010d}.png")), cv2.COLOR_BGR2RGB).astype(np.float32) / 255 + + imgs_f_left += [img_fisheye_left] + imgs_f_right += [img_fisheye_right] + + return imgs_f_left, imgs_f_right + + def load_voxel_gt(self, sequence, img_ids): + voxel_gt = [] + + for id in img_ids: + target_1_path = os.path.join(self.voxel_gt_path, sequence, f"{id:06d}" + "_1_1.npy") + + if not self.load_all or os.path.isfile(target_1_path): + voxel_gt.append(np.load(target_1_path)) + else: + voxel_gt.append(None) + + return voxel_gt + + def process_img(self, img: np.array, color_aug_fn=None, resampler:FisheyeToPinholeSampler=None): + if resampler is not None and not self.is_preprocessed: + img = torch.tensor(img).permute(2, 0, 1) + img = resampler.resample(img) + else: + if self.target_image_size: + img = cv2.resize(img, (self.target_image_size[1], self.target_image_size[0]), interpolation=cv2.INTER_LINEAR) + img = np.transpose(img, (2, 0, 1)) + img = torch.tensor(img) + + if color_aug_fn is not None: + img = color_aug_fn(img) + + img = img * 2 - 1 + return img + + def load_depth(self, seq, img_id, is_right): + points = np.fromfile(os.path.join(self.data_path, "data_3d_raw", seq, "velodyne_points", "data", f"{img_id:010d}.bin"), dtype=np.float32).reshape(-1, 4) + points[:, 3] = 1.0 + + T_velo_to_cam = self._calibs["T_velo_to_cam"]["00" if not is_right else "01"] + K = self._calibs["K_perspective"] + + # project the points to the camera + velo_pts_im = np.dot(K @ T_velo_to_cam[:3, :], points.T).T + velo_pts_im[:, :2] = velo_pts_im[:, :2] / velo_pts_im[:, 2][..., None] + + # the projection is normalized to [-1, 1] -> transform to [0, height-1] x [0, width-1] + velo_pts_im[:, 0] = np.round((velo_pts_im[:, 0] * .5 + .5) * self.target_image_size[1]) + velo_pts_im[:, 1] = np.round((velo_pts_im[:, 1] * .5 + .5) * self.target_image_size[0]) + + # check if in bounds + val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0) + val_inds = val_inds & (velo_pts_im[:, 0] < self.target_image_size[1]) & (velo_pts_im[:, 1] < self.target_image_size[0]) + velo_pts_im = velo_pts_im[val_inds, :] + + # project to image + depth = np.zeros(self.target_image_size) + depth[velo_pts_im[:, 1].astype(np.int32), velo_pts_im[:, 0].astype(np.int32)] = velo_pts_im[:, 2] + + # find the duplicate points and choose the closest depth + inds = velo_pts_im[:, 1] * (self.target_image_size[1] - 1) + velo_pts_im[:, 0] - 1 + dupe_inds = [item for item, count in Counter(inds).items() if count > 1] + for dd in dupe_inds: + pts = np.where(inds == dd)[0] + x_loc = int(velo_pts_im[pts[0], 0]) + y_loc = int(velo_pts_im[pts[0], 1]) + depth[y_loc, x_loc] = velo_pts_im[pts, 2].min() + depth[depth < 0] = 0 + + return depth[None, :, :] + + def __getitem__(self, index: int): + _start_time = time.time() + + if index >= self.length: + raise IndexError() + + if self._skip != 0: + index += self._skip + + sequence, id, is_right = self._datapoints[index] + + load_left = (not is_right) or self.return_stereo + load_right = is_right or self.return_stereo + + ids = [id] + ids_fish = [id + self.fisheye_offset] + + if self.color_aug: + color_aug_fn = get_color_aug_fn(ColorJitter.get_params(brightness=(0.8, 1.2), contrast=(0.8, 1.2), saturation=(0.8, 1.2), hue=(-0.1, 0.1))) + else: + color_aug_fn = None + + _start_time_loading = time.time() + imgs_p_left = self.load_images(sequence, ids) + imgs_f_left, imgs_f_right = self.load_fisheye_images(sequence, ids_fish) + voxel_gt = self.load_voxel_gt(sequence, ids) + _loading_time = np.array(time.time() - _start_time_loading) + + _start_time_processing = time.time() + imgs_p_left = [self.process_img(img, color_aug_fn=color_aug_fn) for img in imgs_p_left] + imgs_f_left = [self.process_img(img, color_aug_fn=color_aug_fn) for img in imgs_f_left] + imgs_f_right = [self.process_img(img, color_aug_fn=color_aug_fn) for img in imgs_f_right] + _processing_time = np.array(time.time() - _start_time_processing) + + # These poses are not camera to world !! + poses_p_left = [self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["00"] for i in ids] if load_left else [] + poses_f_left = [self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["02"] for i in ids_fish] + poses_f_right = [self._poses[sequence][i, :, :] @ self._calibs["T_cam_to_pose"]["03"] for i in ids_fish] + + projs_p_left = [self._calibs["K_perspective"] for _ in ids] if load_left else [] + projs_f_left = [self._calibs["K_fisheye"] for _ in ids_fish] + projs_f_right = [self._calibs["K_fisheye"] for _ in ids_fish] + + imgs = imgs_p_left + projs = projs_p_left + poses = poses_p_left + + if self.load_fisheye: + imgs += imgs_f_left + imgs_f_right + projs += projs_f_left + projs_f_right + poses += poses_f_left + poses_f_right + + _proc_time = np.array(time.time() - _start_time) + + # print(_loading_time, _processing_time, _proc_time) + + data = { + "imgs": imgs, + "projs": projs, + "voxel_gt": voxel_gt, + "poses": poses, + "t__get_item__": np.array([_proc_time]), + "index": np.array([index]) + } + + return data + + def __len__(self) -> int: + return self.length