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import os
import json
import torch
import torchvision
import numpy as np
from tqdm.auto import tqdm
from diffusionsfm.dataset.co3d_v2 import (
Co3dDataset,
full_scene_scale,
)
from pytorch3d.renderer import PerspectiveCameras
from diffusionsfm.utils.visualization import filter_and_align_point_clouds
from diffusionsfm.inference.load_model import load_model
from diffusionsfm.inference.predict import predict_cameras
from diffusionsfm.utils.geometry import (
compute_angular_error_batch,
get_error,
n_to_np_rotations,
)
from diffusionsfm.utils.slurm import init_slurm_signals_if_slurm
from diffusionsfm.utils.rays import cameras_to_rays
from diffusionsfm.utils.rays import normalize_cameras_batch
@torch.no_grad()
def evaluate(
cfg,
model,
dataset,
num_images,
device,
use_pbar=True,
calculate_intrinsics=True,
additional_timesteps=(),
num_evaluate=None,
max_num_images=None,
mode=None,
metrics=True,
load_depth=True,
):
if cfg.training.get("dpt_head", False):
H_in = W_in = 224
H_out = W_out = cfg.training.full_num_patches_y
else:
H_in = H_out = cfg.model.num_patches_x
W_in = W_out = cfg.model.num_patches_y
results = {}
instances = np.arange(0, len(dataset)) if num_evaluate is None else np.linspace(0, len(dataset) - 1, num_evaluate, endpoint=True, dtype=int)
instances = tqdm(instances) if use_pbar else instances
for counter, idx in enumerate(instances):
batch = dataset[idx]
instance = batch["model_id"]
images = batch["image"].to(device)
focal_length = batch["focal_length"].to(device)[:num_images]
R = batch["R"].to(device)[:num_images]
T = batch["T"].to(device)[:num_images]
crop_parameters = batch["crop_parameters"].to(device)[:num_images]
if load_depth:
depths = batch["depth"].to(device)[:num_images]
depth_masks = batch["depth_masks"].to(device)[:num_images]
try:
object_masks = batch["object_masks"].to(device)[:num_images]
except KeyError:
object_masks = depth_masks.clone()
# Normalize cameras and scale depths for output resolution
cameras_gt = PerspectiveCameras(
R=R, T=T, focal_length=focal_length, device=device
)
cameras_gt, _, _ = normalize_cameras_batch(
[cameras_gt],
first_cam_mediod=cfg.training.first_cam_mediod,
normalize_first_camera=cfg.training.normalize_first_camera,
depths=depths.unsqueeze(0),
crop_parameters=crop_parameters.unsqueeze(0),
num_patches_x=H_in,
num_patches_y=W_in,
return_scales=True,
)
cameras_gt = cameras_gt[0]
gt_rays = cameras_to_rays(
cameras=cameras_gt,
num_patches_x=H_in,
num_patches_y=W_in,
crop_parameters=crop_parameters,
depths=depths,
mode=mode,
)
gt_points = gt_rays.get_segments().view(num_images, -1, 3)
resize = torchvision.transforms.Resize(
224,
antialias=False,
interpolation=torchvision.transforms.InterpolationMode.NEAREST_EXACT,
)
else:
cameras_gt = PerspectiveCameras(
R=R, T=T, focal_length=focal_length, device=device
)
pred_cameras, additional_cams = predict_cameras(
model,
images,
device,
crop_parameters=crop_parameters,
num_patches_x=H_out,
num_patches_y=W_out,
max_num_images=max_num_images,
additional_timesteps=additional_timesteps,
calculate_intrinsics=calculate_intrinsics,
mode=mode,
return_rays=True,
use_homogeneous=cfg.model.get("use_homogeneous", False),
)
cameras_to_evaluate = additional_cams + [pred_cameras]
all_cams_batch = dataset.get_data(
sequence_name=instance, ids=np.arange(0, batch["n"]), no_images=True
)
gt_scene_scale = full_scene_scale(all_cams_batch)
R_gt = R
T_gt = T
errors = []
for _, (camera, pred_rays) in enumerate(cameras_to_evaluate):
R_pred = camera.R
T_pred = camera.T
f_pred = camera.focal_length
R_pred_rel = n_to_np_rotations(num_images, R_pred).cpu().numpy()
R_gt_rel = n_to_np_rotations(num_images, batch["R"]).cpu().numpy()
R_error = compute_angular_error_batch(R_pred_rel, R_gt_rel)
CC_error, _ = get_error(True, R_pred, T_pred, R_gt, T_gt, gt_scene_scale)
if load_depth and metrics:
# Evaluate outputs at the same resolution as DUSt3R
pred_points = pred_rays.get_segments().view(num_images, H_out, H_out, 3)
pred_points = pred_points.permute(0, 3, 1, 2)
pred_points = resize(pred_points).permute(0, 2, 3, 1).view(num_images, H_in*W_in, 3)
(
_,
_,
_,
_,
metric_values,
) = filter_and_align_point_clouds(
num_images,
gt_points,
pred_points,
depth_masks,
depth_masks,
images,
metrics=metrics,
num_patches_x=H_in,
)
(
_,
_,
_,
_,
object_metric_values,
) = filter_and_align_point_clouds(
num_images,
gt_points,
pred_points,
depth_masks * object_masks,
depth_masks * object_masks,
images,
metrics=metrics,
num_patches_x=H_in,
)
result = {
"R_pred": R_pred.detach().cpu().numpy().tolist(),
"T_pred": T_pred.detach().cpu().numpy().tolist(),
"f_pred": f_pred.detach().cpu().numpy().tolist(),
"R_gt": R_gt.detach().cpu().numpy().tolist(),
"T_gt": T_gt.detach().cpu().numpy().tolist(),
"f_gt": focal_length.detach().cpu().numpy().tolist(),
"scene_scale": gt_scene_scale,
"R_error": R_error.tolist(),
"CC_error": CC_error,
}
if load_depth and metrics:
result["CD"] = metric_values[1]
result["CD_Object"] = object_metric_values[1]
else:
result["CD"] = 0
result["CD_Object"] = 0
errors.append(result)
results[instance] = errors
if counter == len(dataset) - 1:
break
return results
def save_results(
output_dir,
checkpoint=800_000,
category="hydrant",
num_images=None,
calculate_additional_timesteps=True,
calculate_intrinsics=True,
split="test",
force=False,
sample_num=1,
max_num_images=None,
dataset="co3d",
):
init_slurm_signals_if_slurm()
os.umask(000) # Default to 777 permissions
eval_path = os.path.join(
output_dir,
f"eval_{dataset}",
f"{category}_{num_images}_{sample_num}_ckpt{checkpoint}.json",
)
if os.path.exists(eval_path) and not force:
print(f"File {eval_path} already exists. Skipping.")
return
if num_images is not None and num_images > 8:
custom_keys = {"model.num_images": num_images}
ignore_keys = ["pos_table"]
else:
custom_keys = None
ignore_keys = []
device = torch.device("cuda")
model, cfg = load_model(
output_dir,
checkpoint=checkpoint,
device=device,
custom_keys=custom_keys,
ignore_keys=ignore_keys,
)
if num_images is None:
num_images = cfg.dataset.num_images
if cfg.training.dpt_head:
# Evaluate outputs at the same resolution as DUSt3R
depth_size = 224
else:
depth_size = cfg.model.num_patches_x
dataset = Co3dDataset(
category=category,
split=split,
num_images=num_images,
apply_augmentation=False,
sample_num=None if split == "train" else sample_num,
use_global_intrinsics=cfg.dataset.use_global_intrinsics,
load_depths=True,
center_crop=True,
depth_size=depth_size,
mask_holes=not cfg.training.regression,
img_size=256 if cfg.model.unet_diffuser else 224,
)
print(f"Category {category} {len(dataset)}")
if calculate_additional_timesteps:
additional_timesteps = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
else:
additional_timesteps = []
results = evaluate(
cfg=cfg,
model=model,
dataset=dataset,
num_images=num_images,
device=device,
calculate_intrinsics=calculate_intrinsics,
additional_timesteps=additional_timesteps,
max_num_images=max_num_images,
mode="segment",
)
os.makedirs(os.path.dirname(eval_path), exist_ok=True)
with open(eval_path, "w") as f:
json.dump(results, f) |