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| _base_ = ["../_base_/default_runtime.py"] | |
| # misc custom setting | |
| batch_size = 24 # bs: total bs in all gpus | |
| num_worker = 48 | |
| mix_prob = 0.8 | |
| empty_cache = False | |
| enable_amp = True | |
| find_unused_parameters = True | |
| # trainer | |
| train = dict( | |
| type="MultiDatasetTrainer", | |
| ) | |
| # model settings | |
| model = dict( | |
| type="PPT-v1m1", | |
| backbone=dict( | |
| type="SpUNet-v1m3", | |
| in_channels=6, | |
| num_classes=0, | |
| base_channels=32, | |
| context_channels=256, | |
| channels=(32, 64, 128, 256, 256, 128, 96, 96), | |
| layers=(2, 3, 4, 6, 2, 2, 2, 2), | |
| cls_mode=False, | |
| conditions=("ScanNet", "S3DIS", "Structured3D"), | |
| zero_init=False, | |
| norm_decouple=True, | |
| norm_adaptive=True, | |
| norm_affine=True, | |
| ), | |
| criteria=[dict(type="CrossEntropyLoss", loss_weight=1.0, ignore_index=-1)], | |
| backbone_out_channels=96, | |
| context_channels=256, | |
| conditions=("Structured3D", "ScanNet", "S3DIS"), | |
| template="[x]", | |
| clip_model="ViT-B/16", | |
| # fmt: off | |
| class_name=( | |
| "wall", "floor", "cabinet", "bed", "chair", "sofa", "table", "door", | |
| "window", "bookshelf", "bookcase", "picture", "counter", "desk", "shelves", "curtain", | |
| "dresser", "pillow", "mirror", "ceiling", "refrigerator", "television", "shower curtain", "nightstand", | |
| "toilet", "sink", "lamp", "bathtub", "garbagebin", "board", "beam", "column", | |
| "clutter", "otherstructure", "otherfurniture", "otherprop", | |
| ), | |
| valid_index=( | |
| (0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 25, 26, 33, 34, 35), | |
| (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 20, 22, 24, 25, 27, 34), | |
| (0, 1, 4, 5, 6, 7, 8, 10, 19, 29, 30, 31, 32), | |
| ), | |
| # fmt: on | |
| backbone_mode=False, | |
| ) | |
| # scheduler settings | |
| epoch = 100 | |
| optimizer = dict(type="SGD", lr=0.05, momentum=0.9, weight_decay=0.0001, nesterov=True) | |
| scheduler = dict( | |
| type="OneCycleLR", | |
| max_lr=optimizer["lr"], | |
| pct_start=0.05, | |
| anneal_strategy="cos", | |
| div_factor=10.0, | |
| final_div_factor=10000.0, | |
| ) | |
| # param_dicts = [dict(keyword="modulation", lr=0.005)] | |
| # dataset settings | |
| data = dict( | |
| num_classes=20, | |
| ignore_index=-1, | |
| names=[ | |
| "wall", | |
| "floor", | |
| "cabinet", | |
| "bed", | |
| "chair", | |
| "sofa", | |
| "table", | |
| "door", | |
| "window", | |
| "bookshelf", | |
| "picture", | |
| "counter", | |
| "desk", | |
| "curtain", | |
| "refridgerator", | |
| "shower curtain", | |
| "toilet", | |
| "sink", | |
| "bathtub", | |
| "otherfurniture", | |
| ], | |
| train=dict( | |
| type="ConcatDataset", | |
| datasets=[ | |
| # Structured3D | |
| dict( | |
| type="Structured3DDataset", | |
| split="train", | |
| data_root="data/structured3d", | |
| transform=[ | |
| dict(type="CenterShift", apply_z=True), | |
| dict( | |
| type="RandomDropout", | |
| dropout_ratio=0.2, | |
| dropout_application_ratio=0.2, | |
| ), | |
| # dict(type="RandomRotateTargetAngle", angle=(1/2, 1, 3/2), center=[0, 0, 0], axis="z", p=0.75), | |
| dict( | |
| type="RandomRotate", | |
| angle=[-1, 1], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=0.5, | |
| ), | |
| dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="x", p=0.5), | |
| dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="y", p=0.5), | |
| dict(type="RandomScale", scale=[0.9, 1.1]), | |
| # dict(type="RandomShift", shift=[0.2, 0.2, 0.2]), | |
| dict(type="RandomFlip", p=0.5), | |
| dict(type="RandomJitter", sigma=0.005, clip=0.02), | |
| dict( | |
| type="ElasticDistortion", | |
| distortion_params=[[0.2, 0.4], [0.8, 1.6]], | |
| ), | |
| dict(type="ChromaticAutoContrast", p=0.2, blend_factor=None), | |
| dict(type="ChromaticTranslation", p=0.95, ratio=0.05), | |
| dict(type="ChromaticJitter", p=0.95, std=0.05), | |
| # dict(type="HueSaturationTranslation", hue_max=0.2, saturation_max=0.2), | |
| # dict(type="RandomColorDrop", p=0.2, color_augment=0.0), | |
| dict( | |
| type="GridSample", | |
| grid_size=0.02, | |
| hash_type="fnv", | |
| mode="train", | |
| return_grid_coord=True, | |
| ), | |
| dict(type="SphereCrop", sample_rate=0.8, mode="random"), | |
| dict(type="CenterShift", apply_z=False), | |
| dict(type="NormalizeColor"), | |
| dict(type="ShufflePoint"), | |
| dict(type="Add", keys_dict={"condition": "Structured3D"}), | |
| dict(type="ToTensor"), | |
| dict( | |
| type="Collect", | |
| keys=("coord", "grid_coord", "segment", "condition"), | |
| feat_keys=("color", "normal"), | |
| ), | |
| ], | |
| test_mode=False, | |
| loop=2, # sampling weight | |
| ), | |
| # ScanNet | |
| dict( | |
| type="ScanNetDataset", | |
| split="train", | |
| data_root="data/scannet", | |
| transform=[ | |
| dict(type="CenterShift", apply_z=True), | |
| dict( | |
| type="RandomDropout", | |
| dropout_ratio=0.2, | |
| dropout_application_ratio=0.2, | |
| ), | |
| # dict(type="RandomRotateTargetAngle", angle=(1/2, 1, 3/2), center=[0, 0, 0], axis="z", p=0.75), | |
| dict( | |
| type="RandomRotate", | |
| angle=[-1, 1], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=0.5, | |
| ), | |
| dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="x", p=0.5), | |
| dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="y", p=0.5), | |
| dict(type="RandomScale", scale=[0.9, 1.1]), | |
| # dict(type="RandomShift", shift=[0.2, 0.2, 0.2]), | |
| dict(type="RandomFlip", p=0.5), | |
| dict(type="RandomJitter", sigma=0.005, clip=0.02), | |
| dict( | |
| type="ElasticDistortion", | |
| distortion_params=[[0.2, 0.4], [0.8, 1.6]], | |
| ), | |
| dict(type="ChromaticAutoContrast", p=0.2, blend_factor=None), | |
| dict(type="ChromaticTranslation", p=0.95, ratio=0.05), | |
| dict(type="ChromaticJitter", p=0.95, std=0.05), | |
| # dict(type="HueSaturationTranslation", hue_max=0.2, saturation_max=0.2), | |
| # dict(type="RandomColorDrop", p=0.2, color_augment=0.0), | |
| dict( | |
| type="GridSample", | |
| grid_size=0.02, | |
| hash_type="fnv", | |
| mode="train", | |
| return_grid_coord=True, | |
| ), | |
| dict(type="SphereCrop", point_max=100000, mode="random"), | |
| dict(type="CenterShift", apply_z=False), | |
| dict(type="NormalizeColor"), | |
| dict(type="ShufflePoint"), | |
| dict(type="Add", keys_dict={"condition": "ScanNet"}), | |
| dict(type="ToTensor"), | |
| dict( | |
| type="Collect", | |
| keys=("coord", "grid_coord", "segment", "condition"), | |
| feat_keys=("color", "normal"), | |
| ), | |
| ], | |
| test_mode=False, | |
| loop=1, # sampling weight | |
| ), | |
| ], | |
| ), | |
| val=dict( | |
| type="ScanNetDataset", | |
| split="val", | |
| data_root="data/scannet", | |
| transform=[ | |
| dict(type="CenterShift", apply_z=True), | |
| dict( | |
| type="GridSample", | |
| grid_size=0.02, | |
| hash_type="fnv", | |
| mode="train", | |
| return_grid_coord=True, | |
| ), | |
| # dict(type="SphereCrop", point_max=1000000, mode="center"), | |
| dict(type="CenterShift", apply_z=False), | |
| dict(type="NormalizeColor"), | |
| dict(type="ToTensor"), | |
| dict(type="Add", keys_dict={"condition": "ScanNet"}), | |
| dict( | |
| type="Collect", | |
| keys=("coord", "grid_coord", "segment", "condition"), | |
| feat_keys=("color", "normal"), | |
| ), | |
| ], | |
| test_mode=False, | |
| ), | |
| test=dict( | |
| type="ScanNetDataset", | |
| split="val", | |
| data_root="data/scannet", | |
| transform=[ | |
| dict(type="CenterShift", apply_z=True), | |
| dict(type="NormalizeColor"), | |
| ], | |
| test_mode=True, | |
| test_cfg=dict( | |
| voxelize=dict( | |
| type="GridSample", | |
| grid_size=0.02, | |
| hash_type="fnv", | |
| mode="test", | |
| return_grid_coord=True, | |
| keys=("coord", "color", "normal"), | |
| ), | |
| crop=None, | |
| post_transform=[ | |
| dict(type="CenterShift", apply_z=False), | |
| dict(type="Add", keys_dict={"condition": "ScanNet"}), | |
| dict(type="ToTensor"), | |
| dict( | |
| type="Collect", | |
| keys=("coord", "grid_coord", "index", "condition"), | |
| feat_keys=("color", "normal"), | |
| ), | |
| ], | |
| aug_transform=[ | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[0], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ) | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[1 / 2], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ) | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[1], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ) | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[3 / 2], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ) | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[0], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ), | |
| dict(type="RandomScale", scale=[0.95, 0.95]), | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[1 / 2], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ), | |
| dict(type="RandomScale", scale=[0.95, 0.95]), | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[1], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ), | |
| dict(type="RandomScale", scale=[0.95, 0.95]), | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[3 / 2], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ), | |
| dict(type="RandomScale", scale=[0.95, 0.95]), | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[0], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ), | |
| dict(type="RandomScale", scale=[1.05, 1.05]), | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[1 / 2], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ), | |
| dict(type="RandomScale", scale=[1.05, 1.05]), | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[1], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ), | |
| dict(type="RandomScale", scale=[1.05, 1.05]), | |
| ], | |
| [ | |
| dict( | |
| type="RandomRotateTargetAngle", | |
| angle=[3 / 2], | |
| axis="z", | |
| center=[0, 0, 0], | |
| p=1, | |
| ), | |
| dict(type="RandomScale", scale=[1.05, 1.05]), | |
| ], | |
| [dict(type="RandomFlip", p=1)], | |
| ], | |
| ), | |
| ), | |
| ) | |