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external/Metric3D/mono/configs/_base_/models/backbones/convnext_large.py

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+#_base_ = ['./_model_base_.py',]
+
+#'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-large_3rdparty_in21k_20220301-e6e0ea0a.pth' 
+model = dict(
+    #type='EncoderDecoderAuxi',
+    backbone=dict(
+        type='convnext_large',
+        pretrained=True, 
+        in_22k=True,
+        out_indices=[0, 1, 2, 3],
+        drop_path_rate=0.4,
+        layer_scale_init_value=1.0,
+        checkpoint='data/pretrained_weight_repo/convnext/convnext_large_22k_1k_384.pth',
+        prefix='backbones.',
+        out_channels=[192, 384, 768, 1536]),
+    )

external/Metric3D/mono/configs/_base_/models/backbones/convnext_tiny.py

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+#_base_ = ['./_model_base_.py',]
+
+#'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-large_3rdparty_in21k_20220301-e6e0ea0a.pth' 
+model = dict(
+    #type='EncoderDecoderAuxi',
+    backbone=dict(
+        type='convnext_tiny',
+        pretrained=True, 
+        in_22k=True,
+        out_indices=[0, 1, 2, 3],
+        drop_path_rate=0.4,
+        layer_scale_init_value=1.0,
+        checkpoint='',
+        prefix='backbones.',
+        out_channels=[96, 192, 384, 768]),
+    )

external/Metric3D/mono/configs/_base_/models/backbones/dino_vit_giant2_reg.py

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+model = dict(
+    backbone=dict(
+        type='vit_giant2_reg',
+        prefix='backbones.',
+        out_channels=[1536, 1536, 1536, 1536],
+        drop_path_rate = 0.0),
+    )

external/Metric3D/mono/configs/_base_/models/backbones/dino_vit_large.py

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+model = dict(
+    backbone=dict(
+        type='vit_large',
+        prefix='backbones.',
+        out_channels=[1024, 1024, 1024, 1024],
+        drop_path_rate = 0.0),
+    )

external/Metric3D/mono/configs/_base_/models/backbones/dino_vit_large_reg.py

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+model = dict(
+    backbone=dict(
+        type='vit_large_reg',
+        prefix='backbones.',
+        out_channels=[1024, 1024, 1024, 1024],
+        drop_path_rate = 0.0),
+    )

external/Metric3D/mono/configs/_base_/models/backbones/dino_vit_small_reg.py

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+model = dict(
+    backbone=dict(
+        type='vit_small_reg',
+        prefix='backbones.',
+        out_channels=[384, 384, 384, 384],
+        drop_path_rate = 0.0),
+    )

external/Metric3D/mono/configs/_base_/models/encoder_decoder/convnext_tiny.hourglassdecoder.py

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+# model settings
+_base_ = ['../backbones/convnext_tiny.py',]
+model = dict(
+    type='DensePredModel',
+    decode_head=dict(
+        type='HourglassDecoder',
+        in_channels=[96, 192, 384, 768],
+        decoder_channel=[64, 64, 128, 256],
+        prefix='decode_heads.'),
+)

external/Metric3D/training/data_server_info/__init__.py

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+from .public_datasets import *
+from .pretrained_weight import *

external/Metric3D/training/data_server_info/annos_test_matterport3d_example.json

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+{"files": [{"meta_data": "Matterport3D/data/2n8kARJN3HM/2n8kARJN3HM/meta/add134cc07e64d9d8524d0d9f96c4180_i1_5.pkl"}, {"meta_data": "Matterport3D/data/SN83YJsR3w2/SN83YJsR3w2/meta/4a87c9150e8442a1b8abc51ed5073ca0_i1_4.pkl"}, {"meta_data": "Matterport3D/data/Uxmj2M2itWa/Uxmj2M2itWa/meta/0cef156ab53041da97dd6a70d3d5af0b_i1_4.pkl"}, {"meta_data": "Matterport3D/data/yqstnuAEVhm/yqstnuAEVhm/meta/e9b4d8e951cb4712b3905c8f4c4dabb5_i2_1.pkl"}, {"meta_data": "Matterport3D/data/dhjEzFoUFzH/dhjEzFoUFzH/meta/3d1a8e5759a14f2a81e5d6e2f5045eca_i2_2.pkl"}]}

external/Metric3D/training/data_server_info/annos_test_normal_nyu_example.json

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+{"files": [{"rgb": "NYU/nyu_normal/official/test/0000.png", "depth": "NYU/nyu_normal/official/test/0000_d.png", "cam_in": [518.8579, 519.4691, 325.58245, 253.73617], "normal": "NYU/nyu_normal/official/test/0000_n.png"}, {"rgb": "NYU/nyu_normal/official/test/0001.png", "depth": "NYU/nyu_normal/official/test/0001_d.png", "cam_in": [518.8579, 519.4691, 325.58245, 253.73617], "normal": "NYU/nyu_normal/official/test/0001_n.png"}, {"rgb": "NYU/nyu_normal/official/test/0008.png", "depth": "NYU/nyu_normal/official/test/0008_d.png", "cam_in": [518.8579, 519.4691, 325.58245, 253.73617], "normal": "NYU/nyu_normal/official/test/0008_n.png"}, {"rgb": "NYU/nyu_normal/official/test/0013.png", "depth": "NYU/nyu_normal/official/test/0013_d.png", "cam_in": [518.8579, 519.4691, 325.58245, 253.73617], "normal": "NYU/nyu_normal/official/test/0013_n.png"}, {"rgb": "NYU/nyu_normal/official/test/0014.png", "depth": "NYU/nyu_normal/official/test/0014_d.png", "cam_in": [518.8579, 519.4691, 325.58245, 253.73617], "normal": "NYU/nyu_normal/official/test/0014_n.png"}, {"rgb": "NYU/nyu_normal/official/test/0015.png", "depth": "NYU/nyu_normal/official/test/0015_d.png", "cam_in": [518.8579, 519.4691, 325.58245, 253.73617], "normal": "NYU/nyu_normal/official/test/0015_n.png"}, {"rgb": "NYU/nyu_normal/official/test/0016.png", "depth": "NYU/nyu_normal/official/test/0016_d.png", "cam_in": [518.8579, 519.4691, 325.58245, 253.73617], "normal": "NYU/nyu_normal/official/test/0016_n.png"}, {"rgb": "NYU/nyu_normal/official/test/0017.png", "depth": "NYU/nyu_normal/official/test/0017_d.png", "cam_in": [518.8579, 519.4691, 325.58245, 253.73617], "normal": "NYU/nyu_normal/official/test/0017_n.png"}]}

external/Metric3D/training/data_server_info/pretrained_weight.py

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+db_info={}
+
+
+
+db_info['checkpoint']={
+    'db_root': 'tbd_weight_root', # Config your weight root!
+
+    # pretrained weight for vit
+    'vit_small_reg':  'vit/dinov2_vits14_reg4_pretrain.pth',
+    'vit_large_reg':  'vit/dinov2_vitl14_reg4_pretrain.pth',
+    'vit_giant2_reg':  'vit/dinov2_vitg14_reg4_pretrain.pth',
+
+    'vit_large': 'vit/dinov2_vitl14_pretrain.pth',
+
+    # pretrained weight for convnext
+    'convnext_tiny': 'convnext/convnext_tiny_22k_1k_384.pth',
+    'convnext_small': 'convnext/convnext_small_22k_1k_384.pth',
+    'convnext_base': 'convnext/convnext_base_22k_1k_384.pth',
+    'convnext_large': 'convnext/convnext_large_22k_1k_384.pth',
+    'convnext_xlarge': 'convnext/convnext_xlarge_22k_1k_384_ema.pth',
+}

external/Metric3D/training/data_server_info/public_datasets.py

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+
+db_info={}
+
+
+#### DDAD Dataset
+# RGBD, consecutive frames, and ring cameras annotations
+db_info['DDAD']={
+    'db_root': 'tbd_data_root',  # Config your data root!
+    'data_root': 'DDAD',
+    'semantic_root': 'DDAD',
+    'meta_data_root': 'DDAD',
+    'train_annotations_path': 'DDAD/DDAD/annotations/train.json',
+    'test_annotations_path': 'DDAD/DDAD/annotations/test.json',
+    'val_annotations_path': 'DDAD/DDAD/annotations/val.json',
+}
+
+#### Mapillary Planet Scale Dataset
+# Single frame RGBD annotations
+db_info['Mapillary_PSD']={
+    'db_root': 'tbd_data_root',
+    'data_root': 'Mapillary_PSD',
+    'semantic_root': 'Mapillary_PSD',
+    'train_annotations_path': 'Mapillary_PSD/Mapillary_PSD/annotations/train.json',
+    'test_annotations_path': 'Mapillary_PSD/Mapillary_PSD/annotations/test.json',
+    'val_annotations_path': 'Mapillary_PSD/Mapillary_PSD/annotations/val.json',
+}
+
+#### Cityscapes dataset
+# Cityscapes sequence dataset, RGBD and consecutive frames annotations
+db_info['Cityscapes_sequence'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Cityscapes_sequence',
+    'semantic_root': 'Cityscapes_sequence',
+    'train_annotations_path': 'Cityscapes_sequence/Cityscapes_sequence/annotations/train.json',
+    'test_annotations_path': 'Cityscapes_sequence/Cityscapes_sequence/annotations/test.json',
+    'val_annotations_path': 'Cityscapes_sequence/Cityscapes_sequence/annotations/val.json',
+}
+# Cityscapes extra dataset, RGBD annotations
+db_info['Cityscapes_trainextra'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Cityscapes_trainextra',
+    'train_annotations_path': 'Cityscapes_trainextra/Cityscapes_trainextra/annotations/train.json',
+    'test_annotations_path': 'Cityscapes_trainextra/Cityscapes_trainextra/annotations/test.json',
+    'val_annotations_path': 'Cityscapes_trainextra/Cityscapes_trainextra/annotations/val.json',
+}
+db_info['Cityscapes_sequence_test'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Cityscapes_sequence',
+    'train_annotations_path': 'Cityscapes_sequence/Cityscapes_sequence/annotations/train.json',
+    'test_annotations_path': 'Cityscapes_sequence/Cityscapes_sequence/annotations/test.json',
+    'val_annotations_path': 'Cityscapes_sequence/Cityscapes_sequence/annotations/test.json',
+}
+
+#### Lyft dataset
+# Lyft dataset, RGBD, neighbouring cameras, and consecutive frames annotations
+db_info['Lyft'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Lyft',
+    'depth_root': 'Lyft',
+    'meta_data_root': 'Lyft',
+    'semantic_root': 'Lyft', 
+    'train_annotations_path': 'Lyft/Lyft/annotations/train.json',
+    'test_annotations_path': 'Lyft/Lyft/annotations/test.json',
+    'val_annotations_path': 'Lyft/Lyft/annotations/val.json',
+}
+# Lyft dataset, RGBD for ring cameras
+db_info['Lyft_ring'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Lyft',
+    'depth_root': 'Lyft',
+    'meta_data_root': 'Lyft',
+    'train_annotations_path': 'Lyft/Lyft/annotations/train.json',
+    'test_annotations_path': 'Lyft/Lyft/annotations/test.json',
+    'val_annotations_path': 'Lyft/Lyft/annotations/val.json',
+}
+
+#### DSEC dataset
+# DSEC dataset, RGBD and consecutive frames annotaitons
+db_info['DSEC'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'DSEC',
+    'semantic_root': 'DSEC', 
+    'train_annotations_path': 'DSEC/DSEC/annotations/train.json',
+    'test_annotations_path': 'DSEC/DSEC/annotations/test.json',
+    'val_annotations_path': 'DSEC/DSEC/annotations/val.json',
+}
+
+#### Argovers2 Dataset
+# Argovers2 dataset, RGBD and neighbouring cameras annotaitons
+db_info['Argovers2'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Argovers2',
+    'depth_root': 'Argovers2',
+    'meta_data_root': 'Argovers2',
+    'train_annotations_path': 'Argovers2/Argovers2/annotations/train.json',
+    'test_annotations_path': 'Argovers2/Argovers2/annotations/test.json',
+    'val_annotations_path': 'Argovers2/Argovers2/annotations/val.json',
+} 
+# Argovers2 dataset, RGBD and consecutive cameras annotaitons
+db_info['Argovers2_tmpl'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Argovers2',
+    'depth_root': 'Argovers2',
+    'meta_data_root': 'Argovers2',
+    'train_annotations_path': 'Argovers2/Argovers2/annotations/train.json',
+    'test_annotations_path': 'Argovers2/Argovers2/annotations/test.json',
+    'val_annotations_path': 'Argovers2/Argovers2/annotations/val.json',
+} 
+
+#### DrivingStereo Dataset
+# DrivingStereo dataset, RGBD annotaitons for stereo data
+db_info['DrivingStereo'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'DrivingStereo',
+    'semantic_root': 'DrivingStereo',
+    'train_annotations_path': 'DrivingStereo/DrivingStereo/annotations/train.json',
+    'test_annotations_path': 'DrivingStereo/DrivingStereo/annotations/test.json',
+    'val_annotations_path': 'DrivingStereo/DrivingStereo/annotations/val.json',
+} 
+# DrivingStereo dataset, RGBD and consecutive frames annotaitons for stereo data
+db_info['DrivingStereo_tmpl'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'DrivingStereo',
+    'semantic_root': 'DrivingStereo',
+    'train_annotations_path': 'DrivingStereo/DrivingStereo/annotations/train.json',
+    'test_annotations_path': 'DrivingStereo/DrivingStereo/annotations/test.json',
+    'val_annotations_path': 'DrivingStereo/DrivingStereo/annotations/val.json',
+} 
+
+#### DIML Dataset
+# DIML dataset, RGBD annotaitons for stereo data
+db_info['DIML'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'DIML',
+    'semantic_root': 'DIML',
+    'train_annotations_path': 'DIML/DIML/anotation/train.json',
+    'test_annotations_path': 'DIML/DIML/anotation/test.json',
+    'val_annotations_path': 'DIML/DIML/anotation/val.json',
+} 
+
+db_info['NuScenes'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'NuScenes',
+    'train_annotations_path': 'NuScenes/NuScenes/annotations/train.json',
+    'test_annotations_path': 'NuScenes/NuScenes/annotations/test.json',
+    'val_annotations_path': 'NuScenes/NuScenes/annotations/val.json',
+} 
+db_info['NuScenes_tmpl'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'NuScenes',
+    'train_annotations_path': 'NuScenes/NuScenes/annotations/train.json',
+    'test_annotations_path': 'NuScenes/NuScenes/annotations/test.json',
+    'val_annotations_path': 'NuScenes/NuScenes/annotations/val.json',
+} 
+
+
+# Pandaset, RGBD + tmpl dataset
+db_info['Pandaset'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Pandaset',
+    'meta_data_root': 'Pandaset',
+    'semantic_root': 'Pandaset',
+    'train_annotations_path': 'Pandaset/Pandaset/annotations/train.json',
+    'test_annotations_path': 'Pandaset/Pandaset/annotations/test.json',
+    'val_annotations_path': 'Pandaset/Pandaset/annotations/val.json',
+}
+db_info['Pandaset_ring'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Pandaset',
+    'meta_data_root': 'Pandaset',
+    'semantic_root': 'Pandaset',
+    'train_annotations_path': 'Pandaset/Pandaset/annotations/train.json',
+    'test_annotations_path': 'Pandaset/Pandaset/annotations/test.json',
+    'val_annotations_path': 'Pandaset/Pandaset/annotations/val.json',
+}
+
+# UASOL, RGBD + tmpl dataset
+db_info['UASOL'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'UASOL_data',
+    'meta_data_root': 'UASOL_data',
+    'semantic_root': 'UASOL_data',
+    'train_annotations_path': 'UASOL_data/UASOL_data/annotations/train.json', 
+    'test_annotations_path': 'UASOL_data/UASOL_data/annotations/test.json', 
+    'val_annotations_path': 'UASOL_data/UASOL_data/annotations/test.json',
+}
+
+# Taskonomy, RGBD dataset
+db_info['Taskonomy'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Taskonomy',
+    'meta_data_root': 'Taskonomy',
+    'semantic_root': 'Taskonomy',
+    'normal_root': 'Taskonomy',
+
+    'train_annotations_path': 'Taskonomy/Taskonomy/annotations/train.json',
+    'test_annotations_path': 'Taskonomy/Taskonomy/annotations/test.json',
+    'val_annotations_path': 'Taskonomy/Taskonomy/annotations/test.json',
+}
+
+### WebStereo Datasets
+# HRWSI/Holopix dataset, RGBD and sky masks annotations
+db_info['HRWSI_Holopix'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'WebStereo',
+    'train_annotations_path': 'WebStereo/annotations/train.json',
+    'test_annotations_path': 'WebStereo/annotations/test.json',
+    'val_annotations_path': 'WebStereo/annotations/val.json',
+}
+
+### Waymo Datasets
+db_info['Waymo'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Waymo',
+    'meta_data_root': 'Waymo',
+    'semantic_root': 'Waymo',
+    'train_annotations_path': 'Waymo/Waymo/annotations/training_annos_all_filter.json',
+    'test_annotations_path': 'Waymo/Waymo/annotations/testing_annos_all_filter.json',
+    'val_annotations_path': 'Waymo/Waymo/annotations/validation_annos_all_filter.json',
+}
+
+
+# DIODE, RGBD dataset
+db_info['DIODE'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'DIODE',
+    'depth_mask_root': 'DIODE',
+    'normal_root': 'DIODE',
+    'train_annotations_path': 'DIODE/DIODE/annotations/train.json',
+    'test_annotations_path': 'DIODE/DIODE/annotations/test.json',
+    'val_annotations_path': 'DIODE/DIODE/annotations/val.json',
+}
+db_info['DIODE_indoor'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'DIODE',
+    'depth_mask_root': 'DIODE',
+    'train_annotations_path': 'DIODE/DIODE/annotations/train.json',
+    'test_annotations_path': 'DIODE/DIODE/annotations/test.json',
+    'val_annotations_path': 'DIODE/DIODE/annotations/val.json',
+}
+db_info['DIODE_outdoor'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'DIODE',
+    'depth_mask_root': 'DIODE',
+    'normal_root': 'DIODE',
+    'train_annotations_path': 'DIODE/DIODE/annotations/train.json',
+    'test_annotations_path': 'DIODE/DIODE/annotations/test.json',
+    'val_annotations_path': 'DIODE/DIODE/annotations/val.json',
+}
+db_info['ETH3D'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'ETH3D',
+    'depth_mask_root': 'ETH3D',
+    'train_annotations_path': 'ETH3D/ETH3D/annotations/test.json',
+    'test_annotations_path': 'ETH3D/ETH3D/annotations/test.json',
+    'val_annotations_path': 'ETH3D/ETH3D/annotations/test.json',
+}
+# NYU, RGBD dataset
+db_info['NYU'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'NYU',
+    'normal_root': 'NYU',
+    #'train_annotations_path': 'NYU/NYU/annotations/train.json',
+    'train_annotations_path': 'NYU/NYU/annotations/train_normal.json',
+    #'test_annotations_path': 'NYU/NYU/annotations/test.json',
+    'test_annotations_path': 'NYU/NYU/annotations/test_normal.json',
+    'val_annotations_path': 'NYU/NYU/annotations/test.json',
+}
+# ScanNet, RGBD dataset
+db_info['ScanNet'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'ScanNet',
+    'train_annotations_path': 'ScanNet/ScanNet/annotations/train.json',
+    'test_annotations_path': 'ScanNet/ScanNet/annotations/test.json',
+    'val_annotations_path': 'ScanNet/ScanNet/annotations/test.json',
+}
+# KITTI, RGBD dataset
+db_info['KITTI'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': '',
+    'train_annotations_path': 'KITTI/KITTI/annotations/eigen_train.json',
+    'test_annotations_path': 'KITTI/KITTI/annotations/eigen_test.json',
+    'val_annotations_path': 'KITTI/KITTI/annotations/eigen_test.json',
+}
+
+
+########### new training data
+# Blended_mvg, RGBD dataset
+db_info['BlendedMVG_omni'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Blended_mvg',
+    'meta_data_root': 'Blended_mvg',
+    'normal_root': 'Blended_mvg',
+    'train_annotations_path': 'Blended_mvg/Blended_mvg/annotations/train.json',
+    'test_annotations_path': 'Blended_mvg/Blended_mvg/annotations/test.json',
+    'val_annotations_path': 'Blended_mvg/Blended_mvg/annotations/val.json',
+}
+
+# HM3D, RGBD dataset
+db_info['HM3D'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'HM3D',
+    'meta_data_root': 'HM3D',
+    'normal_root': 'HM3D',
+    'train_annotations_path': 'HM3D/HM3d_omnidata/annotations/train.json', #',
+    'test_annotations_path': 'HM3D/HM3d_omnidata/annotations/val.json',
+    'val_annotations_path': 'HM3D/HM3d_omnidata/annotations/test.json',
+}
+
+# LeddarPixSet, RGBD dataset, some errors in the data
+db_info['LeddarPixSet'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'LeddarPixSet',
+    'meta_data_root': 'LeddarPixSet',
+    'train_annotations_path': 'LeddarPixSet/LeddarPixSet/annotations/train.json',
+    'test_annotations_path': 'LeddarPixSet/LeddarPixSet/annotations/test.json',
+    'val_annotations_path': 'LeddarPixSet/LeddarPixSet/annotations/val.json',
+}
+
+# RGBD dataset
+db_info['Replica'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Replica',
+    'meta_data_root': 'Replica',
+    'normal_root': 'Replica',
+    'train_annotations_path': 'Replica/replica/annotations/train.json',
+    'test_annotations_path': 'Replica/replica/annotations/test.json',
+    'val_annotations_path': 'Replica/replica/annotations/val.json',
+}
+
+db_info['Replica_gso'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Replica',
+    'meta_data_root': 'Replica',
+    'normal_root': 'Replica',
+    'train_annotations_path': 'Replica/replica_gso/annotations/train.json',
+    'test_annotations_path': 'Replica/replica_gso/annotations/test.json',
+    'val_annotations_path': 'Replica/replica_gso/annotations/val.json',
+}
+
+db_info['Matterport3D'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'Matterport3D',
+    'meta_data_root': 'Matterport3D',
+    'normal_root': 'Matterport3D',
+    'train_annotations_path': 'Matterport3D/Matterport3D/annotations/train.json',
+    'test_annotations_path': 'Matterport3D/Matterport3D/annotations/test.json',
+    'val_annotations_path': 'Matterport3D/Matterport3D/annotations/test.json',
+}
+
+db_info['S3DIS'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 's3dis',
+    'meta_data_root': 's3dis',
+    'normal_root': 's3dis',
+    'train_annotations_path': 's3dis/s3dis/annotations/train.json',
+    'test_annotations_path': 's3dis/s3dis/annotations/test.json',
+    'val_annotations_path': 's3dis/s3dis/annotations/test.json',
+}
+
+db_info['Seasons4'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': '4seasons/4seasons',
+    'meta_data_root': '4seasons/4seasons',
+    'train_annotations_path': '4seasons/4seasons/annotations/train.json',
+    'test_annotations_path': '4seasons/4seasons/annotations/test.json',
+    'val_annotations_path': '4seasons/4seasons/annotations/test.json',
+}
+
+db_info['Virtual_KITTI'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'virtual_kitti',
+    'meta_data_root': 'virtual_kitti',
+    'semantic_root': 'virtual_kitti',
+    'train_annotations_path': 'virtual_kitti/virtual_kitti/annotations/train.json',
+    'test_annotations_path': 'virtual_kitti/virtual_kitti/annotations/test.json',
+    'val_annotations_path': 'virtual_kitti/virtual_kitti/annotations/test.json',
+}
+
+db_info['IBIMS'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': '',
+    'train_annotations_path': 'iBims-1/annotations/train.json',
+    'test_annotations_path': 'iBims-1/annotations/test.json',
+    'val_annotations_path': 'iBims-1/annotations/test.json',
+}
+
+db_info['ScanNetAll'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': 'scannet',
+    'normal_root': 'scannet',
+    'meta_data_root': 'scannet',
+    'train_annotations_path': 'scannet/scannet/annotations/train.json',
+    'test_annotations_path': 'scannet/scannet/annotations/test.json',
+    'val_annotations_path': 'scannet/scannet/annotations/test.json',
+}
+
+db_info['Hypersim'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': '',
+    'meta_data_root': '',
+    'normal_root': '',
+    # 'semantic_root': '', # Semantic tags without sky, see https://github.com/apple/ml-hypersim/blob/main/code/cpp/tools/scene_annotation_tool/semantic_label_descs.csv
+    'train_annotations_path': 'Hypersim/annotations/train.json',
+    'test_annotations_path': 'Hypersim/annotations/test.json',
+    'val_annotations_path': 'Hypersim/annotations/test.json',
+}
+
+db_info['DIML_indoor'] = {
+    'db_root': 'tbd_data_root',
+    'data_root': '',
+    # 'semantic_root': '',
+    'train_annotations_path': 'DIML_indoor_new/annotations/train.json',
+    'test_annotations_path': 'DIML_indoor_new/annotations/test.json',
+    'val_annotations_path': 'DIML_indoor_new/annotations/test.json',
+} 

external/Metric3D/training/kitti_json_files/eigen_test.txt

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external/Metric3D/training/kitti_json_files/eigen_val.txt

@@ -0,0 +1,1776 @@
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+2011_10_03/2011_10_03_drive_0034_sync 4478 r
+2011_09_26/2011_09_26_drive_0070_sync 198 l
+2011_10_03/2011_10_03_drive_0034_sync 654 l
+2011_09_30/2011_09_30_drive_0034_sync 528 l
+2011_09_26/2011_09_26_drive_0028_sync 342 l
+2011_09_30/2011_09_30_drive_0033_sync 617 r
+2011_09_30/2011_09_30_drive_0033_sync 1300 l
+2011_10_03/2011_10_03_drive_0034_sync 929 l
+2011_09_26/2011_09_26_drive_0014_sync 219 l
+2011_09_30/2011_09_30_drive_0028_sync 4439 l
+2011_09_30/2011_09_30_drive_0028_sync 909 r
+2011_10_03/2011_10_03_drive_0034_sync 3208 l
+2011_10_03/2011_10_03_drive_0034_sync 3384 r
+2011_10_03/2011_10_03_drive_0034_sync 2328 l
+2011_10_03/2011_10_03_drive_0034_sync 1499 r
+2011_09_26/2011_09_26_drive_0028_sync 65 r
+2011_09_26/2011_09_26_drive_0087_sync 37 l
+2011_09_30/2011_09_30_drive_0028_sync 1906 l
+2011_09_30/2011_09_30_drive_0020_sync 870 l
+2011_10_03/2011_10_03_drive_0042_sync 195 r
+2011_09_30/2011_09_30_drive_0020_sync 1016 r
+2011_09_26/2011_09_26_drive_0070_sync 150 r
+2011_09_30/2011_09_30_drive_0028_sync 4369 l
+2011_09_30/2011_09_30_drive_0028_sync 3446 r
+2011_09_30/2011_09_30_drive_0020_sync 994 l
+2011_09_26/2011_09_26_drive_0095_sync 138 r
+2011_09_26/2011_09_26_drive_0087_sync 101 r
+2011_09_30/2011_09_30_drive_0034_sync 422 l
+2011_09_30/2011_09_30_drive_0034_sync 941 r
+2011_09_26/2011_09_26_drive_0028_sync 12 r
+2011_09_30/2011_09_30_drive_0028_sync 2494 r
+2011_09_30/2011_09_30_drive_0033_sync 1097 r
+2011_09_26/2011_09_26_drive_0061_sync 544 r
+2011_10_03/2011_10_03_drive_0042_sync 634 r
+2011_09_30/2011_09_30_drive_0028_sync 3051 l
+2011_10_03/2011_10_03_drive_0042_sync 974 r
+2011_10_03/2011_10_03_drive_0034_sync 3777 l
+2011_09_30/2011_09_30_drive_0028_sync 2451 r
+2011_09_30/2011_09_30_drive_0034_sync 969 r
+2011_09_30/2011_09_30_drive_0028_sync 514 r
+2011_09_30/2011_09_30_drive_0033_sync 76 r
+2011_10_03/2011_10_03_drive_0034_sync 3384 l
+2011_09_26/2011_09_26_drive_0113_sync 40 r
+2011_09_30/2011_09_30_drive_0020_sync 560 l
+2011_09_26/2011_09_26_drive_0014_sync 41 l
+2011_09_26/2011_09_26_drive_0104_sync 277 r
+2011_09_26/2011_09_26_drive_0113_sync 70 l
+2011_09_30/2011_09_30_drive_0028_sync 4804 r
+2011_09_30/2011_09_30_drive_0034_sync 790 l
+2011_09_26/2011_09_26_drive_0039_sync 104 l
+2011_09_30/2011_09_30_drive_0028_sync 2076 l
+2011_09_26/2011_09_26_drive_0011_sync 42 l
+2011_09_26/2011_09_26_drive_0015_sync 4 l
+2011_09_30/2011_09_30_drive_0033_sync 48 r
+2011_10_03/2011_10_03_drive_0034_sync 481 r
+2011_09_26/2011_09_26_drive_0039_sync 254 r
+2011_10_03/2011_10_03_drive_0034_sync 4117 l
+2011_09_30/2011_09_30_drive_0028_sync 2426 l
+2011_09_30/2011_09_30_drive_0028_sync 4115 l
+2011_09_26/2011_09_26_drive_0022_sync 123 l
+2011_09_26/2011_09_26_drive_0022_sync 746 r
+2011_09_30/2011_09_30_drive_0028_sync 2376 r
+2011_09_29/2011_09_29_drive_0004_sync 90 l
+2011_09_29/2011_09_29_drive_0004_sync 224 l
+2011_09_26/2011_09_26_drive_0057_sync 307 l
+2011_09_30/2011_09_30_drive_0020_sync 598 l
+2011_09_28/2011_09_28_drive_0001_sync 79 r
+2011_09_30/2011_09_30_drive_0034_sync 571 l
+2011_09_26/2011_09_26_drive_0022_sync 656 r
+2011_09_30/2011_09_30_drive_0020_sync 1017 l
+2011_09_26/2011_09_26_drive_0032_sync 147 r
+2011_10_03/2011_10_03_drive_0034_sync 441 r
+2011_09_26/2011_09_26_drive_0014_sync 182 r
+2011_09_30/2011_09_30_drive_0033_sync 1518 r
+2011_09_30/2011_09_30_drive_0028_sync 2174 l
+2011_09_26/2011_09_26_drive_0051_sync 433 r
+2011_09_30/2011_09_30_drive_0028_sync 2749 l

external/Metric3D/training/kitti_json_files/generate_json.py

@@ -0,0 +1,85 @@
+import os
+import json
+
+common_root = 'd:/Datasets'
+depth_root = 'kitti/kitti_depth/depth/data_depth_annotated'
+raw_root = 'kitti_raw/kitti_raw'
+
+#print(os.listdir(os.path.join(common_root, raw_root)))
+
+mid = 'proj_depth/groundtruth'
+mid_raw = 'data'
+
+test_file_dict = {}
+test_file_list = []
+train_file_dict = {}
+
+
+with open('D:/Datasets/eigen_train.txt') as f:
+    lines_train = f.readlines()
+
+cnt = 0
+invalid_cnt = 0
+
+if True:
+    lines = lines_train
+
+    for l in lines:
+        l_ls = l.split(' ')
+        scene = l_ls[0]
+        date = scene.split('/')[0]
+        scene_no_date = scene.split('/')[1]
+        frame = l_ls[1]
+        frame = frame.zfill(10)
+
+        if 'l' in l_ls[2]:
+            cam = 'image_02'
+            P_str = 'P_rect_02'
+        elif 'r' in l_ls[2]:
+            cam = 'image_03'
+            P_str = 'P_rect_03'
+        else:
+            raise NotImplementedError()
+
+        depth_train = os.path.join(depth_root, 'train', scene_no_date, mid, cam , frame + '.png')
+        depth_val = os.path.join(depth_root, 'val', scene_no_date, mid, cam, frame+'.png')
+        rgb = os.path.join(raw_root, scene, cam, mid_raw, frame+'.png')
+
+        with open(os.path.join(common_root, raw_root, date, 'calib_cam_to_cam.txt')) as c:
+            lines_c = c.readlines()
+
+            for l_c in lines_c:
+                if P_str in l_c:
+                    k_str = l_c.split(':')[1:]
+                    k = k_str[0].split(' ')
+                    cam_in = [float(k[1]), float(k[6]), float(k[3]), float(k[7])]
+
+        rgb_path = os.path.join(common_root, rgb)
+        assert  os.path.join(common_root, rgb_path)
+
+        if os.path.exists(os.path.join(common_root, depth_train)):
+            depth_path = os.path.join(common_root, depth_train)
+            depth_rel = depth_train
+
+        else:
+            depth_path = os.path.join(common_root, depth_val)
+            depth_rel = depth_val
+
+        try:
+            assert os.path.exists(depth_path)
+            cnt += 1
+        except:
+            invalid_cnt += 1
+            continue
+
+        curr_file = [{'rgb':rgb.replace("\\", '/'), 'depth':depth_rel.replace("\\", '/'), 'cam_in':cam_in}]
+        test_file_list = test_file_list + curr_file
+
+        if ((cnt + invalid_cnt) % 1000 == 0):
+            print(cnt + invalid_cnt)
+
+print(cnt, invalid_cnt)
+
+train_file_dict['files'] = test_file_list
+with open('eigen_train.json', 'w') as fj:
+    json.dump(train_file_dict, fj)

external/Metric3D/training/mono/datasets/__base_dataset__.py

@@ -0,0 +1,586 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+import mono.utils.transform as img_transform
+import copy 
+from mono.utils.comm import get_func
+import pickle
+import logging
+import multiprocessing as mp
+import ctypes
+"""
+Dataset annotations are saved in a Json file. All data, including rgb, depth, pose, and so on, captured within the same frame are saved in the same dict.
+All frames are organized in a list. In each frame, it may contains the some or all of following data format. 
+
+# Annotations for the current central RGB/depth cameras.
+
+'rgb':          rgb image in the current frame.
+'depth':        depth map in the current frame.
+'sem':          semantic mask in the current frame.
+'cam_in':       camera intrinsic parameters of the current rgb camera. 
+'cam_ex':       camera extrinsic parameters of the current rgb camera.
+'cam_ex_path':  path to the extrinsic parameters.
+'pose':         pose in current frame.
+'timestamp_rgb':    time stamp of current rgb image.
+
+# Annotations for the left hand RGB/depth cameras.
+
+'rgb_l':          rgb image of the left hand camera in the current frame.
+'depth_l':        depth map of the left hand camera in the current frame.
+'sem_l':          semantic mask of the left hand camera in the current frame.
+'cam_in_l':       camera intrinsic parameters of the left hand rgb camera in the current frame.
+'cam_ex_l':       camera extrinsic parameters of the left hand rgb camera in the current frame.
+'cam_ex_path':    path to the extrinsic parameters.
+'pose_l':         pose of the left hand camera  in the incurrent frame.
+'timestamp_rgb_l':    time stamp of the rgb img captured by the left hand camera.
+
+# Annotations for the right RGB/depth cameras, which is on the left hand of the current central cameras.
+
+'rgb_r':          rgb image of the right hand camera in the current frame.
+'depth_r':        depth map of the right hand camera in the current frame.
+'sem_r':          semantic mask of the right hand camera in the current frame.
+'cam_in_r':       camera intrinsic parameters of the right hand rgb camera in the current frame.
+'cam_ex_r':       camera extrinsic parameters of the right hand rgb camera in the current frame.
+'cam_ex_path_r':  path to the extrinsic parameters.
+'pose_r':         pose of the right hand camera  in the incurrent frame.
+'timestamp_rgb_r':    time stamp of the rgb img captured by the right hand camera.
+
+# Annotations for the central RGB/depth cameras in the last frame.
+
+'rgb_pre':          rgb image of the central camera in the last frame.
+'depth_pre':        depth map of the central camera in the last frame.
+'sem_pre':          semantic mask of the central camera in the last frame.
+'cam_in_pre':       camera intrinsic parameters of the central rgb camera in the last frame.
+'cam_ex_pre':       camera extrinsic parameters of the central rgb camera in the last frame.
+'cam_ex_path_pre':  path to the extrinsic parameters.
+'pose_pre':         pose of the central camera  in the last frame.
+'timestamp_rgb_pre':    time stamp of the rgb img captured by the central camera.
+
+# Annotations for the central RGB/depth cameras in the next frame.
+
+'rgb_next':          rgb image of the central camera in the next frame.
+'depth_next':        depth map of the central camera in the next frame.
+'sem_next':          semantic mask of the central camera in the next frame.
+'cam_in_next':       camera intrinsic parameters of the central rgb camera in the next frame.
+'cam_ex_next':       camera extrinsic parameters of the central rgb camera in the next frame.
+'cam_ex_path_next':  path to the extrinsic parameters.
+'pose_next':         pose of the central camera  in the next frame.
+'timestamp_rgb_next':    time stamp of the rgb img captured by the central camera.
+"""
+
+class BaseDataset(Dataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(BaseDataset, self).__init__()
+        self.cfg = cfg
+        self.phase = phase
+        self.db_info = kwargs['db_info']
+        
+        # root dir for data
+        self.data_root = os.path.join(self.db_info['db_root'], self.db_info['data_root']) 
+        # depth/disp data root
+        disp_root = self.db_info['disp_root'] if 'disp_root' in self.db_info else None
+        self.disp_root = os.path.join(self.db_info['db_root'], disp_root) if disp_root is not None else None
+        depth_root = self.db_info['depth_root'] if 'depth_root' in self.db_info else None
+        self.depth_root = os.path.join(self.db_info['db_root'], depth_root) if depth_root is not None \
+            else self.data_root
+        # meta data root
+        meta_data_root = self.db_info['meta_data_root'] if 'meta_data_root' in self.db_info else None
+        self.meta_data_root = os.path.join(self.db_info['db_root'], meta_data_root) if meta_data_root is not None \
+            else None
+        # semantic segmentation labels root
+        sem_root = self.db_info['semantic_root'] if 'semantic_root' in self.db_info else None
+        self.sem_root = os.path.join(self.db_info['db_root'], sem_root) if sem_root is not None \
+            else None
+        # depth valid mask labels root
+        depth_mask_root = self.db_info['depth_mask_root'] if 'depth_mask_root' in self.db_info else None
+        self.depth_mask_root = os.path.join(self.db_info['db_root'], depth_mask_root) if depth_mask_root is not None \
+            else None
+        # surface normal labels root
+        norm_root = self.db_info['normal_root'] if 'normal_root' in self.db_info else None
+        self.norm_root = os.path.join(self.db_info['db_root'], norm_root) if norm_root is not None \
+            else None
+        # data annotations path
+        self.data_annos_path = os.path.join(self.db_info['db_root'], self.db_info['%s_annotations_path' % phase])
+
+        # load annotations
+        self.data_info = self.load_annotations()
+        whole_data_size = len(self.data_info['files']) 
+        
+        # sample a subset for training/validation/testing
+        # such method is deprecated, each training may get different sample list
+        
+        cfg_sample_ratio = cfg.data[phase].sample_ratio 
+        cfg_sample_size = int(cfg.data[phase].sample_size)
+        self.sample_size = int(whole_data_size * cfg_sample_ratio) if cfg_sample_size == -1 \
+                           else (cfg_sample_size if cfg_sample_size < whole_data_size else whole_data_size)
+        random.seed(100) # set the random seed
+        sample_list_of_whole_data = random.sample(list(range(whole_data_size)), self.sample_size)
+
+        self.data_size = self.sample_size
+        self.annotations = {'files': [self.data_info['files'][i] for i in sample_list_of_whole_data]}
+        self.sample_list = list(range(self.data_size))
+        
+        # config transforms for the input and label
+        self.transforms_cfg = cfg.data[phase]['pipeline']
+        self.transforms_lib = 'mono.utils.transform.'
+
+        self.img_file_type = ['.png', '.jpg', '.jpeg', '.bmp', '.tif']
+        self.np_file_type = ['.npz', '.npy']
+
+        # update canonical sparce information
+        self.data_basic = copy.deepcopy(kwargs)
+        canonical = self.data_basic.pop('canonical_space')
+        self.data_basic.update(canonical)
+        self.disp_scale = 10.0
+        self.depth_range = kwargs['depth_range'] # predefined depth range for the network
+        self.clip_depth_range = kwargs['clip_depth_range'] # predefined depth range for data processing
+        self.depth_normalize = kwargs['depth_normalize']
+        
+        self.img_transforms = img_transform.Compose(self.build_data_transforms())
+        self.EPS = 1e-6
+
+        # self.tmpl_info = ['rgb_sr', 'rgb_pre', 'rgb_next']
+        # self.tgt2ref_pose_lookup = {'rgb_sr': 'cam_ex', 'rgb_pre': 'pose_pre', 'rgb_next': 'pose_next'}
+
+        # dataset info
+        self.data_name = cfg.data_name
+        self.data_type = cfg.data_type # there are mainly four types, i.e. ['rel', 'sfm', 'stereo', 'lidar']
+        self.logger = logging.getLogger()
+        self.logger.info(f'{self.data_name} in {self.phase} whole data size: {whole_data_size}')
+        
+        # random crop size for training
+        crop_size = kwargs['crop_size']
+        shared_array_base = mp.Array(ctypes.c_int32, 2)
+        shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
+        shared_array[0] = crop_size[0]
+        shared_array[1] = crop_size[1]
+        # self.random_crop_size = torch.from_numpy(np.array([0,0])) #torch.from_numpy(shared_array)
+        self.random_crop_size = torch.from_numpy(shared_array)
+
+        
+    def __name__(self):
+        return self.data_name
+
+    def __len__(self):
+        return self.data_size
+
+    def load_annotations(self):
+        if not os.path.exists(self.data_annos_path):
+            raise RuntimeError(f'Cannot find {self.data_annos_path} annotations.')
+
+        with open(self.data_annos_path, 'r')  as f:
+            annos = json.load(f)
+        return annos
+    
+    def build_data_transforms(self):
+        transforms_list = []
+        for transform in self.transforms_cfg:
+            args = copy.deepcopy(transform)
+            # insert the canonical space configs
+            args.update(self.data_basic)
+      
+            obj_name = args.pop('type')
+            obj_path = self.transforms_lib + obj_name
+            obj_cls = get_func(obj_path)
+            
+            obj = obj_cls(**args)
+            transforms_list.append(obj)
+        return transforms_list
+    
+        
+    def load_data(self, path: str, is_rgb_img: bool=False):
+        if not os.path.exists(path):
+            self.logger.info(f'>>>>{path} does not exist.')
+            # raise RuntimeError(f'{path} does not exist.')
+
+        data_type = os.path.splitext(path)[-1]
+        if data_type in self.img_file_type:
+            if is_rgb_img:
+                data = cv2.imread(path)
+            else:
+                data = cv2.imread(path, -1)
+        elif data_type in self.np_file_type:
+            data = np.load(path)
+        else:
+            raise RuntimeError(f'{data_type} is not supported in current version.')
+        
+        try:
+            return data.squeeze()
+        except:
+            temp = 1
+            raise RuntimeError(f'{path} is not successfully loaded.')
+    
+    def __getitem__(self, idx: int) -> dict:
+        if self.phase == 'test':
+            return self.get_data_for_test(idx)
+        else:
+            return self.get_data_for_trainval(idx)
+
+    def get_data_for_trainval(self, idx: int):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        
+        data_path = self.load_data_path(meta_data)
+        data_batch = self.load_batch(meta_data, data_path)
+        # if data_path['sem_path'] is not None:
+        #     print(self.data_name)
+
+        curr_rgb, curr_depth, curr_normal, curr_sem, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_sem'], data_batch['curr_cam_model']
+        #curr_stereo_depth = data_batch['curr_stereo_depth']
+        
+        # A patch for stereo depth dataloader (no need to modify specific datasets)
+        if 'curr_stereo_depth' in data_batch.keys():
+            curr_stereo_depth = data_batch['curr_stereo_depth']
+        else:
+            curr_stereo_depth = self.load_stereo_depth_label(None, H=curr_rgb.shape[0], W=curr_rgb.shape[1]) 
+
+        curr_intrinsic = meta_data['cam_in']
+        # data augmentation
+        transform_paras = dict(random_crop_size = self.random_crop_size) # dict() 
+        assert curr_rgb.shape[:2] == curr_depth.shape == curr_normal.shape[:2] == curr_sem.shape
+        rgbs, depths, intrinsics, cam_models, normals, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb, ], 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[curr_intrinsic,], 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   normals = [curr_normal, ],
+                                                                   other_labels=[curr_sem, curr_stereo_depth],
+                                                                   transform_paras=transform_paras)
+        # process sky masks
+        sem_mask = other_labels[0].int()
+        # clip depth map 
+        depth_out = self.normalize_depth(depths[0])
+        # set the depth of sky region to the invalid
+        depth_out[sem_mask==142] = -1 # self.depth_normalize[1] - 1e-6
+        # get inverse depth
+        inv_depth = self.depth2invdepth(depth_out, sem_mask==142)
+        filename = os.path.basename(meta_data['rgb'])[:-4] + '.jpg'
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]
+
+        # stereo_depth
+        if 'label_scale_factor' not in transform_paras.keys():
+            transform_paras['label_scale_factor'] = 1
+        stereo_depth_pre_trans = other_labels[1] * (other_labels[1] > 0.3) * (other_labels[1] < 200)
+        stereo_depth = stereo_depth_pre_trans * transform_paras['label_scale_factor']
+        stereo_depth = self.normalize_depth(stereo_depth)
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]        
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    pad=torch.tensor(pad),
+                    data_type=[self.data_type, ],
+                    sem_mask=sem_mask.int(),
+                    stereo_depth= stereo_depth,
+                    normal=normals[0],
+                    inv_depth=inv_depth,
+                    scale=transform_paras['label_scale_factor'])
+        return data
+
+    def get_data_for_test(self, idx: int):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        data_path = self.load_data_path(meta_data)
+        data_batch = self.load_batch(meta_data, data_path)
+        # load data
+        curr_rgb, curr_depth, curr_normal, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_cam_model']
+        ori_curr_intrinsic = meta_data['cam_in']
+
+        # get crop size
+        transform_paras = dict()
+        rgbs, depths, intrinsics, cam_models, _, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb,],  #+ tmpl_rgbs, 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[ori_curr_intrinsic, ], # * (len(tmpl_rgbs) + 1), 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   transform_paras=transform_paras)
+        # depth in original size and orignial metric***
+        depth_out = self.clip_depth(curr_depth) * self.depth_range[1] # self.clip_depth(depths[0]) #
+        inv_depth = self.depth2invdepth(depth_out, np.zeros_like(depth_out, dtype=np.bool))
+        filename = os.path.basename(meta_data['rgb'])[:-4] + '.jpg'
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+        ori_curr_intrinsic_mat = self.intrinsics_list2mat(ori_curr_intrinsic)
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
+        scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]    
+        raw_rgb = torch.from_numpy(curr_rgb)
+        curr_normal = torch.from_numpy(curr_normal.transpose((2,0,1)))
+        
+
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    pad=pad,
+                    scale=scale_ratio,
+                    raw_rgb=raw_rgb,
+                    sample_id=idx,
+                    data_path=meta_data['rgb'],
+                    inv_depth=inv_depth,
+                    normal=curr_normal,
+                    )
+        return data
+    
+    def load_data_path(self, meta_data):
+        curr_rgb_path = os.path.join(self.data_root, meta_data['rgb'])
+        curr_depth_path = os.path.join(self.depth_root, meta_data['depth'])
+        curr_sem_path = os.path.join(self.sem_root, meta_data['sem']) \
+            if self.sem_root is not None and ('sem' in meta_data) and (meta_data['sem'] is not None)  \
+            else None
+        # matterport3d separates xyz into three images
+        if ('normal' in meta_data) and (meta_data['normal'] is not None) and (self.norm_root is not None):
+            if isinstance(meta_data['normal'], dict):
+                curr_norm_path = {}
+                for k,v in meta_data['normal'].items():
+                    curr_norm_path[k] = os.path.join(self.norm_root, v)
+            else:
+                curr_norm_path = os.path.join(self.norm_root, meta_data['normal'])
+        else:
+            curr_norm_path = None
+        curr_depth_mask_path = os.path.join(self.depth_mask_root, meta_data['depth_mask']) \
+            if self.depth_mask_root is not None and ('depth_mask' in meta_data) and (meta_data['depth_mask'] is not None)  \
+            else None
+
+        if ('disp' in meta_data) and (meta_data['disp'] is not None) and (self.disp_root is not None):
+            if isinstance(meta_data['disp'], dict):
+                curr_disp_path = {}
+                for k,v in meta_data['disp'].items():
+                    curr_disp_path[k] = os.path.join(self.disp_root, v)
+            else:
+                curr_disp_path = os.path.join(self.disp_root, meta_data['disp'])
+        else:
+            curr_disp_path = None
+
+        data_path=dict(
+            rgb_path=curr_rgb_path,
+            depth_path=curr_depth_path,
+            sem_path=curr_sem_path,
+            normal_path=curr_norm_path,
+            disp_path=curr_disp_path,
+            depth_mask_path=curr_depth_mask_path,
+            )
+        return data_path
+    
+    def load_batch(self, meta_data, data_path):
+        curr_intrinsic = meta_data['cam_in']
+        # load rgb/depth
+        curr_rgb, curr_depth = self.load_rgb_depth(data_path['rgb_path'], data_path['depth_path'])
+        # get semantic labels
+        curr_sem = self.load_sem_label(data_path['sem_path'], curr_depth)
+        # create camera model
+        curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], curr_intrinsic)       
+        # get normal labels
+        curr_normal = self.load_norm_label(data_path['normal_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1]) 
+        # get depth mask
+        depth_mask = self.load_depth_valid_mask(data_path['depth_mask_path'])
+        curr_depth[~depth_mask] = -1
+        # get stereo depth
+        curr_stereo_depth = self.load_stereo_depth_label(data_path['disp_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1]) 
+
+        data_batch = dict(
+            curr_rgb = curr_rgb,
+            curr_depth = curr_depth,
+            curr_sem = curr_sem,
+            curr_normal = curr_normal,
+            curr_cam_model=curr_cam_model,
+            curr_stereo_depth=curr_stereo_depth,
+        )
+        return data_batch
+
+
+    def clip_depth(self, depth: np.array) -> np.array:
+        depth[(depth>self.clip_depth_range[1]) | (depth<self.clip_depth_range[0])] = -1
+        depth /= self.depth_range[1]
+        depth[depth<self.EPS] = -1
+        return depth
+    
+    def normalize_depth(self, depth: np.array) -> np.array:
+        depth /= self.depth_range[1]
+        depth[depth<self.EPS] = -1
+        return depth
+    
+    def process_depth(self, depth: np.array, rgb:np.array=None):
+        return depth
+    
+    def create_cam_model(self, H : int, W : int, intrinsics : list) -> np.array:
+        """
+        Encode the camera model (focal length and principle point) to a 4-channel map. 
+        """
+        fx, fy, u0, v0 = intrinsics
+        f = (fx + fy) / 2.0
+        # principle point location
+        x_row = np.arange(0, W).astype(np.float32)
+        x_row_center_norm = (x_row - u0) / W
+        x_center = np.tile(x_row_center_norm, (H, 1)) # [H, W]
+
+        y_col = np.arange(0, H).astype(np.float32) 
+        y_col_center_norm = (y_col - v0) / H
+        y_center = np.tile(y_col_center_norm, (W, 1)).T
+
+        # FoV
+        fov_x = np.arctan(x_center / (f / W))
+        fov_y =  np.arctan(y_center/ (f / H))
+
+        cam_model = np.stack([x_center, y_center, fov_x, fov_y], axis=2)
+        return cam_model
+    
+    def check_data(self, data_dict : dict):
+        for k, v in data_dict.items():
+            if v is None:
+                # print(f'{self.data_name}, {k} cannot be read!')
+                self.logger.info(f'{self.data_name}, {k} cannot be read!')
+    
+    def intrinsics_list2mat(self, intrinsics: torch.tensor) -> torch.tensor:
+        """
+        Create camera intrinsic matrix.
+        Args:
+            intrinsics (torch.tensor, [4,]): list of camera intrinsic parameters.
+        returns:
+            intrinsics_mat (torch.tensor, [3x3]): camera intrinsic parameters matrix.
+        """
+        intrinsics_mat = torch.zeros((3,3)).float()
+        intrinsics_mat[0, 0] = intrinsics[0]
+        intrinsics_mat[1, 1] = intrinsics[1]
+        intrinsics_mat[0, 2] = intrinsics[2]
+        intrinsics_mat[1, 2] = intrinsics[3]
+        intrinsics_mat[2, 2] = 1.0
+        return intrinsics_mat
+        
+    # def load_tmpl_image(self, curr_rgb: np.array, meta_data: dict) -> dict:
+    #     """
+    #     Load  consecutive RGB frames.
+    #     Args:
+    #         anno: the annotation for this group.
+    #         curr_rgb: rgb image of the current frame.
+    #         meta_data: meta data information.
+    #     Returns:
+    #         tmpl_annos: temporal rgbs.
+    #     """
+    #     w_tmpl = False
+        
+    #     tmpl_list = []
+    #     # organize temporal annotations 
+    #     for i in self.tmpl_info:
+    #         if (i in meta_data) and (meta_data[i] is not None) and os.path.exists(os.path.join(self.data_root, meta_data[i])):
+    #             tmpl_list.append(os.path.join(self.data_root, meta_data[i]))
+
+    #     if len(tmpl_list) == 0:
+    #         rgb_tmpl = curr_rgb.copy()
+    #     else:
+    #         id = np.random.randint(len(tmpl_list))
+    #         rgb_tmpl = self.load_data(tmpl_list[id], is_rgb_img=True)
+    #         w_tmpl = True
+
+    #     tmpl_annos = dict(
+    #         tmpl_rgb_list = [rgb_tmpl,],
+    #         w_tmpl = w_tmpl
+    #     )
+    #     return tmpl_annos
+
+    def load_meta_data(self, anno: dict) -> dict:
+        """
+        Load meta data information.
+        """
+        if self.meta_data_root is not None and ('meta_data' in anno or 'meta' in anno):
+            meta_data_path = os.path.join(self.meta_data_root, anno['meta_data']) if 'meta_data' in anno else os.path.join(self.meta_data_root, anno['meta'])
+            with open(meta_data_path, 'rb') as f:
+                meta_data = pickle.load(f)
+            meta_data.update(anno)
+        else:
+            meta_data = anno
+        return meta_data
+    
+    def load_rgb_depth(self, rgb_path: str, depth_path: str):
+        """
+        Load the rgb and depth map with the paths.
+        """
+        rgb = self.load_data(rgb_path, is_rgb_img=True)
+        if rgb is None:
+            self.logger.info(f'>>>>{rgb_path} has errors.')
+       
+        depth = self.load_data(depth_path)
+        if depth is None:
+            self.logger.info(f'{depth_path} has errors.')
+        
+        # self.check_data(dict(
+        #     rgb_path=rgb,
+        #     depth_path=depth,
+        # ))
+        depth = depth.astype(np.float)
+        # if depth.shape != rgb.shape[:2]:
+        #     print(f'no-equal in {self.data_name}')
+        #     depth = cv2.resize(depth, rgb.shape[::-1][1:])
+        
+        depth  = self.process_depth(depth, rgb)
+        return rgb, depth
+    
+    def load_sem_label(self, sem_path, depth=None, sky_id=142) -> np.array:
+        H, W = depth.shape
+        # if sem_path is not None:
+        #     print(self.data_name)
+        sem_label = cv2.imread(sem_path, 0) if sem_path is not None \
+            else np.ones((H, W), dtype=np.int) * -1
+        if sem_label is None:
+            sem_label = np.ones((H, W), dtype=np.int) * -1
+        # set dtype to int before 
+        sem_label = sem_label.astype(np.int) 
+        sem_label[sem_label==255] = -1
+        
+        # mask invalid sky region
+        mask_depth_valid = depth > 1e-8
+        invalid_sky_region = (sem_label==142) & (mask_depth_valid)
+        if self.data_type in ['lidar', 'sfm', 'denselidar', 'denselidar_nometric']:
+            sem_label[invalid_sky_region] = -1
+        return sem_label
+    
+    def load_depth_valid_mask(self, depth_mask_path, depth=None) -> np.array:
+        if depth_mask_path is None:
+            return np.ones_like(depth, dtype=np.bool)
+        data_type = os.path.splitext(depth_mask_path)[-1]
+        if data_type in self.img_file_type:
+            data = cv2.imread(depth_mask_path, -1)
+        elif data_type in self.np_file_type:
+            data = np.load(depth_mask_path)
+        else:
+            raise RuntimeError(f'{data_type} is not supported in current version.')
+        data = data.astype(np.bool)
+        return data
+        
+    def load_norm_label(self, norm_path, H, W):
+        norm_gt = np.zeros((H, W, 3)).astype(np.float32)
+        return norm_gt
+
+    def load_stereo_depth_label(self, disp_path, H, W):
+        stereo_depth_gt = np.zeros((H, W, 1)).astype(np.float32)
+        return stereo_depth_gt
+
+    def depth2invdepth(self, depth, sky_mask):
+        inv_depth = 1.0 / depth * self.disp_scale
+        inv_depth[depth<1e-6] = -1.0
+        inv_depth[inv_depth < 0] = -1.0
+        inv_depth[sky_mask] = 0
+        return inv_depth
+
+
+    def set_random_crop_size(self, random_crop_size):
+        self.random_crop_size[0] = random_crop_size[0]
+        self.random_crop_size[1] = random_crop_size[1]

external/Metric3D/training/mono/datasets/__init__.py

@@ -0,0 +1,38 @@
+from .__base_dataset__ import BaseDataset
+from .ddad_dataset import DDADDataset
+from .mapillary_psd_dataset import MapillaryPSDDataset
+from .argovers2_dataset import Argovers2Dataset
+from .cityscapes_dataset import CityscapesDataset
+from .drivingstereo_dataset import DrivingStereoDataset
+from .dsec_dataset import DSECDataset
+from .lyft_dataset import LyftDataset
+from .diml_dataset import DIMLDataset
+from .any_dataset import AnyDataset
+from .nyu_dataset import NYUDataset
+from .scannet_dataset import ScanNetDataset
+from .diode_dataset import DIODEDataset
+from .kitti_dataset import KITTIDataset
+from .pandaset_dataset import PandasetDataset
+from .taskonomy_dataset import TaskonomyDataset
+from .uasol_dataset import UASOLDataset
+from .nuscenes_dataset import NuScenesDataset
+from .eth3d_dataset import ETH3DDataset
+from .waymo_dataset import WaymoDataset  
+from .ibims_dataset import IBIMSDataset
+
+from .replica_dataset import ReplicaDataset
+from .hm3d_dataset import HM3DDataset
+from .matterport3d_dataset import Matterport3DDataset
+from .virtualkitti_dataset import VKITTIDataset
+from .blendedmvg_omni_dataset import BlendedMVGOmniDataset
+from .hypersim_dataset import HypersimDataset
+
+__all__ = ['BaseDataset', 'DDADDataset', 'MapillaryPSDDataset',
+'Argovers2Dataset', 'CityscapesDataset', 'DrivingStereoDataset', 'DSECDataset', 'LyftDataset', 'DIMLDataset', 'AnyDataset', 
+'NYUDataset', 'ScanNetDataset', 'DIODEDataset', 'KITTIDataset', 'PandasetDataset', 'SUNRGBDDataset',
+'TaskonomyDataset',
+'UASOLDataset', 'NuScenesDataset',
+'G8V1Dataset', 'ETH3DDataset', 'WaymoDataset', 
+'IBIMSDataset',
+'ReplicaDataset', 'HM3DDataset', 'Matterport3DDataset', 'VKITTIDataset',
+'BlendedMVGOmniDataset']

external/Metric3D/training/mono/datasets/any_dataset.py

@@ -0,0 +1,152 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+import copy
+from .__base_dataset__ import BaseDataset
+import mono.utils.transform as img_transform
+
+
+class AnyDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):        
+        super(AnyDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+
+        self.cfg = cfg
+        self.phase = phase
+        self.mldb_info = kwargs['mldb_info']
+        
+        # root dir for data
+        self.data_root = os.path.join(self.mldb_info['mldb_root'], self.mldb_info['data_root'])
+        # depth/disp data root
+        disp_root = self.mldb_info['disp_root'] if 'disp_root' in self.mldb_info else None
+        self.disp_root = os.path.join(self.mldb_info['mldb_root'], disp_root) if disp_root is not None else None
+        depth_root = self.mldb_info['depth_root'] if 'depth_root' in self.mldb_info else None
+        self.depth_root = os.path.join(self.mldb_info['mldb_root'], depth_root) if depth_root is not None \
+            else self.data_root
+        # meta data root
+        meta_data_root = self.mldb_info['meta_data_root'] if 'meta_data_root' in self.mldb_info else None
+        self.meta_data_root = os.path.join(self.mldb_info['mldb_root'], meta_data_root) if meta_data_root is not None \
+            else None
+        # semantic segmentation labels root
+        sem_root = self.mldb_info['semantic_root'] if 'semantic_root' in self.mldb_info else None
+        self.sem_root = os.path.join(self.mldb_info['mldb_root'], sem_root) if sem_root is not None \
+            else None
+
+        # data annotations path
+        self.data_annos_path = '/yvan1/data/NuScenes/NuScenes/annotations/train_ring_annotations.json'  # fill this 
+
+        # load annotations
+        annotations = self.load_annotations()
+        whole_data_size = len(annotations['files']) 
+               
+        cfg_sample_ratio = cfg.data[phase].sample_ratio 
+        cfg_sample_size = int(cfg.data[phase].sample_size)
+        self.sample_size = int(whole_data_size * cfg_sample_ratio) if cfg_sample_size == -1 \
+                           else (cfg_sample_size if cfg_sample_size < whole_data_size else whole_data_size)
+        sample_list_of_whole_data = list(range(whole_data_size))[:self.sample_size]
+        self.data_size = self.sample_size
+        sample_list_of_whole_data = random.sample(list(range(whole_data_size)), whole_data_size)
+        self.annotations = {'files': [annotations['files'][i] for i in sample_list_of_whole_data]}
+        self.sample_list = list(range(self.data_size))
+        
+        # config transforms for the input and label
+        self.transforms_cfg = cfg.data[phase]['pipeline']
+        self.transforms_lib = 'mono.utils.transform.'
+
+        self.img_file_type = ['.png', '.jpg', '.jpeg', '.bmp', '.tif']
+        self.np_file_type = ['.npz', '.npy']
+
+        # update canonical sparce information
+        self.data_basic = copy.deepcopy(kwargs)
+        canonical = self.data_basic.pop('canonical_space')
+        self.data_basic.update(canonical)
+        self.depth_range = kwargs['depth_range'] # predefined depth range for the network
+        self.clip_depth_range = kwargs['clip_depth_range'] # predefined depth range for data processing
+        self.depth_normalize = kwargs['depth_normalize']
+        
+        self.img_transforms = img_transform.Compose(self.build_data_transforms())
+        self.EPS = 1e-8
+
+        self.tmpl_info = ['rgb_sr', 'rgb_pre', 'rgb_next']
+
+        # dataset info
+        self.data_name = cfg.data_name
+        self.data_type = cfg.data_type # there are mainly four types, i.e. ['rel', 'sfm', 'stereo', 'lidar']
+
+    def __getitem__(self, idx: int) -> dict:
+        return self.get_data_for_test(idx)
+
+    def get_data_for_test(self, idx: int):
+        # basic info
+        anno = self.annotations['files'][idx]
+        curr_rgb_path = os.path.join(self.data_root, anno['CAM_FRONT_RIGHT']['rgb']) # Lyft: CAM_FRONT_LEFT
+        curr_depth_path = os.path.join(self.depth_root, anno['CAM_FRONT_RIGHT']['depth'])
+        meta_data = self.load_meta_data(anno['CAM_FRONT_RIGHT'])
+        ori_curr_intrinsic = meta_data['cam_in']
+        
+        curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path)
+        ori_h, ori_w, _ = curr_rgb.shape
+        # create camera model
+        curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], ori_curr_intrinsic)
+        # load tmpl rgb info
+        # tmpl_annos = self.load_tmpl_annos(anno, curr_rgb, meta_data)
+        # tmpl_rgb = tmpl_annos['tmpl_rgb_list'] # list of reference rgbs
+
+        transform_paras = dict()
+        rgbs, depths, intrinsics, cam_models, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb, ], 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[ori_curr_intrinsic,], 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   transform_paras=transform_paras)
+        # depth in augmented size
+        # depth_out = self.clip_depth(depths[0])
+        # depth in original size
+        #depth_out = self.clip_depth(curr_depth)
+        depth_out = curr_depth
+
+        filename = os.path.basename(curr_rgb_path)
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
+        scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]
+        raw_rgb = torch.from_numpy(curr_rgb)
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    # ref_input=rgbs[1:],
+                    # tmpl_flg=tmpl_annos['w_tmpl'],
+                    pad=pad,
+                    scale=scale_ratio,
+                    raw_rgb=raw_rgb) 
+        return data
+
+
+    def process_depth(self, depth):
+        depth[depth>65500] = 0
+        depth /= 200.0
+        return depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/argovers2_dataset.py

@@ -0,0 +1,33 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+import pickle
+
+class Argovers2Dataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(Argovers2Dataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= self.metric_scale
+        return depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/blendedmvg_omni_dataset.py

@@ -0,0 +1,32 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class BlendedMVGOmniDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(BlendedMVGOmniDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+        #self.cap_range = self.depth_range # in meter
+    
+    # def __getitem__(self, idx: int) -> dict:
+    #     if self.phase == 'test':
+    #         return self.get_data_for_test(idx)
+    #     else:
+    #         return self.get_data_for_trainval(idx)
+
+    
+    def process_depth(self, depth: np.array, rgb: np.array) -> np.array:
+        depth[depth>60000] = 0
+        depth = depth / self.metric_scale
+        return depth

external/Metric3D/training/mono/datasets/cityscapes_dataset.py

@@ -0,0 +1,33 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class CityscapesDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(CityscapesDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= self.metric_scale
+        return depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/ddad_dataset.py

@@ -0,0 +1,37 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class DDADDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(DDADDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+        #self.cap_range = self.depth_range # in meter
+    
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= 200.0
+        # depth[(depth>self.cap_range[1]) | (depth<self.cap_range[0])] = -1
+        # depth /= self.cap_range[1]
+        return depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/diml_dataset.py

@@ -0,0 +1,53 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+import pickle
+
+class DIMLDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(DIMLDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    def load_meta_data(self, anno: dict) -> dict:
+        """
+        Load meta data information.
+        """
+        if self.meta_data_root is not None and ('meta_data' in anno or 'meta' in anno):
+            meta_data_path = os.path.join(self.meta_data_root, anno['meta_data']) if 'meta_data' in anno else os.path.join(self.meta_data_root, anno['meta'])
+            with open(meta_data_path, 'rb') as f:
+                meta_data = pickle.load(f)
+            meta_data.update(anno)
+        else:
+            meta_data = anno
+        
+        # DIML_indoor has no cam_in
+        if 'cam_in' not in meta_data:
+            meta_data['cam_in'] = [1081, 1081, 704, 396]
+        return meta_data
+   
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= self.metric_scale
+        h, w, _ = rgb.shape # to rgb size
+        depth_resize = cv2.resize(depth, (w, h), interpolation=cv2.INTER_NEAREST)
+        return depth_resize
+
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = DIMLDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/diode_dataset.py

@@ -0,0 +1,273 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+def creat_uv_mesh(H, W):
+    y, x = np.meshgrid(np.arange(0, H, dtype=np.float), np.arange(0, W, dtype=np.float), indexing='ij')
+    meshgrid = np.stack((x,y))
+    ones = np.ones((1,H*W), dtype=np.float)
+    xy = meshgrid.reshape(2, -1)
+    return np.concatenate([xy, ones], axis=0)
+
+class DIODEDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(DIODEDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+        
+        # meshgrid for depth reprojection
+        self.xy = creat_uv_mesh(768, 1024)
+
+    def get_data_for_test(self, idx: int):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        data_path = self.load_data_path(meta_data)
+        data_batch = self.load_batch(meta_data, data_path)
+        # load data
+        curr_rgb, curr_depth, curr_normal, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_cam_model']
+        ori_curr_intrinsic = meta_data['cam_in']
+
+        # get crop size
+        transform_paras = dict()
+        rgbs, depths, intrinsics, cam_models, _, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb,],  #+ tmpl_rgbs, 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[ori_curr_intrinsic, ], # * (len(tmpl_rgbs) + 1), 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   transform_paras=transform_paras)
+        # depth in original size and orignial metric***
+        depth_out = self.clip_depth(curr_depth) * self.depth_range[1] # self.clip_depth(depths[0]) #
+        inv_depth = self.depth2invdepth(depth_out, np.zeros_like(depth_out, dtype=np.bool))
+        filename = os.path.basename(meta_data['rgb'])[:-4] + '.jpg'
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+        ori_curr_intrinsic_mat = self.intrinsics_list2mat(ori_curr_intrinsic)
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
+        scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]    
+        raw_rgb = torch.from_numpy(curr_rgb)
+        curr_normal = torch.from_numpy(curr_normal.transpose((2,0,1)))
+        
+
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    pad=pad,
+                    scale=scale_ratio,
+                    raw_rgb=raw_rgb,
+                    sample_id=idx,
+                    data_path=meta_data['rgb'],
+                    inv_depth=inv_depth,
+                    normal=curr_normal,
+                    )
+        return data
+
+
+    # def get_data_for_trainval(self, idx: int):
+    #     anno = self.annotations['files'][idx]
+    #     meta_data = self.load_meta_data(anno)
+        
+    #     # curr_rgb_path = os.path.join(self.data_root, meta_data['rgb'])
+    #     # curr_depth_path = os.path.join(self.depth_root, meta_data['depth'])
+    #     # curr_sem_path = os.path.join(self.sem_root, meta_data['sem']) if self.sem_root is not None and ('sem' in meta_data) and (meta_data['sem'] is not None)  else None
+    #     # curr_depth_mask_path = os.path.join(self.depth_mask_root, meta_data['depth_mask']) if self.depth_mask_root is not None and ('depth_mask' in meta_data) and (meta_data['depth_mask'] is not None)  else None
+    #     data_path = self.load_data_path(meta_data)
+    #     data_batch = self.load_batch(meta_data, data_path)
+
+    #     curr_rgb, curr_depth, curr_normal, curr_sem, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_sem'], data_batch['curr_cam_model']
+
+    #     # load data
+    #     # curr_intrinsic = meta_data['cam_in']
+    #     # curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path)
+        
+    #     # # mask the depth
+    #     # curr_depth = curr_depth.squeeze()
+    #     # depth_mask = self.load_depth_valid_mask(curr_depth_mask_path, curr_depth)
+    #     # curr_depth[~depth_mask] = -1
+        
+        
+    #     # # get semantic labels
+    #     # curr_sem = self.load_sem_label(curr_sem_path, curr_depth)
+    #     # # create camera model
+    #     # curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], curr_intrinsic)        
+        
+    #     # get crop size
+    #     transform_paras = dict(random_crop_size = self.random_crop_size)
+    #     rgbs, depths, intrinsics, cam_models, _, other_labels, transform_paras = self.img_transforms(
+    #                                                                images=[curr_rgb, ], 
+    #                                                                labels=[curr_depth, ], 
+    #                                                                intrinsics=[curr_intrinsic,], 
+    #                                                                cam_models=[curr_cam_model, ],
+    #                                                                other_labels=[curr_sem, ],
+    #                                                                transform_paras=transform_paras)
+    #     # process sky masks
+    #     sem_mask = other_labels[0].int()
+        
+    #     # clip depth map 
+    #     depth_out = self.normalize_depth(depths[0])
+    #     # set the depth in sky region to the maximum depth
+    #     depth_out[sem_mask==142] = -1 #self.depth_normalize[1] - 1e-6
+    #     filename = os.path.basename(meta_data['rgb'])
+    #     curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+    #     cam_models_stacks = [
+    #         torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+    #         for i in [2, 4, 8, 16, 32] 
+    #         ]
+    #     pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]        
+    #     data = dict(input=rgbs[0],
+    #                 target=depth_out,
+    #                 intrinsic=curr_intrinsic_mat,
+    #                 filename=filename,
+    #                 dataset=self.data_name,
+    #                 cam_model=cam_models_stacks,
+    #                 #ref_input=rgbs[1:],
+    #                 # tmpl_flg=tmpl_annos['w_tmpl'],
+    #                 pad=torch.tensor(pad),
+    #                 data_type=[self.data_type, ],
+    #                 sem_mask=sem_mask.int())
+    #     return data
+    
+    # def get_data_for_test(self, idx: int):
+    #     anno = self.annotations['files'][idx]
+    #     meta_data = self.load_meta_data(anno)
+    #     curr_rgb_path = os.path.join(self.data_root, meta_data['rgb'])
+    #     curr_depth_path = os.path.join(self.depth_root, meta_data['depth'])
+    #     curr_depth_mask_path = os.path.join(self.depth_mask_root, meta_data['depth_mask']) if self.depth_mask_root is not None and ('depth_mask' in meta_data) and (meta_data['depth_mask'] is not None)  else None
+
+    #     # load data
+    #     ori_curr_intrinsic = meta_data['cam_in']
+    #     curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path)
+        
+    #     # mask the depth
+    #     curr_depth = curr_depth.squeeze()
+    #     depth_mask = self.load_depth_valid_mask(curr_depth_mask_path, curr_depth)
+    #     curr_depth[~depth_mask] = -1
+        
+    #     ori_h, ori_w, _ = curr_rgb.shape
+    #     # create camera model
+    #     curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], ori_curr_intrinsic)
+
+    #     # get crop size
+    #     transform_paras = dict()
+    #     rgbs, depths, intrinsics, cam_models, _,  other_labels, transform_paras = self.img_transforms(
+    #                                                                images=[curr_rgb,],  #+ tmpl_rgbs, 
+    #                                                                labels=[curr_depth, ], 
+    #                                                                intrinsics=[ori_curr_intrinsic, ], # * (len(tmpl_rgbs) + 1), 
+    #                                                                cam_models=[curr_cam_model, ],
+    #                                                                transform_paras=transform_paras)
+    #     # depth in original size and orignial metric***
+    #     depth_out = self.clip_depth(curr_depth) * self.depth_range[1] # self.clip_depth(depths[0]) #
+
+    #     filename = os.path.basename(meta_data['rgb'])
+    #     curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+
+    #     pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
+    #     scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0
+    #     cam_models_stacks = [
+    #         torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+    #         for i in [2, 4, 8, 16, 32] 
+    #         ]    
+    #     raw_rgb = torch.from_numpy(curr_rgb)
+    #     # rel_pose = torch.from_numpy(tmpl_annos['tmpl_pose_list'][0])
+
+    #     data = dict(input=rgbs[0],
+    #                 target=depth_out,
+    #                 intrinsic=curr_intrinsic_mat,
+    #                 filename=filename,
+    #                 dataset=self.data_name,
+    #                 cam_model=cam_models_stacks,
+    #                 pad=pad,
+    #                 scale=scale_ratio,
+    #                 raw_rgb=raw_rgb,
+    #                 sample_id=idx,
+    #                 data_path=meta_data['rgb'],
+    #                 )
+    #     return data
+    
+
+    def load_batch(self, meta_data, data_path):
+        curr_intrinsic = meta_data['cam_in']
+        # load rgb/depth
+        curr_rgb, curr_depth = self.load_rgb_depth(data_path['rgb_path'], data_path['depth_path'])
+        # get semantic labels
+        curr_sem = self.load_sem_label(data_path['sem_path'], curr_depth)
+        # create camera model
+        curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], curr_intrinsic)       
+        # get normal labels
+        
+        try:
+            curr_normal = self.load_norm_label(data_path['normal_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1], depth=curr_depth, K=curr_intrinsic) # !!! this is diff of BaseDataset
+        except:
+            curr_normal = np.zeros_like(curr_rgb)
+        # get depth mask
+        depth_mask = self.load_depth_valid_mask(data_path['depth_mask_path'])
+        curr_depth[~depth_mask] = -1
+        data_batch = dict(
+            curr_rgb = curr_rgb,
+            curr_depth = curr_depth,
+            curr_sem = curr_sem,
+            curr_normal = curr_normal,
+            curr_cam_model=curr_cam_model,
+        )
+        return data_batch
+
+
+    def load_norm_label(self, norm_path, H, W, depth, K):
+        normal = np.load(norm_path)
+        normal[:,:,1:] *= -1
+        normal = self.align_normal(normal, depth, K, H, W)
+
+        return normal
+
+
+    def process_depth(self, depth, rgb):
+        depth[depth>150] = 0
+        depth[depth<0.1] = 0
+        depth /= self.metric_scale
+        return depth
+
+    def align_normal(self, normal, depth, K, H, W):
+        # inv K
+        K = np.array([[K[0], 0 ,K[2]], 
+                      [0, K[1], K[3]], 
+                      [0, 0, 1]])
+        inv_K = np.linalg.inv(K)
+        # reprojection depth to camera points
+        if H == 768 and W == 1024:
+            xy = self.xy
+        else:
+            print('img size no-equal 768x1024')
+            xy = creat_uv_mesh(H, W)
+        points = np.matmul(inv_K[:3, :3], xy).reshape(3, H, W)
+        points = depth * points
+        points = points.transpose((1,2,0))
+
+        # align normal
+        orient_mask = np.sum(normal * points, axis=2) > 0
+        normal[orient_mask] *= -1
+
+        return normal
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = DIODEDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/drivingstereo_dataset.py

@@ -0,0 +1,35 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class DrivingStereoDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(DrivingStereoDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= self.metric_scale
+        return depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/dsec_dataset.py

@@ -0,0 +1,35 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class DSECDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(DSECDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= self.metric_scale
+        return depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/fisheye_dataset.py

@@ -0,0 +1,76 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class FisheyeDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(FisheyeDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    def load_data(self, path: str, is_rgb_img: bool=False):
+        if not os.path.exists(path):
+            self.logger.info(f'>>>>{path} does not exist.')
+            # raise RuntimeError(f'{path} does not exist.')
+
+        data_type = os.path.splitext(path)[-1]
+        if data_type in self.img_file_type:
+            if is_rgb_img:
+                data = cv2.imread(path)
+            else:
+                data = cv2.imread(path, -1)
+                data[data>65500] = 0
+                data &= 0x7FFF
+
+        elif data_type in self.np_file_type:
+            data = np.load(path)
+        else:
+            raise RuntimeError(f'{data_type} is not supported in current version.')
+        
+        return data.squeeze()
+
+    def load_batch(self, meta_data, data_path):
+        curr_intrinsic = meta_data['cam_in']
+        # load rgb/depth
+        curr_rgb, curr_depth = self.load_rgb_depth(data_path['rgb_path'], data_path['depth_path'])
+        # get semantic labels
+        curr_sem = self.load_sem_label(data_path['sem_path'], curr_depth)
+        # create camera model
+        curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], curr_intrinsic)       
+        # get normal labels
+        curr_normal = self.load_norm_label(data_path['normal_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1]) 
+        # get depth mask
+        depth_mask = self.load_depth_valid_mask(data_path['depth_mask_path'])[:, :, :]
+         
+        # with masks from andy
+        curr_depth[~(depth_mask[:, :, 0])] = -1
+        curr_rgb[~(depth_mask[:, :, :])] = 0
+        
+        # get stereo depth
+        curr_stereo_depth = self.load_stereo_depth_label(data_path['disp_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1]) 
+
+        data_batch = dict(
+            curr_rgb = curr_rgb,
+            curr_depth = curr_depth,
+            curr_sem = curr_sem,
+            curr_normal = curr_normal,
+            curr_cam_model=curr_cam_model,
+            curr_stereo_depth=curr_stereo_depth,
+        )
+        return data_batch
+
+    
+    def process_depth(self, depth, rgb):
+
+        depth /= self.metric_scale
+        return depth

external/Metric3D/training/mono/datasets/hm3d_dataset.py

@@ -0,0 +1,35 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from PIL import Image
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class HM3DDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(HM3DDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+        #self.cap_range = self.depth_range # in meter
+
+    def load_norm_label(self, norm_path, H, W):
+        with open(norm_path, 'rb') as f:
+            normal = Image.open(f)
+            normal = np.array(normal.convert(normal.mode), dtype=np.uint8)
+        invalid_mask = np.all(normal == 128, axis=2)
+        normal = normal.astype(np.float64) / 255.0 * 2 - 1
+        normal[invalid_mask, :] = 0
+        return normal
+
+    def process_depth(self, depth: np.array, rgb: np.array) -> np.array:
+        depth[depth>60000] = 0
+        depth = depth / self.metric_scale
+        return depth

external/Metric3D/training/mono/datasets/hypersim_dataset.py

@@ -0,0 +1,141 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from PIL import Image
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+import h5py
+
+def creat_uv_mesh(H, W):
+    y, x = np.meshgrid(np.arange(0, H, dtype=np.float), np.arange(0, W, dtype=np.float), indexing='ij')
+    meshgrid = np.stack((x,y))
+    ones = np.ones((1,H*W), dtype=np.float)
+    xy = meshgrid.reshape(2, -1)
+    return np.concatenate([xy, ones], axis=0)
+
+class HypersimDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(HypersimDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+        #self.cap_range = self.depth_range # in meter
+                # init uv
+
+        # meshgrid for depth reprojection
+        self.xy = creat_uv_mesh(768, 1024)
+        
+    def load_batch(self, meta_data, data_path):
+        curr_intrinsic = meta_data['cam_in']
+        # load rgb/depth
+        curr_rgb, curr_depth = self.load_rgb_depth(data_path['rgb_path'], data_path['depth_path'])
+        # get semantic labels
+        curr_sem = self.load_sem_label(data_path['sem_path'], curr_depth)
+        # create camera model
+        curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], curr_intrinsic)       
+        # get normal labels
+        curr_normal = self.load_norm_label(data_path['normal_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1], depth=curr_depth, K=curr_intrinsic) # !!! this is diff of BaseDataset
+        # get depth mask
+        depth_mask = self.load_depth_valid_mask(data_path['depth_mask_path'])
+        curr_depth[~depth_mask] = -1
+        data_batch = dict(
+            curr_rgb = curr_rgb,
+            curr_depth = curr_depth,
+            curr_sem = curr_sem,
+            curr_normal = curr_normal,
+            curr_cam_model=curr_cam_model,
+        )
+        return data_batch
+
+    def load_data_path(self, meta_data):
+        # 'rgbs': {'rgb_color': 'Hypersim/data/ai_001_001/images/scene_cam_00_final_preview/frame.0008.color.jpg', 
+        #          'rgb_gamma': 'Hypersim/data/ai_001_001/images/scene_cam_00_final_preview/frame.0008.gamma.jpg', 
+        #          'rgb_tonemap': 'Hypersim/data/ai_001_001/images/scene_cam_00_final_preview/frame.0008.tonemap.jpg', 
+        #          'rgb_raw': 'Hypersim/data/ai_001_001/images/scene_cam_00_final_hdf5/frame.0008.color.hdf5'}
+        meta_data['rgb'] = meta_data['rgbs']['rgb_color'] # this is diff of BaseDataset
+        curr_rgb_path = os.path.join(self.data_root, meta_data['rgb'])
+        curr_depth_path = os.path.join(self.depth_root, meta_data['depth'])
+        curr_sem_path = os.path.join(self.sem_root, meta_data['sem']) \
+            if self.sem_root is not None and ('sem' in meta_data) and (meta_data['sem'] is not None)  \
+            else None
+        curr_norm_path = os.path.join(self.norm_root, meta_data['normal']) \
+            if ('normal' in meta_data) and (meta_data['normal'] is not None) and (self.norm_root is not None) \
+            else None
+        curr_depth_mask_path = os.path.join(self.depth_mask_root, meta_data['depth_mask']) \
+            if self.depth_mask_root is not None and ('depth_mask' in meta_data) and (meta_data['depth_mask'] is not None)  \
+            else None
+
+        data_path=dict(
+            rgb_path=curr_rgb_path,
+            depth_path=curr_depth_path,
+            sem_path=curr_sem_path,
+            normal_path=curr_norm_path,
+            depth_mask_path=curr_depth_mask_path,
+            )
+        return data_path
+
+    def load_rgb_depth(self, rgb_path: str, depth_path: str):
+        """
+        Load the rgb and depth map with the paths.
+        """
+        rgb = self.load_data(rgb_path, is_rgb_img=True)
+        if rgb is None:
+            self.logger.info(f'>>>>{rgb_path} has errors.')
+       
+        # depth = self.load_data(depth_path)
+        with h5py.File(depth_path, "r") as f: depth = f["dataset"][:]
+        np.nan_to_num(depth, copy=False, nan=0) # fill nan in gt
+        if depth is None:
+            self.logger.info(f'{depth_path} has errors.')
+        
+        depth = depth.astype(np.float)
+        
+        depth  = self.process_depth(depth, rgb)
+        return rgb, depth
+
+
+    def load_norm_label(self, norm_path, H, W, depth, K):
+        with h5py.File(norm_path, "r") as f: 
+            normal = f["dataset"][:]
+        np.nan_to_num(normal, copy=False, nan=0)
+        normal[:,:,1:] *= -1
+        normal = normal.astype(np.float)
+
+        return self.align_normal(normal, depth, K, H, W)
+
+    def process_depth(self, depth: np.array, rgb: np.array) -> np.array:
+        depth[depth>60000] = 0
+        depth = depth / self.metric_scale
+        return depth
+    
+    def align_normal(self, normal, depth, K, H, W):
+        '''
+        Orientation of surface normals in hypersim is not always consistent
+        see https://github.com/apple/ml-hypersim/issues/26
+        '''
+        # inv K
+        K = np.array([[K[0], 0 ,K[2]], 
+                      [0, K[1], K[3]], 
+                      [0, 0, 1]])
+        inv_K = np.linalg.inv(K)
+        # reprojection depth to camera points
+        if H == 768 and W == 1024:
+            xy = self.xy
+        else:
+            print('img size no-equal 768x1024')
+            xy = creat_uv_mesh(H, W)
+        points = np.matmul(inv_K[:3, :3], xy).reshape(3, H, W)
+        points = depth * points
+        points = points.transpose((1,2,0))
+
+        # align normal
+        orient_mask = np.sum(normal * points, axis=2) > 0
+        normal[orient_mask] *= -1
+
+        return normal

external/Metric3D/training/mono/datasets/ibims_dataset.py

@@ -0,0 +1,92 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class IBIMSDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(IBIMSDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+
+        self.avg = torch.nn.AvgPool2d(kernel_size=7, stride=1, ceil_mode=False, count_include_pad=True, divisor_override=None)
+        self.unfold = torch.nn.Unfold(kernel_size=7, dilation=1, padding=0, stride=1)
+        self.pad = torch.nn.ZeroPad2d(3)
+    
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>50000] = 0
+        depth /= self.metric_scale
+        return depth
+
+    def load_batch(self, meta_data, data_path):
+        curr_intrinsic = meta_data['cam_in']
+        # load rgb/depth
+        curr_rgb, curr_depth = self.load_rgb_depth(data_path['rgb_path'], data_path['depth_path'])
+        # get semantic labels
+        curr_sem = self.load_sem_label(data_path['sem_path'], curr_depth)
+        # create camera model
+        curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], curr_intrinsic)       
+        # get normal labels
+        curr_normal = self.load_norm_label(data_path['normal_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1], depth=curr_depth, K=curr_intrinsic) # !!! this is diff of BaseDataset
+        # get depth mask
+        depth_mask = self.load_depth_valid_mask(data_path['depth_mask_path'])
+        curr_depth[~depth_mask] = -1
+        data_batch = dict(
+            curr_rgb = curr_rgb,
+            curr_depth = curr_depth,
+            curr_sem = curr_sem,
+            curr_normal = curr_normal,
+            curr_cam_model=curr_cam_model,
+        )
+        return data_batch
+
+    def load_norm_label(self, norm_path, H, W, depth, K):
+        depth = torch.from_numpy(depth).squeeze()
+        K = torch.Tensor([[K[0], 0 ,K[2]], 
+                      [0, K[1], K[3]], 
+                      [0, 0, 1]])
+        K_inv = K.inverse()
+
+        y, x = torch.meshgrid([torch.arange(0, 480, dtype=torch.float32),
+                            torch.arange(0, 640, dtype=torch.float32)], indexing='ij')
+        x = x.reshape(1, 480*640)
+        y = y.reshape(1, 480*640)
+        ones = torch.ones_like(x)
+        coord_2d = torch.cat((x, y, ones), dim=0)
+
+        coord_3d = torch.matmul(K_inv, coord_2d).view(3, 480, 640)
+        coord_3d = (coord_3d * depth[None, :])[None, :]
+        coord_3d_mean = self.avg(coord_3d)
+
+        uf_coord_3d = self.unfold(coord_3d.permute(1, 0, 2, 3))
+        coord_3d_decenter = uf_coord_3d - coord_3d_mean.view(3, 1, (480-6)*(640-6))
+        coord_3d_decenter = coord_3d_decenter.permute(2, 0, 1)
+        cov = torch.bmm(coord_3d_decenter, coord_3d_decenter.permute(0, 2, 1))
+        
+        eig = torch.linalg.eigh(cov)
+        #svd = torch.linalg.svd(coord_3d_decenter)
+        normal = (eig[1])[:, :, 0].float()
+        #normal = (svd[1])[:, 2, :]
+        normal = self.pad(normal.permute(1, 0).view(1, 3, (480-6), (640-6)))
+        
+        orient_mask = (torch.sum(normal * coord_3d, axis=1) < 0).unsqueeze(1)
+        normal = normal * orient_mask - normal * (~orient_mask)
+        gt_normal = normal.squeeze().permute(1, 2, 0).numpy()
+        return gt_normal
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = IBIMSDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/kitti_dataset.py

@@ -0,0 +1,190 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class KITTIDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(KITTIDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    def get_data_for_trainval(self, idx: int):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        
+        data_path = self.load_data_path(meta_data)
+        data_batch = self.load_batch(meta_data, data_path)
+        # if data_path['sem_path'] is not None:
+        #     print(self.data_name)
+
+        curr_rgb, curr_depth, curr_normal, curr_sem, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_sem'], data_batch['curr_cam_model']
+        #curr_stereo_depth = data_batch['curr_stereo_depth']
+        
+        th = 352 # target size for bottom cropping, a common practice for kitti training
+        tw = 1216
+
+        ch = curr_rgb.shape[0]
+        cw = curr_rgb.shape[1]
+
+        h_start = ch - th
+        w_start = (cw - tw) // 2
+        w_end = w_start + tw
+
+        curr_intrinsic = meta_data['cam_in']
+
+        curr_rgb = curr_rgb[h_start:, w_start:w_end, :]
+        curr_depth = curr_depth[h_start:, w_start:w_end]
+
+        curr_normal = curr_normal[h_start:, w_start:w_end, :]
+        curr_sem = curr_sem[h_start:, w_start:w_end]
+
+        curr_intrinsic[2] = curr_intrinsic[2] - w_start # cw
+        curr_intrinsic[3] = curr_intrinsic[3] - h_start # ch
+
+        # A patch for stereo depth dataloader (no need to modify specific datasets)
+        if 'curr_stereo_depth' in data_batch.keys():
+            curr_stereo_depth = data_batch['curr_stereo_depth']
+        else:
+            curr_stereo_depth = self.load_stereo_depth_label(None, H=curr_rgb.shape[0], W=curr_rgb.shape[1]) 
+
+
+        # data augmentation
+        transform_paras = dict(random_crop_size = self.random_crop_size) # dict() 
+        assert curr_rgb.shape[:2] == curr_depth.shape == curr_normal.shape[:2] == curr_sem.shape
+        rgbs, depths, intrinsics, cam_models, normals, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb, ], 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[curr_intrinsic,], 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   normals = [curr_normal, ],
+                                                                   other_labels=[curr_sem, curr_stereo_depth],
+                                                                   transform_paras=transform_paras)
+        # process sky masks
+        sem_mask = other_labels[0].int()
+        # clip depth map 
+        depth_out = self.normalize_depth(depths[0])
+        # set the depth of sky region to the invalid
+        depth_out[sem_mask==142] = -1 # self.depth_normalize[1] - 1e-6
+        # get inverse depth
+        inv_depth = self.depth2invdepth(depth_out, sem_mask==142)
+        filename = os.path.basename(meta_data['rgb'])[:-4] + '.jpg'
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]
+
+        # stereo_depth 
+        stereo_depth_pre_trans = other_labels[1] * (other_labels[1] > 0.3) * (other_labels[1] < 200)
+        stereo_depth = stereo_depth_pre_trans * transform_paras['label_scale_factor']
+        stereo_depth = self.normalize_depth(stereo_depth)
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]        
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    pad=torch.tensor(pad),
+                    data_type=[self.data_type, ],
+                    sem_mask=sem_mask.int(),
+                    stereo_depth= stereo_depth,
+                    normal=normals[0],
+                    inv_depth=inv_depth,
+                    scale=transform_paras['label_scale_factor'])
+        return data   
+
+
+    def get_data_for_test(self, idx: int):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        curr_rgb_path = os.path.join(self.data_root, meta_data['rgb'])
+        curr_depth_path = os.path.join(self.depth_root, meta_data['depth'])
+        # load data
+        ori_curr_intrinsic = meta_data['cam_in']
+        curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path)
+        # crop rgb/depth
+        curr_rgb = curr_rgb[:, 43: 1197, :]
+        curr_depth = curr_depth[:, 43: 1197]
+        
+        ori_h, ori_w, _ = curr_rgb.shape
+        # create camera model
+        curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], ori_curr_intrinsic)
+        # load tmpl rgb info
+        # tmpl_annos = self.load_tmpl_image_pose(curr_rgb, meta_data)
+        # tmpl_rgbs = tmpl_annos['tmpl_rgb_list'] # list of reference rgbs
+
+        # get crop size
+        transform_paras = dict()
+        rgbs, depths, intrinsics, cam_models, _, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb,],  #+ tmpl_rgbs, 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[ori_curr_intrinsic, ], # * (len(tmpl_rgbs) + 1), 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   transform_paras=transform_paras)
+        
+        # depth in original size and orignial metric***
+        depth_out = self.clip_depth(curr_depth) * self.depth_range[1] # self.clip_depth(depths[0]) #
+
+        filename = os.path.basename(meta_data['rgb'])
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
+        scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]    
+        raw_rgb = torch.from_numpy(curr_rgb)
+        # rel_pose = torch.from_numpy(tmpl_annos['tmpl_pose_list'][0])
+
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    # ref_input=rgbs[1:],
+                    # tmpl_flg=tmpl_annos['w_tmpl'],
+                    pad=pad,
+                    scale=scale_ratio,
+                    raw_rgb=raw_rgb,
+                    normal = np.zeros_like(curr_rgb.transpose((2,0,1))), 
+                    # rel_pose=rel_pose,
+                    )
+        return data
+    
+    def process_depth(self, depth, rgb):
+        new_depth = np.zeros_like(depth)
+        H, W = depth.shape
+        crop_h_up = int(0.3324324 * H)
+        crop_h_down = int(0.91351351 * H)
+        crop_w_left = int(0.0359477 * W)
+        crop_w_right = int(0.96405229 * W)
+        
+        new_depth[crop_h_up:crop_h_down, crop_w_left: crop_w_right] = depth[crop_h_up:crop_h_down, crop_w_left: crop_w_right]
+        new_depth[new_depth>65500] = 0
+        new_depth /= self.metric_scale
+        #print('image size', new_depth.shape, crop_h_up, crop_h_down, crop_w_left, crop_w_right)
+        #self.logger.info('image size, {new_depth.shape}, {crop_h_up}, {crop_h_down}, {crop_w_left}, {crop_w_right}')
+        return new_depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = KITTIDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/lyft_dataset.py

@@ -0,0 +1,34 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+import pickle
+
+class LyftDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(LyftDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= self.metric_scale
+        return depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/matterport3d_dataset.py

@@ -0,0 +1,44 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from PIL import Image
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class Matterport3DDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(Matterport3DDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+        #self.cap_range = self.depth_range # in meter
+
+    def load_norm_label(self, norm_path, H, W):
+        normal_x = cv2.imread(norm_path['x'], cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
+        normal_y = cv2.imread(norm_path['y'], cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
+        normal_z = cv2.imread(norm_path['z'], cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
+        raw_normal = np.array([normal_x, normal_y, normal_z])
+        invalid_mask = np.all(raw_normal == 0, axis=0)
+
+        ego_normal = raw_normal.astype(np.float64) / 32768.0 - 1
+        ego2cam = np.array([[1,0,0],
+                            [0,-1,0],
+                            [0,0,-1]])
+        normal = (ego2cam @ ego_normal.reshape(3,-1)).reshape(ego_normal.shape)
+        normal[:,invalid_mask] = 0
+        normal = normal.transpose((1,2,0))
+        if normal.shape[0] != H or normal.shape[1] != W:
+            normal = cv2.resize(normal, [W,H], interpolation=cv2.INTER_NEAREST)
+        return normal
+    
+    def process_depth(self, depth: np.array, rgb: np.array) -> np.array:
+        depth[depth>65500] = 0
+        depth = depth / self.metric_scale
+        return depth

external/Metric3D/training/mono/datasets/nuscenes_dataset.py

@@ -0,0 +1,34 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+import pickle
+
+class NuScenesDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(NuScenesDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= self.metric_scale
+        return depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/nyu_dataset.py

@@ -0,0 +1,195 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class NYUDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(NYUDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+
+    def get_data_for_trainval(self, idx: int):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        
+        data_path = self.load_data_path(meta_data)
+        data_batch = self.load_batch(meta_data, data_path)
+        # if data_path['sem_path'] is not None:
+        #     print(self.data_name)
+
+        curr_rgb, curr_depth, curr_normal, curr_sem, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_sem'], data_batch['curr_cam_model']
+        #curr_stereo_depth = data_batch['curr_stereo_depth']
+        new_rgb = np.zeros_like(curr_rgb)
+        new_rgb[6:-6, 6:-6, :] = curr_rgb[6:-6, 6:-6, :]
+        curr_rgb = new_rgb
+
+        # A patch for stereo depth dataloader (no need to modify specific datasets)
+        if 'curr_stereo_depth' in data_batch.keys():
+            curr_stereo_depth = data_batch['curr_stereo_depth']
+        else:
+            curr_stereo_depth = self.load_stereo_depth_label(None, H=curr_rgb.shape[0], W=curr_rgb.shape[1]) 
+
+        curr_intrinsic = meta_data['cam_in']
+        # data augmentation
+        transform_paras = dict(random_crop_size = self.random_crop_size) # dict() 
+        assert curr_rgb.shape[:2] == curr_depth.shape == curr_normal.shape[:2] == curr_sem.shape
+        rgbs, depths, intrinsics, cam_models, normals, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb, ], 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[curr_intrinsic,], 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   normals = [curr_normal, ],
+                                                                   other_labels=[curr_sem, curr_stereo_depth],
+                                                                   transform_paras=transform_paras)
+        # process sky masks
+        sem_mask = other_labels[0].int()
+        # clip depth map 
+        depth_out = self.normalize_depth(depths[0])
+        # set the depth of sky region to the invalid
+        depth_out[sem_mask==142] = -1 # self.depth_normalize[1] - 1e-6
+        # get inverse depth
+        inv_depth = self.depth2invdepth(depth_out, sem_mask==142)
+        filename = os.path.basename(meta_data['rgb'])[:-4] + '.jpg'
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]
+
+        # stereo_depth 
+        stereo_depth_pre_trans = other_labels[1] * (other_labels[1] > 0.3) * (other_labels[1] < 200)
+        stereo_depth = stereo_depth_pre_trans * transform_paras['label_scale_factor']
+        stereo_depth = self.normalize_depth(stereo_depth)
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]        
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    pad=torch.tensor(pad),
+                    data_type=[self.data_type, ],
+                    sem_mask=sem_mask.int(),
+                    stereo_depth= stereo_depth,
+                    normal=normals[0],
+                    inv_depth=inv_depth,
+                    scale=transform_paras['label_scale_factor'])
+        return data
+
+    def get_data_for_test(self, idx: int):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        curr_rgb_path = os.path.join(self.data_root, meta_data['rgb'])
+        curr_depth_path = os.path.join(self.depth_root, meta_data['depth'])
+        # load data
+        ori_curr_intrinsic = meta_data['cam_in']
+        curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path)
+        # crop rgb/depth
+        new_rgb = np.zeros_like(curr_rgb)
+        new_rgb[6:-6, 6:-6, :] = curr_rgb[6:-6, 6:-6, :]
+        curr_rgb = new_rgb
+        
+        ori_h, ori_w, _ = curr_rgb.shape
+        # create camera model
+        curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], ori_curr_intrinsic)
+
+        if 'normal' in meta_data.keys():
+            normal_path = os.path.join(self.data_root, meta_data['normal'])
+        else:
+            normal_path = None
+
+        curr_normal = self.load_norm_label(normal_path, H=curr_rgb.shape[0], W=curr_rgb.shape[1]) 
+        # load tmpl rgb info
+        # tmpl_annos = self.load_tmpl_image_pose(curr_rgb, meta_data)
+        # tmpl_rgbs = tmpl_annos['tmpl_rgb_list'] # list of reference rgbs
+
+        # get crop size
+        transform_paras = dict()
+        rgbs, depths, intrinsics, cam_models, normals, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb,],  #+ tmpl_rgbs, 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[ori_curr_intrinsic, ], # * (len(tmpl_rgbs) + 1), 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   normals = [curr_normal, ],
+                                                                   transform_paras=transform_paras)
+        # depth in original size and orignial metric***
+        depth_out = self.clip_depth(curr_depth) * self.depth_range[1] # self.clip_depth(depths[0]) #
+        
+        filename = os.path.basename(meta_data['rgb'])
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
+        scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]    
+        raw_rgb = torch.from_numpy(curr_rgb)
+        # rel_pose = torch.from_numpy(tmpl_annos['tmpl_pose_list'][0])
+        curr_normal = torch.from_numpy(curr_normal.transpose((2,0,1)))
+
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    # ref_input=rgbs[1:],
+                    # tmpl_flg=tmpl_annos['w_tmpl'],
+                    pad=pad,
+                    scale=scale_ratio,
+                    raw_rgb=raw_rgb,
+                    # rel_pose=rel_pose,
+                    normal=curr_normal
+                    #normal=np.zeros_like(curr_rgb.transpose((2,0,1))),
+                    )
+        return data
+
+    def load_norm_label(self, norm_path, H, W):
+        if norm_path is None:
+            norm_gt = np.zeros((H, W, 3)).astype(np.float32)
+        else:
+            norm_gt = cv2.imread(norm_path)
+
+            norm_gt = np.array(norm_gt).astype(np.uint8)
+            norm_valid_mask = np.logical_not(
+                np.logical_and(
+                    np.logical_and(
+                        norm_gt[:, :, 0] == 0, norm_gt[:, :, 1] == 0),
+                    norm_gt[:, :, 2] == 0))
+            norm_valid_mask = norm_valid_mask[:, :, np.newaxis]
+
+            norm_gt = ((norm_gt.astype(np.float32) / 255.0) * 2.0) - 1.0
+            norm_gt = norm_gt * norm_valid_mask * -1
+            
+        return norm_gt   
+
+    def process_depth(self, depth, rgb):
+        # eign crop
+        new_depth = np.zeros_like(depth)
+        new_depth[45:471, 41:601] = depth[45:471, 41:601]
+        
+        new_depth[new_depth>65500] = 0
+        new_depth /= self.metric_scale
+        return new_depth
+
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = NYUDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/pandaset_dataset.py

@@ -0,0 +1,36 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class PandasetDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(PandasetDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= self.metric_scale
+        # depth[(depth>self.cap_range[1]) | (depth<self.cap_range[0])] = -1
+        # depth /= self.cap_range[1]
+        return depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = PandasetDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/replica_dataset.py

@@ -0,0 +1,35 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from PIL import Image
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class ReplicaDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(ReplicaDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+        #self.cap_range = self.depth_range # in meter
+
+    def load_norm_label(self, norm_path, H, W):
+        with open(norm_path, 'rb') as f:
+            normal = Image.open(f)
+            normal = np.array(normal.convert(normal.mode), dtype=np.uint8)
+        invalid_mask = np.all(normal == 128, axis=2)
+        normal = normal.astype(np.float64) / 255.0 * 2 - 1
+        normal[invalid_mask, :] = 0
+        return normal
+    
+    def process_depth(self, depth: np.array, rgb: np.array) -> np.array:
+        depth[depth>60000] = 0
+        depth = depth / self.metric_scale
+        return depth

external/Metric3D/training/mono/datasets/scannet_dataset.py

@@ -0,0 +1,295 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class ScanNetDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(ScanNetDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    # def get_data_for_test(self, idx):
+    #     anno = self.annotations['files'][idx]
+    #     curr_rgb_path = os.path.join(self.data_root, anno['rgb'])
+    #     curr_depth_path = os.path.join(self.depth_root, anno['depth'])
+    #     meta_data = self.load_meta_data(anno)
+    #     ori_curr_intrinsic = meta_data['cam_in']
+        
+    #     curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path)
+    #     # curr_rgb = cv2.resize(curr_rgb, dsize=(640, 480), interpolation=cv2.INTER_LINEAR)
+    #     ori_h, ori_w, _ = curr_rgb.shape
+    #     # create camera model
+    #     curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], ori_curr_intrinsic)
+    #     # load tmpl rgb info
+    #     # tmpl_annos = self.load_tmpl_annos(anno, curr_rgb, meta_data)
+    #     # tmpl_rgb = tmpl_annos['tmpl_rgb_list'] # list of reference rgbs
+
+    #     transform_paras = dict()
+    #     rgbs, depths, intrinsics, cam_models, _,  other_labels, transform_paras = self.img_transforms(
+    #                                                                images=[curr_rgb, ], 
+    #                                                                labels=[curr_depth, ], 
+    #                                                                intrinsics=[ori_curr_intrinsic,], 
+    #                                                                cam_models=[curr_cam_model, ],
+    #                                                                transform_paras=transform_paras)
+    #     # depth in original size
+    #     depth_out = self.clip_depth(curr_depth) * self.depth_range[1]
+
+    #     filename = os.path.basename(anno['rgb'])
+    #     curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+
+    #     pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
+    #     scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0
+    #     cam_models_stacks = [
+    #         torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+    #         for i in [2, 4, 8, 16, 32] 
+    #         ]    
+    #     raw_rgb = torch.from_numpy(curr_rgb)
+    #     data = dict(input=rgbs[0],
+    #                 target=depth_out,
+    #                 intrinsic=curr_intrinsic_mat,
+    #                 filename=filename,
+    #                 dataset=self.data_name,
+    #                 cam_model=cam_models_stacks,
+    #                 ref_input=rgbs[1:],
+    #                 tmpl_flg=False,
+    #                 pad=pad,
+    #                 scale=scale_ratio,
+    #                 raw_rgb=raw_rgb,
+    #                 normal =np.zeros_like(curr_rgb.transpose((2,0,1))),
+    #     ) 
+    #     return data
+
+    def get_data_for_test(self, idx: int, test_mode=True):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        data_path = self.load_data_path(meta_data)
+        data_batch = self.load_batch(meta_data, data_path, test_mode)
+        # load data
+        curr_rgb, curr_depth, curr_normal, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_cam_model']
+        ori_curr_intrinsic = meta_data['cam_in']
+
+        # get crop size
+        transform_paras = dict()
+        rgbs, depths, intrinsics, cam_models, _, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb,],  #+ tmpl_rgbs, 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[ori_curr_intrinsic, ], # * (len(tmpl_rgbs) + 1), 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   transform_paras=transform_paras)
+        # depth in original size and orignial metric***
+        depth_out = self.clip_depth(curr_depth) * self.depth_range[1] # self.clip_depth(depths[0]) #
+        inv_depth = self.depth2invdepth(depth_out, np.zeros_like(depth_out, dtype=np.bool))
+        filename = os.path.basename(meta_data['rgb'])[:-4] + '.jpg'
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
+        scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]    
+        raw_rgb = torch.from_numpy(curr_rgb)
+        curr_normal = torch.from_numpy(curr_normal.transpose((2,0,1)))
+        
+
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    pad=pad,
+                    scale=scale_ratio,
+                    raw_rgb=raw_rgb,
+                    sample_id=idx,
+                    data_path=meta_data['rgb'],
+                    inv_depth=inv_depth,
+                    normal=curr_normal,
+                    )
+        return data   
+
+    def get_data_for_trainval(self, idx: int):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        
+        data_path = self.load_data_path(meta_data)
+        data_batch = self.load_batch(meta_data, data_path, test_mode=False)
+
+        # if data_path['sem_path'] is not None:
+        #     print(self.data_name)
+
+        curr_rgb, curr_depth, curr_normal, curr_sem, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_sem'], data_batch['curr_cam_model']
+        #curr_stereo_depth = data_batch['curr_stereo_depth']
+        
+        # A patch for stereo depth dataloader (no need to modify specific datasets)
+        if 'curr_stereo_depth' in data_batch.keys():
+            curr_stereo_depth = data_batch['curr_stereo_depth']
+        else:
+            curr_stereo_depth = self.load_stereo_depth_label(None, H=curr_rgb.shape[0], W=curr_rgb.shape[1]) 
+
+        curr_intrinsic = meta_data['cam_in']
+        # data augmentation
+        transform_paras = dict(random_crop_size = self.random_crop_size) # dict() 
+        assert curr_rgb.shape[:2] == curr_depth.shape == curr_normal.shape[:2] == curr_sem.shape
+        rgbs, depths, intrinsics, cam_models, normals, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb, ], 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[curr_intrinsic,], 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   normals = [curr_normal, ],
+                                                                   other_labels=[curr_sem, curr_stereo_depth],
+                                                                   transform_paras=transform_paras)
+        # process sky masks
+        sem_mask = other_labels[0].int()
+        # clip depth map 
+        depth_out = self.normalize_depth(depths[0])
+        # set the depth of sky region to the invalid
+        depth_out[sem_mask==142] = -1 # self.depth_normalize[1] - 1e-6
+        # get inverse depth
+        inv_depth = self.depth2invdepth(depth_out, sem_mask==142)
+        filename = os.path.basename(meta_data['rgb'])[:-4] + '.jpg'
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]
+
+        # stereo_depth 
+        stereo_depth_pre_trans = other_labels[1] * (other_labels[1] > 0.3) * (other_labels[1] < 200)
+        stereo_depth = stereo_depth_pre_trans * transform_paras['label_scale_factor']
+        stereo_depth = self.normalize_depth(stereo_depth)
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]        
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    pad=torch.tensor(pad),
+                    data_type=[self.data_type, ],
+                    sem_mask=sem_mask.int(),
+                    stereo_depth= stereo_depth,
+                    normal=normals[0],
+                    inv_depth=inv_depth,
+                    scale=transform_paras['label_scale_factor'])
+        return data
+
+    def load_batch(self, meta_data, data_path, test_mode):
+
+        # print('############')
+        # print(data_path['rgb_path'])
+        # print(data_path['normal_path'])
+        # print('############')
+
+        curr_intrinsic = meta_data['cam_in']
+        # load rgb/depth
+        curr_rgb, curr_depth = self.load_rgb_depth(data_path['rgb_path'], data_path['depth_path'], test_mode)
+        # get semantic labels
+        curr_sem = self.load_sem_label(data_path['sem_path'], curr_depth)
+        # create camera model
+        curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], curr_intrinsic)       
+        # get normal labels
+        curr_normal = self.load_norm_label(data_path['normal_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1], test_mode=test_mode) 
+        # get depth mask
+        depth_mask = self.load_depth_valid_mask(data_path['depth_mask_path'])
+        curr_depth[~depth_mask] = -1
+        # get stereo depth
+        curr_stereo_depth = self.load_stereo_depth_label(data_path['disp_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1]) 
+
+        data_batch = dict(
+            curr_rgb = curr_rgb,
+            curr_depth = curr_depth,
+            curr_sem = curr_sem,
+            curr_normal = curr_normal,
+            curr_cam_model=curr_cam_model,
+            curr_stereo_depth=curr_stereo_depth,
+        )
+        return data_batch
+
+    def load_rgb_depth(self, rgb_path: str, depth_path: str, test_mode: bool):
+        """
+        Load the rgb and depth map with the paths.
+        """
+        rgb = self.load_data(rgb_path, is_rgb_img=True)
+        if rgb is None:
+            self.logger.info(f'>>>>{rgb_path} has errors.')
+       
+        depth = self.load_data(depth_path)
+        if depth is None:
+            self.logger.info(f'{depth_path} has errors.')
+        
+        # self.check_data(dict(
+        #     rgb_path=rgb,
+        #     depth_path=depth,
+        # ))
+        depth = depth.astype(np.float)
+        # if depth.shape != rgb.shape[:2]:
+        #     print(f'no-equal in {self.data_name}')
+        #     depth = cv2.resize(depth, rgb.shape[::-1][1:])
+        
+        depth  = self.process_depth(depth, rgb, test_mode)
+        return rgb, depth
+    
+    def process_depth(self, depth, rgb, test_mode=False):
+        depth[depth>65500] = 0
+        depth /= self.metric_scale
+        h, w, _ = rgb.shape # to rgb size
+        if test_mode==False:
+            depth = cv2.resize(depth, (w, h), interpolation=cv2.INTER_NEAREST)
+        return depth
+
+    def load_norm_label(self, norm_path, H, W, test_mode):
+        
+        if norm_path is None:
+            norm_gt = np.zeros((H, W, 3)).astype(np.float32)
+        else:
+            norm_gt = cv2.imread(norm_path)
+            norm_gt = cv2.cvtColor(norm_gt, cv2.COLOR_BGR2RGB) 
+
+            norm_gt = np.array(norm_gt).astype(np.uint8)
+
+            mask_path = 'orient-mask'.join(norm_path.rsplit('normal', 1))
+            mask_gt = cv2.imread(mask_path)
+            mask_gt = np.array(mask_gt).astype(np.uint8)
+            valid_mask = np.logical_not(
+                np.logical_and(
+                    np.logical_and(
+                        mask_gt[:, :, 0] == 0, mask_gt[:, :, 1] == 0),
+                    mask_gt[:, :, 2] == 0))
+            valid_mask = valid_mask[:, :, np.newaxis]
+
+            # norm_valid_mask = np.logical_not(
+            #     np.logical_and(
+            #         np.logical_and(
+            #             norm_gt[:, :, 0] == 0, norm_gt[:, :, 1] == 0),
+            #         norm_gt[:, :, 2] == 0))
+            # norm_valid_mask = norm_valid_mask[:, :, np.newaxis]
+
+            norm_gt = ((norm_gt.astype(np.float32) / 255.0) * 2.0) - 1.0
+            norm_valid_mask = (np.linalg.norm(norm_gt, axis=2, keepdims=True) > 0.5) * valid_mask
+            norm_gt = norm_gt * norm_valid_mask
+
+            if test_mode==False:
+                norm_gt = cv2.resize(norm_gt, (W, H), interpolation=cv2.INTER_NEAREST)
+            
+        return norm_gt   
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = NYUDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/taskonomy_dataset.py

@@ -0,0 +1,190 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from PIL import Image
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+import pickle
+
+
+class TaskonomyDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(TaskonomyDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+        #self.cap_range = self.depth_range # in meter
+    
+    def __getitem__(self, idx: int) -> dict:
+        if self.phase == 'test':
+            return self.get_data_for_test(idx)
+        else:
+            return self.get_data_for_trainval(idx)
+
+    def load_meta_data(self, anno: dict) -> dict:
+        """
+        Load meta data information.
+        """
+        if self.meta_data_root is not None and ('meta_data' in anno or 'meta' in anno):
+            meta_data_path = os.path.join(self.meta_data_root, anno['meta_data']) if 'meta_data' in anno else os.path.join(self.meta_data_root, anno['meta'])
+            with open(meta_data_path, 'rb') as f:
+                meta_data = pickle.load(f)
+            meta_data.update(anno)
+        else:
+            meta_data = anno
+        u0, v0, fx, fy = meta_data['cam_in']
+        meta_data['cam_in'] = [fx, fy, u0, v0] # fix data bugs
+        return meta_data
+
+    def get_data_for_trainval(self, idx: int):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        
+        data_path = self.load_data_path(meta_data)
+        data_batch = self.load_batch(meta_data, data_path)
+        curr_rgb, curr_depth, curr_normal, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_cam_model']
+        curr_intrinsic = meta_data['cam_in']
+
+        ins_planes_path = os.path.join(self.data_root, meta_data['ins_planes']) if ('ins_planes' in meta_data) and (meta_data['ins_planes'] is not None) else None
+        # get instance planes
+        ins_planes = self.load_ins_planes(curr_depth, ins_planes_path)
+
+        # load data
+        # u0, v0, fx, fy = meta_data['cam_in'] # this is 
+        # ori_curr_intrinsic = [fx, fy, u0, v0]        
+        # curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path)
+        
+        # get crop size
+        # transform_paras = dict()
+        transform_paras = dict(random_crop_size = self.random_crop_size)
+        rgbs, depths, intrinsics, cam_models, normals, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb, ], 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[curr_intrinsic,], 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   normals = [curr_normal, ],
+                                                                   other_labels=[ins_planes, ],
+                                                                   transform_paras=transform_paras)
+        # process instance planes
+        ins_planes = other_labels[0].int()
+        
+        # clip depth map 
+        depth_out = self.normalize_depth(depths[0])
+        # get inverse depth
+        inv_depth = self.depth2invdepth(depth_out, torch.zeros_like(depth_out, dtype=torch.bool))
+        filename = os.path.basename(meta_data['rgb'])
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32]
+            ]
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]        
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    pad=torch.tensor(pad),
+                    data_type=[self.data_type, ],
+                    sem_mask=ins_planes,
+                    normal=normals[0],
+                    inv_depth=inv_depth,
+                    stereo_depth=torch.zeros_like(inv_depth),
+                    scale= transform_paras['label_scale_factor'])
+        return data
+
+    def get_data_for_test(self, idx: int):
+        anno = self.annotations['files'][idx]
+        meta_data = self.load_meta_data(anno)
+        data_path = self.load_data_path(meta_data)
+        data_batch = self.load_batch(meta_data, data_path)
+
+        curr_rgb, curr_depth, curr_normal, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_cam_model']
+        ori_curr_intrinsic = meta_data['cam_in']
+
+        # curr_rgb_path = os.path.join(self.data_root, meta_data['rgb'])
+        # curr_depth_path = os.path.join(self.depth_root, meta_data['depth'])
+
+        # curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path)
+        # ori_h, ori_w, _ = curr_rgb.shape
+        # # create camera model
+        # curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], ori_curr_intrinsic)
+        # load tmpl rgb info
+        # tmpl_annos = self.load_tmpl_image_pose(curr_rgb, meta_data)
+        # tmpl_rgbs = tmpl_annos['tmpl_rgb_list'] # list of reference rgbs
+
+        transform_paras = dict()
+        rgbs, depths, intrinsics, cam_models, _, other_labels, transform_paras = self.img_transforms(
+                                                                   images=[curr_rgb,], #  + tmpl_rgbs, 
+                                                                   labels=[curr_depth, ], 
+                                                                   intrinsics=[ori_curr_intrinsic, ], # * (len(tmpl_rgbs) + 1), 
+                                                                   cam_models=[curr_cam_model, ],
+                                                                   transform_paras=transform_paras)
+        # depth in original size and orignial metric***
+        depth_out = self.clip_depth(curr_depth) * self.depth_range[1]
+        inv_depth = self.depth2invdepth(depth_out, np.zeros_like(depth_out, dtype=np.bool))
+
+        filename = os.path.basename(meta_data['rgb'])
+        curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
+
+        pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
+        scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0
+        cam_models_stacks = [
+            torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
+            for i in [2, 4, 8, 16, 32] 
+            ]    
+        raw_rgb = torch.from_numpy(curr_rgb)
+        curr_normal = torch.from_numpy(curr_normal.transpose((2,0,1)))
+
+        data = dict(input=rgbs[0],
+                    target=depth_out,
+                    intrinsic=curr_intrinsic_mat,
+                    filename=filename,
+                    dataset=self.data_name,
+                    cam_model=cam_models_stacks,
+                    pad=pad,
+                    scale=scale_ratio,
+                    raw_rgb=raw_rgb,
+                    sample_id=idx,
+                    data_path=meta_data['rgb'],
+                    inv_depth=inv_depth,
+                    normal=curr_normal,
+                    )
+        return data
+    
+    def load_norm_label(self, norm_path, H, W):
+        with open(norm_path, 'rb') as f:
+            normal = Image.open(f)
+            normal = np.array(normal.convert(normal.mode), dtype=np.uint8)
+        invalid_mask = np.all(normal == 128, axis=2)
+        normal = normal.astype(np.float64) / 255.0 * 2 - 1
+        normal[invalid_mask, :] = 0
+        return normal
+    
+    def process_depth(self, depth: np.array, rgb: np.array) -> np.array:
+        depth[depth>60000] = 0
+        depth = depth / self.metric_scale
+        return depth
+    
+    def load_ins_planes(self, depth: np.array, ins_planes_path: str) -> np.array:
+        if ins_planes_path is not None:
+            ins_planes = cv2.imread(ins_planes_path, -1)
+        else:
+            ins_planes = np.zeros_like(depth)
+        return ins_planes
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/uasol_dataset.py

@@ -0,0 +1,52 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class UASOLDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(UASOLDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= self.metric_scale
+        return depth
+    
+    def load_rgb_depth(self, rgb_path: str, depth_path: str) -> (np.array, np.array):
+        """
+        Load the rgb and depth map with the paths.
+        """
+        rgb = self.load_data(rgb_path, is_rgb_img=True)
+        if rgb is None:
+            self.logger.info(f'>>>>{rgb_path} has errors.')
+       
+        depth = self.load_data(depth_path)
+        if depth is None:
+            self.logger.info(f'{depth_path} has errors.')
+        
+        depth = depth.astype(np.float)
+        
+        depth  = self.process_depth(depth, rgb)
+        depth = depth[1:-1, ...]
+        return rgb, depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = UASOLDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/virtualkitti_dataset.py

@@ -0,0 +1,65 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class VKITTIDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(VKITTIDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>(150 * self.metric_scale)] = 0
+        depth /= self.metric_scale
+
+        return depth
+    
+    def load_sem_label(self, sem_path, depth=None, sky_id=142) -> np.array:
+        """
+            Category r g b
+            Terrain 210 0 200
+            Sky     90 200 255
+            Tree     0 199 0
+            Vegetation 90 240 0
+            Building 140 140 140
+            Road 100 60 100
+            GuardRail 250 100 255
+            TrafficSign 255 255 0
+            TrafficLight 200 200 0
+            Pole 255 130 0
+            Misc 80 80 80
+            Truck 160 60 60
+            Car 255 127 80
+            Van 0 139 139
+        """
+        H, W = depth.shape
+        sem_label = np.ones((H, W), dtype=np.int) * -1
+        sem = cv2.imread(sem_path)[:, :, ::-1]
+        if sem is None:
+            return sem_label
+        
+        sky_color = [90, 200, 255]
+        sky_mask = (sem == sky_color).all(axis=2)
+        sem_label[sky_mask] = 142 # set sky region to 142
+        return sem_label
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/datasets/waymo_dataset.py

@@ -0,0 +1,34 @@
+import os
+import json
+import torch
+import torchvision.transforms as transforms
+import os.path
+import numpy as np
+import cv2
+from torch.utils.data import Dataset
+import random
+from .__base_dataset__ import BaseDataset
+
+
+class WaymoDataset(BaseDataset):
+    def __init__(self, cfg, phase, **kwargs):
+        super(WaymoDataset, self).__init__(
+            cfg=cfg,
+            phase=phase,
+            **kwargs)
+        self.metric_scale = cfg.metric_scale
+    
+    
+    def process_depth(self, depth, rgb):
+        depth[depth>65500] = 0
+        depth /= 200.0
+        return depth
+
+
+
+if __name__ == '__main__':
+    from mmcv.utils import Config 
+    cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
+    dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
+    print(dataset_i)
+    

external/Metric3D/training/mono/tools/test.py

@@ -0,0 +1,165 @@
+import os
+import os.path as osp
+import time
+import sys
+CODE_SPACE=os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(CODE_SPACE)
+#os.chdir(CODE_SPACE)
+import argparse
+import mmcv
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+try:
+    from mmcv.utils import Config, DictAction
+except:
+    from mmengine import Config, DictAction
+from datetime import timedelta
+import random
+import numpy as np
+
+from mono.datasets.distributed_sampler import log_canonical_transfer_info
+from mono.utils.comm import init_env
+from mono.utils.logger import setup_logger
+from mono.utils.db import load_data_info, reset_ckpt_path
+from mono.model.monodepth_model import get_configured_monodepth_model
+from mono.datasets.distributed_sampler import build_dataset_n_sampler_with_cfg
+from mono.utils.running import load_ckpt
+from mono.utils.do_test import do_test_with_dataloader, do_test_check_data
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Train a segmentor')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument('--show-dir', help='the dir to save logs and visualization results')
+    parser.add_argument(
+        '--load-from', help='the checkpoint file to load weights from')
+    parser.add_argument('--node_rank', type=int, default=0)
+    parser.add_argument('--nnodes', 
+                        type=int, 
+                        default=1, 
+                        help='number of nodes')
+    parser.add_argument(
+        '--options', nargs='+', action=DictAction, help='custom options')   
+    parser.add_argument(
+        '--launcher', choices=['None', 'pytorch', 'slurm'], default='slurm',
+        help='job launcher')
+    args = parser.parse_args()
+    return args
+
+        
+def main(args):
+    os.chdir(CODE_SPACE)
+    cfg = Config.fromfile(args.config)
+    cfg.dist_params.nnodes = args.nnodes
+    cfg.dist_params.node_rank = args.node_rank
+
+    if args.options is not None:
+        cfg.merge_from_dict(args.options)
+    # set cudnn_benchmark
+    #if cfg.get('cudnn_benchmark', False) and args.launcher != 'ror':
+    #    torch.backends.cudnn.benchmark = True
+
+    # show_dir is determined in this priority: CLI > segment in file > filename
+    if args.show_dir is not None:
+        # update configs according to CLI args if args.show_dir is not None
+        cfg.show_dir = args.show_dir
+    elif cfg.get('show_dir', None) is None:
+        # use config filename + timestamp as default show_dir if cfg.show_dir is None
+        cfg.show_dir = osp.join('./show_dirs',
+                                osp.splitext(osp.basename(args.config))[0], 
+                                args.timestamp)
+
+    # ckpt path
+    if args.load_from is None:
+        raise RuntimeError('Please set model path!')
+    cfg.load_from = args.load_from
+    
+    # create show dir
+    os.makedirs(osp.abspath(cfg.show_dir), exist_ok=True)
+    
+    # init the logger before other steps
+    cfg.log_file = osp.join(cfg.show_dir, f'{args.timestamp}.log')
+    logger = setup_logger(cfg.log_file)
+
+    # log some basic info
+    logger.info(f'Config:\n{cfg.pretty_text}')
+
+    # load db_info for data
+    # load data info
+    data_info = {}
+    load_data_info('data_server_info', data_info=data_info)
+    cfg.db_info = data_info
+    # update check point info
+    reset_ckpt_path(cfg.model, data_info)
+
+    # log data transfer to canonical space info
+    # log_canonical_transfer_info(cfg)
+    
+    # init distributed env first, since logger depends on the dist info.
+    if args.launcher == 'none':
+        cfg.distributed = False
+    else:
+        cfg.distributed = True
+        init_env(args.launcher, cfg)
+    logger.info(f'Distributed training: {cfg.distributed}')
+
+    # dump config
+    cfg.dump(osp.join(cfg.show_dir, osp.basename(args.config)))
+    
+    if not cfg.distributed:
+        main_worker(0, cfg, args.launcher)
+    else:
+        mp.spawn(main_worker, nprocs=cfg.dist_params.num_gpus_per_node, args=(cfg, args.launcher))
+
+def main_worker(local_rank: int, cfg: dict, launcher: str):
+    if cfg.distributed:
+        cfg.dist_params.global_rank = cfg.dist_params.node_rank * cfg.dist_params.num_gpus_per_node + local_rank
+        cfg.dist_params.local_rank = local_rank
+        
+        torch.cuda.set_device(local_rank)
+        default_timeout = timedelta(minutes=30)
+        dist.init_process_group(backend=cfg.dist_params.backend,
+                            init_method=cfg.dist_params.dist_url,
+                            world_size=cfg.dist_params.world_size,
+                            rank=cfg.dist_params.global_rank,
+                            timeout=default_timeout,)
+
+    logger = setup_logger(cfg.log_file)
+    # build model
+    model = get_configured_monodepth_model(cfg,
+                                           None,
+                                           )
+    
+    # build datasets
+    test_dataset, test_sampler = build_dataset_n_sampler_with_cfg(cfg, 'test')
+    # build data loaders
+    test_dataloader = torch.utils.data.DataLoader(dataset=test_dataset,
+                                                   batch_size=1,
+                                                   num_workers=1,
+                                                   sampler=test_sampler,
+                                                   drop_last=False)
+
+   
+    # config distributed training
+    if cfg.distributed:
+        model = torch.nn.parallel.DistributedDataParallel(model.cuda(), 
+                                                          device_ids=[local_rank], 
+                                                          output_device=local_rank, 
+                                                          find_unused_parameters=True)
+    else:
+        model = torch.nn.DataParallel(model.cuda())
+    
+    # load ckpt
+    #model, _, _, _ = load_ckpt(cfg.load_from, model, strict_match=False)
+    model.eval()
+    do_test_with_dataloader(model, cfg, test_dataloader, logger=logger, is_distributed=cfg.distributed)
+    # do_test_check_data(model, cfg, test_dataloader, logger=logger, is_distributed=cfg.distributed, local_rank=local_rank)
+
+
+if __name__=='__main__':
+    # load args
+    args = parse_args()
+    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+    args.timestamp = timestamp
+    main(args)

external/Metric3D/training/mono/tools/train.py

@@ -0,0 +1,254 @@
+import os
+import os.path as osp
+import time
+import sys
+CODE_SPACE=os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(CODE_SPACE)
+#os.chdir(CODE_SPACE)
+import argparse
+import copy
+import mmcv
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+try:
+    from mmcv.utils import Config, DictAction
+except:
+    from mmengine import Config, DictAction
+import socket
+import subprocess
+from datetime import timedelta
+import random
+import numpy as np
+import logging
+
+from mono.datasets.distributed_sampler import log_canonical_transfer_info
+from mono.utils.comm import init_env, collect_env
+from mono.utils.logger import setup_logger
+from mono.utils.db import load_data_info, reset_ckpt_path
+from mono.utils.do_train import do_train
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Train a segmentor')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument('--work-dir', help='the dir to save logs and models')
+    parser.add_argument('--tensorboard-dir', help='the dir to save tensorboard logs')
+    parser.add_argument(
+        '--load-from', help='the checkpoint file to load weights from')
+    parser.add_argument(
+        '--resume-from', help='the checkpoint file to resume from')
+    parser.add_argument(
+        '--no-validate',
+        action='store_true',
+        help='whether not to evaluate the checkpoint during training')
+    parser.add_argument(
+        '--gpu-ids',
+        type=int,
+        nargs='+',
+        help='ids of gpus to use '
+        '(only applicable to non-distributed training)')
+    parser.add_argument('--seed', type=int, default=88, help='random seed')
+    parser.add_argument(
+        '--deterministic',
+        action='store_true',
+        help='whether to set deterministic options for CUDNN backend.')
+    parser.add_argument(
+        '--use-tensorboard',
+        action='store_true',
+        help='whether to set deterministic options for CUDNN backend.')
+    parser.add_argument(
+        '--options', nargs='+', action=DictAction, help='custom options')
+    parser.add_argument('--node_rank', type=int, default=0)
+    parser.add_argument('--nnodes', 
+                        type=int, 
+                        default=1, 
+                        help='number of nodes')
+    parser.add_argument(
+        '--launcher', choices=['None', 'pytorch', 'slurm', 'mpi', 'ror'], default='slurm',
+        help='job launcher') 
+    parser.add_argument('--local_rank', 
+                        type=int, 
+                        default=0, 
+                        help='rank')  
+    parser.add_argument('--experiment_name', default='debug', help='the experiment name for mlflow')
+    args = parser.parse_args()
+    return args
+
+  
+def set_random_seed(seed, deterministic=False):
+    """Set random seed.
+    Args:
+        @seed (int): Seed to be used.
+        @deterministic (bool): Whether to set the deterministic option for
+            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
+            to True and `torch.backends.cudnn.benchmark` to False.
+            Default: False.
+    """
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    #if deterministic:
+    #    torch.backends.cudnn.deterministic = True
+    #    torch.backends.cudnn.benchmark = False
+
+def main(args):
+    os.chdir(CODE_SPACE)
+    cfg = Config.fromfile(args.config)
+    cfg.dist_params.nnodes = args.nnodes
+    cfg.dist_params.node_rank = args.node_rank
+    cfg.deterministic = args.deterministic
+    if args.options is not None:
+        cfg.merge_from_dict(args.options)
+    # set cudnn_benchmark
+    #if cfg.get('cudnn_benchmark', False) and args.launcher != 'ror':
+    #    torch.backends.cudnn.benchmark = True
+    # The flag below controls whether to allow TF32 on matmul. This flag defaults to False
+    # in PyTorch 1.12 and later.
+    # torch.backends.cuda.matmul.allow_tf32 = False
+    # The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
+    # torch.backends.cudnn.allow_tf32 = False
+
+    # work_dir is determined in this priority: CLI > segment in file > filename
+    if args.work_dir is not None:
+        # update configs according to CLI args if args.work_dir is not None
+        cfg.work_dir = args.work_dir
+    elif cfg.get('work_dir', None) is None:
+        # use config filename + timestamp as default work_dir if cfg.work_dir is None
+        cfg.work_dir = osp.join('./work_dirs',
+                                osp.splitext(osp.basename(args.config))[0], 
+                                args.timestamp)
+    # tensorboard_dir is determined in this priority: CLI > segment in file > filename
+    if args.tensorboard_dir is not None:
+        cfg.tensorboard_dir = args.tensorboard_dir
+    elif cfg.get('tensorboard_dir', None) is None:
+        # use cfg.work_dir + 'tensorboard' as default tensorboard_dir if cfg.tensorboard_dir is None
+        cfg.tensorboard_dir = osp.join(cfg.work_dir, 'tensorboard')
+
+    # ckpt path
+    if args.load_from is not None:
+        cfg.load_from = args.load_from
+    # resume training
+    if args.resume_from is not None:
+        cfg.resume_from = args.resume_from
+    
+    # create work_dir and tensorboard_dir
+    os.makedirs(osp.abspath(cfg.work_dir), exist_ok=True)
+    os.makedirs(os.path.abspath(cfg.tensorboard_dir), exist_ok=True)
+    
+    # init the logger before other steps
+    cfg.log_file = osp.join(cfg.work_dir, f'{args.timestamp}.log')
+    logger = setup_logger(cfg.log_file)
+
+    # init the meta dict to record some important information such as
+    # environment info and seed, which will be logged
+    meta = dict()
+    # log env info
+    env_info_dict = collect_env()
+    env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
+    dash_line = '-' * 60 + '\n'
+    logger.info('Environment info:\n' + dash_line + env_info + '\n' +
+                dash_line)
+    meta['env_info'] = env_info
+
+    # log some basic info
+    # logger.info(f'Config:\n{cfg.pretty_text}')
+
+    # mute online evaluation
+    if args.no_validate:
+        cfg.evaluation.online_eval = False
+
+
+    cfg.seed = args.seed
+    meta['seed'] = args.seed
+    meta['exp_name'] = osp.basename(args.config)
+
+    # load data info
+    data_info = {}
+    load_data_info('data_server_info', data_info=data_info)
+    cfg.db_info = data_info
+    # update check point info
+    reset_ckpt_path(cfg.model, data_info)
+
+    # log data transfer to canonical space info``
+    # log_canonical_transfer_info(cfg)
+    
+    # init distributed env first, since logger depends on the dist info.
+    if args.launcher == 'None':
+        cfg.distributed = False
+    else:
+        cfg.distributed = True
+    init_env(args.launcher, cfg)
+    logger.info(f'Distributed training: {cfg.distributed}')
+    logger.info(cfg.dist_params)
+    # dump config
+    cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
+    
+    cfg.experiment_name = args.experiment_name
+
+    if not cfg.distributed:
+        main_worker(0, cfg)
+    else:
+        # distributed training
+        if args.launcher == 'slurm': 
+            mp.spawn(main_worker, nprocs=cfg.dist_params.num_gpus_per_node, args=(cfg, args.launcher))
+        elif args.launcher == 'pytorch':
+            main_worker(args.local_rank, cfg, args.launcher)
+
+def main_worker(local_rank: int, cfg: dict, launcher: str='slurm'):
+    logger = setup_logger(cfg.log_file)
+    if cfg.distributed:
+        if launcher == 'slurm':
+            torch.set_num_threads(8) # without it, the spawn method is much slower than the launch method 
+            cfg.dist_params.global_rank = cfg.dist_params.node_rank * cfg.dist_params.num_gpus_per_node + local_rank
+            cfg.dist_params.local_rank = local_rank
+            os.environ['RANK']=str(cfg.dist_params.global_rank)
+        else:
+            torch.set_num_threads(1)
+        
+        torch.cuda.set_device(local_rank)
+        default_timeout = timedelta(minutes=10)
+        dist.init_process_group(
+                backend=cfg.dist_params.backend,
+                init_method=cfg.dist_params.dist_url,
+                world_size=cfg.dist_params.world_size,
+                rank=cfg.dist_params.global_rank,)
+                #timeout=default_timeout,)
+        dist.barrier()
+
+    # if cfg.distributed:
+        
+    #     cfg.dist_params.global_rank = cfg.dist_params.node_rank * cfg.dist_params.num_gpus_per_node + local_rank
+    #     cfg.dist_params.local_rank = local_rank
+    #     os.environ['RANK']=str(cfg.dist_params.global_rank)
+        
+    #     if launcher == 'ror':
+    #         init_torch_process_group(use_hvd=False)
+    #     else:
+    #         #torch.set_num_threads(4) # without it, the spawn method maybe much slower than the launch method 
+    #         torch.cuda.set_device(local_rank)
+    #         default_timeout = timedelta(minutes=30)
+    #         dist.init_process_group(
+    #             backend=cfg.dist_params.backend,
+    #             init_method=cfg.dist_params.dist_url,
+    #             world_size=cfg.dist_params.world_size,
+    #             rank=cfg.dist_params.global_rank,)
+    #             #timeout=default_timeout,)
+    
+    # set random seeds
+    if cfg.seed is not None:
+        logger.info(f'Set random seed to {cfg.seed}, deterministic: 'f'{cfg.deterministic}')
+        set_random_seed(cfg.seed, deterministic=cfg.deterministic)
+    # with torch.autograd.set_detect_anomaly(True):
+    do_train(local_rank, cfg)
+
+
+if __name__=='__main__':
+    # load args
+    args = parse_args()
+    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+    args.timestamp = timestamp
+    print(args.work_dir, args.tensorboard_dir)
+    main(args)