Update config

configs/gta_human/hmr/resnet50_hmr+_gta_bt.py

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+_base_ = ['../_base_/default_runtime.py']
+use_adversarial_train = True
+# dist_params = dict(backend='nccl', port=29488)
+dist_params = dict(backend='gloo')
+
+# evaluate
+evaluation = dict(interval=1, metric=['pa-mpjpe', 'mpjpe'])
+# optimizer
+optimizer = dict(
+    backbone=dict(type='Adam', lr=2.5e-5), head=dict(type='Adam', lr=2.5e-5))
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(policy='Fixed', by_epoch=False)
+runner = dict(type='EpochBasedRunner', max_epochs=150)
+
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+# checkpoint_config = dict(interval=5)
+img_res = 224
+
+# model settings
+
+body_model = dict(
+    type='SMPL',
+    keypoint_src='smpl_54',
+    keypoint_dst='smpl_49',
+    keypoint_approximate=True,
+    model_path='data/body_models/smpl',
+    extra_joints_regressor='data/body_models/J_regressor_extra.npy')
+
+registrant = dict(
+    type='SMPLify',
+    body_model=body_model,
+    num_epochs=1,
+    stages=[],
+    keypoints2d_loss=dict(
+        type='KeypointMSELoss', loss_weight=1.0, reduction='sum', sigma=100),
+    shape_prior_loss=dict(
+        type='ShapePriorLoss', loss_weight=5.0**2, reduction='sum'),
+    joint_prior_loss=dict(
+        type='JointPriorLoss', loss_weight=15.2**2, reduction='sum'),
+    pose_prior_loss=dict(
+        type='MaxMixturePrior',
+        prior_folder='data',
+        num_gaussians=8,
+        loss_weight=4.78**2,
+        reduction='sum'),
+    ignore_keypoints=[
+        'neck_openpose', 'right_hip_openpose', 'left_hip_openpose',
+        'right_hip_extra', 'left_hip_extra'
+    ],
+    camera=dict(
+        type='PerspectiveCameras',
+        convention='opencv',
+        in_ndc=False,
+        focal_length=5000,
+        image_size=(img_res, img_res),
+        principal_point=(img_res / 2, img_res / 2)))
+registration = dict(mode='static', registrant=registrant)
+
+model = dict(
+    type='ImageBodyModelEstimator',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        out_indices=[3],
+        norm_eval=False,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    head=dict(
+        type='HMRHead',
+        feat_dim=2048,
+        smpl_mean_params='data/body_models/smpl_mean_params.npz'),
+    body_model_train=body_model,
+    body_model_test=dict(
+        type='SMPL',
+        keypoint_src='h36m',
+        keypoint_dst='h36m',
+        model_path='data/body_models/smpl',
+        joints_regressor='data/body_models/J_regressor_h36m.npy'),
+    registration=registration,
+    convention='smpl_49',
+    loss_keypoints3d=dict(type='SmoothL1Loss', loss_weight=100),
+    loss_keypoints2d=dict(type='SmoothL1Loss', loss_weight=10),
+    loss_vertex=dict(type='L1Loss', loss_weight=2),
+    loss_smpl_pose=dict(type='MSELoss', loss_weight=3),
+    loss_smpl_betas=dict(type='MSELoss', loss_weight=0.02),
+    loss_camera=dict(type='CameraPriorLoss', loss_weight=60),
+)
+# dataset settings
+dataset_type = 'HumanImageDataset'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+data_keys = [
+    'has_smpl', 'smpl_body_pose', 'smpl_global_orient', 'smpl_betas',
+    'smpl_transl', 'keypoints2d', 'keypoints3d', 'is_flipped', 'center',
+    'scale', 'rotation', 'sample_idx'
+]
+
+file_client_args = dict(
+    backend='petrel', path_mapping=dict({'data/': 's3://mmhuman3d_datasets/'}))
+train_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='RandomChannelNoise', noise_factor=0.4),
+    dict(type='RandomHorizontalFlip', flip_prob=0.5, convention='smpl_49'),
+    dict(type='GetRandomScaleRotation', rot_factor=30, scale_factor=0.25),
+    dict(type='MeshAffine', img_res=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=data_keys),
+    dict(
+        type='Collect',
+        keys=['img', *data_keys],
+        meta_keys=['image_path', 'center', 'scale', 'rotation'])
+]
+data_keys.remove('is_flipped')
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='GetRandomScaleRotation', rot_factor=0, scale_factor=0),
+    dict(type='MeshAffine', img_res=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=data_keys),
+    dict(
+        type='Collect',
+        keys=['img', *data_keys],
+        meta_keys=['image_path', 'center', 'scale', 'rotation'])
+]
+
+inference_pipeline = [
+    dict(type='MeshAffine', img_res=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(
+        type='Collect',
+        keys=['img', 'sample_idx'],
+        meta_keys=['image_path', 'center', 'scale', 'rotation'])
+]
+
+cache_files = {
+    'h36m': 'data/cache/h36m_mosh_train_smpl_49.npz',
+    'mpi_inf_3dhp': 'data/cache/spin_mpi_inf_3dhp_train_smpl_49.npz',
+    'lsp': 'data/cache/spin_lsp_train_smpl_49.npz',
+    'lspet': 'data/cache/spin_lspet_train_smpl_49.npz',
+    'mpii': 'data/cache/spin_mpii_train_smpl_49.npz',
+    'coco': 'data/cache/spin_coco_2014_train_smpl_49.npz',
+    'gta': 'data/cache/gta_human_4x_smpl_49.npz'
+}
+
+data = dict(
+    samples_per_gpu=128,
+    workers_per_gpu=4,
+    train=dict(
+        type='MixedDataset',
+        configs=[
+            dict(
+                type=dataset_type,
+                dataset_name='h36m',
+                data_prefix='data',
+                pipeline=train_pipeline,
+                convention='smpl_49',
+                cache_data_path=cache_files['h36m'],
+                ann_file='h36m_mosh_train.npz'),
+            dict(
+                type=dataset_type,
+                dataset_name='mpi_inf_3dhp',
+                data_prefix='data',
+                pipeline=train_pipeline,
+                convention='smpl_49',
+                cache_data_path=cache_files['mpi_inf_3dhp'],
+                ann_file='spin_mpi_inf_3dhp_train.npz'),
+            dict(
+                type=dataset_type,
+                dataset_name='lsp',
+                data_prefix='data',
+                pipeline=train_pipeline,
+                convention='smpl_49',
+                cache_data_path=cache_files['lsp'],
+                ann_file='spin_lsp_train.npz'),
+            dict(
+                type=dataset_type,
+                dataset_name='lspet',
+                data_prefix='data',
+                pipeline=train_pipeline,
+                convention='smpl_49',
+                cache_data_path=cache_files['lspet'],
+                ann_file='spin_lspet_train.npz'),
+            dict(
+                type=dataset_type,
+                dataset_name='mpii',
+                data_prefix='data',
+                pipeline=train_pipeline,
+                convention='smpl_49',
+                cache_data_path=cache_files['mpii'],
+                ann_file='spin_mpii_train.npz'),
+            dict(
+                type=dataset_type,
+                dataset_name='coco',
+                data_prefix='data',
+                pipeline=train_pipeline,
+                convention='smpl_49',
+                cache_data_path=cache_files['coco'],
+                ann_file='spin_coco_2014_train.npz'),
+            dict(
+                type=dataset_type,
+                dataset_name='gta',
+                data_prefix='data',
+                pipeline=train_pipeline,
+                convention='smpl_49',
+                cache_data_path=cache_files['gta'],
+                ann_file='gta_human_4x.npz'),
+        ],
+        partition=[0.35, 0.15, 0.1, 0.10, 0.10, 0.2, 1],
+    ),
+    test=dict(
+        type=dataset_type,
+        body_model=dict(
+            type='GenderedSMPL',
+            keypoint_src='h36m',
+            keypoint_dst='h36m',
+            model_path='data/body_models/smpl',
+            joints_regressor='data/body_models/J_regressor_h36m.npy'),
+        dataset_name='pw3d',
+        data_prefix='data',
+        pipeline=test_pipeline,
+        convention='h36m',
+        ann_file='pw3d_test.npz'),
+    val=dict(
+        type=dataset_type,
+        body_model=dict(
+            type='GenderedSMPL',
+            keypoint_src='h36m',
+            keypoint_dst='h36m',
+            model_path='data/body_models/smpl',
+            joints_regressor='data/body_models/J_regressor_h36m.npy'),
+        dataset_name='pw3d',
+        data_prefix='data',
+        pipeline=test_pipeline,
+        ann_file='pw3d_test.npz'))

configs/gta_human/vibe/README.md

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+# GTA-Human (VIBE)
+
+## Notes
+
+- [SMPL](https://smpl.is.tue.mpg.de/) v1.0 is used in our experiments.
+- [J_regressor_extra.npy](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmhuman3d/models/J_regressor_extra.npy?versionId=CAEQHhiBgIDD6c3V6xciIGIwZDEzYWI5NTBlOTRkODU4OTE1M2Y4YTI0NTVlZGM1)
+- [J_regressor_h36m.npy](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmhuman3d/models/J_regressor_h36m.npy?versionId=CAEQHhiBgIDE6c3V6xciIDdjYzE3MzQ4MmU4MzQyNmRiZDA5YTg2YTI5YWFkNjRi)
+- [smpl_mean_params.npz](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmhuman3d/models/smpl_mean_params.npz?versionId=CAEQHhiBgICN6M3V6xciIDU1MzUzNjZjZGNiOTQ3OWJiZTJmNThiZmY4NmMxMTM4)
+- [gmm_08.pkl](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmhuman3d/models/gmm_08.pkl?versionId=CAEQHhiBgIDP6c3V6xciIGU4ZWFlYzlhNDJmODRmOGViYTMzOGRmODg2YjQ4NTg1)
+- Pretrained SPIN model [spin_official.pth](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmhuman3d/data/pretrained_models/spin_official.pth?versionId=CAEQRBiBgMC3zJPvjhgiIDNjODIxODJjYzEyNzRmNDhhNzU3Nzg3N2FlY2Y0ZWMx) for extracting features. Rename it as spin.pth.
+
+Download the above resources and arrange them in the following file structure:
+
+```text
+mmhuman3d
+├── mmhuman3d
+├── docs
+├── tests
+├── tools
+├── configs
+└── data
+    ├── gmm_08.pkl
+    ├── body_models
+    │   ├── J_regressor_extra.npy
+    │   ├── J_regressor_h36m.npy
+    │   ├── smpl_mean_params.npz
+    │   └── smpl
+    │       ├── SMPL_FEMALE.pkl
+    │       ├── SMPL_MALE.pkl
+    │       └── SMPL_NEUTRAL.pkl
+    ├── pretrained
+    │   └── spin.pth
+    ├── preprocessed_datasets
+    │   ├── vibe_insta_variety.npz
+    │   ├── vibe_mpi_inf_3dhp_train.npz
+    │   ├── vibe_pw3d_train.npz
+    │   ├── vibe_pw3d_test.npz
+    │   ├── vibe_gta_train.npz
+    │   └── vibe_gta_96k.npz
+
+
+```