[Refactor] Internet for 3d hand pose estimation

configs/hand_3d_keypoint/internet/README.md

@@ -0,0 +1,10 @@
+# InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image
+
+## Results and Models
+
+### InterHand2.6m 3D Dataset
+
+| Arch                             |    Set    | MPJPE-single | MPJPE-interacting | MPJPE-all | MRRPE | APh  |               ckpt               |               log               |               Details and Download                |
+| :------------------------------- | :-------: | :----------: | :---------------: | :-------: | :---: | :--: | :------------------------------: | :-----------------------------: | :-----------------------------------------------: |
+| [InterNet_resnet_50](/configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/res50_interhand3d_all_256x256.py) | test(H+M) |     9.47     |       13.40       |   11.59   | 29.28 | 0.99 | [ckpt](https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3dv1.0_all_256x256-42b7f2ac_20210702.pth) | [log](https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3dv1.0_all_256x256_20210702.log.json) | [internet_interhand3d.md](./interhand3d/internet_interhand3d.md) |
+| [InterNet_resnet_50](/configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/res50_interhand3d_all_256x256.py) |  val(M)   |    11.22     |       15.23       |   13.16   | 31.73 | 0.98 | [ckpt](https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3dv1.0_all_256x256-42b7f2ac_20210702.pth) | [log](https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3dv1.0_all_256x256_20210702.log.json) | [internet_interhand3d.md](./interhand3d/internet_interhand3d.md) |

configs/hand_3d_keypoint/internet/interhand3d/internet_interhand3d.md

@@ -0,0 +1,59 @@
+<!-- [ALGORITHM] -->
+
+<details>
+<summary align="right"><a href="https://link.springer.com/content/pdf/10.1007/978-3-030-58565-5_33.pdf">InterNet (ECCV'2020)</a></summary>
+
+```bibtex
+@InProceedings{Moon_2020_ECCV_InterHand2.6M,
+author = {Moon, Gyeongsik and Yu, Shoou-I and Wen, He and Shiratori, Takaaki and Lee, Kyoung Mu},
+title = {InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image},
+booktitle = {European Conference on Computer Vision (ECCV)},
+year = {2020}
+}
+```
+
+</details>
+
+<!-- [BACKBONE] -->
+
+<details>
+<summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html">ResNet (CVPR'2016)</a></summary>
+
+```bibtex
+@inproceedings{he2016deep,
+  title={Deep residual learning for image recognition},
+  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={770--778},
+  year={2016}
+}
+```
+
+</details>
+
+<!-- [DATASET] -->
+
+<details>
+<summary align="right"><a href="https://link.springer.com/content/pdf/10.1007/978-3-030-58565-5_33.pdf">InterHand2.6M (ECCV'2020)</a></summary>
+
+```bibtex
+@InProceedings{Moon_2020_ECCV_InterHand2.6M,
+author = {Moon, Gyeongsik and Yu, Shoou-I and Wen, He and Shiratori, Takaaki and Lee, Kyoung Mu},
+title = {InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image},
+booktitle = {European Conference on Computer Vision (ECCV)},
+year = {2020}
+}
+```
+
+</details>
+
+Results on InterHand2.6M val & test set
+
+| Train Set | Set       |                    Arch                    | Input Size | MPJPE-single | MPJPE-interacting | MPJPE-all | MRRPE | APh  |                    ckpt                    |                    log                    |
+| :-------- | :-------- | :----------------------------------------: | :--------: | :----------: | :---------------: | :-------: | :---: | :--: | :----------------------------------------: | :---------------------------------------: |
+| All       | test(H+M) | [InterNet_resnet_50](/configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/res50_interhand3d_all_256x256.py) |  256x256   |     9.69     |       13.72       |   11.86   | 29.27 | 0.99 | [ckpt](https://download.openmmlab.com/mmpose/v1/hand_3d_keypoint/internet/interhand3d/internet_res50_interhand3d-d6ff20d6_20230913.pth) | [log](https://download.openmmlab.com/mmpose/v1/hand_3d_keypoint/internet/interhand3d/internet_res50_interhand3d-d6ff20d6_20230913.json) |
+| All       | val(M)    | [InterNet_resnet_50](/configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/res50_interhand3d_all_256x256.py) |  256x256   |    11.30     |       15.57       |   13.36   | 32.15 | 0.98 | [ckpt](https://download.openmmlab.com/mmpose/v1/hand_3d_keypoint/internet/interhand3d/internet_res50_interhand3d-d6ff20d6_20230913.pth) | [log](https://download.openmmlab.com/mmpose/v1/hand_3d_keypoint/internet/interhand3d/internet_res50_interhand3d-d6ff20d6_20230913.json) |
+| All       | test(H+M) | [InterNet_resnet_50\*](/configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/res50_interhand3d_all_256x256.py) |  256x256   |     9.47     |       13.40       |   11.59   | 29.28 | 0.99 | [ckpt](https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3dv1.0_all_256x256-42b7f2ac_20210702.pth) | [log](https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3dv1.0_all_256x256_20210702.log.json) |
+| All       | val(M)    | [InterNet_resnet_50\*](/configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/res50_interhand3d_all_256x256.py) |  256x256   |    11.22     |       15.23       |   13.16   | 31.73 | 0.98 | [ckpt](https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3dv1.0_all_256x256-42b7f2ac_20210702.pth) | [log](https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3dv1.0_all_256x256_20210702.log.json) |
+
+*Models with * are trained in [MMPose 0.x](https://github.com/open-mmlab/mmpose/tree/0.x). The checkpoints and logs are only for validation.*

configs/hand_3d_keypoint/internet/interhand3d/internet_interhand3d.yml

@@ -0,0 +1,34 @@
+Collections:
+- Name: InterNet
+  Paper:
+    Title: 'InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation
+      from a Single RGB Image'
+    URL: https://link.springer.com/content/pdf/10.1007/978-3-030-58565-5_33.pdf
+  README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/internet.md
+Models:
+- Config: configs/hand_3d_keypoint/internet/interhand3d/internet_res50_4xb16-20e_interhand3d-256x256.py
+  In Collection: InterNet
+  Metadata:
+    Architecture: &id001
+    - InterNet
+    - ResNet
+    Training Data: InterHand2.6M
+  Name: internet_res50_4xb16-20e_interhand3d-256x256
+  Results:
+  - Dataset: InterHand2.6M (H+M)
+    Metrics:
+      APh: 0.99
+      MPJPE-all: 11.86
+      MPJPE-interacting: 13.72
+      MPJPE-single: 9.69
+      MRRPE: 29.27
+    Task: Hand 3D Keypoint
+  - Dataset: InterHand2.6M (M)
+    Metrics:
+      APh: 0.98
+      MPJPE-all: 13.36
+      MPJPE-interacting: 15.57
+      MPJPE-single: 11.30
+      MRRPE: 32.15
+    Task: Hand 3D Keypoint
+  Weights: https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3dv1.0_all_256x256-42b7f2ac_20210702.pth

configs/hand_3d_keypoint/internet/interhand3d/internet_res50_4xb16-20e_interhand3d-256x256.py

@@ -0,0 +1,182 @@
+_base_ = ['../../../_base_/default_runtime.py']
+
+# visualization
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    type='Pose3dLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+# runtime
+train_cfg = dict(max_epochs=20, val_interval=1)
+
+# optimizer
+optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0002))
+
+# learning policy
+param_scheduler = [
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=20,
+        milestones=[15, 17],
+        gamma=0.1,
+        by_epoch=True)
+]
+
+auto_scale_lr = dict(base_batch_size=128)
+
+# hooks
+default_hooks = dict(
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=1,
+        save_best='MPJPE_all',
+        rule='less',
+        max_keep_ckpts=1),
+    logger=dict(type='LoggerHook', interval=20),
+)
+
+# codec settings
+codec = dict(
+    type='Hand3DHeatmap',
+    image_size=[256, 256],
+    root_heatmap_size=64,
+    heatmap_size=[64, 64, 64],
+    sigma=2.5,
+    max_bound=255,
+    depth_size=64)
+
+# model settings
+model = dict(
+    type='TopdownPoseEstimator',
+    data_preprocessor=dict(
+        type='PoseDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True),
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    head=dict(
+        type='InternetHead',
+        keypoint_head_cfg=dict(
+            in_channels=2048,
+            out_channels=21 * 64,
+            depth_size=codec['depth_size'],
+            deconv_out_channels=(256, 256, 256),
+            deconv_kernel_sizes=(4, 4, 4),
+        ),
+        root_head_cfg=dict(
+            in_channels=2048,
+            heatmap_size=codec['root_heatmap_size'],
+            hidden_dims=(512, ),
+        ),
+        hand_type_head_cfg=dict(
+            in_channels=2048,
+            num_labels=2,
+            hidden_dims=(512, ),
+        ),
+        decoder=codec),
+    test_cfg=dict(flip_test=False))
+
+# base dataset settings
+dataset_type = 'InterHand3DDataset'
+data_mode = 'topdown'
+data_root = 'data/interhand2.6m/'
+
+# pipelines
+train_pipeline = [
+    dict(type='LoadImage'),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='HandRandomFlip', prob=0.5),
+    dict(type='RandomBBoxTransform', rotate_factor=90.0),
+    dict(type='TopdownAffine', input_size=codec['image_size']),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(
+        type='PackPoseInputs',
+        meta_keys=('id', 'img_id', 'img_path', 'rotation', 'img_shape',
+                   'focal', 'principal_pt', 'input_size', 'input_center',
+                   'input_scale', 'hand_type', 'hand_type_valid', 'flip',
+                   'flip_indices', 'abs_depth'))
+]
+val_pipeline = [
+    dict(type='LoadImage'),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='TopdownAffine', input_size=codec['image_size']),
+    dict(
+        type='PackPoseInputs',
+        meta_keys=('id', 'img_id', 'img_path', 'rotation', 'img_shape',
+                   'focal', 'principal_pt', 'input_size', 'input_center',
+                   'input_scale', 'hand_type', 'hand_type_valid', 'flip',
+                   'flip_indices', 'abs_depth'))
+]
+
+# data loaders
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        ann_file='annotations/all/InterHand2.6M_train_data.json',
+        camera_param_file='annotations/all/InterHand2.6M_train_camera.json',
+        joint_file='annotations/all/InterHand2.6M_train_joint_3d.json',
+        use_gt_root_depth=True,
+        rootnet_result_file=None,
+        data_mode=data_mode,
+        data_root=data_root,
+        data_prefix=dict(img='images/train/'),
+        pipeline=train_pipeline,
+    ))
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
+    dataset=dict(
+        type=dataset_type,
+        ann_file='annotations/machine_annot/InterHand2.6M_val_data.json',
+        camera_param_file='annotations/machine_annot/'
+        'InterHand2.6M_val_camera.json',
+        joint_file='annotations/machine_annot/InterHand2.6M_val_joint_3d.json',
+        use_gt_root_depth=True,
+        rootnet_result_file=None,
+        data_mode=data_mode,
+        data_root=data_root,
+        data_prefix=dict(img='images/val/'),
+        pipeline=val_pipeline,
+        test_mode=True,
+    ))
+test_dataloader = dict(
+    batch_size=16,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
+    dataset=dict(
+        type=dataset_type,
+        ann_file='annotations/all/'
+        'InterHand2.6M_test_data.json',
+        camera_param_file='annotations/all/'
+        'InterHand2.6M_test_camera.json',
+        joint_file='annotations/all/'
+        'InterHand2.6M_test_joint_3d.json',
+        use_gt_root_depth=True,
+        rootnet_result_file=None,
+        data_mode=data_mode,
+        data_root=data_root,
+        data_prefix=dict(img='images/test/'),
+        pipeline=val_pipeline,
+        test_mode=True,
+    ))
+
+# evaluators
+val_evaluator = [
+    dict(type='InterHandMetric', modes=['MPJPE', 'MRRPE', 'HandednessAcc'])
+]
+test_evaluator = val_evaluator

mmpose/codecs/__init__.py

@@ -3,6 +3,7 @@
 from .associative_embedding import AssociativeEmbedding
 from .decoupled_heatmap import DecoupledHeatmap
 from .edpose_label import EDPoseLabel
+from .hand_3d_heatmap import Hand3DHeatmap
 from .image_pose_lifting import ImagePoseLifting
 from .integral_regression_label import IntegralRegressionLabel
 from .megvii_heatmap import MegviiHeatmap
@@ -18,5 +19,6 @@
     'MSRAHeatmap', 'MegviiHeatmap', 'UDPHeatmap', 'RegressionLabel',
     'SimCCLabel', 'IntegralRegressionLabel', 'AssociativeEmbedding', 'SPR',
     'DecoupledHeatmap', 'VideoPoseLifting', 'ImagePoseLifting',
-    'MotionBERTLabel', 'YOLOXPoseAnnotationProcessor', 'EDPoseLabel'
+    'MotionBERTLabel', 'YOLOXPoseAnnotationProcessor', 'EDPoseLabel',
+    'Hand3DHeatmap'
 ]