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finegym/finegym_ensemble.py

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+from mmcv import load
+import sys
+# Note: please adjust the relative path according to the actual situation.
+sys.path.append('../..')
+from protogcn.smp import *
+
+
+j_1 = load('j_1/best_pred.pkl')
+b_1 = load('b_1/best_pred.pkl')
+k_1 = load('k_1/best_pred.pkl')
+j_2 = load('j_2/best_pred.pkl')
+b_2 = load('b_2/best_pred.pkl')
+k_2 = load('k_2/best_pred.pkl')
+jm = load('jm/best_pred.pkl')
+bm = load('bm/best_pred.pkl')
+km = load('km/best_pred.pkl')
+label = load_label('/data/finegym/gym_hrnet.pkl', 'val')
+
+
+"""
+***************
+InfoGCN  v0:
+j jm b bm k km
+2S:     95.35
+4S:     95.92
+6S:     95.92
+***************
+"""
+print('InfoGCN  v0:')
+print('j jm b bm k km')
+print('2S')
+fused = comb([j_1, b_1], [1, 1])
+print('Top-1', top1(fused, label))
+
+print('4S')
+fused = comb([j_1, b_1, jm, bm], [2, 2, 1, 1])
+print('Top-1', top1(fused, label))
+
+print('6S')
+fused = comb([j_1, b_1, k_1, jm, bm, km], [2, 2, 0, 1, 1, 0])
+print('Top-1', top1(fused, label))
+
+
+"""
+***************
+InfoGCN  v1:
+j j b b k k
+2S:     95.35
+4S:     95.62
+6S:     95.94
+***************
+"""
+print('InfoGCN  v1:')
+print('j j b b k k')
+print('2S')
+fused = comb([j_1, b_1], [1, 1])
+print('Top-1', top1(fused, label))
+
+print('4S')
+fused = comb([j_1, b_1, j_2, b_2], [1, 1, 1, 1])
+print('Top-1', top1(fused, label))
+
+print('6S')
+fused = comb([j_1, j_2, b_1, b_2, k_1, k_2], [5, 5, 5, 5, 4, 4])
+print('Top-1', top1(fused, label))

finegym/j_1/best_pred.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cdd8f339d11c8287cd01cdc79f41e4cffe9fc67bc414350a59578faab38b7433
+size 5256274

finegym/j_1/best_top1_acc_epoch_141.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf64e0df251c6d5ddba56c5fb3e68d31007580ad22d5fff2f43c0feaa9333ce1
+size 32988198

finegym/j_1/j_1.py

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+modality = 'j'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/finegym/j_1'
+model = dict(
+    type='RecognizerGCN_7_1_1',
+    backbone=dict(
+        type='GCN_7_1_1',
+        tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'],
+        graph_cfg=dict(
+            layout='coco_new',
+            mode='random',
+            num_filter=8,
+            init_off=0.04,
+            init_std=0.02)),
+    cls_head=dict(type='SimpleHead_7_4_13', num_classes=99, in_channels=384))
+dataset_type = 'PoseDataset'
+ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+train_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100),
+    dict(type='PoseDecode'),
+    dict(
+        type='Flip',
+        flip_ratio=0.5,
+        left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+        right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+val_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+test_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+data = dict(
+    videos_per_gpu=16,
+    workers_per_gpu=4,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100),
+            dict(type='PoseDecode'),
+            dict(
+                type='Flip',
+                flip_ratio=0.5,
+                left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+                right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='train'),
+    val=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'),
+    test=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'))
+optimizer = dict(
+    type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False)
+total_epochs = 150
+checkpoint_config = dict(interval=1)
+evaluation = dict(
+    interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5))
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+gpu_ids = range(0, 1)

finegym/j_2/best_pred.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a72d3a4445fddc7a6abd18ce43dd1eb4a1851b8b87c53738feb110d9268f4326
+size 5256389

finegym/j_2/best_top1_acc_epoch_139.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f23e35fa982b7264696c742ff543b560d86fc46a122a59d38aad9044e19114ac
+size 34831398

finegym/j_2/j_2.py

@@ -0,0 +1,110 @@
+modality = 'j'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/finegym/j_2'
+model = dict(
+    type='RecognizerGCN_7_1_1',
+    backbone=dict(
+        type='GCN_7_1_2',
+        tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'],
+        graph_cfg=dict(
+            layout='coco_new',
+            mode='random',
+            num_filter=8,
+            init_off=0.04,
+            init_std=0.02)),
+    cls_head=dict(type='SimpleHead_7_4_12', num_classes=99, in_channels=384))
+dataset_type = 'PoseDataset'
+ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+train_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100),
+    dict(type='PoseDecode'),
+    dict(
+        type='Flip',
+        flip_ratio=0.5,
+        left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+        right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+val_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+test_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+data = dict(
+    videos_per_gpu=16,
+    workers_per_gpu=4,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100),
+            dict(type='PoseDecode'),
+            dict(
+                type='Flip',
+                flip_ratio=0.5,
+                left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+                right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='train'),
+    val=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'),
+    test=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['j']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'))
+optimizer = dict(
+    type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False)
+total_epochs = 150
+checkpoint_config = dict(interval=1)
+evaluation = dict(
+    interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5))
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+gpu_ids = range(0, 1)

finegym/jm/best_pred.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fe6f5ad6852df6efdaee4f16c5b2807522e0d7dfeb7cd8f2c862ca09ca251be4
+size 5257496

finegym/jm/best_top1_acc_epoch_133.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3802dac4a8c21075f654ae61faa6f3352b75eb605dfdccb118155fe93ba396ef
+size 34831398

finegym/jm/jm.py

@@ -0,0 +1,110 @@
+modality = 'jm'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/finegym/jm'
+model = dict(
+    type='RecognizerGCN_7_1_1',
+    backbone=dict(
+        type='GCN_7_1_2',
+        tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'],
+        graph_cfg=dict(
+            layout='coco_new',
+            mode='random',
+            num_filter=8,
+            init_off=0.04,
+            init_std=0.02)),
+    cls_head=dict(type='SimpleHead_7_4_12', num_classes=99, in_channels=384))
+dataset_type = 'PoseDataset'
+ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+train_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100),
+    dict(type='PoseDecode'),
+    dict(
+        type='Flip',
+        flip_ratio=0.5,
+        left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+        right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+val_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+test_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+data = dict(
+    videos_per_gpu=16,
+    workers_per_gpu=4,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100),
+            dict(type='PoseDecode'),
+            dict(
+                type='Flip',
+                flip_ratio=0.5,
+                left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+                right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='train'),
+    val=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'),
+    test=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'))
+optimizer = dict(
+    type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False)
+total_epochs = 150
+checkpoint_config = dict(interval=1)
+evaluation = dict(
+    interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5))
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+gpu_ids = range(0, 1)

finegym/k_1/best_pred.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bcea1573792981d7bdff5f465202b1bd8d555dabe5f5d6dc0582c7917daeaada
+size 5255099

finegym/k_1/best_top1_acc_epoch_141.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7170effef12b65c2d95bb43073f50c1bbd040a5f8bb6f768ccbd03ab8a06a524
+size 32988198

finegym/k_1/k_1.py

@@ -0,0 +1,110 @@
+modality = 'k'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/finegym/k_1'
+model = dict(
+    type='RecognizerGCN_7_1_1',
+    backbone=dict(
+        type='GCN_7_1_1',
+        tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'],
+        graph_cfg=dict(
+            layout='coco_new',
+            mode='random',
+            num_filter=8,
+            init_off=0.04,
+            init_std=0.02)),
+    cls_head=dict(type='SimpleHead_7_4_11', num_classes=99, in_channels=384))
+dataset_type = 'PoseDataset'
+ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+train_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100),
+    dict(type='PoseDecode'),
+    dict(
+        type='Flip',
+        flip_ratio=0.5,
+        left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+        right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+val_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+test_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+data = dict(
+    videos_per_gpu=16,
+    workers_per_gpu=4,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100),
+            dict(type='PoseDecode'),
+            dict(
+                type='Flip',
+                flip_ratio=0.5,
+                left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+                right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='train'),
+    val=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'),
+    test=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'))
+optimizer = dict(
+    type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False)
+total_epochs = 150
+checkpoint_config = dict(interval=1)
+evaluation = dict(
+    interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5))
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+gpu_ids = range(0, 1)

finegym/k_2/best_pred.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9f0e0f7faf3b55d2548dd74c1c19f136672717408d2449ba00d1eff56f313e93
+size 5255049

finegym/k_2/best_top1_acc_epoch_146.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4ceaa2e38bf535c7eccc7fa0817d95381f9d507a68ae8cc7899a9afda614ca14
+size 32988198

finegym/k_2/k_2.py

@@ -0,0 +1,110 @@
+modality = 'k'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/finegym/k_2'
+model = dict(
+    type='RecognizerGCN_7_1_1',
+    backbone=dict(
+        type='GCN_7_1_1',
+        tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'],
+        graph_cfg=dict(
+            layout='coco_new',
+            mode='random',
+            num_filter=8,
+            init_off=0.04,
+            init_std=0.02)),
+    cls_head=dict(type='SimpleHead_7_4_11', num_classes=99, in_channels=384))
+dataset_type = 'PoseDataset'
+ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+train_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100),
+    dict(type='PoseDecode'),
+    dict(
+        type='Flip',
+        flip_ratio=0.5,
+        left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+        right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+val_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+test_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+data = dict(
+    videos_per_gpu=16,
+    workers_per_gpu=4,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100),
+            dict(type='PoseDecode'),
+            dict(
+                type='Flip',
+                flip_ratio=0.5,
+                left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+                right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='train'),
+    val=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'),
+    test=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['k']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'))
+optimizer = dict(
+    type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False)
+total_epochs = 150
+checkpoint_config = dict(interval=1)
+evaluation = dict(
+    interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5))
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+gpu_ids = range(0, 1)

finegym/km/best_pred.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83d7ed85ce9ab6ea1661e3207a917164fe727efb634461924ac93bb2d465d380
+size 5255820

finegym/km/best_top1_acc_epoch_146.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e5ff88ded6ec05d9b0054f13a11f87d6eba127ba604a5cabe2dd590f3523b31c
+size 32988198

finegym/km/km.py

@@ -0,0 +1,110 @@
+modality = 'km'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/finegym/km'
+model = dict(
+    type='RecognizerGCN_7_1_1',
+    backbone=dict(
+        type='GCN_7_1_1',
+        tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'],
+        graph_cfg=dict(
+            layout='coco_new',
+            mode='random',
+            num_filter=8,
+            init_off=0.04,
+            init_std=0.02)),
+    cls_head=dict(type='SimpleHead_7_4_11', num_classes=99, in_channels=384))
+dataset_type = 'PoseDataset'
+ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+train_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100),
+    dict(type='PoseDecode'),
+    dict(
+        type='Flip',
+        flip_ratio=0.5,
+        left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+        right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['km']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+val_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['km']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+test_pipeline = [
+    dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+    dict(type='PoseDecode'),
+    dict(type='Kinetics_Transform'),
+    dict(type='GenSkeFeat', dataset='coco_new', feats=['km']),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+data = dict(
+    videos_per_gpu=16,
+    workers_per_gpu=4,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100),
+            dict(type='PoseDecode'),
+            dict(
+                type='Flip',
+                flip_ratio=0.5,
+                left_kp=[1, 3, 5, 7, 9, 11, 13, 15],
+                right_kp=[2, 4, 6, 8, 10, 12, 14, 16]),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['km']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='train'),
+    val=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=1),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['km']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'),
+    test=dict(
+        type='PoseDataset',
+        ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl',
+        pipeline=[
+            dict(type='UniformSampleFrames', clip_len=100, num_clips=10),
+            dict(type='PoseDecode'),
+            dict(type='Kinetics_Transform'),
+            dict(type='GenSkeFeat', dataset='coco_new', feats=['km']),
+            dict(type='FormatGCNInput', num_person=2),
+            dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+            dict(type='ToTensor', keys=['keypoint'])
+        ],
+        split='val'))
+optimizer = dict(
+    type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False)
+total_epochs = 150
+checkpoint_config = dict(interval=1)
+evaluation = dict(
+    interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5))
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+gpu_ids = range(0, 1)