Upload 37 files

k400/j_1/best_pred.pkl

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+oid sha256:da16512966efcb1c3cd4cff6d8bfb6b90f9be7b894d13737ffc602c1a8cf18cf
+size 44885270

k400/j_1/best_top1_acc_epoch_150.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f71b45dd9741828f0cd1ab1940a5d5f5bf687d83231c805e710f9f1b176ef79
+size 35758566

k400/j_1/j_1.py

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+modality = 'j'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/k400/j_1'
+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=400, in_channels=384))
+memcached = True
+mc_cfg = ('localhost', 22077)
+dataset_type = 'PoseDataset'
+ann_file = '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+box_thr = 0.5
+valid_ratio = 0.0
+train_pipeline = [
+    dict(type='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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=32,
+    workers_per_gpu=8,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='train',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        valid_ratio=0.0,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    val=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    test=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)))
+optimizer = dict(
+    type='SGD', lr=0.2, 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, 4)

k400/j_2/best_pred.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4ab4243cbcdae7c27466008c740d2574b40e76dbacb47508f8af9142b249c1b6
+size 44886517

k400/j_2/best_top1_acc_epoch_150.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:07af82c0113dfe4c7aaca3a224dd0b2983dbcb220334b54afe52848680ab44aa
+size 35758566

k400/j_2/j_2.py

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+modality = 'j'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/k400/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=400, in_channels=384))
+memcached = True
+mc_cfg = ('localhost', 22077)
+dataset_type = 'PoseDataset'
+ann_file = '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+box_thr = 0.5
+valid_ratio = 0.0
+train_pipeline = [
+    dict(type='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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=64,
+    workers_per_gpu=16,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='train',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        valid_ratio=0.0,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    val=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    test=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)))
+optimizer = dict(
+    type='SGD', lr=0.1, 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)

k400/j_3/best_pred.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:188c23d49554b681923a37a3e7737329ef624370d1af41542586f571bf14008b
+size 44889810

k400/j_3/best_top1_acc_epoch_146.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0ba3b39852dd147d52ace5fbb83c8686ebe05a29f4dbf7bd5eab5338ec554537
+size 35763942

k400/j_3/j_3.py

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+modality = 'j'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/k400/j_3'
+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=400, in_channels=384))
+memcached = True
+mc_cfg = ('localhost', 22077)
+dataset_type = 'PoseDataset'
+ann_file = '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+box_thr = 0.5
+valid_ratio = 0.0
+train_pipeline = [
+    dict(type='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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=64,
+    workers_per_gpu=16,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='train',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        valid_ratio=0.0,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    val=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    test=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)))
+optimizer = dict(
+    type='SGD', lr=0.1, 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)
+resume_from = './work_dirs/test_prototype/k400/j_3/latest.pth'

k400/k400_ensemble.py

@@ -0,0 +1,71 @@
+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')
+j_3 = load('j_3/best_pred.pkl')
+b_3 = load('b_3/best_pred.pkl')
+k_3 = load('k_3/best_pred.pkl')
+label = load_label('/data/k400/k400_hrnet.pkl', 'val')
+
+
+"""
+***************
+InfoGCN  v1:
+j j b b k k
+2S:     49.85 / 73.96
+4S:     51.33 / 75.23
+6S:     51.73 / 75.58
+***************
+"""
+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('Top-5', topk(fused, label, 5))
+
+print('4S')
+fused = comb([j_1, b_1, j_3, b_2], [1, 1, 1, 1])
+print('Top-1', top1(fused, label))
+print('Top-5', topk(fused, label, 5))
+
+print('6S')
+fused = comb([j_1, j_3, b_1, b_2, k_1, k_2], [7, 7, 5, 5, 3, 3])
+print('Top-1', top1(fused, label))
+print('Top-5', topk(fused, label, 5))
+
+
+"""
+***************
+InfoGCN  v2:
+j b j b j b
+2S:     49.85 / 73.96
+4S:     51.33 / 75.06
+6S:     51.85 / 75.55
+***************
+"""
+print('InfoGCN  v2:')
+print('j b j b j b')
+print('2S')
+fused = comb([j_1, b_1], [1, 1])
+print('Top-1', top1(fused, label))
+print('Top-5', topk(fused, label, 5))
+
+print('4S')
+fused = comb([j_1, b_1, j_2, b_2], [1, 1, 1, 1])
+print('Top-1', top1(fused, label))
+print('Top-5', topk(fused, label, 5))
+
+print('6S')
+fused = comb([j_1, b_1, j_2, b_2, j_3, b_3], [6, 5, 3, 3, 5, 6])
+print('Top-1', top1(fused, label))
+print('Top-5', topk(fused, label, 5))

k400/k_1/best_pred.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 44884333

k400/k_1/best_top1_acc_epoch_150.pth

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+version https://git-lfs.github.com/spec/v1
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+size 33920678

k400/k_1/k_1.py

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+modality = 'k'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/k400/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=400, in_channels=384))
+memcached = True
+mc_cfg = ('localhost', 22077)
+dataset_type = 'PoseDataset'
+ann_file = '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+box_thr = 0.5
+valid_ratio = 0.0
+train_pipeline = [
+    dict(type='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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=64,
+    workers_per_gpu=16,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='train',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        valid_ratio=0.0,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    val=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    test=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)))
+optimizer = dict(
+    type='SGD', lr=0.1, 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)
+resume_from = './work_dirs/test_prototype/k400/k_1/latest.pth'

k400/k_2/best_pred.pkl

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+oid sha256:37034f4c4fb45809ae22f5eb70b40ecf9c54891bf3ccc33407c66038d342005e
+size 44885202

k400/k_2/best_top1_acc_epoch_147.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:03f936902d81b1456a921983f0c92a6b66c3ffda432a01fd9f4c35c247a34c09
+size 33915366

k400/k_2/k_2.py

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+modality = 'k'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/k400/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=400, in_channels=384))
+memcached = True
+mc_cfg = ('localhost', 22077)
+dataset_type = 'PoseDataset'
+ann_file = '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+box_thr = 0.5
+valid_ratio = 0.0
+train_pipeline = [
+    dict(type='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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=64,
+    workers_per_gpu=16,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='train',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        valid_ratio=0.0,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    val=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    test=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)))
+optimizer = dict(
+    type='SGD', lr=0.1, 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)

k400/k_3/best_pred.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:49e37ad26aa519bc4714859ce0d86feda671817a7f80cc3ac533c8a9b39845fe
+size 44888083

k400/k_3/best_top1_acc_epoch_147.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:21233d88db2e6b4cd5a4301b09db594d331a18adb3cb9d1b217ac5a22c95e278
+size 33920678

k400/k_3/k_3.py

@@ -0,0 +1,134 @@
+modality = 'k'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/k400/k_3'
+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=400, in_channels=384))
+memcached = True
+mc_cfg = ('localhost', 22077)
+dataset_type = 'PoseDataset'
+ann_file = '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl'
+left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
+right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
+box_thr = 0.5
+valid_ratio = 0.0
+train_pipeline = [
+    dict(type='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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='DecompressPose', squeeze=True),
+    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=64,
+    workers_per_gpu=16,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='train',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        valid_ratio=0.0,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    val=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)),
+    test=dict(
+        type='PoseDataset',
+        ann_file=
+        '/data1/hao.wang/reproducation/hongda.liu/pyskl_data/k400/k400_hrnet.pkl',
+        split='val',
+        pipeline=[
+            dict(type='DecompressPose', squeeze=True),
+            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'])
+        ],
+        box_thr=0.5,
+        memcached=True,
+        mc_cfg=('localhost', 22077)))
+optimizer = dict(
+    type='SGD', lr=0.1, 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)
+resume_from = './work_dirs/test_prototype/k400/k_3/latest.pth'