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finegym/b_1/b_1.py

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+modality = 'b'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/finegym/b_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=['b']),
+    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=['b']),
+    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=['b']),
+    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=['b']),
+            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=['b']),
+            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=['b']),
+            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/b_1/best_pred.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f9974eb696bb7ea151eb6dafd6812b505cf6b3511d7605773fbe74a5b8cde6f
+size 5257518

finegym/b_1/best_top1_acc_epoch_137.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9fcb8f9b28d8ef7fd1155140d38472d2eb0bad7445a58cd5eb20ecbd869476f4
+size 32988198