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k400/b_1/b_1.py

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+modality = 'b'
+graph = 'coco_new'
+work_dir = './work_dirs/test_prototype/k400/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=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=['b']),
+    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=['b']),
+    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=['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=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=['b']),
+            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=['b']),
+            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=['b']),
+            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/b_1/latest.pth'

k400/b_1/best_pred.pkl

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+oid sha256:262e03c1388ce26e10aa23d3f505c94fa6b2f280c67be8df250090fbc16875cb
+size 44880425

k400/b_1/best_top1_acc_epoch_150.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:daec8e2a02d217bfd21b54794e26e9bdc780acaba9fde6193b9a987e11484817
+size 33920678