187 lines
6.2 KiB
Python
187 lines
6.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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model = dict(
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type='Recognizer2D',
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backbone=dict(
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type='ResNet',
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pretrained='https://download.pytorch.org/models/resnet50-11ad3fa6.pth',
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depth=50,
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norm_eval=False),
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cls_head=dict(
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type='TSNHead',
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num_classes=400,
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in_channels=2048,
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spatial_type='avg',
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consensus=dict(type='AvgConsensus', dim=1),
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dropout_ratio=0.4,
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init_std=0.01,
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average_clips=None),
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data_preprocessor=dict(
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type='ActionDataPreprocessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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format_shape='NCHW'),
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train_cfg=None,
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test_cfg=None)
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train_cfg = dict(
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type='EpochBasedTrainLoop', max_epochs=100, val_begin=1, val_interval=1)
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val_cfg = dict(type='ValLoop')
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test_cfg = dict(type='TestLoop')
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param_scheduler = [
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dict(
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type='MultiStepLR',
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begin=0,
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end=100,
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by_epoch=True,
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milestones=[40, 80],
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gamma=0.1)
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]
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optim_wrapper = dict(
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optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001),
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clip_grad=dict(max_norm=40, norm_type=2))
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default_scope = 'mmaction'
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default_hooks = dict(
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runtime_info=dict(type='RuntimeInfoHook'),
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timer=dict(type='IterTimerHook'),
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logger=dict(type='LoggerHook', interval=20, ignore_last=False),
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param_scheduler=dict(type='ParamSchedulerHook'),
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checkpoint=dict(
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type='CheckpointHook', interval=3, save_best='auto', max_keep_ckpts=3),
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sampler_seed=dict(type='DistSamplerSeedHook'))
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env_cfg = dict(
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cudnn_benchmark=False,
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mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
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dist_cfg=dict(backend='nccl'))
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log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True)
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vis_backends = [dict(type='LocalVisBackend')]
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visualizer = dict(
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type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')])
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log_level = 'INFO'
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load_from = None
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resume = False
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dataset_type = 'VideoDataset'
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data_root = 'data/kinetics400/videos_train'
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data_root_val = 'data/video'
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ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt'
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ann_file_val = 'data/ann.txt'
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train_pipeline = [
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dict(type='DecordInit'),
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dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3),
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dict(type='DecordDecode'),
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dict(type='Resize', scale=(-1, 256)),
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dict(
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type='MultiScaleCrop',
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input_size=224,
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scales=(1, 0.875, 0.75, 0.66),
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random_crop=False,
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max_wh_scale_gap=1),
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dict(type='Resize', scale=(224, 224), keep_ratio=False),
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dict(type='Flip', flip_ratio=0.5),
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dict(type='FormatShape', input_format='NCHW'),
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dict(type='PackActionInputs')
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]
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val_pipeline = [
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dict(type='DecordInit'),
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dict(
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type='SampleFrames',
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clip_len=1,
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frame_interval=1,
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num_clips=3,
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test_mode=True),
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dict(type='DecordDecode'),
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dict(type='Resize', scale=(-1, 256)),
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dict(type='CenterCrop', crop_size=224),
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dict(type='FormatShape', input_format='NCHW'),
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dict(type='PackActionInputs')
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]
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test_pipeline = [
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dict(type='DecordInit'),
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dict(
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type='SampleFrames',
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clip_len=1,
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frame_interval=1,
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num_clips=25,
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test_mode=True),
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dict(type='DecordDecode'),
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dict(type='Resize', scale=(-1, 256)),
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dict(type='TenCrop', crop_size=224),
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dict(type='FormatShape', input_format='NCHW'),
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dict(type='PackActionInputs')
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]
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train_dataloader = dict(
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batch_size=32,
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num_workers=8,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=dict(
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type='VideoDataset',
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ann_file='data/kinetics400/kinetics400_train_list_videos.txt',
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data_prefix=dict(video='data/kinetics400/videos_train'),
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pipeline=[
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dict(type='DecordInit'),
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dict(
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type='SampleFrames', clip_len=1, frame_interval=1,
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num_clips=3),
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dict(type='DecordDecode'),
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dict(type='Resize', scale=(-1, 256)),
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dict(
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type='MultiScaleCrop',
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input_size=224,
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scales=(1, 0.875, 0.75, 0.66),
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random_crop=False,
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max_wh_scale_gap=1),
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dict(type='Resize', scale=(224, 224), keep_ratio=False),
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dict(type='Flip', flip_ratio=0.5),
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dict(type='FormatShape', input_format='NCHW'),
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dict(type='PackActionInputs')
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]))
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val_dataloader = dict(
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batch_size=32,
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num_workers=8,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='VideoDataset',
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ann_file='tests/test_codebase/test_mmaction/data/ann.txt',
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data_prefix=dict(video='tests/test_codebase/test_mmaction/data/video'),
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pipeline=[
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dict(type='DecordInit'),
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dict(
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type='SampleFrames',
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clip_len=1,
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frame_interval=1,
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num_clips=3,
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test_mode=True),
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dict(type='DecordDecode'),
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dict(type='Resize', scale=(-1, 256)),
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dict(type='CenterCrop', crop_size=224),
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dict(type='FormatShape', input_format='NCHW'),
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dict(type='PackActionInputs')
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],
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test_mode=True))
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test_dataloader = dict(
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batch_size=1,
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num_workers=8,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='VideoDataset',
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ann_file='tests/test_codebase/test_mmaction/data/ann.txt',
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data_prefix=dict(video='tests/test_codebase/test_mmaction/data/video'),
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pipeline=[
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dict(type='DecordInit'),
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dict(
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type='SampleFrames',
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clip_len=1,
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frame_interval=1,
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num_clips=25,
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test_mode=True),
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dict(type='DecordDecode'),
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dict(type='Resize', scale=(-1, 256)),
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dict(type='TenCrop', crop_size=224),
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dict(type='FormatShape', input_format='NCHW'),
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dict(type='PackActionInputs')
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],
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test_mode=True))
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val_evaluator = dict(type='AccMetric')
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test_evaluator = dict(type='AccMetric')
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