tin_r50_1x1x8_40e_sthv1_rgb.py

# model settings
model = dict(
    type='Recognizer2D',
    backbone=dict(
        type='ResNetTIN',
        pretrained='torchvision://resnet50',
        depth=50,
        norm_eval=False,
        shift_div=4),
    cls_head=dict(
        type='TSMHead',
        num_classes=174,
        in_channels=2048,
        spatial_type='avg',
        consensus=dict(type='AvgConsensus', dim=1),
        dropout_ratio=0.8,
        init_std=0.001,
        is_shift=False))
# model training and testing settings
train_cfg = None
test_cfg = dict(average_clips=None)
# dataset settings
dataset_type = 'RawframeDataset'
data_root = 'data/sth-v1/rawframes_train/'
data_root_val = 'data/sth-v1/rawframes_val/'
ann_file_train = 'data/sth-v1/sth-v1_train_list.txt'
ann_file_val = 'data/sth-v1/sth-v1_val_list.txt'
ann_file_test = 'data/sth-v1/sth-v1_val_list.txt'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
train_pipeline = [
    dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8),
    dict(type='FrameSelector'),
    dict(type='Resize', scale=(-1, 256)),
    dict(
        type='MultiScaleCrop',
        input_size=224,
        scales=(1, 0.875, 0.75, 0.66),
        random_crop=False,
        max_wh_scale_gap=1),
    dict(type='Resize', scale=(224, 224), keep_ratio=False),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs', 'label'])
]
val_pipeline = [
    dict(
        type='SampleFrames',
        clip_len=1,
        frame_interval=1,
        num_clips=8,
        test_mode=True),
    dict(type='FrameSelector'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='CenterCrop', crop_size=224),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]
test_pipeline = [
    dict(
        type='SampleFrames',
        clip_len=1,
        frame_interval=1,
        num_clips=8,
        test_mode=True),
    dict(type='FrameSelector'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='CenterCrop', crop_size=224),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]
data = dict(
    videos_per_gpu=6,
    workers_per_gpu=4,
    train=dict(
        type=dataset_type,
        ann_file=ann_file_train,
        data_prefix=data_root,
        filename_tmpl='{:05}.jpg',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=ann_file_val,
        data_prefix=data_root_val,
        filename_tmpl='{:05}.jpg',
        pipeline=val_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=ann_file_test,
        data_prefix=data_root_val,
        filename_tmpl='{:05}.jpg',
        pipeline=test_pipeline))
# optimizer
optimizer = dict(
    type='SGD',
    constructor='TSMOptimizerConstructor',
    paramwise_cfg=dict(fc_lr5=True),
    lr=0.02,  # this lr is used for 8 gpus
    momentum=0.9,
    weight_decay=0.0005)
optimizer_config = dict(grad_clip=dict(max_norm=20, norm_type=2))
# learning policy
lr_config = dict(
    policy='CosineAnnealing',
    min_lr_ratio=0.5,
    warmup='linear',
    warmup_ratio=0.1,
    warmup_by_epoch=True,
    warmup_iters=1)
total_epochs = 40
checkpoint_config = dict(interval=1)
evaluation = dict(
    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
log_config = dict(
    interval=20,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook'),
    ])
# runtime settings
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/tin_r50_1x1x8_40e_sthv1_rgb/'
load_from = None
resume_from = None
workflow = [('train', 1)]