config.py

ann_file_train = './dt/labelTrain.txt'
ann_file_val = './dt/labelVal.txt'

auto_scale_lr = dict(base_batch_size=256, enable=False)
data_root = './dt/train/'
data_root_val = './dt/val/'
dataset_type = 'VideoDataset'

default_hooks = dict(
    checkpoint=dict(
        interval=3, max_keep_ckpts=3, save_best='auto', type='CheckpointHook'),
    logger=dict(ignore_last=False, interval=20, type='LoggerHook'),
    param_scheduler=dict(type='ParamSchedulerHook'),
    runtime_info=dict(type='RuntimeInfoHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    sync_buffers=dict(type='SyncBuffersHook'),
    timer=dict(type='IterTimerHook')
)

default_scope = 'mmaction'

env_cfg = dict(
    cudnn_benchmark=False,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)
)

file_client_args = dict(io_backend='disk')

load_from = './FallCheckPoints/epoch_44.pht'
log_level = 'INFO'

log_processor = dict(by_epoch=True, type='LogProcessor', window_size=20)

model = dict(
    backbone=dict(
        depth=50,
        norm_eval=False,
        pretrained='https://download.pytorch.org/models/resnet50-11ad3fa6.pth',
        type='ResNet'
    ),
    cls_head=dict(
        average_clips='prob',
        consensus=dict(dim=1, type='AvgConsensus'),
        dropout_ratio=0.4,
        in_channels=2048,
        init_std=0.01,
        num_classes=2,
        spatial_type='avg',
        type='TSNHead'
    ),
    data_preprocessor=dict(
        format_shape='NCHW',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        type='ActionDataPreprocessor'
    ),
    test_cfg=None,
    train_cfg=None,
    type='Recognizer2D'
)

optim_wrapper = dict(
    clip_grad=dict(max_norm=40, norm_type=2),
    optimizer=dict(
        lr=7.8125e-05, momentum=0.9, type='SGD', weight_decay=0.0001
    )
)

param_scheduler = [
    dict(
        begin=0,
        by_epoch=True,
        end=100,
        gamma=0.1,
        milestones=[40, 80],
        type='MultiStepLR'
    )
]

resume = False

test_cfg = dict(type='TestLoop')

test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='./dt/labelVal.txt',
        data_prefix=dict(video='./dt/val/'),
        pipeline=[
            dict(io_backend='disk', type='DecordInit'),
            dict(
                clip_len=1,
                frame_interval=1,
                num_clips=25,
                test_mode=True,
                type='SampleFrames'
            ),
            dict(type='DecordDecode'),
            dict(scale=(-1, 256), type='Resize'),
            dict(crop_size=224, type='TenCrop'),
            dict(input_format='NCHW', type='FormatShape'),
            dict(type='PackActionInputs'),
        ],
        test_mode=True,
        type='VideoDataset'
    ),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler')
)

test_evaluator = dict(type='AccMetric')

train_cfg = dict(
    max_epochs=50, type='EpochBasedTrainLoop', val_begin=1, val_interval=1
)

train_dataloader = dict(
    batch_size=2,
    dataset=dict(
        ann_file='./dt/labelTrain.txt',
        data_prefix=dict(video='./dt/train/'),
        pipeline=[
            dict(io_backend='disk', type='DecordInit'),
            dict(
                clip_len=1, frame_interval=1, num_clips=3, type='SampleFrames'
            ),
            dict(type='DecordDecode'),
            dict(scale=(-1, 256), type='Resize'),
            dict(
                input_size=224,
                max_wh_scale_gap=1,
                random_crop=False,
                scales=(1, 0.875, 0.75, 0.66),
                type='MultiScaleCrop'
            ),
            dict(keep_ratio=False, scale=(224, 224), type='Resize'),
            dict(flip_ratio=0.5, type='Flip'),
            dict(input_format='NCHW', type='FormatShape'),
            dict(type='PackActionInputs'),
        ],
        type='VideoDataset'
    ),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=True, type='DefaultSampler')
)

train_pipeline = [
    dict(io_backend='disk', type='DecordInit'),
    dict(clip_len=1, frame_interval=1, num_clips=3, type='SampleFrames'),
    dict(type='DecordDecode'),
    dict(scale=(-1, 256), type='Resize'),
    dict(
        input_size=224,
        max_wh_scale_gap=1,
        random_crop=False,
        scales=(1, 0.875, 0.75, 0.66),
        type='MultiScaleCrop'
    ),
    dict(keep_ratio=False, scale=(224, 224), type='Resize'),
    dict(flip_ratio=0.5, type='Flip'),
    dict(input_format='NCHW', type='FormatShape'),
    dict(type='PackActionInputs')
]

val_cfg = dict(type='ValLoop')

val_dataloader = dict(
    batch_size=2,
    dataset=dict(
        ann_file='./dt/labelVal.txt',
        data_prefix=dict(video='./dt/val/'),
        pipeline=[
            dict(io_backend='disk', type='DecordInit'),
            dict(
                clip_len=1, frame_interval=1, num_clips=3,
                test_mode=True, type='SampleFrames'
            ),
            dict(type='DecordDecode'),
            dict(scale=(-1, 256), type='Resize'),
            dict(crop_size=224, type='CenterCrop'),
            dict(input_format='NCHW', type='FormatShape'),
            dict(type='PackActionInputs'),
        ],
        test_mode=True,
        type='VideoDataset'
    ),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler')
)

val_evaluator = dict(type='AccMetric')

vis_backends = [dict(type='LocalVisBackend')]

visualizer = dict(
    type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')]
)

work_dir = './fall_detection_exps'