projects/actionclip/configs/actionclip_vit-base-p32-res224-clip-pre_g8xb16_1x1x8_k400-rgb.py

custom_imports = dict(imports='models')

num_segs = 8

model = dict(
    type='ActionClip',
    clip_arch='ViT-B/32',
    num_adapter_segs=num_segs,
    num_adapter_layers=6,
    to_float32=True,
    labels_or_label_file='configs/label_map_k400.txt',
    data_preprocessor=dict(
        type='ActionDataPreprocessor',
        mean=[122.771, 116.746, 104.093],
        std=[68.500, 66.632, 70.323],
        format_shape='NCHW'))

dataset_type = 'VideoDataset'
data_root = 'data/kinetics400/videos_train'
data_root_val = 'data/kinetics400/videos_val'
ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt'
ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt'
ann_file_test = 'data/kinetics400/kinetics400_val_list_videos.txt'

file_client_args = dict(io_backend='disk')
file_client_args = dict(
    io_backend='petrel',
    path_mapping=dict(
        {'data/kinetics400/': 's3://openmmlab/datasets/action/Kinetics400/'}))

train_pipeline = [
    dict(type='DecordInit', **file_client_args),
    dict(
        type='SampleFrames', clip_len=1, frame_interval=1, num_clips=num_segs),
    dict(type='DecordDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='RandomResizedCrop'),
    dict(
        type='MultiScaleCrop',
        input_size=224,
        scales=(1, .875, .75, .66),
        random_crop=False,
        num_fixed_crops=13,
        max_wh_scale_gap=1),
    dict(type='Resize', scale=(224, 224), keep_ratio=False),
    dict(type='Flip', flip_ratio=0.5),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='PackActionInputs')
]

val_pipeline = [
    dict(type='DecordInit', **file_client_args),
    dict(
        type='SampleFrames',
        clip_len=1,
        frame_interval=1,
        num_clips=num_segs,
        test_mode=True),
    dict(type='DecordDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='CenterCrop', crop_size=224),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='PackActionInputs')
]

test_pipeline = val_pipeline

train_dataloader = dict(
    batch_size=16,
    num_workers=16,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        ann_file=ann_file_train,
        data_prefix=dict(video=data_root),
        pipeline=train_pipeline))
val_dataloader = dict(
    batch_size=16,
    num_workers=16,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        ann_file=ann_file_val,
        data_prefix=dict(video=data_root_val),
        pipeline=val_pipeline,
        test_mode=True))
test_dataloader = dict(
    batch_size=1,
    num_workers=16,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        ann_file=ann_file_test,
        data_prefix=dict(video=data_root_val),
        pipeline=test_pipeline,
        test_mode=True))

val_evaluator = dict(type='AccMetric')
test_evaluator = val_evaluator

train_cfg = dict(
    type='EpochBasedTrainLoop', max_epochs=50, val_begin=1, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

optim_wrapper = dict(
    optimizer=dict(
        type='AdamW', lr=5e-6, betas=(0.9, 0.98), eps=1e-08, weight_decay=0.2),
    paramwise_cfg=dict(custom_keys=dict(adapter=dict(lr_mult=10))))

param_scheduler = [
    dict(
        type='LinearLR',
        start_factor=0.01,
        by_epoch=True,
        begin=0,
        end=5,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingLR',
        T_max=45,
        eta_min=0,
        by_epoch=True,
        begin=5,
        end=50,
        convert_to_iter_based=True)
]

# Default setting for scaling LR automatically
#   - `enable` means enable scaling LR automatically
#       or not by default.
#   - `base_batch_size` = (8 GPUs) x (16 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=128)

default_scope = 'mmaction'

default_hooks = dict(
    runtime_info=dict(type='RuntimeInfoHook'),
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=100, ignore_last=False),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(
        type='CheckpointHook', interval=1, save_best='auto', max_keep_ckpts=5),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    sync_buffers=dict(type='SyncBuffersHook'))

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

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

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

log_level = 'INFO'
load_from = None
resume = False