uniformer-small_imagenet1k-pre_16x4x1_kinetics400-rgb.py

_base_ = ['../../_base_/default_runtime.py']

# model settings
model = dict(
    type='Recognizer3D',
    backbone=dict(
        type='UniFormer',
        depth=[3, 4, 8, 3],
        embed_dim=[64, 128, 320, 512],
        head_dim=64,
        drop_path_rate=0.1),
    cls_head=dict(
        type='I3DHead',
        dropout_ratio=0.,
        num_classes=400,
        in_channels=512,
        average_clips='prob'),
    data_preprocessor=dict(
        type='ActionDataPreprocessor',
        mean=[114.75, 114.75, 114.75],
        std=[57.375, 57.375, 57.375],
        format_shape='NCTHW'))

# dataset settings
dataset_type = 'VideoDataset'
data_root_val = 'data/k400'
ann_file_test = 'data/k400/val.csv'

test_pipeline = [
    dict(type='DecordInit'),
    dict(
        type='SampleFrames',
        clip_len=16,
        frame_interval=4,
        num_clips=4,
        test_mode=True),
    dict(type='DecordDecode'),
    dict(type='Resize', scale=(-1, 224)),
    dict(type='CenterCrop', crop_size=224),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='PackActionInputs')
]

test_dataloader = dict(
    batch_size=32,
    num_workers=8,
    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,
        delimiter=','))

test_evaluator = dict(type='AccMetric')
test_cfg = dict(type='TestLoop')