/
uniformer-base_imagenet1k-pre_16x4x1_kinetics400-rgb.py
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/
uniformer-base_imagenet1k-pre_16x4x1_kinetics400-rgb.py
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_base_ = ['../../_base_/default_runtime.py']
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='UniFormer',
depth=[5, 8, 20, 7],
embed_dim=[64, 128, 320, 512],
head_dim=64,
drop_path_rate=0.3),
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')