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mvit-base-p244_u32_sthv2-rgb.py
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mvit-base-p244_u32_sthv2-rgb.py
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_base_ = [
'../../_base_/models/mvit_small.py', '../../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
arch='base',
temporal_size=32,
drop_path_rate=0.3,
),
data_preprocessor=dict(
type='ActionDataPreprocessor',
mean=[114.75, 114.75, 114.75],
std=[57.375, 57.375, 57.375],
blending=dict(
type='RandomBatchAugment',
augments=[
dict(type='MixupBlending', alpha=0.8, num_classes=174),
dict(type='CutmixBlending', alpha=1, num_classes=174)
]),
format_shape='NCTHW'),
cls_head=dict(num_classes=174))
# dataset settings
dataset_type = 'VideoDataset'
data_root = 'data/sthv2/videos'
data_root_val = 'data/sthv2/videos'
ann_file_train = 'data/sthv2/sthv2_train_list_videos.txt'
ann_file_val = 'data/sthv2/sthv2_val_list_videos.txt'
ann_file_test = 'data/sthv2/sthv2_val_list_videos.txt'
file_client_args = dict(io_backend='disk')
train_pipeline = [
dict(type='DecordInit', **file_client_args),
dict(type='UniformSample', clip_len=32),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='RandomResizedCrop'),
dict(type='Resize', scale=(224, 224), keep_ratio=False),
dict(
type='PytorchVideoWrapper',
op='RandAugment',
magnitude=7,
num_layers=4),
dict(type='RandomErasing', erase_prob=0.25, mode='rand'),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='PackActionInputs')
]
val_pipeline = [
dict(type='DecordInit', **file_client_args),
dict(type='UniformSample', clip_len=32, test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='CenterCrop', crop_size=224),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='PackActionInputs')
]
test_pipeline = [
dict(type='DecordInit', **file_client_args),
dict(type='UniformSample', clip_len=32, test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 224)),
dict(type='ThreeCrop', crop_size=224),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='PackActionInputs')
]
train_dataloader = dict(
batch_size=8,
num_workers=8,
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=8,
num_workers=8,
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=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))
val_evaluator = dict(type='AccMetric')
test_evaluator = val_evaluator
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=100, val_begin=1, val_interval=3)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
base_lr = 1.6e-3
optim_wrapper = dict(
optimizer=dict(
type='AdamW', lr=base_lr, betas=(0.9, 0.999), weight_decay=0.05))
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.1,
by_epoch=True,
begin=0,
end=30,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=70,
eta_min=base_lr / 100,
by_epoch=True,
begin=30,
end=100,
convert_to_iter_based=True)
]
default_hooks = dict(
checkpoint=dict(interval=3, max_keep_ckpts=5), logger=dict(interval=100))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (8 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=64)