/
tsm_imagenet-pretrained-r50_8xb16-dense-1x1x8-50e_kinetics400-rgb.py
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tsm_imagenet-pretrained-r50_8xb16-dense-1x1x8-50e_kinetics400-rgb.py
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_base_ = ['../../_base_/models/tsm_r50.py', '../../_base_/default_runtime.py']
# dataset settings
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'
file_client_args = dict(io_backend='disk')
train_pipeline = [
dict(type='DecordInit', **file_client_args),
dict(type='DenseSampleFrames', clip_len=1, frame_interval=1, num_clips=8),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(
type='MultiScaleCrop',
input_size=224,
scales=(1, 0.875, 0.75, 0.66),
random_crop=False,
max_wh_scale_gap=1,
num_fixed_crops=13),
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='DenseSampleFrames',
clip_len=1,
frame_interval=1,
num_clips=8,
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 = [
dict(type='DecordInit', **file_client_args),
dict(
type='DenseSampleFrames',
clip_len=1,
frame_interval=1,
num_clips=8,
test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='TenCrop', crop_size=224),
dict(type='FormatShape', input_format='NCHW'),
dict(type='PackActionInputs')
]
train_dataloader = dict(
batch_size=16,
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=16,
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_val,
data_prefix=dict(video=data_root_val),
pipeline=test_pipeline,
test_mode=True))
val_evaluator = dict(type='AccMetric')
test_evaluator = val_evaluator
default_hooks = dict(checkpoint=dict(interval=3, max_keep_ckpts=3))
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=50, val_begin=1, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(type='LinearLR', start_factor=0.1, by_epoch=True, begin=0, end=5),
dict(
type='MultiStepLR',
begin=0,
end=50,
by_epoch=True,
milestones=[25, 45],
gamma=0.1)
]
optim_wrapper = dict(
constructor='TSMOptimWrapperConstructor',
paramwise_cfg=dict(fc_lr5=True),
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001),
clip_grad=dict(max_norm=20, norm_type=2))
# 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)