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tsn_imagenet-pretrained-r50_8xb32-1x1x8-100e_kinetics400-rgb.py
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ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt'
ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt'
auto_scale_lr = dict(base_batch_size=256, enable=False)
data_root = 'data/kinetics400/videos_train'
data_root_val = 'data/kinetics400/videos_val'
dataset_type = 'VideoDataset'
default_hooks = dict(
checkpoint=dict(
interval=3, max_keep_ckpts=3, save_best='auto', type='CheckpointHook'),
logger=dict(ignore_last=False, interval=20, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
runtime_info=dict(type='RuntimeInfoHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
sync_buffers=dict(type='SyncBuffersHook'),
timer=dict(type='IterTimerHook'))
default_scope = 'mmaction'
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
file_client_args = dict(io_backend='disk')
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=True, type='LogProcessor', window_size=20)
model = dict(
backbone=dict(
depth=50,
norm_eval=False,
pretrained='https://download.pytorch.org/models/resnet50-11ad3fa6.pth',
type='ResNet'),
cls_head=dict(
average_clips='prob',
consensus=dict(dim=1, type='AvgConsensus'),
dropout_ratio=0.4,
in_channels=2048,
init_std=0.01,
num_classes=400,
spatial_type='avg',
type='TSNHead'),
data_preprocessor=dict(
format_shape='NCHW',
mean=[
123.675,
116.28,
103.53,
],
std=[
58.395,
57.12,
57.375,
],
type='ActionDataPreprocessor'),
test_cfg=None,
train_cfg=None,
type='Recognizer2D')
optim_wrapper = dict(
clip_grad=dict(max_norm=40, norm_type=2),
optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0001))
param_scheduler = [
dict(
begin=0,
by_epoch=True,
end=100,
gamma=0.1,
milestones=[
40,
80,
],
type='MultiStepLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='data/kinetics400/kinetics400_val_list_videos.txt',
data_prefix=dict(video='data/kinetics400/videos_val'),
pipeline=[
dict(io_backend='disk', type='DecordInit'),
dict(
clip_len=1,
frame_interval=1,
num_clips=25,
test_mode=True,
type='SampleFrames'),
dict(type='DecordDecode'),
dict(scale=(
-1,
256,
), type='Resize'),
dict(crop_size=224, type='TenCrop'),
dict(input_format='NCHW', type='FormatShape'),
dict(type='PackActionInputs'),
],
test_mode=True,
type='VideoDataset'),
num_workers=8,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(type='AccMetric')
test_pipeline = [
dict(io_backend='disk', type='DecordInit'),
dict(
clip_len=1,
frame_interval=1,
num_clips=25,
test_mode=True,
type='SampleFrames'),
dict(type='DecordDecode'),
dict(scale=(
-1,
256,
), type='Resize'),
dict(crop_size=224, type='TenCrop'),
dict(input_format='NCHW', type='FormatShape'),
dict(type='PackActionInputs'),
]
train_cfg = dict(
max_epochs=100, type='EpochBasedTrainLoop', val_begin=1, val_interval=1)
train_dataloader = dict(
batch_size=32,
dataset=dict(
ann_file='data/kinetics400/kinetics400_train_list_videos.txt',
data_prefix=dict(video='data/kinetics400/videos_train'),
pipeline=[
dict(io_backend='disk', type='DecordInit'),
dict(
clip_len=1, frame_interval=1, num_clips=8,
type='SampleFrames'),
dict(type='DecordDecode'),
dict(scale=(
-1,
256,
), type='Resize'),
dict(
input_size=224,
max_wh_scale_gap=1,
random_crop=False,
scales=(
1,
0.875,
0.75,
0.66,
),
type='MultiScaleCrop'),
dict(keep_ratio=False, scale=(
224,
224,
), type='Resize'),
dict(flip_ratio=0.5, type='Flip'),
dict(input_format='NCHW', type='FormatShape'),
dict(type='PackActionInputs'),
],
type='VideoDataset'),
num_workers=8,
persistent_workers=True,
sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
dict(io_backend='disk', type='DecordInit'),
dict(clip_len=1, frame_interval=1, num_clips=8, type='SampleFrames'),
dict(type='DecordDecode'),
dict(scale=(
-1,
256,
), type='Resize'),
dict(
input_size=224,
max_wh_scale_gap=1,
random_crop=False,
scales=(
1,
0.875,
0.75,
0.66,
),
type='MultiScaleCrop'),
dict(keep_ratio=False, scale=(
224,
224,
), type='Resize'),
dict(flip_ratio=0.5, type='Flip'),
dict(input_format='NCHW', type='FormatShape'),
dict(type='PackActionInputs'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=32,
dataset=dict(
ann_file='data/kinetics400/kinetics400_val_list_videos.txt',
data_prefix=dict(video='data/kinetics400/videos_val'),
pipeline=[
dict(io_backend='disk', type='DecordInit'),
dict(
clip_len=1,
frame_interval=1,
num_clips=8,
test_mode=True,
type='SampleFrames'),
dict(type='DecordDecode'),
dict(scale=(
-1,
256,
), type='Resize'),
dict(crop_size=224, type='CenterCrop'),
dict(input_format='NCHW', type='FormatShape'),
dict(type='PackActionInputs'),
],
test_mode=True,
type='VideoDataset'),
num_workers=8,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(type='AccMetric')
val_pipeline = [
dict(io_backend='disk', type='DecordInit'),
dict(
clip_len=1,
frame_interval=1,
num_clips=8,
test_mode=True,
type='SampleFrames'),
dict(type='DecordDecode'),
dict(scale=(
-1,
256,
), type='Resize'),
dict(crop_size=224, type='CenterCrop'),
dict(input_format='NCHW', type='FormatShape'),
dict(type='PackActionInputs'),
]
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
type='ActionVisualizer', vis_backends=[
dict(type='LocalVisBackend'),
])