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tcanet_2xb8-700x100-9e_hacs-feature.py
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tcanet_2xb8-700x100-9e_hacs-feature.py
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_base_ = '../../_base_/default_runtime.py'
# model settings
model = dict(
type='TCANet',
feat_dim=700,
se_sample_num=32,
action_sample_num=64,
temporal_dim=100,
window_size=9,
lgte_num=2,
soft_nms_alpha=0.4,
soft_nms_low_threshold=0.0,
soft_nms_high_threshold=0.0,
post_process_top_k=100,
feature_extraction_interval=16)
# dataset settings
dataset_type = 'ActivityNetDataset'
data_root = 'data/HACS/slowonly_feature/'
data_root_val = 'data/HACS/slowonly_feature/'
ann_file_train = 'data/HACS/hacs_anno_train.json'
ann_file_val = 'data/HACS/hacs_anno_val.json'
ann_file_test = 'data/HACS/hacs_anno_val.json'
train_pipeline = [
dict(type='LoadLocalizationFeature'),
dict(type='GenerateLocalizationLabels'),
dict(
type='PackLocalizationInputs',
keys=('gt_bbox', 'proposals'),
meta_keys=('video_name', ))
]
val_pipeline = [
dict(type='LoadLocalizationFeature'),
dict(type='GenerateLocalizationLabels'),
dict(
type='PackLocalizationInputs',
keys=('gt_bbox', 'proposals'),
meta_keys=(
'video_name',
'duration_second',
'duration_frame',
'annotations',
'feature_frame',
))
]
test_pipeline = [
dict(type='LoadLocalizationFeature'),
dict(
type='PackLocalizationInputs',
keys=('gt_bbox', 'proposals'),
meta_keys=(
'video_name',
'duration_second',
'duration_frame',
'annotations',
'feature_frame',
))
]
train_dataloader = dict(
batch_size=8,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
drop_last=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=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=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))
max_epochs = 9
train_cfg = dict(
type='EpochBasedTrainLoop',
max_epochs=max_epochs,
val_begin=1,
val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
optim_wrapper = dict(
optimizer=dict(type='Adam', lr=0.001, weight_decay=0.0001),
clip_grad=dict(max_norm=40, norm_type=2))
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[
7,
],
gamma=0.1)
]
work_dir = './work_dirs/tcanet_2xb8-2048x100-9e_hacs-feature/'
test_evaluator = dict(
type='ANetMetric',
metric_type='AR@AN',
dump_config=dict(out=f'{work_dir}/results.json', output_format='json'))
val_evaluator = test_evaluator