bsn_tem_1xb16-400x100-20e_activitynet-feature.py

_base_ = ['../../_base_/models/bsn_tem.py', '../../_base_/default_runtime.py']

# dataset settings
dataset_type = 'ActivityNetDataset'
data_root = 'data/ActivityNet/activitynet_feature_cuhk/csv_mean_100/'
data_root_val = 'data/ActivityNet/activitynet_feature_cuhk/csv_mean_100/'
ann_file_train = 'data/ActivityNet/anet_anno_train.json'
ann_file_val = 'data/ActivityNet/anet_anno_val.json'
ann_file_test = 'data/ActivityNet/anet_anno_full.json'

train_pipeline = [
    dict(type='LoadLocalizationFeature'),
    dict(type='GenerateLocalizationLabels'),
    dict(
        type='PackLocalizationInputs',
        keys=('gt_bbox', ),
        meta_keys=('video_name', ))
]
val_pipeline = [
    dict(type='LoadLocalizationFeature'),
    dict(type='GenerateLocalizationLabels'),
    dict(
        type='PackLocalizationInputs',
        keys=('gt_bbox', ),
        meta_keys=('video_name', ))
]
test_pipeline = [
    dict(type='LoadLocalizationFeature'),
    dict(type='PackLocalizationInputs', meta_keys=('video_name', ))
]

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_test,
        data_prefix=dict(video=data_root_val),
        pipeline=test_pipeline,
        test_mode=True))

train_cfg = dict(
    type='EpochBasedTrainLoop', max_epochs=20, val_begin=1, val_interval=20)
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=20,
        by_epoch=True,
        milestones=[7, 14],
        gamma=0.1)
]

work_dir = 'work_dirs/bsn_400x100_20e_1x16_activitynet_feature/'
tem_results_dir = f'{work_dir}/tem_results/'

test_evaluator = dict(
    type='ANetMetric',
    metric_type='TEM',
    dump_config=dict(out=tem_results_dir, output_format='csv'))
val_evaluator = test_evaluator

default_hooks = dict(checkpoint=dict(filename_tmpl='tem_epoch_{}.pth'))