_base_ = [
    '../../_base_/models/audioonly_r50.py', '../../_base_/default_runtime.py'
]

# dataset settings
dataset_type = 'AudioDataset'
data_root = 'data/kinetics400/audio_features_train'
data_root_val = 'data/kinetics400/audio_features_val'
ann_file_train = 'data/kinetics400/kinetics400_train_list_audio_features.txt'
ann_file_val = 'data/kinetics400/kinetics400_val_list_audio_features.txt'
ann_file_test = 'data/kinetics400/kinetics400_val_list_audio_features.txt'
train_pipeline = [
    dict(type='LoadAudioFeature'),
    dict(type='SampleFrames', clip_len=64, frame_interval=1, num_clips=1),
    dict(type='AudioFeatureSelector'),
    dict(type='FormatAudioShape', input_format='NCTF'),
    dict(type='PackActionInputs')
]
val_pipeline = [
    dict(type='LoadAudioFeature'),
    dict(
        type='SampleFrames',
        clip_len=64,
        frame_interval=1,
        num_clips=1,
        test_mode=True),
    dict(type='AudioFeatureSelector'),
    dict(type='FormatAudioShape', input_format='NCTF'),
    dict(type='PackActionInputs')
]
test_pipeline = [
    dict(type='LoadAudioFeature'),
    dict(
        type='SampleFrames',
        clip_len=64,
        frame_interval=1,
        num_clips=10,
        test_mode=True),
    dict(type='AudioFeatureSelector'),
    dict(type='FormatAudioShape', input_format='NCTF'),
    dict(type='PackActionInputs')
]

train_dataloader = dict(
    batch_size=160,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        ann_file=ann_file_train,
        pipeline=train_pipeline,
        data_prefix=dict(audio=data_root),
        suffix='.npy'))
val_dataloader = dict(
    batch_size=160,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        ann_file=ann_file_val,
        pipeline=val_pipeline,
        data_prefix=dict(audio=data_root_val),
        suffix='.npy',
        test_mode=True))
test_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        ann_file=ann_file_test,
        pipeline=test_pipeline,
        data_prefix=dict(audio=data_root_val),
        suffix='.npy',
        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=5)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

param_scheduler = [
    dict(type='CosineAnnealingLR', eta_min=0, T_max=100, by_epoch=True)
]

optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=2.0, momentum=0.9, weight_decay=0.0001),
    clip_grad=dict(max_norm=40, norm_type=2))

default_hooks = dict(
    checkpoint=dict(max_keep_ckpts=3, interval=5), logger=dict(interval=20))