_base_ = ['slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py']

model = dict(
    backbone=dict(
        _delete_=True,
        type='ResNet3dSlowFast',
        _scope_='mmaction',
        pretrained=None,
        resample_rate=8,
        speed_ratio=8,
        channel_ratio=8,
        slow_pathway=dict(
            type='resnet3d',
            depth=50,
            pretrained=None,
            lateral=True,
            conv1_kernel=(1, 7, 7),
            dilations=(1, 1, 1, 1),
            conv1_stride_t=1,
            pool1_stride_t=1,
            inflate=(0, 0, 1, 1),
            spatial_strides=(1, 2, 2, 1)),
        fast_pathway=dict(
            type='resnet3d',
            depth=50,
            pretrained=None,
            lateral=False,
            base_channels=8,
            conv1_kernel=(5, 7, 7),
            conv1_stride_t=1,
            pool1_stride_t=1,
            spatial_strides=(1, 2, 2, 1))),
    roi_head=dict(bbox_head=dict(in_channels=2304)))

dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'

ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'

exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'

label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'

proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
                       'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'

train_pipeline = [
    dict(type='SampleAVAFrames', clip_len=32, frame_interval=2),
    dict(type='RawFrameDecode'),
    dict(type='RandomRescale', scale_range=(256, 320)),
    dict(type='RandomCrop', size=256),
    dict(type='Flip', flip_ratio=0.5),
    dict(type='FormatShape', input_format='NCTHW', collapse=True),
    dict(type='PackActionInputs')
]

# The testing is w/o. any cropping / flipping
val_pipeline = [
    dict(
        type='SampleAVAFrames', clip_len=32, frame_interval=2, test_mode=True),
    dict(type='RawFrameDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='FormatShape', input_format='NCTHW', collapse=True),
    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,
        exclude_file=exclude_file_train,
        pipeline=train_pipeline,
        label_file=label_file,
        proposal_file=proposal_file_train,
        data_prefix=dict(img=data_root)))
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,
        exclude_file=exclude_file_val,
        pipeline=val_pipeline,
        label_file=label_file,
        proposal_file=proposal_file_val,
        data_prefix=dict(img=data_root),
        test_mode=True))
test_dataloader = val_dataloader

load_from = ('https://download.openmmlab.com/mmaction/recognition/slowfast/'
             'slowfast_r50_4x16x1_256e_kinetics400_rgb/'
             'slowfast_r50_4x16x1_256e_kinetics400_rgb_20200704-bcde7ed7.pth')
