_base_ = '../../_base_/default_runtime.py'

model = dict(
    type='Recognizer3D',
    backbone=dict(
        type='ResNet3dSlowOnly',
        depth=50,
        pretrained=None,
        in_channels=17,
        base_channels=32,
        num_stages=3,
        out_indices=(2, ),
        stage_blocks=(3, 4, 6),
        conv1_stride_s=1,
        pool1_stride_s=1,
        inflate=(0, 1, 1),
        spatial_strides=(2, 2, 2),
        temporal_strides=(1, 1, 2),
        dilations=(1, 1, 1)),
    cls_head=dict(
        type='I3DHead',
        in_channels=512,
        num_classes=51,
        spatial_type='avg',
        dropout_ratio=0.5,
        average_clips='prob'),
    train_cfg=None,
    test_cfg=None)

dataset_type = 'PoseDataset'
ann_file = 'data/posec3d/hmdb51.pkl'
left_kp = [1, 3, 5, 7, 9, 11, 13, 15]
right_kp = [2, 4, 6, 8, 10, 12, 14, 16]
train_pipeline = [
    dict(type='UniformSampleFrames', clip_len=48),
    dict(type='PoseDecode'),
    dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True),
    dict(type='Resize', scale=(-1, 64)),
    dict(type='RandomResizedCrop', area_range=(0.56, 1.0)),
    dict(type='Resize', scale=(48, 48), keep_ratio=False),
    dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp),
    dict(
        type='GeneratePoseTarget',
        sigma=0.6,
        use_score=True,
        with_kp=True,
        with_limb=False),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='PackActionInputs')
]
val_pipeline = [
    dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True),
    dict(type='PoseDecode'),
    dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True),
    dict(type='Resize', scale=(-1, 56)),
    dict(type='CenterCrop', crop_size=56),
    dict(
        type='GeneratePoseTarget',
        sigma=0.6,
        use_score=True,
        with_kp=True,
        with_limb=False),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='PackActionInputs')
]
test_pipeline = [
    dict(
        type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True),
    dict(type='PoseDecode'),
    dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True),
    dict(type='Resize', scale=(-1, 56)),
    dict(type='CenterCrop', crop_size=56),
    dict(
        type='GeneratePoseTarget',
        sigma=0.6,
        use_score=True,
        with_kp=True,
        with_limb=False,
        double=True,
        left_kp=left_kp,
        right_kp=right_kp),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='PackActionInputs')
]

train_dataloader = dict(
    batch_size=16,
    num_workers=8,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type='RepeatDataset',
        times=10,
        dataset=dict(
            type=dataset_type,
            ann_file=ann_file,
            split='train1',
            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,
        split='test1',
        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,
        split='test1',
        pipeline=test_pipeline,
        test_mode=True))

val_evaluator = dict(type='AccMetric')
test_evaluator = val_evaluator

train_cfg = dict(
    type='EpochBasedTrainLoop', max_epochs=12, val_begin=1, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

param_scheduler = [
    dict(
        type='MultiStepLR',
        begin=0,
        end=12,
        by_epoch=True,
        milestones=[9, 11],
        gamma=0.1)
]

optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0003),
    clip_grad=dict(max_norm=40, norm_type=2))

default_hooks = dict(checkpoint=dict(max_keep_ckpts=3))

load_from = 'https://download.openmmlab.com/mmaction/v2.0/skeleton/posec3d/k400_posec3d-041f49c6.pth'  # noqa: E501
