# =========================================================
# from 'mmdetection/configs/_base_/default_runtime.py'
# =========================================================
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
custom_hooks = [dict(type='NumClassCheckHook')]
# =========================================================

# =========================================================
# from 'mmdetection/configs/_base_/datasets/coco_detection.py'
# =========================================================
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_train2017.json',
        img_prefix=data_root + 'train2017/',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')
# =========================================================

model = dict(
    type='SingleStageDetector',
    backbone=dict(
        type='MobileNetV2',
        out_indices=(4, 7),
        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
    neck=dict(
        type='SSDNeck',
        in_channels=(96, 1280),
        out_channels=(96, 1280, 512, 256, 256, 128),
        level_strides=(2, 2, 2, 2),
        level_paddings=(1, 1, 1, 1),
        l2_norm_scale=None,
        use_depthwise=True,
        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
        act_cfg=dict(type='ReLU6'),
        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
    bbox_head=dict(
        type='SSDHead',
        in_channels=(96, 1280, 512, 256, 256, 128),
        num_classes=1,
        use_depthwise=True,
        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
        act_cfg=dict(type='ReLU6'),
        init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),

        # set anchor size manually instead of using the predefined
        # SSD300 setting.
        anchor_generator=dict(
            type='SSDAnchorGenerator',
            scale_major=False,
            strides=[16, 32, 64, 107, 160, 320],
            ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
            min_sizes=[48, 100, 150, 202, 253, 304],
            max_sizes=[100, 150, 202, 253, 304, 320]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[0.1, 0.1, 0.2, 0.2])),
    # model training and testing settings
    train_cfg=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.5,
            neg_iou_thr=0.5,
            min_pos_iou=0.,
            ignore_iof_thr=-1,
            gt_max_assign_all=False),
        smoothl1_beta=1.,
        allowed_border=-1,
        pos_weight=-1,
        neg_pos_ratio=3,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        nms=dict(type='nms', iou_threshold=0.45),
        min_bbox_size=0,
        score_thr=0.02,
        max_per_img=200))
cudnn_benchmark = True

# dataset settings
file_client_args = dict(
    backend='petrel',
    path_mapping=dict({
        '.data/onehand10k/':
        'openmmlab:s3://openmmlab/datasets/pose/OneHand10K/',
        'data/onehand10k/':
        'openmmlab:s3://openmmlab/datasets/pose/OneHand10K/'
    }))

dataset_type = 'CocoDataset'
data_root = 'data/onehand10k/'
classes = ('hand', )
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=file_client_args),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='Expand',
        mean=img_norm_cfg['mean'],
        to_rgb=img_norm_cfg['to_rgb'],
        ratio_range=(1, 4)),
    dict(
        type='MinIoURandomCrop',
        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
        min_crop_size=0.3),
    dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='PhotoMetricDistortion',
        brightness_delta=32,
        contrast_range=(0.5, 1.5),
        saturation_range=(0.5, 1.5),
        hue_delta=18),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=320),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=file_client_args),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(320, 320),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=False),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=320),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=24,
    workers_per_gpu=4,
    train=dict(
        _delete_=True,
        type='RepeatDataset',  # use RepeatDataset to speed up training
        times=5,
        dataset=dict(
            type=dataset_type,
            ann_file=data_root + 'annotations/onehand10k_train.json',
            img_prefix=data_root,
            classes=classes,
            pipeline=train_pipeline)),
    val=dict(
        ann_file=data_root + 'annotations/onehand10k_test.json',
        img_prefix=data_root,
        classes=classes,
        pipeline=test_pipeline),
    test=dict(
        ann_file=data_root + 'annotations/onehand10k_test.json',
        img_prefix=data_root,
        classes=classes,
        pipeline=test_pipeline))

# optimizer
optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4.0e-5)
optimizer_config = dict(grad_clip=None)

# learning policy
lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    min_lr=0)
runner = dict(type='EpochBasedRunner', max_epochs=120)

# Avoid evaluation and saving weights too frequently
evaluation = dict(interval=5, metric='bbox')
checkpoint_config = dict(interval=5)
custom_hooks = [
    dict(type='NumClassCheckHook'),
    dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
]

log_config = dict(interval=5)

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (24 samples per GPU)
auto_scale_lr = dict(base_batch_size=192)

load_from = 'https://download.openmmlab.com/mmdetection/'
'v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/'
'ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth'
