_base_ = [
    '../_base_/datasets/coco_minidetection.py', '../_base_/default_runtime.py'
]

model = dict(
    type='SingleStageDetector',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        plugins=[dict(cfg=dict(type='ReceptiveFieldAttention'),
                      stages=(True, False, False, False),
                      position='after_conv3'),
                 dict(cfg=dict(type='ReceptiveFieldAttention'),
                      stages=(False, True, False, False),
                      position='after_conv3'),
                 dict(cfg=dict(type='ReceptiveFieldAttention'),
                      stages=(False, False, True, False),
                      position='after_conv3'),
                 dict(cfg=dict(type='ReceptiveFieldAttention'),
                      stages=(False, False, False, True),
                      position='after_conv3'),],
        init_cfg=dict(type='Pretrained', checkpoint='/data/home/scv6681/run/project/mmdetection/work_dirs/resnet50_rf/model_r50_rf.pth.tar')),
    neck=dict(
        type='SSDNeck',
        in_channels=(1024, 2048),
        out_channels=(1024, 2048, 512, 256, 256, 256),
        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=(1024, 2048, 512, 256, 256, 256),
        num_classes=80,
        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
dataset_type = 'CocoDataset'
data_root = '/data/public/coco2017/'
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='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'),
    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=8,
    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/home/scv6681/run/data/subcoco/instances_minitrain2017.json", #ata_root + 'annotations/instances_train2017.json',
            img_prefix=data_root + 'train2017/',
            pipeline=train_pipeline)),
    val=dict(pipeline=test_pipeline),
    test=dict(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=40)

# 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')
]