_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    backbone=dict(
        type='ResNeSt',
        stem_channels=128,
        depth=101,
        radix=2,
        reduction_factor=4,
        avg_down_stride=True,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=norm_cfg,
        norm_eval=False,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest101')),
    roi_head=dict(
        bbox_head=[
            dict(
                type='Shared4Conv1FCBBoxHead',
                in_channels=256,
                conv_out_channels=256,
                fc_out_channels=1024,
                norm_cfg=norm_cfg,
                roi_feat_size=7,
                num_classes=11,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.1, 0.1, 0.2, 0.2]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='Shared4Conv1FCBBoxHead',
                in_channels=256,
                conv_out_channels=256,
                fc_out_channels=1024,
                norm_cfg=norm_cfg,
                roi_feat_size=7,
                num_classes=11,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.05, 0.05, 0.1, 0.1]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='Shared4Conv1FCBBoxHead',
                in_channels=256,
                conv_out_channels=256,
                fc_out_channels=1024,
                norm_cfg=norm_cfg,
                roi_feat_size=7,
                num_classes=11,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.033, 0.033, 0.067, 0.067]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
        ], ))
# # use ResNeSt img_norm
img_norm_cfg = dict(
    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)

dataset_type = 'CocoDataset'
classes = ('yiwei','loujian','celi','libei','fantie','lianxi','duojian','shunjian','shaoxi','jiahan','yiwu')

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='Resize',
        img_scale=[(400, 400), (500, 500)],
        multiscale_mode='value',
        keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=[0.2,0.2,0.2], direction=['horizontal', 'vertical', 'diagonal']),
    dict(type='BrightnessTransform', level=5, prob=0.5),
    dict(type='ContrastTransform', level=5, prob=0.5),
    dict(type='RandomShift', shift_ratio=0.5),
    dict(type='MinIoURandomCrop', min_ious=(0.5, 0.7, 0.9), min_crop_size=0.8),
    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=[(400, 400)],
        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=8,
    workers_per_gpu=8,
    train=dict(
        type=dataset_type,
        img_prefix='/home/shanwei-luo/userdata/datasets/dsxw_dataset_v5/dsxw_train/images/',
        classes=classes,
        ann_file='/home/shanwei-luo/userdata/datasets/dsxw_dataset_v5/dsxw_train/annotations/train.json',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        img_prefix='/home/shanwei-luo/userdata/datasets/dsxw_dataset_v5/dsxw_test/images/',
        classes=classes,
        ann_file='/home/shanwei-luo/userdata/datasets/dsxw_dataset_v5/dsxw_test/annotations/test.json',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        img_prefix='/home/shanwei-luo/userdata/datasets/dsxw_dataset_v5/dsxw_test/images/',
        classes=classes,
        ann_file='/home/shanwei-luo/userdata/datasets/dsxw_dataset_v5/dsxw_test/annotations/test.json',
        pipeline=test_pipeline))

# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=5000,
    warmup_ratio=1.0 / 10,
    min_lr_ratio=1e-5)
runner = dict(type='EpochBasedRunner', max_epochs=40)

evaluation = dict(interval=5, metric='bbox')