model_poly = dict(
    type='PSENet',
    backbone=dict(
        type='mmdet.ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=-1,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
        norm_eval=True,
        style='caffe'),
    neck=dict(
        type='FPNF',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        fusion_type='concat'),
    det_head=dict(
        type='PSEHead',
        in_channels=[256],
        hidden_dim=256,
        out_channel=7,
        module_loss=dict(type='PSEModuleLoss'),
        postprocessor=dict(type='PSEPostprocessor', text_repr_type='poly')),
    data_preprocessor=dict(
        type='TextDetDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32))

model_quad = dict(
    type='PSENet',
    backbone=dict(
        type='mmdet.ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=-1,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=True,
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
        style='pytorch'),
    neck=dict(
        type='FPNF',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        fusion_type='concat'),
    det_head=dict(
        type='PSEHead',
        in_channels=[256],
        hidden_dim=256,
        out_channel=7,
        module_loss=dict(type='PSEModuleLoss'),
        postprocessor=dict(type='PSEPostprocessor', text_repr_type='quad')),
    data_preprocessor=dict(
        type='TextDetDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32))