66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
model = dict(
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type="DBNet",
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backbone=dict(
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type="CLIPResNet",
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=-1,
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norm_cfg=dict(type="BN", requires_grad=True),
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norm_eval=False,
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style="pytorch",
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dcn=dict(type="DCNv2", deform_groups=1, fallback_on_stride=False),
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# init_cfg=dict(
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# type='Pretrained',
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# checkpoint='https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth'),
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stage_with_dcn=(False, True, True, True),
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),
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neck=dict(
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type="FPNC",
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in_channels=[256, 512, 1024, 2048],
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lateral_channels=256,
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asf_cfg=dict(attention_type="ScaleChannelSpatial"),
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),
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det_head=dict(
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type="DBHead",
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in_channels=256,
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module_loss=dict(type="DBModuleLoss"),
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postprocessor=dict(
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type="DBPostprocessor",
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text_repr_type="quad",
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epsilon_ratio=0.002,
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),
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),
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data_preprocessor=dict(
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type="TextDetDataPreprocessor",
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True,
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pad_size_divisor=32,
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),
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init_cfg=dict(
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type="Pretrained",
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checkpoint="https://download.openmmlab.com/mmocr/textdet/dbnetpp/"
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"dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015/"
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"dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015_20221101_124139-4ecb39ac.pth",
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),
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)
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test_pipeline = [
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# dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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dict(type="Resize", scale=(4068, 1024), keep_ratio=True),
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dict(
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type="PackTextDetInputs",
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# meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'),
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meta_keys=("img_shape", "scale_factor"),
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),
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]
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# Visualization
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vis_backends = [dict(type="LocalVisBackend")]
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visualizer = dict(
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type="TextDetLocalVisualizer",
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name="visualizer",
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vis_backends=vis_backends,
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)
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