add SVTR LCNet PP-OCRv3 CH model #614
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tonytonglt
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| ## 2. 权重转换 | ||
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| 如您已经有采用PaddleOCR训练好的PaddlePaddle模型，想在MindOCR下直接进行推理或进行微调续训，您可以对训练好的模型转换为MindSpore格式的ckpt文件。 |
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"对训练好的模型转换为"->"将训练好的模型转换为"
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已修改，同步修改DBNet README
| data_out = dict() | ||
| data_out["img_path"] = data.get("img_path", None) | ||
| data_out["image"] = data["image"] | ||
| ctc = self.ctc_encode.__call__(data_ctc) |
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此处由于会预生成预处理transform_pipeline的实例列表，后续对每个batch使用transform(data)进行分步预处理，因此需要写为__call__调用形式，参考代码https://github.com/mindspore-lab/mindocr/blob/main/mindocr/data/transforms/transforms_factory.py#L68
| return padding_im, valid_ratio, valid_width_mask | ||
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| def resize_norm_img( |
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应该有同功能函数或函数组合，建议此次或后续重构，避免冗余代码。
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此处由于需要将参考代码中valid_ratio转换为valid_width_mask以规避动态长度tensor切片引入的动态shape，有一定特殊性
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| class SEModule(nn.Cell): |
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与utils文件夹下SE模块有何区别？代码能否复用。
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已修改，复用utils下SEModule
| return result | ||
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| class ConvBNLayer(nn.Cell): |
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mindcv文件夹下可能有类似代码，建议确认是否可复用，以规避和减少冗余代码。
本文件夹下的MLP等代码亦建议做相同检视。
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此处由于需要判断是否使用自定义的Swish激活函数，有一定特殊性；另外通常从mindcv引用模型作为backbone使用，较少引入单一功能模块
mindocr/models/heads/rec_sar_head.py
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| valid_width_masks = img_metas[-1] | ||
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| lab_embedding = self.embedding(label) | ||
| # bsz * seq_len * emb_dim |
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无需使用的代码直接删除，不应用注释的方式保留。下同
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此处主要为tensor shape的注释，便于代码阅读和调试，已修改为代码后注释形式
tools/infer/text/postprocess.py
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| character_dict_path=dict_path, | ||
| use_space_char=False, | ||
| ) | ||
| dict_path = "mindocr/utils/dict/ch_dict.txt" if algo == "CRNN_CH" or algo == "SVTR_PPOCRv3_CH" else None |
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if algo in ["CRNN_CH", "SVTR_PPOCRv3_CH"]
方便后续拓展
tonytonglt
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Thank you for your contribution to the MindOCR repo.
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