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建议: 将ppocr_keys等信息直接存储到onnx模型 #42
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好主意,我去调研一下。 |
再次感谢。 |
issue,我这里先关闭了,以后有问题或者建议可以再次打开。 |
character_dict_path 这个怎么指定?报错了 rapid_ocr = RapidOCR(use_text_det=False, character_dict_path=r"F:\japan_dict.txt",keys_path=r"F:\japan_dict.txt", File "F:\pycharm2020.2\RapidStructure-0.0.0\rapidocr_onnxruntime\rapid_ocr_api.py", line 43, in init |
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用1楼的代码可以存入字典信息了 import onnx model_path = r'L:\resnet34\4lan\res34.onnx' meta.key = 'dictionary'meta.key = 'character' meta.value = open('/path/to/ppocr_keys_v1.txt', 'r', -1, 'u8').read()meta.value = open(r'F:/japan_dict.txt', 'r', -1, 'u8').read() meta = model.metadata_props.add() model_path_save = r'L:\resnet34\4lan\res34_withDictPath.onnx' onnx.save_model(model, '/path/to/model.onnx')onnx.save_model(model, model_path_save ) 获取meta信息import json sess = ort.InferenceSession('/path/to/model.onnx')sess = ort.InferenceSession(model_path_save) chars = metamap['dictionary'].splitlines()chars = metamap['character'].splitlines() |
目前仓库最新版,已经这么干了。可下载的识别模型里面都有对应的字典了。 |
建议将ppocr_keys, rec_img_shape等信息直接存储到onnx模型
目前, ppocr_keys是单独存放在txt文件, 然后在config.yaml中配置文件路径; rec_img_shape是在config.yaml中配置
这两个参数是和onnx模型强相关的, 可以直接作为元数据存储到onnx模型内, 减少配置的需求.
尤其是ppocr_keys, 目前通过另一个文件来分发, 容易出现两边不一致的情况.
ONNX本身支持自定义元信息的存储. 使用这种方式, 部署相关的配置应该会更简单.
参考代码:
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