fix problems responding to inference
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README.md
54
README.md
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@ -15,39 +15,63 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
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## 文档教程
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- [快速安装](./doc/installation.md)
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- [快速开始]()
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- [文本识别模型训练/评估/预测](./doc/detection.md)
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- [文本预测模型训练/评估/预测](./doc/recognition.md)
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- [基于inference model预测](./doc/)
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### **快速开始**
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下载inference模型
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```
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# 创建inference模型保存目录
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mkdir inference && cd inference && mkdir det && mkdir rec
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# 下载检测inference模型
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wget -P ./inference/det 检测inference模型链接
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# 下载识别inference模型
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wget -P ./inferencee/rec 识别inference模型链接
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```
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实现文本检测、识别串联推理，预测$image_dir$指定的单张图像：
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```
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export PYTHONPATH=.
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python tools/infer/predict_eval.py --image_dir="/Demo.jpg" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
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```
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在执行预测时，通过参数det_model_dir以及rec_model_dir设置存储inference 模型的路径。
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实现文本检测、识别串联推理，预测$image_dir$指指定文件夹下的所有图像：
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```
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python tools/infer/predict_eval.py --image_dir="/test_imgs/" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
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```
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## 文本检测算法:
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PaddleOCR开源的文本检测算法列表：
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- [x] [EAST](https://arxiv.org/abs/1704.03155)
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- [x] [DB](https://arxiv.org/abs/1911.08947)
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- [x] [SAST](https://arxiv.org/abs/1908.05498)
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- []
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- [x] [EAST](https://arxiv.org/abs/1704.03155)
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- [x] [DB](https://arxiv.org/abs/1911.08947)
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- [ ] [SAST](https://arxiv.org/abs/1908.05498)
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算法效果：
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|模型|骨干网络|Hmean|
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|EAST^[1]^|ResNet50_vd|85.85%|
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|EAST^[1]^|MobileNetV3|79.08%|
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|DB^[2]^|ResNet50_vd|83.30%|
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|DB^[2]^|MobileNetV3|73.00%|
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|EAST|ResNet50_vd|85.85%|
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|EAST|MobileNetV3|79.08%|
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|DB|ResNet50_vd|83.30%|
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|DB|MobileNetV3|73.00%|
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PaddleOCR文本检测算法的训练与使用请参考[文档](./doc/detection.md)。
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## 文本识别算法:
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PaddleOCR开源的文本识别算法列表：
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- [CRNN](https://arxiv.org/abs/1507.05717)
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- [Rosetta](https://arxiv.org/abs/1910.05085)
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- [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
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- [RARE](https://arxiv.org/abs/1603.03915v1)
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- [SRN]((https://arxiv.org/abs/2003.12294))(百度自研)
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- [x] [CRNN](https://arxiv.org/abs/1507.05717)
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- [x] [DTRB](https://arxiv.org/abs/1904.01906)
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- [ ] [Rosetta](https://arxiv.org/abs/1910.05085)
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- [ ] [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
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- [ ] [RARE](https://arxiv.org/abs/1603.03915v1)
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- [ ] [SRN]((https://arxiv.org/abs/2003.12294))(百度自研)
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算法效果如下表所示，精度指标是在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上的评测结果的平均值。
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@ -67,7 +91,7 @@ PaddleOCR文本识别算法的训练与使用请参考[文档](./doc/recognition
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## TODO
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**端到端OCR算法**
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PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.org/abs/1909.07808)，敬请关注。
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- End2End-PSL (comming soon)
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- [ ] End2End-PSL (comming soon)
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@ -14,6 +14,7 @@ Global:
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pretrain_weights: ./pretrain_models/MobileNetV3_pretrained/MobileNetV3_large_x0_5_pretrained/
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checkpoints:
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save_res_path: ./output/predicts_db.txt
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.det_model,DetModel
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@ -0,0 +1,58 @@
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# 基于inference model的推理
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inference 模型（fluid.io.save_inference_model保存的模型）
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一般是模型训练完成后保存的固化模型，多用于预测部署。
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训练过程中保存的模型是checkpoints模型，保存的是模型的参数，多用于恢复训练等。
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与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越。
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PaddleOCR提供了将checkpoints转换成inference model的实现。
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## 文本检测模型推理
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将文本检测模型训练过程中保存的模型，转换成inference model，可以使用如下命令：
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```
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python tools/export_model.py -c configs/det/det_db_mv3.yml -o Global.checkpoints="./output/best_accuracy" \
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Global.save_inference_dir="./inference/det/"
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```
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推理模型保存在$./inference/det/model$, $./inference/det/params$
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使用保存的inference model实现在单张图像上的预测：
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```
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python tools/infer/predict_det.py --image_dir="/demo.jpg" --det_model_dir="./inference/det/"
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```
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## 文本识别模型推理
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将文本识别模型训练过程中保存的模型，转换成inference model，可以使用如下命令：
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```
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python tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints="./output/best_accuracy" \
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Global.save_inference_dir="./inference/rec/"
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```
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推理模型保存在$./inference/rec/model$, $./inference/rec/params$
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使用保存的inference model实现在单张图像上的预测：
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```
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python tools/infer/predict_rec.py --image_dir="/demo.jpg" --rec_model_dir="./inference/rec/"
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```
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## 文本检测、识别串联推理
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实现文本检测、识别串联推理，预测$image_dir$指定的单张图像：
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```
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python tools/infer/predict_eval.py --image_dir="/Demo.jpg" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
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```
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实现文本检测、识别串联推理，预测$image_dir$指指定文件夹下的所有图像：
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```
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python tools/infer/predict_eval.py --image_dir="/test_imgs/" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
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```
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@ -22,7 +22,7 @@ import string
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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from ppocr.utils.utility import create_module
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from tools.infer.utility import get_image_file_list
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from ppocr.utils.utility import get_image_file_list
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import time
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@ -13,6 +13,7 @@
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# limitations under the License.
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import logging
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import os
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def initial_logger():
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@ -55,6 +56,23 @@ def get_check_reader_params(mode):
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return check_params
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def get_image_file_list(img_file):
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imgs_lists = []
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if img_file is None or not os.path.exists(img_file):
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raise Exception("not found any img file in {}".format(img_file))
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img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
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if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
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imgs_lists.append(img_file)
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elif os.path.isdir(img_file):
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for single_file in os.listdir(img_file):
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if single_file.split('.')[-1] in img_end:
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imgs_lists.append(os.path.join(img_file, single_file))
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if len(imgs_lists) == 0:
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raise Exception("not found any img file in {}".format(img_file))
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return imgs_lists
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from paddle import fluid
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@ -71,14 +71,19 @@ def main():
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init_model(config, eval_program, exe)
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save_inference_dir = config['Global']['save_inference_dir']
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if not os.path.exists(save_inference_dir):
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os.makedirs(save_inference_dir)
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fluid.io.save_inference_model(
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dirname="./output/",
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dirname=save_inference_dir,
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feeded_var_names=feeded_var_names,
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main_program=eval_program,
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target_vars=target_vars,
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executor=exe,
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model_filename='model',
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params_filename='params')
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print("inference model saved in {}/model and {}/params".format(
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save_inference_dir, save_inference_dir))
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print("save success, output_name_list:", fetches_var_name)
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@ -61,23 +61,6 @@ def parse_args():
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return parser.parse_args()
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def get_image_file_list(img_file):
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imgs_lists = []
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if img_file is None or not os.path.exists(img_file):
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raise Exception("not found any img file in {}".format(img_file))
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img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
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if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
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imgs_lists.append(img_file)
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elif os.path.isdir(img_file):
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for single_file in os.listdir(img_file):
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if single_file.split('.')[-1] in img_end:
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imgs_lists.append(os.path.join(img_file, single_file))
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if len(imgs_lists) == 0:
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raise Exception("not found any img file in {}".format(img_file))
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return imgs_lists
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def create_predictor(args, mode):
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if mode == "det":
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model_dir = args.det_model_dir
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