commit
4cac91eb7b
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@ -36,6 +36,9 @@ 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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#### 2.inference模型下载
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*windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载,并解压放置在相应目录下*
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#### (1)超轻量级中文OCR模型下载
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```
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mkdir inference && cd inference
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@ -63,6 +66,9 @@ cd ..
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# 设置PYTHONPATH环境变量
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export PYTHONPATH=.
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# windows下设置环境变量
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SET PYTHONPATH=.
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# 预测image_dir指定的单张图像
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/"
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@ -10,4 +10,3 @@ EvalReader:
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TestReader:
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reader_function: ppocr.data.rec.dataset_traversal,LMDBReader
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lmdb_sets_dir: ./train_data/data_lmdb_release/evaluation/
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infer_img: ./infer_img
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@ -18,6 +18,8 @@ Global:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -11,4 +11,3 @@ EvalReader:
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TestReader:
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reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
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infer_img: ./infer_img
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@ -11,4 +11,3 @@ EvalReader:
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TestReader:
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reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
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infer_img: ./infer_img
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@ -17,6 +17,8 @@ Global:
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pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -17,6 +17,7 @@ Global:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -17,6 +17,7 @@ Global:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -13,10 +13,13 @@ Global:
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max_text_length: 25
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character_type: en
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loss_type: attention
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tps: true
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -13,10 +13,12 @@ Global:
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max_text_length: 25
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character_type: en
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loss_type: ctc
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tps: true
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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@ -17,6 +17,8 @@ Global:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -17,6 +17,7 @@ Global:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -17,6 +17,8 @@ Global:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -17,6 +17,8 @@ Global:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -46,6 +46,9 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/Res
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```
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**启动训练**
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*如果您安装的是cpu版本,请将配置文件中的 `use_gpu` 字段修改为false*
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```
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python3 tools/train.py -c configs/det/det_mv3_db.yml
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```
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@ -165,6 +165,16 @@ STAR-Net文本识别模型推理,可以执行如下命令:
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```
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
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```
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### 3.基于Attention损失的识别模型推理
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基于Attention损失的识别模型与ctc不同,需要额外设置识别算法参数 --rec_algorithm="RARE"
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RARE 文本识别模型推理,可以执行如下命令:
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```
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/sare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE"
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```
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![](imgs_words_en/word_336.png)
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执行命令后,上面图像的识别结果如下:
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@ -8,6 +8,8 @@ PaddleOCR 工作环境
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建议使用我们提供的docker运行PaddleOCR,有关docker使用请参考[链接](https://docs.docker.com/get-started/)。
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*如您希望使用 mac 或 windows直接运行预测代码,可以从第2步开始执行。*
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1. (建议)准备docker环境。第一次使用这个镜像,会自动下载该镜像,请耐心等待。
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```
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# 切换到工作目录下
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@ -54,6 +56,10 @@ python3 -m pip install paddlepaddle-gpu==1.7.2.post97 -i https://pypi.tuna.tsing
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如果您的机器安装的是CUDA10,请运行以下命令安装
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python3 -m pip install paddlepaddle-gpu==1.7.2.post107 -i https://pypi.tuna.tsinghua.edu.cn/simple
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如果您的机器是CPU,请运行以下命令安装
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python3 -m pip install paddlepaddle==1.7.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
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更多的版本需求,请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
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```
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@ -41,6 +41,8 @@ PaddleOCR 提供了一份用于训练 icdar2015 数据集的标签文件,通
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wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
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# 测试集标签
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wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
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```
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最终训练集应有如下文件结构:
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@ -111,6 +113,8 @@ tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
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开始训练:
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*如果您安装的是cpu版本,请将配置文件中的 `use_gpu` 字段修改为false*
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```
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# 设置PYTHONPATH路径
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export PYTHONPATH=$PYTHONPATH:.
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@ -168,10 +172,11 @@ Global:
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评估数据集可以通过 `configs/rec/rec_icdar15_reader.yml` 修改EvalReader中的 `label_file_path` 设置。
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*注意* 评估时必须确保配置文件中 infer_img 字段为空
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```
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export CUDA_VISIBLE_DEVICES=0
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# GPU 评估, Global.checkpoints 为待测权重
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python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
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python3 tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
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```
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### 预测
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@ -184,7 +189,7 @@ python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkp
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```
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# 预测英文结果
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python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
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python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
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```
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预测图片:
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@ -61,8 +61,6 @@ class TrainReader(object):
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if len(batch_outs) == self.batch_size:
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yield batch_outs
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batch_outs = []
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if len(batch_outs) != 0:
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yield batch_outs
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return batch_iter_reader
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@ -17,6 +17,8 @@ import cv2
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import numpy as np
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import json
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import sys
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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from .data_augment import AugmentData
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from .random_crop_data import RandomCropData
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@ -100,6 +102,7 @@ class DBProcessTrain(object):
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img_path, gt_label = self.convert_label_infor(label_infor)
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imgvalue = cv2.imread(img_path)
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if imgvalue is None:
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logger.info("{} does not exist!".format(img_path))
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return None
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data = self.make_data_dict(imgvalue, gt_label)
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data = AugmentData(data)
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@ -41,13 +41,18 @@ class LMDBReader(object):
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self.loss_type = params['loss_type']
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self.max_text_length = params['max_text_length']
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self.mode = params['mode']
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self.drop_last = False
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self.use_tps = False
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if "tps" in params:
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self.ues_tps = True
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if params['mode'] == 'train':
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self.batch_size = params['train_batch_size_per_card']
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elif params['mode'] == "eval":
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self.drop_last = True
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else:
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self.batch_size = params['test_batch_size_per_card']
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elif params['mode'] == "test":
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self.batch_size = 1
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self.infer_img = params["infer_img"]
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self.drop_last = False
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self.infer_img = params['infer_img']
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def load_hierarchical_lmdb_dataset(self):
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lmdb_sets = {}
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dataset_idx = 0
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@ -100,13 +105,18 @@ class LMDBReader(object):
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process_id = 0
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def sample_iter_reader():
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if self.mode == 'test':
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if self.mode != 'train' and self.infer_img is not None:
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image_file_list = get_image_file_list(self.infer_img)
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for single_img in image_file_list:
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img = cv2.imread(single_img)
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if img.shape[-1] == 1 or len(list(img.shape)) == 2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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norm_img = process_image(img, self.image_shape)
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norm_img = process_image(
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img=img,
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image_shape=self.image_shape,
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char_ops=self.char_ops,
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tps=self.use_tps,
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infer_mode=True)
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yield norm_img
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else:
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lmdb_sets = self.load_hierarchical_lmdb_dataset()
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@ -126,9 +136,13 @@ class LMDBReader(object):
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if sample_info is None:
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continue
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img, label = sample_info
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outs = process_image(img, self.image_shape, label,
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self.char_ops, self.loss_type,
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self.max_text_length)
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outs = process_image(
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img=img,
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image_shape=self.image_shape,
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label=label,
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char_ops=self.char_ops,
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loss_type=self.loss_type,
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max_text_length=self.max_text_length)
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if outs is None:
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continue
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yield outs
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if finish_read_num == len(lmdb_sets):
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break
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self.close_lmdb_dataset(lmdb_sets)
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def batch_iter_reader():
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batch_outs = []
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for outs in sample_iter_reader():
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if len(batch_outs) == self.batch_size:
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yield batch_outs
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batch_outs = []
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if not self.drop_last:
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if len(batch_outs) != 0:
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yield batch_outs
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if self.mode != 'test':
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if self.infer_img is None:
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return batch_iter_reader
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return sample_iter_reader
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self.loss_type = params['loss_type']
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self.max_text_length = params['max_text_length']
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self.mode = params['mode']
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self.infer_img = params['infer_img']
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self.use_tps = False
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if "tps" in params:
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self.ues_tps = True
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if params['mode'] == 'train':
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self.batch_size = params['train_batch_size_per_card']
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elif params['mode'] == 'eval':
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self.batch_size = params['test_batch_size_per_card']
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self.drop_last = True
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else:
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self.batch_size = 1
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self.infer_img = params['infer_img']
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self.batch_size = params['test_batch_size_per_card']
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self.drop_last = False
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def __call__(self, process_id):
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if self.mode != 'train':
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process_id = 0
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def sample_iter_reader():
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if self.mode == 'test':
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if self.mode != 'train' and self.infer_img is not None:
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image_file_list = get_image_file_list(self.infer_img)
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for single_img in image_file_list:
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img = cv2.imread(single_img)
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if img.shape[-1] == 1 or len(list(img.shape)) == 2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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norm_img = process_image(img, self.image_shape)
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norm_img = process_image(
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img=img,
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image_shape=self.image_shape,
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char_ops=self.char_ops,
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tps=self.use_tps,
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infer_mode=True)
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yield norm_img
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else:
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with open(self.label_file_path, "rb") as fin:
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@ -222,9 +246,10 @@ class SimpleReader(object):
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if len(batch_outs) == self.batch_size:
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yield batch_outs
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batch_outs = []
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if not self.drop_last:
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if len(batch_outs) != 0:
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yield batch_outs
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if self.mode != 'test':
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if self.infer_img is None:
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return batch_iter_reader
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return sample_iter_reader
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@ -48,6 +48,32 @@ def resize_norm_img(img, image_shape):
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return padding_im
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def resize_norm_img_chinese(img, image_shape):
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imgC, imgH, imgW = image_shape
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# todo: change to 0 and modified image shape
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max_wh_ratio = 0
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h, w = img.shape[0], img.shape[1]
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ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, ratio)
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imgW = int(32 * max_wh_ratio)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype('float32')
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if image_shape[0] == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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def get_img_data(value):
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"""get_img_data"""
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if not value:
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@ -66,8 +92,13 @@ def process_image(img,
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label=None,
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char_ops=None,
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loss_type=None,
|
||||
max_text_length=None):
|
||||
max_text_length=None,
|
||||
tps=None,
|
||||
infer_mode=False):
|
||||
if not infer_mode or char_ops.character_type == "en" or tps != None:
|
||||
norm_img = resize_norm_img(img, image_shape)
|
||||
else:
|
||||
norm_img = resize_norm_img_chinese(img, image_shape)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
if label is not None:
|
||||
char_num = char_ops.get_char_num()
|
||||
|
|
|
@ -30,6 +30,8 @@ class RecModel(object):
|
|||
global_params = params['Global']
|
||||
char_num = global_params['char_ops'].get_char_num()
|
||||
global_params['char_num'] = char_num
|
||||
self.char_type = global_params['character_type']
|
||||
self.infer_img = global_params['infer_img']
|
||||
if "TPS" in params:
|
||||
tps_params = deepcopy(params["TPS"])
|
||||
tps_params.update(global_params)
|
||||
|
@ -60,8 +62,8 @@ class RecModel(object):
|
|||
def create_feed(self, mode):
|
||||
image_shape = deepcopy(self.image_shape)
|
||||
image_shape.insert(0, -1)
|
||||
image = fluid.data(name='image', shape=image_shape, dtype='float32')
|
||||
if mode == "train":
|
||||
image = fluid.data(name='image', shape=image_shape, dtype='float32')
|
||||
if self.loss_type == "attention":
|
||||
label_in = fluid.data(
|
||||
name='label_in',
|
||||
|
@ -86,6 +88,16 @@ class RecModel(object):
|
|||
use_double_buffer=True,
|
||||
iterable=False)
|
||||
else:
|
||||
if self.char_type == "ch" and self.infer_img:
|
||||
image_shape[-1] = -1
|
||||
if self.tps != None:
|
||||
logger.info(
|
||||
"WARNRNG!!!\n"
|
||||
"TPS does not support variable shape in chinese!"
|
||||
"We set img_shape to be the same , it may affect the inference effect"
|
||||
)
|
||||
image_shape = deepcopy(self.image_shape)
|
||||
image = fluid.data(name='image', shape=image_shape, dtype='float32')
|
||||
labels = None
|
||||
loader = None
|
||||
return image, labels, loader
|
||||
|
@ -110,7 +122,11 @@ class RecModel(object):
|
|||
return loader, outputs
|
||||
elif mode == "export":
|
||||
predict = predicts['predict']
|
||||
if self.loss_type == "ctc":
|
||||
predict = fluid.layers.softmax(predict)
|
||||
return [image, {'decoded_out': decoded_out, 'predicts': predict}]
|
||||
else:
|
||||
return loader, {'decoded_out': decoded_out}
|
||||
predict = predicts['predict']
|
||||
if self.loss_type == "ctc":
|
||||
predict = fluid.layers.softmax(predict)
|
||||
return loader, {'decoded_out': decoded_out, 'predicts': predict}
|
||||
|
|
|
@ -123,6 +123,8 @@ class AttentionPredict(object):
|
|||
|
||||
full_ids = fluid.layers.fill_constant_batch_size_like(
|
||||
input=init_state, shape=[-1, 1], dtype='int64', value=1)
|
||||
full_scores = fluid.layers.fill_constant_batch_size_like(
|
||||
input=init_state, shape=[-1, 1], dtype='float32', value=1)
|
||||
|
||||
cond = layers.less_than(x=counter, y=array_len)
|
||||
while_op = layers.While(cond=cond)
|
||||
|
@ -171,6 +173,9 @@ class AttentionPredict(object):
|
|||
new_ids = fluid.layers.concat([full_ids, topk_indices], axis=1)
|
||||
fluid.layers.assign(new_ids, full_ids)
|
||||
|
||||
new_scores = fluid.layers.concat([full_scores, topk_scores], axis=1)
|
||||
fluid.layers.assign(new_scores, full_scores)
|
||||
|
||||
layers.increment(x=counter, value=1, in_place=True)
|
||||
|
||||
# update the memories
|
||||
|
@ -184,7 +189,7 @@ class AttentionPredict(object):
|
|||
length_cond = layers.less_than(x=counter, y=array_len)
|
||||
finish_cond = layers.logical_not(layers.is_empty(x=topk_indices))
|
||||
layers.logical_and(x=length_cond, y=finish_cond, out=cond)
|
||||
return full_ids
|
||||
return full_ids, full_scores
|
||||
|
||||
def __call__(self, inputs, labels=None, mode=None):
|
||||
encoder_features = self.encoder(inputs)
|
||||
|
@ -225,8 +230,8 @@ class AttentionPredict(object):
|
|||
decoded_out = layers.lod_reset(decoded_out, y=label_out)
|
||||
predicts = {'predict':predict, 'decoded_out':decoded_out}
|
||||
else:
|
||||
ids = self.gru_attention_infer(
|
||||
ids, predict = self.gru_attention_infer(
|
||||
decoder_boot, self.max_length, char_num, word_vector_dim,
|
||||
encoded_vector, encoded_proj, decoder_size)
|
||||
predicts = {'decoded_out': ids}
|
||||
predicts = {'predict':predict, 'decoded_out':ids}
|
||||
return predicts
|
||||
|
|
|
@ -48,7 +48,7 @@ def eval_rec_run(exe, config, eval_info_dict, mode):
|
|||
total_sample_num = 0
|
||||
total_acc_num = 0
|
||||
total_batch_num = 0
|
||||
if mode == "test":
|
||||
if mode == "eval":
|
||||
is_remove_duplicate = False
|
||||
else:
|
||||
is_remove_duplicate = True
|
||||
|
@ -91,11 +91,11 @@ def test_rec_benchmark(exe, config, eval_info_dict):
|
|||
total_correct_number = 0
|
||||
eval_data_acc_info = {}
|
||||
for eval_data in eval_data_list:
|
||||
config['EvalReader']['lmdb_sets_dir'] = \
|
||||
config['TestReader']['lmdb_sets_dir'] = \
|
||||
eval_data_dir + "/" + eval_data
|
||||
eval_reader = reader_main(config=config, mode="eval")
|
||||
eval_reader = reader_main(config=config, mode="test")
|
||||
eval_info_dict['reader'] = eval_reader
|
||||
metrics = eval_rec_run(exe, config, eval_info_dict, "eval")
|
||||
metrics = eval_rec_run(exe, config, eval_info_dict, "test")
|
||||
total_evaluation_data_number += metrics['total_sample_num']
|
||||
total_correct_number += metrics['total_acc_num']
|
||||
eval_data_acc_info[eval_data] = metrics
|
||||
|
|
|
@ -32,10 +32,16 @@ class TextRecognizer(object):
|
|||
self.rec_image_shape = image_shape
|
||||
self.character_type = args.rec_char_type
|
||||
self.rec_batch_num = args.rec_batch_num
|
||||
self.rec_algorithm = args.rec_algorithm
|
||||
char_ops_params = {}
|
||||
char_ops_params["character_type"] = args.rec_char_type
|
||||
char_ops_params["character_dict_path"] = args.rec_char_dict_path
|
||||
if self.rec_algorithm != "RARE":
|
||||
char_ops_params['loss_type'] = 'ctc'
|
||||
self.loss_type = 'ctc'
|
||||
else:
|
||||
char_ops_params['loss_type'] = 'attention'
|
||||
self.loss_type = 'attention'
|
||||
self.char_ops = CharacterOps(char_ops_params)
|
||||
|
||||
def resize_norm_img(self, img, max_wh_ratio):
|
||||
|
@ -80,13 +86,14 @@ class TextRecognizer(object):
|
|||
starttime = time.time()
|
||||
self.input_tensor.copy_from_cpu(norm_img_batch)
|
||||
self.predictor.zero_copy_run()
|
||||
|
||||
if self.loss_type == "ctc":
|
||||
rec_idx_batch = self.output_tensors[0].copy_to_cpu()
|
||||
rec_idx_lod = self.output_tensors[0].lod()[0]
|
||||
predict_batch = self.output_tensors[1].copy_to_cpu()
|
||||
predict_lod = self.output_tensors[1].lod()[0]
|
||||
elapse = time.time() - starttime
|
||||
predict_time += elapse
|
||||
starttime = time.time()
|
||||
for rno in range(len(rec_idx_lod) - 1):
|
||||
beg = rec_idx_lod[rno]
|
||||
end = rec_idx_lod[rno + 1]
|
||||
|
@ -100,6 +107,22 @@ class TextRecognizer(object):
|
|||
valid_ind = np.where(ind != (blank - 1))[0]
|
||||
score = np.mean(probs[valid_ind, ind[valid_ind]])
|
||||
rec_res.append([preds_text, score])
|
||||
else:
|
||||
rec_idx_batch = self.output_tensors[0].copy_to_cpu()
|
||||
predict_batch = self.output_tensors[1].copy_to_cpu()
|
||||
elapse = time.time() - starttime
|
||||
predict_time += elapse
|
||||
for rno in range(len(rec_idx_batch)):
|
||||
end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
|
||||
if len(end_pos) <= 1:
|
||||
preds = rec_idx_batch[rno, 1:]
|
||||
score = np.mean(predict_batch[rno, 1:])
|
||||
else:
|
||||
preds = rec_idx_batch[rno, 1:end_pos[1]]
|
||||
score = np.mean(predict_batch[rno, 1:end_pos[1]])
|
||||
preds_text = self.char_ops.decode(preds)
|
||||
rec_res.append([preds_text, score])
|
||||
|
||||
return rec_res, predict_time
|
||||
|
||||
|
||||
|
@ -116,7 +139,17 @@ if __name__ == "__main__":
|
|||
continue
|
||||
valid_image_file_list.append(image_file)
|
||||
img_list.append(img)
|
||||
try:
|
||||
rec_res, predict_time = text_recognizer(img_list)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
logger.info(
|
||||
"ERROR!!!! \n"
|
||||
"Please read the FAQ:https://github.com/PaddlePaddle/PaddleOCR#faq \n"
|
||||
"If your model has tps module: "
|
||||
"TPS does not support variable shape.\n"
|
||||
"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
|
||||
exit()
|
||||
for ino in range(len(img_list)):
|
||||
print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
|
||||
print("Total predict time for %d images:%.3f" %
|
||||
|
|
|
@ -21,6 +21,7 @@ import time
|
|||
import multiprocessing
|
||||
import numpy as np
|
||||
|
||||
|
||||
def set_paddle_flags(**kwargs):
|
||||
for key, value in kwargs.items():
|
||||
if os.environ.get(key, None) is None:
|
||||
|
@ -54,6 +55,7 @@ def main():
|
|||
program.merge_config(FLAGS.opt)
|
||||
logger.info(config)
|
||||
char_ops = CharacterOps(config['Global'])
|
||||
loss_type = config['Global']['loss_type']
|
||||
config['Global']['char_ops'] = char_ops
|
||||
|
||||
# check if set use_gpu=True in paddlepaddle cpu version
|
||||
|
@ -78,35 +80,44 @@ def main():
|
|||
init_model(config, eval_prog, exe)
|
||||
|
||||
blobs = reader_main(config, 'test')()
|
||||
infer_img = config['TestReader']['infer_img']
|
||||
infer_img = config['Global']['infer_img']
|
||||
infer_list = get_image_file_list(infer_img)
|
||||
max_img_num = len(infer_list)
|
||||
if len(infer_list) == 0:
|
||||
logger.info("Can not find img in infer_img dir.")
|
||||
for i in range(max_img_num):
|
||||
print("infer_img:",infer_list[i])
|
||||
print("infer_img:%s" % infer_list[i])
|
||||
img = next(blobs)
|
||||
predict = exe.run(program=eval_prog,
|
||||
feed={"image": img},
|
||||
fetch_list=fetch_varname_list,
|
||||
return_numpy=False)
|
||||
|
||||
if loss_type == "ctc":
|
||||
preds = np.array(predict[0])
|
||||
if preds.shape[1] == 1:
|
||||
preds = preds.reshape(-1)
|
||||
preds_lod = predict[0].lod()[0]
|
||||
preds_text = char_ops.decode(preds)
|
||||
else:
|
||||
probs = np.array(predict[1])
|
||||
ind = np.argmax(probs, axis=1)
|
||||
blank = probs.shape[1]
|
||||
valid_ind = np.where(ind != (blank - 1))[0]
|
||||
score = np.mean(probs[valid_ind, ind[valid_ind]])
|
||||
elif loss_type == "attention":
|
||||
preds = np.array(predict[0])
|
||||
probs = np.array(predict[1])
|
||||
end_pos = np.where(preds[0, :] == 1)[0]
|
||||
if len(end_pos) <= 1:
|
||||
preds_text = preds[0, 1:]
|
||||
preds = preds[0, 1:]
|
||||
score = np.mean(probs[0, 1:])
|
||||
else:
|
||||
preds_text = preds[0, 1:end_pos[1]]
|
||||
preds_text = preds_text.reshape(-1)
|
||||
preds_text = char_ops.decode(preds_text)
|
||||
preds = preds[0, 1:end_pos[1]]
|
||||
score = np.mean(probs[0, 1:end_pos[1]])
|
||||
preds = preds.reshape(-1)
|
||||
preds_text = char_ops.decode(preds)
|
||||
|
||||
print("\t index:", preds)
|
||||
print("\t word :", preds_text)
|
||||
print("\t score :", score)
|
||||
|
||||
# save for inference model
|
||||
target_var = []
|
||||
|
|
Loading…
Reference in New Issue