zhoujun
GitHub
commit
c305acb60c
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@ -57,24 +57,22 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs
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**注意：**
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- 我们提供的数据集，即[`CROHME数据集`](https://paddleocr.bj.bcebos.com/dataset/CROHME.tar)将手写公式存储为黑底白字的格式，若您自行准备的数据集与之相反，即以白底黑字模式存储，请在训练时做出如下修改
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```
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python3 tools/train.py -c configs/rec/rec_d28_can.yml
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-o Train.dataset.transforms.GrayImageChannelFormat.inverse=False
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python3 tools/train.py -c configs/rec/rec_d28_can.yml -o Train.dataset.transforms.GrayImageChannelFormat.inverse=False
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```
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- 默认每训练1个epoch（1105次iteration）进行1次评估，若您更改训练的batch_size，或更换数据集，请在训练时作出如下修改
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```
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python3 tools/train.py -c configs/rec/rec_d28_can.yml
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-o Global.eval_batch_step=[0, {length_of_dataset//batch_size}]
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python3 tools/train.py -c configs/rec/rec_d28_can.yml -o Global.eval_batch_step=[0, {length_of_dataset//batch_size}]
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```
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#
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<a name="3-2"></a>
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### 3.2 评估
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可下载已训练完成的[模型文件](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)，使用如下命令进行评估：
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可下载已训练完成的[模型文件](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar)，使用如下命令进行评估：
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```shell
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# 注意将pretrained_model的路径设置为本地路径。若使用自行训练保存的模型，请注意修改路径和文件名为{path/to/weights}/{model_name}。
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python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/CAN
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python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams
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```
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<a name="3-3"></a>
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@ -83,7 +81,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec
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使用如下命令进行单张图片预测：
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```shell
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# 注意将pretrained_model的路径设置为本地路径。
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python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/datasets/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/CAN
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python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/datasets/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams
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# 预测文件夹下所有图像时，可修改infer_img为文件夹，如 Global.infer_img='./doc/datasets/crohme_demo/'。
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```
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@ -94,11 +92,11 @@ python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.a
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<a name="4-1"></a>
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### 4.1 Python推理
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首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/contribution/can_train.tar) )，可以使用如下命令进行转换：
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首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar) )，可以使用如下命令进行转换：
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```shell
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# 注意将pretrained_model的路径设置为本地路径。
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python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/CAN Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
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python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
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# 目前的静态图模型默认的输出长度最大为36，如果您需要预测更长的序列，请在导出模型时指定其输出序列为合适的值，例如 Architecture.Head.max_text_length=72
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```
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@ -53,14 +53,14 @@ Evaluation:
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```
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# GPU evaluation
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python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/CAN
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python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams
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```
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Prediction:
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```
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# The configuration file used for prediction must match the training
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python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/CAN
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python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams
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```
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<a name="4"></a>
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@ -71,7 +71,7 @@ python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.a
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First, the model saved during the CAN handwritten mathematical expression recognition training process is converted into an inference model. you can use the following command to convert:
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```
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python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
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python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
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# The default output max length of the model is 36. If you need to predict a longer sequence, please specify its output sequence as an appropriate value when exporting the model, as: Architecture.Head.max_ text_ length=72
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```
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@ -79,7 +79,7 @@ python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_infe
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For CAN handwritten mathematical expression recognition model inference, the following commands can be executed:
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```
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python3 tools/infer/predict_rec.py --image_dir="./doc/crohme_demo/hme_00.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt"
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python3 tools/infer/predict_rec.py --image_dir="./doc/datasets/crohme_demo/hme_00.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt"
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# If you need to predict on a picture with black characters on a white background, please set: -- rec_ image_ inverse=False
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```
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@ -51,7 +51,7 @@ Metric:
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Train:
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dataset:
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name: LMDBDataSetSR
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data_dir: ./train_data/TextZoom/train
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data_dir: ./train_data/TextZoom/test
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transforms:
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- SRResize:
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imgH: 32
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@ -4,12 +4,12 @@ python:python3.7
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gpu_list:0|0,1
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Global.use_gpu:True|True
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Global.auto_cast:null
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Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
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Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
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Global.save_model_dir:./output/
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Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=16
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Global.pretrained_model:null
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train_model_name:latest
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train_infer_img_dir:./inference/sr_inference
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train_infer_img_dir:./inference/rec_inference
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null:null
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##
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trainer:norm_train
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@ -21,7 +21,7 @@ null:null
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null:null
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##
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===========================eval_params===========================
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eval:tools/eval.py -c test_tipc/configs/sr_telescope/sr_telescope.yml -o
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eval:null
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null:null
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##
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===========================infer_params===========================
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@ -44,8 +44,8 @@ inference:tools/infer/predict_sr.py --sr_image_shape="1,32,128" --rec_algorithm=
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--rec_batch_num:1
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--use_tensorrt:False
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--precision:fp32
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--rec_model_dir:
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--image_dir:./inference/sr_inference
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--sr_model_dir:
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--image_dir:./inference/rec_inference
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--save_log_path:./test/output/
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--benchmark:True
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null:null
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@ -150,6 +150,7 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
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# pretrain lite train data
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wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
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wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar --no-check-certificate
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cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
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if [[ ${model_name} =~ "ch_PP-OCRv2_det" ]];then
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wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar --no-check-certificate
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cd ./pretrain_models/ && tar xf ch_PP-OCRv2_det_distill_train.tar && cd ../
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@ -179,7 +180,6 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
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wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar --no-check-certificate
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cd ./pretrain_models/ && tar xf table_structure_tablemaster_train.tar && cd ../
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fi
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cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
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rm -rf ./train_data/icdar2015
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rm -rf ./train_data/ic15_data
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rm -rf ./train_data/pubtabnet
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