mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-11-03 11:19:20 +00:00
update can data loading method and tipc configs, revert precommit config
This commit is contained in:
dorren
parent
25e56a6f44
commit
c57effb84f
@ -1,11 +1,10 @@
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repos:
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- repo: https://github.com/PaddlePaddle/mirrors-yapf.git
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rev: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
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sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
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hooks:
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- id: yapf
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files: \.py$
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: a11d9314b22d8f8c7556443875b731ef05965464
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sha: a11d9314b22d8f8c7556443875b731ef05965464
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hooks:
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- id: check-merge-conflict
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- id: check-symlinks
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@ -16,7 +15,7 @@ repos:
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- id: trailing-whitespace
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files: \.md$
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- repo: https://github.com/Lucas-C/pre-commit-hooks
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rev: v1.0.1
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sha: v1.0.1
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hooks:
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- id: forbid-crlf
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files: \.md$
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@ -5,14 +5,14 @@ Global:
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print_batch_step: 10
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save_model_dir: ./output/rec/can/
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save_epoch_step: 1
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# evaluation is run every 1105 iterations
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# evaluation is run every 1105 iterations (1 epoch)(batch_size = 8)
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eval_batch_step: [0, 1105]
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cal_metric_during_train: True
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pretrained_model: ./output/rec/can/CAN
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checkpoints: ./output/rec/can/CAN
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save_inference_dir: ./inference/rec_d28_can/
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pretrained_model:
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img: doc/imgs_hme/hme_01.jpeg
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infer_img: doc/datasets/crohme_demo/hme_00.jpg
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# for data or label process
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character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
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max_text_length: 36
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@ -75,7 +75,7 @@ Metric:
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Train:
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dataset:
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name: HMERDataSet
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name: PGDataSet
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data_dir: ./train_data/CROHME/training/images/
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transforms:
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- DecodeImage:
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@ -83,19 +83,22 @@ Train:
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- GrayImageChannelFormat:
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normalize: True
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inverse: True
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- SeqLabelEncode:
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character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
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lower: False
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- KeepKeys:
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keep_keys: ['image', 'label']
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label_file_list: ["./train_data/CROHME/training/labels.json"]
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label_file_list: ["./train_data/CROHME/training/labels.txt"]
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loader:
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shuffle: True
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batch_size_per_card: 2
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drop_last: True
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num_workers: 1
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batch_size_per_card: 8
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drop_last: False
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num_workers: 4
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collate_fn: DyMaskCollator
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Eval:
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dataset:
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name: HMERDataSet
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name: PGDataSet
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data_dir: ./train_data/CROHME/evaluation/images/
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transforms:
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- DecodeImage:
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@ -103,9 +106,12 @@ Eval:
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- GrayImageChannelFormat:
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normalize: True
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inverse: True
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- SeqLabelEncode:
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character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
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lower: False
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- KeepKeys:
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keep_keys: ['image', 'label']
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label_file_list: ["./train_data/CROHME/evaluation/labels.json"]
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label_file_list: ["./train_data/CROHME/evaluation/labels.txt"]
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loader:
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shuffle: False
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drop_last: False
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|
Before Width: | Height: | Size: 12 KiB After Width: | Height: | Size: 12 KiB |
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Before Width: | Height: | Size: 15 KiB After Width: | Height: | Size: 15 KiB |
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Before Width: | Height: | Size: 4.8 KiB After Width: | Height: | Size: 4.8 KiB |
@ -1,4 +1,4 @@
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# 手写数学公式识别算法-ABINet
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# 手写数学公式识别算法-CAN
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- [1. 算法简介](#1)
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- [2. 环境配置](#2)
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@ -27,7 +27,7 @@
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|模型 |骨干网络|配置文件|ExpRate|下载链接|
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| ----- | ----- | ----- | ----- | ----- |
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|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar)|
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|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[训练模型](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)|
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<a name="2"></a>
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## 2. 环境配置
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@ -60,16 +60,21 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs
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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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```
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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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```
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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](#model)，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/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。
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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
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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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```
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<a name="3-3"></a>
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@ -78,9 +83,9 @@ 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/imgs_hme/hme_01.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy
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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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# 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/imgs_hme/'。
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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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@ -89,17 +94,16 @@ 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/rec_d28_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/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.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/CAN 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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**注意：**
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- 如果您是在自己的数据集上训练的模型，并且调整了字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
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- 如果您修改了训练时的输入大小，请修改`tools/export_model.py`文件中的对应ABINet的`infer_shape`。
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转换成功后，在目录下有三个文件：
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```
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@ -112,18 +116,18 @@ python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_infe
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执行如下命令进行模型推理：
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```shell
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_hme/hme_01.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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# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/imgs_hme/'。
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# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/datasets/crohme_demo/'。
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# 如果您需要在白底黑字的图片上进行预测，请设置 --rec_image_inverse=False
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```
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执行命令后，上面图像的预测结果（识别的文本）会打印到屏幕上，示例如下：
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```shell
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Predicts of ./doc/imgs_hme/hme_03.jpg:['x _ { k } x x _ { k } + y _ { k } y x _ { k }', []]
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Predicts of ./doc/imgs_hme/hme_00.jpg:['x _ { k } x x _ { k } + y _ { k } y x _ { k }', []]
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```
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@ -25,7 +25,7 @@ Using CROHME handwrittem mathematical expression recognition datasets for traini
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|Model|Backbone|config|exprate|Download link|
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| --- | --- | --- | --- | --- |
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|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|coming soon|
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|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[trained model](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)|
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<a name="2"></a>
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## 2. Environment
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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/best_accuracy
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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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```
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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/imgs_hme/hme_01.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy
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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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```
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<a name="4"></a>
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@ -68,16 +68,20 @@ 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 Inference
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First, the model saved during the RobustScanner text recognition training process is converted into an inference model. you can use the following command to convert:
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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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# 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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For RobustScanner text recognition model inference, the following commands can be executed:
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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/imgs_hme/hme_01.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_image_shape="1, 100, 100" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt"
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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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# 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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<a name="4-2"></a>
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@ -37,7 +37,6 @@ from ppocr.data.simple_dataset import SimpleDataSet
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from ppocr.data.lmdb_dataset import LMDBDataSet, LMDBDataSetSR
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from ppocr.data.pgnet_dataset import PGDataSet
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from ppocr.data.pubtab_dataset import PubTabDataSet
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from ppocr.data.hmer_dataset import HMERDataSet
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__all__ = ['build_dataloader', 'transform', 'create_operators']
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@ -56,7 +55,7 @@ def build_dataloader(config, mode, device, logger, seed=None):
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support_dict = [
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'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet',
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'LMDBDataSetSR', 'HMERDataSet'
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'LMDBDataSetSR'
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]
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module_name = config[mode]['dataset']['name']
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assert module_name in support_dict, Exception(
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@ -95,8 +95,8 @@ class DyMaskCollator(object):
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1] > max_height else max_height
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max_width = item[0].shape[2] if item[0].shape[
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2] > max_width else max_width
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max_length = item[1].shape[0] if item[1].shape[
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0] > max_length else max_length
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max_length = len(item[1]) if len(item[
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1]) > max_length else max_length
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proper_items.append(item)
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images, image_masks = np.zeros(
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@ -111,7 +111,7 @@ class DyMaskCollator(object):
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_, h, w = proper_items[i][0].shape
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images[i][:, :h, :w] = proper_items[i][0]
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image_masks[i][:, :h, :w] = 1
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l = proper_items[i][1].shape[0]
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l = len(proper_items[i][1])
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labels[i][:l] = proper_items[i][1]
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label_masks[i][:l] = 1
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@ -1,99 +0,0 @@
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# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os, json, random, traceback
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import numpy as np
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from PIL import Image
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from paddle.io import Dataset
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from .imaug import transform, create_operators
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class HMERDataSet(Dataset):
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def __init__(self, config, mode, logger, seed=None):
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super(HMERDataSet, self).__init__()
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self.logger = logger
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self.seed = seed
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self.mode = mode
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global_config = config['Global']
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dataset_config = config[mode]['dataset']
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self.data_dir = config[mode]['dataset']['data_dir']
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label_file_list = dataset_config['label_file_list']
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data_source_num = len(label_file_list)
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ratio_list = dataset_config.get("ratio_list", [1.0])
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self.data_lines, self.labels = self.get_image_info_list(label_file_list,
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ratio_list)
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self.data_idx_order_list = list(range(len(self.data_lines)))
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if self.mode == "train" and self.do_shuffle:
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self.shuffle_data_random()
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if isinstance(ratio_list, (float, int)):
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ratio_list = [float(ratio_list)] * int(data_source_num)
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assert len(
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ratio_list
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) == data_source_num, "The length of ratio_list should be the same as the file_list."
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self.ops = create_operators(dataset_config['transforms'], global_config)
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self.need_reset = True in [x < 1 for x in ratio_list]
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|
||||
def get_image_info_list(self, file_list, ratio_list):
|
||||
if isinstance(file_list, str):
|
||||
file_list = [file_list]
|
||||
labels = {}
|
||||
for idx, file in enumerate(file_list):
|
||||
with open(file, "r") as f:
|
||||
lines = json.load(f)
|
||||
labels.update(lines)
|
||||
data_lines = [name for name in labels.keys()]
|
||||
return data_lines, labels
|
||||
|
||||
def shuffle_data_random(self):
|
||||
random.seed(self.seed)
|
||||
random.shuffle(self.data_lines)
|
||||
return
|
||||
|
||||
def __len__(self):
|
||||
return len(self.data_idx_order_list)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
file_idx = self.data_idx_order_list[idx]
|
||||
data_name = self.data_lines[file_idx]
|
||||
try:
|
||||
file_name = data_name + '.jpg'
|
||||
img_path = os.path.join(self.data_dir, file_name)
|
||||
if not os.path.exists(img_path):
|
||||
raise Exception("{} does not exist!".format(img_path))
|
||||
with open(img_path, 'rb') as f:
|
||||
img = f.read()
|
||||
|
||||
label = self.labels.get(data_name).split()
|
||||
label = np.array([int(item) for item in label])
|
||||
|
||||
data = {'image': img, 'label': label}
|
||||
outs = transform(data, self.ops)
|
||||
except:
|
||||
self.logger.error(
|
||||
"When parsing line {}, error happened with msg: {}".format(
|
||||
file_name, traceback.format_exc()))
|
||||
outs = None
|
||||
if outs is None:
|
||||
# during evaluation, we should fix the idx to get same results for many times of evaluation.
|
||||
rnd_idx = np.random.randint(self.__len__())
|
||||
return self.__getitem__(rnd_idx)
|
||||
return outs
|
||||
@ -1476,4 +1476,33 @@ class CTLabelEncode(object):
|
||||
|
||||
data['polys'] = boxes
|
||||
data['texts'] = txts
|
||||
return data
|
||||
return data
|
||||
|
||||
|
||||
class SeqLabelEncode(BaseRecLabelEncode):
|
||||
def __init__(self,
|
||||
character_dict_path,
|
||||
max_text_length=100,
|
||||
use_space_char=False,
|
||||
lower=True,
|
||||
**kwargs):
|
||||
super(SeqLabelEncode, self).__init__(
|
||||
max_text_length, character_dict_path, use_space_char, lower)
|
||||
|
||||
def encode(self, text_seq):
|
||||
text_seq_encoded = []
|
||||
for text in text_seq:
|
||||
if text not in self.character:
|
||||
continue
|
||||
text_seq_encoded.append(self.dict.get(text))
|
||||
if len(text_seq_encoded) == 0:
|
||||
return None
|
||||
return text_seq_encoded
|
||||
|
||||
def __call__(self, data):
|
||||
label = data['label']
|
||||
if isinstance(label, str):
|
||||
label = label.strip().split()
|
||||
label.append(self.end_str)
|
||||
data['label'] = self.encode(label)
|
||||
return data
|
||||
|
||||
@ -5,14 +5,14 @@ Global:
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/can/
|
||||
save_epoch_step: 1
|
||||
# evaluation is run every 1105 iterations
|
||||
# evaluation is run every 1105 iterations (1 epoch)(batch_size = 8)
|
||||
eval_batch_step: [0, 1105]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model: ./output/rec/can/CAN
|
||||
checkpoints: ./output/rec/can/CAN
|
||||
save_inference_dir: ./inference/rec_d28_can/
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_hme/hme_01.jpeg
|
||||
infer_img: doc/datasets/crohme_demo/hme_00.jpg
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
|
||||
max_text_length: 36
|
||||
@ -75,37 +75,43 @@ Metric:
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: HMERDataSet
|
||||
data_dir: ./train_data/CROHME/training/images/
|
||||
name: PGDataSet
|
||||
data_dir: ./train_data/CROHME_lite/training/images/
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
channel_first: False
|
||||
- GrayImageChannelFormat:
|
||||
normalize: True
|
||||
inverse: True
|
||||
- SeqLabelEncode:
|
||||
character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
|
||||
lower: False
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label']
|
||||
label_file_list: ["./train_data/CROHME/training/labels.json"]
|
||||
label_file_list: ["./train_data/CROHME_lite/training/labels.txt"]
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 2
|
||||
drop_last: True
|
||||
num_workers: 1
|
||||
batch_size_per_card: 8
|
||||
drop_last: False
|
||||
num_workers: 4
|
||||
collate_fn: DyMaskCollator
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: HMERDataSet
|
||||
data_dir: ./train_data/CROHME/evaluation/images/
|
||||
name: PGDataSet
|
||||
data_dir: ./train_data/CROHME_lite/evaluation/images/
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
channel_first: False
|
||||
- GrayImageChannelFormat:
|
||||
normalize: True
|
||||
inverse: True
|
||||
- SeqLabelEncode:
|
||||
character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
|
||||
lower: False
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label']
|
||||
label_file_list: ["./train_data/CROHME/evaluation/labels.json"]
|
||||
label_file_list: ["./train_data/CROHME_lite/evaluation/labels.txt"]
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
===========================train_params===========================
|
||||
model_name:rec_d28_can
|
||||
python:python
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:null
|
||||
@ -9,7 +9,7 @@ Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=8
|
||||
Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./doc/imgs_hme
|
||||
train_infer_img_dir:./doc/datasets/crohme_demo
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
@ -37,15 +37,15 @@ export2:null
|
||||
train_model:./inference/rec_d28_can_train/best_accuracy
|
||||
infer_export:tools/export_model.py -c test_tipc/configs/rec_d28_can/rec_d28_can.yml -o
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict/latex_symbol_dict.txt --rec_image_shape="1,100,100" --rec_algorithm="CAN"
|
||||
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict/latex_symbol_dict.txt --rec_algorithm="CAN"
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:False
|
||||
--cpu_threads:6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False
|
||||
--precision:fp32
|
||||
--rec_model_dir:./output/
|
||||
--image_dir:./doc/imgs_hme
|
||||
--rec_model_dir:
|
||||
--image_dir:./doc/datasets/crohme_demo
|
||||
--save_log_path:./test/output/
|
||||
--benchmark:True
|
||||
null:null
|
||||
|
||||
@ -257,6 +257,13 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
|
||||
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_r32_gaspin_bilstm_att_train.tar --no-check-certificate
|
||||
cd ./pretrain_models/ && tar xf rec_r32_gaspin_bilstm_att_train.tar && cd ../
|
||||
fi
|
||||
if [ ${model_name} == "rec_d28_can" ]; then
|
||||
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/contribution/can_train.tar --no-check-certificate
|
||||
cd ./pretrain_models/ && tar xf can_train.tar && cd ../
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/CROHME_lite.tar --no-check-certificate
|
||||
cd ./train_data/ && tar xf CROHME_lite.tar && cd ../
|
||||
|
||||
fi
|
||||
if [ ${model_name} == "layoutxlm_ser" ]; then
|
||||
${python_name} -m pip install -r ppstructure/kie/requirements.txt
|
||||
${python_name} -m pip install opencv-python -U
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
| SAST |det_r50_vd_sast_totaltext_v2.0 | 检测 | 支持 | 多机多卡 <br> 混合精度 | - | - |
|
||||
| Rosetta|rec_mv3_none_none_ctc_v2.0 | 识别 | 支持 | 多机多卡 <br> 混合精度 | - | - |
|
||||
| Rosetta|rec_r34_vd_none_none_ctc_v2.0 | 识别 | 支持 | 多机多卡 <br> 混合精度 | - | - |
|
||||
| CAN |rec_d28_can | 识别 | 支持 | 多机多卡 <br> 混合精度 | - | - |
|
||||
| CRNN |rec_mv3_none_bilstm_ctc_v2.0 | 识别 | 支持 | 多机多卡 <br> 混合精度 | - | - |
|
||||
| CRNN |rec_r34_vd_none_bilstm_ctc_v2.0| 识别 | 支持 | 多机多卡 <br> 混合精度 | - | - |
|
||||
| StarNet|rec_mv3_tps_bilstm_ctc_v2.0 | 识别 | 支持 | 多机多卡 <br> 混合精度 | - | - |
|
||||
|
||||
@ -544,7 +544,7 @@ def eval(model,
|
||||
elif model_type in ['sr']:
|
||||
eval_class(preds, batch_numpy)
|
||||
elif model_type in ['can']:
|
||||
eval_class(preds[0], batch_numpy[2:], epoch_reset=False)
|
||||
eval_class(preds[0], batch_numpy[2:], epoch_reset=(idx == 0))
|
||||
else:
|
||||
post_result = post_process_class(preds, batch_numpy[1])
|
||||
eval_class(post_result, batch_numpy)
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user