Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into dygraph

benchmark/run_benchmark_det.sh

@@ -17,7 +17,7 @@ function _set_params(){
     skip_steps=2                 # 解析日志，有些模型前几个step耗时长，需要跳过                                    (必填)
     keyword="ips:"               # 解析日志，筛选出数据所在行的关键字                                             (必填)
     index="1"
-    model_name=${model_item}_${run_mode}_bs${batch_size}_${fp_item}        # model_item 用于yml文件名匹配，model_name 用于数据入库前端展示
+    model_name=${model_item}_bs${batch_size}_${fp_item}        # model_item 用于yml文件名匹配，model_name 用于数据入库前端展示
 #   以下不用修改   
     device=${CUDA_VISIBLE_DEVICES//,/ }
     arr=(${device})

benchmark/run_det.sh

@@ -2,6 +2,7 @@
 # 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7  paddle=2.1.2  py=37
 # 执行目录: ./PaddleOCR
 # 1 安装该模型需要的依赖 (如需开启优化策略请注明)
+log_path=${LOG_PATH_INDEX_DIR:-$(pwd)}
 python -m pip install -r requirements.txt
 # 2 拷贝该模型需要数据、预训练模型
 wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data  && tar xf icdar2015.tar && cd ../
@@ -12,18 +13,22 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg
 
 model_mode_list=(det_res18_db_v2.0 det_r50_vd_east det_r50_vd_pse)
 fp_item_list=(fp32)
-bs_list=(8 16)
 for model_mode in ${model_mode_list[@]}; do
       for fp_item in ${fp_item_list[@]}; do
+          if [ ${model_mode} == "det_r50_vd_east" ]; then
+              bs_list=(16)
+          else
+              bs_list=(8 16)
+          fi
           for bs_item in ${bs_list[@]}; do
             echo "index is speed, 1gpus, begin, ${model_name}"
             run_mode=sp
-            log_name=ocr_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}
+            log_name=ocr_${model_mode}_bs${bs_item}_${fp_item}_${run_mode}
             CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode} | tee ${log_path}/${log_name}_speed_1gpus 2>&1    #  (5min)
             sleep 60
             echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
             run_mode=mp
-            log_name=ocr_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}
+            log_name=ocr_${model_mode}_bs${bs_item}_${fp_item}_${run_mode}
             CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 2 ${model_mode} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1
             sleep 60
             done

deploy/slim/prune/export_prune_model.py

@@ -52,12 +52,17 @@ def main(config, device, logger, vdl_writer):
         config['Architecture']["Head"]['out_channels'] = char_num
     model = build_model(config['Architecture'])
 
-    flops = paddle.flops(model, [1, 3, 640, 640])
-    logger.info(f"FLOPs before pruning: {flops}")
+    if config['Architecture']['model_type'] == 'det':
+        input_shape = [1, 3, 640, 640]
+    elif config['Architecture']['model_type'] == 'rec':
+        input_shape = [1, 3, 32, 320]
+
+    flops = paddle.flops(model, input_shape)
+    logger.info("FLOPs before pruning: {}".format(flops))
 
     from paddleslim.dygraph import FPGMFilterPruner
     model.train()
-    pruner = FPGMFilterPruner(model, [1, 3, 640, 640])
+    pruner = FPGMFilterPruner(model, input_shape)
 
     # build metric
     eval_class = build_metric(config['Metric'])
@@ -65,8 +70,13 @@ def main(config, device, logger, vdl_writer):
     def eval_fn():
         metric = program.eval(model, valid_dataloader, post_process_class,
                               eval_class)
-        logger.info(f"metric['hmean']: {metric['hmean']}")
-        return metric['hmean']
+        if config['Architecture']['model_type'] == 'det':
+            main_indicator = 'hmean'
+        else:
+            main_indicator = 'acc'
+        logger.info("metric[{}]: {}".format(main_indicator, metric[
+            main_indicator]))
+        return metric[main_indicator]
 
     params_sensitive = pruner.sensitive(
         eval_func=eval_fn,
@@ -81,18 +91,22 @@ def main(config, device, logger, vdl_writer):
     # calculate pruned params's ratio
     params_sensitive = pruner._get_ratios_by_loss(params_sensitive, loss=0.02)
     for key in params_sensitive.keys():
-        logger.info(f"{key}, {params_sensitive[key]}")
+        logger.info("{}, {}".format(key, params_sensitive[key]))
 
     plan = pruner.prune_vars(params_sensitive, [0])
 
-    flops = paddle.flops(model, [1, 3, 640, 640])
-    logger.info(f"FLOPs after pruning: {flops}")
+    flops = paddle.flops(model, input_shape)
+    logger.info("FLOPs after pruning: {}".format(flops))
 
     # load pretrain model
     load_model(config, model)
     metric = program.eval(model, valid_dataloader, post_process_class,
                           eval_class)
-    logger.info(f"metric['hmean']: {metric['hmean']}")
+    if config['Architecture']['model_type'] == 'det':
+        main_indicator = 'hmean'
+    else:
+        main_indicator = 'acc'
+    logger.info("metric['']: {}".format(main_indicator, metric[main_indicator]))
 
     # start export model
     from paddle.jit import to_static

deploy/slim/prune/sensitivity_anal.py

@@ -73,13 +73,18 @@ def main(config, device, logger, vdl_writer):
         char_num = len(getattr(post_process_class, 'character'))
         config['Architecture']["Head"]['out_channels'] = char_num
     model = build_model(config['Architecture'])
+    if config['Architecture']['model_type'] == 'det':
+        input_shape = [1, 3, 640, 640]
+    elif config['Architecture']['model_type'] == 'rec':
+        input_shape = [1, 3, 32, 320]
+    flops = paddle.flops(model, input_shape)
 
-    flops = paddle.flops(model, [1, 3, 640, 640])
     logger.info("FLOPs before pruning: {}".format(flops))
 
     from paddleslim.dygraph import FPGMFilterPruner
     model.train()
-    pruner = FPGMFilterPruner(model, [1, 3, 640, 640])
+
+    pruner = FPGMFilterPruner(model, input_shape)
 
     # build loss
     loss_class = build_loss(config['Loss'])
@@ -107,8 +112,14 @@ def main(config, device, logger, vdl_writer):
     def eval_fn():
         metric = program.eval(model, valid_dataloader, post_process_class,
                               eval_class, False)
-        logger.info("metric['hmean']: {}".format(metric['hmean']))
-        return metric['hmean']
+        if config['Architecture']['model_type'] == 'det':
+            main_indicator = 'hmean'
+        else:
+            main_indicator = 'acc'
+
+        logger.info("metric[{}]: {}".format(main_indicator, metric[
+            main_indicator]))
+        return metric[main_indicator]
 
     run_sensitive_analysis = False
     """
@@ -149,7 +160,7 @@ def main(config, device, logger, vdl_writer):
 
     plan = pruner.prune_vars(params_sensitive, [0])
 
-    flops = paddle.flops(model, [1, 3, 640, 640])
+    flops = paddle.flops(model, input_shape)
     logger.info("FLOPs after pruning: {}".format(flops))
 
     # start train

doc/doc_ch/code_and_doc.md

+# 附录
+
+本附录包含了Python、文档规范以及Pull Request流程，请各位开发者遵循相关内容
+
+- [附录1：Python代码规范](#附录1)
+
+- [附录2：文档规范](#附录2)
+
+- [附录3：Pull Request说明](#附录3)
+
+<a name="附录1"></a>
+
+## 附录1：Python代码规范
+
+PaddleOCR的Python代码遵循 [PEP8规范](https://www.python.org/dev/peps/pep-0008/)，其中一些关注的重点包括如下内容
+
+- 空格 
+
+  - 空格应该加在逗号、分号、冒号前，而非他们的后面
+
+    ```python
+    # 正确：
+    print(x, y)
+    
+    # 错误：
+    print(x , y)
+    ```
+
+  - 在函数中指定关键字参数或默认参数值时, 不要在其两侧使用空格
+
+    ```python
+    # 正确：
+    def complex(real, imag=0.0)
+    # 错误：
+    def complex(real, imag = 0.0)
+    ```
+
+- 注释
+
+  - 行内注释：行内注释使用 `#` 号表示，在代码与 `#` 之间需要空两个空格， `#`  与注释之间应当空一个空格，例如
+
+    ```python
+    x = x + 1  # Compensate for border
+    ```
+
+  - 函数和方法：每个函数的定义后的描述应该包括以下内容：
+
+    - 函数描述：函数的作用，输入输出的
+
+    - Args：每个参数的名字以及对该参数的描述
+    - Returns：返回值的含义和类型
+
+    ```python
+    def fetch_bigtable_rows(big_table, keys, other_silly_variable=None):
+        """Fetches rows from a Bigtable.
+    
+        Retrieves rows pertaining to the given keys from the Table instance
+        represented by big_table.  Silly things may happen if
+        other_silly_variable is not None.
+    
+        Args:
+            big_table: An open Bigtable Table instance.
+            keys: A sequence of strings representing the key of each table row
+                to fetch.
+            other_silly_variable: Another optional variable, that has a much
+                longer name than the other args, and which does nothing.
+    
+        Returns:
+            A dict mapping keys to the corresponding table row data
+            fetched. Each row is represented as a tuple of strings. For
+            example:
+    
+            {'Serak': ('Rigel VII', 'Preparer'),
+             'Zim': ('Irk', 'Invader'),
+             'Lrrr': ('Omicron Persei 8', 'Emperor')}
+    
+            If a key from the keys argument is missing from the dictionary,
+            then that row was not found in the table.
+        """
+        pass
+    ```
+
+<a name="附录2"></a>
+
+## 附录2：文档规范
+
+### 2.1 总体说明
+
+- 文档位置：如果您增加的新功能可以补充在原有的Markdown文件中，请**不要重新新建**一个文件。如果您对添加的位置不清楚，可以先PR代码，然后在commit中询问官方人员。
+
+- 新增Markdown文档名称：使用英文描述文档内容，一般由小写字母与下划线组合而成，例如  `add_new_algorithm.md`
+
+- 新增Markdown文档格式：目录 - 正文 - FAQ
+
+  > 目录生成方法可以使用 [此网站](https://ecotrust-canada.github.io/markdown-toc/) 将md内容复制之后自动提取目录，然后在md文件的每个标题前添加 `<a name="XXXX"></a>` 
+
+- 中英双语：任何对文档的改动或新增都需要分别在中文和英文文档上进行。
+
+### 2.2 格式规范
+
+- 标题格式：文档标题格式按照：阿拉伯数字小数点组合 - 空格 - 标题的格式（例如 `2.1 XXXX` ， `2. XXXX`）
+
+- 代码块：通过代码块格式展示需要运行的代码，在代码块前描述命令参数的含义。例如：
+
+  > 检测+方向分类器+识别全流程：设置方向分类器参数 `--use_angle_cls true` 后可对竖排文本进行识别。
+  >
+  > ```
+  > paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true
+  > ```
+
+- 变量引用：如果在行内引用到代码变量或命令参数，需要用行内代码表示，例如上方  `--use_angle_cls true` ，并在前后各空一格
+
+- 补充说明：通过引用格式 `>` 补充说明，或对注意事项进行说明
+
+- 图片：如果在说明文档中增加了图片，请规范图片的命名形式（描述图片内容），并将图片添加在 `doc/` 下
+
+<a name="附录3"></a>
+
+## 附录3：Pull Request说明
+
+### 3.1 PaddleOCR分支说明
+
+PaddleOCR未来将维护2种分支，分别为：
+
+- release/x.x系列分支：为稳定的发行版本分支，也是默认分支。PaddleOCR会根据功能更新情况发布新的release分支，同时适配Paddle的release版本。随着版本迭代，release/x.x系列分支会越来越多，默认维护最新版本的release分支。
+- dygraph分支：为开发分支，适配Paddle动态图的dygraph版本，主要用于开发新功能。如果有同学需要进行二次开发，请选择dygraph分支。为了保证dygraph分支能在需要的时候拉出release/x.x分支，dygraph分支的代码只能使用Paddle最新release分支中有效的api。也就是说，如果Paddle dygraph分支中开发了新的api，但尚未出现在release分支代码中，那么请不要在PaddleOCR中使用。除此之外，对于不涉及api的性能优化、参数调整、策略更新等，都可以正常进行开发。
+
+PaddleOCR的历史分支，未来将不再维护。考虑到一些同学可能仍在使用，这些分支还会继续保留：
+
+- develop分支：这个分支曾用于静态图的开发与测试，目前兼容>=1.7版本的Paddle。如果有特殊需求，要适配旧版本的Paddle，那还可以使用这个分支，但除了修复bug外不再更新代码。
+
+PaddleOCR欢迎大家向repo中积极贡献代码，下面给出一些贡献代码的基本流程。
+
+### 3.2 PaddleOCR代码提交流程与规范
+
+> 如果你熟悉Git使用，可以直接跳转到 [3.2.10 提交代码的一些约定](#提交代码的一些约定)
+
+#### 3.2.1 创建你的 `远程仓库`
+
+- 在PaddleOCR的 [GitHub首页](https://github.com/PaddlePaddle/PaddleOCR)，点击左上角 `Fork`  按钮，在你的个人目录下创建 `远程仓库`，比如`https://github.com/{your_name}/PaddleOCR`。
+
+![banner](/Users/zhulingfeng01/OCR/PaddleOCR/doc/banner.png)
+
+- 将 `远程仓库` Clone到本地
+
+```
+# 拉取develop分支的代码
+git clone https://github.com/{your_name}/PaddleOCR.git -b dygraph
+cd PaddleOCR
+```
+
+> 多数情况下clone失败是由于网络原因，请稍后重试或配置代理
+
+#### 3.2.2 和 `远程仓库` 建立连接
+
+首先查看当前 `远程仓库` 的信息。
+
+```
+git remote -v
+# origin    https://github.com/{your_name}/PaddleOCR.git (fetch)
+# origin    https://github.com/{your_name}/PaddleOCR.git (push)
+```
+
+只有clone的 `远程仓库` 的信息，也就是自己用户名下的 PaddleOCR，接下来我们创建一个原始 PaddleOCR 仓库的远程主机，命名为 upstream。
+
+```
+git remote add upstream https://github.com/PaddlePaddle/PaddleOCR.git
+```
+
+使用 `git remote -v` 查看当前 `远程仓库` 的信息，输出如下，发现包括了origin和upstream 2个 `远程仓库` 。
+
+```
+origin    https://github.com/{your_name}/PaddleOCR.git (fetch)
+origin    https://github.com/{your_name}/PaddleOCR.git (push)
+upstream    https://github.com/PaddlePaddle/PaddleOCR.git (fetch)
+upstream    https://github.com/PaddlePaddle/PaddleOCR.git (push)
+```
+
+这主要是为了后续在提交pull request(PR)时，始终保持本地仓库最新。
+
+#### 3.2.3 创建本地分支
+
+可以基于当前分支创建新的本地分支，命令如下。
+
+```
+git checkout -b new_branch
+```
+
+也可以基于远程或者上游的分支创建新的分支，命令如下。
+
+```
+# 基于用户远程仓库(origin)的develop创建new_branch分支
+git checkout -b new_branch origin/develop
+# 基于上游远程仓库(upstream)的develop创建new_branch分支
+# 如果需要从upstream创建新的分支，需要首先使用git fetch upstream获取上游代码
+git checkout -b new_branch upstream/develop
+```
+
+最终会显示切换到新的分支，输出信息如下
+
+```
+Branch new_branch set up to track remote branch develop from upstream.
+Switched to a new branch 'new_branch'
+```
+
+#### 3.2.4 使用pre-commit勾子
+
+Paddle 开发人员使用 pre-commit 工具来管理 Git 预提交钩子。 它可以帮助我们格式化源代码（C++，Python），在提交（commit）前自动检查一些基本事宜（如每个文件只有一个 EOL，Git 中不要添加大文件等）。
+
+pre-commit测试是 Travis-CI 中单元测试的一部分，不满足钩子的 PR 不能被提交到 PaddleOCR，首先安装并在当前目录运行它：
+
+```
+pip install pre-commit
+pre-commit install
+```
+
+ >  1. Paddle 使用 clang-format 来调整 C/C++ 源代码格式，请确保 `clang-format` 版本在 3.8 以上。
+ >
+ >  2. 通过pip install pre-commit和conda install -c conda-forge pre-commit安装的yapf稍有不同的，PaddleOCR 开发人员使用的是 `pip install pre-commit`。
+
+#### 3.2.5 修改与提交代码
+
+ 假设对PaddleOCR的 `README.md` 做了一些修改，可以通过 `git status` 查看改动的文件，然后使用 `git add` 添加改动文件。
+
+```
+git status # 查看改动文件
+git add README.md
+pre-commit
+```
+
+重复上述步骤，直到pre-comit格式检查不报错。如下所示。
+
+[![img](https://github.com/PaddlePaddle/PaddleClas/raw/release/2.3/docs/images/quick_start/community/003_precommit_pass.png)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/images/quick_start/community/003_precommit_pass.png)
+
+使用下面的命令完成提交。
+
+```
+git commit -m "your commit info"
+```
+
+#### 3.2.6 保持本地仓库最新
+
+获取 upstream 的最新代码并更新当前分支。这里的upstream来自于2.2节的`和远程仓库建立连接`部分。
+
+```
+git fetch upstream
+# 如果是希望提交到其他分支，则需要从upstream的其他分支pull代码，这里是develop
+git pull upstream develop
+```
+
+#### 3.2.7 push到远程仓库
+
+```
+git push origin new_branch
+```
+
+#### 3.2.7 提交Pull Request
+
+点击new pull request，选择本地分支和目标分支，如下图所示。在PR的描述说明中，填写该PR所完成的功能。接下来等待review，如果有需要修改的地方，参照上述步骤更新 origin 中的对应分支即可。
+
+![banner](/Users/zhulingfeng01/OCR/PaddleOCR/doc/pr.png)
+
+#### 3.2.8 签署CLA协议和通过单元测试
+
+- 签署CLA 在首次向PaddlePaddle提交Pull Request时，您需要您签署一次CLA(Contributor License Agreement)协议，以保证您的代码可以被合入，具体签署方式如下：
+
+  1. 请您查看PR中的Check部分，找到license/cla，并点击右侧detail，进入CLA网站
+
+  2. 点击CLA网站中的“Sign in with GitHub to agree”,点击完成后将会跳转回您的Pull Request页面
+
+#### 3.2.9 删除分支
+
+- 删除远程分支
+
+  在 PR 被 merge 进主仓库后，我们可以在 PR 的页面删除远程仓库的分支。
+
+  也可以使用 `git push origin :分支名` 删除远程分支，如：
+
+  ```
+  git push origin :new_branch
+  ```
+
+- 删除本地分支
+
+  ```
+  # 切换到develop分支，否则无法删除当前分支
+  git checkout develop
+  
+  # 删除new_branch分支
+  git branch -D new_branch
+  ```
+
+<a name="提交代码的一些约定"></a>
+
+#### 3.2.10 提交代码的一些约定
+
+为了使官方维护人员在评审代码时更好地专注于代码本身，请您每次提交代码时，遵守以下约定：
+
+1）请保证Travis-CI 中单元测试能顺利通过。如果没过，说明提交的代码存在问题，官方维护人员一般不做评审。
+
+2）提交Pull Request前：
+
+- 请注意commit的数量。
+
+  原因：如果仅仅修改一个文件但提交了十几个commit，每个commit只做了少量的修改，这会给评审人带来很大困扰。评审人需要逐一查看每个commit才能知道做了哪些修改，且不排除commit之间的修改存在相互覆盖的情况。
+
+  建议：每次提交时，保持尽量少的commit，可以通过git commit --amend补充上次的commit。对已经Push到远程仓库的多个commit，可以参考[squash commits after push](https://stackoverflow.com/questions/5667884/how-to-squash-commits-in-git-after-they-have-been-pushed)。
+
+- 请注意每个commit的名称：应能反映当前commit的内容，不能太随意。
+
+
+3）如果解决了某个Issue的问题，请在该Pull Request的第一个评论框中加上：fix #issue_number，这样当该Pull Request被合并后，会自动关闭对应的Issue。关键词包括：close, closes, closed, fix, fixes, fixed, resolve, resolves, resolved，请选择合适的词汇。详细可参考[Closing issues via commit messages](https://help.github.com/articles/closing-issues-via-commit-messages)。
+
+此外，在回复评审人意见时，请您遵守以下约定：
+
+1）官方维护人员的每一个review意见都希望得到回复，这样会更好地提升开源社区的贡献。
+
+- 对评审意见同意且按其修改完的，给个简单的Done即可；
+- 对评审意见不同意的，请给出您自己的反驳理由。
+
+2）如果评审意见比较多:
+
+- 请给出总体的修改情况。
+- 请采用`start a review`进行回复，而非直接回复的方式。原因是每个回复都会发送一封邮件，会造成邮件灾难。
\ No newline at end of file

doc/doc_ch/detection.md

@@ -247,3 +247,7 @@ Q1: 训练模型转inference 模型之后预测效果不一致？
 **A**：此类问题出现较多，问题多是trained model预测时候的预处理、后处理参数和inference model预测的时候的预处理、后处理参数不一致导致的。以det_mv3_db.yml配置文件训练的模型为例，训练模型、inference模型预测结果不一致问题解决方式如下：
 - 检查[trained model预处理](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L116)，和[inference model的预测预处理](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/predict_det.py#L42)函数是否一致。算法在评估的时候，输入图像大小会影响精度，为了和论文保持一致，训练icdar15配置文件中将图像resize到[736, 1280]，但是在inference model预测的时候只有一套默认参数，会考虑到预测速度问题，默认限制图像最长边为960做resize的。训练模型预处理和inference模型的预处理函数位于[ppocr/data/imaug/operators.py](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/ppocr/data/imaug/operators.py#L147)
 - 检查[trained model后处理](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L51)，和[inference 后处理参数](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/utility.py#L50)是否一致。
+
+Q1: 训练EAST模型提示找不到lanms库？
+
+**A**：执行pip3 install lanms-nova 即可。

doc/doc_ch/inference.md

@@ -34,6 +34,8 @@ inference 模型（`paddle.jit.save`保存的模型）
     - [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
     - [2. 其他模型推理](#其他模型推理)
 
+- [六、参数解释](参数解释)
+
 
 <a name="训练模型转inference模型"></a>
 ## 一、训练模型转inference模型
@@ -394,3 +396,127 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --d
 执行命令后，识别结果图像如下：
 
 ![](../imgs_results/img_10_east_starnet.jpg)
+
+
+
+<a name="参数解释"></a>
+# 六、参数解释
+
+更多关于预测过程的参数解释如下所示。
+
+* 全局信息
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  image_dir | str | 无，必须显式指定 | 图像或者文件夹路径 |
+|  vis_font_path | str | "./doc/fonts/simfang.ttf" | 用于可视化的字体路径 |
+|  drop_score | float | 0.5 | 识别得分小于该值的结果会被丢弃，不会作为返回结果 |
+|  use_pdserving | bool | False | 是否使用Paddle Serving进行预测 |
+|  warmup | bool | False | 是否开启warmup，在统计预测耗时的时候，可以使用这种方法 |
+|  draw_img_save_dir | str | "./inference_results" | 系统串联预测OCR结果的保存文件夹 |
+|  save_crop_res | bool | False  | 是否保存OCR的识别文本图像 |
+|  crop_res_save_dir | str | "./output" | 保存OCR识别出来的文本图像路径 |
+|  use_mp | bool | False | 是否开启多进程预测  |
+|  total_process_num | int | 6 | 开启的进城数，`use_mp`为`True`时生效  |
+|  process_id | int | 0 | 当前进程的id号，无需自己修改  |
+|  benchmark | bool | False | 是否开启benchmark，对预测速度、显存占用等进行统计  |
+|  save_log_path | str | "./log_output/" | 开启`benchmark`时，日志结果的保存文件夹 |
+|  show_log | bool | True | 是否显示预测中的日志信息  |
+|  use_onnx | bool | False | 是否开启onnx预测 |
+
+
+* 预测引擎相关
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  use_gpu | bool | True | 是否使用GPU进行预测 |
+|  ir_optim | bool | True | 是否对计算图进行分析与优化，开启后可以加速预测过程 |
+|  use_tensorrt | bool | False | 是否开启tensorrt |
+|  min_subgraph_size | int | 15 | tensorrt中最小子图size，当子图的size大于该值时，才会尝试对该子图使用trt engine计算 |
+|  precision | str | fp32 | 预测的精度，支持`fp32`, `fp16`, `int8` 3种输入 |
+|  enable_mkldnn | bool | True | 是否开启mkldnn |
+|  cpu_threads | int | 10 | 开启mkldnn时，cpu预测的线程数 |
+
+* 文本检测模型相关
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_algorithm | str | "DB" | 文本检测算法名称，目前支持`DB`, `EAST`, `SAST`, `PSE`  |
+|  det_model_dir | str | xx | 检测inference模型路径 |
+|  det_limit_side_len | int | 960 | 检测的图像边长限制 |
+|  det_limit_type | str | "max" | 检测的变成限制类型，目前支持`min`, `max`，`min`表示保证图像最短边不小于`det_limit_side_len`，`max`表示保证图像最长边不大于`det_limit_side_len` |
+
+其中，DB算法相关参数如下
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_db_thresh | float | 0.3 | DB输出的概率图中，得分大于该阈值的像素点才会被认为是文字像素点 |
+|  det_db_box_thresh | float | 0.6 | 检测结果边框内，所有像素点的平均得分大于该阈值时，该结果会被认为是文字区域 |
+|  det_db_unclip_ratio | float | 1.5 | `Vatti clipping`算法的扩张系数，使用该方法对文字区域进行扩张 |
+|  max_batch_size | int | 10 | 预测的batch size |
+|  use_dilation | bool | False | 是否对分割结果进行膨胀以获取更优检测效果 |
+|  det_db_score_mode | str | "fast" | DB的检测结果得分计算方法，支持`fast`和`slow`，`fast`是根据polygon的外接矩形边框内的所有像素计算平均得分，`slow`是根据原始polygon内的所有像素计算平均得分，计算速度相对较慢一些，但是更加准确一些。 |
+
+EAST算法相关参数如下
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_east_score_thresh | float | 0.8 | EAST后处理中score map的阈值 |
+|  det_east_cover_thresh | float | 0.1 | EAST后处理中文本框的平均得分阈值 |
+|  det_east_nms_thresh | float | 0.2 | EAST后处理中nms的阈值 |
+
+SAST算法相关参数如下
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_sast_score_thresh | float | 0.5 | SAST后处理中的得分阈值 |
+|  det_sast_nms_thresh | float | 0.5 | SAST后处理中nms的阈值 |
+|  det_sast_polygon | bool | False | 是否多边形检测，弯曲文本场景（如Total-Text）设置为True |
+
+PSE算法相关参数如下
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_pse_thresh | float | 0.0 | 对输出图做二值化的阈值 |
+|  det_pse_box_thresh | float | 0.85 | 对box进行过滤的阈值，低于此阈值的丢弃 |
+|  det_pse_min_area | float | 16 | box的最小面积，低于此阈值的丢弃 |
+|  det_pse_box_type | str | "box" | 返回框的类型，box:四点坐标，poly: 弯曲文本的所有点坐标 |
+|  det_pse_scale | int | 1 | 输入图像相对于进后处理的图的比例，如`640*640`的图像，网络输出为`160*160`，scale为2的情况下，进后处理的图片shape为`320*320`。这个值调大可以加快后处理速度，但是会带来精度的下降 |
+
+* 文本识别模型相关
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  rec_algorithm | str | "CRNN" | 文本识别算法名称，目前支持`CRNN`, `SRN`, `RARE`, `NETR`, `SAR` |
+|  rec_model_dir | str | 无，如果使用识别模型，该项是必填项 | 识别inference模型路径 |
+|  rec_image_shape | list | [3, 32, 320] | 识别时的图像尺寸， |
+|  rec_batch_num | int | 6 | 识别的batch size |
+|  max_text_length | int | 25 | 识别结果最大长度，在`SRN`中有效 |
+|  rec_char_dict_path | str | "./ppocr/utils/ppocr_keys_v1.txt" | 识别的字符字典文件 |
+|  use_space_char | bool | True | 是否包含空格，如果为`True`，则会在最后字符字典中补充`空格`字符 |
+
+
+* 端到端文本检测与识别模型相关
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  e2e_algorithm | str | "PGNet" | 端到端算法名称，目前支持`PGNet` |
+|  e2e_model_dir | str | 无，如果使用端到端模型，该项是必填项 | 端到端模型inference模型路径 |
+|  e2e_limit_side_len | int | 768 | 端到端的输入图像边长限制 |
+|  e2e_limit_type | str | "max" | 端到端的边长限制类型，目前支持`min`, `max`，`min`表示保证图像最短边不小于`e2e_limit_side_len`，`max`表示保证图像最长边不大于`e2e_limit_side_len` |
+|  e2e_pgnet_score_thresh | float | xx | xx |
+|  e2e_char_dict_path | str | "./ppocr/utils/ic15_dict.txt" | 识别的字典文件路径 |
+|  e2e_pgnet_valid_set | str | "totaltext" | 验证集名称，目前支持`totaltext`, `partvgg`，不同数据集对应的后处理方式不同，与训练过程保持一致即可 |
+|  e2e_pgnet_mode | str | "fast" | PGNet的检测结果得分计算方法，支持`fast`和`slow`，`fast`是根据polygon的外接矩形边框内的所有像素计算平均得分，`slow`是根据原始polygon内的所有像素计算平均得分，计算速度相对较慢一些，但是更加准确一些。 |
+
+
+* 方向分类器模型相关
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  use_angle_cls | bool | False | 是否使用方向分类器 |
+|  cls_model_dir | str | 无，如果需要使用，则必须显式指定路径 | 方向分类器inference模型路径 |
+|  cls_image_shape | list | [3, 48, 192] | 预测尺度 |
+|  label_list | list | ['0', '180'] | class id对应的角度值 |
+|  cls_batch_num | int | 6 | 方向分类器预测的batch size |
+|  cls_thresh | float | 0.9 | 预测阈值，模型预测结果为180度，且得分大于该阈值时，认为最终预测结果为180度，需要翻转 |

ppocr/modeling/backbones/__init__.py

@@ -16,7 +16,7 @@ __all__ = ["build_backbone"]
 
 
 def build_backbone(config, model_type):
-    if model_type == "det":
+    if model_type == "det" or model_type == "table":
         from .det_mobilenet_v3 import MobileNetV3
         from .det_resnet_vd import ResNet
         from .det_resnet_vd_sast import ResNet_SAST
@@ -36,10 +36,6 @@ def build_backbone(config, model_type):
     elif model_type == "e2e":
         from .e2e_resnet_vd_pg import ResNet
         support_dict = ["ResNet"]
-    elif model_type == "table":
-        from .table_resnet_vd import ResNet
-        from .table_mobilenet_v3 import MobileNetV3
-        support_dict = ["ResNet", "MobileNetV3"]
     else:
         raise NotImplementedError
 

ppocr/modeling/backbones/rec_mobilenet_v3.py

@@ -26,8 +26,10 @@ class MobileNetV3(nn.Layer):
                  scale=0.5,
                  large_stride=None,
                  small_stride=None,
+                 disable_se=False,
                  **kwargs):
         super(MobileNetV3, self).__init__()
+        self.disable_se = disable_se
         if small_stride is None:
             small_stride = [2, 2, 2, 2]
         if large_stride is None:
@@ -101,6 +103,7 @@ class MobileNetV3(nn.Layer):
         block_list = []
         inplanes = make_divisible(inplanes * scale)
         for (k, exp, c, se, nl, s) in cfg:
+            se = se and not self.disable_se
             block_list.append(
                 ResidualUnit(
                     in_channels=inplanes,

ppocr/modeling/backbones/table_mobilenet_v3.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import paddle
-from paddle import nn
-import paddle.nn.functional as F
-from paddle import ParamAttr
-
-__all__ = ['MobileNetV3']
-
-
-def make_divisible(v, divisor=8, min_value=None):
-    if min_value is None:
-        min_value = divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    if new_v < 0.9 * v:
-        new_v += divisor
-    return new_v
-
-
-class MobileNetV3(nn.Layer):
-    def __init__(self,
-                 in_channels=3,
-                 model_name='large',
-                 scale=0.5,
-                 disable_se=False,
-                 **kwargs):
-        """
-        the MobilenetV3 backbone network for detection module.
-        Args:
-            params(dict): the super parameters for build network
-        """
-        super(MobileNetV3, self).__init__()
-
-        self.disable_se = disable_se
-
-        if model_name == "large":
-            cfg = [
-                # k, exp, c,  se,     nl,  s,
-                [3, 16, 16, False, 'relu', 1],
-                [3, 64, 24, False, 'relu', 2],
-                [3, 72, 24, False, 'relu', 1],
-                [5, 72, 40, True, 'relu', 2],
-                [5, 120, 40, True, 'relu', 1],
-                [5, 120, 40, True, 'relu', 1],
-                [3, 240, 80, False, 'hardswish', 2],
-                [3, 200, 80, False, 'hardswish', 1],
-                [3, 184, 80, False, 'hardswish', 1],
-                [3, 184, 80, False, 'hardswish', 1],
-                [3, 480, 112, True, 'hardswish', 1],
-                [3, 672, 112, True, 'hardswish', 1],
-                [5, 672, 160, True, 'hardswish', 2],
-                [5, 960, 160, True, 'hardswish', 1],
-                [5, 960, 160, True, 'hardswish', 1],
-            ]
-            cls_ch_squeeze = 960
-        elif model_name == "small":
-            cfg = [
-                # k, exp, c,  se,     nl,  s,
-                [3, 16, 16, True, 'relu', 2],
-                [3, 72, 24, False, 'relu', 2],
-                [3, 88, 24, False, 'relu', 1],
-                [5, 96, 40, True, 'hardswish', 2],
-                [5, 240, 40, True, 'hardswish', 1],
-                [5, 240, 40, True, 'hardswish', 1],
-                [5, 120, 48, True, 'hardswish', 1],
-                [5, 144, 48, True, 'hardswish', 1],
-                [5, 288, 96, True, 'hardswish', 2],
-                [5, 576, 96, True, 'hardswish', 1],
-                [5, 576, 96, True, 'hardswish', 1],
-            ]
-            cls_ch_squeeze = 576
-        else:
-            raise NotImplementedError("mode[" + model_name +
-                                      "_model] is not implemented!")
-
-        supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
-        assert scale in supported_scale, \
-            "supported scale are {} but input scale is {}".format(supported_scale, scale)
-        inplanes = 16
-        # conv1
-        self.conv = ConvBNLayer(
-            in_channels=in_channels,
-            out_channels=make_divisible(inplanes * scale),
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            groups=1,
-            if_act=True,
-            act='hardswish',
-            name='conv1')
-
-        self.stages = []
-        self.out_channels = []
-        block_list = []
-        i = 0
-        inplanes = make_divisible(inplanes * scale)
-        for (k, exp, c, se, nl, s) in cfg:
-            se = se and not self.disable_se
-            start_idx = 2 if model_name == 'large' else 0
-            if s == 2 and i > start_idx:
-                self.out_channels.append(inplanes)
-                self.stages.append(nn.Sequential(*block_list))
-                block_list = []
-            block_list.append(
-                ResidualUnit(
-                    in_channels=inplanes,
-                    mid_channels=make_divisible(scale * exp),
-                    out_channels=make_divisible(scale * c),
-                    kernel_size=k,
-                    stride=s,
-                    use_se=se,
-                    act=nl,
-                    name="conv" + str(i + 2)))
-            inplanes = make_divisible(scale * c)
-            i += 1
-        block_list.append(
-            ConvBNLayer(
-                in_channels=inplanes,
-                out_channels=make_divisible(scale * cls_ch_squeeze),
-                kernel_size=1,
-                stride=1,
-                padding=0,
-                groups=1,
-                if_act=True,
-                act='hardswish',
-                name='conv_last'))
-        self.stages.append(nn.Sequential(*block_list))
-        self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
-        for i, stage in enumerate(self.stages):
-            self.add_sublayer(sublayer=stage, name="stage{}".format(i))
-
-    def forward(self, x):
-        x = self.conv(x)
-        out_list = []
-        for stage in self.stages:
-            x = stage(x)
-            out_list.append(x)
-        return out_list
-
-
-class ConvBNLayer(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride,
-                 padding,
-                 groups=1,
-                 if_act=True,
-                 act=None,
-                 name=None):
-        super(ConvBNLayer, self).__init__()
-        self.if_act = if_act
-        self.act = act
-        self.conv = nn.Conv2D(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            groups=groups,
-            weight_attr=ParamAttr(name=name + '_weights'),
-            bias_attr=False)
-
-        self.bn = nn.BatchNorm(
-            num_channels=out_channels,
-            act=None,
-            param_attr=ParamAttr(name=name + "_bn_scale"),
-            bias_attr=ParamAttr(name=name + "_bn_offset"),
-            moving_mean_name=name + "_bn_mean",
-            moving_variance_name=name + "_bn_variance")
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        if self.if_act:
-            if self.act == "relu":
-                x = F.relu(x)
-            elif self.act == "hardswish":
-                x = F.hardswish(x)
-            else:
-                print("The activation function({}) is selected incorrectly.".
-                      format(self.act))
-                exit()
-        return x
-
-
-class ResidualUnit(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 mid_channels,
-                 out_channels,
-                 kernel_size,
-                 stride,
-                 use_se,
-                 act=None,
-                 name=''):
-        super(ResidualUnit, self).__init__()
-        self.if_shortcut = stride == 1 and in_channels == out_channels
-        self.if_se = use_se
-
-        self.expand_conv = ConvBNLayer(
-            in_channels=in_channels,
-            out_channels=mid_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            if_act=True,
-            act=act,
-            name=name + "_expand")
-        self.bottleneck_conv = ConvBNLayer(
-            in_channels=mid_channels,
-            out_channels=mid_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=int((kernel_size - 1) // 2),
-            groups=mid_channels,
-            if_act=True,
-            act=act,
-            name=name + "_depthwise")
-        if self.if_se:
-            self.mid_se = SEModule(mid_channels, name=name + "_se")
-        self.linear_conv = ConvBNLayer(
-            in_channels=mid_channels,
-            out_channels=out_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            if_act=False,
-            act=None,
-            name=name + "_linear")
-
-    def forward(self, inputs):
-        x = self.expand_conv(inputs)
-        x = self.bottleneck_conv(x)
-        if self.if_se:
-            x = self.mid_se(x)
-        x = self.linear_conv(x)
-        if self.if_shortcut:
-            x = paddle.add(inputs, x)
-        return x
-
-
-class SEModule(nn.Layer):
-    def __init__(self, in_channels, reduction=4, name=""):
-        super(SEModule, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2D(1)
-        self.conv1 = nn.Conv2D(
-            in_channels=in_channels,
-            out_channels=in_channels // reduction,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            weight_attr=ParamAttr(name=name + "_1_weights"),
-            bias_attr=ParamAttr(name=name + "_1_offset"))
-        self.conv2 = nn.Conv2D(
-            in_channels=in_channels // reduction,
-            out_channels=in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            weight_attr=ParamAttr(name + "_2_weights"),
-            bias_attr=ParamAttr(name=name + "_2_offset"))
-
-    def forward(self, inputs):
-        outputs = self.avg_pool(inputs)
-        outputs = self.conv1(outputs)
-        outputs = F.relu(outputs)
-        outputs = self.conv2(outputs)
-        outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)
-        return inputs * outputs
\ No newline at end of file

ppocr/modeling/backbones/table_resnet_vd.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import paddle
-from paddle import ParamAttr
-import paddle.nn as nn
-import paddle.nn.functional as F
-
-__all__ = ["ResNet"]
-
-
-class ConvBNLayer(nn.Layer):
-    def __init__(
-            self,
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride=1,
-            groups=1,
-            is_vd_mode=False,
-            act=None,
-            name=None, ):
-        super(ConvBNLayer, self).__init__()
-
-        self.is_vd_mode = is_vd_mode
-        self._pool2d_avg = nn.AvgPool2D(
-            kernel_size=2, stride=2, padding=0, ceil_mode=True)
-        self._conv = nn.Conv2D(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=(kernel_size - 1) // 2,
-            groups=groups,
-            weight_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False)
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        self._batch_norm = nn.BatchNorm(
-            out_channels,
-            act=act,
-            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
-
-    def forward(self, inputs):
-        if self.is_vd_mode:
-            inputs = self._pool2d_avg(inputs)
-        y = self._conv(inputs)
-        y = self._batch_norm(y)
-        return y
-
-
-class BottleneckBlock(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 stride,
-                 shortcut=True,
-                 if_first=False,
-                 name=None):
-        super(BottleneckBlock, self).__init__()
-
-        self.conv0 = ConvBNLayer(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=1,
-            act='relu',
-            name=name + "_branch2a")
-        self.conv1 = ConvBNLayer(
-            in_channels=out_channels,
-            out_channels=out_channels,
-            kernel_size=3,
-            stride=stride,
-            act='relu',
-            name=name + "_branch2b")
-        self.conv2 = ConvBNLayer(
-            in_channels=out_channels,
-            out_channels=out_channels * 4,
-            kernel_size=1,
-            act=None,
-            name=name + "_branch2c")
-
-        if not shortcut:
-            self.short = ConvBNLayer(
-                in_channels=in_channels,
-                out_channels=out_channels * 4,
-                kernel_size=1,
-                stride=1,
-                is_vd_mode=False if if_first else True,
-                name=name + "_branch1")
-
-        self.shortcut = shortcut
-
-    def forward(self, inputs):
-        y = self.conv0(inputs)
-        conv1 = self.conv1(y)
-        conv2 = self.conv2(conv1)
-
-        if self.shortcut:
-            short = inputs
-        else:
-            short = self.short(inputs)
-        y = paddle.add(x=short, y=conv2)
-        y = F.relu(y)
-        return y
-
-
-class BasicBlock(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 stride,
-                 shortcut=True,
-                 if_first=False,
-                 name=None):
-        super(BasicBlock, self).__init__()
-        self.stride = stride
-        self.conv0 = ConvBNLayer(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=3,
-            stride=stride,
-            act='relu',
-            name=name + "_branch2a")
-        self.conv1 = ConvBNLayer(
-            in_channels=out_channels,
-            out_channels=out_channels,
-            kernel_size=3,
-            act=None,
-            name=name + "_branch2b")
-
-        if not shortcut:
-            self.short = ConvBNLayer(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=1,
-                stride=1,
-                is_vd_mode=False if if_first else True,
-                name=name + "_branch1")
-
-        self.shortcut = shortcut
-
-    def forward(self, inputs):
-        y = self.conv0(inputs)
-        conv1 = self.conv1(y)
-
-        if self.shortcut:
-            short = inputs
-        else:
-            short = self.short(inputs)
-        y = paddle.add(x=short, y=conv1)
-        y = F.relu(y)
-        return y
-
-
-class ResNet(nn.Layer):
-    def __init__(self, in_channels=3, layers=50, **kwargs):
-        super(ResNet, self).__init__()
-        
-        self.layers = layers
-        supported_layers = [18, 34, 50, 101, 152, 200]
-        assert layers in supported_layers, \
-            "supported layers are {} but input layer is {}".format(
-                supported_layers, layers)
-
-        if layers == 18:
-            depth = [2, 2, 2, 2]
-        elif layers == 34 or layers == 50:
-            depth = [3, 4, 6, 3]
-        elif layers == 101:
-            depth = [3, 4, 23, 3]
-        elif layers == 152:
-            depth = [3, 8, 36, 3]
-        elif layers == 200:
-            depth = [3, 12, 48, 3]
-        num_channels = [64, 256, 512,
-                        1024] if layers >= 50 else [64, 64, 128, 256]
-        num_filters = [64, 128, 256, 512]
-
-        self.conv1_1 = ConvBNLayer(
-            in_channels=in_channels,
-            out_channels=32,
-            kernel_size=3,
-            stride=2,
-            act='relu',
-            name="conv1_1")
-        self.conv1_2 = ConvBNLayer(
-            in_channels=32,
-            out_channels=32,
-            kernel_size=3,
-            stride=1,
-            act='relu',
-            name="conv1_2")
-        self.conv1_3 = ConvBNLayer(
-            in_channels=32,
-            out_channels=64,
-            kernel_size=3,
-            stride=1,
-            act='relu',
-            name="conv1_3")
-        self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
-
-        self.stages = []
-        self.out_channels = []
-        if layers >= 50:
-            for block in range(len(depth)):
-                block_list = []
-                shortcut = False
-                for i in range(depth[block]):
-                    if layers in [101, 152] and block == 2:
-                        if i == 0:
-                            conv_name = "res" + str(block + 2) + "a"
-                        else:
-                            conv_name = "res" + str(block + 2) + "b" + str(i)
-                    else:
-                        conv_name = "res" + str(block + 2) + chr(97 + i)
-                    bottleneck_block = self.add_sublayer(
-                        'bb_%d_%d' % (block, i),
-                        BottleneckBlock(
-                            in_channels=num_channels[block]
-                            if i == 0 else num_filters[block] * 4,
-                            out_channels=num_filters[block],
-                            stride=2 if i == 0 and block != 0 else 1,
-                            shortcut=shortcut,
-                            if_first=block == i == 0,
-                            name=conv_name))
-                    shortcut = True
-                    block_list.append(bottleneck_block)
-                self.out_channels.append(num_filters[block] * 4)
-                self.stages.append(nn.Sequential(*block_list))
-        else:
-            for block in range(len(depth)):
-                block_list = []
-                shortcut = False
-                for i in range(depth[block]):
-                    conv_name = "res" + str(block + 2) + chr(97 + i)
-                    basic_block = self.add_sublayer(
-                        'bb_%d_%d' % (block, i),
-                        BasicBlock(
-                            in_channels=num_channels[block]
-                            if i == 0 else num_filters[block],
-                            out_channels=num_filters[block],
-                            stride=2 if i == 0 and block != 0 else 1,
-                            shortcut=shortcut,
-                            if_first=block == i == 0,
-                            name=conv_name))
-                    shortcut = True
-                    block_list.append(basic_block)
-                self.out_channels.append(num_filters[block])
-                self.stages.append(nn.Sequential(*block_list))
-
-    def forward(self, inputs):
-        y = self.conv1_1(inputs)
-        y = self.conv1_2(y)
-        y = self.conv1_3(y)
-        y = self.pool2d_max(y)
-        out = []
-        for block in self.stages:
-            y = block(y)
-            out.append(y)
-        return out

ppocr/modeling/transforms/tps_spatial_transformer.py

@@ -53,7 +53,7 @@ def compute_partial_repr(input_points, control_points):
                                                                          1]
     repr_matrix = 0.5 * pairwise_dist * paddle.log(pairwise_dist)
     # fix numerical error for 0 * log(0), substitute all nan with 0
-    mask = repr_matrix != repr_matrix
+    mask = np.array(repr_matrix != repr_matrix)
     repr_matrix[mask] = 0
     return repr_matrix
 

ppocr/postprocess/east_postprocess.py

@@ -20,7 +20,6 @@ import numpy as np
 from .locality_aware_nms import nms_locality
 import cv2
 import paddle
-import lanms
 
 import os
 import sys
@@ -61,6 +60,7 @@ class EASTPostProcess(object):
         """
         restore text boxes from score map and geo map
         """
+
         score_map = score_map[0]
         geo_map = np.swapaxes(geo_map, 1, 0)
         geo_map = np.swapaxes(geo_map, 1, 2)
@@ -76,8 +76,15 @@ class EASTPostProcess(object):
         boxes = np.zeros((text_box_restored.shape[0], 9), dtype=np.float32)
         boxes[:, :8] = text_box_restored.reshape((-1, 8))
         boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]]
+
+        try:
+            import lanms
             boxes = lanms.merge_quadrangle_n9(boxes, nms_thresh)
-        # boxes = nms_locality(boxes.astype(np.float64), nms_thresh)
+        except:
+            print(
+                'you should install lanms by pip3 install lanms-nova to speed up nms_locality'
+            )
+            boxes = nms_locality(boxes.astype(np.float64), nms_thresh)
         if boxes.shape[0] == 0:
             return []
         # Here we filter some low score boxes by the average score map, 

ppocr/utils/save_load.py

@@ -67,6 +67,7 @@ def load_model(config, model, optimizer=None):
             if key not in params:
                 logger.warning("{} not in loaded params {} !".format(
                     key, params.keys()))
+                continue
             pre_value = params[key]
             if list(value.shape) == list(pre_value.shape):
                 new_state_dict[key] = pre_value
@@ -76,9 +77,14 @@ def load_model(config, model, optimizer=None):
                     format(key, value.shape, pre_value.shape))
         model.set_state_dict(new_state_dict)
 
-        optim_dict = paddle.load(checkpoints + '.pdopt')
         if optimizer is not None:
+            if os.path.exists(checkpoints + '.pdopt'):
+                optim_dict = paddle.load(checkpoints + '.pdopt')
                 optimizer.set_state_dict(optim_dict)
+            else:
+                logger.warning(
+                    "{}.pdopt is not exists, params of optimizer is not loaded".
+                    format(checkpoints))
 
         if os.path.exists(checkpoints + '.states'):
             with open(checkpoints + '.states', 'rb') as f:

requirements.txt

@@ -13,4 +13,3 @@ lxml
 premailer
 openpyxl
 fasttext==0.9.1
\ No newline at end of file
-lanms-nova
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

-===========================ch_ppocr_mobile_v2.0===========================
+===========================ch_PP-OCRv2===========================
 model_name:ch_PP-OCRv2
 python:python3.7
 infer_model:./inference/ch_PP-OCRv2_det_infer/
 infer_export:null
-infer_quant:True
+infer_quant:False
 inference:tools/infer/predict_system.py
---use_gpu:False
---enable_mkldnn:False
+--use_gpu:False|True
+--enable_mkldnn:False|True
 --cpu_threads:1|6
 --rec_batch_num:1
---use_tensorrt:False
---precision:int8
+--use_tensorrt:False|True
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 --rec_model_dir:./inference/ch_PP-OCRv2_rec_infer/

test_tipc/configs/ch_PP-OCRv2_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

 ===========================kl_quant_params===========================
 model_name:PPOCRv2_ocr_det_kl
 python:python3.7
+Global.pretrained_model:null
+Global.save_inference_dir:null
 infer_model:./inference/ch_PP-OCRv2_det_infer/
 infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
 infer_quant:True
 inference:tools/infer/predict_det.py
---use_gpu:False
---enable_mkldnn:False
+--use_gpu:False|True
+--enable_mkldnn:True
 --cpu_threads:1|6
 --rec_batch_num:1
---use_tensorrt:False
+--use_tensorrt:False|True
 --precision:int8
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/

test_tipc/configs/ch_PP-OCRv2_rec_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

 ===========================kl_quant_params===========================
 model_name:PPOCRv2_ocr_rec_kl
 python:python3.7
+Global.pretrained_model:null
+Global.save_inference_dir:null
 infer_model:./inference/ch_PP-OCRv2_rec_infer/
 infer_export:deploy/slim/quantization/quant_kl.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o
 infer_quant:True
 inference:tools/infer/predict_rec.py
---use_gpu:False
---enable_mkldnn:False
+--use_gpu:False|True
+--enable_mkldnn:False|True
 --cpu_threads:1|6
 --rec_batch_num:1|6
---use_tensorrt:False
+--use_tensorrt:True
 --precision:int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference

test_tipc/configs/ch_ppocr_mobile_V2.0_det_FPGM/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:null
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=300
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null
@@ -15,7 +15,7 @@ null:null
 trainer:fpgm_train
 norm_train:null
 pact_train:null
-fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
+fpgm_train:deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
 distill_train:null
 null:null
 null:null
@@ -29,7 +29,7 @@ Global.save_inference_dir:./output/
 Global.pretrained_model:
 norm_export:null
 quant_export:null
-fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
+fpgm_export:deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o 
 distill_export:null
 export1:null
 export2:null

test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

@@ -3,14 +3,14 @@ model_name:ch_ppocr_mobile_v2.0
 python:python3.7
 infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
 infer_export:null
-infer_quant:True
+infer_quant:False
 inference:tools/infer/predict_system.py
---use_gpu:False
---enable_mkldnn:False
+--use_gpu:False|True
+--enable_mkldnn:False|True
 --cpu_threads:1|6
 --rec_batch_num:1
---use_tensorrt:False
---precision:int8
+--use_tensorrt:False|True
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 --rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/

test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:null
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=300
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null

test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt

@@ -4,7 +4,7 @@ python:python
 gpu_list:-1
 Global.use_gpu:False
 Global.auto_cast:null
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=300
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null
@@ -12,10 +12,10 @@ train_model_name:latest
 train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
-trainer:norm_train|pact_train|fpgm_train
-norm_train:tools/train.py -c test_tipc/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
-pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/det_mv3_db.yml -o
-fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
+trainer:norm_train
+norm_train:tools/train.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+pact_train:null
+fpgm_train:null
 distill_train:null
 null:null
 null:null
@@ -27,9 +27,9 @@ null:null
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 Global.pretrained_model:
-norm_export:tools/export_model.py -c test_tipc/configs/det_mv3_db.yml -o 
-quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/det_mv3_db.yml -o 
-fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/det_mv3_db.yml -o 
+norm_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o 
+quant_export:null
+fpgm_export:null 
 distill_export:null
 export1:null
 export2:null

test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt

@@ -4,7 +4,7 @@ python:python
 gpu_list:0
 Global.use_gpu:True
 Global.auto_cast:fp32|amp
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=300
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null
@@ -12,10 +12,10 @@ train_model_name:latest
 train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
-trainer:norm_train|pact_train|fpgm_train
-norm_train:tools/train.py -c test_tipc/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
-pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/det_mv3_db.yml -o
-fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
+trainer:norm_train
+norm_train:tools/train.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+pact_train:null
+fpgm_train:null
 distill_train:null
 null:null
 null:null
@@ -27,9 +27,9 @@ null:null
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 Global.pretrained_model:
-norm_export:tools/export_model.py -c test_tipc/configs/det_mv3_db.yml -o 
-quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/det_mv3_db.yml -o 
-fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/det_mv3_db.yml -o 
+norm_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o 
+quant_export:null
+fpgm_export:null
 distill_export:null
 export1:null
 export2:null
@@ -49,63 +49,4 @@ inference:tools/infer/predict_det.py
 null:null
 --benchmark:True
 null:null
-===========================cpp_infer_params===========================
-use_opencv:True
-infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
-infer_quant:False
-inference:./deploy/cpp_infer/build/ppocr det
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16
---det_model_dir:
---image_dir:./inference/ch_det_data_50/all-sum-510/
-null:null
---benchmark:True
-===========================serving_params===========================
-model_name:ocr_det
-python:python3.7
-trans_model:-m paddle_serving_client.convert
---dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
---model_filename:inference.pdmodel
---params_filename:inference.pdiparams
---serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
---serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
-serving_dir:./deploy/pdserving
-web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
-op.det.local_service_conf.devices:null|0
-op.det.local_service_conf.use_mkldnn:True|False
-op.det.local_service_conf.thread_num:1|6
-op.det.local_service_conf.use_trt:False|True
-op.det.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py|pipeline_rpc_client.py
---image_dir=../../doc/imgs
-===========================kl_quant_params===========================
-infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
-infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
-infer_quant:True
-inference:tools/infer/predict_det.py
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:False|True
---precision:int8
---det_model_dir:
---image_dir:./inference/ch_det_data_50/all-sum-510/
-null:null
---benchmark:True
-null:null
-null:null
-===========================lite_params===========================
-inference:./ocr_db_crnn det
-infer_model:./models/ch_ppocr_mobile_v2.0_det_opt.nb|./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb
---cpu_threads:1|4
---batch_size:1
---power_mode:LITE_POWER_HIGH|LITE_POWER_LOW
---image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/|./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg
---config_dir:./config.txt
---rec_dict_dir:./ppocr_keys_v1.txt
---benchmark:True
+

test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

 ===========================kl_quant_params===========================
-model_name:PPOCRv2_ocr_det
+model_name:ch_ppocr_mobile_v2.0_det_KL
 python:python3.7
+Global.pretrained_model:null
+Global.save_inference_dir:null
 infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
 infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
 infer_quant:True
 inference:tools/infer/predict_det.py
---use_gpu:False
---enable_mkldnn:False
+--use_gpu:False|True
+--enable_mkldnn:True
 --cpu_threads:1|6
 --rec_batch_num:1
---use_tensorrt:False
+--use_tensorrt:False|True
 --precision:int8
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/

test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:null
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=300
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null
@@ -14,7 +14,7 @@ null:null
 ##
 trainer:pact_train
 norm_train:null
-pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
+pact_train:deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
 fpgm_train:null
 distill_train:null
 null:null
@@ -28,7 +28,7 @@ null:null
 Global.save_inference_dir:./output/
 Global.pretrained_model:
 norm_export:null
-quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
+quant_export:deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o 
 fpgm_export:null
 distill_export:null
 export1:null

test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_chinese_lite_v2.0
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+    disable_se: True
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/ic15_data
+    label_file_list: ["train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/ic15_data
+    label_file_list: ["train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/train_infer_python.txt

+===========================train_params===========================
+model_name:ch_ppocr_mobile_v2.0_rec_FPGM
+python:python3.7
+gpu_list:0
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/ic15_data/test/word_1.png
+null:null
+##
+trainer:fpgm_train
+norm_train:null
+pact_train:null
+fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./pretrain_models/ch_ppocr_mobile_v2.0_rec_train/best_accuracy
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:null
+quant_export:null
+fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml -o 
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:null
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_rec.py
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+null:null
+--benchmark:True
+null:null
\ No newline at end of file

test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

+===========================kl_quant_params===========================
+model_name:ch_ppocr_mobile_v2.0_rec_KL
+python:python3.7
+Global.pretrained_model:null
+Global.save_inference_dir:null
+infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
+infer_export:deploy/slim/quantization/quant_kl.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/rec_chinese_lite_train_v2.0.yml -o
+infer_quant:True
+inference:tools/infer/predict_rec.py
+--use_gpu:False|True
+--enable_mkldnn:True
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:int8
+--det_model_dir:
+--image_dir:./inference/rec_inference
+null:null
+--benchmark:True
+null:null
+null:null

test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/rec_chinese_lite_train_v2.0.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_chinese_lite_v2.0
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/ic15_data
+    label_file_list: ["train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/ic15_data
+    label_file_list: ["train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_chinese_lite_v2.0
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/ic15_data
+    label_file_list: ["train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/ic15_data
+    label_file_list: ["train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/train_infer_python.txt

+===========================train_params===========================
+model_name:ch_ppocr_mobile_v2.0_rec_PACT
+python:python3.7
+gpu_list:0
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.checkpoints:null
+train_model_name:latest
+train_infer_img_dir:./train_data/ic15_data/test/word_1.png
+null:null
+##
+trainer:pact_train
+norm_train:null
+pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml -o
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.checkpoints:
+norm_export:null
+quant_export:deploy/slim/quantization/export_model.py -ctest_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml -o 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:null
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.txt --rec_image_shape="3,32,100"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:False|True
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
\ No newline at end of file

test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

-===========================ch_ppocr_mobile_v2.0===========================
+===========================ch_ppocr_server_v2.0===========================
 model_name:ch_ppocr_server_v2.0
 python:python3.7
 infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
 infer_export:null
 infer_quant:True
 inference:tools/infer/predict_system.py
---use_gpu:False
---enable_mkldnn:False
+--use_gpu:False|True
+--enable_mkldnn:False|True
 --cpu_threads:1|6
 --rec_batch_num:1
 --use_tensorrt:False
---precision:int8
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 --rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/

test_tipc/configs/ch_ppocr_server_v2.0_rec/train_infer_python.txt

@@ -37,13 +37,13 @@ export2:null
 infer_model:null
 infer_export:tools/export_model.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
 infer_quant:False
-inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
+inference:tools/infer/predict_rec.py
 --use_gpu:True|False
 --enable_mkldnn:True|False
 --cpu_threads:1|6
 --rec_batch_num:1|6
 --use_tensorrt:True|False
---precision:fp32|fp16|int8
+--precision:fp32|int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 --save_log_path:./test/output/

test_tipc/configs/det_mv3_pse_v2.0/det_mv3_pse.yml

+Global:
+  use_gpu: true
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_mv3_pse/
+  save_epoch_step: 600
+  # evaluation is run every 63 iterations
+  eval_batch_step: [ 0,1000 ]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_pse/predicts_pse.txt
+
+Architecture:
+  model_type: det
+  algorithm: PSE
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: FPN
+    out_channels: 96
+  Head:
+    name: PSEHead
+    hidden_dim: 96
+    out_channels: 7
+
+Loss:
+  name: PSELoss
+  alpha: 0.7
+  ohem_ratio: 3
+  kernel_sample_mask: pred
+  reduction: none
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Step
+    learning_rate: 0.001
+    step_size: 200
+    gamma: 0.1
+  regularizer:
+    name: 'L2'
+    factor: 0.0005
+
+PostProcess:
+  name: PSEPostProcess
+  thresh: 0
+  box_thresh: 0.85
+  min_area: 16
+  box_type: box # 'box' or 'poly'
+  scale: 1
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - ColorJitter:
+          brightness: 0.12549019607843137
+          saturation: 0.5
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
+      - MakePseGt:
+          kernel_num: 7
+          min_shrink_ratio: 0.4
+          size: 640
+      - RandomCropImgMask:
+          size: [ 640,640 ]
+          main_key: gt_text
+          crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 16
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          limit_side_len: 736
+          limit_type: min
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 8
\ No newline at end of file

test_tipc/configs/det_mv3_pse_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:det_mv3_pse_v2.0
+python:python3.7
+gpu_list:0
+Global.use_gpu:True|True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/det_mv3_pse_v2.0/det_mv3_pse.yml -o 
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/det_mv3_pse_v2.0/det_mv3_pse.yml -o 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/det_mv3_pse/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/cconfigs/det_mv3_pse_v2.0/det_mv3_pse.yml  -o
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:fp32|fp16|int8
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+--save_log_path:null
+--benchmark:True
+--det_algorithm:PSE

test_tipc/configs/det_r50_vd_east_v2.0/train_infer_python.txt

@@ -34,7 +34,7 @@ distill_export:null
 export1:null
 export2:null
 ##
-train_model:./inference/det_mv3_east/best_accuracy
+train_model:./inference/det_r50_vd_east/best_accuracy
 infer_export:tools/export_model.py -c test_tipc/cconfigs/det_r50_vd_east_v2.0/det_r50_vd_east.yml  -o
 infer_quant:False
 inference:tools/infer/predict_det.py

test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml

+Global:
+  use_gpu: true
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_r50_vd_pse/
+  save_epoch_step: 600
+  # evaluation is run every 125 iterations
+  eval_batch_step: [ 0,1000 ]
+  cal_metric_during_train: False
+  pretrained_model: 
+  checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_pse/predicts_pse.txt
+
+Architecture:
+  model_type: det
+  algorithm: PSE
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 50
+  Neck:
+    name: FPN
+    out_channels: 256
+  Head:
+    name: PSEHead
+    hidden_dim: 256
+    out_channels: 7
+
+Loss:
+  name: PSELoss
+  alpha: 0.7
+  ohem_ratio: 3
+  kernel_sample_mask: pred
+  reduction: none
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Step
+    learning_rate: 0.0001
+    step_size: 200
+    gamma: 0.1
+  regularizer:
+    name: 'L2'
+    factor: 0.0005
+
+PostProcess:
+  name: PSEPostProcess
+  thresh: 0
+  box_thresh: 0.85
+  min_area: 16
+  box_type: box # 'box' or 'poly'
+  scale: 1
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - ColorJitter:
+          brightness: 0.12549019607843137
+          saturation: 0.5
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
+      - MakePseGt:
+          kernel_num: 7
+          min_shrink_ratio: 0.4
+          size: 640
+      - RandomCropImgMask:
+          size: [ 640,640 ]
+          main_key: gt_text
+          crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          limit_side_len: 736
+          limit_type: min
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 8
\ No newline at end of file

test_tipc/configs/det_r50_vd_pse_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:det_r50_vd_pse_v2.0
+python:python3.7
+gpu_list:0
+Global.use_gpu:True|True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml -o 
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml -o 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/det_r50_vd_pse/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/cconfigs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml  -o
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:fp32|fp16|int8
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+--save_log_path:null
+--benchmark:True
+--det_algorithm:PSE

test_tipc/configs/en_server_pgnetA/train_infer_python.txt

@@ -44,7 +44,7 @@ inference:tools/infer/predict_e2e.py
 --rec_batch_num:1
 --use_tensorrt:False|True
 --precision:fp32|fp16|int8
---det_model_dir:
+--e2e_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null
 --benchmark:True

test_tipc/configs/rec_mtb_nrtr/rec_mtb_nrtr.yml

+Global:
+  use_gpu: True
+  epoch_num: 21
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/nrtr/
+  save_epoch_step: 1
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path: ppocr/utils/EN_symbol_dict.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_nrtr.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.99
+  clip_norm: 5.0
+  lr:
+    name: Cosine
+    learning_rate: 0.0005
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0.
+
+Architecture:
+  model_type: rec
+  algorithm: NRTR
+  in_channels: 1
+  Transform:
+  Backbone:
+    name: MTB
+    cnn_num: 2
+  Head:
+    name: Transformer
+    d_model: 512
+    num_encoder_layers: 6
+    beam_size: -1 # When Beam size is greater than 0, it means to use beam search when evaluation.
+    
+
+Loss:
+  name: NRTRLoss
+  smoothing: True
+
+PostProcess:
+  name: NRTRLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - NRTRLabelEncode: # Class handling label
+      - NRTRRecResizeImg:
+          image_shape: [100, 32]
+          resize_type: PIL # PIL or OpenCV
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 512
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - NRTRLabelEncode: # Class handling label
+      - NRTRRecResizeImg:
+          image_shape: [100, 32]
+          resize_type: PIL # PIL or OpenCV
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 1
+    use_shared_memory: False

test_tipc/configs/rec_mtb_nrtr/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_mtb_nrtr
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_mtb_nrtr/rec_mtb_nrtr.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_mtb_nrtr/rec_mtb_nrtr.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_mtb_nrtr/rec_mtb_nrtr.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_mtb_nrtr/rec_mtb_nrtr.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/EN_symbol_dict.txt --rec_image_shape="1,32,100" --rec_algorithm="NRTR"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
+

test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/train_infer_python.txt

@@ -43,7 +43,7 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
 --cpu_threads:1|6
 --rec_batch_num:1|6
 --use_tensorrt:True|False
---precision:fp32|fp16|int8
+--precision:fp32|int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 --save_log_path:./test/output/

test_tipc/configs/rec_mv3_none_none_ctc_v2.0/train_infer_python.txt

@@ -43,7 +43,7 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
 --cpu_threads:1|6
 --rec_batch_num:1|6
 --use_tensorrt:True|False
---precision:fp32|fp16|int8
+--precision:fp32|int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 --save_log_path:./test/output/

test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml

+Global:
+  use_gpu: True
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/rec_mv3_tps_bilstm_att/
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_tps_bilstm_att.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: RARE
+  Transform:
+    name: TPS
+    num_fiducial: 20
+    loc_lr: 0.1
+    model_name: small
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn 
+    hidden_size: 96
+  Head:
+    name: AttentionHead  
+    hidden_size: 96
+    
+
+Loss:
+  name: AttentionLoss
+
+PostProcess:
+  name: AttnLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - AttnLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - AttnLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 1

test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_mv3_tps_bilstm_att_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="RARE"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
+

test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/train_infer_python.txt

@@ -43,7 +43,7 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
 --cpu_threads:1|6
 --rec_batch_num:1|6
 --use_tensorrt:True|False
---precision:fp32|fp16|int8
+--precision:fp32|int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 --save_log_path:./test/output/

test_tipc/configs/rec_r31_sar/rec_r31_sar.yml

+Global:
+  use_gpu: true
+  epoch_num: 5
+  log_smooth_window: 20
+  print_batch_step: 20
+  save_model_dir: ./sar_rec
+  save_epoch_step: 1
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: 
+  # for data or label process
+  character_dict_path: ppocr/utils/dict90.txt
+  max_text_length: 30
+  infer_mode: False
+  use_space_char: False
+  rm_symbol: True
+  save_res_path: ./output/rec/predicts_sar.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Piecewise
+    decay_epochs: [3, 4]
+    values: [0.001, 0.0001, 0.00001] 
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: SAR
+  Transform:
+  Backbone:
+    name: ResNet31
+  Head:
+    name: SARHead
+
+Loss:
+  name: SARLoss
+
+PostProcess:
+  name: SARLabelDecode
+
+Metric:
+  name: RecMetric
+
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - SARLabelEncode: # Class handling label
+      - SARRecResizeImg:
+          image_shape: [3, 48, 48, 160] # h:48 w:[48,160]
+          width_downsample_ratio: 0.25
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 64
+    drop_last: True
+    num_workers: 8
+    use_shared_memory: False
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - SARLabelEncode: # Class handling label
+      - SARRecResizeImg:
+          image_shape: [3, 48, 48, 160]
+          width_downsample_ratio: 0.25
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 64
+    num_workers: 4
+    use_shared_memory: False
+  

test_tipc/configs/rec_r31_sar/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_r31_sar
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_r31_sar/rec_r31_sar.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_r31_sar/rec_r31_sar.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_r31_sar/rec_r31_sar.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_r31_sar/rec_r31_sar.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict90.txt --rec_image_shape="3,48,48,160" --rec_algorithm="SAR"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
+

test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/train_infer_python.txt

@@ -43,7 +43,7 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
 --cpu_threads:1|6
 --rec_batch_num:1|6
 --use_tensorrt:True|False
---precision:fp32|fp16|int8
+--precision:fp32|int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 --save_log_path:./test/output/

test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/train_infer_python.txt

@@ -43,7 +43,7 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
 --cpu_threads:1|6
 --rec_batch_num:1|6
 --use_tensorrt:True|False
---precision:fp32|fp16|int8
+--precision:fp32|int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 --save_log_path:./test/output/

test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml

+Global:
+  use_gpu: True
+  epoch_num: 400
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/b3_rare_r34_none_gru/
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_b3_rare_r34_none_gru.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0.00000
+
+Architecture:
+  model_type: rec
+  algorithm: RARE
+  Transform:
+    name: TPS
+    num_fiducial: 20
+    loc_lr: 0.1
+    model_name: large
+  Backbone:
+    name: ResNet  
+    layers: 34
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn 
+    hidden_size: 256 #96
+  Head:
+    name: AttentionHead  # AttentionHead
+    hidden_size: 256 #
+    l2_decay: 0.00001
+
+Loss:
+  name: AttentionLoss
+
+PostProcess:
+  name: AttnLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - AttnLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - AttnLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_r34_vd_tps_bilstm_att_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="RARE"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
+

test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/train_infer_python.txt

@@ -43,7 +43,7 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
 --cpu_threads:1|6
 --rec_batch_num:1|6
 --use_tensorrt:True|False
---precision:fp32|fp16|int8
+--precision:fp32|int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 --save_log_path:./test/output/

test_tipc/configs/rec_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml

+Global:
+  use_gpu: True
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 5
+  save_model_dir: ./output/rec/srn_new
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 5000]
+  cal_metric_during_train: True
+  pretrained_model: 
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  num_heads: 8
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_srn.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  clip_norm: 10.0
+  lr:
+    learning_rate: 0.0001
+
+Architecture:
+  model_type: rec
+  algorithm: SRN
+  in_channels: 1
+  Transform:
+  Backbone:
+    name: ResNetFPN
+  Head:
+    name: SRNHead
+    max_text_length: 25
+    num_heads: 8
+    num_encoder_TUs: 2
+    num_decoder_TUs: 4
+    hidden_dims: 512
+
+Loss:
+  name: SRNLoss
+
+PostProcess:
+  name: SRNLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - SRNLabelEncode: # Class handling label
+      - SRNRecResizeImg:
+          image_shape: [1, 64, 256]
+      - KeepKeys:
+          keep_keys: ['image',
+                      'label',
+                      'length',
+                      'encoder_word_pos',
+                      'gsrm_word_pos',
+                      'gsrm_slf_attn_bias1',
+                      'gsrm_slf_attn_bias2'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    batch_size_per_card: 64
+    drop_last: False
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - SRNLabelEncode: # Class handling label
+      - SRNRecResizeImg:
+          image_shape: [1, 64, 256]
+      - KeepKeys:
+          keep_keys: ['image',
+                      'label',
+                      'length',
+                      'encoder_word_pos',
+                      'gsrm_word_pos',
+                      'gsrm_slf_attn_bias1',
+                      'gsrm_slf_attn_bias2'] 
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 32
+    num_workers: 4

test_tipc/configs/rec_r50_fpn_vd_none_srn/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_r50_fpn_vd_none_srn
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="1,64,256" --rec_algorithm="SRN" --use_space_char=False
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
+

test_tipc/docs/jeston_test_train_inference_python.md

 # Jeston端基础训练预测功能测试
 
-Jeston端基础训练预测功能测试的主程序为`test_train_inference_python.sh`，由于Jeston端CPU较差，Jeston只需要测试TIPC关于GPU和TensorRT预测推理的部分即可。
+Jeston端基础训练预测功能测试的主程序为`test_inference_inference.sh`，由于Jeston端CPU较差，Jeston只需要测试TIPC关于GPU和TensorRT预测推理的部分即可。
 
 ## 1. 测试结论汇总
 
@@ -40,21 +40,21 @@ Jeston端基础训练预测功能测试的主程序为`test_train_inference_pyth
 
 ### 2.2 功能测试
 
-先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。
+先运行`prepare.sh`准备数据和模型，然后运行`test_inference_inference.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。
 
-`test_train_inference_python.sh`包含5种[运行模式](./test_train_inference_python.md)，在Jeston端，仅需要测试预测推理的模式即可：
+`test_inference_inference.sh`仅有一个模式`whole_infer`，在Jeston端，仅需要测试预测推理的模式即可：
 
 ```
 - 模式3：whole_infer，不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
 ```shell
 bash test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer'
 # 用法1:
-bash test_tipc/test_inference_jeston.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer'
+bash test_tipc/test_inference_inference.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer'
 # 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
 bash test_tipc/test_inference_jeston.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer' '1'
 ```
 
-运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如`lite_train_lite_infer`模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件：
+运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如`whole_infer`模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件：
 ```
 test_tipc/output/
 |- results_python.log    # 运行指令状态的日志

test_tipc/prepare.sh

@@ -45,7 +45,7 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/total_text_lite.tar --no-check-certificate
         wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar --no-check-certificate
         cd ./pretrain_models/ && tar xf en_server_pgnetA.tar && cd ../
-        cd ./train_data && tar xf total_text_lite.tar && ln -s total_text && cd ../
+        cd ./train_data && tar xf total_text_lite.tar && ln -s total_text_lite total_text && cd ../
     fi
     if [ ${model_name} == "det_r50_vd_sast_icdar15_v2.0" ] || [ ${model_name} == "det_r50_vd_sast_totaltext_v2.0" ]; then
         wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate
@@ -61,6 +61,10 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar  --no-check-certificate
         cd ./inference/ && tar xf det_r50_vd_db_v2.0_train.tar && cd ../
     fi
+    if [ ${model_name} == "ch_ppocr_mobile_v2.0_rec_FPGM" ]; then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf ch_ppocr_mobile_v2.0_rec_train.tar && cd ../
+    fi
 
 elif [ ${MODE} = "whole_train_whole_infer" ];then
     wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
@@ -137,11 +141,6 @@ elif [ ${MODE} = "whole_infer" ];then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar --no-check-certificate
         cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
     fi
-    if [ ${model_name} = "ch_PPOCRv2_det" ]; then
-        wget -nc -P ./inference/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
-        wget -nc -P ./inference/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar --no-check-certificate
-         cd ./inference && tar xf e2e_server_pgnetA_infer.tar && tar xf ch_det_data_50.tar && cd ../ 
-    fi
     if [ ${model_name} == "en_server_pgnetA" ]; then
         wget -nc -P ./inference/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar  --no-check-certificate
         cd ./inference && tar xf en_server_pgnetA.tar && cd ../
@@ -160,7 +159,10 @@ elif [ ${MODE} = "whole_infer" ];then
     fi
 fi
 if [ ${MODE} = "klquant_whole_infer" ]; then
-    if [ ${model_name} = "ch_ppocr_mobile_v2.0_det" ]; then
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar --no-check-certificate
+    cd ./train_data/ && tar xf icdar2015_lite.tar
+    ln -s ./icdar2015_lite ./icdar2015 && cd ../
+    if [ ${model_name} = "ch_ppocr_mobile_v2.0_det_KL" ]; then
         wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar --no-check-certificate
         wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar  --no-check-certificate
         cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../ 
@@ -171,6 +173,13 @@ if [ ${MODE} = "klquant_whole_infer" ]; then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar  --no-check-certificate
         cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
     fi 
+    if [ ${model_name} = "ch_ppocr_mobile_v2.0_rec_KL" ]; then
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar --no-check-certificate
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar  --no-check-certificate
+        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar --no-check-certificate
+        cd ./train_data/ && tar xf ic15_data.tar && cd ../
+        cd ./inference && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf rec_inference.tar &&  cd ../ 
+    fi 
 fi
 
 if [ ${MODE} = "cpp_infer" ];then

test_tipc/test_inference_jeston.sh → test_tipc/test_inference_python.sh

 #!/bin/bash
 source test_tipc/common_func.sh
-source test_tipc/test_train_inference_python.sh
+#source test_tipc/test_train_inference_python.sh
 
 FILENAME=$1
 # MODE be one of ['whole_infer']
 MODE=$2
 
-dataline=$(awk 'NR==1, NR==17{print}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==20{print}'  $FILENAME)
 
 # parser params
 IFS=$'\n'
@@ -35,18 +35,100 @@ precision_list=$(func_parser_value "${lines[12]}")
 infer_model_key=$(func_parser_key "${lines[13]}")
 image_dir_key=$(func_parser_key "${lines[14]}")
 infer_img_dir=$(func_parser_value "${lines[14]}")
-save_log_key=$(func_parser_key "${lines[15]}")
+rec_model_key=$(func_parser_key "${lines[15]}")
+rec_model_value=$(func_parser_value "${lines[15]}")
 benchmark_key=$(func_parser_key "${lines[16]}")
 benchmark_value=$(func_parser_value "${lines[16]}")
 infer_key1=$(func_parser_key "${lines[17]}")
 infer_value1=$(func_parser_value "${lines[17]}")
 
 
+
 LOG_PATH="./test_tipc/output"
 mkdir -p ${LOG_PATH}
 status_log="${LOG_PATH}/results_python.log"
 
 
+function func_inference(){
+    IFS='|'
+    _python=$1
+    _script=$2
+    _model_dir=$3
+    _log_path=$4
+    _img_dir=$5
+    _flag_quant=$6
+    # inference 
+    for use_gpu in ${use_gpu_list[*]}; do
+        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
+            for use_mkldnn in ${use_mkldnn_list[*]}; do
+                if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
+                    continue
+                fi
+                for threads in ${cpu_threads_list[*]}; do
+                    for batch_size in ${batch_size_list[*]}; do
+                        for precision in ${precision_list[*]}; do
+                            if [ ${use_mkldnn} = "False" ] && [ ${precision} = "fp16" ]; then
+                                continue
+                            fi # skip when enable fp16 but disable mkldnn
+                            if [ ${_flag_quant} = "True" ] && [ ${precision} != "int8" ]; then
+                                continue
+                            fi # skip when quant model inference but precision is not int8
+                            set_precision=$(func_set_params "${precision_key}" "${precision}")
+                            
+                            _save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
+                            set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
+                            set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
+                            set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
+                            set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
+                            set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
+                            set_infer_params0=$(func_set_params "${rec_model_key}" "${rec_model_value}")
+                            set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
+                            command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_params0} ${set_infer_data} ${set_benchmark} ${set_precision} ${set_infer_params1} > ${_save_log_path} 2>&1 "
+                            eval $command
+                            last_status=${PIPESTATUS[0]}
+                            eval "cat ${_save_log_path}"
+                            status_check $last_status "${command}" "${status_log}"
+                        done
+                    done
+                done
+            done
+        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
+            for use_trt in ${use_trt_list[*]}; do
+                for precision in ${precision_list[*]}; do
+                    if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
+                        continue
+                    fi 
+                    if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
+                        continue
+                    fi
+                    if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
+                        continue
+                    fi
+                    for batch_size in ${batch_size_list[*]}; do
+                        _save_log_path="${_log_path}/python_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
+                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
+                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
+                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
+                        set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
+                        set_precision=$(func_set_params "${precision_key}" "${precision}")
+                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
+                        set_infer_params0=$(func_set_params "${save_log_key}" "${save_log_value}")
+                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} ${set_infer_params0} > ${_save_log_path} 2>&1 "
+                        eval $command
+                        last_status=${PIPESTATUS[0]}
+                        eval "cat ${_save_log_path}"
+                        status_check $last_status "${command}" "${status_log}"
+                        
+                    done
+                done
+            done
+        else
+            echo "Does not support hardware other than CPU and GPU Currently!"
+        fi
+    done
+}
+
 if [ ${MODE} = "whole_infer" ]; then
     GPUID=$3
     if [ ${#GPUID} -le 0 ];then
@@ -68,7 +150,6 @@ if [ ${MODE} = "whole_infer" ]; then
             set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
             export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}"
             echo ${infer_run_exports[Count]} 
-            echo  $export_cmd
             eval $export_cmd
             status_export=$?
             status_check $status_export "${export_cmd}" "${status_log}"
@@ -85,3 +166,4 @@ if [ ${MODE} = "whole_infer" ]; then
     done
 fi
 
+

test_tipc/test_train_inference_python.sh

@@ -90,36 +90,39 @@ infer_value1=$(func_parser_value "${lines[50]}")
 
 # parser klquant_infer
 if [ ${MODE} = "klquant_whole_infer" ]; then
-    dataline=$(awk 'NR==1 NR==17{print}'  $FILENAME)
+    dataline=$(awk 'NR==1, NR==17{print}'  $FILENAME)
     lines=(${dataline})
     model_name=$(func_parser_value "${lines[1]}")
     python=$(func_parser_value "${lines[2]}")
+    export_weight=$(func_parser_key "${lines[3]}")
+    save_infer_key=$(func_parser_key "${lines[4]}")
     # parser inference model 
-    infer_model_dir_list=$(func_parser_value "${lines[3]}")
-    infer_export_list=$(func_parser_value "${lines[4]}")
-    infer_is_quant=$(func_parser_value "${lines[5]}")
+    infer_model_dir_list=$(func_parser_value "${lines[5]}")
+    infer_export_list=$(func_parser_value "${lines[6]}")
+    infer_is_quant=$(func_parser_value "${lines[7]}")
     # parser inference 
-    inference_py=$(func_parser_value "${lines[6]}")
-    use_gpu_key=$(func_parser_key "${lines[7]}")
-    use_gpu_list=$(func_parser_value "${lines[7]}")
-    use_mkldnn_key=$(func_parser_key "${lines[8]}")
-    use_mkldnn_list=$(func_parser_value "${lines[8]}")
-    cpu_threads_key=$(func_parser_key "${lines[9]}")
-    cpu_threads_list=$(func_parser_value "${lines[9]}")
-    batch_size_key=$(func_parser_key "${lines[10]}")
-    batch_size_list=$(func_parser_value "${lines[10]}")
-    use_trt_key=$(func_parser_key "${lines[11]}")
-    use_trt_list=$(func_parser_value "${lines[11]}")
-    precision_key=$(func_parser_key "${lines[12]}")
-    precision_list=$(func_parser_value "${lines[12]}")
-    infer_model_key=$(func_parser_key "${lines[13]}")
-    image_dir_key=$(func_parser_key "${lines[14]}")
-    infer_img_dir=$(func_parser_value "${lines[14]}")
-    save_log_key=$(func_parser_key "${lines[15]}")
-    benchmark_key=$(func_parser_key "${lines[16]}")
-    benchmark_value=$(func_parser_value "${lines[16]}")
-    infer_key1=$(func_parser_key "${lines[17]}")
-    infer_value1=$(func_parser_value "${lines[17]}")
+    inference_py=$(func_parser_value "${lines[8]}")
+    use_gpu_key=$(func_parser_key "${lines[9]}")
+    use_gpu_list=$(func_parser_value "${lines[9]}")
+    use_mkldnn_key=$(func_parser_key "${lines[10]}")
+    use_mkldnn_list=$(func_parser_value "${lines[10]}")
+    cpu_threads_key=$(func_parser_key "${lines[11]}")
+    cpu_threads_list=$(func_parser_value "${lines[11]}")
+    batch_size_key=$(func_parser_key "${lines[12]}")
+    batch_size_list=$(func_parser_value "${lines[12]}")
+    use_trt_key=$(func_parser_key "${lines[13]}")
+    use_trt_list=$(func_parser_value "${lines[13]}")
+    precision_key=$(func_parser_key "${lines[14]}")
+    precision_list=$(func_parser_value "${lines[14]}")
+    infer_model_key=$(func_parser_key "${lines[15]}")
+    image_dir_key=$(func_parser_key "${lines[16]}")
+    infer_img_dir=$(func_parser_value "${lines[16]}")
+    save_log_key=$(func_parser_key "${lines[17]}")
+    save_log_value=$(func_parser_value "${lines[17]}")
+    benchmark_key=$(func_parser_key "${lines[18]}")
+    benchmark_value=$(func_parser_value "${lines[18]}")
+    infer_key1=$(func_parser_key "${lines[19]}")
+    infer_value1=$(func_parser_value "${lines[19]}")
 fi
 
 LOG_PATH="./test_tipc/output"
@@ -159,8 +162,9 @@ function func_inference(){
                             set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
                             set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
                             set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
+                            set_infer_params0=$(func_set_params "${save_log_key}" "${save_log_value}")
                             set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
-                            command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_precision} ${set_infer_params1} > ${_save_log_path} 2>&1 "
+                            command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_params0} ${set_infer_data} ${set_benchmark} ${set_precision} ${set_infer_params1} > ${_save_log_path} 2>&1 "
                             eval $command
                             last_status=${PIPESTATUS[0]}
                             eval "cat ${_save_log_path}"
@@ -189,8 +193,9 @@ function func_inference(){
                         set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
                         set_precision=$(func_set_params "${precision_key}" "${precision}")
                         set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
+                        set_infer_params0=$(func_set_params "${save_log_key}" "${save_log_value}")
                         set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} ${set_infer_params0} > ${_save_log_path} 2>&1 "
                         eval $command
                         last_status=${PIPESTATUS[0]}
                         eval "cat ${_save_log_path}"
@@ -235,7 +240,7 @@ if [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ]; then
         fi
         #run inference
         is_quant=${infer_quant_flag[Count]}
-        if [ ${MODE} = "klquant_infer" ]; then
+        if [ ${MODE} = "klquant_whole_infer" ]; then
             is_quant="True"
         fi
         func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}

tools/infer/utility.py

@@ -211,7 +211,7 @@ def create_predictor(args, mode, logger):
                     "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
                 }
                 max_input_shape = {
-                    "x": [1, 3, 1280, 1280],
+                    "x": [1, 3, 1536, 1536],
                     "conv2d_92.tmp_0": [1, 120, 400, 400],
                     "conv2d_91.tmp_0": [1, 24, 200, 200],
                     "conv2d_59.tmp_0": [1, 96, 400, 400],
@@ -261,7 +261,7 @@ def create_predictor(args, mode, logger):
                 opt_input_shape.update(opt_pact_shape)
             elif mode == "rec":
                 min_input_shape = {"x": [1, 3, 32, 10]}
-                max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1024]}
+                max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1536]}
                 opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
             elif mode == "cls":
                 min_input_shape = {"x": [1, 3, 48, 10]}

tools/infer_det.py

@@ -53,6 +53,7 @@ def draw_det_res(dt_boxes, config, img, img_name, save_path):
         logger.info("The detected Image saved in {}".format(save_path))
 
 
+@paddle.no_grad()
 def main():
     global_config = config['Global']