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

README.md

@@ -189,7 +189,7 @@ PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训
 请扫描下面二维码，完成问卷填写，获取加群二维码和OCR方向的炼丹秘籍
 
 <div align="center">
-<img src="./doc/joinus.jpg"  width = "200" height = "200" />
+<img src="./doc/joinus.PNG"  width = "200" height = "200" />
 </div>
 
 <a name="许可证书"></a>

README_en.md

@@ -56,7 +56,6 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr
 - Algorithm introduction
     - [Text Detection Algorithm](#TEXTDETECTIONALGORITHM)
     - [Text Recognition Algorithm](#TEXTRECOGNITIONALGORITHM)
-    - [END-TO-END OCR Algorithm](#ENDENDOCRALGORITHM)
 - Model training/evaluation
     - [Text Detection](./doc/doc_en/detection_en.md)
     - [Text Recognition](./doc/doc_en/recognition_en.md)
@@ -158,10 +157,6 @@ We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/
 
 Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
 
-<a name="ENDENDOCRALGORITHM"></a>
-## END-TO-END OCR Algorithm
-- [ ]  [End2End-PSL](https://arxiv.org/abs/1909.07808)(Baidu Self-Research, coming soon)
-
 ## Visualization
 
 <a name="UCOCRVIS"></a>
@@ -211,7 +206,7 @@ Please refer to the document for training guide and use of PaddleOCR text recogn
 Scan  the QR code below with your wechat and completing the questionnaire, you can access to offical technical exchange group.
 
 <div align="center">
-<img src="./doc/joinus.jpg"  width = "200" height = "200" />
+<img src="./doc/joinus.PNG"  width = "200" height = "200" />
 </div>
 
 <a name="LICENSE"></a>

deploy/cpp_infer/src/ocr_det.cpp

@@ -26,6 +26,8 @@ void DBDetector::LoadModel(const std::string &model_dir) {
     config.DisableGpu();
     if (this->use_mkldnn_) {
       config.EnableMKLDNN();
+      // cache 10 different shapes for mkldnn to avoid memory leak
+      config.SetMkldnnCacheCapacity(10);
     }
     config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
   }

deploy/cpp_infer/src/ocr_rec.cpp

@@ -126,6 +126,8 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
     config.DisableGpu();
     if (this->use_mkldnn_) {
       config.EnableMKLDNN();
+      // cache 10 different shapes for mkldnn to avoid memory leak
+      config.SetMkldnnCacheCapacity(10);
     }
     config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
   }

deploy/cpp_infer/tools/config.txt

@@ -3,7 +3,7 @@ use_gpu  0
 gpu_id  0
 gpu_mem  4000
 cpu_math_library_num_threads  10
-use_mkldnn 0
+use_mkldnn 1
 use_zero_copy_run 1
 
 # det config

docker/hubserving/README.md → deploy/docker/hubserving/README.md

@@ -20,7 +20,7 @@ git clone https://github.com/PaddlePaddle/PaddleOCR.git
 ```
 b. Goto Dockerfile directory（ps：Need to distinguish between cpu and gpu version, the following takes cpu as an example, gpu version needs to replace the keyword）
 ```
-cd docker/cpu
+cd deploy/docker/cpu
 ```
 c. Build image
 ```

docker/hubserving/README_cn.md → deploy/docker/hubserving/README_cn.md

@@ -20,7 +20,7 @@ git clone https://github.com/PaddlePaddle/PaddleOCR.git
 ```
 b.切换至Dockerfile目录（注：需要区分cpu或gpu版本，下文以cpu为例，gpu版本需要替换一下关键字即可）
 ```
-cd docker/cpu
+cd deploy/docker/cpu
 ```
 c.生成镜像
 ```

deploy/slim/quantization/README.md

+> 运行示例前请先安装1.2.0或更高版本PaddleSlim
+
+# 模型量化压缩教程
+
+## 概述
+
+该示例使用PaddleSlim提供的[量化压缩API](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)对OCR模型进行压缩。
+在阅读该示例前，建议您先了解以下内容：
+
+- [OCR模型的常规训练方法](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/detection.md)
+- [PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)
+
+## 安装PaddleSlim
+可按照[PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)中的步骤安装PaddleSlim。
+
+
+
+## 量化训练
+
+进入PaddleOCR根目录，通过以下命令对模型进行量化：
+
+```bash
+python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=det_mv3_db/best_accuracy Global.save_model_dir=./output/quant_model
+```
+
+
+
+## 评估并导出
+
+在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
+
+```bash
+python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_model
+```

deploy/slim/quantization/export_model.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 os
+import sys
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
+sys.path.append(
+    os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
+
+
+def set_paddle_flags(**kwargs):
+    for key, value in kwargs.items():
+        if os.environ.get(key, None) is None:
+            os.environ[key] = str(value)
+
+
+# NOTE(paddle-dev): All of these flags should be
+# set before `import paddle`. Otherwise, it would
+# not take any effect.
+set_paddle_flags(
+    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
+)
+
+import program
+from paddle import fluid
+from ppocr.utils.utility import initial_logger
+logger = initial_logger()
+from ppocr.utils.save_load import init_model, load_params
+from ppocr.utils.character import CharacterOps
+from ppocr.utils.utility import create_module
+from ppocr.data.reader_main import reader_main
+
+from paddleslim.quant import quant_aware, convert
+from paddle.fluid.layer_helper import LayerHelper
+from eval_utils.eval_det_utils import eval_det_run
+from eval_utils.eval_rec_utils import eval_rec_run
+
+
+def main():
+    # 1. quantization configs
+    quant_config = {
+        # weight quantize type, default is 'channel_wise_abs_max'
+        'weight_quantize_type': 'channel_wise_abs_max',
+        # activation quantize type, default is 'moving_average_abs_max'
+        'activation_quantize_type': 'moving_average_abs_max',
+        # weight quantize bit num, default is 8
+        'weight_bits': 8,
+        # activation quantize bit num, default is 8
+        'activation_bits': 8,
+        # ops of name_scope in not_quant_pattern list, will not be quantized
+        'not_quant_pattern': ['skip_quant'],
+        # ops of type in quantize_op_types, will be quantized
+        'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'],
+        # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
+        'dtype': 'int8',
+        # window size for 'range_abs_max' quantization. defaulf is 10000
+        'window_size': 10000,
+        # The decay coefficient of moving average, default is 0.9
+        'moving_rate': 0.9,
+    }
+
+    startup_prog, eval_program, place, config, alg_type = program.preprocess()
+
+    feeded_var_names, target_vars, fetches_var_name = program.build_export(
+        config, eval_program, startup_prog)
+
+    eval_program = eval_program.clone(for_test=True)
+    exe = fluid.Executor(place)
+    exe.run(startup_prog)
+
+    eval_program = quant_aware(
+        eval_program, place, quant_config, scope=None, for_test=True)
+
+    init_model(config, eval_program, exe)
+
+    # 2. Convert the program before save inference program
+    #    The dtype of eval_program's weights is float32, but in int8 range.
+
+    eval_program = convert(eval_program, place, quant_config, scope=None)
+
+    eval_fetch_name_list = fetches_var_name
+    eval_fetch_varname_list = [v.name for v in target_vars]
+    eval_reader = reader_main(config=config, mode="eval")
+    quant_info_dict = {'program':eval_program,\
+        'reader':eval_reader,\
+        'fetch_name_list':eval_fetch_name_list,\
+        'fetch_varname_list':eval_fetch_varname_list}
+
+    if alg_type == 'det':
+        final_metrics = eval_det_run(exe, config, quant_info_dict, "eval")
+    else:
+        final_metrics = eval_rec_run(exe, config, quant_info_dict, "eval")
+    print(final_metrics)
+
+    # 3. Save inference model
+    model_path = "./quant_model"
+    if not os.path.isdir(model_path):
+        os.makedirs(model_path)
+
+    fluid.io.save_inference_model(
+        dirname=model_path,
+        feeded_var_names=feeded_var_names,
+        target_vars=target_vars,
+        executor=exe,
+        main_program=eval_program,
+        model_filename=model_path + '/model',
+        params_filename=model_path + '/params')
+    print("model saved as {}".format(model_path))
+
+
+if __name__ == '__main__':
+    main()

deploy/slim/quantization/quant.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 os
+import sys
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
+sys.path.append(
+    os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
+
+
+def set_paddle_flags(**kwargs):
+    for key, value in kwargs.items():
+        if os.environ.get(key, None) is None:
+            os.environ[key] = str(value)
+
+
+# NOTE(paddle-dev): All of these flags should be
+# set before `import paddle`. Otherwise, it would
+# not take any effect.
+set_paddle_flags(
+    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
+)
+
+import tools.program as program
+from paddle import fluid
+from ppocr.utils.utility import initial_logger
+logger = initial_logger()
+from ppocr.data.reader_main import reader_main
+from ppocr.utils.save_load import init_model
+from paddle.fluid.contrib.model_stat import summary
+
+# quant dependencies
+import paddle
+import paddle.fluid as fluid
+from paddleslim.quant import quant_aware, convert
+from paddle.fluid.layer_helper import LayerHelper
+
+
+def pact(x):
+    """
+    Process a variable using the pact method you define
+    Args:
+        x(Tensor): Paddle Tensor, need to be preprocess before quantization
+    Returns:
+        The processed Tensor x.
+    """
+    helper = LayerHelper("pact", **locals())
+    dtype = 'float32'
+    init_thres = 20
+    u_param_attr = fluid.ParamAttr(
+        name=x.name + '_pact',
+        initializer=fluid.initializer.ConstantInitializer(value=init_thres),
+        regularizer=fluid.regularizer.L2Decay(0.0001),
+        learning_rate=1)
+    u_param = helper.create_parameter(attr=u_param_attr, shape=[1], dtype=dtype)
+    x = fluid.layers.elementwise_sub(
+        x, fluid.layers.relu(fluid.layers.elementwise_sub(x, u_param)))
+    x = fluid.layers.elementwise_add(
+        x, fluid.layers.relu(fluid.layers.elementwise_sub(-u_param, x)))
+    return x
+
+
+def get_optimizer():
+    """
+    Build a program using a model and an optimizer
+    """
+    return fluid.optimizer.AdamOptimizer(0.001)
+
+
+def main():
+    train_build_outputs = program.build(
+        config, train_program, startup_program, mode='train')
+    train_loader = train_build_outputs[0]
+    train_fetch_name_list = train_build_outputs[1]
+    train_fetch_varname_list = train_build_outputs[2]
+    train_opt_loss_name = train_build_outputs[3]
+    model_average = train_build_outputs[-1]
+
+    eval_program = fluid.Program()
+    eval_build_outputs = program.build(
+        config, eval_program, startup_program, mode='eval')
+    eval_fetch_name_list = eval_build_outputs[1]
+    eval_fetch_varname_list = eval_build_outputs[2]
+    eval_program = eval_program.clone(for_test=True)
+
+    train_reader = reader_main(config=config, mode="train")
+    train_loader.set_sample_list_generator(train_reader, places=place)
+
+    eval_reader = reader_main(config=config, mode="eval")
+
+    exe = fluid.Executor(place)
+    exe.run(startup_program)
+
+    # 1. quantization configs
+    quant_config = {
+        # weight quantize type, default is 'channel_wise_abs_max'
+        'weight_quantize_type': 'channel_wise_abs_max',
+        # activation quantize type, default is 'moving_average_abs_max'
+        'activation_quantize_type': 'moving_average_abs_max',
+        # weight quantize bit num, default is 8
+        'weight_bits': 8,
+        # activation quantize bit num, default is 8
+        'activation_bits': 8,
+        # ops of name_scope in not_quant_pattern list, will not be quantized
+        'not_quant_pattern': ['skip_quant'],
+        # ops of type in quantize_op_types, will be quantized
+        'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'],
+        # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
+        'dtype': 'int8',
+        # window size for 'range_abs_max' quantization. defaulf is 10000
+        'window_size': 10000,
+        # The decay coefficient of moving average, default is 0.9
+        'moving_rate': 0.9,
+    }
+
+    # 2. quantization transform programs (training aware)
+    #    Make some quantization transforms in the graph before training and testing.
+    #    According to the weight and activation quantization type, the graph will be added
+    #    some fake quantize operators and fake dequantize operators.
+    act_preprocess_func = pact
+    optimizer_func = get_optimizer
+    executor = exe
+
+    eval_program = quant_aware(
+        eval_program,
+        place,
+        quant_config,
+        scope=None,
+        act_preprocess_func=act_preprocess_func,
+        optimizer_func=optimizer_func,
+        executor=executor,
+        for_test=True)
+    quant_train_program = quant_aware(
+        train_program,
+        place,
+        quant_config,
+        scope=None,
+        act_preprocess_func=act_preprocess_func,
+        optimizer_func=optimizer_func,
+        executor=executor,
+        for_test=False,
+        return_program=True)
+
+    # compile program for multi-devices
+    train_compile_program = program.create_multi_devices_program(
+        quant_train_program, train_opt_loss_name, for_quant=True)
+
+    init_model(config, quant_train_program, exe)
+
+    train_info_dict = {'compile_program':train_compile_program,\
+        'train_program':quant_train_program,\
+        'reader':train_loader,\
+        'fetch_name_list':train_fetch_name_list,\
+        'fetch_varname_list':train_fetch_varname_list,\
+        'model_average': model_average}
+
+    eval_info_dict = {'program':eval_program,\
+        'reader':eval_reader,\
+        'fetch_name_list':eval_fetch_name_list,\
+        'fetch_varname_list':eval_fetch_varname_list}
+
+    if train_alg_type == 'det':
+        program.train_eval_det_run(config, exe, train_info_dict, eval_info_dict)
+    else:
+        program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
+
+
+if __name__ == '__main__':
+    startup_program, train_program, place, config, train_alg_type = program.preprocess(
+    )
+    main()

doc/doc_ch/recognition.md

@@ -140,7 +140,7 @@ PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入
 
 训练过程中每种扰动方式以50%的概率被选择，具体代码实现请参考：[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
 
-*由于OpenCV的兼容性问题，扰动操作暂时只支持GPU*
+*由于OpenCV的兼容性问题，扰动操作暂时只支持Linux*
 
 - 训练
 

doc/doc_ch/serving.md

@@ -61,6 +61,14 @@ hub install deploy\hubserving\ocr_rec\
 hub install deploy\hubserving\ocr_system\
 ```
 
+#### 安装模型
+安装服务模块前，需要将训练好的模型放到对应的文件夹内。默认使用的是：
+./inference/ch_det_mv3_db/
+和
+./inference/ch_rec_mv3_crnn/
+这两个模型可以在https://github.com/PaddlePaddle/PaddleOCR 下载
+可以在./deploy/hubserving/ocr_system/params.py 里面修改成自己的模型
+
 ### 3. 启动服务
 #### 方式1. 命令行命令启动（仅支持CPU）
 **启动命令：**  

docker/hubserving/readme.md

-English | [简体中文](README_cn.md)
-
-## Introduction
-Many user hopes package the PaddleOCR service into an docker image, so that it can be quickly released and used in the docker or k8s environment.
-
-This page provide some standardized code to achieve this goal. You can quickly publish the PaddleOCR project into a callable Restful API service through the following steps. (At present, the deployment based on the HubServing mode is implemented first, and author plans to increase the deployment of the PaddleServing mode in the futrue)
-
-## 1. Prerequisites
-
-You need to install the following basic components first：
-a. Docker
-b. Graphics driver and CUDA 10.0+（GPU）
-c. NVIDIA Container Toolkit（GPU，Docker 19.03+ can skip this）
-d. cuDNN 7.6+（GPU）
-
-## 2. Build Image
-a. Download PaddleOCR sourcecode
-```
-git clone https://github.com/PaddlePaddle/PaddleOCR.git
-```
-b. Goto Dockerfile directory（ps：Need to distinguish between cpu and gpu version, the following takes cpu as an example, gpu version needs to replace the keyword）
-```
-cd docker/cpu
-```
-c. Build image
-```
-docker build -t paddleocr:cpu . 
-```
-
-## 3. Start container
-a. CPU version
-```
-sudo docker run -dp 8866:8866 --name paddle_ocr paddleocr:cpu
-```
-b. GPU version (base on NVIDIA Container Toolkit)
-```
-sudo nvidia-docker run -dp 8866:8866 --name paddle_ocr paddleocr:gpu
-```
-c. GPU version (Docker 19.03++)
-```
-sudo docker run -dp 8866:8866 --gpus all --name paddle_ocr paddleocr:gpu
-```
-d. Check service status（If you can see the following statement then it means completed：Successfully installed ocr_system && Running on http://0.0.0.0:8866/）
-```
-docker logs -f paddle_ocr
-```
-
-## 4. Test
-a. Calculate the Base64 encoding of the picture to be recognized (if you just test, you can use a free online tool, like：https://freeonlinetools24.com/base64-image/）
-b. Post a service request（sample request in sample_request.txt）
-
-```
-curl -H "Content-Type:application/json" -X POST --data "{\"images\": [\"Input image Base64 encode(need to delete the code 'data:image/jpg;base64,'）\"]}" http://localhost:8866/predict/ocr_system
-```
-c. Get resposne（If the call is successful, the following result will be returned）
-```
-{"msg":"","results":[[{"confidence":0.8403433561325073,"text":"约定","text_region":[[345,377],[641,390],[634,540],[339,528]]},{"confidence":0.8131805658340454,"text":"最终相遇","text_region":[[356,532],[624,530],[624,596],[356,598]]}]],"status":"0"}
-```

ppocr/data/det/random_crop_data.py

-# -*- coding:utf-8 -*- 
+# -*- coding:utf-8 -*-
 
 from __future__ import absolute_import
 from __future__ import division
@@ -121,24 +121,22 @@ def RandomCropData(data, size):
     all_care_polys = [
         text_polys[i] for i, tag in enumerate(ignore_tags) if not tag
     ]
-    # 计算crop区域
     crop_x, crop_y, crop_w, crop_h = crop_area(im, all_care_polys,
                                                min_crop_side_ratio, max_tries)
-    # crop 图片 保持比例填充
-    scale_w = size[0] / crop_w
-    scale_h = size[1] / crop_h
+    dh, dw = size
+    scale_w = dw / crop_w
+    scale_h = dh / crop_h
     scale = min(scale_w, scale_h)
     h = int(crop_h * scale)
     w = int(crop_w * scale)
     if keep_ratio:
-        padimg = np.zeros((size[1], size[0], im.shape[2]), im.dtype)
+        padimg = np.zeros((dh, dw, im.shape[2]), im.dtype)
         padimg[:h, :w] = cv2.resize(
             im[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w], (w, h))
         img = padimg
     else:
         img = cv2.resize(im[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w],
-                         tuple(size))
-    # crop 文本框
+                         (dw, dh))
     text_polys_crop = []
     ignore_tags_crop = []
     texts_crop = []

ppocr/modeling/architectures/det_model.py

@@ -67,6 +67,7 @@ class DetModel(object):
 
         image = fluid.layers.data(
             name='image', shape=image_shape, dtype='float32')
+        image.stop_gradient = False
         if mode == "train":
             if self.algorithm == "EAST":
                 h, w = int(image_shape[1] // 4), int(image_shape[2] // 4)
@@ -108,7 +109,10 @@ class DetModel(object):
                     name='tvo', shape=[9, 128, 128], dtype='float32')
                 input_tco = fluid.layers.data(
                     name='tco', shape=[3, 128, 128], dtype='float32')
-                feed_list = [image, input_score, input_border, input_mask, input_tvo, input_tco]
+                feed_list = [
+                    image, input_score, input_border, input_mask, input_tvo,
+                    input_tco
+                ]
                 labels = {'input_score': input_score,\
                     'input_border': input_border,\
                     'input_mask': input_mask,\

ppocr/modeling/architectures/rec_model.py

@@ -68,6 +68,7 @@ class RecModel(object):
         image_shape.insert(0, -1)
         if mode == "train":
             image = fluid.data(name='image', shape=image_shape, dtype='float32')
+            image.stop_gradient = False
             if self.loss_type == "attention":
                 label_in = fluid.data(
                     name='label_in',
@@ -136,7 +137,7 @@ class RecModel(object):
         else:
             labels = None
             loader = None
-            if self.char_type == "ch" and self.infer_img:
+            if self.char_type == "ch" and self.infer_img and self.loss_type != "srn":
                 image_shape[-1] = -1
                 if self.tps != None:
                     logger.info(
@@ -146,6 +147,7 @@ class RecModel(object):
                     )
                     image_shape = deepcopy(self.image_shape)
             image = fluid.data(name='image', shape=image_shape, dtype='float32')
+            image.stop_gradient = False
             if self.loss_type == "srn":
                 encoder_word_pos = fluid.data(
                     name="encoder_word_pos",
@@ -172,16 +174,13 @@ class RecModel(object):
                         self.max_text_length
                     ],
                     dtype="float32")
-                feed_list = [
-                    image, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
-                    gsrm_slf_attn_bias2
-                ]
                 labels = {
                     'encoder_word_pos': encoder_word_pos,
                     'gsrm_word_pos': gsrm_word_pos,
                     'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1,
                     'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2
                 }
+
         return image, labels, loader
 
     def __call__(self, mode):
@@ -218,8 +217,13 @@ class RecModel(object):
             if self.loss_type == "ctc":
                 predict = fluid.layers.softmax(predict)
             if self.loss_type == "srn":
-                raise Exception(
-                    "Warning! SRN does not support export model currently")
+                return [
+                    image, labels, {
+                        'decoded_out': decoded_out,
+                        'predicts': predict
+                    }
+                ]
+
             return [image, {'decoded_out': decoded_out, 'predicts': predict}]
         else:
             predict = predicts['predict']

ppocr/modeling/heads/rec_ctc_head.py

@@ -35,12 +35,13 @@ class CTCPredict(object):
         self.fc_decay = params.get("fc_decay", 0.0004)
 
     def __call__(self, inputs, labels=None, mode=None):
-        encoder_features = self.encoder(inputs)
-        if self.encoder_type != "reshape":
-            encoder_features = fluid.layers.concat(encoder_features, axis=1)
-        name = "ctc_fc"
-        para_attr, bias_attr = get_para_bias_attr(
-            l2_decay=self.fc_decay, k=encoder_features.shape[1], name=name)
+        with fluid.scope_guard("skip_quant"):
+            encoder_features = self.encoder(inputs)
+            if self.encoder_type != "reshape":
+                encoder_features = fluid.layers.concat(encoder_features, axis=1)
+            name = "ctc_fc"
+            para_attr, bias_attr = get_para_bias_attr(
+                l2_decay=self.fc_decay, k=encoder_features.shape[1], name=name)
         predict = fluid.layers.fc(input=encoder_features,
                                   size=self.char_num + 1,
                                   param_attr=para_attr,

ppocr/utils/utility.py

@@ -90,15 +90,3 @@ def check_and_read_gif(img_path):
         return imgvalue, True
     return None, False
 
-
-def create_multi_devices_program(program, loss_var_name):
-    build_strategy = fluid.BuildStrategy()
-    build_strategy.memory_optimize = False
-    build_strategy.enable_inplace = True
-    exec_strategy = fluid.ExecutionStrategy()
-    exec_strategy.num_iteration_per_drop_scope = 1
-    compile_program = fluid.CompiledProgram(program).with_data_parallel(
-        loss_name=loss_var_name,
-        build_strategy=build_strategy,
-        exec_strategy=exec_strategy)
-    return compile_program

tools/infer/predict_rec.py

@@ -40,6 +40,7 @@ class TextRecognizer(object):
         self.character_type = args.rec_char_type
         self.rec_batch_num = args.rec_batch_num
         self.rec_algorithm = args.rec_algorithm
+        self.text_len = args.max_text_length
         self.use_zero_copy_run = args.use_zero_copy_run
         char_ops_params = {
             "character_type": args.rec_char_type,
@@ -47,12 +48,15 @@ class TextRecognizer(object):
             "use_space_char": args.use_space_char,
             "max_text_length": args.max_text_length
         }
-        if self.rec_algorithm != "RARE":
+        if self.rec_algorithm in ["CRNN", "Rosetta", "STAR-Net"]:
             char_ops_params['loss_type'] = 'ctc'
             self.loss_type = 'ctc'
-        else:
+        elif self.rec_algorithm == "RARE":
             char_ops_params['loss_type'] = 'attention'
             self.loss_type = 'attention'
+        elif self.rec_algorithm == "SRN":
+            char_ops_params['loss_type'] = 'srn'
+            self.loss_type = 'srn'
         self.char_ops = CharacterOps(char_ops_params)
 
     def resize_norm_img(self, img, max_wh_ratio):
@@ -75,6 +79,83 @@ class TextRecognizer(object):
         padding_im[:, :, 0:resized_w] = resized_image
         return padding_im
 
+    def resize_norm_img_srn(self, img, image_shape):
+        imgC, imgH, imgW = image_shape
+
+        img_black = np.zeros((imgH, imgW))
+        im_hei = img.shape[0]
+        im_wid = img.shape[1]
+
+        if im_wid <= im_hei * 1:
+            img_new = cv2.resize(img, (imgH * 1, imgH))
+        elif im_wid <= im_hei * 2:
+            img_new = cv2.resize(img, (imgH * 2, imgH))
+        elif im_wid <= im_hei * 3:
+            img_new = cv2.resize(img, (imgH * 3, imgH))
+        else:
+            img_new = cv2.resize(img, (imgW, imgH))
+
+        img_np = np.asarray(img_new)
+        img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
+        img_black[:, 0:img_np.shape[1]] = img_np
+        img_black = img_black[:, :, np.newaxis]
+
+        row, col, c = img_black.shape
+        c = 1
+
+        return np.reshape(img_black, (c, row, col)).astype(np.float32)
+
+    def srn_other_inputs(self, image_shape, num_heads, max_text_length,
+                         char_num):
+
+        imgC, imgH, imgW = image_shape
+        feature_dim = int((imgH / 8) * (imgW / 8))
+
+        encoder_word_pos = np.array(range(0, feature_dim)).reshape(
+            (feature_dim, 1)).astype('int64')
+        gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
+            (max_text_length, 1)).astype('int64')
+
+        gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
+        gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
+            [-1, 1, max_text_length, max_text_length])
+        gsrm_slf_attn_bias1 = np.tile(
+            gsrm_slf_attn_bias1,
+            [1, num_heads, 1, 1]).astype('float32') * [-1e9]
+
+        gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
+            [-1, 1, max_text_length, max_text_length])
+        gsrm_slf_attn_bias2 = np.tile(
+            gsrm_slf_attn_bias2,
+            [1, num_heads, 1, 1]).astype('float32') * [-1e9]
+
+        encoder_word_pos = encoder_word_pos[np.newaxis, :]
+        gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
+
+        return [
+            encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+            gsrm_slf_attn_bias2
+        ]
+
+    def process_image_srn(self,
+                          img,
+                          image_shape,
+                          num_heads,
+                          max_text_length,
+                          char_ops=None):
+        norm_img = self.resize_norm_img_srn(img, image_shape)
+        norm_img = norm_img[np.newaxis, :]
+        char_num = char_ops.get_char_num()
+
+        [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
+            self.srn_other_inputs(image_shape, num_heads, max_text_length, char_num)
+
+        gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
+        gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
+
+        return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+                gsrm_slf_attn_bias2)
+
     def __call__(self, img_list):
         img_num = len(img_list)
         # Calculate the aspect ratio of all text bars
@@ -84,7 +165,7 @@ class TextRecognizer(object):
         # Sorting can speed up the recognition process
         indices = np.argsort(np.array(width_list))
 
-        # rec_res = []
+        #rec_res = []
         rec_res = [['', 0.0]] * img_num
         batch_num = self.rec_batch_num
         predict_time = 0
@@ -98,20 +179,62 @@ class TextRecognizer(object):
                 wh_ratio = w * 1.0 / h
                 max_wh_ratio = max(max_wh_ratio, wh_ratio)
             for ino in range(beg_img_no, end_img_no):
-                # norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
-                norm_img = self.resize_norm_img(img_list[indices[ino]],
-                                                max_wh_ratio)
-                norm_img = norm_img[np.newaxis, :]
-                norm_img_batch.append(norm_img)
-            norm_img_batch = np.concatenate(norm_img_batch)
+                if self.loss_type != "srn":
+                    norm_img = self.resize_norm_img(img_list[indices[ino]],
+                                                    max_wh_ratio)
+                    norm_img = norm_img[np.newaxis, :]
+                    norm_img_batch.append(norm_img)
+                else:
+                    norm_img = self.process_image_srn(img_list[indices[ino]],
+                                                      self.rec_image_shape, 8,
+                                                      25, self.char_ops)
+                    encoder_word_pos_list = []
+                    gsrm_word_pos_list = []
+                    gsrm_slf_attn_bias1_list = []
+                    gsrm_slf_attn_bias2_list = []
+                    encoder_word_pos_list.append(norm_img[1])
+                    gsrm_word_pos_list.append(norm_img[2])
+                    gsrm_slf_attn_bias1_list.append(norm_img[3])
+                    gsrm_slf_attn_bias2_list.append(norm_img[4])
+                    norm_img_batch.append(norm_img[0])
+
+            norm_img_batch = np.concatenate(norm_img_batch, axis=0)
             norm_img_batch = norm_img_batch.copy()
-            starttime = time.time()
-            if self.use_zero_copy_run:
-                self.input_tensor.copy_from_cpu(norm_img_batch)
-                self.predictor.zero_copy_run()
-            else:
+
+            if self.loss_type == "srn":
+                starttime = time.time()
+                encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
+                gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
+                gsrm_slf_attn_bias1_list = np.concatenate(
+                    gsrm_slf_attn_bias1_list)
+                gsrm_slf_attn_bias2_list = np.concatenate(
+                    gsrm_slf_attn_bias2_list)
+                starttime = time.time()
+
                 norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
-                self.predictor.run([norm_img_batch])
+                encoder_word_pos_list = fluid.core.PaddleTensor(
+                    encoder_word_pos_list)
+                gsrm_word_pos_list = fluid.core.PaddleTensor(gsrm_word_pos_list)
+                gsrm_slf_attn_bias1_list = fluid.core.PaddleTensor(
+                    gsrm_slf_attn_bias1_list)
+                gsrm_slf_attn_bias2_list = fluid.core.PaddleTensor(
+                    gsrm_slf_attn_bias2_list)
+
+                inputs = [
+                    norm_img_batch, encoder_word_pos_list,
+                    gsrm_slf_attn_bias1_list, gsrm_slf_attn_bias2_list,
+                    gsrm_word_pos_list
+                ]
+
+                self.predictor.run(inputs)
+            else:
+                starttime = time.time()
+                if self.use_zero_copy_run:
+                    self.input_tensor.copy_from_cpu(norm_img_batch)
+                    self.predictor.zero_copy_run()
+                else:
+                    norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
+                    self.predictor.run([norm_img_batch])
 
             if self.loss_type == "ctc":
                 rec_idx_batch = self.output_tensors[0].copy_to_cpu()
@@ -136,6 +259,26 @@ class TextRecognizer(object):
                     score = np.mean(probs[valid_ind, ind[valid_ind]])
                     # rec_res.append([preds_text, score])
                     rec_res[indices[beg_img_no + rno]] = [preds_text, score]
+            elif self.loss_type == 'srn':
+                rec_idx_batch = self.output_tensors[0].copy_to_cpu()
+                probs = self.output_tensors[1].copy_to_cpu()
+                char_num = self.char_ops.get_char_num()
+                preds = rec_idx_batch.reshape(-1)
+                elapse = time.time() - starttime
+                predict_time += elapse
+                total_preds = preds.copy()
+                for ino in range(int(len(rec_idx_batch) / self.text_len)):
+                    preds = total_preds[ino * self.text_len:(ino + 1) *
+                                        self.text_len]
+                    ind = np.argmax(probs, axis=1)
+                    valid_ind = np.where(preds != int(char_num - 1))[0]
+                    if len(valid_ind) == 0:
+                        continue
+                    score = np.mean(probs[valid_ind, ind[valid_ind]])
+                    preds = preds[:valid_ind[-1] + 1]
+                    preds_text = self.char_ops.decode(preds)
+
+                    rec_res[indices[beg_img_no + ino]] = [preds_text, score]
             else:
                 rec_idx_batch = self.output_tensors[0].copy_to_cpu()
                 predict_batch = self.output_tensors[1].copy_to_cpu()
@@ -170,6 +313,7 @@ def main(args):
             continue
         valid_image_file_list.append(image_file)
         img_list.append(img)
+
     try:
         rec_res, predict_time = text_recognizer(img_list)
     except Exception as e:

tools/infer/predict_system.py

@@ -122,7 +122,6 @@ def main(args):
     image_file_list = get_image_file_list(args.image_dir)
     text_sys = TextSystem(args)
     is_visualize = True
-    tackle_img_num = 0
     for image_file in image_file_list:
         img, flag = check_and_read_gif(image_file)
         if not flag:
@@ -131,9 +130,6 @@ def main(args):
             logger.info("error in loading image:{}".format(image_file))
             continue
         starttime = time.time()
-        tackle_img_num += 1
-        if not args.use_gpu and args.enable_mkldnn and tackle_img_num % 30 == 0:
-            text_sys = TextSystem(args)
         dt_boxes, rec_res = text_sys(img)
         elapse = time.time() - starttime
         print("Predict time of %s: %.3fs" % (image_file, elapse))
@@ -153,11 +149,7 @@ def main(args):
             scores = [rec_res[i][1] for i in range(len(rec_res))]
 
             draw_img = draw_ocr(
-                image,
-                boxes,
-                txts,
-                scores,
-                drop_score=drop_score)
+                image, boxes, txts, scores, drop_score=drop_score)
             draw_img_save = "./inference_results/"
             if not os.path.exists(draw_img_save):
                 os.makedirs(draw_img_save)

tools/infer/utility.py

@@ -101,6 +101,8 @@ def create_predictor(args, mode):
         config.disable_gpu()
         config.set_cpu_math_library_num_threads(6)
         if args.enable_mkldnn:
+            # cache 10 different shapes for mkldnn to avoid memory leak
+            config.set_mkldnn_cache_capacity(10)
             config.enable_mkldnn()
 
     #config.enable_memory_optim()
@@ -114,7 +116,8 @@ def create_predictor(args, mode):
 
     predictor = create_paddle_predictor(config)
     input_names = predictor.get_input_names()
-    input_tensor = predictor.get_input_tensor(input_names[0])
+    for name in input_names:
+        input_tensor = predictor.get_input_tensor(name)
     output_names = predictor.get_output_names()
     output_tensors = []
     for output_name in output_names:

tools/infer_rec.py

@@ -145,7 +145,7 @@ def main():
             preds = preds.reshape(-1)
             probs = np.array(predict[1])
             ind = np.argmax(probs, axis=1)
-            valid_ind = np.where(preds != int(char_num-1))[0]
+            valid_ind = np.where(preds != int(char_num - 1))[0]
             if len(valid_ind) == 0:
                 continue
             score = np.mean(probs[valid_ind, ind[valid_ind]])

tools/program.py

@@ -208,18 +208,29 @@ def build_export(config, main_prog, startup_prog):
         with fluid.unique_name.guard():
             func_infor = config['Architecture']['function']
             model = create_module(func_infor)(params=config)
-            image, outputs = model(mode='export')
+            algorithm = config['Global']['algorithm']
+            if algorithm == "SRN":
+                image, others, outputs = model(mode='export')
+            else:
+                image, outputs = model(mode='export')
             fetches_var_name = sorted([name for name in outputs.keys()])
             fetches_var = [outputs[name] for name in fetches_var_name]
-    feeded_var_names = [image.name]
+    if algorithm == "SRN":
+        others_var_names = sorted([name for name in others.keys()])
+        feeded_var_names = [image.name] + others_var_names
+    else:
+        feeded_var_names = [image.name]
+
     target_vars = fetches_var
     return feeded_var_names, target_vars, fetches_var_name
 
 
-def create_multi_devices_program(program, loss_var_name):
+def create_multi_devices_program(program, loss_var_name, for_quant=False):
     build_strategy = fluid.BuildStrategy()
     build_strategy.memory_optimize = False
     build_strategy.enable_inplace = True
+    if for_quant:
+        build_strategy.fuse_all_reduce_ops = False
     exec_strategy = fluid.ExecutionStrategy()
     exec_strategy.num_iteration_per_drop_scope = 1
     compile_program = fluid.CompiledProgram(program).with_data_parallel(
@@ -409,7 +420,9 @@ def preprocess():
     check_gpu(use_gpu)
 
     alg = config['Global']['algorithm']
-    assert alg in ['EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN']
+    assert alg in [
+        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN'
+    ]
     if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN']:
         config['Global']['char_ops'] = CharacterOps(config['Global'])