[Serving] add ocr serving example (PaddlePaddle#627)

* add ocr serving example

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* Update README.md

* Delete ocr_pipeline.png

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Co-authored-by: Jason <[email protected]>
Co-authored-by: heliqi <[email protected]>

examples/vision/ocr/PP-OCRv3/serving/README.md

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+# PP-OCR服务化部署示例
+
+## 介绍
+本文介绍了使用FastDeploy搭建OCR文字识别服务的方法.
+
+服务端必须在docker内启动,而客户端不是必须在docker容器内.
+
+**本文所在路径($PWD)下的models里包含模型的配置和代码(服务端会加载模型和代码以启动服务), 需要将其映射到docker中使用.**
+
+OCR由det(检测)、cls(分类)和rec(识别)三个模型组成.
+
+服务化部署串联的示意图如下图所示,其中`pp_ocr`串联了`det_preprocess`、`det_runtime`和`det_postprocess`,`cls_pp`串联了`cls_runtime`和`cls_postprocess`,`rec_pp`串联了`rec_runtime`和`rec_postprocess`.
+
+特别的是,在`det_postprocess`中会多次调用`cls_pp`和`rec_pp`服务,来实现对检测结果(多个框)进行分类和识别,,最后返回给用户最终的识别结果。
+
+<p align="center">
+    <br>
+<img src='./ppocr.png'">
+    <br>
+<p>
+
+## 使用
+### 1. 服务端
+#### 1.1 Docker
+```bash
+# 下载仓库代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/ocr/PP-OCRv3/serving/
+
+# 下载模型,图片和字典文件
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
+tar xvf ch_PP-OCRv3_det_infer.tar && mv ch_PP-OCRv3_det_infer 1
+mv 1/inference.pdiparams 1/model.pdiparams && mv 1/inference.pdmodel 1/model.pdmodel
+mv 1 models/det_runtime/ && rm -rf ch_PP-OCRv3_det_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar xvf ch_ppocr_mobile_v2.0_cls_infer.tar && mv ch_ppocr_mobile_v2.0_cls_infer 1
+mv 1/inference.pdiparams 1/model.pdiparams && mv 1/inference.pdmodel 1/model.pdmodel
+mv 1 models/cls_runtime/ && rm -rf ch_ppocr_mobile_v2.0_cls_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
+tar xvf ch_PP-OCRv3_rec_infer.tar && mv ch_PP-OCRv3_rec_infer 1
+mv 1/inference.pdiparams 1/model.pdiparams && mv 1/inference.pdmodel 1/model.pdmodel
+mv 1 models/rec_runtime/ && rm -rf ch_PP-OCRv3_rec_infer.tar
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
+mv ppocr_keys_v1.txt models/rec_postprocess/1/
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
+
+
+docker pull paddlepaddle/fastdeploy:0.6.0-gpu-cuda11.4-trt8.4-21.10
+docker run -dit --net=host --name fastdeploy --shm-size="1g" -v $PWD:/ocr_serving paddlepaddle/fastdeploy:0.6.0-gpu-cuda11.4-trt8.4-21.10 bash
+docker exec -it -u root fastdeploy bash
+```
+
+#### 1.2 安装(在docker内)
+```bash
+ldconfig
+apt-get install libgl1
+```
+
+#### 1.3 启动服务端(在docker内)
+```bash
+fastdeployserver --model-repository=/ocr_serving/models
+```
+
+参数:
+  - `model-repository`(required): 整套模型streaming_pp_tts存放的路径.
+  - `http-port`(optional): HTTP服务的端口号. 默认: `8000`. 本示例中未使用该端口.
+  - `grpc-port`(optional): GRPC服务的端口号. 默认: `8001`.
+  - `metrics-port`(optional): 服务端指标的端口号. 默认: `8002`. 本示例中未使用该端口.
+
+
+### 2. 客户端
+#### 2.1 安装
+```bash
+pip3 install tritonclient[all]
+```
+
+#### 2.2 发送请求
+```bash
+python3 client.py
+```
+
+## 配置修改
+
+当前默认配置在GPU上运行， 如果要在CPU或其他推理引擎上运行。 需要修改`models/runtime/config.pbtxt`中配置，详情请参考[配置文档](../../../../../serving/docs/zh_CN/model_configuration.md)

examples/vision/ocr/PP-OCRv3/serving/client.py

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+import logging
+import numpy as np
+import time
+from typing import Optional
+import cv2
+import json
+
+from tritonclient import utils as client_utils
+from tritonclient.grpc import InferenceServerClient, InferInput, InferRequestedOutput, service_pb2_grpc, service_pb2
+
+LOGGER = logging.getLogger("run_inference_on_triton")
+
+
+class SyncGRPCTritonRunner:
+    DEFAULT_MAX_RESP_WAIT_S = 120
+
+    def __init__(
+            self,
+            server_url: str,
+            model_name: str,
+            model_version: str,
+            *,
+            verbose=False,
+            resp_wait_s: Optional[float]=None, ):
+        self._server_url = server_url
+        self._model_name = model_name
+        self._model_version = model_version
+        self._verbose = verbose
+        self._response_wait_t = self.DEFAULT_MAX_RESP_WAIT_S if resp_wait_s is None else resp_wait_s
+
+        self._client = InferenceServerClient(
+            self._server_url, verbose=self._verbose)
+        error = self._verify_triton_state(self._client)
+        if error:
+            raise RuntimeError(
+                f"Could not communicate to Triton Server: {error}")
+
+        LOGGER.debug(
+            f"Triton server {self._server_url} and model {self._model_name}:{self._model_version} "
+            f"are up and ready!")
+
+        model_config = self._client.get_model_config(self._model_name,
+                                                     self._model_version)
+        model_metadata = self._client.get_model_metadata(self._model_name,
+                                                         self._model_version)
+        LOGGER.info(f"Model config {model_config}")
+        LOGGER.info(f"Model metadata {model_metadata}")
+
+        self._inputs = {tm.name: tm for tm in model_metadata.inputs}
+        self._input_names = list(self._inputs)
+        self._outputs = {tm.name: tm for tm in model_metadata.outputs}
+        self._output_names = list(self._outputs)
+        self._outputs_req = [
+            InferRequestedOutput(name) for name in self._outputs
+        ]
+
+    def Run(self, inputs):
+        """
+        Args:
+            inputs: list, Each value corresponds to an input name of self._input_names
+        Returns:
+            results: dict, {name : numpy.array}
+        """
+        infer_inputs = []
+        for idx, data in enumerate(inputs):
+            infer_input = InferInput(self._input_names[idx], data.shape,
+                                     "UINT8")
+            infer_input.set_data_from_numpy(data)
+            infer_inputs.append(infer_input)
+
+        results = self._client.infer(
+            model_name=self._model_name,
+            model_version=self._model_version,
+            inputs=infer_inputs,
+            outputs=self._outputs_req,
+            client_timeout=self._response_wait_t, )
+        results = {name: results.as_numpy(name) for name in self._output_names}
+        return results
+
+    def _verify_triton_state(self, triton_client):
+        if not triton_client.is_server_live():
+            return f"Triton server {self._server_url} is not live"
+        elif not triton_client.is_server_ready():
+            return f"Triton server {self._server_url} is not ready"
+        elif not triton_client.is_model_ready(self._model_name,
+                                              self._model_version):
+            return f"Model {self._model_name}:{self._model_version} is not ready"
+        return None
+
+
+if __name__ == "__main__":
+    model_name = "pp_ocr"
+    model_version = "1"
+    url = "localhost:9001"
+    runner = SyncGRPCTritonRunner(url, model_name, model_version)
+    im = cv2.imread("12.jpg")
+    im = np.array([im, ])
+    for i in range(1):
+        result = runner.Run([im, ])
+        batch_texts = result['rec_texts']
+        batch_scores = result['rec_scores']
+        for i_batch in range(len(batch_texts)):
+            texts = batch_texts[i_batch]
+            scores = batch_scores[i_batch]
+            for i_box in range(len(texts)):
+                print('text=', texts[i_box].decode('utf-8'), '  score=',
+                      scores[i_box])

examples/vision/ocr/PP-OCRv3/serving/models/cls_postprocess/1/model.py

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+# Copyright (c) 2022 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.
+
+import json
+import numpy as np
+import time
+
+import fastdeploy as fd
+
+# triton_python_backend_utils is available in every Triton Python model. You
+# need to use this module to create inference requests and responses. It also
+# contains some utility functions for extracting information from model_config
+# and converting Triton input/output types to numpy types.
+import triton_python_backend_utils as pb_utils
+
+
+class TritonPythonModel:
+    """Your Python model must use the same class name. Every Python model
+    that is created must have "TritonPythonModel" as the class name.
+    """
+
+    def initialize(self, args):
+        """`initialize` is called only once when the model is being loaded.
+        Implementing `initialize` function is optional. This function allows
+        the model to intialize any state associated with this model.
+        Parameters
+        ----------
+        args : dict
+          Both keys and values are strings. The dictionary keys and values are:
+          * model_config: A JSON string containing the model configuration
+          * model_instance_kind: A string containing model instance kind
+          * model_instance_device_id: A string containing model instance device ID
+          * model_repository: Model repository path
+          * model_version: Model version
+          * model_name: Model name
+        """
+        # You must parse model_config. JSON string is not parsed here
+        self.model_config = json.loads(args['model_config'])
+        print("model_config:", self.model_config)
+
+        self.input_names = []
+        for input_config in self.model_config["input"]:
+            self.input_names.append(input_config["name"])
+        print("postprocess input names:", self.input_names)
+
+        self.output_names = []
+        self.output_dtype = []
+        for output_config in self.model_config["output"]:
+            self.output_names.append(output_config["name"])
+            dtype = pb_utils.triton_string_to_numpy(output_config["data_type"])
+            self.output_dtype.append(dtype)
+        print("postprocess output names:", self.output_names)
+        self.postprocessor = fd.vision.ocr.ClassifierPostprocessor()
+
+    def execute(self, requests):
+        """`execute` must be implemented in every Python model. `execute`
+        function receives a list of pb_utils.InferenceRequest as the only
+        argument. This function is called when an inference is requested
+        for this model. Depending on the batching configuration (e.g. Dynamic
+        Batching) used, `requests` may contain multiple requests. Every
+        Python model, must create one pb_utils.InferenceResponse for every
+        pb_utils.InferenceRequest in `requests`. If there is an error, you can
+        set the error argument when creating a pb_utils.InferenceResponse.
+        Parameters
+        ----------
+        requests : list
+          A list of pb_utils.InferenceRequest
+        Returns
+        -------
+        list
+          A list of pb_utils.InferenceResponse. The length of this list must
+          be the same as `requests`
+        """
+        responses = []
+        for request in requests:
+            infer_outputs = pb_utils.get_input_tensor_by_name(
+                request, self.input_names[0])
+            infer_outputs = infer_outputs.as_numpy()
+            results = self.postprocessor.run([infer_outputs])
+            out_tensor_0 = pb_utils.Tensor(self.output_names[0],
+                                           np.array(results[0]))
+            out_tensor_1 = pb_utils.Tensor(self.output_names[1],
+                                           np.array(results[1]))
+            inference_response = pb_utils.InferenceResponse(
+                output_tensors=[out_tensor_0, out_tensor_1])
+            responses.append(inference_response)
+        return responses
+
+    def finalize(self):
+        """`finalize` is called only once when the model is being unloaded.
+        Implementing `finalize` function is optional. This function allows
+        the model to perform any necessary clean ups before exit.
+        """
+        print('Cleaning up...')

examples/vision/ocr/PP-OCRv3/serving/models/cls_postprocess/config.pbtxt

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+name: "cls_postprocess"
+backend: "python"
+max_batch_size: 128
+input [
+  {
+    name: "POST_INPUT_0"
+    data_type: TYPE_FP32
+    dims: [ 2 ]
+  }
+]
+
+output [
+  {
+    name: "POST_OUTPUT_0"
+    data_type: TYPE_INT32
+    dims: [ 1 ]
+  },
+  {
+    name: "POST_OUTPUT_1"
+    data_type: TYPE_FP32
+    dims: [ 1 ]
+  }
+]
+
+instance_group [
+  {
+      count: 1
+      kind: KIND_CPU
+  }
+]

examples/vision/ocr/PP-OCRv3/serving/models/cls_pp/config.pbtxt

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+name: "cls_pp"
+platform: "ensemble"
+max_batch_size: 128
+input [
+  {
+    name: "x"
+    data_type: TYPE_FP32
+    dims: [ 3, -1, -1 ]
+  }
+]
+output [
+  {
+    name: "cls_labels"
+    data_type: TYPE_INT32
+    dims: [ 1 ]
+  },
+  {
+    name: "cls_scores"
+    data_type: TYPE_FP32
+    dims: [ 1 ]
+  }
+]
+ensemble_scheduling {
+  step [
+    {
+      model_name: "cls_runtime"
+      model_version: 1
+      input_map {
+        key: "x"
+        value: "x"
+      }
+      output_map {
+        key: "softmax_0.tmp_0"
+        value: "infer_output"
+      }
+    },
+    {
+      model_name: "cls_postprocess"
+      model_version: 1
+      input_map {
+        key: "POST_INPUT_0"
+        value: "infer_output"
+      }
+      output_map {
+        key: "POST_OUTPUT_0"
+        value: "cls_labels"
+      }
+      output_map {
+        key: "POST_OUTPUT_1"
+        value: "cls_scores"
+      }
+    }
+  ]
+}