update serving
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@ -29,7 +29,9 @@ PaddleOCR提供2种服务部署方式：
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需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。
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- 准备PaddleOCR的运行环境参考[链接](../../doc/doc_ch/installation.md)
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- 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md)
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根据环境下载对应的paddle whl包，
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推荐2.0.1版本：https://www.paddlepaddle.org.cn/whl/mkl/stable.html
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- 准备PaddleServing的运行环境，步骤如下
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@ -45,25 +47,16 @@ PaddleOCR提供2种服务部署方式：
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```
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2. 安装client，用于向服务发送请求
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```
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pip3 install paddle-serving-client==0.5.0 # for CPU
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在[下载链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)中找到对应python版本的client安装包，这里推荐python3.7版本：
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pip3 install paddle-serving-client-gpu==0.5.0 # for GPU
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```
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wget https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
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pip3 install paddle_serving_client-0.0.0-cp37-none-any.whl
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```
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3. 安装serving-app
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```
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pip3 install paddle-serving-app==0.3.0
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```
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**note:** 安装0.3.0版本的serving-app后，为了能加载动态图模型，需要修改serving_app的源码，具体为：
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```
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# 找到paddle_serving_app的安装目录，找到并编辑local_predict.py文件
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vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py
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# 将local_predict.py 的第85行 config = AnalysisConfig(model_path) 替换为：
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if os.path.exists(os.path.join(model_path, "__params__")):
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config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__"))
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else:
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config = AnalysisConfig(model_path)
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pip3 install paddle-serving-app==0.3.1
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```
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**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)。
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@ -1,32 +1,32 @@
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#rpc端口, rpc_port和http_port不允许同时为空。当rpc_port为空且http_port不为空时，会自动将rpc_port设置为http_port+1
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rpc_port: 18090
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rpc_port: 18091
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#http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时，不自动生成http_port
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http_port: 9999
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http_port: 9998
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#worker_num, 最大并发数。当build_dag_each_worker=True时, 框架会创建worker_num个进程，每个进程内构建grpcSever和DAG
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##当build_dag_each_worker=False时，框架会设置主线程grpc线程池的max_workers=worker_num
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worker_num: 20
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worker_num: 5
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#build_dag_each_worker, False，框架在进程内创建一条DAG；True，框架会每个进程内创建多个独立的DAG
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build_dag_each_worker: false
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build_dag_each_worker: False
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dag:
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#op资源类型, True, 为线程模型；False，为进程模型
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is_thread_op: False
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#重试次数
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retry: 1
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retry: 10
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#使用性能分析, True，生成Timeline性能数据，对性能有一定影响；False为不使用
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use_profile: False
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use_profile: True
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tracer:
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interval_s: 10
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op:
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det:
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#并发数，is_thread_op=True时，为线程并发；否则为进程并发
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concurrency: 4
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concurrency: 2
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#当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
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local_service_conf:
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@ -34,15 +34,15 @@ op:
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client_type: local_predictor
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#det模型路径
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model_config: /paddle/serving/models/det_serving_server/ #ocr_det_model
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model_config: ./ppocr_det_server_2.0_serving
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#Fetch结果列表，以client_config中fetch_var的alias_name为准
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fetch_list: ["save_infer_model/scale_0.tmp_1"]
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#计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
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devices: "2"
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devices: "0"
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ir_optim: True
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ir_optim: False
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rec:
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#并发数，is_thread_op=True时，为线程并发；否则为进程并发
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concurrency: 1
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@ -60,12 +60,12 @@ op:
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client_type: local_predictor
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#rec模型路径
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model_config: /paddle/serving/models/rec_serving_server/ #ocr_rec_model
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model_config: ./ppocr_rec_server_2.0_serving
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#Fetch结果列表，以client_config中fetch_var的alias_name为准
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fetch_list: ["save_infer_model/scale_0.tmp_1"] #["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
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fetch_list: ["save_infer_model/scale_0.tmp_1"]
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#计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
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devices: "2"
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devices: "0"
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ir_optim: True
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ir_optim: False
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@ -21,7 +21,6 @@ import sys
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import argparse
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import string
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from copy import deepcopy
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import paddle
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class DetResizeForTest(object):
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@ -227,8 +226,8 @@ class CTCLabelDecode(BaseRecLabelDecode):
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super(CTCLabelDecode, self).__init__(config)
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def __call__(self, preds, label=None, *args, **kwargs):
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if isinstance(preds, paddle.Tensor):
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preds = preds.numpy()
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#if isinstance(preds, paddle.Tensor):
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# preds = preds.numpy()
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preds_idx = preds.argmax(axis=2)
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preds_prob = preds.max(axis=2)
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text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
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@ -23,8 +23,8 @@ def cv2_to_base64(image):
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return base64.b64encode(image).decode('utf8')
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url = "http://127.0.0.1:9999/ocr/prediction"
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test_img_dir = "../doc/imgs/"
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url = "http://127.0.0.1:9998/ocr/prediction"
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test_img_dir = "../../doc/imgs/"
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for idx, img_file in enumerate(os.listdir(test_img_dir)):
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with open(os.path.join(test_img_dir, img_file), 'rb') as file:
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image_data1 = file.read()
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@ -36,5 +36,5 @@ for idx, img_file in enumerate(os.listdir(test_img_dir)):
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r = requests.post(url=url, data=json.dumps(data))
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print(r.json())
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test_img_dir = "../doc/imgs/"
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test_img_dir = "../../doc/imgs/"
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print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))
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@ -23,7 +23,7 @@ import base64
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import os
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client = PipelineClient()
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client.connect(['127.0.0.1:18090'])
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client.connect(['127.0.0.1:18091'])
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def cv2_to_base64(image):
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@ -39,4 +39,3 @@ for img_file in os.listdir(test_img_dir):
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for i in range(1):
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ret = client.predict(feed_dict={"image": image}, fetch=["res"])
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print(ret)
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#print(ret)
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@ -56,11 +56,9 @@ class DetOp(Op):
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det_img = self.det_preprocess(self.im)
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_, self.new_h, self.new_w = det_img.shape
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print("det image shape", det_img.shape)
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return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
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def postprocess(self, input_dicts, fetch_dict, log_id):
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print("input_dicts: ", input_dicts)
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det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
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ratio_list = [
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float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
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@ -69,7 +67,6 @@ class DetOp(Op):
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dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
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out_dict = {"dt_boxes": dt_boxes, "image": self.im}
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print("out dict", out_dict["dt_boxes"])
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return out_dict, None, ""
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@ -89,8 +86,36 @@ class RecOp(Op):
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feed_list = []
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img_list = []
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max_wh_ratio = 0
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for i, dtbox in enumerate(dt_boxes):
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boximg = self.get_rotate_crop_image(im, dt_boxes[i])
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## Many mini-batchs, the type of feed_data is list.
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max_batch_size = 6 # len(dt_boxes)
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# If max_batch_size is 0, skipping predict stage
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if max_batch_size == 0:
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return {}, True, None, ""
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boxes_size = len(dt_boxes)
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batch_size = boxes_size // max_batch_size
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rem = boxes_size % max_batch_size
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#_LOGGER.info("max_batch_len:{}, batch_size:{}, rem:{}, boxes_size:{}".format(max_batch_size, batch_size, rem, boxes_size))
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for bt_idx in range(0, batch_size + 1):
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imgs = None
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boxes_num_in_one_batch = 0
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if bt_idx == batch_size:
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if rem == 0:
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continue
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else:
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boxes_num_in_one_batch = rem
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elif bt_idx < batch_size:
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boxes_num_in_one_batch = max_batch_size
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else:
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_LOGGER.error("batch_size error, bt_idx={}, batch_size={}".
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format(bt_idx, batch_size))
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break
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start = bt_idx * max_batch_size
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end = start + boxes_num_in_one_batch
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img_list = []
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for box_idx in range(start, end):
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boximg = self.get_rotate_crop_image(im, dt_boxes[box_idx])
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img_list.append(boximg)
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h, w = boximg.shape[0:2]
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wh_ratio = w * 1.0 / h
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@ -98,20 +123,30 @@ class RecOp(Op):
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_, w, h = self.ocr_reader.resize_norm_img(img_list[0],
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max_wh_ratio).shape
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imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
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imgs = np.zeros((boxes_num_in_one_batch, 3, w, h)).astype('float32')
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for id, img in enumerate(img_list):
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norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
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imgs[id] = norm_img
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print("rec image shape", imgs.shape)
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feed = {"x": imgs.copy()}
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return feed, False, None, ""
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feed_list.append(feed)
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def postprocess(self, input_dicts, fetch_dict, log_id):
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rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True)
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res_lst = []
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for res in rec_res:
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res_lst.append(res[0])
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res = {"res": str(res_lst)}
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return feed_list, False, None, ""
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def postprocess(self, input_dicts, fetch_data, log_id):
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res_list = []
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if isinstance(fetch_data, dict):
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if len(fetch_data) > 0:
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rec_batch_res = self.ocr_reader.postprocess(
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fetch_data, with_score=True)
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for res in rec_batch_res:
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res_list.append(res[0])
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elif isinstance(fetch_data, list):
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for one_batch in fetch_data:
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one_batch_res = self.ocr_reader.postprocess(
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one_batch, with_score=True)
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for res in one_batch_res:
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res_list.append(res[0])
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res = {"res": str(res_list)}
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return res, None, ""
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