diff --git a/deploy/pdserving/README_CN.md b/deploy/pdserving/README_CN.md index 3e3f1bde0e824fe6133a1c169b9b03e614904c26..38715a4a7f34d74fb098186c908f612ce0807437 100644 --- a/deploy/pdserving/README_CN.md +++ b/deploy/pdserving/README_CN.md @@ -29,7 +29,9 @@ PaddleOCR提供2种服务部署方式： 需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。 -- 准备PaddleOCR的运行环境参考[链接](../../doc/doc_ch/installation.md) +- 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md) + 根据环境下载对应的paddle whl包， + 推荐2.0.1版本：https://www.paddlepaddle.org.cn/whl/mkl/stable.html - 准备PaddleServing的运行环境，步骤如下 @@ -45,25 +47,16 @@ PaddleOCR提供2种服务部署方式： ``` 2. 安装client，用于向服务发送请求 - ``` - pip3 install paddle-serving-client==0.5.0 # for CPU + 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： - pip3 install paddle-serving-client-gpu==0.5.0 # for GPU + ``` + wget https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp37-none-any.whl + pip3 install paddle_serving_client-0.0.0-cp37-none-any.whl ``` 3. 安装serving-app ``` - pip3 install paddle-serving-app==0.3.0 - ``` - **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： - ``` - # 找到paddle_serving_app的安装目录，找到并编辑local_predict.py文件 - vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py - # 将local_predict.py 的第85行 config = AnalysisConfig(model_path) 替换为： - if os.path.exists(os.path.join(model_path, "__params__")): - config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__")) - else: - config = AnalysisConfig(model_path) + pip3 install paddle-serving-app==0.3.1 ``` **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)。 diff --git a/deploy/pdserving/config.yml b/deploy/pdserving/config.yml index aef735dbfab5b314f9209a7cc91e7fd5b6fc615c..8ab27dbc20901f6674156e86e82cd22f5ff946a0 100644 --- a/deploy/pdserving/config.yml +++ b/deploy/pdserving/config.yml @@ -1,32 +1,32 @@ #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 -rpc_port: 18090 +rpc_port: 18091 #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 -http_port: 9999 +http_port: 9998 #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 ##当build_dag_each_worker=False时，框架会设置主线程grpc线程池的max_workers=worker_num -worker_num: 20 +worker_num: 5 #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 -build_dag_each_worker: false +build_dag_each_worker: False dag: #op资源类型, True, 为线程模型；False，为进程模型 is_thread_op: False #重试次数 - retry: 1 + retry: 10 #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 - use_profile: False + use_profile: True tracer: interval_s: 10 op: det: #并发数，is_thread_op=True时，为线程并发；否则为进程并发 - concurrency: 4 + concurrency: 2 #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置 local_service_conf: @@ -34,15 +34,15 @@ op: client_type: local_predictor #det模型路径 - model_config: /paddle/serving/models/det_serving_server/ #ocr_det_model + model_config: ./ppocr_det_server_2.0_serving #Fetch结果列表，以client_config中fetch_var的alias_name为准 fetch_list: ["save_infer_model/scale_0.tmp_1"] #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 - devices: "2" + devices: "0" - ir_optim: True + ir_optim: False rec: #并发数，is_thread_op=True时，为线程并发；否则为进程并发 concurrency: 1 @@ -60,12 +60,12 @@ op: client_type: local_predictor #rec模型路径 - model_config: /paddle/serving/models/rec_serving_server/ #ocr_rec_model + model_config: ./ppocr_rec_server_2.0_serving #Fetch结果列表，以client_config中fetch_var的alias_name为准 - fetch_list: ["save_infer_model/scale_0.tmp_1"] #["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"] + fetch_list: ["save_infer_model/scale_0.tmp_1"] #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 - devices: "2" + devices: "0" - ir_optim: True + ir_optim: False diff --git a/deploy/pdserving/ocr_reader.py b/deploy/pdserving/ocr_reader.py index 95110706af13662de11ef0f668558d0dd3abcf52..71682e6728e8fca3dbbfe07d2fd5b2a77222bd88 100644 --- a/deploy/pdserving/ocr_reader.py +++ b/deploy/pdserving/ocr_reader.py @@ -21,7 +21,6 @@ import sys import argparse import string from copy import deepcopy -import paddle class DetResizeForTest(object): @@ -227,8 +226,8 @@ class CTCLabelDecode(BaseRecLabelDecode): super(CTCLabelDecode, self).__init__(config) def __call__(self, preds, label=None, *args, **kwargs): - if isinstance(preds, paddle.Tensor): - preds = preds.numpy() + #if isinstance(preds, paddle.Tensor): + # preds = preds.numpy() preds_idx = preds.argmax(axis=2) preds_prob = preds.max(axis=2) text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True) diff --git a/deploy/pdserving/pipeline_http_client.py b/deploy/pdserving/pipeline_http_client.py index 88c4a81ea8bbed80d37b5fbfea6bf01b38f9613a..0befe2f6144d18e24fb3f72ed1d919fd8cd7d5a4 100644 --- a/deploy/pdserving/pipeline_http_client.py +++ b/deploy/pdserving/pipeline_http_client.py @@ -23,8 +23,8 @@ def cv2_to_base64(image): return base64.b64encode(image).decode('utf8') -url = "http://127.0.0.1:9999/ocr/prediction" -test_img_dir = "../doc/imgs/" +url = "http://127.0.0.1:9998/ocr/prediction" +test_img_dir = "../../doc/imgs/" for idx, img_file in enumerate(os.listdir(test_img_dir)): with open(os.path.join(test_img_dir, img_file), 'rb') as file: image_data1 = file.read() @@ -36,5 +36,5 @@ for idx, img_file in enumerate(os.listdir(test_img_dir)): r = requests.post(url=url, data=json.dumps(data)) print(r.json()) -test_img_dir = "../doc/imgs/" +test_img_dir = "../../doc/imgs/" print("==> total number of test imgs: ", len(os.listdir(test_img_dir))) diff --git a/deploy/pdserving/pipeline_rpc_client.py b/deploy/pdserving/pipeline_rpc_client.py index 7471f7ed6c1254d550bcf2c19f6ee7c610a2e20e..79f898faf37f946cdbf4a87d4d62c8b1f9d5c93b 100644 --- a/deploy/pdserving/pipeline_rpc_client.py +++ b/deploy/pdserving/pipeline_rpc_client.py @@ -23,7 +23,7 @@ import base64 import os client = PipelineClient() -client.connect(['127.0.0.1:18090']) +client.connect(['127.0.0.1:18091']) def cv2_to_base64(image): @@ -39,4 +39,3 @@ for img_file in os.listdir(test_img_dir): for i in range(1): ret = client.predict(feed_dict={"image": image}, fetch=["res"]) print(ret) - #print(ret) diff --git a/deploy/pdserving/web_service.py b/deploy/pdserving/web_service.py index b47ef65d09dd7aad0e4d00ca852a5c32161ad45b..43de8355f099e7a5dd059b954e7e61c1dddf0e7d 100644 --- a/deploy/pdserving/web_service.py +++ b/deploy/pdserving/web_service.py @@ -56,11 +56,9 @@ class DetOp(Op): det_img = self.det_preprocess(self.im) _, self.new_h, self.new_w = det_img.shape - print("det image shape", det_img.shape) return {"x": det_img[np.newaxis, :].copy()}, False, None, "" def postprocess(self, input_dicts, fetch_dict, log_id): - print("input_dicts: ", input_dicts) det_out = fetch_dict["save_infer_model/scale_0.tmp_1"] ratio_list = [ float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w @@ -69,7 +67,6 @@ class DetOp(Op): dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w]) out_dict = {"dt_boxes": dt_boxes, "image": self.im} - print("out dict", out_dict["dt_boxes"]) return out_dict, None, "" @@ -89,29 +86,67 @@ class RecOp(Op): feed_list = [] img_list = [] max_wh_ratio = 0 - for i, dtbox in enumerate(dt_boxes): - boximg = self.get_rotate_crop_image(im, dt_boxes[i]) - img_list.append(boximg) - h, w = boximg.shape[0:2] - wh_ratio = w * 1.0 / h - max_wh_ratio = max(max_wh_ratio, wh_ratio) - _, w, h = self.ocr_reader.resize_norm_img(img_list[0], - max_wh_ratio).shape - - imgs = np.zeros((len(img_list), 3, w, h)).astype('float32') - for id, img in enumerate(img_list): - norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio) - imgs[id] = norm_img - print("rec image shape", imgs.shape) - feed = {"x": imgs.copy()} - return feed, False, None, "" - - def postprocess(self, input_dicts, fetch_dict, log_id): - rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True) - res_lst = [] - for res in rec_res: - res_lst.append(res[0]) - res = {"res": str(res_lst)} + ## Many mini-batchs, the type of feed_data is list. + max_batch_size = 6 # len(dt_boxes) + + # If max_batch_size is 0, skipping predict stage + if max_batch_size == 0: + return {}, True, None, "" + boxes_size = len(dt_boxes) + batch_size = boxes_size // max_batch_size + rem = boxes_size % max_batch_size + #_LOGGER.info("max_batch_len:{}, batch_size:{}, rem:{}, boxes_size:{}".format(max_batch_size, batch_size, rem, boxes_size)) + for bt_idx in range(0, batch_size + 1): + imgs = None + boxes_num_in_one_batch = 0 + if bt_idx == batch_size: + if rem == 0: + continue + else: + boxes_num_in_one_batch = rem + elif bt_idx < batch_size: + boxes_num_in_one_batch = max_batch_size + else: + _LOGGER.error("batch_size error, bt_idx={}, batch_size={}". + format(bt_idx, batch_size)) + break + + start = bt_idx * max_batch_size + end = start + boxes_num_in_one_batch + img_list = [] + for box_idx in range(start, end): + boximg = self.get_rotate_crop_image(im, dt_boxes[box_idx]) + img_list.append(boximg) + h, w = boximg.shape[0:2] + wh_ratio = w * 1.0 / h + max_wh_ratio = max(max_wh_ratio, wh_ratio) + _, w, h = self.ocr_reader.resize_norm_img(img_list[0], + max_wh_ratio).shape + + imgs = np.zeros((boxes_num_in_one_batch, 3, w, h)).astype('float32') + for id, img in enumerate(img_list): + norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio) + imgs[id] = norm_img + feed = {"x": imgs.copy()} + feed_list.append(feed) + + return feed_list, False, None, "" + def postprocess(self, input_dicts, fetch_data, log_id): + res_list = [] + if isinstance(fetch_data, dict): + if len(fetch_data) > 0: + rec_batch_res = self.ocr_reader.postprocess( + fetch_data, with_score=True) + for res in rec_batch_res: + res_list.append(res[0]) + elif isinstance(fetch_data, list): + for one_batch in fetch_data: + one_batch_res = self.ocr_reader.postprocess( + one_batch, with_score=True) + for res in one_batch_res: + res_list.append(res[0]) + + res = {"res": str(res_list)} return res, None, ""