upload cplusplus serving
stephon
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nohup python3 -m paddle_serving_server.serve \
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--model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving \
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--port 9293 >>log_mainbody_detection.txt 1&>2 &
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nohup python3 -m paddle_serving_server.serve \
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--model general_PPLCNet_x2_5_lite_v1.0_serving \
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--port 9294 >>log_feature_extraction.txt 1&>2 &
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@ -0,0 +1,201 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import numpy as np
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from paddle_serving_client import Client
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from paddle_serving_app.reader import *
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import cv2
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import faiss
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import os
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import pickle
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class MainbodyDetect():
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"""
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pp-shitu mainbody detect.
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include preprocess, process, postprocess
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return detect results
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Attention: Postprocess include num limit and box filter; no nms
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"""
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def __init__(self):
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self.preprocess = DetectionSequential([
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DetectionFile2Image(), DetectionNormalize(
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[0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True),
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DetectionResize(
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(640, 640), False, interpolation=2), DetectionTranspose(
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(2, 0, 1))
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])
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self.client = Client()
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self.client.load_client_config(
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"picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/serving_client_conf.prototxt"
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)
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self.client.connect(['127.0.0.1:9293'])
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self.max_det_result = 5
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self.conf_threshold = 0.2
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def predict(self, imgpath):
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im, im_info = self.preprocess(sys.argv[1])
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im_shape = np.array(im.shape[1:]).reshape(-1)
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scale_factor = np.array(list(im_info['scale_factor'])).reshape(-1)
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fetch_map = self.client.predict(
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feed={
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"image": im,
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"im_shape": im_shape,
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"scale_factor": scale_factor,
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},
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fetch=["save_infer_model/scale_0.tmp_1"],
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batch=False)
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return self.postprocess(fetch_map, imgpath)
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def postprocess(self, fetch_map, imgpath):
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#1. get top max_det_result
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det_results = fetch_map["save_infer_model/scale_0.tmp_1"]
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if len(det_results) > self.max_det_result:
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boxes_reserved = fetch_map[
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"save_infer_model/scale_0.tmp_1"][:self.max_det_result]
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else:
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boxes_reserved = det_results
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#2. do conf threshold
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boxes_list = []
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for i in range(boxes_reserved.shape[0]):
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if (boxes_reserved[i, 1]) > self.conf_threshold:
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boxes_list.append(boxes_reserved[i, :])
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#3. add origin image box
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origin_img = cv2.imread(imgpath)
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boxes_list.append(
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np.array([0, 1.0, 0, 0, origin_img.shape[1], origin_img.shape[0]]))
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return np.array(boxes_list)
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class ObjectRecognition():
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"""
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pp-shitu object recognion for all objects detected by MainbodyDetect.
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include preprocess, process, postprocess
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preprocess include preprocess for each image and batching.
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Batch process
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postprocess include retrieval and nms
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"""
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def __init__(self):
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self.client = Client()
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self.client.load_client_config(
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"general_PPLCNet_x2_5_lite_v1.0_client/serving_client_conf.prototxt"
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)
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self.client.connect(["127.0.0.1:9294"])
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self.seq = Sequential([
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BGR2RGB(), Resize((224, 224)), Div(255),
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Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225],
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False), Transpose((2, 0, 1))
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])
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self.searcher, self.id_map = self.init_index()
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self.rec_nms_thresold = 0.05
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self.rec_score_thres = 0.5
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self.feature_normalize = True
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self.return_k = 1
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def init_index(self):
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index_dir = "../../drink_dataset_v1.0/index"
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assert os.path.exists(os.path.join(
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index_dir, "vector.index")), "vector.index not found ..."
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assert os.path.exists(os.path.join(
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index_dir, "id_map.pkl")), "id_map.pkl not found ... "
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searcher = faiss.read_index(os.path.join(index_dir, "vector.index"))
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with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
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id_map = pickle.load(fd)
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return searcher, id_map
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def predict(self, det_boxes, imgpath):
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#1. preprocess
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batch_imgs = []
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origin_img = cv2.imread(imgpath)
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for i in range(det_boxes.shape[0]):
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box = det_boxes[i]
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x1, y1, x2, y2 = [int(x) for x in box[2:]]
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cropped_img = origin_img[y1:y2, x1:x2, :].copy()
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tmp = self.seq(cropped_img)
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batch_imgs.append(tmp)
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batch_imgs = np.array(batch_imgs)
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#2. process
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fetch_map = self.client.predict(
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feed={"x": batch_imgs}, fetch=["features"], batch=True)
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batch_features = fetch_map["features"]
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#3. postprocess
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if self.feature_normalize:
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feas_norm = np.sqrt(
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np.sum(np.square(batch_features), axis=1, keepdims=True))
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batch_features = np.divide(batch_features, feas_norm)
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scores, docs = self.searcher.search(batch_features, self.return_k)
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results = []
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for i in range(scores.shape[0]):
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pred = {}
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if scores[i][0] >= self.rec_score_thres:
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pred["bbox"] = [int(x) for x in det_boxes[i, 2:]]
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pred["rec_docs"] = self.id_map[docs[i][0]].split()[1]
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pred["rec_scores"] = scores[i][0]
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results.append(pred)
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return self.nms_to_rec_results(results)
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def nms_to_rec_results(self, results):
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filtered_results = []
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x1 = np.array([r["bbox"][0] for r in results]).astype("float32")
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y1 = np.array([r["bbox"][1] for r in results]).astype("float32")
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x2 = np.array([r["bbox"][2] for r in results]).astype("float32")
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y2 = np.array([r["bbox"][3] for r in results]).astype("float32")
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scores = np.array([r["rec_scores"] for r in results])
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = scores.argsort()[::-1]
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while order.size > 0:
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i = order[0]
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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w = np.maximum(0.0, xx2 - xx1 + 1)
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h = np.maximum(0.0, yy2 - yy1 + 1)
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inter = w * h
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= self.rec_nms_thresold)[0]
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order = order[inds + 1]
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filtered_results.append(results[i])
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return filtered_results
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if __name__ == "__main__":
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det = MainbodyDetect()
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rec = ObjectRecognition()
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#1. get det_results
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det_results = det.predict(sys.argv[1])
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print(det_results)
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#2. get rec_results
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rec_results = rec.predict(det_results, sys.argv[1])
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print(rec_results)
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@ -0,0 +1,2 @@
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#run cls server:
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nohup python3 -m paddle_serving_server.serve --model ResNet50_vd_serving --port 9292 &
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@ -0,0 +1,52 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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from paddle_serving_client import Client
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#app
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from paddle_serving_app.reader import Sequential, URL2Image, Resize
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from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize
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import time
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client = Client()
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client.load_client_config("./ResNet50_vd_serving/serving_server_conf.prototxt")
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client.connect(["127.0.0.1:9292"])
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label_dict = {}
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label_idx = 0
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with open("imagenet.label") as fin:
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for line in fin:
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label_dict[label_idx] = line.strip()
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label_idx += 1
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#preprocess
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seq = Sequential([
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URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
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Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
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])
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start = time.time()
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image_file = "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"
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for i in range(1):
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img = seq(image_file)
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fetch_map = client.predict(
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feed={"inputs": img}, fetch=["prediction"], batch=False)
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prob = max(fetch_map["prediction"][0])
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label = label_dict[fetch_map["prediction"][0].tolist().index(prob)].strip(
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).replace(",", "")
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print("prediction: {}, probability: {}".format(label, prob))
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end = time.time()
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print(end - start)
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@ -4,9 +4,13 @@
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- [3. 图像分类服务部署](#3)
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- [3.1 模型转换](#3.1)
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- [3.2 服务部署和请求](#3.2)
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- [3.2.1 Python Serving](#3.2.1)
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- [3.2.2 C++ Serving](#3.2.2)
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- [4. 图像识别服务部署](#4)
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- [4.1 模型转换](#4.1)
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- [4.2 服务部署和请求](#4.2)
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- [4.2.1 Python Serving](#4.2.1)
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- [4.2.2 C++ Serving](#4.2.2)
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- [5. FAQ](#5)
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<a name="1"></a>
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@ -88,7 +92,7 @@ ResNet50_vd 推理模型转换完成后，会在当前文件夹多出 `ResNet50_
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|- serving_client_conf.prototxt
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|- serving_client_conf.stream.prototxt
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```
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得到模型文件之后，需要修改 `ResNet50_vd_server` 下文件 `serving_server_conf.prototxt` 中的 alias 名字：将 `fetch_var` 中的 `alias_name` 改为 `prediction`
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得到模型文件之后，需要分别修改 `ResNet50_vd_server` 和 `ResNet50_vd_client` 下文件 `serving_server_conf.prototxt` 中的 alias 名字：将 `fetch_var` 中的 `alias_name` 改为 `prediction`
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**备注**: Serving 为了兼容不同模型的部署，提供了输入输出重命名的功能。这样，不同的模型在推理部署时，只需要修改配置文件的 alias_name 即可，无需修改代码即可完成推理部署。
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修改后的 serving_server_conf.prototxt 如下所示:
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@ -112,15 +116,18 @@ fetch_var {
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```
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<a name="3.2"></a>
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### 3.2 服务部署和请求
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paddleserving 目录包含了启动 pipeline 服务和发送预测请求的代码，包括：
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paddleserving 目录包含了启动 pipeline 服务、C++ serving服务和发送预测请求的代码，包括：
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```shell
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__init__.py
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config.yml # 启动服务的配置文件
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config.yml # 启动pipeline服务的配置文件
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pipeline_http_client.py # http方式发送pipeline预测请求的脚本
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pipeline_rpc_client.py # rpc方式发送pipeline预测请求的脚本
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classification_web_service.py # 启动pipeline服务端的脚本
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run_cpp_serving.sh # 启动C++ Serving部署的脚本
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test_cpp_serving_client.py # rpc方式发送C++ serving预测请求的脚本
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```
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<a name="3.2.1"></a>
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#### 3.2.1 Python Serving
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- 启动服务：
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```shell
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# 启动服务，运行日志保存在 log.txt
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@ -137,6 +144,22 @@ python3 pipeline_http_client.py
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成功运行后，模型预测的结果会打印在 cmd 窗口中，结果示例为：
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<a name="3.2.2"></a>
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#### 3.2.2 C++ Serving
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- 启动服务：
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```shell
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# 启动服务， 服务在后台运行，运行日志保存在 nohup.txt
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sh run_cpp_serving.sh
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```
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- 发送请求：
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```shell
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# 发送服务请求
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python3 test_cpp_serving_client.py
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```
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成功运行后，模型预测的结果会打印在 cmd 窗口中，结果示例为：
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<a name="4"></a>
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## 4.图像识别服务部署
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使用 PaddleServing 做服务化部署时，需要将保存的 inference 模型转换为 Serving 模型。 下面以 PP-ShiTu 中的超轻量图像识别模型为例，介绍图像识别服务的部署。
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@ -162,7 +185,11 @@ python3 -m paddle_serving_client.convert --dirname ./general_PPLCNet_x2_5_lite_v
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--serving_server ./general_PPLCNet_x2_5_lite_v1.0_serving/ \
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--serving_client ./general_PPLCNet_x2_5_lite_v1.0_client/
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```
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<<<<<<< HEAD
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识别推理模型转换完成后，会在当前文件夹多出 `general_PPLCNet_x2_5_lite_v1.0_serving/` 和 `general_PPLCNet_x2_5_lite_v1.0_serving/` 的文件夹。分别修改 `general_PPLCNet_x2_5_lite_v1.0_serving/` 和 `general_PPLCNet_x2_5_lite_v1.0_client/` 目录下的 serving_server_conf.prototxt 中的 alias 名字： 将 `fetch_var` 中的 `alias_name` 改为 `features`。
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=======
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识别推理模型转换完成后，会在当前文件夹多出 `general_PPLCNet_x2_5_lite_v1.0_serving/` 和 `general_PPLCNet_x2_5_lite_v1.0_client/` 的文件夹。修改 `general_PPLCNet_x2_5_lite_v1.0_serving/` 目录下的 serving_server_conf.prototxt 中的 alias 名字： 将 `fetch_var` 中的 `alias_name` 改为 `features`。
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>>>>>>> d69a6e8f242cd894b41e9608bbca23172bcd3193
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修改后的 serving_server_conf.prototxt 内容如下：
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```
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feed_var {
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@ -207,14 +234,19 @@ wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_da
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```shell
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cd ./deploy/paddleserving/recognition
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```
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paddleserving 目录包含启动 pipeline 服务和发送预测请求的代码，包括：
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paddleserving 目录包含启动 Python Pipeline 服务、C++ Serving 服务和发送预测请求的代码，包括：
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```
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__init__.py
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config.yml # 启动服务的配置文件
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config.yml # 启动python pipeline服务的配置文件
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pipeline_http_client.py # http方式发送pipeline预测请求的脚本
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pipeline_rpc_client.py # rpc方式发送pipeline预测请求的脚本
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recognition_web_service.py # 启动pipeline服务端的脚本
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run_cpp_serving.sh # 启动C++ Serving部署的脚本
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test_cpp_serving_client.py # rpc方式发送C++ serving预测请求的脚本
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```
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<a name="4.2.1"></a>
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#### 4.2.1 Python Serving
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- 启动服务：
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```
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# 启动服务，运行日志保存在 log.txt
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@ -230,6 +262,23 @@ python3 pipeline_http_client.py
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成功运行后，模型预测的结果会打印在 cmd 窗口中，结果示例为：
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<a name="4.2.2"></a>
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#### 4.2.2 C++ Serving
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- 启动服务：
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```shell
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# 启动服务： 此处会在后台同时启动主体检测和特征提取服务，端口号分别为9293和9294；
|
||||
# 运行日志分别保存在 log_mainbody_detection.txt 和 log_feature_extraction.txt中
|
||||
sh run_cpp_serving.sh
|
||||
```
|
||||
|
||||
- 发送请求：
|
||||
```shell
|
||||
# 发送服务请求
|
||||
python3 test_cpp_serving_client.py
|
||||
```
|
||||
成功运行后，模型预测的结果会打印在 cmd 窗口中，结果示例为：
|
||||
|
||||
|
||||
<a name="5"></a>
|
||||
## 5.FAQ
|
||||
**Q1**： 发送请求后没有结果返回或者提示输出解码报错
|
||||
|
|
|
|||
Loading…
Reference in New Issue