From b574a47d6e19c794fac0745db8f4ee1129d4273a Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 8 Aug 2022 23:02:05 +0800 Subject: [PATCH] update bash scripts and related python file to develop version --- deploy/paddleserving/recognition/config.yml | 2 +- .../recognition/run_cpp_serving.sh | 19 +- .../recognition/test_cpp_serving_client.py | 275 ++++++------------ deploy/paddleserving/run_cpp_serving.sh | 16 +- 4 files changed, 125 insertions(+), 187 deletions(-) diff --git a/deploy/paddleserving/recognition/config.yml b/deploy/paddleserving/recognition/config.yml index 6ecc32e2..e4108006 100644 --- a/deploy/paddleserving/recognition/config.yml +++ b/deploy/paddleserving/recognition/config.yml @@ -31,7 +31,7 @@ op: #Fetch结果列表，以client_config中fetch_var的alias_name为准 fetch_list: ["features"] - + det: concurrency: 1 local_service_conf: diff --git a/deploy/paddleserving/recognition/run_cpp_serving.sh b/deploy/paddleserving/recognition/run_cpp_serving.sh index affca99c..72e7af80 100644 --- a/deploy/paddleserving/recognition/run_cpp_serving.sh +++ b/deploy/paddleserving/recognition/run_cpp_serving.sh @@ -1,7 +1,14 @@ -nohup python3 -m paddle_serving_server.serve \ ---model ../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving \ - --port 9293 >>log_mainbody_detection.txt 1&>2 & +gpu_id=$1 -nohup python3 -m paddle_serving_server.serve \ ---model ../../models/general_PPLCNet_x2_5_lite_v1.0_serving \ ---port 9294 >>log_feature_extraction.txt 1&>2 & +# PP-ShiTu CPP serving script +if [[ -n "${gpu_id}" ]]; then + nohup python3.7 -m paddle_serving_server.serve \ + --model ../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving ../../models/general_PPLCNet_x2_5_lite_v1.0_serving \ + --op GeneralPicodetOp GeneralFeatureExtractOp \ + --port 9400 --gpu_id="${gpu_id}" > log_PPShiTu.txt 2>&1 & +else + nohup python3.7 -m paddle_serving_server.serve \ + --model ../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving ../../models/general_PPLCNet_x2_5_lite_v1.0_serving \ + --op GeneralPicodetOp GeneralFeatureExtractOp \ + --port 9400 > log_PPShiTu.txt 2>&1 & +fi \ No newline at end of file diff --git a/deploy/paddleserving/recognition/test_cpp_serving_client.py b/deploy/paddleserving/recognition/test_cpp_serving_client.py index a2bf1ae3..e2cd17e8 100644 --- a/deploy/paddleserving/recognition/test_cpp_serving_client.py +++ b/deploy/paddleserving/recognition/test_cpp_serving_client.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -import sys import numpy as np from paddle_serving_client import Client @@ -22,181 +21,101 @@ import faiss import os import pickle - -class MainbodyDetect(): - """ - pp-shitu mainbody detect. - include preprocess, process, postprocess - return detect results - Attention: Postprocess include num limit and box filter; no nms - """ - - def __init__(self): - self.preprocess = DetectionSequential([ - DetectionFile2Image(), DetectionNormalize( - [0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True), - DetectionResize( - (640, 640), False, interpolation=2), DetectionTranspose( - (2, 0, 1)) - ]) - - self.client = Client() - self.client.load_client_config( - "../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/serving_client_conf.prototxt" - ) - self.client.connect(['127.0.0.1:9293']) - - self.max_det_result = 5 - self.conf_threshold = 0.2 - - def predict(self, imgpath): - im, im_info = self.preprocess(imgpath) - im_shape = np.array(im.shape[1:]).reshape(-1) - scale_factor = np.array(list(im_info['scale_factor'])).reshape(-1) - - fetch_map = self.client.predict( - feed={ - "image": im, - "im_shape": im_shape, - "scale_factor": scale_factor, - }, - fetch=["save_infer_model/scale_0.tmp_1"], - batch=False) - return self.postprocess(fetch_map, imgpath) - - def postprocess(self, fetch_map, imgpath): - #1. get top max_det_result - det_results = fetch_map["save_infer_model/scale_0.tmp_1"] - if len(det_results) > self.max_det_result: - boxes_reserved = fetch_map[ - "save_infer_model/scale_0.tmp_1"][:self.max_det_result] - else: - boxes_reserved = det_results - - #2. do conf threshold - boxes_list = [] - for i in range(boxes_reserved.shape[0]): - if (boxes_reserved[i, 1]) > self.conf_threshold: - boxes_list.append(boxes_reserved[i, :]) - - #3. add origin image box - origin_img = cv2.imread(imgpath) - boxes_list.append( - np.array([0, 1.0, 0, 0, origin_img.shape[1], origin_img.shape[0]])) - return np.array(boxes_list) - - -class ObjectRecognition(): - """ - pp-shitu object recognion for all objects detected by MainbodyDetect. - include preprocess, process, postprocess - preprocess include preprocess for each image and batching. - Batch process - postprocess include retrieval and nms - """ - - def __init__(self): - self.client = Client() - self.client.load_client_config( - "../../models/general_PPLCNet_x2_5_lite_v1.0_client/serving_client_conf.prototxt" - ) - self.client.connect(["127.0.0.1:9294"]) - - self.seq = Sequential([ - BGR2RGB(), Resize((224, 224)), Div(255), - Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], - False), Transpose((2, 0, 1)) - ]) - - self.searcher, self.id_map = self.init_index() - - self.rec_nms_thresold = 0.05 - self.rec_score_thres = 0.5 - self.feature_normalize = True - self.return_k = 1 - - def init_index(self): - index_dir = "../../drink_dataset_v1.0/index" - assert os.path.exists(os.path.join( - index_dir, "vector.index")), "vector.index not found ..." - assert os.path.exists(os.path.join( - index_dir, "id_map.pkl")), "id_map.pkl not found ... " - - searcher = faiss.read_index(os.path.join(index_dir, "vector.index")) - - with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd: - id_map = pickle.load(fd) - return searcher, id_map - - def predict(self, det_boxes, imgpath): - #1. preprocess - batch_imgs = [] - origin_img = cv2.imread(imgpath) - for i in range(det_boxes.shape[0]): - box = det_boxes[i] - x1, y1, x2, y2 = [int(x) for x in box[2:]] - cropped_img = origin_img[y1:y2, x1:x2, :].copy() - tmp = self.seq(cropped_img) - batch_imgs.append(tmp) - batch_imgs = np.array(batch_imgs) - - #2. process - fetch_map = self.client.predict( - feed={"x": batch_imgs}, fetch=["features"], batch=True) - batch_features = fetch_map["features"] - - #3. postprocess - if self.feature_normalize: - feas_norm = np.sqrt( - np.sum(np.square(batch_features), axis=1, keepdims=True)) - batch_features = np.divide(batch_features, feas_norm) - scores, docs = self.searcher.search(batch_features, self.return_k) - - results = [] - for i in range(scores.shape[0]): - pred = {} - if scores[i][0] >= self.rec_score_thres: - pred["bbox"] = [int(x) for x in det_boxes[i, 2:]] - pred["rec_docs"] = self.id_map[docs[i][0]].split()[1] - pred["rec_scores"] = scores[i][0] - results.append(pred) - return self.nms_to_rec_results(results) - - def nms_to_rec_results(self, results): - filtered_results = [] - x1 = np.array([r["bbox"][0] for r in results]).astype("float32") - y1 = np.array([r["bbox"][1] for r in results]).astype("float32") - x2 = np.array([r["bbox"][2] for r in results]).astype("float32") - y2 = np.array([r["bbox"][3] for r in results]).astype("float32") - scores = np.array([r["rec_scores"] for r in results]) - - areas = (x2 - x1 + 1) * (y2 - y1 + 1) - order = scores.argsort()[::-1] - while order.size > 0: - i = order[0] - xx1 = np.maximum(x1[i], x1[order[1:]]) - yy1 = np.maximum(y1[i], y1[order[1:]]) - xx2 = np.minimum(x2[i], x2[order[1:]]) - yy2 = np.minimum(y2[i], y2[order[1:]]) - - w = np.maximum(0.0, xx2 - xx1 + 1) - h = np.maximum(0.0, yy2 - yy1 + 1) - inter = w * h - ovr = inter / (areas[i] + areas[order[1:]] - inter) - inds = np.where(ovr <= self.rec_nms_thresold)[0] - order = order[inds + 1] - filtered_results.append(results[i]) - return filtered_results - - +rec_nms_thresold = 0.05 +rec_score_thres = 0.5 +feature_normalize = True +return_k = 1 +index_dir = "../../drink_dataset_v1.0/index" + + +def init_index(index_dir): + assert os.path.exists(os.path.join( + index_dir, "vector.index")), "vector.index not found ..." + assert os.path.exists(os.path.join( + index_dir, "id_map.pkl")), "id_map.pkl not found ... " + + searcher = faiss.read_index(os.path.join(index_dir, "vector.index")) + + with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd: + id_map = pickle.load(fd) + return searcher, id_map + + +#get box +def nms_to_rec_results(results, thresh=0.1): + filtered_results = [] + + x1 = np.array([r["bbox"][0] for r in results]).astype("float32") + y1 = np.array([r["bbox"][1] for r in results]).astype("float32") + x2 = np.array([r["bbox"][2] for r in results]).astype("float32") + y2 = np.array([r["bbox"][3] for r in results]).astype("float32") + scores = np.array([r["rec_scores"] for r in results]) + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + while order.size > 0: + i = order[0] + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + filtered_results.append(results[i]) + return filtered_results + + +def postprocess(fetch_dict, feature_normalize, det_boxes, searcher, id_map, + return_k, rec_score_thres, rec_nms_thresold): + batch_features = fetch_dict["features"] + + #do feature norm + if feature_normalize: + feas_norm = np.sqrt( + np.sum(np.square(batch_features), axis=1, keepdims=True)) + batch_features = np.divide(batch_features, feas_norm) + + scores, docs = searcher.search(batch_features, return_k) + + results = [] + for i in range(scores.shape[0]): + pred = {} + if scores[i][0] >= rec_score_thres: + pred["bbox"] = [int(x) for x in det_boxes[i, 2:]] + pred["rec_docs"] = id_map[docs[i][0]].split()[1] + pred["rec_scores"] = scores[i][0] + results.append(pred) + + #do nms + results = nms_to_rec_results(results, rec_nms_thresold) + return results + + +#do client if __name__ == "__main__": - det = MainbodyDetect() - rec = ObjectRecognition() - - #1. get det_results - imgpath = "../../drink_dataset_v1.0/test_images/001.jpeg" - det_results = det.predict(imgpath) - - #2. get rec_results - rec_results = rec.predict(det_results, imgpath) - print(rec_results) + client = Client() + client.load_client_config([ + "../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client", + "../../models/general_PPLCNet_x2_5_lite_v1.0_client" + ]) + client.connect(['127.0.0.1:9400']) + + im = cv2.imread("../../drink_dataset_v1.0/test_images/001.jpeg") + im_shape = np.array(im.shape[:2]).reshape(-1) + fetch_map = client.predict( + feed={"image": im, + "im_shape": im_shape}, + fetch=["features", "boxes"], + batch=False) + + #add retrieval procedure + det_boxes = fetch_map["boxes"] + searcher, id_map = init_index(index_dir) + results = postprocess(fetch_map, feature_normalize, det_boxes, searcher, + id_map, return_k, rec_score_thres, rec_nms_thresold) + print(results) diff --git a/deploy/paddleserving/run_cpp_serving.sh b/deploy/paddleserving/run_cpp_serving.sh index 05794b7d..5defa03e 100644 --- a/deploy/paddleserving/run_cpp_serving.sh +++ b/deploy/paddleserving/run_cpp_serving.sh @@ -1,2 +1,14 @@ -#run cls server: -nohup python3 -m paddle_serving_server.serve --model ResNet50_vd_serving --port 9292 & +gpu_id=$1 + +# ResNet50_vd CPP serving script +if [[ -n "${gpu_id}" ]]; then + nohup python3.7 -m paddle_serving_server.serve \ + --model ./ResNet50_vd_serving \ + --op GeneralClasOp \ + --port 9292 & +else + nohup python3.7 -m paddle_serving_server.serve \ + --model ./ResNet50_vd_serving \ + --op GeneralClasOp \ + --port 9292 --gpu_id="${gpu_id}" & +fi \ No newline at end of file -- GitLab