debug

deploy/paddleserving/recognition/test_cpp_serving_client.py

@@ -22,181 +22,102 @@ 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)
+    print(fetch_map.keys())
+    #add retrieval procedure
+    det_boxes = fetch_map["boxes"]
+    print(det_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)

test_tipc/docs/test_serving_infer_cpp.md

@@ -52,7 +52,7 @@ Linux GPU/CPU  PYTHON 服务化部署测试的主程序为`test_serving_infer.sh
     ```
 - 安装 PaddleServing 相关组件，包括serving_client、serving-app，自动编译带自定义OP的serving_server包（测试PP-ShiTu时），以及自动下载并解压推理模型
   ```bash
-  bash test_tipc/prepare.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt serving_infer
+  bash test_tipc/prepare.sh test_tipc/configs/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt serving_infer
   ```
 
 ### 2.3 功能测试
@@ -63,24 +63,28 @@ Linux GPU/CPU  PYTHON 服务化部署测试的主程序为`test_serving_infer.sh
 bash test_tipc/test_serving_infer.sh ${your_params_file}
 ```
 
-以`ResNet50`的`Linux GPU/CPU PYTHON 服务化部署测试`为例，命令如下所示。
+以`PPLCNet_x1_0`的`Linux GPU/CPU C++ 服务化部署测试`为例，命令如下所示。
 
 
 ```bash
-bash test_tipc/test_serving_infer.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
+bash test_tipc/test_serving_infer.sh test_tipc/configs/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt
 ```
 
 输出结果如下，表示命令运行成功。
 
 ```
-Run successfully with command - python3.7 pipeline_http_client.py > ../../test_tipc/output/ResNet50/server_infer_gpu_pipeline_http_batchsize_1.log 2>&1!
-Run successfully with command - python3.7 pipeline_http_client.py > ../../test_tipc/output/ResNet50/server_infer_cpu_pipeline_http_batchsize_1.log 2>&1 !
+Run successfully with command - PPLCNet_x1_0 - python3.7 test_cpp_serving_client.py > ../../test_tipc/output/PPLCNet_x1_0/server_infer_cpp_gpu_pipeline_batchsize_1.log 2>&1 !
+Run successfully with command - PPLCNet_x1_0 - python3.7 test_cpp_serving_client.py > ../../test_tipc/output/PPLCNet_x1_0/server_infer_cpp_cpu_pipeline_batchsize_1.log 2>&1 !
 ```
 
-预测结果会自动保存在 `./test_tipc/output/ResNet50/server_infer_gpu_pipeline_http_batchsize_1.log` ，可以看到 PaddleServing 的运行结果：
+预测结果会自动保存在 `./test_tipc/output/PPLCNet_x1_0/server_infer_gpu_pipeline_http_batchsize_1.log` ，可以看到 PaddleServing 的运行结果：
 
 ```
-{'err_no': 0, 'err_msg': '', 'key': ['label', 'prob'], 'value': ["['daisy']", '[0.998314619064331]']}
+WARNING: Logging before InitGoogleLogging() is written to STDERR
+I0612 09:55:16.109890 38303 naming_service_thread.cpp:202] brpc::policy::ListNamingService("127.0.0.1:9292"): added 1
+I0612 09:55:16.172924 38303 general_model.cpp:490] [client]logid=0,client_cost=60.772ms,server_cost=57.6ms.
+prediction: daisy, probability: 0.9099399447441101
+0.06275796890258789
 ```
 
 

test_tipc/prepare.sh

@@ -204,7 +204,9 @@ if [[ ${MODE} = "serving_infer" ]]; then
     ${python_name} -m pip install paddle-serving-app==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
     python_name=$(func_parser_value "${lines[2]}")
     if [[ ${FILENAME} =~ "cpp" ] && [ ${model_name} =~ "ShiTu" ]]; then
+        pushd ./deploy/paddleserving
         bash build_server.sh ${python_name}
+        popd
     else
         ${python_name} -m pip install install paddle-serving-server-gpu==0.9.0.post101 -i https://pypi.tuna.tsinghua.edu.cn/simple
     fi

test_tipc/test_serving_infer.sh

@@ -263,13 +263,13 @@ function func_serving_rec(){
     det_trans_model_cmd="${python_interp} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
     eval $det_trans_model_cmd
 
-    cp_prototxt_cmd="cp ./paddleserving/preprocess/general_PPLCNet_x2_5_lite_v1.0_serving/*.prototxt ${cls_serving_server_value}"
+    cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/general_PPLCNet_x2_5_lite_v1.0_serving/*.prototxt ${cls_serving_server_value}"
     eval ${cp_prototxt_cmd}
-    cp_prototxt_cmd="cp ./paddleserving/preprocess/general_PPLCNet_x2_5_lite_v1.0_client/*.prototxt ${cls_serving_client_value}"
+    cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/general_PPLCNet_x2_5_lite_v1.0_client/*.prototxt ${cls_serving_client_value}"
     eval ${cp_prototxt_cmd}
-    cp_prototxt_cmd="cp ./paddleserving/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/*.prototxt ${det_serving_client_value}"
+    cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/*.prototxt ${det_serving_client_value}"
     eval ${cp_prototxt_cmd}
-    cp_prototxt_cmd="cp ./paddleserving/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/*.prototxt ${det_serving_server_value}"
+    cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/*.prototxt ${det_serving_server_value}"
     eval ${cp_prototxt_cmd}
 
     prototxt_dataline=$(awk 'NR==1, NR==3{print}'  ${cls_serving_server_value}/serving_server_conf.prototxt)