update bash scripts and related python file to develop version

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:

deploy/paddleserving/recognition/run_cpp_serving.sh

-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

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)

deploy/paddleserving/run_cpp_serving.sh

-#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