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 | 259 ++++++------------
deploy/paddleserving/run_cpp_serving.sh | 16 +-
4 files changed, 117 insertions(+), 179 deletions(-)
diff --git a/deploy/paddleserving/recognition/config.yml b/deploy/paddleserving/recognition/config.yml
index 6ecc32e22..e4108006e 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 affca99c6..72e7af804 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 a2bf1ae3e..e2cd17e85 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)
+rec_nms_thresold = 0.05
+rec_score_thres = 0.5
+feature_normalize = True
+return_k = 1
+index_dir = "../../drink_dataset_v1.0/index"
-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_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 ... "
- 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"])
+ searcher = faiss.read_index(os.path.join(index_dir, "vector.index"))
- 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
+ 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()
+ 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'])
- #1. get det_results
- imgpath = "../../drink_dataset_v1.0/test_images/001.jpeg"
- det_results = det.predict(imgpath)
+ 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)
- #2. get rec_results
- rec_results = rec.predict(det_results, imgpath)
- print(rec_results)
+ #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 05794b7d9..5defa03e0 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