update bash scripts and related python file to develop version
HydrogenSulfate
parent
d76705288d
commit
b574a47d6e
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@ -1,7 +1,14 @@
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nohup python3 -m paddle_serving_server.serve \
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--model ../../models/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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gpu_id=$1
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nohup python3 -m paddle_serving_server.serve \
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--model ../../models/general_PPLCNet_x2_5_lite_v1.0_serving \
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--port 9294 >>log_feature_extraction.txt 1&>2 &
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# PP-ShiTu CPP serving script
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if [[ -n "${gpu_id}" ]]; then
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nohup python3.7 -m paddle_serving_server.serve \
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--model ../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving ../../models/general_PPLCNet_x2_5_lite_v1.0_serving \
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--op GeneralPicodetOp GeneralFeatureExtractOp \
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--port 9400 --gpu_id="${gpu_id}" > log_PPShiTu.txt 2>&1 &
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else
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nohup python3.7 -m paddle_serving_server.serve \
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--model ../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving ../../models/general_PPLCNet_x2_5_lite_v1.0_serving \
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--op GeneralPicodetOp GeneralFeatureExtractOp \
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--port 9400 > log_PPShiTu.txt 2>&1 &
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fi
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@ -12,7 +12,6 @@
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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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@ -22,181 +21,101 @@ 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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"../../models/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(imgpath)
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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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rec_nms_thresold = 0.05
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rec_score_thres = 0.5
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feature_normalize = True
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return_k = 1
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index_dir = "../../drink_dataset_v1.0/index"
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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_index(index_dir):
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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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def __init__(self):
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self.client = Client()
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self.client.load_client_config(
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"../../models/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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searcher = faiss.read_index(os.path.join(index_dir, "vector.index"))
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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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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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#get box
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def nms_to_rec_results(results, thresh=0.1):
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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 <= thresh)[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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def postprocess(fetch_dict, feature_normalize, det_boxes, searcher, id_map,
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return_k, rec_score_thres, rec_nms_thresold):
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batch_features = fetch_dict["features"]
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#do feature norm
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if 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 = searcher.search(batch_features, 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] >= 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"] = 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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#do nms
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results = nms_to_rec_results(results, rec_nms_thresold)
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return results
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#do client
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if __name__ == "__main__":
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det = MainbodyDetect()
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rec = ObjectRecognition()
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client = Client()
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client.load_client_config([
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"../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client",
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"../../models/general_PPLCNet_x2_5_lite_v1.0_client"
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])
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client.connect(['127.0.0.1:9400'])
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#1. get det_results
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imgpath = "../../drink_dataset_v1.0/test_images/001.jpeg"
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det_results = det.predict(imgpath)
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im = cv2.imread("../../drink_dataset_v1.0/test_images/001.jpeg")
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im_shape = np.array(im.shape[:2]).reshape(-1)
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fetch_map = client.predict(
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feed={"image": im,
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"im_shape": im_shape},
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fetch=["features", "boxes"],
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batch=False)
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#2. get rec_results
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rec_results = rec.predict(det_results, imgpath)
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print(rec_results)
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#add retrieval procedure
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det_boxes = fetch_map["boxes"]
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searcher, id_map = init_index(index_dir)
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results = postprocess(fetch_map, feature_normalize, det_boxes, searcher,
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id_map, return_k, rec_score_thres, rec_nms_thresold)
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print(results)
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@ -1,2 +1,14 @@
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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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gpu_id=$1
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# ResNet50_vd CPP serving script
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if [[ -n "${gpu_id}" ]]; then
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nohup python3.7 -m paddle_serving_server.serve \
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--model ./ResNet50_vd_serving \
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--op GeneralClasOp \
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--port 9292 &
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else
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nohup python3.7 -m paddle_serving_server.serve \
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--model ./ResNet50_vd_serving \
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--op GeneralClasOp \
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--port 9292 --gpu_id="${gpu_id}" &
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fi
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