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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """YoloV5 310 infer."""
- import os
- import sys
- import argparse
- import datetime
- import time
- import ast
- from collections import defaultdict
- import numpy as np
- from pycocotools.coco import COCO
- from pycocotools.cocoeval import COCOeval
- from src.logger import get_logger
-
- parser = argparse.ArgumentParser('yolov5 postprocess')
-
- # dataset related
- parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu')
-
- # logging related
- parser.add_argument('--log_path', type=str, default='outputs/', help='checkpoint save location')
-
- # detect_related
- parser.add_argument('--nms_thresh', type=float, default=0.6, help='threshold for NMS')
- parser.add_argument('--ann_file', type=str, default='', help='path to annotation')
- parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
-
- parser.add_argument('--dataset_path', type=str, default='', help='path of image dataset')
- parser.add_argument('--result_files', type=str, default='./result_Files', help='path to 310 infer result path')
- parser.add_argument('--multi_label', type=ast.literal_eval, default=True, help='whether to use multi label')
- parser.add_argument('--multi_label_thresh', type=float, default=0.1, help='threshold to throw low quality boxes')
-
- args, _ = parser.parse_known_args()
-
-
- class Redirct:
- def __init__(self):
- self.content = ""
-
- def write(self, content):
- self.content += content
-
- def flush(self):
- self.content = ""
-
-
- class DetectionEngine:
- """Detection engine."""
-
- def __init__(self, args_detection):
- self.ignore_threshold = args_detection.ignore_threshold
- self.labels = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat',
- 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat',
- 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',
- 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
- 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
- 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
- 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
- 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
- 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',
- 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
- self.num_classes = len(self.labels)
- self.results = {}
- self.file_path = ''
- self.save_prefix = args_detection.outputs_dir
- self.ann_file = args_detection.ann_file
- self._coco = COCO(self.ann_file)
- self._img_ids = list(sorted(self._coco.imgs.keys()))
- self.det_boxes = []
- self.nms_thresh = args_detection.nms_thresh
- self.multi_label = args_detection.multi_label
- self.multi_label_thresh = args_detection.multi_label_thresh
- # self.coco_catids = self._coco.getCatIds()
- self.coco_catIds = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27,
- 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53,
- 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80,
- 81, 82, 84, 85, 86, 87, 88, 89, 90]
-
- def do_nms_for_results(self):
- """Get result boxes."""
- # np.save('/opt/disk1/hjc/yolov5_positive_policy/result.npy', self.results)
- for image_id in self.results:
- for clsi in self.results[image_id]:
- dets = self.results[image_id][clsi]
- dets = np.array(dets)
- keep_index = self._diou_nms(dets, thresh=self.nms_thresh)
-
- keep_box = [{'image_id': int(image_id),
- 'category_id': int(clsi),
- 'bbox': list(dets[i][:4].astype(float)),
- 'score': dets[i][4].astype(float)}
- for i in keep_index]
- self.det_boxes.extend(keep_box)
-
- def _nms(self, predicts, threshold):
- """Calculate NMS."""
- # convert xywh -> xmin ymin xmax ymax
- x1 = predicts[:, 0]
- y1 = predicts[:, 1]
- x2 = x1 + predicts[:, 2]
- y2 = y1 + predicts[:, 3]
- scores = predicts[:, 4]
-
- areas = (x2 - x1 + 1) * (y2 - y1 + 1)
- order = scores.argsort()[::-1]
-
- reserved_boxes = []
- while order.size > 0:
- i = order[0]
- reserved_boxes.append(i)
- max_x1 = np.maximum(x1[i], x1[order[1:]])
- max_y1 = np.maximum(y1[i], y1[order[1:]])
- min_x2 = np.minimum(x2[i], x2[order[1:]])
- min_y2 = np.minimum(y2[i], y2[order[1:]])
-
- intersect_w = np.maximum(0.0, min_x2 - max_x1 + 1)
- intersect_h = np.maximum(0.0, min_y2 - max_y1 + 1)
- intersect_area = intersect_w * intersect_h
- ovr = intersect_area / (areas[i] + areas[order[1:]] - intersect_area)
-
- indexes = np.where(ovr <= threshold)[0]
- order = order[indexes + 1]
- return reserved_boxes
-
- def _diou_nms(self, dets, thresh=0.5):
- """
- convert xywh -> xmin ymin xmax ymax
- """
- x1 = dets[:, 0]
- y1 = dets[:, 1]
- x2 = x1 + dets[:, 2]
- y2 = y1 + dets[:, 3]
- scores = dets[:, 4]
- areas = (x2 - x1 + 1) * (y2 - y1 + 1)
- order = scores.argsort()[::-1]
- keep = []
- while order.size > 0:
- i = order[0]
- keep.append(i)
- 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)
- center_x1 = (x1[i] + x2[i]) / 2
- center_x2 = (x1[order[1:]] + x2[order[1:]]) / 2
- center_y1 = (y1[i] + y2[i]) / 2
- center_y2 = (y1[order[1:]] + y2[order[1:]]) / 2
- inter_diag = (center_x2 - center_x1) ** 2 + (center_y2 - center_y1) ** 2
- out_max_x = np.maximum(x2[i], x2[order[1:]])
- out_max_y = np.maximum(y2[i], y2[order[1:]])
- out_min_x = np.minimum(x1[i], x1[order[1:]])
- out_min_y = np.minimum(y1[i], y1[order[1:]])
- outer_diag = (out_max_x - out_min_x) ** 2 + (out_max_y - out_min_y) ** 2
- diou = ovr - inter_diag / outer_diag
- diou = np.clip(diou, -1, 1)
- inds = np.where(diou <= thresh)[0]
- order = order[inds + 1]
- return keep
-
- def write_result(self):
- """Save result to file."""
- import json
- t = datetime.datetime.now().strftime('_%Y_%m_%d_%H_%M_%S')
- try:
- self.file_path = self.save_prefix + '/predict' + t + '.json'
- f = open(self.file_path, 'w')
- json.dump(self.det_boxes, f)
- except IOError as e:
- raise RuntimeError("Unable to open json file to dump. What(): {}".format(str(e)))
- else:
- f.close()
- return self.file_path
-
- def get_eval_result(self):
- """Get eval result."""
- coco_gt = COCO(self.ann_file)
- coco_dt = coco_gt.loadRes(self.file_path)
- coco_eval = COCOeval(coco_gt, coco_dt, 'bbox')
- coco_eval.evaluate()
- coco_eval.accumulate()
- rdct = Redirct()
- stdout = sys.stdout
- sys.stdout = rdct
- coco_eval.summarize()
- sys.stdout = stdout
- return rdct.content
-
- def detect(self, outputs, batch, img_shape, image_id):
- """Detect boxes."""
- outputs_num = len(outputs)
- # output [|32, 52, 52, 3, 85| ]
- for batch_id in range(batch):
- for out_id in range(outputs_num):
- # 32, 52, 52, 3, 85
- out_item = outputs[out_id]
- # 52, 52, 3, 85
- out_item_single = out_item[batch_id, :]
- # get number of items in one head, [B, gx, gy, anchors, 5+80]
- dimensions = out_item_single.shape[:-1]
- out_num = 1
- for d in dimensions:
- out_num *= d
- ori_w, ori_h = img_shape[batch_id]
- img_id = int(image_id[batch_id])
- x = out_item_single[..., 0] * ori_w
- y = out_item_single[..., 1] * ori_h
- w = out_item_single[..., 2] * ori_w
- h = out_item_single[..., 3] * ori_h
-
- conf = out_item_single[..., 4:5]
- cls_emb = out_item_single[..., 5:]
- cls_argmax = np.expand_dims(np.argmax(cls_emb, axis=-1), axis=-1)
- x = x.reshape(-1)
- y = y.reshape(-1)
- w = w.reshape(-1)
- h = h.reshape(-1)
- x_top_left = x - w / 2.
- y_top_left = y - h / 2.
- cls_emb = cls_emb.reshape(-1, self.num_classes)
- if self.multi_label:
- conf = conf.reshape(-1, 1)
- # create all False
- confidence = cls_emb * conf
- flag = cls_emb > self.multi_label_thresh
- flag = flag.nonzero()
- for index in range(len(flag[0])):
- i = flag[0][index]
- j = flag[1][index]
- confi = confidence[i][j]
- if confi < self.ignore_threshold:
- continue
- if img_id not in self.results:
- self.results[img_id] = defaultdict(list)
- x_lefti = max(0, x_top_left[i])
- y_lefti = max(0, y_top_left[i])
- wi = min(w[i], ori_w)
- hi = min(h[i], ori_h)
- clsi = j
- # transform catId to match coco
- coco_clsi = self.coco_catIds[clsi]
- self.results[img_id][coco_clsi].append([x_lefti, y_lefti, wi, hi, confi])
- else:
- cls_argmax = np.expand_dims(np.argmax(cls_emb, axis=-1), axis=-1)
- conf = conf.reshape(-1)
- cls_argmax = cls_argmax.reshape(-1)
-
- # create all False
- flag = np.random.random(cls_emb.shape) > sys.maxsize
- for i in range(flag.shape[0]):
- c = cls_argmax[i]
- flag[i, c] = True
- confidence = cls_emb[flag] * conf
-
- for x_lefti, y_lefti, wi, hi, confi, clsi in zip(x_top_left, y_top_left, w, h, confidence,
- cls_argmax):
- if confi < self.ignore_threshold:
- continue
- if img_id not in self.results:
- self.results[img_id] = defaultdict(list)
- x_lefti = max(0, x_lefti)
- y_lefti = max(0, y_lefti)
- wi = min(wi, ori_w)
- hi = min(hi, ori_h)
- # transform catId to match coco
- coco_clsi = self.coco_catids[clsi]
- self.results[img_id][coco_clsi].append([x_lefti, y_lefti, wi, hi, confi])
-
-
- if __name__ == "__main__":
- start_time = time.time()
-
- args.outputs_dir = os.path.join(args.log_path,
- datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
- args.logger = get_logger(args.outputs_dir, 0)
-
- # init detection engine
- detection = DetectionEngine(args)
-
- coco = COCO(args.ann_file)
- result_path = args.result_files
-
- files = os.listdir(args.dataset_path)
-
- for file in files:
- img_ids_name = file.split('.')[0]
- img_id_ = int(np.squeeze(img_ids_name))
- imgIds = coco.getImgIds(imgIds=[img_id_])
- img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
- image_shape = ((img['width'], img['height']),)
- img_id_ = (np.squeeze(img_ids_name),)
-
- result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
- result_path_1 = os.path.join(result_path, img_ids_name + "_1.bin")
- result_path_2 = os.path.join(result_path, img_ids_name + "_2.bin")
-
- output_small = np.fromfile(result_path_0, dtype=np.float32).reshape(1, 20, 20, 3, 85)
- output_me = np.fromfile(result_path_1, dtype=np.float32).reshape(1, 40, 40, 3, 85)
- output_big = np.fromfile(result_path_2, dtype=np.float32).reshape(1, 80, 80, 3, 85)
-
- detection.detect([output_small, output_me, output_big], args.per_batch_size, image_shape, img_id_)
-
- args.logger.info('Calculating mAP...')
- detection.do_nms_for_results()
- result_file_path = detection.write_result()
- args.logger.info('result file path: {}'.format(result_file_path))
- eval_result = detection.get_eval_result()
-
- cost_time = time.time() - start_time
- args.logger.info('\n=============coco 310 infer reulst=========\n' + eval_result)
- args.logger.info('testing cost time {:.2f}h'.format(cost_time / 3600.))
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