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import argparse
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from models import * # set ONNX_EXPORT in models.py
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from utils.datasets import *
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from utils.utils import *
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def detect(save_img=False):
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imgsz = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
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out, source, weights, half, view_img, save_txt = opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt
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webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
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# Initialize
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device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device)
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if os.path.exists(out):
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shutil.rmtree(out) # delete output folder
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os.makedirs(out) # make new output folder
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# Initialize model
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model = Darknet(opt.cfg, imgsz)
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# Load weights
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attempt_download(weights)
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if weights.endswith('.pt'): # pytorch format
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model.load_state_dict(torch.load(weights, map_location=device)['model'])
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else: # darknet format
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load_darknet_weights(model, weights)
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# Second-stage classifier
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classify = False
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if classify:
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modelc = torch_utils.load_classifier(name='resnet101', n=2) # initialize
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modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights
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modelc.to(device).eval()
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# Eval mode
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model.to(device).eval()
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# Fuse Conv2d + BatchNorm2d layers
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# model.fuse()
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# Export mode
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if ONNX_EXPORT:
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model.fuse()
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img = torch.zeros((1, 3) + imgsz) # (1, 3, 320, 192)
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f = opt.weights.replace(opt.weights.split('.')[-1], 'onnx') # *.onnx filename
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torch.onnx.export(model, img, f, verbose=False, opset_version=11,
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input_names=['images'], output_names=['classes', 'boxes'])
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# Validate exported model
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import onnx
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model = onnx.load(f) # Load the ONNX model
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onnx.checker.check_model(model) # Check that the IR is well formed
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print(onnx.helper.printable_graph(model.graph)) # Print a human readable representation of the graph
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return
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# Half precision
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half = half and device.type != 'cpu' # half precision only supported on CUDA
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if half:
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model.half()
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# Set Dataloader
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vid_path, vid_writer = None, None
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if webcam:
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view_img = True
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torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference
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dataset = LoadStreams(source, img_size=imgsz)
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else:
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save_img = True
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dataset = LoadImages(source, img_size=imgsz)
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# Get names and colors
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names = load_classes(opt.names)
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colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
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output_json_file = open(opt.json, 'w')
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# Run inference
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t0 = time.time()
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img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
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_ = model(img.half() if half else img.float()) if device.type != 'cpu' else None # run once
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for i, (path, img, im0s, vid_cap) in enumerate(dataset):
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json = {"detections": []}
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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# Inference
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t1 = torch_utils.time_synchronized()
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pred = model(img, augment=opt.augment)[0]
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t2 = torch_utils.time_synchronized()
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# to float
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if half:
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pred = pred.float()
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# Apply NMS
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pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres,
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multi_label=False, classes=opt.classes, agnostic=opt.agnostic_nms)
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# Apply Classifier
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if classify:
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pred = apply_classifier(pred, modelc, img, im0s)
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# Process detections
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for i, det in enumerate(pred): # detections for image i
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if webcam: # batch_size >= 1
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p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
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else:
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p, s, im0 = path, '', im0s
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save_path = str(Path(out) / Path(p).name)
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s += '%gx%g ' % img.shape[2:] # print string
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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if det is not None and len(det):
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# Rescale boxes from imgsz to im0 size
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
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# Print results
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum() # detections per class
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s += '%g %ss, ' % (n, names[int(c)]) # add to string
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# Write results
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for *xyxy, conf, cls in det:
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if save_txt: # Write to file
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file:
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file.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format
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label_to_display = '%s %.2f' % (names[int(cls)], conf)
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name = '%s' % names[int(cls)]
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probability = float('%.3f' % conf)
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left, top, right, bottom = plot_one_box(xyxy, im0, label=label_to_display, color=colors[int(cls)])
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json["detections"].append({"classes": [{"name": name, "probability": probability}],
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"rect": {"bottom": bottom, "left": left, "right": right, "top": top}})
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if save_img or view_img: # Add bbox to image
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label = '%s %.2f' % (names[int(cls)], conf)
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plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
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# Print time (inference + NMS)
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print('%sDone. (%.3fs)' % (s, t2 - t1))
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json["frameId"] = i
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line = str(json).replace("\'", "\"")
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output_json_file.write(line)
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output_json_file.write("\n")
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# Stream results
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if view_img:
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cv2.imshow(p, im0)
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if cv2.waitKey(1) == ord('q'): # q to quit
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raise StopIteration
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# Save results (image with detections)
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if save_img:
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if dataset.mode == 'images':
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cv2.imwrite(save_path, im0)
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else:
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if vid_path != save_path: # new video
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vid_path = save_path
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if isinstance(vid_writer, cv2.VideoWriter):
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vid_writer.release() # release previous video writer
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h))
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vid_writer.write(im0)
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output_json_file.close()
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if save_txt or save_img:
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print('Results saved to %s' % os.getcwd() + os.sep + out)
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if platform == 'darwin': # MacOS
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os.system('open ' + save_path)
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print('Done. (%.3fs)' % (time.time() - t0))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
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parser.add_argument('--names', type=str, default='data/coco.names', help='*.names path')
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parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path')
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parser.add_argument('--source', type=str, default='data/samples', help='source') # input file/folder, 0 for webcam
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parser.add_argument('--output', type=str, default='output', help='output folder') # output folder
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parser.add_argument('--img-size', type=int, default=512, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
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parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
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parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
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parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
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parser.add_argument('--view-img', action='store_true', help='display results')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
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parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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parser.add_argument('--json', type=str, default='output.txt', help='output json file ')
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parser.add_argument('--save-video', type=bool, default=True, help='saving output video')
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opt = parser.parse_args()
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opt.cfg = check_file(opt.cfg) # check file
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opt.names = check_file(opt.names) # check file
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print(opt)
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with torch.no_grad():
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detect(opt.save_video)
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