minor fix
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17
detect.py
17
detect.py
@ -63,6 +63,9 @@ def detect(save_img=False):
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# Run inference
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if device.type != 'cpu':
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model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
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old_img_w = old_img_h = imgsz
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old_img_b = 1
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t0 = time.time()
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for path, img, im0s, vid_cap in dataset:
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img = torch.from_numpy(img).to(device)
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@ -71,13 +74,22 @@ def detect(save_img=False):
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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# Warmup
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if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
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old_img_b = img.shape[0]
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old_img_h = img.shape[2]
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old_img_w = img.shape[3]
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for i in range(3):
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model(img, augment=opt.augment)[0]
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# Inference
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t1 = time_synchronized()
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pred = model(img, augment=opt.augment)[0]
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t2 = time_synchronized()
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# Apply NMS
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pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
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t2 = time_synchronized()
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t3 = time_synchronized()
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# Apply Classifier
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if classify:
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@ -93,7 +105,6 @@ def detect(save_img=False):
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # img.jpg
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txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
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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 len(det):
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# Rescale boxes from img_size to im0 size
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@ -117,7 +128,7 @@ def detect(save_img=False):
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plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
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# Print time (inference + NMS)
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#print(f'{s}Done. ({t2 - t1:.3f}s)')
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#print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}s) NMS')
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# Stream results
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if view_img:
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