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e19f87eb4b
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@ -703,7 +703,7 @@ def clip_coords(boxes, shape):
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def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
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labels=(), max_det=300):
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"""Runs Non-Maximum Suppression (NMS) on inference results
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"""Non-Maximum Suppression (NMS) on inference results to reject overlapping bounding boxes
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Returns:
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list of detections, on (n,6) tensor per image [xyxy, conf, cls]
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@ -718,18 +718,19 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
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assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
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# Settings
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min_wh, max_wh = 2, 7680 # (pixels) minimum and maximum box width and height
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# min_wh = 2 # (pixels) minimum box width and height
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max_wh = 7680 # (pixels) maximum box width and height
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max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
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time_limit = 0.030 * bs # seconds to quit after
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redundant = True # require redundant detections
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multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
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merge = False # use merge-NMS
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t, warn_time = time.time(), True
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t = time.time()
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output = [torch.zeros((0, 6), device=prediction.device)] * bs
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for xi, x in enumerate(prediction): # image index, image inference
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# Apply constraints
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x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
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# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
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x = x[xc[xi]] # confidence
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# Cat apriori labels if autolabelling
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@ -790,9 +791,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
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output[xi] = x[i]
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if (time.time() - t) > time_limit:
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if warn_time:
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LOGGER.warning(f'WARNING: NMS time limit {time_limit:3f}s exceeded')
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warn_time = False
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LOGGER.warning(f'WARNING: NMS time limit {time_limit:.3f}s exceeded')
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break # time limit exceeded
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return output
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