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Adjust NMS time limit warning to batch size (#7156)
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@ -709,6 +709,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
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list of detections, on (n,6) tensor per image [xyxy, conf, cls]
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"""
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bs = prediction.shape[0] # batch size
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nc = prediction.shape[2] - 5 # number of classes
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xc = prediction[..., 4] > conf_thres # candidates
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@ -719,13 +720,13 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
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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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max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
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time_limit = 10.0 # seconds to quit after
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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 = time.time()
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output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
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t, warn_time = time.time(), True
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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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@ -789,7 +790,9 @@ 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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LOGGER.warning(f'WARNING: NMS time limit {time_limit}s exceeded')
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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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break # time limit exceeded
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return output
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