Unverified 提交 3373aab5 authored 作者: Glenn Jocher's avatar Glenn Jocher 提交者: GitHub

NMS unused variable fix (#7161)

* NMS unused variable fix * Update general.py
上级 e19f87eb
......@@ -703,7 +703,7 @@ def clip_coords(boxes, shape):
def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
labels=(), max_det=300):
"""Runs Non-Maximum Suppression (NMS) on inference results
"""Non-Maximum Suppression (NMS) on inference results to reject overlapping bounding boxes
Returns:
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
......@@ -718,18 +718,19 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
# Settings
min_wh, max_wh = 2, 7680 # (pixels) minimum and maximum box width and height
# min_wh = 2 # (pixels) minimum box width and height
max_wh = 7680 # (pixels) maximum box width and height
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
time_limit = 0.030 * bs # seconds to quit after
redundant = True # require redundant detections
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
merge = False # use merge-NMS
t, warn_time = time.time(), True
t = time.time()
output = [torch.zeros((0, 6), device=prediction.device)] * bs
for xi, x in enumerate(prediction): # image index, image inference
# Apply constraints
x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
x = x[xc[xi]] # confidence
# Cat apriori labels if autolabelling
......@@ -790,9 +791,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
output[xi] = x[i]
if (time.time() - t) > time_limit:
if warn_time:
LOGGER.warning(f'WARNING: NMS time limit {time_limit:3f}s exceeded')
warn_time = False
LOGGER.warning(f'WARNING: NMS time limit {time_limit:.3f}s exceeded')
break # time limit exceeded
return output
......
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