zjc
/
yolov5
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updates

This commit is contained in:
Glenn Jocher 2020-05-30 15:22:09 -07:00
parent 4f44aaf26b
commit 5a79b5f65c
1 changed files with 3 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
utils/utils.py
View File

@ -473,6 +473,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
max_det = 300 # maximum number of detections per image max_det = 300 # maximum number of detections per image
time_limit = 10.0 # seconds to quit after time_limit = 10.0 # seconds to quit after
redundant = conf_thres == 0.001 # require redundant detections
t = time.time() t = time.time()
nc = prediction[0].shape[1] - 5 # number of classes nc = prediction[0].shape[1] - 5 # number of classes
@ -528,7 +529,8 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
weights = iou * scores[None] # box weights weights = iou * scores[None] # box weights
x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
# i = i[iou.sum(1) > 1] # require redundancy if redundant:
i = i[iou.sum(1) > 1] # require redundancy
except: # possible CUDA error https://github.com/ultralytics/yolov3/issues/1139 except: # possible CUDA error https://github.com/ultralytics/yolov3/issues/1139
print(x, i, x.shape, i.shape) print(x, i, x.shape, i.shape)
pass pass