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car-detection-bayes
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NMS and test batch_size updates

This commit is contained in:
Glenn Jocher 2020-03-29 20:41:32 -07:00
parent c6b59a0e8a
commit eb151a881e
2 changed files with 10 additions and 8 deletions
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6
train.py
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@ -180,12 +180,12 @@ def train():
collate_fn=dataset.collate_fn) collate_fn=dataset.collate_fn)
# Testloader # Testloader
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, img_size_test, batch_size * 2, testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, img_size_test, batch_size,
hyp=hyp, hyp=hyp,
rect=True, rect=True,
cache_images=opt.cache_images, cache_images=opt.cache_images,
single_cls=opt.single_cls), single_cls=opt.single_cls),
batch_size=batch_size * 2, batch_size=batch_size,
num_workers=nw, num_workers=nw,
pin_memory=True, pin_memory=True,
collate_fn=dataset.collate_fn) collate_fn=dataset.collate_fn)
@ -311,7 +311,7 @@ def train():
is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80 is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80
results, maps = test.test(cfg, results, maps = test.test(cfg,
data, data,
batch_size=batch_size * 2, batch_size=batch_size,
img_size=img_size_test, img_size=img_size_test,
model=ema.ema, model=ema.ema,
conf_thres=0.001 if final_epoch else 0.01, # 0.001 for best mAP, 0.01 for speed conf_thres=0.001 if final_epoch else 0.01, # 0.001 for best mAP, 0.01 for speed

4
utils/utils.py
View File

@ -543,7 +543,8 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
x = x[torch.isfinite(x).all(1)] x = x[torch.isfinite(x).all(1)]
# If none remain process next image # If none remain process next image
if not x.shape[0]: n = x.shape[0] # number of boxes
if not n:
continue continue
# Sort by confidence # Sort by confidence
@ -555,6 +556,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
boxes, scores = x[:, :4].clone() + c.view(-1, 1) * max_wh, x[:, 4] # boxes (offset by class), scores boxes, scores = x[:, :4].clone() + c.view(-1, 1) * max_wh, x[:, 4] # boxes (offset by class), scores
if method == 'merge': # Merge NMS (boxes merged using weighted mean) if method == 'merge': # Merge NMS (boxes merged using weighted mean)
i = torchvision.ops.boxes.nms(boxes, scores, iou_thres) i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
if n < 1000: # update boxes
iou = box_iou(boxes, boxes).tril_() # lower triangular iou matrix iou = box_iou(boxes, boxes).tril_() # lower triangular iou matrix
weights = (iou > iou_thres) * scores.view(-1, 1) weights = (iou > iou_thres) * scores.view(-1, 1)
weights /= weights.sum(0) weights /= weights.sum(0)