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Rename img_size to imgsz (#86)

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Glenn Jocher
2022-12-24 00:39:09 +01:00
committed by GitHub
parent ae2443c210
commit 6432afc5f9
25 changed files with 98 additions and 98 deletions
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18
ultralytics/yolo/utils/modeling/tasks.py
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@ -51,8 +51,8 @@ class BaseModel(nn.Module):
self.info()
return self
def info(self, verbose=False, img_size=640): # print model information
model_info(self, verbose, img_size)
def info(self, verbose=False, imgsz=640): # print model information
model_info(self, verbose, imgsz)
def _apply(self, fn):
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
@ -117,7 +117,7 @@ class DetectionModel(BaseModel):
return self._forward_once(x, profile, visualize) # single-scale inference, train
def _forward_augment(self, x):
img_size = x.shape[-2:] # height, width
imgsz = x.shape[-2:] # height, width
s = [1, 0.83, 0.67] # scales
f = [None, 3, None] # flips (2-ud, 3-lr)
y = [] # outputs
@ -125,25 +125,25 @@ class DetectionModel(BaseModel):
xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
yi = self._forward_once(xi)[0] # forward
# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
yi = self._descale_pred(yi, fi, si, img_size)
yi = self._descale_pred(yi, fi, si, imgsz)
y.append(yi)
y = self._clip_augmented(y) # clip augmented tails
return torch.cat(y, 1), None # augmented inference, train
def _descale_pred(self, p, flips, scale, img_size):
def _descale_pred(self, p, flips, scale, imgsz):
# de-scale predictions following augmented inference (inverse operation)
if self.inplace:
p[..., :4] /= scale # de-scale
if flips == 2:
p[..., 1] = img_size[0] - p[..., 1] # de-flip ud
p[..., 1] = imgsz[0] - p[..., 1] # de-flip ud
elif flips == 3:
p[..., 0] = img_size[1] - p[..., 0] # de-flip lr
p[..., 0] = imgsz[1] - p[..., 0] # de-flip lr
else:
x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale
if flips == 2:
y = img_size[0] - y # de-flip ud
y = imgsz[0] - y # de-flip ud
elif flips == 3:
x = img_size[1] - x # de-flip lr
x = imgsz[1] - x # de-flip lr
p = torch.cat((x, y, wh, p[..., 4:]), -1)
return p