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augmented inference

This commit is contained in:
Glenn Jocher 2020-06-05 21:14:15 -07:00
parent d5d160449c
commit b810b21299
2 changed files with 3 additions and 3 deletions
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4
models/yolo.py
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@ -72,8 +72,8 @@ class Model(nn.Module):
s = [0.83, 0.67] # scales s = [0.83, 0.67] # scales
y = [] y = []
for i, xi in enumerate((x, for i, xi in enumerate((x,
torch_utils.scale_img(x.flip(3), s[0], same_shape=False), # flip-lr and scale torch_utils.scale_img(x.flip(3), s[0]), # flip-lr and scale
torch_utils.scale_img(x, s[1], same_shape=False), # scale torch_utils.scale_img(x, s[1]), # scale
)): )):
# cv2.imwrite('img%g.jpg' % i, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # cv2.imwrite('img%g.jpg' % i, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])
y.append(self.forward_once(xi)[0]) y.append(self.forward_once(xi)[0])

2
utils/torch_utils.py
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@ -135,7 +135,7 @@ def load_classifier(name='resnet101', n=2):
return model return model
def scale_img(img, ratio=1.0, same_shape=True): # img(16,3,256,416), r=ratio def scale_img(img, ratio=1.0, same_shape=False): # img(16,3,256,416), r=ratio
# scales img(bs,3,y,x) by ratio # scales img(bs,3,y,x) by ratio
h, w = img.shape[2:] h, w = img.shape[2:]
s = (int(h * ratio), int(w * ratio)) # new size s = (int(h * ratio), int(w * ratio)) # new size