|
|
@@ -259,8 +259,8 @@ class ResizeByShort(ClsTransform):
|
|
|
im_short_size = min(im.shape[0], im.shape[1])
|
|
|
im_long_size = max(im.shape[0], im.shape[1])
|
|
|
scale = float(self.short_size) / im_short_size
|
|
|
- if self.max_size > 0 and np.round(
|
|
|
- scale * im_long_size) > self.max_size:
|
|
|
+ if self.max_size > 0 and np.round(scale *
|
|
|
+ im_long_size) > self.max_size:
|
|
|
scale = float(self.max_size) / float(im_long_size)
|
|
|
resized_width = int(round(im.shape[1] * scale))
|
|
|
resized_height = int(round(im.shape[0] * scale))
|
|
|
@@ -455,7 +455,7 @@ class ArrangeClassifier(ClsTransform):
|
|
|
tuple: 当mode为'train'或'eval'时,返回(im, label),分别对应图像np.ndarray数据、
|
|
|
图像类别id;当mode为'test'或'quant'时,返回(im, ),对应图像np.ndarray数据。
|
|
|
"""
|
|
|
- im = permute(im, False)
|
|
|
+ im = permute(im, False).astype('float32')
|
|
|
if self.mode == 'train' or self.mode == 'eval':
|
|
|
outputs = (im, label)
|
|
|
else:
|