def test_img_normalize():
dummy_tensor = torch.rand(3, 64, 64).add_(-0.5).div_(0.5)
normalized_tensor = img_normalize(dummy_tensor)
assert normalized_tensor.min() == 0
assert normalized_tensor.max() == 1
dummy_tensor = torch.randn(3, 8, 8)
normalized_tensor = img_normalize(dummy_tensor, val_range=(-2, 2))
assert normalized_tensor.min() < 0
assert normalized_tensor.max() > 1
dummy_zero_tensor = torch.zeros(8, 8)
img_normalize(dummy_zero_tensor)
def test_img_normalize():
dummy_img_tensor = torch.rand(3, 64, 64)
transformed_tensor = dummy_img_tensor.add_(-0.5).div_(0.5)
normalized_tensor = img_normalize(transformed_tensor)
assert normalized_tensor.min() == 0
assert normalized_tensor.max() == 1
normalized_var = img_normalize(to_var(dummy_img_tensor))
assert normalized_var.data.min() == 0
assert normalized_var.data.max() == 1
dummy_tensor = torch.LongTensor(3, 8, 8).random_(-2, to=2).float()
normalized_tensor = img_normalize(dummy_tensor, img_range=(-2, 2))
assert normalized_tensor.min() >= 0
assert normalized_tensor.max() <= 1
dummy_zero_tensor = torch.zeros(8, 8)
img_normalize(dummy_zero_tensor)
def image(self, kw_images, epoch, prefix):
'''
Args:
kw_images: 4-D tensor [batch, channel, height, width]
epoch: step for TensorBoard logging
prefix: prefix string for tag
'''
[
self.writer.add_image(
f'{prefix}{tag}/{self._tag_base_counter + i}',
img_normalize(image), epoch)
for tag, images in kw_images.items()
for i, image in enumerate(images)
]
def test_psnr():
dummy_output = to_var(img_normalize(torch.rand(4, 3, 64, 64)))
dummy_target = to_var(img_normalize(torch.rand(4, 3, 64, 64)))
psnr = onegan.metrics.psnr(dummy_output, dummy_target)
assert psnr
def normalize(t):
return img_normalize(t, img_range=img_range)