data_loading_tutorial.py 文件源码

def __call__(self, sample):
        image, landmarks = sample['image'], sample['landmarks']

        # swap color axis because
        # numpy image: H x W x C
        # torch image: C X H X W
        image = image.transpose((2, 0, 1))
        return {'image': torch.from_numpy(image),
                'landmarks': torch.from_numpy(landmarks)}


######################################################################
# Compose transforms
# ~~~~~~~~~~~~~~~~~~
#
# Now, we apply the transforms on an sample.
#
# Let's say we want to rescale the shorter side of the image to 256 and
# then randomly crop a square of size 224 from it. i.e, we want to compose
# ``Rescale`` and ``RandomCrop`` transforms.
# ``torchvision.transforms.Compose`` is a simple callable class which allows us
# to do this.
#