stacked_autoencoder.py
def images(self, image):
        """Get dictionary of numpy images for logging."""
        decoding = self.full_decode(self.full_encode(image))
        images = {
            'input': image,
            'recons': decoding,
            'diff': image - decoding,
        }
        images = {
            k: image_utils.images_to_grid(
                image_utils.normalize_0_1(v.detach().cpu().numpy()),
                padding_value=1.)
            for k, v in images.items()
        }
        prev_images = self._prev_model.images(image)
        images.update(
            {'prev_' + k: v for k, v in prev_images.items()}
        )
        return images
Get dictionary of numpy images for logging.
Get dictionary of numpy images for logging.
Get dictionary of numpy images for logging .
def images(self, image):
        decoding = self.full_decode(self.full_encode(image))
        images = {
            'input': image,
            'recons': decoding,
            'diff': image - decoding,
        }
        images = {
            k: image_utils.images_to_grid(
                image_utils.normalize_0_1(v.detach().cpu().numpy()),
                padding_value=1.)
            for k, v in images.items()
        }
        prev_images = self._prev_model.images(image)
        images.update(
            {'prev_' + k: v for k, v in prev_images.items()}
        )
        return images
def images ( self , image ) : decoding = self . full_decode ( self . full_encode ( image ) ) images = { 'input' : image , 'recons' : decoding , 'diff' : image - decoding , } images = { k : image_utils . images_to_grid ( image_utils . normalize_0_1 ( v . detach ( ) . cpu ( ) . numpy ( ) ) , padding_value = 1. ) for k , v in images . items ( ) } prev_images = self . _prev_model . images ( image ) images . update ( { 'prev_' + k : v for k , v in prev_images . items ( ) } ) return images
Get dictionary of numpy images for logging.
Get dictionary of numpy images for logging .
"""Get dictionary of numpy images for logging."""
self
image