Merge branch 'fix-style-transfer' into 'master'

[style] Fixed issues with style-transfer

Closes #82

See merge request !80

bob/learn/tensorflow/loss/StyleLoss.py

@@ -5,6 +5,7 @@
 import logging
 import tensorflow as tf
 logger = logging.getLogger("bob.learn.tensorflow")
+import functools
 
 
 def content_loss(noises, content_features):
@@ -33,7 +34,7 @@ def content_loss(noises, content_features):
     content_losses = []
     for n,c in zip(noises, content_features):
         content_losses.append((2 * tf.nn.l2_loss(n - c) / c.size))
-    return reduce(tf.add, content_losses)
+    return functools.reduce(tf.add, content_losses)
 
      
 def linear_gram_style_loss(noises, gram_style_features):
@@ -63,7 +64,7 @@ def linear_gram_style_loss(noises, gram_style_features):
     for n,s in zip(noises, gram_style_features):
         style_losses.append((2 * tf.nn.l2_loss(n - s)) / s.size)
 
-    return reduce(tf.add, style_losses)
+    return functools.reduce(tf.add, style_losses)
 
 
 
@@ -82,7 +83,7 @@ def denoising_loss(noise):
     """
     def _tensor_size(tensor):
         from operator import mul
-        return reduce(mul, (d.value for d in tensor.get_shape()), 1)
+        return functools.reduce(mul, (d.value for d in tensor.get_shape()), 1)
 
     shape = noise.get_shape().as_list()
 

bob/learn/tensorflow/script/style_transfer.py

@@ -49,7 +49,7 @@ logger = logging.getLogger(__name__)
               help='Number of iterations to generate the image',
               default=1000)
 @click.option('--learning_rate',
-              '-i',
+              '-r',
               type=click.types.FLOAT,
               help='Learning rate.',
               default=1.)

bob/learn/tensorflow/style_transfer/neural_transfer.py

@@ -163,6 +163,7 @@ def do_style_transfer(content_image, style_images,
 
     # Reshaping to NxWxHxC and converting to the tensorflow format
     # content
+    original_image = content_image
     content_image = bob.io.image.to_matplotlib(content_image).astype("float32")
     content_image = numpy.reshape(content_image, wise_shape(content_image.shape))
 
@@ -253,11 +254,12 @@ def do_style_transfer(content_image, style_images,
                 if normalized_style_image.shape[0] == 1:
                     normalized_style_image_yuv = bob.ip.color.rgb_to_yuv(bob.ip.color.gray_to_rgb(normalized_style_image[0,:,:]))
                     # Loading the content image and clipping from 0-255 in case is in another scale
-                    scaled_content_image = normalize4save(bob.io.base.load(content_image_path).astype("float32")).astype("float64")
+                    #scaled_content_image = normalize4save(bob.io.base.load(content_image_path).astype("float32")).astype("float64")
+                    scaled_content_image = original_image.astype("float64")
                     content_image_yuv = bob.ip.color.rgb_to_yuv(bob.ip.color.gray_to_rgb(scaled_content_image))
                 else:
                     normalized_style_image_yuv = bob.ip.color.rgb_to_yuv(bob.ip.color.gray_to_rgb(bob.ip.color.rgb_to_gray(normalized_style_image)))
-                    content_image_yuv = bob.ip.color.rgb_to_yuv(bob.io.base.load(content_image_path))
+                    content_image_yuv = bob.ip.color.rgb_to_yuv(original_image)
 
                 output_image = numpy.zeros(shape=content_image_yuv.shape, dtype="uint8")
                 output_image[0,:,:] = normalized_style_image_yuv[0,:,:]