testing

3 files changed, 6 insertions, 6 deletions

diff --git a/__pycache__/losses.cpython-38.pyc b/__pycache__/losses.cpython-38.pyc
index d25c70d0..1f2c9bf4 100644
--- a/__pycache__/losses.cpython-38.pyc
+++ b/__pycache__/losses.cpython-38.pyc
diff --git a/losses.py b/losses.py
index 51f387a4..99b1eded 100644
--- a/losses.py
+++ b/losses.py
@@ -11,16 +11,16 @@ class YourModel(tf.keras.Model):
     def __init__(self, content_image, style_image): #normalize these images to float values
         super(YourModel, self).__init__()
        
-        self.content_image = transform.resize(content_image, tf.shape(style_image), anti_aliasing=True, preserve_range=True).astype('uint8')
+        self.content_image = transform.resize(content_image, tf.shape(style_image), anti_aliasing=True)
         self.content_image = tf.expand_dims(self.content_image, axis=0)
         print(self.content_image)
         
         #perhaps consider cropping to avoid distortion
-        self.style_image = transform.resize(style_image, tf.shape(style_image), anti_aliasing=True, preserve_range=True).astype('uint8')
+        self.style_image = transform.resize(style_image, tf.shape(style_image), anti_aliasing=True)
         self.style_image = tf.expand_dims(self.style_image, axis=0)
         #self.x = tf.Variable(initial_value = self.content_image.numpy().astype(np.float32), trainable=True) 
         self.x = tf.Variable(initial_value = np.random.rand(self.content_image.shape[0],
-        self.content_image.shape[1], self.content_image.shape[2], self.content_image.shape[3]).astype('uint8'), trainable=True) 
+        self.content_image.shape[1], self.content_image.shape[2], self.content_image.shape[3]).astype(np.float32), trainable=True) 
 
         self.alpha = hp.alpha
         self.beta = hp.beta
@@ -117,7 +117,7 @@ class YourModel(tf.keras.Model):
        return (self.alpha * content_l) + (self.beta * style_l)
         
     def content_loss(self, photo_layers, input_layers):
-        L_content = tf.constant(0.0).astype('uint8')
+        L_content = tf.constant(0.0)
         for i in range(len(photo_layers)):
               pl = photo_layers[i]
               il = input_layers[i]
diff --git a/main.py b/main.py
index 2d8c216a..605ddee7 100644
--- a/main.py
+++ b/main.py
@@ -54,7 +54,7 @@ def main():
 
     content_image = imread(ARGS.content)
     style_image = imread(ARGS.style)
-    style_image = transform.resize(style_image, content_image.shape).astype('uint8')
+    style_image = transform.resize(style_image, content_image.shape)
     my_model = YourModel(content_image=content_image, style_image=style_image)
     my_model.vgg16.build([1, 255, 255, 3])
     my_model.vgg16.load_weights('vgg16_imagenet.h5', by_name=True)
@@ -62,7 +62,7 @@ def main():
     
     final_image = tf.squeeze(my_model.x)
 
-    plt.imshow(final_image)
+    plt.imshow(final_image).astype('uint8')
 
     imsave(ARGS.savefile, final_image)