got it to run, but it hangs

5 files changed, 18 insertions, 9 deletions

diff --git a/__pycache__/hyperparameters.cpython-38.pyc b/__pycache__/hyperparameters.cpython-38.pyc
index 9b86a7da..8654cf2c 100644
--- a/__pycache__/hyperparameters.cpython-38.pyc
+++ b/__pycache__/hyperparameters.cpython-38.pyc
diff --git a/__pycache__/losses.cpython-38.pyc b/__pycache__/losses.cpython-38.pyc
index ebfd7772..c409cebf 100644
--- a/__pycache__/losses.cpython-38.pyc
+++ b/__pycache__/losses.cpython-38.pyc
diff --git a/__pycache__/preprocess.cpython-38.pyc b/__pycache__/preprocess.cpython-38.pyc
index e2f42bca..a4fcfb04 100644
--- a/__pycache__/preprocess.cpython-38.pyc
+++ b/__pycache__/preprocess.cpython-38.pyc
diff --git a/losses.py b/losses.py
index 2cb51e15..bf6f6973 100644
--- a/losses.py
+++ b/losses.py
@@ -68,9 +68,12 @@ class YourModel(tf.keras.Model):
        #  for layer in self.vgg16:
        #         layer.trainable = False
 
-        self.indexed_layers = [layer for layer in self.vgg16 if layer.name == "conv1"]
+        self.indexed_layers = [layer for layer in self.vgg16 if "conv1" in layer.name]
         print(self.indexed_layers)
-        self.desired = [layer.name for layer in self.vgg16 if layer.name == "conv1"]
+        self.desired = [layer.name for layer in self.vgg16 if "conv1" in layer.name]
+
+        # create a map of the layers to their corresponding number of filters if it is a convolutional layer
+        self.layer_to_filters = {layer.name: layer.filters for layer in self.vgg16 if "conv" in layer.name}
 
     def call(self, x):
         layers = []
@@ -99,15 +102,18 @@ class YourModel(tf.keras.Model):
         return (self.alpha * content_l) + (self.beta * style_l)
         
     def content_loss(self, photo_layers, input_layers):
+        print(photo_layers, input_layers)
         L_content = tf.reduce_mean(tf.square(photo_layers - input_layers))
         print(L_content)
         return L_content
     
-    def layer_loss(art_layers, input_layers, layer):
+    def layer_loss(self, art_layers, input_layers, layer):
+        # vectorize the art_layers
+        art_layers = tf.reshape(art_layers, (-1, art_layers.shape[-1]))
+        # vectorize the input_layers
+        input_layers = tf.reshape(input_layers, (-1, input_layers.shape[-1]))
 
-        #vectorize the inputs
-        art_vector = art_layers.reshape(-1, 224**2)
-        input_vector = input_layers.reshape(-1, 224**2)
+        print('layer', layer.name, self.layer_to_filters[layer.name])
 
         # get the gram matrix
         input_dim = input_layers.shape[0]
@@ -122,18 +128,19 @@ class YourModel(tf.keras.Model):
         # N depends on # of filters in the layer, M depends on hight and width of feature map
         M_l = art_layers.shape[0] * art_layers.shape[1]
         
-        # layer.filteres might not work
-        E_l = 1/4 * (layer.filters**(-2)) * (M_l**(-2)) * np.sum(np.square(G - input_layers))
+        # layer.filters might not work
+        E_l = 1/4 * (self.layer_to_filters[layer.name]**(-2)) * (M_l**(-2)) * np.sum(np.square(G - input_layers))
 
         # while Sotech is botty:
         #         Jayson_tatum.tear_acl()
         #         return ("this is just another day")
+        return E_l
 
     def style_loss(self, art_layers, input_layers):
         L_style = 0
         for layer in self.indexed_layers:
             L_style += self.layer_loss(art_layers, input_layers, layer)
-            print('this is style loss',L_style)
+            print('this is style loss', L_style)
         return L_style
             
     def train_step(self):
diff --git a/main.py b/main.py
index 063670b8..8363fcef 100644
--- a/main.py
+++ b/main.py
@@ -55,7 +55,9 @@ def main():
     print('this is',ARGS.content)
 
     content_image = imread(ARGS.content)
+    content_image = np.resize(content_image, (255, 255, 3))
     style_image = imread(ARGS.style)
+    style_image = np.resize(style_image, (255, 255, 3))
     my_model = YourModel(content_image=content_image, style_image=style_image)
     train(my_model)