From d802c988a57d6afe4fca979384ba377ecc7edb66 Mon Sep 17 00:00:00 2001
From: Logan Bauman <logan_bauman@brown.edu>
Date: Fri, 6 May 2022 23:32:17 -0400
Subject: hi

---
 __pycache__/hyperparameters.cpython-38.pyc | Bin 341 -> 332 bytes
 __pycache__/losses.cpython-38.pyc          | Bin 4409 -> 4546 bytes
 hyperparameters.py                         |   6 ++---
 losses.py                                  |  37 +++++++++++++++++++++--------
 main.py                                    |  12 ++++++++--
 save.jpg                                   | Bin 39903 -> 46064 bytes
 6 files changed, 40 insertions(+), 15 deletions(-)

diff --git a/__pycache__/hyperparameters.cpython-38.pyc b/__pycache__/hyperparameters.cpython-38.pyc
index e1a90bd4..7d32eefa 100644
Binary files a/__pycache__/hyperparameters.cpython-38.pyc and b/__pycache__/hyperparameters.cpython-38.pyc differ
diff --git a/__pycache__/losses.cpython-38.pyc b/__pycache__/losses.cpython-38.pyc
index 71b86245..66e565ec 100644
Binary files a/__pycache__/losses.cpython-38.pyc and b/__pycache__/losses.cpython-38.pyc differ
diff --git a/hyperparameters.py b/hyperparameters.py
index b03db017..460543dc 100644
--- a/hyperparameters.py
+++ b/hyperparameters.py
@@ -9,17 +9,17 @@ Number of epochs. If you experiment with more complex networks you
 might need to increase this. Likewise if you add regularization that
 slows training.
 """
-num_epochs = 5000
+num_epochs = 1000
 
 """
 A critical parameter that can dramatically affect whether training
 succeeds or fails. The value for this depends significantly on which
 optimizer is used. Refer to the default learning rate parameter
 """
-learning_rate = 3e-2
+learning_rate = 1e-2
 
 momentum = 0.01
 
 alpha = 1e-2
 
-beta = 1e-5
+beta = 1e-4
diff --git a/losses.py b/losses.py
index 7198ebf4..5564ca33 100644
--- a/losses.py
+++ b/losses.py
@@ -1,8 +1,10 @@
 import tensorflow as tf
+import numpy as np
 from tensorflow.keras.layers import \
     Conv2D, AveragePooling2D
 from skimage import transform
 import hyperparameters as hp
+
 class YourModel(tf.keras.Model):
     """ Your own neural network model. """
 
@@ -11,17 +13,26 @@ class YourModel(tf.keras.Model):
        
         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)
         self.style_image = tf.expand_dims(self.style_image, axis=0)
-        self.x = tf.Variable(tf.expand_dims(tf.random.uniform(tf.shape(content_image)), axis=0), trainable=True) 
+        #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(np.float32), trainable=True) 
+
         self.alpha = hp.alpha
         self.beta = hp.beta
 
-        print(self.x.shape)
+        self.photo_layers = None
+        self.art_layers = None
+
 
-        print(self.content_image.shape, self.style_image.shape)
+
+        #(self.x.shape)
+
+        #print(self.content_image.shape, self.style_image.shape)
 
         self.optimizer = tf.keras.optimizers.Adam()
 
@@ -88,14 +99,20 @@ class YourModel(tf.keras.Model):
         return x, layers
 
     def loss_fn(self, p, a, x):
-        _, photo_layers = self.call(p)
-        _, art_layers = self.call(a)
-        _, input_layers = self.call(x)
-
-        content_l = self.content_loss(photo_layers, input_layers)
-        style_l = self.style_loss(art_layers, input_layers)
+       # print(p)
+       if(self.photo_layers == None):
+              _, self.photo_layers = self.call(p)
+       # print(a)
+       if(self.art_layers == None): 
+              _, self.art_layers = self.call(a)
+       # print(x) 
+       _, input_layers = self.call(x)
+         
+
+       content_l = self.content_loss(self.photo_layers, input_layers)
+       style_l = self.style_loss(self.art_layers, input_layers)
         # Equation 7
-        return (self.alpha * content_l) + (self.beta * style_l)
+       return (self.alpha * content_l) + (self.beta * style_l)
         
     def content_loss(self, photo_layers, input_layers):
         L_content = tf.constant(0.0)
diff --git a/main.py b/main.py
index 60574e63..a074f71d 100644
--- a/main.py
+++ b/main.py
@@ -1,7 +1,9 @@
 import os
 import sys
 import argparse
+import cv2
 import tensorflow as tf
+from skimage import transform
 
 import hyperparameters as hp
 from losses import YourModel
@@ -50,9 +52,15 @@ 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))
+    cv2.imshow('hi1', style_image)
+    cv2.waitKey(0)
+    
+    style_image = transform.resize(style_image, content_image.shape)
+    cv2.imshow('hi2', style_image)
+    cv2.waitKey(0)
     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)
diff --git a/save.jpg b/save.jpg
index 86b5f854..f4f49d6e 100644
Binary files a/save.jpg and b/save.jpg differ
-- 
cgit v1.2.3-70-g09d2