djghdfkjMerge branch 'master' of github.com:Unity-Technologies/Keras-GAN

cgan/cgan.py

@@ -1,4 +1,6 @@
 from __future__ import print_function, division
+import warnings
+warnings.filterwarnings("ignore")
 
 from keras.datasets import mnist
 from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
@@ -9,17 +11,19 @@ from keras.models import Sequential, Model
 from keras.optimizers import Adam
 
 import matplotlib.pyplot as plt
-
+import sys
 import numpy as np
+sys.path.append('../unity')
+from unity import semantic_maps, semantic_maps_shape
 
 class CGAN():
-    def __init__(self):
+    def __init__(self, image_size, channels, num_classes):
         # Input shape
-        self.img_rows = 28
-        self.img_cols = 28
-        self.channels = 1
+        self.img_rows = image_size
+        self.img_cols = image_size
+        self.channels = channels
         self.img_shape = (self.img_rows, self.img_cols, self.channels)
-        self.num_classes = 10
+        self.num_classes = num_classes
         self.latent_dim = 100
 
         optimizer = Adam(0.0002, 0.5)
@@ -109,11 +113,11 @@ class CGAN():
     def train(self, epochs, batch_size=128, sample_interval=50):
 
         # Load the dataset
-        (X_train, y_train), (_, _) = mnist.load_data()
-
+        #(X_train, y_train), (_, _) = mnist.load_data()
+        (X_train, y_train), (_, _) = semantic_maps(resize=self.img_cols)
         # Configure input
         X_train = (X_train.astype(np.float32) - 127.5) / 127.5
-        X_train = np.expand_dims(X_train, axis=3)
+        #X_train = np.expand_dims(X_train, axis=3)
         y_train = y_train.reshape(-1, 1)
 
         # Adversarial ground truths
@@ -161,8 +165,8 @@ class CGAN():
     def sample_images(self, epoch):
         r, c = 2, 5
         noise = np.random.normal(0, 1, (r * c, 100))
-        sampled_labels = np.arange(0, 10).reshape(-1, 1)
-
+        sampled_labels = np.zeros(10).reshape(-1, 1)
+        print("sampled_labels", sampled_labels)
         gen_imgs = self.generator.predict([noise, sampled_labels])
 
         # Rescale images 0 - 1
@@ -172,8 +176,11 @@ class CGAN():
         cnt = 0
         for i in range(r):
             for j in range(c):
-                axs[i,j].imshow(gen_imgs[cnt,:,:,0], cmap='gray')
-                axs[i,j].set_title("Digit: %d" % sampled_labels[cnt])
+                if (self.channels==1):
+                    axs[i,j].imshow(gen_imgs[cnt,:,:,0], cmap='gray')
+                else:
+                    axs[i,j].imshow(gen_imgs[cnt,:,:])
+                axs[i,j].set_title("Trait: %d" % sampled_labels[cnt])
                 axs[i,j].axis('off')
                 cnt += 1
         fig.savefig("images/%d.png" % epoch)
@@ -181,5 +188,8 @@ class CGAN():
 
 
 if __name__ == '__main__':
-    cgan = CGAN()
-    cgan.train(epochs=20000, batch_size=32, sample_interval=200)
+    image_size, channels, classes = semantic_maps_shape()
+    if (len(sys.argv)>1):
+        image_size = int(sys.argv[1])
+    cgan = CGAN(image_size, channels, 1)
+    cgan.train(epochs=20000, batch_size=8, sample_interval=200)

infogan/infogan.py

@@ -257,7 +257,7 @@ class INFOGAN():
         save(self.discriminator, "discriminator")
 
 
-if __name__ == '__main__':
+if __name__ == '__main__':    
     image_size, channels, classes = semantic_maps_shape()
     infogan = INFOGAN(image_size, channels, classes)
     infogan.train(epochs=50000, batch_size=32, sample_interval=50)