commit for RRC

model.py

@@ -5,6 +5,7 @@
 import tensorflow as tf
 import numpy as np
 from six.moves import xrange
+import cv2
 
 from ops import *
 from utils import *
@@ -91,7 +92,7 @@ def build_model(self):
 
             self.sampler = self.sampler(self.z)
             self.D_, self.D_logits_ = self.discriminator(self.G, reuse=True)
-        
+
 
         self.d_sum = histogram_summary("d", self.D)
         self.d__sum = histogram_summary("d_", self.D_)
@@ -103,7 +104,7 @@ def build_model(self):
 
         self.d_loss_real_sum = scalar_summary("d_loss_real", self.d_loss_real)
         self.d_loss_fake_sum = scalar_summary("d_loss_fake", self.d_loss_fake)
-                                                    
+
         self.d_loss = self.d_loss_real + self.d_loss_fake
 
         self.g_loss_sum = scalar_summary("g_loss", self.g_loss)
@@ -119,7 +120,9 @@ def build_model(self):
     def train(self, config):
         """Train DCGAN"""
         if config.dataset == 'mnist':
-            data_X, data_y = self.load_mnist()
+#             data_X, data_y = self.load_mnist()
+            #import pdb;pdb.set_trace()
+            data_X, data_y = self.load_mnist_handcrafted()
         else:
             data = glob(os.path.join("./data", config.dataset, "*.jpg"))
         #np.random.shuffle(data)
@@ -137,7 +140,7 @@ def train(self, config):
         self.writer = SummaryWriter("./logs", self.sess.graph)
 
         sample_z = np.random.uniform(-1, 1, size=(self.sample_size , self.z_dim))
-        
+
         if config.dataset == 'mnist':
             sample_images = data_X[0:self.sample_size]
             sample_labels = data_y[0:self.sample_size]
@@ -146,9 +149,10 @@ def train(self, config):
             sample = [get_image(sample_file, self.image_size, is_crop=self.is_crop, resize_w=self.output_size, is_grayscale = self.is_grayscale) for sample_file in sample_files]
             if (self.is_grayscale):
                 sample_images = np.array(sample).astype(np.float32)[:, :, :, None]
+
             else:
                 sample_images = np.array(sample).astype(np.float32)
-            
+
         counter = 1
         start_time = time.time()
 
@@ -160,7 +164,7 @@ def train(self, config):
         for epoch in xrange(config.epoch):
             if config.dataset == 'mnist':
                 batch_idxs = min(len(data_X), config.train_size) // config.batch_size
-            else:            
+            else:
                 data = glob(os.path.join("./data", config.dataset, "*.jpg"))
                 batch_idxs = min(len(data), config.train_size) // config.batch_size
 
@@ -194,7 +198,7 @@ def train(self, config):
                     _, summary_str = self.sess.run([g_optim, self.g_sum],
                         feed_dict={ self.z: batch_z, self.y:batch_labels })
                     self.writer.add_summary(summary_str, counter)
-                    
+
                     errD_fake = self.d_loss_fake.eval({self.z: batch_z, self.y:batch_labels})
                     errD_real = self.d_loss_real.eval({self.images: batch_images, self.y:batch_labels})
                     errG = self.g_loss.eval({self.z: batch_z, self.y:batch_labels})
@@ -213,7 +217,7 @@ def train(self, config):
                     _, summary_str = self.sess.run([g_optim, self.g_sum],
                         feed_dict={ self.z: batch_z })
                     self.writer.add_summary(summary_str, counter)
-                    
+
                     errD_fake = self.d_loss_fake.eval({self.z: batch_z})
                     errD_real = self.d_loss_real.eval({self.images: batch_images})
                     errG = self.g_loss.eval({self.z: batch_z})
@@ -261,14 +265,14 @@ def discriminator(self, image, y=None, reuse=False):
             h0 = conv_cond_concat(h0, yb)
 
             h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim + self.y_dim, name='d_h1_conv')))
-            h1 = tf.reshape(h1, [self.batch_size, -1])            
+            h1 = tf.reshape(h1, [self.batch_size, -1])
             h1 = tf.concat(1, [h1, y])
-            
+
             h2 = lrelu(self.d_bn2(linear(h1, self.dfc_dim, 'd_h2_lin')))
             h2 = tf.concat(1, [h2, y])
 
             h3 = linear(h2, 1, 'd_h3_lin')
-            
+
             return tf.nn.sigmoid(h3), h3
 
     def generator(self, z, y=None):
@@ -282,7 +286,7 @@ def generator(self, z, y=None):
             self.h0 = tf.reshape(self.z_, [-1, s16, s16, self.gf_dim * 8])
             h0 = tf.nn.relu(self.g_bn0(self.h0))
 
-            self.h1, self.h1_w, self.h1_b = deconv2d(h0, 
+            self.h1, self.h1_w, self.h1_b = deconv2d(h0,
                 [self.batch_size, s8, s8, self.gf_dim*4], name='g_h1', with_w=True)
             h1 = tf.nn.relu(self.g_bn1(self.h1))
 
@@ -300,7 +304,7 @@ def generator(self, z, y=None):
             return tf.nn.tanh(h4)
         else:
             s = self.output_size
-            s2, s4 = int(s/2), int(s/4) 
+            s2, s4 = int(s/2), int(s/4)
 
             # yb = tf.expand_dims(tf.expand_dims(y, 1),2)
             yb = tf.reshape(y, [self.batch_size, 1, 1, self.y_dim])
@@ -323,7 +327,7 @@ def sampler(self, z, y=None):
         tf.get_variable_scope().reuse_variables()
 
         if not self.y_dim:
-            
+
             s = self.output_size
             s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)
 
@@ -366,10 +370,11 @@ def sampler(self, z, y=None):
 
     def load_mnist(self):
         data_dir = os.path.join("./data", self.dataset_name)
-        
+
         fd = open(os.path.join(data_dir,'train-images-idx3-ubyte'))
         loaded = np.fromfile(file=fd,dtype=np.uint8)
         trX = loaded[16:].reshape((60000,28,28,1)).astype(np.float)
+        print trX.shape
 
         fd = open(os.path.join(data_dir,'train-labels-idx1-ubyte'))
         loaded = np.fromfile(file=fd,dtype=np.uint8)
@@ -385,22 +390,41 @@ def load_mnist(self):
 
         trY = np.asarray(trY)
         teY = np.asarray(teY)
-        
+
         X = np.concatenate((trX, teX), axis=0)
         y = np.concatenate((trY, teY), axis=0)
-        
+
         seed = 547
         np.random.seed(seed)
         np.random.shuffle(X)
         np.random.seed(seed)
         np.random.shuffle(y)
-        
+
         y_vec = np.zeros((len(y), self.y_dim), dtype=np.float)
         for i, label in enumerate(y):
             y_vec[i,y[i]] = 1.0
-        
+
         return X/255.,y_vec
-
+
+    def load_mnist_handcrafted(self):
+        X=[]
+        for j in range(1,5500+1):
+            for i in range(1,7):
+                image = cv2.imread("data/mnist_handcrafted/fiveBin"+str(i)+".png",0)
+                image=image/255
+                X.append(image)
+        X=np.array(X)
+        X= X.reshape((X.shape[0],28,28,1)).astype(np.float)
+        print X.shape
+        Y = [np.array([0,0,0,0,1,0,0,0,0,0]) for _ in range(1,X.shape[0]+1)]
+        print len(Y)
+        Y=np.array(Y)
+        print Y.shape
+        #import pdb;pdb.set_trace()
+        Y= Y.reshape(X.shape[0],10).astype(np.float)
+
+        return X,Y
+
     def save(self, checkpoint_dir, step):
         model_name = "DCGAN.model"
         model_dir = "%s_%s_%s" % (self.dataset_name, self.batch_size, self.output_size)