(self, lr_images)
Args:[-0.5, 0.5]
      lr_images: [batch_size, lr_height, lr_width, in_channels]
    Returns:
      conditioning_logits: [batch_size, hr_height, hr_width, 3*256]
Args:[-0.5, 0.5]
      lr_images: [batch_size, lr_height, lr_width, in_channels]
    Returns:
      conditioning_logits: [batch_size, hr_height, hr_width, 3*256]
Args : [ - 0 . 5 0 . 5 ] lr_images : [ batch_size lr_height lr_width in_channels ] Returns : conditioning_logits : [ batch_size hr_height hr_width 3 * 256 ]
def conditioning_network(self, lr_images):
    """
    Args:[-0.5, 0.5]
      lr_images: [batch_size, lr_height, lr_width, in_channels]
    Returns:
      conditioning_logits: [batch_size, hr_height, hr_width, 3*256]
    """
    res_num = 6
    with tf.variable_scope('conditioning') as scope:
      inputs = lr_images
      inputs = conv2d(inputs, 32, [1, 1], strides=[1, 1], mask_type=None, scope="conv_init")
      for i in range(2):
        for j in range(res_num):
          inputs = resnet_block(inputs, 32, [3, 3], strides=[1, 1], scope='res' + str(i) + str(j), train=self.train)
        inputs = deconv2d(inputs, 32, [3, 3], strides=[2, 2], scope="deconv" + str(i))
        inputs = tf.nn.relu(inputs)
      for i in range(res_num):
        inputs = resnet_block(inputs, 32, [3, 3], strides=[1, 1], scope='res3' + str(i), train=self.train)
      conditioning_logits = conv2d(inputs, 3*256, [1, 1], strides=[1, 1], mask_type=None, scope="conv")

      return conditioning_logits
def conditioning_network ( self , lr_images ) : res_num = 6 with tf . variable_scope ( 'conditioning' ) as scope : inputs = lr_images inputs = conv2d ( inputs , 32 , [ 1 , 1 ] , strides = [ 1 , 1 ] , mask_type = None , scope = "conv_init" ) for i in range ( 2 ) : for j in range ( res_num ) : inputs = resnet_block ( inputs , 32 , [ 3 , 3 ] , strides = [ 1 , 1 ] , scope = 'res' + str ( i ) + str ( j ) , train = self . train ) inputs = deconv2d ( inputs , 32 , [ 3 , 3 ] , strides = [ 2 , 2 ] , scope = "deconv" + str ( i ) ) inputs = tf . nn . relu ( inputs ) for i in range ( res_num ) : inputs = resnet_block ( inputs , 32 , [ 3 , 3 ] , strides = [ 1 , 1 ] , scope = 'res3' + str ( i ) , train = self . train ) conditioning_logits = conv2d ( inputs , 3 * 256 , [ 1 , 1 ] , strides = [ 1 , 1 ] , mask_type = None , scope = "conv" ) return conditioning_logits