discriminator.py

import tensorflow as tf
import numpy as np
import ops

class Discriminator:
  def __init__(self, name, is_training, norm='instance', use_sigmoid=False):
    self.name = name
    self.is_training = is_training
    self.norm = norm
    self.reuse = False
    self.use_sigmoid = use_sigmoid

  def __call__(self, input,class_number = -1):
    """
    Args:
      input: batch_size x image_size x image_size x 3
    Returns:
      output: 4D tensor batch_size x out_size x out_size x 1 (default 1x5x5x1)
              filled with 0.9 if real, 0.0 if fake
    """
    print("At discriminator ",self.name," with ",class_number)
    with tf.variable_scope(self.name):
      # convolution layers
      if class_number!=-1:
        class_feature = self.generate_class_feature(class_number)
        input = tf.concat([input,class_feature],axis=3,name="concat")
        print("input_descrimator",input.shape)
      C64 = ops.Ck(input, 64, reuse=self.reuse, norm=None,
          is_training=self.is_training, name='C64')             # (?, w/2, h/2, 64)
      C128 = ops.Ck(C64, 128, reuse=self.reuse, norm=self.norm,
          is_training=self.is_training, name='C128')            # (?, w/4, h/4, 128)
      C256 = ops.Ck(C128, 256, reuse=self.reuse, norm=self.norm,
          is_training=self.is_training, name='C256')            # (?, w/8, h/8, 256)
      C512 = ops.Ck(C256, 512,reuse=self.reuse, norm=self.norm,
          is_training=self.is_training, name='C512')            # (?, w/16, h/16, 512)
      
      # apply a convolution to produce a 1 dimensional output (1 channel?)
      # use_sigmoid = False if use_lsgan = True
      output = ops.last_conv(C512, reuse=self.reuse,
          use_sigmoid=self.use_sigmoid, name='output')          # (?, w/16, h/16, 1)

    self.reuse = True
    self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)

    return output
  
  def generate_class_feature(self,class_number):
    array = np.zeros((1,256,256,3))
    array[:,:,:,class_number] = 1
    return array