discriminator.py

import tensorflow as tf
from utils import logger
import ops


class Discriminator(object):
    def __init__(self, name, is_train, norm='instance', activation='leaky', image_size=128):
        logger.info('Init Discriminator %s', name)
        self.name = name
        self._is_train = is_train
        self._norm = norm
        self._activation = activation
        self._reuse = False
        self._image_size = image_size

    def __call__(self, input):
        with tf.variable_scope(self.name, reuse=self._reuse):
            D = ops.conv_block(input, 64, 'C64', 4, 2, self._is_train,
                               self._reuse, norm=None, activation=self._activation)
            D = ops.conv_block(D, 128, 'C128', 4, 2, self._is_train,
                               self._reuse, self._norm, self._activation)
            D = ops.conv_block(D, 256, 'C256', 4, 2, self._is_train,
                               self._reuse, self._norm, self._activation)
            num_layers = 3 if self._image_size == 256 else 1
            for i in range(num_layers):
                D = ops.conv_block(D, 512, 'C512_{}'.format(i), 4, 2, self._is_train,
                                   self._reuse, self._norm, self._activation)
            D = ops.conv_block(D, 1, 'C1', 4, 1, self._is_train,
                               self._reuse, norm=None, activation=None, bias=True)
            D = tf.reduce_mean(D, axis=[1,2,3])

            self._reuse = True
            self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.name)
            return D