model.py 文件源码

def model(self):
    X_reader = Reader(self.X_train_file, name='X',
        image_size=self.image_size, batch_size=self.batch_size)
    Y_reader = Reader(self.Y_train_file, name='Y',
        image_size=self.image_size, batch_size=self.batch_size)

    x = X_reader.feed()
    y = Y_reader.feed()

    cycle_loss = self.cycle_consistency_loss(self.G, self.F, x, y)

    # X -> Y
    fake_y = self.G(x)
    G_gan_loss = self.generator_loss(self.D_Y, fake_y, use_lsgan=self.use_lsgan)
    G_loss =  G_gan_loss + cycle_loss
    D_Y_loss = self.discriminator_loss(self.D_Y, y, self.fake_y, use_lsgan=self.use_lsgan)

    # Y -> X
    fake_x = self.F(y)
    F_gan_loss = self.generator_loss(self.D_X, fake_x, use_lsgan=self.use_lsgan)
    F_loss = F_gan_loss + cycle_loss
    D_X_loss = self.discriminator_loss(self.D_X, x, self.fake_x, use_lsgan=self.use_lsgan)

    # summary
    tf.summary.histogram('D_Y/true', self.D_Y(y))
    tf.summary.histogram('D_Y/fake', self.D_Y(self.G(x)))
    tf.summary.histogram('D_X/true', self.D_X(x))
    tf.summary.histogram('D_X/fake', self.D_X(self.F(y)))

    tf.summary.scalar('loss/G', G_gan_loss)
    tf.summary.scalar('loss/D_Y', D_Y_loss)
    tf.summary.scalar('loss/F', F_gan_loss)
    tf.summary.scalar('loss/D_X', D_X_loss)
    tf.summary.scalar('loss/cycle', cycle_loss)

    tf.summary.image('X/generated', utils.batch_convert2int(self.G(x)))
    tf.summary.image('X/reconstruction', utils.batch_convert2int(self.F(self.G(x))))
    tf.summary.image('Y/generated', utils.batch_convert2int(self.F(y)))
    tf.summary.image('Y/reconstruction', utils.batch_convert2int(self.G(self.F(y))))

    return G_loss, D_Y_loss, F_loss, D_X_loss, fake_y, fake_x