unet.py 文件源码

def encoder(self, images, is_training, reuse=False):
        with tf.variable_scope("generator"):
            if reuse:
                tf.get_variable_scope().reuse_variables()

            encode_layers = dict()

            def encode_layer(x, output_filters, layer):
                act = lrelu(x)
                conv = conv2d(act, output_filters=output_filters, scope="g_e%d_conv" % layer)
                enc = batch_norm(conv, is_training, scope="g_e%d_bn" % layer)
                encode_layers["e%d" % layer] = enc
                return enc

            e1 = conv2d(images, self.generator_dim, scope="g_e1_conv")
            encode_layers["e1"] = e1
            e2 = encode_layer(e1, self.generator_dim * 2, 2)
            e3 = encode_layer(e2, self.generator_dim * 4, 3)
            e4 = encode_layer(e3, self.generator_dim * 8, 4)
            e5 = encode_layer(e4, self.generator_dim * 8, 5)
            e6 = encode_layer(e5, self.generator_dim * 8, 6)
            e7 = encode_layer(e6, self.generator_dim * 8, 7)
            e8 = encode_layer(e7, self.generator_dim * 8, 8)

            return e8, encode_layers