1d7b7345ab3254ed9b450dbd4208a3d63cb4963f,dataset/models/tf/linknet.py,LinkNet,_build,#LinkNet#,17

Before Change

            layout_transpose = "tna" if b_norm else "ta"

            net = conv_block(dim, net, 32, 3, layout_transpose, "output_conv_1", 2, **kwargs)
            net = conv_block(dim, net, 32, 3, layout, "output_conv_2", **kwargs)
            net = conv_block(dim, net, n_classes, 2, "t", "output_conv_3", 2, **kwargs)

        logits = tf.identity(net, "predictions")
        tf.nn.softmax(logits, name="predicted_prob")

After Change

                net = self.downsampling_block(dim, net, filters, "downsampling-"+str(i), enable_batch_norm, **kwargs)
                encoder_output.append(net)

            for i, filters in enumerate(linknet_filters[::-1][1:]):
                net = self.upsampling_block(dim, net, filters, "upsampling-"+str(i), enable_batch_norm, **kwargs)
                net = tf.add(net, encoder_output[-2-i])

Instances