2b60e6de22b0d59ed4cc4b55e451a36060e622e1,layer/upsample.py,UpSampleLayer,layer_op,#UpSampleLayer#Any#,46

Before Change

            output_tensor = tf.tile(input=input_tensor, multiples=repmat)
            output_tensor = tf.batch_to_space_nd(
                output_tensor,
                [self.kernel_size] * spatial_rank,
                [[0, 0]] * spatial_rank)

        elif self.func == "CHANNELWISE_DECONV":

After Change

                    "consider using `CHANNELWISE_DECONV` operation.")
            // simply replicate input values to
            // local regions of (kernel_size ** spatial_rank) element
            kernel_size_all_dims = get_list_parameter(self.kernel_size, spatial_rank)
            pixel_num = 1
            for x in kernel_size_all_dims:
                pixel_num = pixel_num * x
            repmat = np.hstack((pixel_num, [1] * spatial_rank, 1)).flatten()
            output_tensor = tf.tile(input=input_tensor, multiples=repmat)
            output_tensor = tf.batch_to_space_nd(
                output_tensor,

Instances