863a9dcee691e850e79d97a16abb977e24324fa1,niftynet/layer/downsample.py,DownSampleLayer,layer_op,#DownSampleLayer#Any#,32

Before Change

        spatial_rank = layer_util.infer_spatial_rank(input_tensor)
        look_up_operations(self.func, SUPPORTED_OP)
        if self.func == "CONSTANT":
            kernel_shape = np.hstack((
                [self.kernel_size] * spatial_rank, 1, 1)).flatten()
            np_kernel = layer_util.trivial_kernel(kernel_shape)
            kernel = tf.constant(np_kernel, dtype=tf.float32)
            output_tensor = [tf.expand_dims(x, -1)
                             for x in tf.unstack(input_tensor, axis=-1)]
            output_tensor = [tf.nn.convolution(
                input=inputs,
                filter=kernel,
                strides=[self.stride] * spatial_rank,
                padding=self.padding,
                name="conv") for inputs in output_tensor]
            output_tensor = tf.concat(output_tensor, axis=-1)

After Change

    def layer_op(self, input_tensor):
        spatial_rank = layer_util.infer_spatial_rank(input_tensor)
        look_up_operations(self.func, SUPPORTED_OP)
        kernel_size_all_dims = layer_util.expand_spatial_params(
            self.kernel_size, spatial_rank)
        stride_all_dims = layer_util.expand_spatial_params(
            self.stride, spatial_rank)
        if self.func == "CONSTANT":
            full_kernel_size = kernel_size_all_dims + [1, 1]
            np_kernel = layer_util.trivial_kernel(full_kernel_size)
            kernel = tf.constant(np_kernel, dtype=tf.float32)

Instances