984d17836d7a6240942cd44f2f61c92a427bb9bb,niftynet/layer/crop.py,CropLayer,layer_op,#CropLayer#Any#,25

Before Change

    def layer_op(self, inputs):
        spatial_rank = layer_util.infer_spatial_rank(inputs)
        kernel_shape = np.hstack((
            [self.border * 2 + 1] * spatial_rank, 1, 1)).flatten()
        // initializer a kernel with all 0s, and set the central element to 1
        np_kernel = layer_util.trivial_kernel(kernel_shape)
        crop_kernel = tf.constant(np_kernel, dtype=inputs.dtype)
        // split channel dim
        output_tensor = [tf.expand_dims(x, -1)
                         for x in tf.unstack(inputs, axis=-1)]
        output_tensor = [tf.nn.convolution(input=inputs,
                                           filter=crop_kernel,
                                           strides=[1] * spatial_rank,
                                           padding="VALID",
                                           name="conv")
                         for inputs in output_tensor]
        output_tensor = tf.concat(output_tensor, axis=-1)
        return output_tensor

After Change

    def layer_op(self, inputs):
        spatial_rank = layer_util.infer_spatial_rank(inputs)
        offsets = [0, *([int(self.border)] * spatial_rank), 0]
        out_shape = [-1, *[int(d) - 2 * int(self.border) for d in list(inputs.shape)[1:-1]], -1]
        output_tensor = tf.slice(inputs, offsets, out_shape)
        return output_tensor

Instances