31 using namespace tensorflow;
33 static_assert(
sizeof(int64) ==
sizeof(int64_t),
34 "int64 type is not compatible");
36 const Tensor& filters =
context->input(0);
37 const Tensor& out_importance =
context->input(1);
38 const Tensor& inp_features =
context->input(2);
39 const Tensor& inp_neighbors_importance_sum =
context->input(3);
40 const Tensor& inp_neighbors_row_splits =
context->input(4);
41 const Tensor& neighbors_index =
context->input(5);
42 const Tensor& neighbors_kernel_index =
context->input(6);
43 const Tensor& neighbors_importance =
context->input(7);
44 const Tensor& neighbors_row_splits =
context->input(8);
45 const Tensor& out_features_gradient =
context->input(9);
47 Dim num_out(
"num_out");
48 Dim num_inp(
"num_inp");
49 Dim num_kernel_elements(
"num_kernel_elements");
50 Dim in_channels(
"in_channels");
51 Dim out_channels(
"out_channels");
52 Dim num_neighbors(
"num_neighbors");
55 in_channels, out_channels);
66 TensorShape filter_backprop_shape(filters.shape());
67 Tensor* filter_backprop =
nullptr;
69 context->allocate_output(0, filter_backprop_shape,
72 std::vector<int> filter_dims;
73 for (
int i = 0; i < filters.dims(); ++i) {
74 filter_dims.push_back(filters.dim_size(i));
77 bool point_importances = out_importance.shape().dim_size(0) != 0;
79 bool has_neighbors_importances =
80 neighbors_importance.shape().dim_size(0) != 0;
83 inp_neighbors_importance_sum, inp_neighbors_row_splits,
84 neighbors_index, neighbors_kernel_index, neighbors_importance,
85 neighbors_row_splits, out_features_gradient, filter_dims,
86 point_importances, has_neighbors_importances, *filter_backprop);