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/*
* SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/** @file vec_pybind11.h
* @author Thomas Müller, NVIDIA
* @brief pybind11 bindings for NGP's vector and matrix types. Adapted from
* Patrik Huber's glm binding code per the BSD license of pybind11.
*/
#pragma once
#include <tiny-cuda-nn/vec.h>
#include <cstddef>
#if defined(_MSC_VER)
#pragma warning(push)
#pragma warning(disable: 4127) // warning C4127: Conditional expression is constant
#endif
namespace pybind11 {
namespace detail {
template <typename T, uint32_t N>
struct type_caster<tcnn::tvec<T, N>> {
using vector_type = tcnn::tvec<T, N>;
using Scalar = T;
static constexpr std::size_t num_elements = N;
bool load(handle src, bool) {
auto buf = array_t<Scalar>::ensure(src);
if (!buf) {
return false;
}
if (buf.ndim() != 1) {
return false; // not a rank-1 tensor (i.e. vector)
}
if (buf.shape(0) != num_elements) {
return false; // not a 2-elements vector
}
for (size_t i = 0; i < num_elements; ++i) {
value[i] = *buf.data(i);
}
return true;
}
static handle cast(const vector_type& src, return_value_policy, handle) {
return array(
num_elements,
src.data()
).release();
}
// Specifies the doc-string for the type in Python:
PYBIND11_TYPE_CASTER(vector_type, _("vec"));
};
template <typename T, uint32_t N, uint32_t M>
struct type_caster<tcnn::tmat<T, N, M>> {
using matrix_type = tcnn::tmat<T, N, M>;
using Scalar = T;
static constexpr std::size_t num_rows = M;
static constexpr std::size_t num_cols = N;
bool load(handle src, bool) {
auto buf = array_t<Scalar>::ensure(src);
if (!buf) {
return false;
}
if (buf.ndim() != 2) {
return false; // not a rank-2 tensor (i.e. matrix)
}
if (buf.shape(0) != num_rows || buf.shape(1) != num_cols) {
return false; // not a 4x4 matrix
}
for (size_t i = 0; i < num_cols; ++i) {
for (size_t j = 0; j < num_rows; ++j) {
value[i][j] = *buf.data(j, i);
}
}
return true;
}
static handle cast(const matrix_type& src, return_value_policy, handle) {
return array(
{ num_rows, num_cols },
{ sizeof(Scalar), sizeof(Scalar) * num_rows }, // strides - flip the row/col layout!
src.data()
).release();
}
// Specifies the doc-string for the type in Python:
PYBIND11_TYPE_CASTER(matrix_type, _("mat"));
};
}
}
#if defined(_MSC_VER)
#pragma warning(pop)
#endif
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