scale_shift.cu
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include "types.hpp"
#include "vector_traits.hpp"
#include "grid_stride_range.hpp"
#include "execution.hpp"
#include "../cuda4dnn/csl/stream.hpp"
#include "../cuda4dnn/csl/tensor.hpp"
#include "../cuda4dnn/csl/span.hpp"
#include <opencv2/core.hpp>
#include <cstddef>
using namespace cv::dnn::cuda4dnn::csl;
using namespace cv::dnn::cuda4dnn::csl::device;
namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
namespace raw {
template <class T, std::size_t N>
__global__ void biasN_vec(Span<T> output, View<T> input, size_type inner_size, View<T> bias) {
using vector_type = get_vector_type_t<T, N>;
auto output_vPtr = vector_type::get_pointer(output.data());
auto input_vPtr = vector_type::get_pointer(input.data());
inner_size /= vector_type::size();
for (auto i : grid_stride_range(output.size() / vector_type::size())) {
const index_type bias_idx = (i / inner_size) % bias.size();
vector_type vec;
v_load(vec, input_vPtr[i]);
for(int j = 0; j < vec.size(); j++)
vec.data[j] = vec.data[j] + bias[bias_idx];
v_store(output_vPtr[i], vec);
}
}
template <class T, std::size_t N>
__global__ void scaleN_vec(Span<T> output, View<T> input, size_type inner_size, View<T> weights)
{
using vector_type = get_vector_type_t<T, N>;
auto output_vPtr = vector_type::get_pointer(output.data());
auto input_vPtr = vector_type::get_pointer(input.data());
inner_size /= vector_type::size();
for (auto i : grid_stride_range(output.size() / vector_type::size())) {
const index_type scale_idx = (i / inner_size) % weights.size();
vector_type vec;
v_load(vec, input_vPtr[i]);
for (int j = 0; j < vec.size(); j++)
vec.data[j] = vec.data[j] * weights[scale_idx];
v_store(output_vPtr[i], vec);
}
}
template <class T, std::size_t N>
__global__ void scale1_with_bias1_vec(Span<T> output, View<T> input, T alpha, T beta)
{
using vector_type = get_vector_type_t<T, N>;
auto output_vPtr = vector_type::get_pointer(output.data());
auto input_vPtr = vector_type::get_pointer(input.data());
for (auto i : grid_stride_range(output.size() / vector_type::size())) {
vector_type vec;
v_load(vec, input_vPtr[i]);
for (int j = 0; j < vec.size(); j++)
vec.data[j] = alpha * vec.data[j] + beta;
v_store(output_vPtr[i], vec);
}
}
template <class T, std::size_t N>
__global__ void scaleN_with_biasN_vec(Span<T> output, View<T> input, size_type inner_size, View<T> weights, View<T> bias)
{
using vector_type = get_vector_type_t<T, N>;
auto output_vPtr = vector_type::get_pointer(output.data());
auto input_vPtr = vector_type::get_pointer(input.data());
inner_size /= vector_type::size();
for (auto i : grid_stride_range(output.size() / vector_type::size())) {
const index_type scale_idx = (i / inner_size) % weights.size();
vector_type vec;
v_load(vec, input_vPtr[i]);
for (int j = 0; j < vec.size(); j++)
vec.data[j] = vec.data[j] * weights[scale_idx] + bias[scale_idx];
v_store(output_vPtr[i], vec);
}
}
}
template <class T, std::size_t N> static
void launch_biasN_vec_kernel(const Stream& stream, Span<T> output, View<T> input, std::size_t inner_size, View<T> bias){
CV_Assert(is_fully_aligned<T>(output, N));
CV_Assert(is_fully_aligned<T>(input, N));
CV_Assert(inner_size % N == 0);
auto kernel = raw::biasN_vec<T, N>;
auto policy = make_policy(kernel, output.size() / N, 0, stream);
launch_kernel(kernel, policy, output, input, inner_size, bias);
}
template <class T>
void biasN(
const Stream& stream,
TensorSpan<T> output,
TensorView<T> input, std::size_t inner_size,
TensorView<T> bias)
{
CV_Assert(is_shape_same(input, output));
if (is_fully_aligned<T>(output, 4) && is_fully_aligned<T>(input, 4) && inner_size % 4 == 0) {
launch_biasN_vec_kernel<T, 4>(stream, output, input, inner_size, bias);
} else if (is_fully_aligned<T>(output, 2) && is_fully_aligned<T>(input, 2) && inner_size % 2 == 0) {
launch_biasN_vec_kernel<T, 2>(stream, output, input, inner_size, bias);
} else {
launch_biasN_vec_kernel<T, 1>(stream, output, input, inner_size, bias);
}
}
#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
template void biasN<__half>(const Stream&, TensorSpan<__half>, TensorView<__half>, std::size_t, TensorView<__half>);
#endif
template void biasN<float>(const Stream&, TensorSpan<float>, TensorView<float>, std::size_t, TensorView<float>);
template <class T, std::size_t N> static
void launch_scaleN_vec_kernel(const Stream& stream, Span<T> output, View<T> input, std::size_t inner_size, View<T> weights) {
CV_Assert(is_fully_aligned<T>(output, N));
CV_Assert(is_fully_aligned<T>(input, N));
CV_Assert(inner_size % N == 0);
auto kernel = raw::scaleN_vec<T, N>;
auto policy = make_policy(kernel, output.size() / N, 0, stream);
launch_kernel(kernel, policy, output, input, inner_size, weights);
}
template <class T>
void scaleN(
const Stream& stream,
TensorSpan<T> output,
TensorView<T> input, std::size_t inner_size,
TensorView<T> weights)
{
CV_Assert(is_shape_same(input, output));
if (is_fully_aligned<T>(output, 4) && is_fully_aligned<T>(input, 4) && inner_size % 4 == 0) {
launch_scaleN_vec_kernel<T, 4>(stream, output, input, inner_size, weights);
} else if (is_fully_aligned<T>(output, 2) && is_fully_aligned<T>(input, 2) && inner_size % 2 == 0) {
launch_scaleN_vec_kernel<T, 2>(stream, output, input, inner_size, weights);
} else {
launch_scaleN_vec_kernel<T, 1>(stream, output, input, inner_size, weights);
}
}
#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
template void scaleN<__half>(const Stream&, TensorSpan<__half>, TensorView<__half>, std::size_t, TensorView<__half>);
#endif
template void scaleN<float>(const Stream&, TensorSpan<float>, TensorView<float>, std::size_t, TensorView<float>);
template <class T, std::size_t N> static
void launch_scale1_with_bias1_vec_kernel(const Stream& stream, Span<T> output, View<T> input, T alpha, T beta) {
CV_Assert(is_fully_aligned<T>(output, N));
CV_Assert(is_fully_aligned<T>(input, N));
auto kernel = raw::scale1_with_bias1_vec<T, N>;
auto policy = make_policy(kernel, output.size() / N, 0, stream);
launch_kernel(kernel, policy, output, input, alpha, beta);
}
template <class T>
void scale1_with_bias1(const Stream& stream, Span<T> output, View<T> input, T alpha, T beta) {
CV_Assert(output.size() == input.size());
if (is_fully_aligned<T>(output, 4) && is_fully_aligned<T>(input, 4)) {
launch_scale1_with_bias1_vec_kernel<T, 4>(stream, output, input, alpha, beta);
} else if (is_fully_aligned<T>(output, 2) && is_fully_aligned<T>(input, 2)) {
launch_scale1_with_bias1_vec_kernel<T, 2>(stream, output, input, alpha, beta);
} else {
launch_scale1_with_bias1_vec_kernel<T, 1>(stream, output, input, alpha, beta);
}
}
#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
template void scale1_with_bias1<__half>(const Stream&, Span<__half>, View<__half>, __half, __half);
#endif
template void scale1_with_bias1<float>(const Stream&, Span<float>, View<float>, float, float);
template <class T, std::size_t N> static
void launch_scaleN_with_biasN_vec_kernel(const Stream& stream, Span<T> output, View<T> input, std::size_t inner_size, View<T> weights, View<T> bias) {
CV_Assert(is_fully_aligned<T>(output, N));
CV_Assert(is_fully_aligned<T>(input, N));
CV_Assert(inner_size % N == 0);
auto kernel = raw::scaleN_with_biasN_vec<T, N>;
auto policy = make_policy(kernel, output.size() / N, 0, stream);
launch_kernel(kernel, policy, output, input, inner_size, weights, bias);
}
template <class T>
void scaleN_with_biasN(
const Stream& stream,
TensorSpan<T> output,
TensorView<T> input, std::size_t inner_size,
TensorView<T> weights, TensorView<T> bias)
{
CV_Assert(is_shape_same(input, output));
CV_Assert(weights.size() == bias.size());
if (is_fully_aligned<T>(output, 4) && is_fully_aligned<T>(input, 4) && inner_size % 4 == 0) {
launch_scaleN_with_biasN_vec_kernel<T, 4>(stream, output, input, inner_size, weights, bias);
} else if (is_fully_aligned<T>(output, 2) && is_fully_aligned<T>(input, 2) && inner_size % 2 == 0) {
launch_scaleN_with_biasN_vec_kernel<T, 2>(stream, output, input, inner_size, weights, bias);
} else {
launch_scaleN_with_biasN_vec_kernel<T, 1>(stream, output, input, inner_size, weights, bias);
}
}
#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
template void scaleN_with_biasN<__half>(const Stream&, TensorSpan<__half>, TensorView<__half>, std::size_t, TensorView<__half>, TensorView<__half>);
#endif
template void scaleN_with_biasN<float>(const Stream&, TensorSpan<float>, TensorView<float>, std::size_t, TensorView<float>, TensorView<float>);
}}}} /* namespace cv::dnn::cuda4dnn::kernels */