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init - 初始化项目

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Lee Nony
2022-05-06 01:58:53 +08:00
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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 "math.hpp"
#include "types.hpp"
#include "grid_stride_range.hpp"
#include "execution.hpp"
#include "memory.hpp"
#include "../cuda4dnn/csl/stream.hpp"
#include "../cuda4dnn/csl/tensor.hpp"
#include "../cuda4dnn/csl/span.hpp"
#include <cuda_runtime.h>
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 CHANNELS_PER_ITER>
__global__ void resize_nn(
Span<T> output, size_type out_height, size_type out_width,
View<T> input, size_type in_height, size_type in_width,
float o2i_fy, float o2i_fx, bool round, bool half_pixel_centers)
{
auto in_image_size = in_height * in_width;
auto out_image_size = out_height * out_width;
/* think of the output and input as a collection of 2d images with the last axis
* representing the width and the last but one axis representing the height
*
* the remaining axis together form a collection of these images/channels
*/
auto num_effective_channels = output.size() / out_image_size;
/* we process multiple channels every iteration to reuse the identical computation
* involved with the spatial dimensions
*
* if we are processing `CHANNELS_PER_ITER` channels per iteration, we will need
* (num_effective_channels / CHANNELS_PER_ITER) iterations per (x, y) location
*/
auto num_channel_iters_per_xy = (num_effective_channels / CHANNELS_PER_ITER);
/* we need `num_channel_iters_per_xy` iterations per (x, y) and there are `out_image_size`
* combinations of (x, y); hence, we'll need `num_channel_iters_per_xy * out_image_size`
* iterations in total to finish the resize operation
*/
auto iters_required = num_channel_iters_per_xy * out_image_size;
for (auto iter : grid_stride_range(iters_required)) {
const index_type c_start = (iter / out_image_size) * CHANNELS_PER_ITER;
/* note here that consecutive `iter` values will often have consecutive `x` values
* => stores into output will be coalesced across threads
*/
const index_type y = (iter % out_image_size) / out_width;
const index_type x = iter % out_width;
auto in_yf = half_pixel_centers ? (y + 0.5f) * o2i_fy : y * o2i_fy;
auto in_xf = half_pixel_centers ? (x + 0.5f) * o2i_fx : x * o2i_fx;
using device::lround;
index_type in_y = round ? lround(in_yf) : static_cast<index_type>(in_yf);
index_type in_x = round ? lround(in_xf) : static_cast<index_type>(in_xf);
using device::min;
in_y = min(in_y, in_height - 1);
in_x = min(in_x, in_width - 1);
index_type in_idx = c_start * in_image_size + in_y * in_width + in_x;
index_type out_idx = c_start * out_image_size + y * out_width + x;
for (int i = 0; i < CHANNELS_PER_ITER; i++) {
output[out_idx] = load_ldg(input[in_idx]);
in_idx += in_image_size;
out_idx += out_image_size;
}
}
}
template <class T, std::size_t CHANNELS_PER_ITER>
__global__ void resize_bilinear(
Span<T> output, size_type out_height, size_type out_width,
View<T> input, size_type in_height, size_type in_width,
float o2i_fy, float o2i_fx, bool half_pixel_centers)
{
auto in_image_size = in_height * in_width;
auto out_image_size = out_height * out_width;
/* think of the output and input as a collection of 2d images with the last axis
* representing the width and the last but one axis representing the height
*
* the remaining axis together form a collection of these images/channels
*/
auto num_effective_channels = output.size() / out_image_size;
/* we process multiple channels every iteration to reuse the identical computation
* involved with the spatial dimensions
*
* if we are processing `CHANNELS_PER_ITER` channels per iteration, we will need
* (num_effective_channels / CHANNELS_PER_ITER) iterations per (x, y) location
*/
auto num_channel_iters_per_xy = (num_effective_channels / CHANNELS_PER_ITER);
/* we need `num_channel_iters_per_xy` iterations per (x, y) and there are `out_image_size`
* combinations of (x, y); hence, we'll need `num_channel_iters_per_xy * out_image_size`
* iterations in total to finish the resize operation
*/
auto iters_required = num_channel_iters_per_xy * out_image_size;
for (auto iter : grid_stride_range(iters_required)) {
const index_type c_start = (iter / out_image_size) * CHANNELS_PER_ITER;
const index_type c_end = c_start + CHANNELS_PER_ITER;
/* note here that consecutive `iter` values will often have consecutive `x` values
* => stores into output will be coalesced across threads
*/
const index_type y = (iter % out_image_size) / out_width;
const index_type x = iter % out_width;
using device::max;
auto in_x = half_pixel_centers ? max<float>((x + 0.5f) * o2i_fx - 0.5f, 0.0f) : x * o2i_fx;
auto in_y = half_pixel_centers ? max<float>((y + 0.5f) * o2i_fy - 0.5f, 0.0f) : y * o2i_fy;
auto in_x0 = static_cast<index_type>(in_x);
auto in_y0 = static_cast<index_type>(in_y);
using device::min;
auto in_x1 = min<index_type>(in_x0 + 1, in_width - 1);
auto in_y1 = min<index_type>(in_y0 + 1, in_height - 1);
index_type in_offset_r0 = c_start * in_image_size + in_y0 * in_width;
index_type in_offset_r1 = c_start * in_image_size + in_y1 * in_width;
index_type out_idx = c_start * out_image_size + y * out_width + x;
#pragma unroll 1 /* disable unrolling to reduce register pressure; not sure how but it works */
for (auto c = c_start; c < c_end; c++) {
auto v_00 = load_ldg(input[in_offset_r0 + in_x0]),
v_01 = load_ldg(input[in_offset_r0 + in_x1]),
v_10 = load_ldg(input[in_offset_r1 + in_x0]),
v_11 = load_ldg(input[in_offset_r1 + in_x1]);
output[out_idx] =
v_00 +
T(in_y - in_y0) * T(v_10 - v_00) +
T(in_x - in_x0) * T(v_01 - v_00) +
T(in_y - in_y0) * T(in_x - in_x0) * T(v_11 - v_01 - v_10 + v_00);
in_offset_r0 += in_image_size;
in_offset_r1 += in_image_size;
out_idx += out_image_size;
}
}
}
}
template <class T, std::size_t CHANNELS_PER_ITER> static
void launch_multichannel_resize_nn(const Stream& stream,
Span<T> output, size_type out_height, size_type out_width,
View<T> input, size_type in_height, size_type in_width,
float scale_y, float scale_x, bool round, bool half_pixel_centers)
{
auto kernel = raw::resize_nn<T, CHANNELS_PER_ITER>;
auto policy = make_policy(kernel, output.size() / CHANNELS_PER_ITER, 0, stream);
launch_kernel(kernel, policy, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, round, half_pixel_centers);
}
template <class T>
void resize_nn(const Stream& stream, TensorSpan<T> output, TensorView<T> input, float scale_y, float scale_x, bool round, bool half_pixel_centers) {
auto out_height = output.get_axis_size(-2);
auto out_width = output.get_axis_size(-1);
auto in_height = input.get_axis_size(-2);
auto in_width = input.get_axis_size(-1);
auto num_effective_channels = input.size_range(0, 2);
auto num_iters = num_effective_channels * out_height * out_width;
if (num_effective_channels % 32 == 0 && num_iters > 655360) {
launch_multichannel_resize_nn<T, 32>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, round, half_pixel_centers);
} else if (num_effective_channels % 16 == 0 && num_iters > 327680) {
launch_multichannel_resize_nn<T, 16>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, round, half_pixel_centers);
} else if (num_effective_channels % 8 == 0 && num_iters > 163840) {
launch_multichannel_resize_nn<T, 8>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, round, half_pixel_centers);
} else if (num_effective_channels % 4 == 0 && num_iters > 81920) {
launch_multichannel_resize_nn<T, 4>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, round, half_pixel_centers);
} else if (num_effective_channels % 2 == 0) {
launch_multichannel_resize_nn<T, 2>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, round, half_pixel_centers);
} else {
launch_multichannel_resize_nn<T, 1>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, round, half_pixel_centers);
}
}
#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
template void resize_nn<__half>(const Stream&, TensorSpan<__half>, TensorView<__half>, float, float, bool, bool);
#endif
template void resize_nn<float>(const Stream&, TensorSpan<float>, TensorView<float>, float, float, bool,bool);
template <class T, std::size_t CHANNELS_PER_ITER> static
void launch_multichannel_resize_bilinear(const Stream& stream,
Span<T> output, size_type out_height, size_type out_width,
View<T> input, size_type in_height, size_type in_width,
float scale_y, float scale_x, bool half_pixel_centers)
{
auto kernel = raw::resize_bilinear<T, CHANNELS_PER_ITER>;
auto policy = make_policy(kernel, output.size() / CHANNELS_PER_ITER, 0, stream);
launch_kernel(kernel, policy, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, half_pixel_centers);
}
template <class T>
void resize_bilinear(const Stream& stream, TensorSpan<T> output, TensorView<T> input, float scale_y, float scale_x, bool half_pixel_centers) {
auto out_height = output.get_axis_size(-2);
auto out_width = output.get_axis_size(-1);
auto in_height = input.get_axis_size(-2);
auto in_width = input.get_axis_size(-1);
auto num_effective_channels = input.size_range(0, 2);
auto num_iters = num_effective_channels * out_height * out_width;
if (num_effective_channels % 16 == 0 && num_iters > 163840) {
launch_multichannel_resize_bilinear<T, 16>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, half_pixel_centers);
} else if (num_effective_channels % 8 == 0 && num_iters > 81920) {
launch_multichannel_resize_bilinear<T, 8>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, half_pixel_centers);
} else if (num_effective_channels % 4 == 0 && num_iters > 40960) {
launch_multichannel_resize_bilinear<T, 4>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, half_pixel_centers);
} else if (num_effective_channels % 2 == 0) {
launch_multichannel_resize_bilinear<T, 2>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, half_pixel_centers);
} else {
launch_multichannel_resize_bilinear<T, 1>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x, half_pixel_centers);
}
}
#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
template void resize_bilinear<__half>(const Stream&, TensorSpan<__half>, TensorView<__half>, float, float, bool);
#endif
template void resize_bilinear<float>(const Stream&, TensorSpan<float>, TensorView<float>, float, float, bool);
}}}} /* namespace cv::dnn::cuda4dnn::kernels */