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4e2ef8c8f5
Enable cuda4dnn on hardware without support for __half * Enable cuda4dnn on hardware without support for half (ie. compute capability < 5.3) Update CMakeLists.txt Lowered minimum CC to 3.0 * UPD: added ifdef on new copy kernel * added fp16 support detection at runtime * Clarified #if condition on atomicAdd definition * More explicit CMake error message
238 lines
12 KiB
Plaintext
238 lines
12 KiB
Plaintext
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include <cuda_runtime.h>
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#include <cuda_fp16.h>
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#include "math.hpp"
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#include "types.hpp"
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#include "grid_stride_range.hpp"
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#include "execution.hpp"
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#include "../cuda4dnn/csl/stream.hpp"
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#include "../cuda4dnn/csl/tensor.hpp"
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#include "../cuda4dnn/csl/span.hpp"
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#include <cuda_runtime.h>
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using namespace cv::dnn::cuda4dnn::csl;
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using namespace cv::dnn::cuda4dnn::csl::device;
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namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
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namespace raw {
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template <class T, std::size_t CHANNELS_PER_ITER>
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__global__ void resize_nn(
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Span<T> output, size_type out_height, size_type out_width,
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View<T> input, size_type in_height, size_type in_width)
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{
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auto in_image_size = in_height * in_width;
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auto out_image_size = out_height * out_width;
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/* think of the output and input as a collection of 2d images with the last axis
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* representing the width and the last but one axis representing the height
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*
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* the remaining axis together form a collection of these images/channels
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*/
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auto num_effective_channels = output.size() / out_image_size;
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/* we process multiple channels every iteration to reuse the identical computation
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* involved with the spatial dimensions
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*
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* if we are processing `CHANNELS_PER_ITER` channels per iteration, we will need
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* (num_effective_channels / CHANNELS_PER_ITER) iterations per (x, y) location
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*/
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auto num_channel_iters_per_xy = (num_effective_channels / CHANNELS_PER_ITER);
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/* we need `num_channel_iters_per_xy` iterations per (x, y) and there are `out_image_size`
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* combinations of (x, y); hence, we'll need `num_channel_iters_per_xy * out_image_size`
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* iterations in total to finish the resize operation
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*/
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auto iters_required = num_channel_iters_per_xy * out_image_size;
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for (auto iter : grid_stride_range(iters_required)) {
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const index_type c_start = (iter / out_image_size) * CHANNELS_PER_ITER;
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/* note here that consecutive `iter` values will often have consecutive `x` values
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* => stores into output will be coalesced across threads
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*/
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const index_type y = (iter % out_image_size) / out_width;
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const index_type x = iter % out_width;
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/* o2i = output to input */
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auto o2i_fy = static_cast<float>(in_height) / out_height;
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auto o2i_fx = static_cast<float>(in_width) / out_width;
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auto in_y = static_cast<index_type>(y * o2i_fy);
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auto in_x = static_cast<index_type>(x * o2i_fx);
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index_type in_idx = c_start * in_image_size + in_y * in_width + in_x;
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index_type out_idx = c_start * out_image_size + y * out_width + x;
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for (int i = 0; i < CHANNELS_PER_ITER; i++) {
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output[out_idx] = input[in_idx];
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in_idx += in_image_size;
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out_idx += out_image_size;
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}
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}
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}
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template <class T, std::size_t CHANNELS_PER_ITER>
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__global__ void resize_bilinear(
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Span<T> output, size_type out_height, size_type out_width,
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View<T> input, size_type in_height, size_type in_width,
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float o2i_fy, float o2i_fx)
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{
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auto in_image_size = in_height * in_width;
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auto out_image_size = out_height * out_width;
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/* think of the output and input as a collection of 2d images with the last axis
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* representing the width and the last but one axis representing the height
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*
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* the remaining axis together form a collection of these images/channels
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*/
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auto num_effective_channels = output.size() / out_image_size;
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/* we process multiple channels every iteration to reuse the identical computation
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* involved with the spatial dimensions
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*
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* if we are processing `CHANNELS_PER_ITER` channels per iteration, we will need
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* (num_effective_channels / CHANNELS_PER_ITER) iterations per (x, y) location
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*/
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auto num_channel_iters_per_xy = (num_effective_channels / CHANNELS_PER_ITER);
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/* we need `num_channel_iters_per_xy` iterations per (x, y) and there are `out_image_size`
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* combinations of (x, y); hence, we'll need `num_channel_iters_per_xy * out_image_size`
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* iterations in total to finish the resize operation
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*/
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auto iters_required = num_channel_iters_per_xy * out_image_size;
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for (auto iter : grid_stride_range(iters_required)) {
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const index_type c_start = (iter / out_image_size) * CHANNELS_PER_ITER;
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const index_type c_end = c_start + CHANNELS_PER_ITER;
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/* note here that consecutive `iter` values will often have consecutive `x` values
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* => stores into output will be coalesced across threads
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*/
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const index_type y = (iter % out_image_size) / out_width;
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const index_type x = iter % out_width;
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auto in_x = x * o2i_fx;
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auto in_y = y * o2i_fy;
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auto in_x0 = static_cast<index_type>(in_x);
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auto in_y0 = static_cast<index_type>(in_y);
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using device::min;
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auto in_x1 = min<index_type>(in_x0 + 1, in_width - 1);
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auto in_y1 = min<index_type>(in_y0 + 1, in_height - 1);
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index_type in_offset_r0 = c_start * in_image_size + in_y0 * in_width;
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index_type in_offset_r1 = c_start * in_image_size + in_y1 * in_width;
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index_type out_idx = c_start * out_image_size + y * out_width + x;
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#pragma unroll 1 /* disable unrolling to reduce register pressure; not sure how but it works */
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for (auto c = c_start; c < c_end; c++) {
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auto v_00 = input[in_offset_r0 + in_x0],
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v_01 = input[in_offset_r0 + in_x1],
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v_10 = input[in_offset_r1 + in_x0],
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v_11 = input[in_offset_r1 + in_x1];
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output[out_idx] =
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v_00 +
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T(in_y - in_y0) * T(v_10 - v_00) +
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T(in_x - in_x0) * T(v_01 - v_00) +
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T(in_y - in_y0) * T(in_x - in_x0) * T(v_11 - v_01 - v_10 + v_00);
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in_offset_r0 += in_image_size;
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in_offset_r1 += in_image_size;
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out_idx += out_image_size;
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}
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}
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}
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}
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template <class T, std::size_t CHANNELS_PER_ITER> static
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void launch_multichannel_resize_nn(const Stream& stream,
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Span<T> output, size_type out_height, size_type out_width,
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View<T> input, size_type in_height, size_type in_width)
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{
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auto kernel = raw::resize_nn<T, CHANNELS_PER_ITER>;
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auto policy = make_policy(kernel, output.size() / CHANNELS_PER_ITER, 0, stream);
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launch_kernel(kernel, policy, output, out_height, out_width, input, in_height, in_width);
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}
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template <class T>
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void resize_nn(const Stream& stream, TensorSpan<T> output, TensorView<T> input) {
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auto out_height = output.get_axis_size(-2);
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auto out_width = output.get_axis_size(-1);
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auto in_height = input.get_axis_size(-2);
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auto in_width = input.get_axis_size(-1);
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auto num_effective_channels = input.size_range(0, 2);
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auto num_iters = num_effective_channels * out_height * out_width;
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if (num_effective_channels % 32 == 0 && num_iters > 655360) {
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launch_multichannel_resize_nn<T, 32>(stream, output, out_height, out_width, input, in_height, in_width);
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} else if (num_effective_channels % 16 == 0 && num_iters > 327680) {
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launch_multichannel_resize_nn<T, 16>(stream, output, out_height, out_width, input, in_height, in_width);
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} else if (num_effective_channels % 8 == 0 && num_iters > 163840) {
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launch_multichannel_resize_nn<T, 8>(stream, output, out_height, out_width, input, in_height, in_width);
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} else if (num_effective_channels % 4 == 0 && num_iters > 81920) {
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launch_multichannel_resize_nn<T, 4>(stream, output, out_height, out_width, input, in_height, in_width);
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} else if (num_effective_channels % 2 == 0) {
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launch_multichannel_resize_nn<T, 2>(stream, output, out_height, out_width, input, in_height, in_width);
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} else {
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launch_multichannel_resize_nn<T, 1>(stream, output, out_height, out_width, input, in_height, in_width);
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}
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}
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#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
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template void resize_nn<__half>(const Stream&, TensorSpan<__half>, TensorView<__half>);
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#endif
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template void resize_nn<float>(const Stream&, TensorSpan<float>, TensorView<float>);
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template <class T, std::size_t CHANNELS_PER_ITER> static
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void launch_multichannel_resize_bilinear(const Stream& stream,
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Span<T> output, size_type out_height, size_type out_width,
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View<T> input, size_type in_height, size_type in_width,
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float scale_y, float scale_x)
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{
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auto kernel = raw::resize_bilinear<T, CHANNELS_PER_ITER>;
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auto policy = make_policy(kernel, output.size() / CHANNELS_PER_ITER, 0, stream);
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launch_kernel(kernel, policy, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x);
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}
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template <class T>
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void resize_bilinear(const Stream& stream, TensorSpan<T> output, TensorView<T> input, float scale_y, float scale_x) {
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auto out_height = output.get_axis_size(-2);
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auto out_width = output.get_axis_size(-1);
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auto in_height = input.get_axis_size(-2);
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auto in_width = input.get_axis_size(-1);
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auto num_effective_channels = input.size_range(0, 2);
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auto num_iters = num_effective_channels * out_height * out_width;
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if (num_effective_channels % 16 == 0 && num_iters > 163840) {
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launch_multichannel_resize_bilinear<T, 16>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x);
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} else if (num_effective_channels % 8 == 0 && num_iters > 81920) {
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launch_multichannel_resize_bilinear<T, 8>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x);
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} else if (num_effective_channels % 4 == 0 && num_iters > 40960) {
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launch_multichannel_resize_bilinear<T, 4>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x);
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} else if (num_effective_channels % 2 == 0) {
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launch_multichannel_resize_bilinear<T, 2>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x);
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} else {
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launch_multichannel_resize_bilinear<T, 1>(stream, output, out_height, out_width, input, in_height, in_width, scale_y, scale_x);
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}
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}
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#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
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template void resize_bilinear<__half>(const Stream&, TensorSpan<__half>, TensorView<__half>, float, float);
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#endif
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template void resize_bilinear<float>(const Stream&, TensorSpan<float>, TensorView<float>, float, float);
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}}}} /* namespace cv::dnn::cuda4dnn::kernels */
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