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36124f6205
* check in cmake * move backend_ops to csrc/backend_ops * check in preprocess, model, some codebase and their c-apis * check in CMakeLists.txt * check in parts of test_csrc * commit everything else * add readme * update core's BUILD_INTERFACE directory * skip codespell on third_party * update trt_net and ort_net's CMakeLists * ignore clion's build directory * check in pybind11 * add onnx.proto. Remove MMDeploy's dependency on ncnn's source code * export MMDeployTargets only when MMDEPLOY_BUILD_SDK is ON * remove useless message * target include directory is wrong * change target name from mmdeploy_ppl_net to mmdeploy_pplnn_net * skip install directory * update project's cmake * remove useless code * set CMAKE_BUILD_TYPE to Release by force if it isn't set by user * update custom ops CMakeLists * pass object target's source lists * fix lint end-of-file * fix lint: trailing whitespace * fix codespell hook * remove bicubic_interpolate to csrc/backend_ops/ * set MMDEPLOY_BUILD_SDK OFF * change custom ops build command * add spdlog installation command * update docs on how to checkout pybind11 * move bicubic_interpolate to backend_ops/tensorrt directory * remove useless code * correct cmake * fix typo * fix typo * fix install directory * correct sdk's readme * set cub dir when cuda version < 11.0 * change directory where clang-format will apply to * fix build command * add .clang-format * change clang-format style from google to file * reformat csrc/backend_ops * format sdk's code * turn off clang-format for some files * add -Xcompiler=-fno-gnu-unique * fix trt topk initialize * check in config for sdk demo * update cmake script and csrc's readme * correct config's path * add cuda include directory, otherwise compile failed in case of tensorrt8.2 * clang-format onnx2ncnn.cpp Co-authored-by: zhangli <lzhang329@gmail.com> Co-authored-by: grimoire <yaoqian@sensetime.com>
60 lines
2.5 KiB
Plaintext
60 lines
2.5 KiB
Plaintext
// Copyright (c) OpenMMLab. All rights reserved.
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#include <cstdint>
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#include <cstdio>
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namespace mmdeploy {
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namespace cuda {
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template <typename T, int channels>
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__global__ void normalize(const T* src, int height, int width, int stride, float* output,
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const float3 mean, const float3 std, bool to_rgb) {
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int x = (int)(blockIdx.x * blockDim.x + threadIdx.x);
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int y = (int)(blockIdx.y * blockDim.y + threadIdx.y);
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if (x >= width or y >= height) {
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return;
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}
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int loc = y * stride + x * channels;
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auto mean_ptr = &mean.x;
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auto std_ptr = &std.x;
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if (to_rgb) {
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for (int c = 0; c < channels; ++c) {
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output[loc + c] = ((float)src[loc + channels - 1 - c] - mean_ptr[c]) / std_ptr[c];
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}
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} else {
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for (int c = 0; c < channels; ++c) {
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output[loc + c] = ((float)src[loc + c] - mean_ptr[c]) / std_ptr[c];
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}
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}
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}
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template <typename T, int channels>
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void Normalize(const T* src, int height, int width, int stride, float* output, const float* mean,
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const float* std, bool to_rgb, cudaStream_t stream) {
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const dim3 thread_block(16, 16);
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const dim3 num_blocks((width + thread_block.x - 1) / thread_block.x,
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(height + thread_block.y - 1) / thread_block.y);
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const float3 _mean{mean[0], mean[1], mean[2]};
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const float3 _std{std[0], std[1], std[2]};
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normalize<T, channels><<<num_blocks, thread_block, 0, stream>>>(src, height, width, stride,
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output, _mean, _std, to_rgb);
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}
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template void Normalize<uint8_t, 3>(const uint8_t* src, int height, int width, int stride,
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float* output, const float* mean, const float* std, bool to_rgb,
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cudaStream_t stream);
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template void Normalize<uint8_t, 1>(const uint8_t* src, int height, int width, int stride,
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float* output, const float* mean, const float* std, bool to_rgb,
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cudaStream_t stream);
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template void Normalize<float, 3>(const float* src, int height, int width, int stride,
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float* output, const float* mean, const float* std, bool to_rgb,
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cudaStream_t stream);
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template void Normalize<float, 1>(const float* src, int height, int width, int stride,
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float* output, const float* mean, const float* std, bool to_rgb,
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cudaStream_t stream);
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} // namespace cuda
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} // namespace mmdeploy
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