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* minor changes * support windows * fix GCC build * fix lint * reformat * fix Windows build * fix GCC build * search backend ops for onnxruntime * fix lint * fix lint * code clean-up * code clean-up * fix clang build * fix trt support * fix cmake for ncnn * fix cmake for openvino * fix SDK Python API * handle ops for other backends (ncnn, trt) * handle SDK Python API library location * robustify linkage * fix cuda * minor fix for openvino & ncnn * use CMAKE_CUDA_ARCHITECTURES if set * fix cuda preprocessor * fix misc * fix pplnn & pplcv, drop support for pplcv<0.6.0 * robustify cmake * update build.md (#2) * build dynamic modules as module library & fix demo (partially) * fix candidate path for mmdeploy_python * move "enable CUDA" to cmake config for demo * refine demo cmake * add comment * fix ubuntu build * revert docs/en/build.md * fix C API * fix lint * Windows build doc (#3) * check in docs related to mmdeploy build on windows * update build guide on windows platform * update build guide on windows platform * make path of thirdparty libraries consistent * make path consistency * correct build command for custom ops * correct build command for sdk * update sdk build instructions * update doc * correct build command * fix lint * correct build command and fix lint Co-authored-by: lvhan <lvhan@pjlab.org> * trailing whitespace (#4) * minor fix * fix sr sdk model * fix type deduction * fix cudaFree after driver shutting down * update ppl.cv installation warning (#5) * fix device allocator threshold & fix lint * update doc (#6) * update ppl.cv installation warning * missing 'git clone' Co-authored-by: chenxin <chenxin2@sensetime.com> Co-authored-by: zhangli <zhangli@sensetime.com> Co-authored-by: lvhan028 <lvhan_028@163.com> Co-authored-by: lvhan <lvhan@pjlab.org>
62 lines
2.6 KiB
Plaintext
62 lines
2.6 KiB
Plaintext
// Copyright (c) OpenMMLab. All rights reserved.
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#include <cuda_runtime.h>
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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 || 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{float(1. / std[0]), float(1. / std[1]), float(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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