94 lines
3.1 KiB
C++
94 lines
3.1 KiB
C++
// Copyright (2019-2024) Paul Scherrer Institute
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#include "NeuralNetResPredictor.h"
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#include <cmath>
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#include "../common/JFJochException.h"
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NeuralNetResPredictor::NeuralNetResPredictor(const DiffractionExperiment &in_experiment)
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: experiment(in_experiment), model_input(512*512)
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#ifdef JFJOCH_USE_TORCH
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, device(torch::kCUDA)
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#endif
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{
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float max_direction = std::max(in_experiment.GetXPixelsNum(), in_experiment.GetYPixelsNum()) / 2.0;
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pool_factor = std::lround(max_direction / 512.0f);
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if (pool_factor <= 0)
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throw JFJochException(JFJochExceptionCategory::InputParameterInvalid, "Detector size is too small");
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if (pool_factor > 8)
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throw JFJochException(JFJochExceptionCategory::InputParameterInvalid, "Detector size is too large");
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#ifdef JFJOCH_USE_TORCH
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module = torch::jit::load(experiment.GetNeuralNetModelPath());
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module.to(device);
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#endif
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}
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template<class T>
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void NeuralNetResPredictor::PrepareInternal(const T* image) {
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size_t xpixel = experiment.GetXPixelsNum();
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size_t ypixel = experiment.GetYPixelsNum();
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size_t start_x = std::lround(experiment.GetBeamX_pxl());
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size_t start_y = std::lround(experiment.GetBeamY_pxl());
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for (size_t y = 0; y < 512; y++) {
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size_t y0 = y * pool_factor + start_y;
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size_t min_yp = std::min(y0 + pool_factor, ypixel);
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for (size_t x = 0; x < 512; x++) {
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float val = 0.0;
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size_t x0 = x * pool_factor + start_x;
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size_t min_xp = std::min(x0 + pool_factor, ypixel);
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for (size_t yp = y0; yp < min_yp; yp++) {
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for (size_t xp = x0; xp < min_xp; xp++) {
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int16_t pxl = image[yp * xpixel + xp];
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if (val < pxl)
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val = pxl;
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}
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}
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float max_pool = floorf(sqrtf(val));
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model_input[512 * y + x] = max_pool;
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}
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}
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}
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void NeuralNetResPredictor::Prepare(const int16_t *image) {
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PrepareInternal(image);
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}
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void NeuralNetResPredictor::Prepare(const int32_t *image) {
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PrepareInternal(image);
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}
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size_t NeuralNetResPredictor::GetMaxPoolFactor() const {
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return pool_factor;
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}
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float NeuralNetResPredictor::Inference(const void *image) {
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if (experiment.GetPixelDepth() == 2)
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Prepare((int16_t *) image);
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else
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Prepare((int32_t *) image);
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#ifdef JFJOCH_USE_TORCH
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auto options = torch::TensorOptions().dtype(at::kFloat);
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auto model_input_tensor = torch::from_blob(model_input.data(), {1,1,512,512}, options).to(device);
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std::vector<torch::jit::IValue> pixels;
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pixels.emplace_back(std::move(model_input_tensor));
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auto output = module.forward(pixels).toTensor();
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auto tensor_output = output[0].item<float>();
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float two_theta = atanf(((2.0f * experiment.GetPixelSize_mm() / experiment.GetDetectorDistance_mm()) * tensor_output));
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float stheta = sinf(two_theta * 0.5f);
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float resolution = experiment.GetWavelength_A() / (2.0f * stheta);
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#else
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float resolution = 50.0;
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#endif
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return resolution;
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}
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const std::vector<float> &NeuralNetResPredictor::GetModelInput() const {
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return model_input;
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}
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