95 lines
3.4 KiB
C++
95 lines
3.4 KiB
C++
// SPDX-FileCopyrightText: 2025 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
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// SPDX-License-Identifier: GPL-3.0-only
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// Copyright (2019-2024) Paul Scherrer Institute
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#include <catch2/catch_all.hpp>
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#include "../writer/HDF5Objects.h"
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#include "../image_analysis/NeuralNetInferenceClient.h"
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TEST_CASE("NeuralNetResPredictor_Prepare", "[LinearAlgebra][Coord]") {
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DiffractionExperiment experiment(DetJF4M());
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experiment.DetectorDistance_mm(75).IncidentEnergy_keV(12.4).BeamX_pxl(1000).BeamY_pxl(1000);
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std::vector<int16_t> v(experiment.GetPixelsNum(),0);
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v[1000 * experiment.GetXPixelsNum() + 1000] = 100;
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v[1000 * experiment.GetXPixelsNum() + 1001] = 20;
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v[1001 * experiment.GetXPixelsNum() + 1000] = 30;
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v[1001 * experiment.GetXPixelsNum() + 1001] = INT16_MIN;
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v[600 * experiment.GetXPixelsNum() + 600] = 121;
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v[1050 * experiment.GetXPixelsNum() + 1050] = 52;
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v[2000 * experiment.GetXPixelsNum() + 1500] = 160;
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v[800 * experiment.GetXPixelsNum() + 600] = 49;
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v[1200 * experiment.GetXPixelsNum() + 600] = 36;
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v[800 * experiment.GetXPixelsNum() + 1400] = 64;
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NeuralNetInferenceClient predictor;
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REQUIRE(predictor.GetMaxPoolFactor(experiment) == 2);
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PixelMask mask(experiment);
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std::vector<uint32_t> mask_vec(experiment.GetPixelsNum());
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mask.LoadUserMask(experiment, mask_vec);
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auto br = predictor.Prepare(experiment, mask, v.data(), Quarter::BottomRight);
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REQUIRE(br.size() == 512 * 512);
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CHECK(br[0] == 10);
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CHECK(br[25 * 512 + 25] == 7);
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CHECK(br[500 * 512 + 250] == 12);
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auto tl = predictor.Prepare(experiment, mask, v.data(), Quarter::TopLeft);
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REQUIRE(tl.size() == 512 * 512);
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CHECK(tl[100 * 512 + 200] == 7);
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CHECK(tl[200 * 512 + 200] == 11);
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auto tr = predictor.Prepare(experiment, mask, v.data(), Quarter::TopRight);
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REQUIRE(tr.size() == 512 * 512);
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CHECK(tr[100 * 512 + 200] == 8);
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auto bl = predictor.Prepare(experiment, mask, v.data(), Quarter::BottomLeft);
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REQUIRE(bl.size() == 512 * 512);
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CHECK(bl[100 * 512 + 200] == 6);
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}
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TEST_CASE("NeuralNetResPredictor_Prepare_PixelMask", "[LinearAlgebra][Coord]") {
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DiffractionExperiment experiment(DetJF4M());
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experiment.DetectorDistance_mm(75).IncidentEnergy_keV(12.4).BeamX_pxl(1000).BeamY_pxl(1000);
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std::vector<int16_t> v(experiment.GetPixelsNum(),0);
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v[1000 * experiment.GetXPixelsNum() + 1000] = 100;
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v[1000 * experiment.GetXPixelsNum() + 1001] = 20;
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v[1001 * experiment.GetXPixelsNum() + 1000] = 30;
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v[1001 * experiment.GetXPixelsNum() + 1001] = INT16_MIN;
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v[600 * experiment.GetXPixelsNum() + 600] = 121;
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v[1050 * experiment.GetXPixelsNum() + 1050] = 52;
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v[2000 * experiment.GetXPixelsNum() + 1500] = 160;
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v[800 * experiment.GetXPixelsNum() + 600] = 49;
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v[1200 * experiment.GetXPixelsNum() + 600] = 36;
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v[800 * experiment.GetXPixelsNum() + 1400] = 64;
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NeuralNetInferenceClient predictor;
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REQUIRE(predictor.GetMaxPoolFactor(experiment) == 2);
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PixelMask mask(experiment);
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std::vector<uint32_t> mask_vec(experiment.GetPixelsNum());
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mask_vec[600 * experiment.GetXPixelsNum() + 600] = 8;
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mask.LoadUserMask(experiment, mask_vec);
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auto tl = predictor.Prepare(experiment, mask, v.data(), Quarter::TopLeft);
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REQUIRE(tl.size() == 512 * 512);
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CHECK(tl[100 * 512 + 200] == 7);
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CHECK(tl[200 * 512 + 200] == 0);
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}
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