mx/Jungfraujoch
2
1
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases 80 Wiki Activity
Files
dd18d8c21dafb5d2ee3038ec0b3eaee96b2db008
Jungfraujoch/image_analysis/MXAnalysisWithoutFPGA.cpp
T
leonarski_fandClaude Opus 5.5 2b476336ae rugnux: report-only supercell probe - the eight parity classes of the doubled cell 
Every lattice decision counts spots or frames, and the sub-lattice's strong
reflections win every count, so a crystal whose cell is doubled by a weak
superstructure class (9min, 6z9g) is adopted at the half cell. This asks the
question in intensities instead. On 60 frames spread over the sweep, after
each frame's own integration, the 2a x 2b x 2c supercell of its primitive
lattice is predicted to 3 A with the frame's own refined orientation and
geometry and integrated on the same engine; the reflections are summed per
parity class in two shells (20-5, 5-3 A), with a fit of intensity against
partiality (I = a + b p) that separates what rocks like a Bragg reflection
from what sits at the node whatever the rocking. Only the tested frames are
predicted and nothing is retained, so memory is bounded (the previous
prototype predicted the whole run through the merge and ran out of GPU
memory). The probe's integrations are kept out of the engine's own counts,
which the two-pass stencil guard reads. Results are bit-identical with and
without it.

REPORT ONLY - it decides nothing, because on the battery it does not yet
separate a weak real class from what sits at the half-integer nodes of
crystals whose cell is right. Real classes: 6z9g class 101 at 24 % of the
lattice's intensity (29 % rocking), 9min 100 at 19 % (4 % rocking - its real
class does not rock like the lattice either). On correct cells the largest
classes reach 9-12 % raw (7n2s, 9i0a, 7os3) and 3-4 % rocking (7dkp, 7os3),
and 7mzt reads 40 % / 19 % on a class the deposition does not have. The log
line is the population a decision has to be calibrated on.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01D1G8gJVAy6gp1K5Dz3NE5C
2026-09-25 06:02:50 +02:00

412 lines
22 KiB
C++
Raw Blame History

// SPDX-FileCopyrightText: 2024 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include "MXAnalysisWithoutFPGA.h"
#include <algorithm>
#include <spdlog/spdlog.h>
#include "spot_finding/StrongPixelSet.h"
#include "../compression/JFJochDecompress.h"
#include "../common/CUDAWrapper.h"
#include "spot_finding/SpotUtils.h"
#include "bragg_prediction/BraggPredictionFactory.h"
#include "image_preprocessing/ImagePreprocessorCPU.h"
#include "azint/AzIntEngineCPU.h"
#include "roi/ROIIntegrationCPU.h"
#include "spot_finding/ImageSpotFinderCPU.h"
#include "spot_finding/AdaptiveSpotFinderCPU.h"
#include "bragg_integration/BraggIntegrationEngineCPU.h"
#ifdef JFJOCH_USE_CUDA
#include "azint/AzIntEngineGPU.h"
#include "roi/ROIIntegrationGPU.h"
#include "spot_finding/ImageSpotFinderGPU.h"
#include "spot_finding/AdaptiveSpotFinderGPU.h"
#include "image_preprocessing/ImagePreprocessorGPU.h"
#include "image_preprocessing/ImagePreprocessorBufferGPU.h"
#include "bragg_integration/BraggIntegrationEngineGPU.h"
#include "../common/CUDAWrapper.h"
#endif
MXAnalysisWithoutFPGA::MXAnalysisWithoutFPGA(const DiffractionExperiment &in_experiment,
const AzimuthalIntegrationMapping &in_integration,
const PixelMask &in_mask,
IndexAndRefine &in_indexer,
bool in_enable_fused_adaptive_gpu)
: experiment(in_experiment),
integration(in_integration),
enable_fused_adaptive_gpu(in_enable_fused_adaptive_gpu),
npixels(experiment.GetPixelsNum()),
xpixels(experiment.GetXPixelsNum()),
indexer(in_indexer),
prediction(CreateBraggPrediction(experiment.IsRotationIndexing())),
mask(in_mask),
mask_high_res(-1),
mask_low_res(-1) {
#ifdef JFJOCH_USE_CUDA
if (get_gpu_count() == 0) {
#endif
preprocessor_buffer = std::make_unique<ImagePreprocessorBuffer>(experiment.GetPixelsNum());
preprocessor = std::make_unique<ImagePreprocessorCPU>(in_experiment, in_mask);
bragg_engine = std::make_unique<BraggIntegrationEngineCPU>(in_experiment);
if (experiment.ROI().size() >= 1)
roi = std::make_unique<ROIIntegrationCPU>(experiment);
#ifdef JFJOCH_USE_CUDA
} else {
stream = std::make_shared<CudaStream>();
// The host copy of the preprocessed image is only read when a CPU engine wants it, which is
// the same condition that drives copy_image_to_host below. Skipping it also skips page-locking
// 4 bytes per pixel per worker.
preprocessor_buffer = std::make_unique<ImagePreprocessorBufferGPU>(
experiment.GetPixelsNum(), /*host_mirror=*/!enable_fused_adaptive_gpu);
// The preprocessed image only has to come back to the host if a CPU engine reads it. Every
// engine built below runs on the GPU, except the CPU adaptive finder that is kept when the fused
// GPU engine is off - so that is the one case that needs the copy. Every caller currently passes
// enable_fused_adaptive_gpu = true, so on the GPU path the copy is off in practice.
preprocessor = std::make_unique<ImagePreprocessorGPU>(in_experiment, in_mask, stream,
/*copy_image_to_host=*/!enable_fused_adaptive_gpu);
bragg_engine = std::make_unique<BraggIntegrationEngineGPU>(in_experiment, stream);
if (experiment.ROI().size() >= 1)
roi = std::make_unique<ROIIntegrationGPU>(experiment, stream);
if (enable_fused_adaptive_gpu) {
// One GPU engine that computes the azimuthal profile and the adaptive spot mask in a single
// image pass. fused_adaptive aliases it so Analyze() can lift the profile out of it.
auto fused = std::make_unique<AdaptiveSpotFinderGPU>(integration, stream);
fused_adaptive = fused.get();
adaptiveSpotFinder = std::move(fused);
}
}
#endif
if (!adaptiveSpotFinder)
adaptiveSpotFinder = std::make_unique<AdaptiveSpotFinderCPU>(integration);
}
void MXAnalysisWithoutFPGA::Analyze(DataMessage &output,
AzimuthalIntegrationProfile &profile,
const SpotFindingSettings &spot_finding_settings) {
if ((output.image.GetWidth() != xpixels)
|| (output.image.GetWidth() * output.image.GetHeight() != npixels))
throw JFJochException(JFJochExceptionCategory::InputParameterInvalid,
"Mismatch in pixel size");
// Decompress on the device where the preprocessor can, so only the compressed chunk crosses PCIe
// and the host does no decompression at all. AnalyzeCompressed says whether it took the image;
// when it declines (a CPU preprocessor, or an algorithm with no device decoder) fall through to
// the host route unchanged. The two produce the same preprocessed image.
const auto compression_start_time = std::chrono::steady_clock::now();
ImageStatistics ret{};
bool decoded_on_device = false;
try {
decoded_on_device = preprocessor->AnalyzeCompressed(*preprocessor_buffer, output.image, ret);
} catch (const JFJochException &e) {
// The device route must never be the reason a frame fails: whatever it could not handle, the
// host decoder gets its turn. If the data really is bad the host throws too and the caller
// sees the same error it saw before any of this existed - but a GPU-side problem costs speed
// rather than the acquisition.
spdlog::warn("Device decoding failed ({}), falling back to host decompression", e.what());
decoded_on_device = false;
// Handled here, so it must not stay behind as the thread's last error: the host route below
// runs kernels of its own and checks cudaGetLastError() after them, which would report this
// failure again - fatally - over an image that decoded perfectly well on the host.
cuda_clear_error();
}
const auto compression_end_time = std::chrono::steady_clock::now();
if (!decoded_on_device) {
const uint8_t *image_ptr = Decompress(output.image);
const auto decompressed_time = std::chrono::steady_clock::now();
if (output.image.GetCompressionAlgorithm() != CompressionAlgorithm::NO_COMPRESSION)
output.compression_time_s = std::chrono::duration<float>(decompressed_time - compression_start_time).count();
const auto preprocessing_start_time = std::chrono::steady_clock::now();
ret = preprocessor->Analyze(*preprocessor_buffer, image_ptr, output.image.GetMode());
const auto preprocessing_end_time = std::chrono::steady_clock::now();
output.preprocessing_time_s = std::chrono::duration<float>(preprocessing_end_time - preprocessing_start_time).count();
} else {
// Decode and preprocess are one device operation here, but the decompression is still a real,
// separately measurable cost - the decoder brackets it with CUDA events - so it is still
// reported as one. Leaving compression_time_s unset instead would blank the broker's
// "compression" plot trace and fill /entry/profiling/compressionTime with NaN.
const float total_s = std::chrono::duration<float>(compression_end_time - compression_start_time).count();
const float decompress_s = std::min(preprocessor->GetLastDecompressionTime_s(), total_s);
output.compression_time_s = decompress_s;
output.preprocessing_time_s = total_s - decompress_s;
}
// The fused GPU engine (rugnux offline, GPU, adaptive detection) produces the azimuthal profile as
// a byproduct of spot finding, so the separate azint pass is skipped in that case and the profile is
// lifted out of the finder below.
const bool fused = enable_fused_adaptive_gpu && spot_finding_settings.enable
&& spot_finding_settings.adaptive_threshold && fused_adaptive != nullptr;
if (!fused) {
const auto azint_start_time = std::chrono::steady_clock::now();
AzInt().Run(*preprocessor_buffer, profile);
const auto azint_end_time = std::chrono::steady_clock::now();
output.azint_time_s = std::chrono::duration<float>(azint_end_time - azint_start_time).count();
}
if (roi)
roi->Run(*preprocessor_buffer, output.roi);
if (spot_finding_settings.enable) {
// Update resolution mask
if (mask_high_res != spot_finding_settings.high_resolution_limit
|| mask_low_res != spot_finding_settings.low_resolution_limit)
UpdateMaskResolution(spot_finding_settings);
ImageSpotFinder &finder = spot_finding_settings.adaptive_threshold
? static_cast<ImageSpotFinder &>(*adaptiveSpotFinder)
: FixedThresholdFinder();
const auto integrate_fn = [this](const std::vector<Reflection> &predicted, size_t npredicted,
int64_t image_number) {
return bragg_engine->Run(*preprocessor_buffer, predicted, npredicted, image_number);
};
// The supercell probe integrates on the same engine, but what the engine counts is the run's
// measurement of its own stencil, and the probe's reflections are not the run's: keep them out.
const auto probe_integrate_fn = [this](const std::vector<Reflection> &predicted, size_t npredicted,
int64_t image_number) {
const BraggIntegrationCounts before = bragg_engine->Counts();
auto ret = bragg_engine->Run(*preprocessor_buffer, predicted, npredicted, image_number);
const BraggIntegrationCounts after = bragg_engine->Counts();
probe_counts.predicted += after.predicted - before.predicted;
probe_counts.bkg_starved += after.bkg_starved - before.bkg_starved;
probe_counts.bkg_starved_by_neighbour += after.bkg_starved_by_neighbour - before.bkg_starved_by_neighbour;
probe_counts.profile_fallback += after.profile_fallback - before.profile_fallback;
return ret;
};
// The radial background correction has to be decided BEFORE this image is integrated, so the
// ice score is taken here rather than with the other per-image quantities at the end of the
// function. It needs the peak-excluded per-ring background, which the adaptive finder has as
// soon as it has detected - so this must be called after detection and before integration.
// Where no such background exists (no adaptive finder), auto leaves the correction off: the
// plain profile carries the Bragg peaks and cannot support an absolute threshold.
const auto decide_radial_background = [this, &spot_finding_settings, &output]() {
if (!bragg_engine->IsBackgroundRadialAuto())
return;
// Same condition as the score at the end of this function: the adaptive finder holds its
// ring background from whenever it last ran, so requiring that it ran for THIS image is
// what keeps a stale curve out.
if (!spot_finding_settings.adaptive_threshold)
return;
const std::vector<float> &ring_bkg = adaptiveSpotFinder->GetRingBackground();
if (ring_bkg.empty())
return;
output.ice_ring_score = AzimuthalIntegrationProfile::IceRingScore(
ring_bkg, integration.GetQBinCount(), integration.Settings(),
spot_finding_settings.ice_ring_width_Q_recipA);
bragg_engine->BackgroundRadial(*output.ice_ring_score
>= experiment.GetScalingSettings().GetIceMinScore());
};
// A missing min-pix (std::nullopt) means "choose it per image". This applies only to the stills
// indexing path (each frame is indexed independently); rotation indexing builds one lattice from
// all frames, so it keeps the fixed min-pix and the single-pass finder.
const bool adaptive_min_pix = !spot_finding_settings.min_pix_per_spot.has_value()
&& spot_finding_settings.indexing
&& !experiment.IsRotationIndexing();
if (adaptive_min_pix) {
// Choose the per-image min-pix adaptively instead of a fixed one. min-pix filters connected
// components AFTER detection, so BOTH the detection (the expensive per-pixel pass) and the
// connected-component search run ONCE and only the filter is repeated; the azimuthal
// profile is the one Detect() computed.
// Index at 3/2/1 (index-only, no integration/accumulation), keep whichever maximises
// n_indexed^2 / n_total (indexed count weighted by indexed fraction) together with its spot
// list, and integrate that one.
const auto detect_start_time = std::chrono::steady_clock::now();
finder.Detect(*preprocessor_buffer, spot_finding_settings);
const auto &components = finder.ExtractComponents(*preprocessor_buffer, spot_finding_settings);
float spot_finding_time_s =
std::chrono::duration<float>(std::chrono::steady_clock::now() - detect_start_time).count();
float indexing_time_s = 0.0f;
SpotFindingSettings s = spot_finding_settings;
std::vector<DiffractionSpot> best_spots;
int best_mp = 0;
double best_score = -1.0;
for (int mp : {3, 2, 1}) {
s.min_pix_per_spot = mp;
const auto extract_start_time = std::chrono::steady_clock::now();
std::vector<DiffractionSpot> spots = ImageSpotFinder::Filter(components, s);
spot_finding_time_s +=
std::chrono::duration<float>(std::chrono::steady_clock::now() - extract_start_time).count();
SpotAnalyze(experiment, s, spots, output);
const bool indexed = indexer.IndexFrameOnly(output, s);
indexing_time_s += output.indexing_time_s.value_or(0.0f);
if (indexed) {
const double n_idx = static_cast<double>(output.spot_count_indexed.value_or(0));
const double n_tot = static_cast<double>(std::max<int64_t>(1, output.spot_count.value_or(1)));
const double score = n_idx * n_idx / n_tot;
if (score > best_score) {
best_score = score;
best_mp = mp;
best_spots = std::move(spots);
}
}
}
if (best_mp != 0) {
// Index and integrate the winning spot list; no spot finding left to do.
s.min_pix_per_spot = best_mp;
SpotAnalyze(experiment, s, best_spots, output);
decide_radial_background();
indexer.ProcessImage(output, s, *prediction, integrate_fn, probe_integrate_fn);
indexing_time_s += output.indexing_time_s.value_or(0.0f);
}
// Each indexer call reports only its own time, so the escalation's total is summed here.
output.spot_finding_time_s = spot_finding_time_s;
output.indexing_time_s = indexing_time_s;
} else {
const auto spot_finding_start_time = std::chrono::steady_clock::now();
const std::vector<DiffractionSpot> spots = finder.Run(*preprocessor_buffer, spot_finding_settings);
SpotAnalyze(experiment, spot_finding_settings, spots, output);
output.spot_finding_time_s = std::chrono::duration<float>(std::chrono::steady_clock::now() - spot_finding_start_time).count();
decide_radial_background();
if (spot_finding_settings.indexing)
indexer.ProcessImage(output, spot_finding_settings, *prediction, integrate_fn, probe_integrate_fn);
}
// Recorded whichever way the frame went. A frame holding StrongPixelLimit of them is given up
// on and reports no spots at all, and this is the only thing that tells such a frame from one
// that did not diffract.
output.strong_pixel_count = finder.StrongPixelCount();
#ifdef JFJOCH_USE_CUDA
if (fused) {
// Lift the azimuthal profile the fused engine computed in the same detection pass; its azint
// cost is folded into spot_finding_time_s above.
profile.Clear(integration);
profile += fused_adaptive->GetProfile();
output.azint_time_s = 0.0f;
}
#endif
}
output.max_viable_pixel_value = ret.max_value;
output.min_viable_pixel_value = ret.min_value;
output.error_pixel_count = ret.error_pixel_count;
output.saturated_pixel_count = ret.saturated_pixel_count;
output.az_int_profile = profile.GetResult();
output.az_int_profile_count = profile.GetPixelCount();
output.az_int_profile_std = profile.GetStd();
output.bkg_estimate = profile.GetBkgEstimate(integration.Settings());
// The ice score wants a radial profile with the Bragg peaks taken OUT of it. The azimuthal profile
// is a plain per-ring mean, so a strong low-resolution reflection landing in a ring's bin is
// indistinguishable from ice sitting there - measured, that alone lifts clean crystals to a score
// of 1.5-4.2, right into the range real ice occupies. The adaptive spot finder already computes
// exactly what is wanted: a sigma-clipped per-ring background, in the same bins, from which the
// peaks have been removed (an ice ring is azimuthally smooth, so it survives the clip). It is in
// raw counts rather than corrected ones, which the score does not care about - it is a ratio to the
// background interpolated under the ring, and the corrections are smooth in radius.
const std::vector<float> &ring_bkg = adaptiveSpotFinder->GetRingBackground();
const bool have_ring_bkg = spot_finding_settings.enable && spot_finding_settings.adaptive_threshold
&& !ring_bkg.empty();
output.ice_ring_score = AzimuthalIntegrationProfile::IceRingScore(
have_ring_bkg ? ring_bkg : profile.GetResult1D(), integration.GetQBinCount(),
integration.Settings(), spot_finding_settings.ice_ring_width_Q_recipA);
}
ImageSpotFinder &MXAnalysisWithoutFPGA::FixedThresholdFinder() {
if (!spotFinder) {
#ifdef JFJOCH_USE_CUDA
if (stream)
spotFinder = std::make_unique<ImageSpotFinderGPU>(experiment.GetXPixelsNum(),
experiment.GetYPixelsNum(), stream);
else
#endif
spotFinder = std::make_unique<ImageSpotFinderCPU>(experiment.GetXPixelsNum(),
experiment.GetYPixelsNum());
// It missed every mask update that happened before it existed, so it takes the current one
// now. Without this it would find spots outside the resolution limits.
if (mask_resolution)
spotFinder->SetResolutionMaskBits(*mask_resolution);
}
return *spotFinder;
}
AzIntEngine &MXAnalysisWithoutFPGA::AzInt() {
if (!azint) {
#ifdef JFJOCH_USE_CUDA
if (stream)
azint = std::make_unique<AzIntEngineGPU>(integration, stream);
else
#endif
azint = std::make_unique<AzIntEngineCPU>(integration);
}
return *azint;
}
void MXAnalysisWithoutFPGA::RebuildROI() {
if (experiment.ROI().empty()) {
roi.reset();
return;
}
#ifdef JFJOCH_USE_CUDA
if (stream) {
roi = std::make_unique<ROIIntegrationGPU>(experiment, stream);
return;
}
#endif
roi = std::make_unique<ROIIntegrationCPU>(experiment);
}
void MXAnalysisWithoutFPGA::AnalyzeROIOnly(DataMessage &output) {
if ((output.image.GetWidth() != xpixels)
|| (output.image.GetWidth() * output.image.GetHeight() != npixels))
throw JFJochException(JFJochExceptionCategory::InputParameterInvalid,
"Mismatch in pixel size");
const uint8_t *image_ptr = Decompress(output.image);
preprocessor->Analyze(*preprocessor_buffer, image_ptr, output.image.GetMode());
RunROIOnly(output);
}
const uint8_t *MXAnalysisWithoutFPGA::Decompress(const CompressedImage &image) {
// An uncompressed image is read straight out of the message and never touches decompression_buffer,
// so what the upload reads is the reader's own bytes: page-lock those instead. Pageable memory is
// staged by the driver a chunk at a time, which on a 4-byte 16 Mpx frame is 12.9 ms against 4.0 ms
// pinned - and a miniCBF sweep takes this path for every image.
if (image.GetCompressionAlgorithm() == CompressionAlgorithm::NO_COMPRESSION)
preprocessor->PinInputRegion(image.GetCompressed(), image.GetUncompressedSize());
else
preprocessor->PinInputBuffer(decompression_buffer, image.GetUncompressedSize());
return image.GetUncompressedPtr(decompression_buffer);
}
void MXAnalysisWithoutFPGA::RunROIOnly(DataMessage &output) {
output.roi.clear();
if (roi)
roi->Run(*preprocessor_buffer, output.roi);
}
BraggIntegrationCounts MXAnalysisWithoutFPGA::BraggCounts() const {
if (!bragg_engine)
return {};
BraggIntegrationCounts c = bragg_engine->Counts();
c.predicted -= probe_counts.predicted;
c.bkg_starved -= probe_counts.bkg_starved;
c.bkg_starved_by_neighbour -= probe_counts.bkg_starved_by_neighbour;
c.profile_fallback -= probe_counts.profile_fallback;
return c;
}
void MXAnalysisWithoutFPGA::UpdateMaskResolution(const SpotFindingSettings &settings) {
mask_low_res = settings.low_resolution_limit;
mask_high_res = settings.high_resolution_limit;
// The mask is a pure function of the resolution map and the two limits, so the mapping builds it -
// once for all the workers, which otherwise each walked every pixel of the detector to arrive at
// the same bits.
mask_resolution = integration.ResolutionMaskBits(mask_high_res, mask_low_res);
// The finders keep their own copy (the GPU ones a bit-packed device copy), so the mask is handed
// over here - when the limits change - rather than with every image.
if (spotFinder)
spotFinder->SetResolutionMaskBits(*mask_resolution);
adaptiveSpotFinder->SetResolutionMaskBits(*mask_resolution);
}
Reference in New Issue View Git Blame Copy Permalink