leonarski_fandClaude Opus 5.5
d4f4bab158
The hot-pixel step of the pre-scan (MaskDefectivePixels) on a GPU build:
- The device half is built once, before the workers start (HotPixelFinder::PrepareDevice),
instead of by the first worker's frame while the others waited. The unmasked pixels
grouped by key are sorted on the device (stable radix sort: the same order the host
fill gave) instead of scattered on the host and uploaded.
- No per-frame host round trip: the ring-sector levels and lit thresholds are made on
the device from the order statistics, and the per-key frame and level sums are kept
there too; frames queue on their workers' streams and the per-pixel accumulation is
ordered by an event instead of a host synchronisation.
- The mask: the chance rate's per-ring counts are summed on the device, and only the
pixels the tests can pass (error value on most frames, or lit on at least
min(max(2, k_chance), valid frames)) come back with their sums - not the five
per-pixel arrays (470 MB pageable on a 16 Mpx detector). The host tests run on them
unchanged.
The threshold is written as fma(nsigma, noise, level) + offset on the host - what GCC
already contracted it to - and the device takes the same two roundings, so the levels
are bit-identical (and no longer depend on whether a compiler contracts).
Exact: hot-pixel mask and p.mtz byte-identical to f849e2d1b on myoglobin, cytochrome C
and thaumatin, GPU and CPU builds. Hot-pixel step (GPU, box at load 18-23, interleaved
A/B, two pairs each): 0.94-1.32 s -> 0.65-0.81 s; frames 0.37-0.46 -> 0.18-0.23 s,
mask 0.21-0.34 -> 0.08-0.18 s. Device memory of the finder: 543 MB as before, plus a
~0.36 GB transient for the sort while it is built.
Tests: [HotPixelFinder] (HotPixelFinder_DeviceMatchesHost bit-exact), [ShadowFinder],
[BeamCenter].
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01K5K8jvPPbmCrbqnWkddTuB
379 lines
17 KiB
C++
379 lines
17 KiB
C++
// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
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// SPDX-License-Identifier: GPL-3.0-only
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#include "HotPixels.h"
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#include <algorithm>
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#include <cmath>
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#include <cstring>
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#include <limits>
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#include <optional>
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#include "../common/JFJochMath.h"
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#include "../common/ParallelFor.h"
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namespace {
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// The lower median of v[0..n), reordering it.
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int32_t median_of(int32_t *v, size_t n) {
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std::nth_element(v, v + n / 2, v + n);
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return v[n / 2];
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}
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// A ring's background is a few counts, so its order statistics are read off a histogram of the values
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// 0..RING_HIST_VALUES-1 - exactly, as long as the one asked for lands inside that range.
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constexpr int32_t RING_HIST_VALUES = 1024;
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// The rank-th smallest value, if it is inside the histogram; `below` counts the values under 0.
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std::optional<int32_t> hist_order_statistic(const std::vector<uint32_t> &hist, size_t below, size_t rank) {
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if (rank < below)
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return std::nullopt;
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size_t seen = below;
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for (int32_t v = 0; v < RING_HIST_VALUES; v++) {
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seen += hist[v];
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if (seen > rank)
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return v;
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}
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return std::nullopt;
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}
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// The rank-th smallest |value - m|, if every value that close to m is inside the histogram (none
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// below 0 anywhere, and m + d still in range).
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std::optional<int32_t> hist_deviation_order_statistic(const std::vector<uint32_t> &hist, size_t below,
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int32_t m, size_t rank) {
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if (below > 0)
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return std::nullopt;
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size_t seen = hist[m];
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for (int32_t d = 0; seen <= rank; ) {
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d++;
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if (m + d >= RING_HIST_VALUES)
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return std::nullopt;
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seen += hist[m + d] + (m - d >= 0 ? hist[m - d] : 0);
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if (seen > rank)
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return d;
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}
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return 0;
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}
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// The smallest k with npix * P(Binomial(n, q) >= k) below the family-wise rate, n + 1 if none.
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int binomial_bound(int n, double q, double npix, double rate) {
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double tail = 0.0;
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int k = n + 1;
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for (int j = n; j >= 0; j--) {
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tail += std::exp(std::lgamma(n + 1.0) - std::lgamma(j + 1.0) - std::lgamma(n - j + 1.0)
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+ j * std::log(q) + (n - j) * std::log1p(-q));
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if (npix * tail >= rate) break;
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k = j;
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}
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return k;
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}
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} // namespace
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HotPixelFinder::HotPixelFinder(const DiffractionExperiment &experiment, const PixelMask &mask, size_t nthreads)
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: geometry(experiment.GetDiffractionGeometry()),
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axis(experiment.GetGoniometer() ? experiment.GetGoniometer()->GetAxis().Normalize() : Coord(0, 0, 0)),
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width(experiment.GetXPixelsNumConv()), height(experiment.GetYPixelsNumConv()),
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key(std::make_unique_for_overwrite<int32_t[]>(width * height)),
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n_lit(std::make_unique_for_overwrite<uint16_t[]>(width * height)),
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n_error(std::make_unique_for_overwrite<uint16_t[]>(width * height)),
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sum_value(std::make_unique_for_overwrite<int64_t[]>(width * height)),
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n_error_ring_ok(std::make_unique_for_overwrite<uint16_t[]>(width * height)),
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error_level_sum(std::make_unique_for_overwrite<int64_t[]>(width * height)) {
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const auto &m = mask.GetMask();
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// Rings of equal 2theta, RING_WIDTH_PX wide where the beam meets the detector.
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const float ring_rad = RING_WIDTH_PX * geometry.GetPixelSize_mm() / geometry.GetDetectorDistance_mm();
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ParallelChunks(static_cast<int>(height), nthreads, [&](int y0, int y1) {
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for (size_t y = y0; y < static_cast<size_t>(y1); y++)
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for (size_t x = 0; x < width; x++) {
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const size_t i = y * width + x;
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n_lit[i] = n_error[i] = n_error_ring_ok[i] = 0;
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sum_value[i] = error_level_sum[i] = 0;
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key[i] = -1;
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if (m[i] != 0) continue;
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const float fx = static_cast<float>(x), fy = static_cast<float>(y);
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const int ring = static_cast<int>(geometry.TwoTheta_rad(fx, fy) / ring_rad);
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const int sector = std::min(SECTORS - 1, static_cast<int>(geometry.Phi_rad(fx, fy)
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/ static_cast<float>(2.0 * PI) * SECTORS));
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key[i] = ring * SECTORS + sector;
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}
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});
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for (size_t i = 0; i < width * height; i++)
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if (key[i] >= 0) {
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nrings = std::max(nrings, key[i] / SECTORS + 1);
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unmasked++;
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}
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key_frames.assign(static_cast<size_t>(nrings) * SECTORS, 0);
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key_level_sum.assign(static_cast<size_t>(nrings) * SECTORS, 0);
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key_begin.assign(static_cast<size_t>(nrings) * SECTORS + 1, 0);
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for (size_t i = 0; i < width * height; i++)
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if (key[i] >= 0) key_begin[key[i] + 1]++;
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for (size_t k = 1; k < key_begin.size(); k++)
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key_begin[k] += key_begin[k - 1];
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}
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void HotPixelFinder::AddImage(const int32_t *image, std::vector<int32_t> &scratch) {
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const size_t npix = width * height;
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const size_t nkeys = static_cast<size_t>(nrings) * SECTORS;
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// The frame's valid counts laid out by ring and sector. A saturated pixel has no count to add.
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scratch.resize(npix);
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std::vector<uint32_t> count(nkeys, 0);
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for (size_t i = 0; i < npix; i++) {
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const int32_t k = key[i], v = image[i];
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if (k < 0 || v == INT32_MIN || v == INT32_MAX) continue;
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scratch[key_begin[k] + count[k]++] = v;
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}
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std::vector<int32_t> sector_level(nkeys, 0);
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for (size_t k = 0; k < nkeys; k++)
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if (count[k] >= MIN_SECTOR_PIXELS)
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sector_level[k] = median_of(scratch.data() + key_begin[k], count[k]);
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// Each ring's median and its robust spread about that median: off the histogram of its values
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// where both land inside it, otherwise with the ring's sectors packed together - forward into the
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// space the ring owns, so nothing is overwritten before it is moved - and selected.
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std::vector<int32_t> ring_level(nrings, 0);
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std::vector<float> ring_spread(nrings, 0.0f);
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std::vector<char> ring_ok(nrings, 0);
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std::vector<uint32_t> hist(RING_HIST_VALUES);
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for (int r = 0; r < nrings; r++) {
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size_t n = 0;
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for (int s = 0; s < SECTORS; s++)
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n += count[r * SECTORS + s];
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if (n < MIN_RING_PIXELS) continue;
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ring_ok[r] = 1;
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std::fill(hist.begin(), hist.end(), 0);
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size_t below = 0;
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for (int s = 0; s < SECTORS; s++) {
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const size_t k = r * SECTORS + s;
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for (size_t j = 0; j < count[k]; j++) {
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const int32_t v = scratch[key_begin[k] + j];
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if (v < 0) below++;
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else if (v < RING_HIST_VALUES) hist[v]++;
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}
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}
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const auto median = hist_order_statistic(hist, below, n / 2);
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const auto mad = median ? hist_deviation_order_statistic(hist, below, *median, n / 2) : std::nullopt;
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if (median && mad) {
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ring_level[r] = *median;
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ring_spread[r] = 1.4826f * static_cast<float>(*mad);
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continue;
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}
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int32_t *ring = scratch.data() + key_begin[r * SECTORS];
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size_t packed = 0;
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for (int s = 0; s < SECTORS; s++) {
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const size_t k = r * SECTORS + s;
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std::memmove(ring + packed, scratch.data() + key_begin[k], count[k] * sizeof(int32_t));
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packed += count[k];
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}
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ring_level[r] = median_of(ring, n);
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for (size_t j = 0; j < n; j++)
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ring[j] = std::abs(ring[j] - ring_level[r]);
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ring_spread[r] = 1.4826f * static_cast<float>(median_of(ring, n));
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}
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std::vector<int32_t> level;
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std::vector<float> threshold;
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AddLevels(sector_level, ring_level, ring_spread, ring_ok, level, threshold);
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const size_t rows_per_band = (height + BANDS - 1) / BANDS;
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const size_t first = next_band.fetch_add(1);
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for (size_t b = 0; b < BANDS; b++) {
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const size_t band = (first + b) % BANDS;
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const size_t begin = std::min(npix, band * rows_per_band * width);
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const size_t end = std::min(npix, (band + 1) * rows_per_band * width);
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std::lock_guard lock(band_mutex[band]);
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for (size_t i = begin; i < end; i++) {
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const int32_t k = key[i], v = image[i];
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if (k < 0) continue;
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if (v == INT32_MIN) {
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n_error[i]++;
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if (ring_ok[k / SECTORS]) { // counted in its ring-sector's frames, but not valid here
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n_error_ring_ok[i]++;
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error_level_sum[i] += level[k];
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}
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continue;
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}
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if (!ring_ok[k / SECTORS]) continue;
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sum_value[i] += v;
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if (v == INT32_MAX || static_cast<float>(v) > threshold[k])
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n_lit[i]++;
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}
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}
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}
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void HotPixelFinder::AddLevels(const std::vector<int32_t> §or_level, const std::vector<int32_t> &ring_level,
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const std::vector<float> &ring_spread, const std::vector<char> &ring_ok,
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std::vector<int32_t> &level, std::vector<float> &threshold) {
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const size_t nkeys = static_cast<size_t>(nrings) * SECTORS;
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level.assign(nkeys, 0);
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threshold.assign(nkeys, 0.0f);
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for (size_t k = 0; k < nkeys; k++) {
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const int r = static_cast<int>(k / SECTORS);
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level[k] = std::max(ring_level[r], sector_level[k]);
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const float noise = std::max(std::sqrt(static_cast<float>(std::max(level[k], 0))), ring_spread[r]);
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// One rounding for level + nsigma * noise and one for the offset, written out so that every
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// compiler takes the same two - the device takes them too (HotPixelsGPU.cu).
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threshold[k] = std::fma(LIT_NSIGMA, noise, static_cast<float>(level[k])) + LIT_OFFSET;
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}
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// Every sum is an integer, so the result does not depend on the order the frames arrive in.
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std::lock_guard lock(m);
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frames++;
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for (size_t k = 0; k < nkeys; k++)
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if (ring_ok[k / SECTORS]) {
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key_frames[k]++;
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key_level_sum[k] += level[k];
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}
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}
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#ifdef JFJOCH_USE_CUDA
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void HotPixelFinder::PrepareDevice() {
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std::lock_guard lock(m);
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if (!gpu)
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gpu = std::make_unique<HotPixelFinderGPU>(key.get(), width * height, key_begin, nrings, SECTORS,
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HotPixelLevelRules{MIN_SECTOR_PIXELS, MIN_RING_PIXELS,
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LIT_NSIGMA, LIT_OFFSET});
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}
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void HotPixelFinder::AddDeviceImage(const int32_t *device_image, HotPixelFinderGPU::Frame &frame) {
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PrepareDevice();
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gpu->Add(device_image, frame);
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std::lock_guard lock(m);
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frames++;
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}
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#endif
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HotPixelFinder::Result HotPixelFinder::GetMask(double oscillation_deg, double spacing_deg, size_t nthreads) {
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std::lock_guard lock(m);
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Result ret;
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ret.frames = frames;
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ret.mask.assign(width * height, 0);
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const int n = static_cast<int>(frames);
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// The chance rate per ring, from the pixels lit on no more than half of their frames: whatever
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// lights those - reflections, zingers, noise above the bound - lights a defect-free pixel too.
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// Counted in integers by blocks of rows in parallel, so the totals do not depend on the split.
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std::vector<double> lit(nrings, 0.0), seen(nrings, 0.0);
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#ifdef JFJOCH_USE_CUDA
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if (gpu) {
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std::vector<int64_t> device_lit, device_seen;
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gpu->ChanceCounts(device_lit, device_seen);
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for (int r = 0; r < nrings; r++) {
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lit[r] = static_cast<double>(device_lit[r]);
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seen[r] = static_cast<double>(device_seen[r]);
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}
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} else
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#endif
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{
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std::vector<std::vector<int64_t>> block_lit(BANDS), block_seen(BANDS);
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const size_t rows_per_band = (height + BANDS - 1) / BANDS;
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ParallelFor(static_cast<int>(BANDS), nthreads, [&](int b) {
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block_lit[b].assign(nrings, 0);
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block_seen[b].assign(nrings, 0);
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const size_t begin = std::min(width * height, b * rows_per_band * width);
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const size_t end = std::min(width * height, (b + 1) * rows_per_band * width);
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for (size_t i = begin; i < end; i++)
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if (key[i] >= 0 && n_valid(i) > 0 && 2 * n_lit[i] <= n_valid(i)) {
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block_lit[b][key[i] / SECTORS] += n_lit[i];
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block_seen[b][key[i] / SECTORS] += n_valid(i);
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}
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});
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for (int r = 0; r < nrings; r++)
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for (size_t b = 0; b < BANDS; b++) {
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lit[r] += static_cast<double>(block_lit[b][r]);
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seen[r] += static_cast<double>(block_seen[b][r]);
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}
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}
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std::vector<int> k_chance(nrings, n + 1);
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for (int r = 0; r < nrings; r++)
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if (seen[r] > 0.0)
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k_chance[r] = binomial_bound(n, std::clamp(lit[r] / seen[r], 1e-6, 0.999),
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static_cast<double>(unmasked), FAMILY_WISE_RATE);
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// Persistent: lit on more frames than one reflection or chance explains.
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const int min_valid = std::max(10, n / 2);
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#ifdef JFJOCH_USE_CUDA
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// With a GPU the per-pixel sums stay there. Only the pixels that can be masked come back - those the
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// tests below could pass (see GetCandidates) - into the host arrays, which are zero everywhere else,
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// so the tests below run on them unchanged.
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if (gpu) {
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const auto c = gpu->GetCandidates(frames, min_valid, spacing_deg > 0.0, k_chance);
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for (size_t j = 0; j < c.index.size(); j++) {
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const size_t i = c.index[j];
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n_lit[i] = c.n_lit[j];
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n_error[i] = c.n_error[j];
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n_error_ring_ok[i] = c.n_error_ring_ok[j];
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sum_value[i] = c.sum_value[j];
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error_level_sum[i] = c.error_level_sum[j];
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}
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for (size_t k = 0; k < key_frames.size(); k++) {
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key_frames[k] = static_cast<uint16_t>(c.key_frames[k]);
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key_level_sum[k] = c.key_level_sum[k];
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}
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}
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#endif
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std::vector<uint8_t> persistent(width * height, 0);
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ParallelChunks(static_cast<int>(height), nthreads, [&](int y0, int y1) {
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for (size_t y = y0; y < static_cast<size_t>(y1); y++)
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for (size_t x = 0; x < width; x++) {
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const size_t i = y * width + x;
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if (key[i] < 0 || n_valid(i) < min_valid || n_lit[i] == 0 || !(spacing_deg > 0.0)) continue;
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// One reflection's stay on this pixel, in sampled frames: 1 / |zeta| turns the rocking
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// width into rotation, and zeta = |axis . (s1 x s0)| with s0 along z.
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const Coord s1 = geometry.LabCoord(static_cast<float>(x), static_cast<float>(y)).Normalize();
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const double zeta = std::max(1e-4, static_cast<double>(std::fabs(axis * (s1 % Coord(0, 0, 1)))));
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const double stay = (oscillation_deg + ROCKING_WIDTH_DEG) / zeta;
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const double k_bragg = 1.0 + std::ceil(stay / spacing_deg);
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const double k = std::min<double>(std::max<double>(k_bragg, k_chance[key[i] / SECTORS]),
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n_valid(i));
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persistent[i] = n_lit[i] >= k;
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}
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});
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// Masked: a persistent pixel standing alone - on its own or as one of a pair; a larger patch is
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// a feature of the scattering, not a defect - whose mean is STRONG_RATIO times its ring's and
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// whose excess is above the Poisson bound. A weaker one cannot make an outlier.
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const auto persistent_neighbours = [&](size_t x, size_t y) {
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int count = 0;
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for (size_t yy = (y > 0 ? y - 1 : 0); yy <= std::min(height - 1, y + 1); yy++)
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for (size_t xx = (x > 0 ? x - 1 : 0); xx <= std::min(width - 1, x + 1); xx++)
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if ((xx != x || yy != y) && persistent[yy * width + xx]) count++;
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return count;
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};
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ParallelChunks(static_cast<int>(height), nthreads, [&](int y0, int y1) {
|
|
for (size_t y = y0; y < static_cast<size_t>(y1); y++)
|
|
for (size_t x = 0; x < width; x++) {
|
|
const size_t i = y * width + x;
|
|
if (key[i] < 0) continue;
|
|
if (2 * n_error[i] > n) {
|
|
ret.mask[i] = 2;
|
|
continue;
|
|
}
|
|
if (!persistent[i]) continue;
|
|
const int neighbours = persistent_neighbours(x, y);
|
|
bool isolated = neighbours == 0;
|
|
if (neighbours == 1)
|
|
for (size_t yy = (y > 0 ? y - 1 : 0); yy <= std::min(height - 1, y + 1); yy++)
|
|
for (size_t xx = (x > 0 ? x - 1 : 0); xx <= std::min(width - 1, x + 1); xx++)
|
|
if ((xx != x || yy != y) && persistent[yy * width + xx])
|
|
isolated = persistent_neighbours(xx, yy) == 1;
|
|
const double mean_level = static_cast<double>(sum_level(i)) / n_valid(i);
|
|
const double excess = static_cast<double>(sum_value[i] - sum_level(i)) / n_valid(i);
|
|
if (isolated
|
|
&& static_cast<double>(sum_value[i]) >= STRONG_RATIO * static_cast<double>(sum_level(i))
|
|
&& excess >= LIT_NSIGMA * std::sqrt(std::max(mean_level, 0.0)) + LIT_OFFSET)
|
|
ret.mask[i] = 1;
|
|
}
|
|
});
|
|
for (const uint32_t v : ret.mask) {
|
|
ret.hot += v == 1;
|
|
ret.error += v == 2;
|
|
}
|
|
return ret;
|
|
}
|