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// MoGe-2 host pre/post-processing kernels (fp32, multithreaded, no torch dependency).
// Built on first use by tt_moge/tt/host_ext.py (g++ -O3 -march=native) and loaded with ctypes.
//
// moge_tail: the fused-path host tail of MoGeModel.forward in ONE pass over the full-resolution
// output: bf16 channel-major low-res device output (o-major phase rows, see
// fused_host.chanmajor_to_planar) -> bilinear upsample (align_corners=False, no
// antialias; torch's index/weight rules and its width-then-height evaluation order)
// -> exp remap of the points, L2 normalize of the normals, sigmoid of the mask,
// written straight into channels-last fp32 outputs [H, W, 3] / [H, W, 3] / [H, W].
// moge_input: the encoder's host input: separable resize with precomputed (torch-extracted)
// weights, (x - mean) / std, 14x14 patch rows (c, kh, kw) -> bf16 (round to nearest
// even) [rows_total, k_pad], zero padding rows/cols.
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <cstdlib>
#include <cstring>
#include <limits>
#if defined(__F16C__) || defined(__AVX512F__) || defined(__SSE4_1__)
#include <immintrin.h>
#endif
#include <atomic>
#include <chrono>
#include <condition_variable>
#include <mutex>
#include <memory>
#include <thread>
#include <vector>
namespace {
inline float bf16_to_f32(uint16_t v) {
uint32_t u = static_cast<uint32_t>(v) << 16;
float f;
std::memcpy(&f, &u, 4);
return f;
}
inline uint16_t f32_to_bf16(float f) { // round to nearest even (torch's .to(bfloat16)); no NaN inputs
uint32_t u;
std::memcpy(&u, &f, 4);
u += 0x7FFFu + ((u >> 16) & 1u);
return static_cast<uint16_t>(u >> 16);
}
#if defined(__AVX512F__)
#include <immintrin.h>
#define MOGE_AVX512 1
// expf, Cephes polynomial (~1 ulp in fp32), input clamped to [-88.37, 88.37]
inline __m512 exp16(__m512 x) {
x = _mm512_min_ps(_mm512_max_ps(x, _mm512_set1_ps(-88.3762626647949f)), _mm512_set1_ps(88.3762626647949f));
__m512 fx = _mm512_roundscale_ps(_mm512_mul_ps(x, _mm512_set1_ps(1.44269504088896341f)),
_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
x = _mm512_fnmadd_ps(fx, _mm512_set1_ps(0.693359375f), x);
x = _mm512_fnmadd_ps(fx, _mm512_set1_ps(-2.12194440e-4f), x);
__m512 y = _mm512_set1_ps(1.9875691500E-4f);
y = _mm512_fmadd_ps(y, x, _mm512_set1_ps(1.3981999507E-3f));
y = _mm512_fmadd_ps(y, x, _mm512_set1_ps(8.3334519073E-3f));
y = _mm512_fmadd_ps(y, x, _mm512_set1_ps(4.1665795894E-2f));
y = _mm512_fmadd_ps(y, x, _mm512_set1_ps(1.6666665459E-1f));
y = _mm512_fmadd_ps(y, x, _mm512_set1_ps(5.0000001201E-1f));
y = _mm512_fmadd_ps(y, _mm512_mul_ps(x, x), _mm512_add_ps(x, _mm512_set1_ps(1.f)));
return _mm512_scalef_ps(y, fx);
}
struct Interleave3 { // out[3i + k] = v_k[i] for 16 pixels -> 3 vectors
__m512i ab[3], c[3];
__mmask16 mc[3];
Interleave3() {
alignas(64) int iab[3][16], ic[3][16];
for (int k = 0; k < 3; ++k) {
uint32_t m = 0;
for (int j = 0; j < 16; ++j) {
int g = 16 * k + j, i = g / 3, ch = g % 3;
iab[k][j] = (ch == 1) ? 16 + i : i;
ic[k][j] = i;
if (ch == 2) m |= (1u << j);
}
ab[k] = _mm512_load_si512(iab[k]);
c[k] = _mm512_load_si512(ic[k]);
mc[k] = static_cast<__mmask16>(m);
}
}
inline void store(float* dst, __m512 a, __m512 b, __m512 cc, bool nt) const {
for (int k = 0; k < 3; ++k) {
__m512 t = _mm512_permutex2var_ps(a, ab[k], b);
t = _mm512_mask_permutexvar_ps(t, mc[k], c[k], cc);
if (nt) _mm512_stream_ps(dst + 16 * k, t); // full-resolution outputs: no read-for-ownership
else _mm512_storeu_ps(dst + 16 * k, t);
}
}
};
#endif
// torch: area_pixel_compute_source_index(scale, i, align_corners=false) + guard_index_and_lambda
inline void lin_index(int i, int in_size, float scale, int& i0, int& i1, float& w0, float& w1) {
float src = std::fma(scale, static_cast<float>(i) + 0.5f, -0.5f); // torch's build contracts this to an fma
if (src < 0.f) src = 0.f;
int idx = std::min(static_cast<int>(std::floor(src)), in_size - 1);
float lam = std::min(std::max(src - static_cast<float>(idx), 0.f), 1.f);
i0 = idx;
i1 = idx + ((idx < in_size - 1) ? 1 : 0);
w0 = 1.f - lam;
w1 = lam;
}
// run_parallel(n, fn): fn(0..n-1), fn(0) on the caller, one fresh std::thread per other share. (A
// persistent condvar pool was tried in round 5: ~5x lower empty-call latency, but the memory-bound input
// prep ran 0.2-0.7 ms SLOWER after the usual idle gap between calls (cold workers), so: not used.)
template <class F>
void run_parallel(int nthreads, F&& fn) {
if (nthreads <= 1) { fn(0); return; }
std::vector<std::thread> th;
th.reserve(nthreads - 1);
for (int t = 1; t < nthreads; ++t) th.emplace_back([&fn, t] { fn(t); });
fn(0);
for (auto& x : th) x.join();
}
template <class F>
void parallel_rows(int n, int nthreads, F&& fn) {
nthreads = std::max(1, std::min(nthreads, n));
if (nthreads == 1) {
fn(0, n);
return;
}
const int chunk = (n + nthreads - 1) / nthreads;
run_parallel(nthreads, [&](int t) {
const int a = t * chunk, b = std::min(n, a + chunk);
if (a < b) fn(a, b);
});
}
} // namespace
extern "C" {
// y : bf16 [rows, ld]; row (o*4 + dy*2 + dx), column (Y*W3 + X) = low-res pixel (2Y+dy, 2X+dx)
// of channel o (low-res image is [2*H3, 2*W3]).
// c_pts/c_nrm/c_msk : channel index o of points (3 ch) / normal (3 ch) / mask (1 ch), -1 = absent.
// pts/nrm: fp32 [H, W, 3] contiguous; msk: fp32 [H, W]; rows [y_begin, y_end) are written.
// Banded input: the low-res rows Y live in nbands buffers of band_Y rows each (Y-major bands,
// read back one by one); band b holds rows [b*band_Y, (b+1)*band_Y) as bf16 [rows, ld], ld = band_Y*W3.
// One band (band_Y = H3) is the plain [rows, H3*W3] layout.
int moge_tail_banded(const uint16_t* const* bands, int nbands, int band_Y, int64_t ld, int H3, int W3,
int c_pts, int c_nrm, int c_msk, int H, int W, float* pts, float* nrm, float* msk,
int y_begin, int y_end, int nthreads) {
(void)nbands;
const int Hl = 2 * H3, Wl = 2 * W3;
const float sh = static_cast<float>(Hl) / static_cast<float>(H);
const float sw = static_cast<float>(Wl) / static_cast<float>(W);
std::vector<int> xi0(W), xi1(W);
std::vector<float> xw0(W), xw1(W);
for (int x = 0; x < W; ++x) lin_index(x, Wl, sw, xi0[x], xi1[x], xw0[x], xw1[x]);
// per 16-output block: window start (or -1 -> gather) and the in-window tap indices
std::vector<int> pbase(W / 16 + 1, -1), prel0(W), prel1(W);
for (int b0 = 0; b0 + 16 <= W; b0 += 16) {
int lo = xi0[b0], hi = 0;
for (int j = 0; j < 16; ++j) hi = std::max(hi, xi1[b0 + j]);
if (hi - lo < 16 && lo + 16 <= Wl + 32) { // dec has 32 floats of slack past Wl
pbase[b0 / 16] = lo;
for (int j = 0; j < 16; ++j) { prel0[b0 + j] = xi0[b0 + j] - lo; prel1[b0 + j] = xi1[b0 + j] - lo; }
}
}
int chans[7];
int nch = 0;
if (c_pts >= 0) for (int k = 0; k < 3; ++k) chans[nch++] = c_pts + k;
if (c_nrm >= 0) for (int k = 0; k < 3; ++k) chans[nch++] = c_nrm + k;
if (c_msk >= 0) chans[nch++] = c_msk;
if (y_end > H) y_end = H;
const int n = y_end - y_begin;
if (n <= 0) return 0;
parallel_rows(n, nthreads, [&](int a, int b) {
// per thread: decoded low-res row (Wl) and two horizontally interpolated rows (W) per channel
std::vector<float> dec(Wl + 32);
#ifdef MOGE_AVX512
alignas(64) int lo[16], hi[16];
for (int j = 0; j < 8; ++j) { lo[2 * j] = j; lo[2 * j + 1] = 16 + j; hi[2 * j] = 8 + j; hi[2 * j + 1] = 24 + j; }
const __m512i ilo = _mm512_load_si512(lo), ihi = _mm512_load_si512(hi);
const Interleave3 il3;
#endif
std::vector<float> hrow[2];
hrow[0].resize(static_cast<size_t>(nch) * W);
hrow[1].resize(static_cast<size_t>(nch) * W);
int cached[2] = {-1, -1};
auto hinterp = [&](int r, float* dst) { // low-res row r of every channel -> width W
const int Y = r >> 1, dy = r & 1;
for (int ci = 0; ci < nch; ++ci) {
const uint16_t* yb = bands[Y / band_Y];
const int64_t yo = static_cast<int64_t>(Y % band_Y) * W3;
const uint16_t* r0 = yb + (static_cast<int64_t>(chans[ci]) * 4 + dy * 2 + 0) * ld + yo;
const uint16_t* r1 = yb + (static_cast<int64_t>(chans[ci]) * 4 + dy * 2 + 1) * ld + yo;
int X = 0;
float* d = dst + static_cast<size_t>(ci) * W;
const float* s = dec.data();
int x = 0;
#ifdef MOGE_AVX512
for (; X + 16 <= W3; X += 16) {
__m512i a = _mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256(reinterpret_cast<const __m256i*>(r0 + X))), 16);
__m512i b = _mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256(reinterpret_cast<const __m256i*>(r1 + X))), 16);
_mm512_storeu_si512(dec.data() + 2 * X, _mm512_permutex2var_epi32(a, ilo, b));
_mm512_storeu_si512(dec.data() + 2 * X + 16, _mm512_permutex2var_epi32(a, ihi, b));
}
#endif
for (; X < W3; ++X) {
dec[2 * X] = bf16_to_f32(r0[X]);
dec[2 * X + 1] = bf16_to_f32(r1[X]);
}
#ifdef MOGE_AVX512
for (; x + 16 <= W; x += 16) {
__m512 g0, g1;
const int blk = x >> 4;
if (pbase[blk] >= 0) { // all 16 taps inside one 16-float window: load + 2 permutes
const __m512 win = _mm512_loadu_ps(s + pbase[blk]);
g0 = _mm512_permutexvar_ps(_mm512_loadu_si512(prel0.data() + x), win);
g1 = _mm512_permutexvar_ps(_mm512_loadu_si512(prel1.data() + x), win);
} else {
g0 = _mm512_i32gather_ps(_mm512_loadu_si512(xi0.data() + x), s, 4);
g1 = _mm512_i32gather_ps(_mm512_loadu_si512(xi1.data() + x), s, 4);
}
_mm512_storeu_ps(d + x, _mm512_fmadd_ps(g0, _mm512_loadu_ps(xw0.data() + x),
_mm512_mul_ps(g1, _mm512_loadu_ps(xw1.data() + x))));
}
#endif
for (; x < W; ++x) d[x] = std::fma(s[xi0[x]], xw0[x], s[xi1[x]] * xw1[x]);
}
};
auto get = [&](int r) -> const float* {
for (int k = 0; k < 2; ++k)
if (cached[k] == r) return hrow[k].data();
// evict the slot not holding the other needed row: rows are visited in increasing order
int slot = (cached[0] < cached[1]) ? 0 : 1;
hinterp(r, hrow[slot].data());
cached[slot] = r;
return hrow[slot].data();
};
for (int yy = y_begin + a; yy < y_begin + b; ++yy) {
int h0, h1;
float wh0, wh1;
lin_index(yy, Hl, sh, h0, h1, wh0, wh1);
const float* A = get(h0);
const float* B = get(h1);
const size_t row = static_cast<size_t>(yy) * W;
int x = 0;
#ifdef MOGE_AVX512
const __m512 v0 = _mm512_set1_ps(wh0), v1 = _mm512_set1_ps(wh1);
auto al = [](const float* p) { return (reinterpret_cast<uintptr_t>(p) & 63) == 0; };
const bool nt_p = pts && al(pts + row * 3) && (W % 16 == 0);
const bool nt_n = nrm && al(nrm + row * 3) && (W % 16 == 0);
const bool nt_m = msk && al(msk + row) && (W % 16 == 0);
for (; x + 16 <= W; x += 16) {
__m512 v[7];
for (int ci = 0; ci < nch; ++ci) {
const size_t off = static_cast<size_t>(ci) * W + x;
v[ci] = _mm512_fmadd_ps(_mm512_loadu_ps(A + off), v0, _mm512_mul_ps(_mm512_loadu_ps(B + off), v1));
}
int ci = 0;
if (c_pts >= 0) {
__m512 z = exp16(v[2]);
il3.store(pts + (row + x) * 3, _mm512_mul_ps(v[0], z), _mm512_mul_ps(v[1], z), z, nt_p);
ci = 3;
}
if (c_nrm >= 0) {
__m512 nx = v[ci], ny = v[ci + 1], nz = v[ci + 2];
__m512 s2 = _mm512_add_ps(_mm512_add_ps(_mm512_mul_ps(nx, nx), _mm512_mul_ps(ny, ny)), _mm512_mul_ps(nz, nz));
__m512 sn = _mm512_max_ps(_mm512_sqrt_ps(s2), _mm512_set1_ps(1e-12f));
il3.store(nrm + (row + x) * 3, _mm512_div_ps(nx, sn), _mm512_div_ps(ny, sn), _mm512_div_ps(nz, sn), nt_n);
ci += 3;
}
if (c_msk >= 0) {
__m512 e = exp16(_mm512_sub_ps(_mm512_setzero_ps(), v[ci]));
__m512 sg = _mm512_div_ps(_mm512_set1_ps(1.f), _mm512_add_ps(_mm512_set1_ps(1.f), e));
if (nt_m) _mm512_stream_ps(msk + row + x, sg);
else _mm512_storeu_ps(msk + row + x, sg);
}
}
#endif
for (; x < W; ++x) {
float v[7];
for (int ci = 0; ci < nch; ++ci) {
const size_t off = static_cast<size_t>(ci) * W + x;
v[ci] = std::fma(A[off], wh0, B[off] * wh1);
}
int ci = 0;
if (c_pts >= 0) {
float z = std::exp(v[2]);
float* dst = pts + (row + x) * 3;
dst[0] = v[0] * z;
dst[1] = v[1] * z;
dst[2] = z;
ci = 3;
}
if (c_nrm >= 0) {
float s = std::max(std::sqrt(v[ci] * v[ci] + v[ci + 1] * v[ci + 1] + v[ci + 2] * v[ci + 2]), 1e-12f);
float* dst = nrm + (row + x) * 3;
dst[0] = v[ci] / s;
dst[1] = v[ci + 1] / s;
dst[2] = v[ci + 2] / s;
ci += 3;
}
if (c_msk >= 0) msk[row + x] = 1.f / (1.f + std::exp(-v[ci]));
}
}
#ifdef MOGE_AVX512
_mm_sfence();
#endif
});
return 0;
}
int moge_tail(const uint16_t* y, int64_t ld, int H3, int W3, int c_pts, int c_nrm, int c_msk,
int H, int W, float* pts, float* nrm, float* msk, int y_begin, int y_end, int nthreads) {
const uint16_t* bands[1] = {y};
return moge_tail_banded(bands, 1, H3, ld, H3, W3, c_pts, c_nrm, c_msk, H, W, pts, nrm, msk, y_begin, y_end, nthreads);
}
// number of leading output rows (of H) whose bilinear source rows all lie in low-res band rows Y < Y_avail
int moge_tail_ready(int H, int H3, int Y_avail) {
const int Hl = 2 * H3;
const float sh = static_cast<float>(Hl) / static_cast<float>(H);
int n = 0;
for (int yy = 0; yy < H; ++yy) {
int h0, h1;
float w0, w1;
lin_index(yy, Hl, sh, h0, h1, w0, w1);
if ((h1 >> 1) >= Y_avail) break;
n = yy + 1;
}
return n;
}
// asynchronous banded tail: runs on its own std::thread (which fans out to nthreads), so the caller
// can read back the next band meanwhile; moge_tail_wait joins it.
struct TailJob {
std::vector<const uint16_t*> bands;
std::thread th;
};
void* moge_tail_start(const uint16_t* const* bands, int nbands, int band_Y, int64_t ld, int H3, int W3,
int c_pts, int c_nrm, int c_msk, int H, int W, float* pts, float* nrm, float* msk,
int y_begin, int y_end, int nthreads) {
auto* job = new TailJob;
job->bands.assign(bands, bands + nbands);
const uint16_t* const* bp = job->bands.data();
job->th = std::thread([=] {
moge_tail_banded(bp, nbands, band_Y, ld, H3, W3, c_pts, c_nrm, c_msk, H, W, pts, nrm, msk, y_begin, y_end, nthreads);
});
return job;
}
int moge_tail_wait(void* handle) {
auto* job = static_cast<TailJob*>(handle);
if (job->th.joinable()) job->th.join();
delete job;
return 0;
}
// img : fp32 image, element (c, h, w) at img[c*sc + h*shs + w*sws] (any stride layout), C <= 4.
// ry_* : vertical taps per output row y (Ho rows): start ry_s[y], weights ry_w[y*ky + k], k < ky
// rx_* : horizontal taps per output col x (Wo cols): start rx_s[x], weights rx_w[x*kx + k], k < kx
// (zero-weight padding taps allowed; start + k must stay in range)
// Evaluation order: height pass first (contiguous rows), then width (torch does width first: the
// results differ by fp32 rounding only); each output is s0*w0 + unfused accumulation of the taps.
// out : bf16 [rows_total, k_pad]; patch row (i*bw + j), column (c*p*p + kh*p + kw)
} // extern "C"
namespace {
// One input-prep job: output rows (patch row i, kh) are independent units; unit (i, 0) also zeroes the
// padding columns of patch row i's tokens, unit (bh-1, 0) the trailing rows. Rows go to the band buffer
// that holds patch row i (bands = consecutive patch-row ranges; one band = the whole-image layout).
// u8 -> float32 exactly as postprocess.fit_to_canvas / moge_fit_canvas: float32(u8) / 255.0f (IEEE division:
// the same value as their lookup table; written as a division so the loops vectorize)
inline float ldf(const float* p, int64_t i) { return p[i]; }
inline float ldf(const uint8_t* p, int64_t i) { return static_cast<float>(p[i]) / 255.0f; }
struct InputPrep {
const float* img; int C, Hi, Wi; int64_t sc, shs, sws; int Ho, Wo;
const int* ry_s; const float* ry_w; int ky; const int* rx_s; const float* rx_w; int kx;
const float* mean; const float* stdv; int patch, rows_total, k_pad;
// round 12: uint8 source (the served canvas, HWC or any strides; elements = u8 / 255 as float32, bit-identical
// to reading the float32 canvas that moge_fit_canvas would have produced from it). nullptr = float `img`.
const uint8_t* img8 = nullptr;
int bh = 0, bw = 0, K = 0;
bool hwc = false;
std::vector<float> wxT;
// round 12: width pass without gathers. Per 16-output block, its taps lie in a 48-float window from wbase[blk]
// (planar rows): tap k of lane j = window[widx[(blk * kx + k) * 16 + j]], fetched with two permutex2var + a
// blend instead of a gather; the multiply-adds are unchanged (bit-identical). wok[blk] = 0 -> gather.
std::vector<int> wbase, widx;
std::vector<uint8_t> wok;
std::vector<int> band_of; // patch row -> band
std::vector<uint16_t*> base; // band buffer shifted so that token row r is base + r * k_pad
void init(int i_lo, int i_hi, const int* edges, int nb, uint16_t* const* outs) {
bh = Ho / patch; bw = Wo / patch; K = C * patch * patch;
wxT.assign(static_cast<size_t>(kx) * Wo, 0.f);
for (int x = 0; x < Wo; ++x)
for (int k = 0; k < kx; ++k) wxT[static_cast<size_t>(k) * Wo + x] = rx_w[static_cast<size_t>(x) * kx + k];
hwc = (sc == 1 && sws == C);
const int nblk = Wo / 16;
wbase.assign(nblk, 0);
wok.assign(nblk, 0);
widx.assign(static_cast<size_t>(nblk) * kx * 16, 0);
for (int b = 0; b < nblk; ++b) {
int lo = rx_s[16 * b], hi = lo;
for (int j = 0; j < 16; ++j) { lo = std::min(lo, rx_s[16 * b + j]); hi = std::max(hi, rx_s[16 * b + j] + kx - 1); }
wbase[b] = lo;
wok[b] = (hi - lo < 48) ? 1 : 0;
for (int k = 0; k < kx; ++k)
for (int j = 0; j < 16; ++j) widx[(static_cast<size_t>(b) * kx + k) * 16 + j] = rx_s[16 * b + j] + k - lo;
}
band_of.assign(bh, -1);
base.assign(nb, nullptr);
for (int k = 0; k < nb; ++k) {
for (int i = std::max(edges[k], i_lo); i < std::min(edges[k + 1], i_hi); ++i) band_of[i] = k;
base[k] = outs[k] - static_cast<int64_t>(edges[k]) * bw * k_pad;
}
}
struct Bufs {
std::vector<float> tmp, il, vrow;
std::vector<uint16_t> brow;
// u8 source: a per-thread ring of input rows converted once to planar float32 (u8 / 255.0f); slot of input
// row r = r % ring_n (ring_n >= ky + 4: the taps of consecutive output rows overlap and advance ~2.4 rows)
int ring_n = 0;
std::vector<float> ring;
std::vector<int> ring_tag;
explicit Bufs(const InputPrep& P)
: tmp(static_cast<size_t>(P.Wi) * P.C + 64), il(static_cast<size_t>(P.Wi) * P.C + 64), vrow(P.Wo), brow(P.Wo + 32) {
if (P.img8) {
ring_n = P.ky + 4;
ring.resize(static_cast<size_t>(ring_n) * P.C * P.Wi);
ring_tag.assign(ring_n, -1);
}
}
};
#ifdef MOGE_AVX512
// [Wi][3] interleaved -> planar dst[c * Wi + w] (16 pixels per step: 2 permutes per channel)
void deinterleave3(const float* il, float* dst) const {
alignas(64) int iab[3][16], ic[3][16];
__mmask16 mc[3];
for (int c = 0; c < 3; ++c) {
mc[c] = 0;
for (int j = 0; j < 16; ++j) {
const int p = 3 * j + c;
iab[c][j] = p & 31;
ic[c][j] = (p - 32) & 15;
if (p >= 32) mc[c] |= static_cast<__mmask16>(1u << j);
}
}
const __m512i I0 = _mm512_load_si512(iab[0]), I1 = _mm512_load_si512(iab[1]), I2 = _mm512_load_si512(iab[2]);
const __m512i J0 = _mm512_load_si512(ic[0]), J1 = _mm512_load_si512(ic[1]), J2 = _mm512_load_si512(ic[2]);
float* d0 = dst; float* d1 = dst + Wi; float* d2 = dst + 2 * static_cast<size_t>(Wi);
int w = 0;
for (; w + 16 <= Wi; w += 16) {
const float* s = il + 3 * w;
const __m512 a = _mm512_loadu_ps(s), b = _mm512_loadu_ps(s + 16), c = _mm512_loadu_ps(s + 32);
_mm512_storeu_ps(d0 + w, _mm512_mask_permutexvar_ps(_mm512_permutex2var_ps(a, I0, b), mc[0], J0, c));
_mm512_storeu_ps(d1 + w, _mm512_mask_permutexvar_ps(_mm512_permutex2var_ps(a, I1, b), mc[1], J1, c));
_mm512_storeu_ps(d2 + w, _mm512_mask_permutexvar_ps(_mm512_permutex2var_ps(a, I2, b), mc[2], J2, c));
}
for (; w < Wi; ++w) { d0[w] = il[3 * w]; d1[w] = il[3 * w + 1]; d2[w] = il[3 * w + 2]; }
}
#endif
// u8 path (HWC source: sc = 1, sws = C): input row r as interleaved float32 [Wi * C] (converted on first use
// by this thread; contiguous, vectorized)
const float* ring_row(int r, Bufs& B) const {
const int slot = r % B.ring_n;
const int n = Wi * C;
float* d = B.ring.data() + static_cast<size_t>(slot) * n;
if (B.ring_tag[slot] != r) {
const uint8_t* src = img8 + static_cast<int64_t>(r) * shs;
int j = 0;
#ifdef MOGE_AVX512
// u8 / 255.0f without the (slow) division: q0 = v * fl(1/255), then one Markstein correction step with
// explicit FMAs (q1 = q0 + (v - q0 * 255) * fl(1/255)) gives the correctly rounded quotient for all 256
// values (checked exhaustively by moge_check_u8_div / tests: bit-identical to the division)
const __m512 c = _mm512_set1_ps(1.0f / 255.0f), k255 = _mm512_set1_ps(255.0f);
for (; j + 16 <= n; j += 16) {
const __m512 v = _mm512_cvtepi32_ps(_mm512_cvtepu8_epi32(_mm_loadu_si128(reinterpret_cast<const __m128i*>(src + j))));
const __m512 q0 = _mm512_mul_ps(v, c);
const __m512 e = _mm512_fnmadd_ps(q0, k255, v);
_mm512_storeu_ps(d + j, _mm512_fmadd_ps(e, c, q0));
}
#endif
for (; j < n; ++j) d[j] = static_cast<float>(src[j]) / 255.0f;
B.ring_tag[slot] = r;
}
return d;
}
// u8 height pass from the ring: the same multiply-adds in the same tap order as vpass on the float32 canvas
// (bit-identical), then the HWC -> planar transpose of the hwc path
void hpass_ring(int yo, const float* wv, Bufs& B, float* tmp) const {
const int s = ry_s[yo];
const int n = Wi * C;
const float* rows[64];
for (int k = 0; k < ky; ++k) rows[k] = ring_row(s + k, B);
float* il = B.il.data();
for (int j = 0; j < n; ++j) il[j] = rows[0][j] * wv[0];
for (int k = 1; k < ky; ++k) {
const float wk = wv[k];
const float* rk = rows[k];
for (int j = 0; j < n; ++j) il[j] += rk[j] * wk;
}
(void)tmp; // result stays interleaved in B.il: the width pass reads it with stride C
}
template <class T>
void vpass(const T* src, int64_t stride_k, const float* wv, int s, int n, float* dst) const {
const T* r0 = src + static_cast<int64_t>(s) * stride_k;
for (int w = 0; w < n; ++w) dst[w] = ldf(r0, w) * wv[0];
for (int k = 1; k < ky; ++k) {
const float wk = wv[k];
const T* rk = r0 + static_cast<int64_t>(k) * stride_k;
for (int w = 0; w < n; ++w) dst[w] += ldf(rk, w) * wk;
}
}
// height pass (contiguous rows, vectorized), planar result tmp[c * Wi + w]
template <class T>
void hpass(const T* im, int yo, const float* wv, Bufs& B, float* tmp) const {
if (hwc) {
vpass(im, shs, wv, ry_s[yo], Wi * C, B.il.data());
for (int w = 0; w < Wi; ++w)
for (int c = 0; c < C; ++c) tmp[static_cast<size_t>(c) * Wi + w] = B.il[static_cast<size_t>(w) * C + c];
} else if (sws == 1) {
for (int c = 0; c < C; ++c) vpass(im + c * sc, shs, wv, ry_s[yo], Wi, tmp + static_cast<size_t>(c) * Wi);
} else {
for (int c = 0; c < C; ++c) {
const T* r0 = im + c * sc + static_cast<int64_t>(ry_s[yo]) * shs;
float* d = tmp + static_cast<size_t>(c) * Wi;
for (int w = 0; w < Wi; ++w) d[w] = ldf(r0, w * sws) * wv[0];
for (int k = 1; k < ky; ++k)
for (int w = 0; w < Wi; ++w) d[w] += ldf(r0, static_cast<int64_t>(k) * shs + w * sws) * wv[k];
}
}
}
void unit(int i, int kh, Bufs& B) const {
uint16_t* out = base[band_of[i]];
if (kh == 0) {
for (int r = i * bw; r < (i + 1) * bw; ++r) std::memset(out + static_cast<size_t>(r) * k_pad + K, 0, sizeof(uint16_t) * (k_pad - K));
if (i == bh - 1)
std::memset(out + static_cast<size_t>(bh) * bw * k_pad, 0, sizeof(uint16_t) * static_cast<size_t>(rows_total - bh * bw) * k_pad);
}
float* tmp = B.tmp.data();
const int yo = i * patch + kh;
const float* wv = ry_w + static_cast<size_t>(yo) * ky;
// height pass first
// height-pass result interleaved [Wi][C] in B.il (HWC sources; round 12: also float32 HWC, i.e. the bench's
// permuted HWC image: the width pass reads with stride C instead of transposing; same arithmetic)
bool il_src = hwc && (img8 ? ky <= 64 : true);
if (il_src && img8) hpass_ring(yo, wv, B, tmp);
else if (il_src) vpass(img, shs, wv, ry_s[yo], Wi * C, B.il.data());
else if (img8) hpass(img8, yo, wv, B, tmp);
else hpass(img, yo, wv, B, tmp);
#ifdef MOGE_AVX512
if (il_src && C == 3) { // vectorized de-interleave [Wi][3] -> planar tmp (pure data movement)
deinterleave3(B.il.data(), tmp);
il_src = false;
}
#endif
const int cs = il_src ? C : 1; // element stride of one channel's row
float* vrow = B.vrow.data();
uint16_t* brow = B.brow.data();
for (int c = 0; c < C; ++c) {
const float m = mean[c], sd = stdv[c];
const float* src = il_src ? B.il.data() + c : tmp + static_cast<size_t>(c) * Wi;
// width pass (gathers inside one row: planar tmp, or the interleaved u8-path row with stride C)
int x = 0;
#ifdef MOGE_AVX512
const __m512i vcs = _mm512_set1_epi32(cs);
const __m512i v16 = _mm512_set1_epi32(16), v32 = _mm512_set1_epi32(32);
for (; x + 16 <= Wo; x += 16) {
const int blk = x >> 4;
if (cs == 1 && wok[blk]) { // taps from a 48-float window: permutes instead of gathers
const float* wsrc = src + wbase[blk];
const __m512 A = _mm512_loadu_ps(wsrc), Bv = _mm512_loadu_ps(wsrc + 16), Cv = _mm512_loadu_ps(wsrc + 32);
const int* ib = widx.data() + static_cast<size_t>(blk) * kx * 16;
auto tap = [&](int k) {
const __m512i id = _mm512_loadu_si512(ib + k * 16);
const __m512 lo = _mm512_permutex2var_ps(A, id, Bv);
const __m512 hi = _mm512_permutex2var_ps(Bv, _mm512_sub_epi32(id, v16), Cv);
return _mm512_mask_blend_ps(_mm512_cmpge_epi32_mask(id, v32), lo, hi);
};
__m512 acc = _mm512_mul_ps(tap(0), _mm512_loadu_ps(wxT.data() + x));
for (int k = 1; k < kx; ++k)
acc = _mm512_add_ps(acc, _mm512_mul_ps(tap(k), _mm512_loadu_ps(wxT.data() + static_cast<size_t>(k) * Wo + x)));
_mm512_storeu_ps(vrow + x, acc);
continue;
}
__m512i idx = _mm512_mullo_epi32(_mm512_loadu_si512(rx_s + x), vcs);
__m512 acc = _mm512_mul_ps(_mm512_i32gather_ps(idx, src, 4), _mm512_loadu_ps(wxT.data() + x));
for (int k = 1; k < kx; ++k) {
idx = _mm512_add_epi32(idx, vcs);
acc = _mm512_add_ps(acc, _mm512_mul_ps(_mm512_i32gather_ps(idx, src, 4),
_mm512_loadu_ps(wxT.data() + static_cast<size_t>(k) * Wo + x)));
}
_mm512_storeu_ps(vrow + x, acc);
}
#endif
for (; x < Wo; ++x) {
const float* sv = src + static_cast<int64_t>(rx_s[x]) * cs;
float acc = sv[0] * wxT[x];
for (int k = 1; k < kx; ++k) acc += sv[static_cast<int64_t>(k) * cs] * wxT[static_cast<size_t>(k) * Wo + x];
vrow[x] = acc;
}
x = 0;
#ifdef MOGE_AVX512
const __m512 vm = _mm512_set1_ps(m), vsd = _mm512_set1_ps(sd);
const __m512i rb = _mm512_set1_epi32(0x7FFF), one = _mm512_set1_epi32(1);
for (; x + 16 <= Wo; x += 16) { // (v - m) / sd, then round-to-nearest-even to bf16
__m512i u = _mm512_castps_si512(_mm512_div_ps(_mm512_sub_ps(_mm512_loadu_ps(vrow + x), vm), vsd));
u = _mm512_add_epi32(u, _mm512_add_epi32(rb, _mm512_and_si512(_mm512_srli_epi32(u, 16), one)));
_mm256_storeu_si256(reinterpret_cast<__m256i*>(brow + x), _mm512_cvtepi32_epi16(_mm512_srli_epi32(u, 16)));
}
#endif
for (; x < Wo; ++x) brow[x] = f32_to_bf16((vrow[x] - m) / sd);
uint16_t* o0 = out + static_cast<size_t>(i * bw) * k_pad + c * patch * patch + kh * patch;
#ifdef MOGE_AVX512
if (patch <= 16) { // one masked 16-lane store per patch (brow has 32 elements of slack)
const __mmask16 pm = static_cast<__mmask16>((1u << patch) - 1u);
for (int j = 0; j < bw; ++j)
_mm256_mask_storeu_epi16(o0 + static_cast<size_t>(j) * k_pad, pm,
_mm256_loadu_si256(reinterpret_cast<const __m256i*>(brow + j * patch)));
continue;
}
#endif
for (int j = 0; j < bw; ++j)
std::memcpy(o0 + static_cast<size_t>(j) * k_pad, brow + j * patch, sizeof(uint16_t) * patch);
}
}
};
// asynchronous banded prep: units claimed in order by nthreads workers (on a driver thread), per-band
// completion counters so the caller can upload band k while later bands are still being computed
struct InputJob {
InputPrep P;
int nb = 0, nthreads = 1, chunk = 8;
std::vector<int> edges;
std::unique_ptr<std::atomic<int>[]> done;
std::atomic<int> next{0};
std::thread driver;
};
} // namespace
extern "C" {
// moge_input_band: patch rows [i0, i1) only (i.e. token rows [i0*bw, i1*bw)), written to `out` whose
// row 0 is token row i0*bw; the band with i1 == bh also gets the zero rows [bh*bw, rows_total). Work is
// split over (patch row, kh) pairs. Same arithmetic per output as the whole-image call.
static int input_band_impl(const float* img, const uint8_t* img8, int C, int Hi, int Wi, int64_t sc, int64_t shs,
int64_t sws, int Ho, int Wo, const int* ry_s, const float* ry_w, int ky,
const int* rx_s, const float* rx_w, int kx, const float* mean, const float* stdv,
int patch, uint16_t* out, int rows_total, int k_pad, int nthreads, int i0, int i1) {
const int bh = Ho / patch;
if (i0 < 0 || i1 > bh || i0 >= i1) return -1;
InputPrep P{img, C, Hi, Wi, sc, shs, sws, Ho, Wo, ry_s, ry_w, ky, rx_s, rx_w, kx, mean, stdv, patch, rows_total, k_pad};
P.img8 = img8;
const int edges[2] = {i0, i1};
P.init(i0, i1, edges, 1, &out);
parallel_rows((i1 - i0) * patch, nthreads, [&](int ua, int ub) {
InputPrep::Bufs B(P);
for (int u = ua; u < ub; ++u) P.unit(i0 + u / patch, u % patch, B);
});
return 0;
}
int moge_input_band(const float* img, int C, int Hi, int Wi, int64_t sc, int64_t shs, int64_t sws,
int Ho, int Wo, const int* ry_s, const float* ry_w, int ky,
const int* rx_s, const float* rx_w, int kx, const float* mean, const float* stdv,
int patch, uint16_t* out, int rows_total, int k_pad, int nthreads, int i0, int i1) {
return input_band_impl(img, nullptr, C, Hi, Wi, sc, shs, sws, Ho, Wo, ry_s, ry_w, ky, rx_s, rx_w, kx, mean, stdv,
patch, out, rows_total, k_pad, nthreads, i0, i1);
}
// round 12: the same from a uint8 image (strides in elements; HWC: sc = 1, sws = C): == moge_input_band on the
// float32 image u8 / 255.0f (the served canvas without materializing it as float32)
int moge_input_band_u8(const uint8_t* img, int C, int Hi, int Wi, int64_t sc, int64_t shs, int64_t sws,
int Ho, int Wo, const int* ry_s, const float* ry_w, int ky,
const int* rx_s, const float* rx_w, int kx, const float* mean, const float* stdv,
int patch, uint16_t* out, int rows_total, int k_pad, int nthreads, int i0, int i1) {
return input_band_impl(nullptr, img, C, Hi, Wi, sc, shs, sws, Ho, Wo, ry_s, ry_w, ky, rx_s, rx_w, kx, mean, stdv,
patch, out, rows_total, k_pad, nthreads, i0, i1);
}
// moge_input_start: the whole image as nb bands (patch-row edges[0..nb], edges[0] = 0, edges[nb] = bh)
// into outs[k] (rows of band k; the last band also holds the trailing zero rows). Returns a handle;
// moge_input_wait_band(h, k) blocks until band k is complete, moge_input_join(h) joins and frees.
static void* input_start_impl(const float* img, const uint8_t* img8, int C, int Hi, int Wi, int64_t sc,
int64_t shs, int64_t sws, int Ho, int Wo, const int* ry_s, const float* ry_w, int ky,
const int* rx_s, const float* rx_w, int kx, const float* mean, const float* stdv,
int patch, int rows_total, int k_pad, int nthreads, int nb, const int* edges,
uint16_t* const* outs) {
auto* J = new InputJob;
J->P = InputPrep{img, C, Hi, Wi, sc, shs, sws, Ho, Wo, ry_s, ry_w, ky, rx_s, rx_w, kx, mean, stdv, patch, rows_total, k_pad};
J->P.img8 = img8;
J->nb = nb;
J->nthreads = std::max(1, nthreads);
if (const char* e = std::getenv("MOGE_IN_CHUNK")) J->chunk = std::max(1, std::atoi(e));
J->edges.assign(edges, edges + nb + 1);
J->P.init(0, Ho / patch, J->edges.data(), nb, outs);
J->done.reset(new std::atomic<int>[nb]);
for (int k = 0; k < nb; ++k) J->done[k].store(0);
J->driver = std::thread([J] {
const int units = J->P.bh * J->P.patch;
run_parallel(std::min(J->nthreads, units), [J, units](int) {
InputPrep::Bufs B(J->P);
// units are claimed MOGE_IN_CHUNK (default 8) consecutive output rows at a time (round 11): the vertical
// taps of neighbouring output rows overlap (~2.4 input rows apart, 6 taps), so one thread reuses its
// input rows from cache (5 threads: 1.36-1.79 -> 1.08-1.33 ms; outputs identical)
const int CH = J->chunk;
for (;;) {
const int u0 = J->next.fetch_add(CH, std::memory_order_relaxed);
if (u0 >= units) break;
for (int u = u0; u < std::min(u0 + CH, units); ++u) {
const int i = u / J->P.patch;
J->P.unit(i, u % J->P.patch, B);
J->done[J->P.band_of[i]].fetch_add(1, std::memory_order_release);
}
}
});
});
return J;
}
void* moge_input_start(const float* img, int C, int Hi, int Wi, int64_t sc, int64_t shs, int64_t sws,
int Ho, int Wo, const int* ry_s, const float* ry_w, int ky,
const int* rx_s, const float* rx_w, int kx, const float* mean, const float* stdv,
int patch, int rows_total, int k_pad, int nthreads, int nb, const int* edges,
uint16_t* const* outs) {
return input_start_impl(img, nullptr, C, Hi, Wi, sc, shs, sws, Ho, Wo, ry_s, ry_w, ky, rx_s, rx_w, kx, mean, stdv,
patch, rows_total, k_pad, nthreads, nb, edges, outs);
}
void* moge_input_start_u8(const uint8_t* img, int C, int Hi, int Wi, int64_t sc, int64_t shs, int64_t sws,
int Ho, int Wo, const int* ry_s, const float* ry_w, int ky,
const int* rx_s, const float* rx_w, int kx, const float* mean, const float* stdv,
int patch, int rows_total, int k_pad, int nthreads, int nb, const int* edges,
uint16_t* const* outs) {
return input_start_impl(nullptr, img, C, Hi, Wi, sc, shs, sws, Ho, Wo, ry_s, ry_w, ky, rx_s, rx_w, kx, mean, stdv,
patch, rows_total, k_pad, nthreads, nb, edges, outs);
}
// round 12: 0 if the vectorized u8 / 255 of the input ring equals the IEEE division for all 256 values
int moge_check_u8_div() {
#ifdef MOGE_AVX512
alignas(64) uint8_t u[256];
alignas(64) float q[256];
for (int i = 0; i < 256; ++i) u[i] = static_cast<uint8_t>(i);
const __m512 c = _mm512_set1_ps(1.0f / 255.0f), k255 = _mm512_set1_ps(255.0f);
for (int j = 0; j < 256; j += 16) {
const __m512 v = _mm512_cvtepi32_ps(_mm512_cvtepu8_epi32(_mm_loadu_si128(reinterpret_cast<const __m128i*>(u + j))));
const __m512 q0 = _mm512_mul_ps(v, c);
_mm512_storeu_ps(q + j, _mm512_fmadd_ps(_mm512_fnmadd_ps(q0, k255, v), c, q0));
}
int bad = 0;
for (int i = 0; i < 256; ++i) {
const float want = static_cast<float>(i) / 255.0f;
if (std::memcmp(&q[i], &want, 4) != 0) ++bad;
}
return bad;
#else
return 0;
#endif
}
int moge_input_wait_band(void* h, int k) {
auto* J = static_cast<InputJob*>(h);
const int need = (J->edges[k + 1] - J->edges[k]) * J->P.patch;
int spins = 0;
while (J->done[k].load(std::memory_order_acquire) < need) {
_mm_pause();
if (++spins > 256) { std::this_thread::yield(); spins = 0; }
}
return 0;
}
int moge_input_join(void* h) {
auto* J = static_cast<InputJob*>(h);
if (J->driver.joinable()) J->driver.join();
delete J;
return 0;
}
int moge_input(const float* img, int C, int Hi, int Wi, int64_t sc, int64_t shs, int64_t sws,
int Ho, int Wo, const int* ry_s, const float* ry_w, int ky,
const int* rx_s, const float* rx_w, int kx, const float* mean, const float* stdv,
int patch, uint16_t* out, int rows_total, int k_pad, int nthreads) {
return moge_input_band(img, C, Hi, Wi, sc, shs, sws, Ho, Wo, ry_s, ry_w, ky, rx_s, rx_w, kx, mean, stdv,
patch, out, rows_total, k_pad, nthreads, 0, Ho / patch);
}
// ---------------------------------------------------------------------------------------------
// Served postprocess (server/fastpath.py): the tail of MoGeModel.infer() + the server's
// crop / resize / re-projection / masking, fused into one pass that writes the response arrays.
//
// pts/nrm/mprob : canvas outputs of TtMoGe (points affine [Hc,Wc,3], unit normals [Hc,Wc,3], mask
// probability [Hc,Wc]), fp32 contiguous.
// region : x0,y0,x1,y1 crop on the canvas (the letterbox is outside it); Ho x Wo output size.
// shift, ms : recovered z-shift and metric scale (postprocess.canvas_to_camera semantics:
// z' = z + shift; canvas mask &= z' > 0; depth = z' * ms; points = (x*ms, y*ms, z'*ms)).
// nidx_y/nidx_x : nearest-neighbour source rows / cols inside the crop (torch's 'nearest' indices,
// computed by the caller with F.interpolate on an arange).
// u/v : utils3d uv_map linspaces (Wo / Ho), kinv: row-major 3x4 of inv(4x4 intrinsics).
// Outputs (Ho x Wo): depth f32, points f32 x3, normal x3 (f16, round to nearest even, if nrm_half, else
// f32), mask u8 (0/1).
// Bilinear resize = torch upsample_bilinear2d (align_corners=False, no antialias), evaluated as
// torch's build does: fma(fma(x00,w0,x01*w1), h0, fma(x10,w0,x11*w1)*h1). Normalize = x / max(||x||,
// 1e-12) with ||x|| = sqrt((a*a + b*b) + c*c) (the resized map is NCHW: torch's strided reduction). Re-projection = [u*d, v*d, d, 1] . kinv rows, summed
// left to right. stats (per call): [count, min, max] of the valid depths (mask == 1), double.
namespace {
inline uint16_t f32_to_f16(float f) {
#if defined(__F16C__)
return static_cast<uint16_t>(_cvtss_sh(f, 0));
#else // portable round-to-nearest-even (== F16C / numpy for every non-NaN float, checked exhaustively)
uint32_t x; std::memcpy(&x, &f, 4);
const uint16_t sgn = static_cast<uint16_t>((x >> 16) & 0x8000u);
const uint32_t ax = x & 0x7fffffffu;
if (ax > 0x7f800000u) return static_cast<uint16_t>(sgn | 0x7e00u | ((ax >> 13) & 0x3ffu)); // NaN
if (ax >= 0x477ff000u) return static_cast<uint16_t>(sgn | 0x7c00u); // >= 65520 -> inf
if (ax < 0x38800000u) { // half subnormal
float a; std::memcpy(&a, &ax, 4);
return static_cast<uint16_t>(sgn | static_cast<uint16_t>(std::nearbyint(a * 16777216.0f)));
}
const uint32_t r = ax - 0x38000000u;
uint32_t res = r >> 13;
const uint32_t rem = r & 0x1fffu;
if (rem > 0x1000u || (rem == 0x1000u && (res & 1u))) ++res;
return static_cast<uint16_t>(sgn | res);
#endif
}
} // namespace
// output rows [ya_all, yb_all) only (moge_finish = all rows); stats cover those rows
int moge_finish_rows(const float* pts, const float* nrm, const float* mprob, int Hc, int Wc,
int x0, int y0, int x1, int y1, float shift, float ms, int Ho, int Wo,
const int* nidx_y, const int* nidx_x, const float* u, const float* v, const float* kinv,
int force_projection, int apply_mask,
float* out_depth, float* out_pts, void* out_nrm, int nrm_half, uint8_t* out_mask,
double* stats, int nthreads, int ya_all, int yb_all) {
const int rh = y1 - y0, rw = x1 - x0;
if (rh <= 0 || rw <= 0 || x0 < 0 || y0 < 0 || x1 > Wc || y1 > Hc) return -1;
if (ya_all < 0 || yb_all > Ho || ya_all >= yb_all) return -1;
const float sh = static_cast<float>(rh) / static_cast<float>(Ho);
const float sw = static_cast<float>(rw) / static_cast<float>(Wo);
std::vector<int> cx0(Wo), cx1(Wo);
std::vector<float> cw0(Wo), cw1(Wo);
for (int x = 0; x < Wo; ++x) lin_index(x, rw, sw, cx0[x], cx1[x], cw0[x], cw1[x]);
const float inf = std::numeric_limits<float>::infinity();
const int nrows = yb_all - ya_all;
nthreads = std::max(1, std::min(nthreads, nrows));
std::vector<double> st(3 * nthreads, 0.0);
int chunk = (nrows + nthreads - 1) / nthreads;
auto work = [&](int t) {
int ya = ya_all + t * chunk, yb = std::min(yb_all, ya + chunk);
double cnt = 0, mn = inf, mx = -inf;
for (int yo = ya; yo < yb; ++yo) {
int r0, r1; float h0, h1;
lin_index(yo, rh, sh, r0, r1, h0, h1);
r0 += y0; r1 += y0;
const int rn = y0 + nidx_y[yo];
const float vv = v[yo];
for (int xo = 0; xo < Wo; ++xo) {
const int c0 = x0 + cx0[xo], c1 = x0 + cx1[xo];
const float w0 = cw0[xo], w1 = cw1[xo];
const int64_t i00 = static_cast<int64_t>(r0) * Wc + c0, i01 = static_cast<int64_t>(r0) * Wc + c1;
const int64_t i10 = static_cast<int64_t>(r1) * Wc + c0, i11 = static_cast<int64_t>(r1) * Wc + c1;
auto bil = [&](float a00, float a01, float a10, float a11) {
return std::fma(std::fma(a00, w0, a01 * w1), h0, std::fma(a10, w0, a11 * w1) * h1);
};
// depth taps: (z + shift) * ms
const float d00 = (pts[3 * i00 + 2] + shift) * ms, d01 = (pts[3 * i01 + 2] + shift) * ms;
const float d10 = (pts[3 * i10 + 2] + shift) * ms, d11 = (pts[3 * i11 + 2] + shift) * ms;
const float d = bil(d00, d01, d10, d11);
// normal
float n[3];
for (int c = 0; c < 3; ++c) n[c] = bil(nrm[3 * i00 + c], nrm[3 * i01 + c], nrm[3 * i10 + c], nrm[3 * i11 + c]);
float nn = std::sqrt((n[0] * n[0] + n[1] * n[1]) + n[2] * n[2]); // torch: strided (NCHW) norm, unfused
nn = std::max(nn, 1e-12f);
for (int c = 0; c < 3; ++c) n[c] = n[c] / nn;
// mask: nearest of the canvas mask (prob > 0.5 and z' > 0), then finite / positive depth
const int64_t in_ = static_cast<int64_t>(rn) * Wc + x0 + nidx_x[xo];
bool m = (mprob[in_] > 0.5f) && ((pts[3 * in_ + 2] + shift) > 0.f);
m = m && std::isfinite(d) && (d > 0.f);
float p[3];
if (force_projection) {
const float uu = u[xo];
const float ud = uu * d, vd = vv * d;
for (int c = 0; c < 3; ++c) {
const float* k = kinv + 4 * c;
p[c] = ((ud * k[0] + vd * k[1]) + d * k[2]) + 1.f * k[3];
}
} else {
for (int c = 0; c < 2; ++c)
p[c] = bil(pts[3 * i00 + c] * ms, pts[3 * i01 + c] * ms, pts[3 * i10 + c] * ms, pts[3 * i11 + c] * ms);
p[2] = d; // bilinear of (z + shift) * ms == the depth map
}
const int64_t o = static_cast<int64_t>(yo) * Wo + xo;
float dd = d;
if (apply_mask && !m) {
dd = inf; p[0] = p[1] = p[2] = inf; n[0] = n[1] = n[2] = 0.f;
}
out_depth[o] = dd;
out_pts[3 * o] = p[0]; out_pts[3 * o + 1] = p[1]; out_pts[3 * o + 2] = p[2];
if (nrm_half) {
uint16_t* on = static_cast<uint16_t*>(out_nrm) + 3 * o;
on[0] = f32_to_f16(n[0]); on[1] = f32_to_f16(n[1]); on[2] = f32_to_f16(n[2]);
} else {
float* on = static_cast<float*>(out_nrm) + 3 * o;
on[0] = n[0]; on[1] = n[1]; on[2] = n[2];
}
out_mask[o] = m ? 1 : 0;
if (m) { cnt += 1; mn = std::min(mn, static_cast<double>(dd)); mx = std::max(mx, static_cast<double>(dd)); }
}
}
st[3 * t] = cnt; st[3 * t + 1] = mn; st[3 * t + 2] = mx;
};
run_parallel(nthreads, work);
double cnt = 0, mn = inf, mx = -inf;
for (int t = 0; t < nthreads; ++t) { cnt += st[3 * t]; mn = std::min(mn, st[3 * t + 1]); mx = std::max(mx, st[3 * t + 2]); }
stats[0] = cnt; stats[1] = mn; stats[2] = mx;
return 0;
}
int moge_finish(const float* pts, const float* nrm, const float* mprob, int Hc, int Wc,
int x0, int y0, int x1, int y1, float shift, float ms, int Ho, int Wo,
const int* nidx_y, const int* nidx_x, const float* u, const float* v, const float* kinv,
int force_projection, int apply_mask,
float* out_depth, float* out_pts, void* out_nrm, int nrm_half, uint8_t* out_mask,
double* stats, int nthreads) {
return moge_finish_rows(pts, nrm, mprob, Hc, Wc, x0, y0, x1, y1, shift, ms, Ho, Wo, nidx_y, nidx_x, u, v, kinv,
force_projection, apply_mask, out_depth, out_pts, out_nrm, nrm_half, out_mask, stats,
nthreads, 0, Ho);
}
// k-th smallest (0-based) of the depths where mask == 1 (positive finite floats: their bit patterns
// sort like the values). Radix select: a parallel 16-bit histogram of the high halves, then an
// nth_element over the one bucket. Returns NaN when k is out of range.
float moge_masked_kth(const float* depth, const uint8_t* mask, int64_t n, int64_t k, int nthreads) {
nthreads = std::max(1, nthreads);
std::vector<std::vector<int64_t>> hist(nthreads, std::vector<int64_t>(65536, 0));
int64_t chunk = (n + nthreads - 1) / nthreads;
auto pass1 = [&](int t) {
int64_t a = t * chunk, b = std::min(n, a + chunk);
auto& h = hist[t];
for (int64_t i = a; i < b; ++i) if (mask[i]) { uint32_t u; std::memcpy(&u, depth + i, 4); ++h[u >> 16]; }
};
run_parallel(nthreads, pass1);
int64_t acc = 0; int bucket = -1; int64_t before = 0;
for (int bkt = 0; bkt < 65536; ++bkt) {
int64_t c = 0; for (int t = 0; t < nthreads; ++t) c += hist[t][bkt];
if (k < acc + c) { bucket = bkt; before = acc; break; }
acc += c;
}
if (bucket < 0 || k < 0) return std::numeric_limits<float>::quiet_NaN();
std::vector<float> vals;
for (int64_t i = 0; i < n; ++i)
if (mask[i]) { uint32_t u; std::memcpy(&u, depth + i, 4); if (static_cast<int>(u >> 16) == bucket) vals.push_back(depth[i]); }
auto kk = static_cast<size_t>(k - before);
std::nth_element(vals.begin(), vals.begin() + kk, vals.end());
return vals[kk];
}
// standard base64 (RFC 4648 alphabet, '=' padding) of n bytes into out (4 * ceil(n / 3) chars)
int moge_b64(const uint8_t* src, int64_t n, char* out, int nthreads) {
static const char tbl[] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
const int64_t groups = n / 3;
nthreads = std::max<int64_t>(1, std::min<int64_t>(nthreads, groups / 65536 + 1));
int64_t chunk = (groups + nthreads - 1) / nthreads;
auto work = [&](int t) {
int64_t a = t * chunk, b = std::min(groups, a + chunk);
const uint8_t* s = src + 3 * a;
char* o = out + 4 * a;
for (int64_t g = a; g < b; ++g, s += 3, o += 4) {
uint32_t w = (uint32_t(s[0]) << 16) | (uint32_t(s[1]) << 8) | s[2];
o[0] = tbl[w >> 18]; o[1] = tbl[(w >> 12) & 63]; o[2] = tbl[(w >> 6) & 63]; o[3] = tbl[w & 63];
}
};
run_parallel(nthreads, work);
const int64_t rem = n - 3 * groups;
if (rem) {
const uint8_t* s = src + 3 * groups;
char* o = out + 4 * groups;
uint32_t w = uint32_t(s[0]) << 16;
if (rem == 2) w |= uint32_t(s[1]) << 8;
o[0] = tbl[w >> 18]; o[1] = tbl[(w >> 12) & 63];
o[2] = rem == 2 ? tbl[(w >> 6) & 63] : '=';
o[3] = '=';
}
return 0;
}
// base64 of the concatenation of nseg byte segments (ptrs[i], lens[i]) without gathering them first:
// each thread encodes a range of 3-byte groups, walking the segments with a cursor (groups that straddle
// a segment boundary are assembled byte by byte). Same output as moge_b64 on the joined bytes.
int moge_b64_segs(const uint8_t* const* ptrs, const int64_t* lens, int nseg, char* out, int nthreads) {
static const char tbl[] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
std::vector<int64_t> start(nseg + 1, 0);
for (int i = 0; i < nseg; ++i) start[i + 1] = start[i] + lens[i];
const int64_t n = start[nseg];
const int64_t groups = n / 3;
nthreads = std::max<int64_t>(1, std::min<int64_t>(nthreads, groups / 65536 + 1));
const int64_t chunk = (groups + nthreads - 1) / nthreads;
auto work = [&](int t) {
int64_t g = t * chunk;
const int64_t gend = std::min(groups, g + chunk);
if (g >= gend) return;
int64_t pos = 3 * g;
int s = int(std::upper_bound(start.begin(), start.end(), pos) - start.begin()) - 1;
char* o = out + 4 * g;
while (g < gend) {
while (s < nseg && pos >= start[s + 1]) ++s;
const int64_t off = pos - start[s];
const int64_t avail = start[s + 1] - pos;
int64_t bulk = std::min(avail / 3, gend - g);
if (bulk > 0) {
const uint8_t* p = ptrs[s] + off;
for (int64_t k = 0; k < bulk; ++k, p += 3, o += 4) {
uint32_t w = (uint32_t(p[0]) << 16) | (uint32_t(p[1]) << 8) | p[2];
o[0] = tbl[w >> 18]; o[1] = tbl[(w >> 12) & 63]; o[2] = tbl[(w >> 6) & 63]; o[3] = tbl[w & 63];
}
g += bulk; pos += 3 * bulk;
continue;
}
uint8_t b[3];
int ss = s;
for (int j = 0; j < 3; ++j) {
const int64_t q = pos + j;
while (q >= start[ss + 1]) ++ss;
b[j] = ptrs[ss][q - start[ss]];
}
uint32_t w = (uint32_t(b[0]) << 16) | (uint32_t(b[1]) << 8) | b[2];
o[0] = tbl[w >> 18]; o[1] = tbl[(w >> 12) & 63]; o[2] = tbl[(w >> 6) & 63]; o[3] = tbl[w & 63];
o += 4; ++g; pos += 3;
}
};
run_parallel(nthreads, work);
const int64_t rem = n - 3 * groups;
if (rem) {
uint8_t b[2] = {0, 0};
int ss = 0;
for (int64_t j = 0; j < rem; ++j) {
const int64_t q = 3 * groups + j;
while (q >= start[ss + 1]) ++ss;
b[j] = ptrs[ss][q - start[ss]];
}
char* o = out + 4 * groups;
uint32_t w = (uint32_t(b[0]) << 16) | (uint32_t(b[1]) << 8);
o[0] = tbl[w >> 18]; o[1] = tbl[(w >> 12) & 63];
o[2] = rem == 2 ? tbl[(w >> 6) & 63] : '=';
o[3] = '=';
}
return 0;
}
} // extern "C"
namespace {
#if defined(__SSE4_1__)
template <int BPP>
inline void paeth_sse(const uint8_t* s, const uint8_t* p, uint8_t* c, int64_t rowbytes) {
const __m128i zero = _mm_setzero_si128();
__m128i a = zero, cc = zero;
for (int64_t i = 0; i < rowbytes; i += BPP) {
uint32_t ub, us; // 4-byte loads / stores (3-byte memcpy stalls): buffers carry 4 bytes of padding
std::memcpy(&ub, p + i, 4);
std::memcpy(&us, s + i, 4);
const __m128i b = _mm_unpacklo_epi8(_mm_cvtsi32_si128(static_cast<int>(ub)), zero);
const __m128i x = _mm_unpacklo_epi8(_mm_cvtsi32_si128(static_cast<int>(us)), zero);
__m128i pa = _mm_sub_epi16(b, cc), pb = _mm_sub_epi16(a, cc);
__m128i pc = _mm_abs_epi16(_mm_add_epi16(pa, pb));
pa = _mm_abs_epi16(pa);
pb = _mm_abs_epi16(pb);
const __m128i sm = _mm_min_epi16(pc, _mm_min_epi16(pa, pb));
const __m128i pred = _mm_blendv_epi8(_mm_blendv_epi8(cc, b, _mm_cmpeq_epi16(sm, pb)), a,
_mm_cmpeq_epi16(sm, pa));
a = _mm_and_si128(_mm_add_epi8(x, pred), _mm_set1_epi16(0xFF));
const uint32_t r = static_cast<uint32_t>(_mm_cvtsi128_si32(_mm_packus_epi16(a, a)));
std::memcpy(c + i, &r, 4);
cc = b;
}
}
#endif
} // namespace
extern "C" {
// PNG scanline unfilter (filters 0..4, 8-bit samples, non-interlaced): src = the inflated IDAT stream,
// H rows of (1 + rowbytes) bytes; dst = H x rowbytes pixels, then (out_ch < bpp) the first out_ch
// channels of every pixel are kept in place (RGBA -> RGB, as PIL's convert('RGB')). Returns -1 on a bad
// filter type.
namespace {
struct Unfilter {
int64_t rowbytes;
int bpp, out_ch;
std::vector<uint8_t> prev, cur, srow;
Unfilter(int64_t rb, int b, int oc) : rowbytes(rb), bpp(b), out_ch(oc), prev(rb + 4, 0), cur(rb + 4, 0), srow(rb + 4, 0) {}
// src: nrows filtered rows (filter byte + rowbytes each); dst: nrows output rows (W * out_ch)
int rows(const uint8_t* src, int nrows, uint8_t* dst) {
for (int y = 0; y < nrows; ++y) {
const uint8_t* s = src + static_cast<int64_t>(y) * (rowbytes + 1);
const int ft = s[0];
++s;
uint8_t* c = cur.data();
const uint8_t* p = prev.data();
switch (ft) {
case 0: std::memcpy(c, s, rowbytes); break;
case 1:
for (int64_t i = 0; i < rowbytes; ++i) c[i] = static_cast<uint8_t>(s[i] + (i >= bpp ? c[i - bpp] : 0));
break;
case 2:
for (int64_t i = 0; i < rowbytes; ++i) c[i] = static_cast<uint8_t>(s[i] + p[i]);
break;
case 3:
for (int64_t i = 0; i < rowbytes; ++i)
c[i] = static_cast<uint8_t>(s[i] + ((static_cast<int>(i >= bpp ? c[i - bpp] : 0) + p[i]) >> 1));
break;
case 4: {
#if defined(__SSE4_1__)
// libpng's SSE paeth: one pixel (bpp <= 4 bytes) per step as 16-bit lanes;
// |p-a| = |b-c|, |p-b| = |a-c|, |p-c| = |(b-c)+(a-c)|; ties a, then b, then c.
std::memcpy(srow.data(), s, rowbytes); // padded copy of the filtered row
if (bpp == 3) paeth_sse<3>(srow.data(), p, c, rowbytes);
else if (bpp == 4) paeth_sse<4>(srow.data(), p, c, rowbytes);
else return -1;
#else
for (int64_t i = 0; i < rowbytes; ++i) {
const int a = i >= bpp ? c[i - bpp] : 0, b = p[i], cc = i >= bpp ? p[i - bpp] : 0;
const int pp = a + b - cc, pa = std::abs(pp - a), pb = std::abs(pp - b), pc = std::abs(pp - cc);
const int pred = (pa <= pb && pa <= pc) ? a : (pb <= pc ? b : cc);
c[i] = static_cast<uint8_t>(s[i] + pred);
}
#endif
break;
}
default: return -1;
}
const int64_t W = rowbytes / bpp;
uint8_t* d = dst + static_cast<int64_t>(y) * W * out_ch;
if (out_ch == bpp) std::memcpy(d, c, rowbytes);
else for (int64_t x = 0; x < W; ++x) for (int k = 0; k < out_ch; ++k) d[x * out_ch + k] = c[x * bpp + k];
std::swap(prev, cur);
}
return 0;
}
};
} // namespace
extern "C" {
int moge_png_unfilter(const uint8_t* src, int H, int64_t rowbytes, int bpp, int out_ch, uint8_t* dst) {
Unfilter u(rowbytes, bpp, out_ch);
return u.rows(src, H, dst);
}
// incremental form (rows unfiltered while the IDAT stream is still being inflated)
void* moge_unf_new(int64_t rowbytes, int bpp, int out_ch) { return new Unfilter(rowbytes, bpp, out_ch); }
int moge_unf_rows(void* st, const uint8_t* src, int nrows, uint8_t* dst) { return static_cast<Unfilter*>(st)->rows(src, nrows, dst); }
void moge_unf_free(void* st) { delete static_cast<Unfilter*>(st); }
} // extern "C"
// PNG IDAT inflate with ISA-L (isal_inflate through function pointers the caller resolves from
// python-isal's shared object; the inflate_state layout of ISA-L 2.31 -- offsets checked by the caller's
// version gate and self test) pipelined with the serial Unfilter on a std::thread that follows the inflated
// row count. No GIL, no per-chunk Python objects. Returns 0, or -1 (bad stream / checksum / size: the
// caller falls back to PIL). z: H * (rowbytes + 1) scratch; dst: H x W x 3.
namespace {
constexpr size_t kIsalStateBytes = 87368; // sizeof(struct inflate_state), ISA-L 2.31
constexpr size_t kIsalOffTotalOut = 12, kIsalOffNextIn = 16, kIsalOffAvailIn = 32;
constexpr size_t kIsalOffBlockState = 21160, kIsalOffCrcFlag = 21172;
constexpr int kIsalZlib = 3, kIsalBlockFinish = 5;
} // namespace
extern "C" {
int64_t moge_isal_state_bytes() { return static_cast<int64_t>(kIsalStateBytes); }
int moge_png_inflate_unfilter(void* fn_init, void* fn_inflate, const uint8_t* const* chunks, const int64_t* lens,
int nchunks, int H, int64_t rowbytes, int bpp, uint8_t* z, uint8_t* dst,
double* times_us) {
const auto t_start = std::chrono::steady_clock::now();
auto us = [&] { return std::chrono::duration<double, std::micro>(std::chrono::steady_clock::now() - t_start).count(); };
using InitFn = void (*)(void*);
using InflFn = int (*)(void*);
auto init = reinterpret_cast<InitFn>(fn_init);
auto infl = reinterpret_cast<InflFn>(fn_inflate);
thread_local std::vector<uint64_t> stbuf;
if (stbuf.size() * 8 < kIsalStateBytes + 64) stbuf.assign((kIsalStateBytes + 64) / 8 + 1, 0);
uint8_t* st = reinterpret_cast<uint8_t*>(stbuf.data());
init(st);
std::memcpy(st + kIsalOffCrcFlag, &kIsalZlib, 4);
const int64_t stride = rowbytes + 1, total = static_cast<int64_t>(H) * stride;
uint8_t* next_out = z;
uint32_t avail_out = static_cast<uint32_t>(std::min<int64_t>(total, 0xFFFFFFFFll));
std::memcpy(st + 0, &next_out, sizeof(next_out));
std::memcpy(st + 8, &avail_out, 4);
std::atomic<int> ready{0}, stop{0};
Unfilter u(rowbytes, bpp, 3);
int unf_rc = 0;
// the worker sleeps on a condition variable between published row counts (a spinning worker that the
// scheduler put on the inflating thread's core doubled the decode time on the loaded host)
std::mutex mu;
std::condition_variable cv;
std::thread worker([&] {
int done = 0;
for (;;) {
int r = ready.load(std::memory_order_acquire);
if (r <= done) {
if (done >= H || stop.load(std::memory_order_acquire)) return;
std::unique_lock<std::mutex> l(mu);
cv.wait(l, [&] { return ready.load(std::memory_order_acquire) > done || stop.load(std::memory_order_acquire); });
continue;
}
const int n = std::min(r - done, 64);
if (u.rows(z + static_cast<int64_t>(done) * stride, n, dst + static_cast<int64_t>(done) * (rowbytes / bpp) * 3)) { unf_rc = -1; return; }
done += n;
}
});
int rc = 0;
auto publish = [&] {
uint32_t tot;
std::memcpy(&tot, st + kIsalOffTotalOut, 4);
const int r = static_cast<int>(std::min<int64_t>(tot, total) / stride);
if (r > ready.load(std::memory_order_relaxed)) {
{ std::lock_guard<std::mutex> l(mu); ready.store(r, std::memory_order_release); }
cv.notify_one();
}
};
thread_local std::vector<uint8_t> discard(1 << 16);
auto overflow = [&] { // output full: the image is complete, keep inflating into a discard buffer
uint32_t ao0;
std::memcpy(&ao0, st + 8, 4);
if (ao0 != 0) return false;
uint32_t tot;
std::memcpy(&tot, st + kIsalOffTotalOut, 4);
if (static_cast<int64_t>(tot) < total) return false;
publish();
uint8_t* no = discard.data();
const uint32_t ao = static_cast<uint32_t>(discard.size());
std::memcpy(st + 0, &no, sizeof(no));
std::memcpy(st + 8, &ao, 4);
return true;
};
for (int k = 0; k < nchunks && rc == 0; ++k) {
const uint8_t* ni = chunks[k];
uint32_t ai = static_cast<uint32_t>(lens[k]);
std::memcpy(st + kIsalOffNextIn, &ni, sizeof(ni));
std::memcpy(st + kIsalOffAvailIn, &ai, 4);
uint32_t prev_left = ai + 1;
while (rc == 0) {
const int r = infl(st);
if ((r == 0 || r == 2) && overflow()) continue; // bytes beyond the image: inflated and ignored
if (r != 0 && r != 1) { rc = -1; break; } // ISAL_DECOMP_OK / ISAL_END_INPUT; errors -> fallback
publish();
uint32_t left;
std::memcpy(&left, st + kIsalOffAvailIn, 4);
int bs;
std::memcpy(&bs, st + kIsalOffBlockState, 4);
if (left == 0 || bs == kIsalBlockFinish) break;
if (left >= prev_left) { rc = -1; break; } // no progress: bail out to the fallback
prev_left = left;
}
}
if (rc == 0) { // flush anything still buffered (no more input)
for (int it = 0; it < 4; ++it) {
int bs;
std::memcpy(&bs, st + kIsalOffBlockState, 4);
if (bs == kIsalBlockFinish) break;
uint32_t ai = 0;
std::memcpy(st + kIsalOffAvailIn, &ai, 4);
const int r = infl(st);
if ((r == 0 || r == 2) && overflow()) continue;
if (r != 0 && r != 1) { rc = -1; break; }
publish();
}
int bs;
uint32_t tot;
std::memcpy(&bs, st + kIsalOffBlockState, 4);
std::memcpy(&tot, st + kIsalOffTotalOut, 4);
if (bs != kIsalBlockFinish || static_cast<int64_t>(tot) < total) rc = -1;
}
if (times_us) times_us[0] = us(); // inflate done
if (rc) {
{ std::lock_guard<std::mutex> l(mu); stop.store(1, std::memory_order_release); }
cv.notify_one();
}
worker.join();
if (times_us) times_us[1] = us(); // unfilter done
return rc ? rc : unf_rc;
}
} // extern "C"
extern "C" {
// base64 decode of a CLEAN standard-alphabet string (length % 4 == 0, '=' only as 1-2 trailing pad
// characters): returns the decoded length, or -1 for anything else (the caller then uses Python's
// base64.b64decode, whose non-validating mode skips foreign characters). On clean input the result
// equals base64.b64decode(validate=False) (leftover pad bits are ignored as in binascii).
int64_t moge_b64dec(const uint8_t* s, int64_t n, uint8_t* out, int nthreads) {
if (n % 4) return -1;
if (n == 0) return 0;
int pad = 0;
if (s[n - 1] == '=') { pad = 1; if (s[n - 2] == '=') pad = 2; }
static int8_t tbl[256];
static bool init = false;
if (!init) {
for (int i = 0; i < 256; ++i) tbl[i] = -1;
const char* a = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
for (int i = 0; i < 64; ++i) tbl[static_cast<uint8_t>(a[i])] = static_cast<int8_t>(i);
init = true;
}
const int64_t groups = n / 4;
const int64_t full = pad ? groups - 1 : groups;
nthreads = std::max<int64_t>(1, std::min<int64_t>(nthreads, full / 65536 + 1));
const int64_t chunk = (full + nthreads - 1) / nthreads;
std::vector<int> bad(nthreads, 0);
auto work = [&](int t) {
const int64_t a = t * chunk, b = std::min(full, a + chunk);
const uint8_t* p = s + 4 * a;
uint8_t* o = out + 3 * a;
int acc = 0;
for (int64_t g = a; g < b; ++g, p += 4, o += 3) {
const int v0 = tbl[p[0]], v1 = tbl[p[1]], v2 = tbl[p[2]], v3 = tbl[p[3]];
acc |= v0 | v1 | v2 | v3;
const uint32_t w = (uint32_t(v0 & 63) << 18) | (uint32_t(v1 & 63) << 12) | (uint32_t(v2 & 63) << 6) | uint32_t(v3 & 63);
o[0] = uint8_t(w >> 16); o[1] = uint8_t(w >> 8); o[2] = uint8_t(w);
}
bad[t] = acc < 0;
};
run_parallel(nthreads, work);
for (int t = 0; t < nthreads; ++t) if (bad[t]) return -1;
int64_t len = 3 * full;
if (pad) {
const uint8_t* p = s + 4 * full;
const int v0 = tbl[p[0]], v1 = tbl[p[1]];
const int v2 = pad == 1 ? tbl[p[2]] : 0;
if (v0 < 0 || v1 < 0 || v2 < 0) return -1;
const uint32_t w = (uint32_t(v0) << 18) | (uint32_t(v1) << 12) | (uint32_t(v2) << 6);
out[len++] = uint8_t(w >> 16);
if (pad == 1) out[len++] = uint8_t(w >> 8);
}
return len;
}
} // extern "C"
// ---------------------------------------------------------------------------------------------
// Served preprocess: postprocess.fit_to_canvas in one pass. Pillow's BILINEAR resize of an 8-bit RGB
// image (ImagingResample: separable, double-precision triangle-filter coefficients normalized per
// output pixel and converted to 22-bit fixed point, horizontal pass into uint8 rows then vertical
// pass, round-half-up + clip), pasted at (x0, y0) on a canvas filled with `pad`, converted to float32
// as u8 / 255.0f and written planar NCHW [3, Ch, Cw]. rw x rh = resized size (== Wi x Hi -> copy).
namespace {
constexpr int kPrec = 22;
inline uint8_t clip8(int v) { v >>= kPrec; return static_cast<uint8_t>(v < 0 ? 0 : (v > 255 ? 255 : v)); }
int pil_coeffs(int in_size, int out_size, std::vector<int>& bounds, std::vector<int32_t>& kk) {
const double in0 = 0.0, in1 = static_cast<double>(in_size);
const double scale = (in1 - in0) / out_size;
double filterscale = scale < 1.0 ? 1.0 : scale;
const double support = 1.0 * filterscale;
const int ksize = static_cast<int>(std::ceil(support)) * 2 + 1;
bounds.assign(2 * out_size, 0);
kk.assign(static_cast<size_t>(out_size) * ksize, 0);
std::vector<double> k(ksize);
for (int xx = 0; xx < out_size; ++xx) {
const double center = in0 + (xx + 0.5) * scale;
double ww = 0.0;
const double ss = 1.0 / filterscale;
int xmin = static_cast<int>(center - support + 0.5);
if (xmin < 0) xmin = 0;
int xmax = static_cast<int>(center + support + 0.5);
if (xmax > in_size) xmax = in_size;
xmax -= xmin;
for (int x = 0; x < xmax; ++x) {
double t = (x + xmin - center + 0.5) * ss;
if (t < 0.0) t = -t;
const double w = t < 1.0 ? 1.0 - t : 0.0;
k[x] = w;
ww += w;
}
for (int x = 0; x < xmax; ++x) if (ww != 0.0) k[x] /= ww;
for (int x = xmax; x < ksize; ++x) k[x] = 0;
for (int x = 0; x < ksize; ++x) {
const double v = k[x] * (1 << kPrec);
kk[static_cast<size_t>(xx) * ksize + x] = static_cast<int32_t>(k[x] < 0 ? -0.5 + v : 0.5 + v);
}
bounds[2 * xx] = xmin;
bounds[2 * xx + 1] = xmax;
}
return ksize;
}
} // namespace
// U8 = false: float32 planar NCHW [3, Ch, Cw] = u8 / 255.0f (pad: the float pad value);
// U8 = true (round 12): the uint8 canvas itself, interleaved HWC [Ch, Cw, 3] (pad: the gray level), i.e. the
// float32 canvas before the / 255 (the input prep reads it as u8 / 255.0f: bit-identical model input)
extern "C++" {
template <bool U8>
static int fit_canvas_impl(const uint8_t* img, int Hi, int Wi, int64_t row_stride, int rw, int rh, int x0, int y0,
int Cw, int Ch, float pad, void* out_v, int nthreads) {
if (x0 < 0 || y0 < 0 || x0 + rw > Cw || y0 + rh > Ch || rw < 1 || rh < 1) return -1;
const bool need_h = rw != Wi, need_v = rh != Hi;
std::vector<int> bh, bv;
std::vector<int32_t> kh, kv;
const int ksh = need_h ? pil_coeffs(Wi, rw, bh, kh) : 0;
const int ksv = need_v ? pil_coeffs(Hi, rh, bv, kv) : 0;
// horizontal pass over the source rows the vertical pass needs (all of them here: box = full image)
const int ybox_first = need_v ? bv[0] : 0;
const int ybox_last = need_v ? bv[2 * rh - 2] + bv[2 * rh - 1] : Hi;
const int tmp_rows = ybox_last - ybox_first;
std::vector<uint8_t> tmp;
const uint8_t* src = img;
int64_t src_stride = row_stride;
if (need_h) {
tmp.resize(static_cast<size_t>(tmp_rows) * rw * 3);
parallel_rows(tmp_rows, nthreads, [&](int a, int b) {
for (int yy = a; yy < b; ++yy) {
const uint8_t* r = img + static_cast<int64_t>(yy + ybox_first) * row_stride;
uint8_t* o = tmp.data() + static_cast<size_t>(yy) * rw * 3;
for (int xx = 0; xx < rw; ++xx) {
const int xmin = bh[2 * xx], xmax = bh[2 * xx + 1];
const int32_t* k = kh.data() + static_cast<size_t>(xx) * ksh;
int s0 = 1 << (kPrec - 1), s1 = s0, s2 = s0;
const uint8_t* px = r + 3 * xmin;
if (ksh == 3 && xmin + 3 <= Wi) { // upscale: fixed 3 taps (the zero-padded ones add 0)
s0 += px[0] * k[0] + px[3] * k[1] + px[6] * k[2];
s1 += px[1] * k[0] + px[4] * k[1] + px[7] * k[2];
s2 += px[2] * k[0] + px[5] * k[1] + px[8] * k[2];
} else {
for (int x = 0; x < xmax; ++x, px += 3) {
s0 += px[0] * k[x]; s1 += px[1] * k[x]; s2 += px[2] * k[x];
}
}
o[3 * xx] = clip8(s0); o[3 * xx + 1] = clip8(s1); o[3 * xx + 2] = clip8(s2);
}
}
});
src = tmp.data();
src_stride = static_cast<int64_t>(rw) * 3;
} else if (need_v) {
src = img + static_cast<int64_t>(ybox_first) * row_stride; // rows relative to ybox_first
}
const int64_t plane = static_cast<int64_t>(Ch) * Cw;
float lut[256];
for (int v = 0; v < 256; ++v) lut[v] = static_cast<float>(v) / 255.0f; // == float32(u8) / 255
if constexpr (U8) {
uint8_t* out8 = static_cast<uint8_t*>(out_v);
const uint8_t pad8 = static_cast<uint8_t>(pad);
parallel_rows(Ch, nthreads, [&](int a, int b) {
std::vector<int32_t> acc;
for (int y = a; y < b; ++y) {
uint8_t* o = out8 + static_cast<int64_t>(y) * Cw * 3;
const int yy = y - y0;
if (yy < 0 || yy >= rh) { std::memset(o, pad8, static_cast<size_t>(Cw) * 3); continue; }
std::memset(o, pad8, static_cast<size_t>(x0) * 3);
std::memset(o + static_cast<size_t>(x0 + rw) * 3, pad8, static_cast<size_t>(Cw - x0 - rw) * 3);
uint8_t* ox = o + static_cast<size_t>(x0) * 3;
if (need_v) {
const int ymin = bv[2 * yy] - ybox_first, ymax = bv[2 * yy + 1];
const int32_t* k = kv.data() + static_cast<size_t>(yy) * ksv;
const int n = 3 * rw;
acc.assign(n, 1 << (kPrec - 1));
for (int t = 0; t < ymax; ++t) {
const uint8_t* r = src + static_cast<int64_t>(ymin + t) * src_stride;
const int32_t kt = k[t];
int32_t* ap = acc.data();
for (int i = 0; i < n; ++i) ap[i] += r[i] * kt;
}
for (int i = 0; i < n; ++i) ox[i] = clip8(acc[i]);
} else {
std::memcpy(ox, src + static_cast<int64_t>(yy) * src_stride, static_cast<size_t>(rw) * 3);
}
}
});
return 0;
}
float* out = static_cast<float*>(out_v);
parallel_rows(Ch, nthreads, [&](int a, int b) {
std::vector<int32_t> acc;
for (int y = a; y < b; ++y) {
float* o0 = out + static_cast<int64_t>(y) * Cw;
float* o1 = o0 + plane;
float* o2 = o1 + plane;
const int yy = y - y0;
if (yy < 0 || yy >= rh) {
for (int x = 0; x < Cw; ++x) { o0[x] = pad; o1[x] = pad; o2[x] = pad; }
continue;
}
for (int x = 0; x < x0; ++x) { o0[x] = pad; o1[x] = pad; o2[x] = pad; }
for (int x = x0 + rw; x < Cw; ++x) { o0[x] = pad; o1[x] = pad; o2[x] = pad; }
if (need_v) {
const int ymin = bv[2 * yy] - ybox_first, ymax = bv[2 * yy + 1];
const int32_t* k = kv.data() + static_cast<size_t>(yy) * ksv;
const int n = 3 * rw;
acc.assign(n, 1 << (kPrec - 1));
for (int t = 0; t < ymax; ++t) { // row-vector accumulation (vectorizes over x)
const uint8_t* r = src + static_cast<int64_t>(ymin + t) * src_stride;
const int32_t kt = k[t];
int32_t* ap = acc.data();
for (int i = 0; i < n; ++i) ap[i] += r[i] * kt;
}
for (int xx = 0; xx < rw; ++xx) {
o0[x0 + xx] = lut[clip8(acc[3 * xx])];
o1[x0 + xx] = lut[clip8(acc[3 * xx + 1])];
o2[x0 + xx] = lut[clip8(acc[3 * xx + 2])];
}
} else {
const uint8_t* r = src + static_cast<int64_t>(yy) * src_stride;
for (int xx = 0; xx < rw; ++xx) {
o0[x0 + xx] = lut[r[3 * xx]];
o1[x0 + xx] = lut[r[3 * xx + 1]];
o2[x0 + xx] = lut[r[3 * xx + 2]];
}
}
}
});
return 0;
}
} // extern "C++"
int moge_fit_canvas(const uint8_t* img, int Hi, int Wi, int64_t row_stride, int rw, int rh, int x0, int y0,
int Cw, int Ch, float pad, float* out, int nthreads) {
return fit_canvas_impl<false>(img, Hi, Wi, row_stride, rw, rh, x0, y0, Cw, Ch, pad, out, nthreads);
}
int moge_fit_canvas_u8(const uint8_t* img, int Hi, int Wi, int64_t row_stride, int rw, int rh, int x0, int y0,
int Cw, int Ch, float pad, uint8_t* out, int nthreads) {
return fit_canvas_impl<true>(img, Hi, Wi, row_stride, rw, rh, x0, y0, Cw, Ch, pad, out, nthreads);
}
} // extern "C"
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