just a couple of 'shadowed local variables' compiler warning fixes

Makefile.am

@@ -147,41 +147,53 @@ libtesseract_native_la_CXXFLAGS = -O3 -ffast-math
 if OPENMP_SIMD
 libtesseract_native_la_CXXFLAGS += -fopenmp-simd -DOPENMP_SIMD
 endif
+if HAVE_AVX
+libtesseract_native_la_CXXFLAGS += -mavx
+endif
 if MARCH_NATIVE_OPT
 libtesseract_native_la_CXXFLAGS += -march=native -mtune=native
 endif
+libtesseract_native_la_CXXFLAGS += -I$(top_srcdir)/src/ccutil
 libtesseract_native_la_SOURCES = src/arch/dotproduct.cpp
 
 if HAVE_AVX
 libtesseract_avx_la_CXXFLAGS = -mavx
+libtesseract_avx_la_CXXFLAGS += -I$(top_srcdir)/src/ccutil
 libtesseract_avx_la_SOURCES = src/arch/dotproductavx.cpp
 libtesseract_la_LIBADD += libtesseract_avx.la
 noinst_LTLIBRARIES += libtesseract_avx.la
 endif
 
 if HAVE_AVX2
 libtesseract_avx2_la_CXXFLAGS = -mavx2
+libtesseract_avx2_la_CXXFLAGS += -I$(top_srcdir)/src/ccutil
 libtesseract_avx2_la_SOURCES = src/arch/intsimdmatrixavx2.cpp
 libtesseract_la_LIBADD += libtesseract_avx2.la
 noinst_LTLIBRARIES += libtesseract_avx2.la
 endif
 
 if HAVE_FMA
 libtesseract_fma_la_CXXFLAGS = -mfma
+libtesseract_fma_la_CXXFLAGS += -I$(top_srcdir)/src/ccutil
 libtesseract_fma_la_SOURCES = src/arch/dotproductfma.cpp
 libtesseract_la_LIBADD += libtesseract_fma.la
 noinst_LTLIBRARIES += libtesseract_fma.la
 endif
 
 if HAVE_SSE4_1
 libtesseract_sse_la_CXXFLAGS = -msse4.1
+libtesseract_sse_la_CXXFLAGS += -I$(top_srcdir)/src/ccutil
+if OPENMP_SIMD
+libtesseract_sse_la_CXXFLAGS += -fopenmp-simd -DOPENMP_SIMD
+endif
 libtesseract_sse_la_SOURCES = src/arch/dotproductsse.cpp src/arch/intsimdmatrixsse.cpp
 libtesseract_la_LIBADD += libtesseract_sse.la
 noinst_LTLIBRARIES += libtesseract_sse.la
 endif
 
 if HAVE_NEON
 libtesseract_neon_la_CXXFLAGS = $(NEON_CXXFLAGS)
+libtesseract_neon_la_CXXFLAGS += -I$(top_srcdir)/src/ccutil
 libtesseract_neon_la_SOURCES = src/arch/intsimdmatrixneon.cpp
 libtesseract_la_LIBADD += libtesseract_neon.la
 noinst_LTLIBRARIES += libtesseract_neon.la
@@ -1233,6 +1245,7 @@ check_PROGRAMS += commandlineflags_test
 check_PROGRAMS += dawg_test
 endif # ENABLE_TRAINING
 check_PROGRAMS += denorm_test
+check_PROGRAMS += dotproduct_test
 if !DISABLED_LEGACY_ENGINE
 check_PROGRAMS += equationdetect_test
 endif # !DISABLED_LEGACY_ENGINE
@@ -1359,6 +1372,16 @@ denorm_test_SOURCES = unittest/denorm_test.cc
 denorm_test_CPPFLAGS = $(unittest_CPPFLAGS)
 denorm_test_LDADD = $(TESS_LIBS)
 
+dotproduct_test_SOURCES = unittest/dotproduct_test.cc
+dotproduct_test_CPPFLAGS = $(unittest_CPPFLAGS)
+if HAVE_AVX2
+dotproduct_test_CPPFLAGS += -DHAVE_AVX2
+endif
+if HAVE_SSE4_1
+dotproduct_test_CPPFLAGS += -DHAVE_SSE4_1
+endif
+dotproduct_test_LDADD = $(TESS_LIBS)
+
 if !DISABLED_LEGACY_ENGINE
 equationdetect_test_SOURCES = unittest/equationdetect_test.cc
 equationdetect_test_CPPFLAGS = $(unittest_CPPFLAGS)

configure.ac

@@ -297,13 +297,14 @@ OPENCL_CPPFLAGS=''
 OPENCL_LDFLAGS=''
 case "${host_os}" in
   *darwin* | *-macos10*)
-    echo "checking for OpenCL framework"
-    MY_CHECK_FRAMEWORK([OpenCL])
-    if test $my_cv_framework_OpenCL = yes; then
-       have_opencl_lib=true
+    MY_CHECK_FRAMEWORK([Accelerate])
+    if test $my_cv_framework_Accelerate = yes; then
+      AM_CPPFLAGS="-DHAVE_FRAMEWORK_ACCELERATE $AM_CPPFLAGS"
+      LDFLAGS="$LDFLAGS -framework Accelerate"
     fi
+    MY_CHECK_FRAMEWORK([OpenCL])
     if test "$enable_opencl" = "yes"; then
-      if !($have_opencl_lib); then
+      if test $my_cv_framework_OpenCL = no; then
         AC_MSG_ERROR([Required OpenCL library not found!])
       fi
       AM_CPPFLAGS="-DUSE_OPENCL $AM_CPPFLAGS"

src/api/hocrrenderer.cpp

@@ -319,7 +319,6 @@ char *TessBaseAPI::GetHOCRText(ETEXT_DESC *monitor, int page_number) {
             hocr_str << "\n        </span>";
             ++scnt;
           } else if (lstm_choice_mode == 2) {
-            tesseract::ChoiceIterator ci(*res_it);
             hocr_str << "\n        <span class='ocrx_cinfo'"
                      << " id='"
                      << "lstm_choices_" << page_id << "_" << wcnt << "_" << tcnt << "'>";

src/arch/dotproduct.cpp

@@ -19,12 +19,12 @@
 namespace tesseract {
 
 // Computes and returns the dot product of the two n-vectors u and v.
-double DotProductNative(const double *u, const double *v, int n) {
-  double total = 0.0;
+TFloat DotProductNative(const TFloat *u, const TFloat *v, int n) {
+  TFloat total = 0;
 #if defined(OPENMP_SIMD)
 #pragma omp simd reduction(+:total)
 #endif
-  for (int k = 0; k < n; ++k) {
+  for (int k = 0; k < n; k++) {
     total += u[k] * v[k];
   }
   return total;

src/arch/dotproduct.h

@@ -17,20 +17,27 @@
 #ifndef TESSERACT_ARCH_DOTPRODUCT_H_
 #define TESSERACT_ARCH_DOTPRODUCT_H_
 
+#include "tfloat.h"
+
 namespace tesseract {
 
 // Computes and returns the dot product of the n-vectors u and v.
-double DotProductNative(const double *u, const double *v, int n);
+TFloat DotProductNative(const TFloat *u, const TFloat *v, int n);
 
 // Uses Intel AVX intrinsics to access the SIMD instruction set.
-double DotProductAVX(const double *u, const double *v, int n);
+TFloat DotProductAVX(const TFloat *u, const TFloat *v, int n);
+TFloat DotProductAVX1(const TFloat *u, const TFloat *v, int n);
+TFloat DotProductAVX2(const TFloat *u, const TFloat *v, int n);
+TFloat DotProductAVX3(const TFloat *u, const TFloat *v, int n);
+TFloat DotProductAVX4(const TFloat *u, const TFloat *v, int n);
 
 // Use Intel FMA.
-double DotProductFMA(const double *u, const double *v, int n);
+TFloat DotProductFMA(const TFloat *u, const TFloat *v, int n);
 
 // Uses Intel SSE intrinsics to access the SIMD instruction set.
-double DotProductSSE(const double *u, const double *v, int n);
+TFloat DotProductSSE(const TFloat *u, const TFloat *v, int n);
 
+TFloat DotProductAccelerate(const TFloat *u, const TFloat *v, int n);
 } // namespace tesseract.
 
 #endif // TESSERACT_ARCH_DOTPRODUCT_H_

src/arch/dotproductavx.cpp

@@ -29,6 +29,79 @@ namespace tesseract {
 
 // Computes and returns the dot product of the n-vectors u and v.
 // Uses Intel AVX intrinsics to access the SIMD instruction set.
+#if defined(FAST_FLOAT)
+float DotProductAVX(const float *u, const float *v, int n) {
+  const unsigned quot = n / 8;
+  const unsigned rem = n % 8;
+  __m256 t0 = _mm256_setzero_ps();
+  for (unsigned k = 0; k < quot; k++) {
+    __m256 f0 = _mm256_loadu_ps(u);
+    __m256 f1 = _mm256_loadu_ps(v);
+    f0 = _mm256_mul_ps(f0, f1);
+    t0 = _mm256_add_ps(t0, f0);
+    u += 8;
+    v += 8;
+  }
+  alignas(32) float tmp[8];
+  _mm256_store_ps(tmp, t0);
+  float result = tmp[0] + tmp[1] + tmp[2] + tmp[3] + tmp[4] + tmp[5] + tmp[6] + tmp[7];
+  for (unsigned k = 0; k < rem; k++) {
+    result += *u++ * *v++;
+  }
+  return result;
+}
+float DotProductAVX1(const float *u, const float *v, int n) {
+  const unsigned quot = n / 16;
+  const unsigned rem = n % 16;
+  __m256 t0 = _mm256_setzero_ps();
+  __m256 t1 = _mm256_setzero_ps();
+  for (unsigned k = 0; k < quot; k++) {
+    __m256 f0 = _mm256_loadu_ps(u);
+    __m256 f1 = _mm256_loadu_ps(v);
+    __m256 f2 = _mm256_loadu_ps(u + 8);
+    __m256 f3 = _mm256_loadu_ps(v + 8);
+    f0 = _mm256_mul_ps(f0, f1);
+    f2 = _mm256_mul_ps(f2, f3);
+    t0 = _mm256_add_ps(t0, f0);
+    t1 = _mm256_add_ps(t1, f2);
+    u += 16;
+    v += 16;
+  }
+  t0 = _mm256_hadd_ps(t0, t1);
+  alignas(32) float tmp[8];
+  _mm256_store_ps(tmp, t0);
+  float result = tmp[0] + tmp[1] + tmp[2] + tmp[3] + tmp[4] + tmp[5] + tmp[6] + tmp[7];
+  for (unsigned k = 0; k < rem; k++) {
+    result += *u++ * *v++;
+  }
+  return result;
+}
+#else
+double DotProductAVX1(const double *u, const double *v, int n) {
+  __m256d t0 = _mm256_setzero_pd();
+  __m256d t1 = _mm256_setzero_pd();
+  for (unsigned quot = n / 8; quot > 0; quot--) {
+    __m256d f0 = _mm256_loadu_pd(u);
+    __m256d f1 = _mm256_loadu_pd(v);
+    __m256d f2 = _mm256_loadu_pd(u + 4);
+    __m256d f3 = _mm256_loadu_pd(v + 4);
+    f0 = _mm256_mul_pd(f0, f1);
+    f2 = _mm256_mul_pd(f2, f3);
+    t0 = _mm256_add_pd(t0, f0);
+    t1 = _mm256_add_pd(t1, f2);
+    u += 8;
+    v += 8;
+  }
+  t0 = _mm256_hadd_pd(t0, t1);
+  alignas(32) double tmp[4];
+  _mm256_store_pd(tmp, t0);
+  double result = tmp[0] + tmp[1] + tmp[2] + tmp[3];
+  for (unsigned rem = n % 8; rem > 0; rem--) {
+    result += *u++ * *v++;
+  }
+  return result;
+}
+
 double DotProductAVX(const double *u, const double *v, int n) {
   const unsigned quot = n / 8;
   const unsigned rem = n % 8;
@@ -57,6 +130,7 @@ double DotProductAVX(const double *u, const double *v, int n) {
   }
   return result;
 }
+#endif
 
 } // namespace tesseract.
 

src/arch/dotproductfma.cpp

@@ -29,6 +29,34 @@ namespace tesseract {
 
 // Computes and returns the dot product of the n-vectors u and v.
 // Uses Intel FMA intrinsics to access the SIMD instruction set.
+#if defined(FAST_FLOAT)
+TFloat DotProductFMA(const TFloat *u, const TFloat *v, int n) {
+  const unsigned quot = n / 16;
+  const unsigned rem = n % 16;
+  __m256 t0 = _mm256_setzero_ps();
+  __m256 t1 = _mm256_setzero_ps();
+  for (unsigned k = 0; k < quot; k++) {
+    __m256 f0 = _mm256_loadu_ps(u);
+    __m256 f1 = _mm256_loadu_ps(v);
+    t0 = _mm256_fmadd_ps(f0, f1, t0);
+    u += 8;
+    v += 8;
+    __m256 f2 = _mm256_loadu_ps(u);
+    __m256 f3 = _mm256_loadu_ps(v);
+    t1 = _mm256_fmadd_ps(f2, f3, t1);
+    u += 8;
+    v += 8;
+  }
+  t0 = _mm256_hadd_ps(t0, t1);
+  alignas(32) TFloat tmp[8];
+  _mm256_store_ps(tmp, t0);
+  TFloat result = tmp[0] + tmp[1] + tmp[2] + tmp[3] + tmp[4] + tmp[5] + tmp[6] + tmp[7];
+  for (unsigned k = 0; k < rem; k++) {
+    result += *u++ * *v++;
+  }
+  return result;
+}
+#else
 double DotProductFMA(const double *u, const double *v, int n) {
   const unsigned quot = n / 8;
   const unsigned rem = n % 8;
@@ -55,6 +83,7 @@ double DotProductFMA(const double *u, const double *v, int n) {
   }
   return result;
 }
+#endif
 
 } // namespace tesseract.
 

src/arch/dotproductsse.cpp

@@ -30,6 +30,66 @@ namespace tesseract {
 
 // Computes and returns the dot product of the n-vectors u and v.
 // Uses Intel SSE intrinsics to access the SIMD instruction set.
+#if defined(FAST_FLOAT)
+float DotProductSSE(const float *u, const float *v, int n) {
+  int max_offset = n - 4;
+  int offset = 0;
+  // Accumulate a set of 4 sums in sum, by loading pairs of 4 values from u and
+  // v, and multiplying them together in parallel.
+  __m128 sum = _mm_setzero_ps();
+  if (offset <= max_offset) {
+    offset = 4;
+    // Aligned load is reputedly faster but requires 16 byte aligned input.
+    if ((reinterpret_cast<uintptr_t>(u) & 15) == 0 &&
+        (reinterpret_cast<uintptr_t>(v) & 15) == 0) {
+      // Use aligned load.
+      sum = _mm_load_ps(u);
+      __m128 floats2 = _mm_load_ps(v);
+      // Multiply.
+      sum = _mm_mul_ps(sum, floats2);
+      while (offset <= max_offset) {
+        __m128 floats1 = _mm_load_ps(u + offset);
+        floats2 = _mm_load_ps(v + offset);
+        floats1 = _mm_mul_ps(floats1, floats2);
+        sum = _mm_add_ps(sum, floats1);
+        offset += 4;
+      }
+    } else {
+      // Use unaligned load.
+      sum = _mm_loadu_ps(u);
+      __m128 floats2 = _mm_loadu_ps(v);
+      // Multiply.
+      sum = _mm_mul_ps(sum, floats2);
+      while (offset <= max_offset) {
+        __m128 floats1 = _mm_loadu_ps(u + offset);
+        floats2 = _mm_loadu_ps(v + offset);
+        floats1 = _mm_mul_ps(floats1, floats2);
+        sum = _mm_add_ps(sum, floats1);
+        offset += 4;
+      }
+    }
+  }
+  // Add the 4 sums in sum horizontally.
+#if 0
+	alignas(32) float tmp[4];
+	_mm_store_ps(tmp, sum);
+	float result = tmp[0] + tmp[1] + tmp[2] + tmp[3];
+#else
+  __m128 zero = _mm_setzero_ps();
+  // https://www.felixcloutier.com/x86/haddps
+  sum = _mm_hadd_ps(sum, zero);
+  sum = _mm_hadd_ps(sum, zero);
+  // Extract the low result.
+  float result = _mm_cvtss_f32(sum);
+#endif
+  // Add on any left-over products.
+  while (offset < n) {
+    result += u[offset] * v[offset];
+    ++offset;
+  }
+  return result;
+}
+#else
 double DotProductSSE(const double *u, const double *v, int n) {
   int max_offset = n - 2;
   int offset = 0;
@@ -39,7 +99,8 @@ double DotProductSSE(const double *u, const double *v, int n) {
   if (offset <= max_offset) {
     offset = 2;
     // Aligned load is reputedly faster but requires 16 byte aligned input.
-    if ((reinterpret_cast<uintptr_t>(u) & 15) == 0 && (reinterpret_cast<uintptr_t>(v) & 15) == 0) {
+    if ((reinterpret_cast<uintptr_t>(u) & 15) == 0 &&
+        (reinterpret_cast<uintptr_t>(v) & 15) == 0) {
       // Use aligned load.
       sum = _mm_load_pd(u);
       __m128d floats2 = _mm_load_pd(v);
@@ -78,6 +139,7 @@ double DotProductSSE(const double *u, const double *v, int n) {
   }
   return result;
 }
+#endif
 
 } // namespace tesseract.
 

src/arch/intsimdmatrix.cpp

@@ -76,7 +76,7 @@ void IntSimdMatrix::Init(const GENERIC_2D_ARRAY<int8_t> &w, std::vector<int8_t>
 // u is imagined to have an extra element at the end with value 1, to
 // implement the bias, but it doesn't actually have it.
 void IntSimdMatrix::MatrixDotVector(const GENERIC_2D_ARRAY<int8_t> &w,
-                                    const std::vector<double> &scales, const int8_t *u, double *v) {
+                                    const std::vector<TFloat> &scales, const int8_t *u, TFloat *v) {
   int num_out = w.dim1();
   int num_in = w.dim2() - 1;
   // Base implementation.