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fftconvolve.cpp
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267 lines (236 loc) · 8.79 KB
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/*******************************************************
* Copyright (c) 2014, ArrayFire
* All rights reserved.
*
* This file is distributed under 3-clause BSD license.
* The complete license agreement can be obtained at:
* http://arrayfire.com/licenses/BSD-3-Clause
********************************************************/
#include <fftconvolve.hpp>
#include <arith.hpp>
#include <backend.hpp>
#include <common/cast.hpp>
#include <common/dispatch.hpp>
#include <common/err_common.hpp>
#include <complex.hpp>
#include <fft_common.hpp>
#include <handle.hpp>
#include <af/defines.h>
#include <af/dim4.hpp>
#include <af/signal.h>
#include <algorithm>
#include <type_traits>
#include <vector>
using af::dim4;
using arrayfire::common::cast;
using detail::arithOp;
using detail::Array;
using detail::cdouble;
using detail::cfloat;
using detail::createSubArray;
using detail::fftconvolve;
using detail::intl;
using detail::real;
using detail::schar;
using detail::uchar;
using detail::uint;
using detail::uintl;
using detail::ushort;
using std::conditional;
using std::is_integral;
using std::is_same;
using std::max;
using std::swap;
using std::vector;
template<typename T>
af_array fftconvolve_fallback(const af_array signal, const af_array filter,
const bool expand, const int baseDim) {
using convT = typename conditional<is_integral<T>::value ||
is_same<T, float>::value ||
is_same<T, cfloat>::value,
float, double>::type;
using cT = typename conditional<is_same<convT, float>::value, cfloat,
cdouble>::type;
const Array<cT> S = castArray<cT>(signal);
const Array<cT> F = castArray<cT>(filter);
const dim4 &sdims = S.dims();
const dim4 &fdims = F.dims();
dim4 odims(1, 1, 1, 1);
dim4 psdims(1, 1, 1, 1);
dim4 pfdims(1, 1, 1, 1);
vector<af_seq> index(AF_MAX_DIMS);
int count = 1;
for (int i = 0; i < baseDim; i++) {
dim_t tdim_i = sdims[i] + fdims[i] - 1;
// Pad temporary buffers to power of 2 for performance
odims[i] = nextpow2(tdim_i);
psdims[i] = nextpow2(tdim_i);
pfdims[i] = nextpow2(tdim_i);
// The normalization factor
count *= odims[i];
// Get the indexing params for output
if (expand) {
index[i].begin = 0.;
index[i].end = static_cast<double>(tdim_i) - 1.;
} else {
index[i].begin = static_cast<double>(fdims[i]) / 2.0;
index[i].end = static_cast<double>(index[i].begin + sdims[i]) - 1.;
}
index[i].step = 1.;
}
for (int i = baseDim; i < AF_MAX_DIMS; i++) {
odims[i] = max(sdims[i], fdims[i]);
psdims[i] = sdims[i];
pfdims[i] = fdims[i];
index[i] = af_span;
}
// fft(signal)
Array<cT> T1 = fft<cT, cT>(S, 1.0, baseDim, psdims.get(), baseDim, true);
// fft(filter)
Array<cT> T2 = fft<cT, cT>(F, 1.0, baseDim, pfdims.get(), baseDim, true);
// fft(signal) * fft(filter)
T1 = arithOp<cT, af_mul_t>(T1, T2, odims);
// ifft(ffit(signal) * fft(filter))
T1 = fft<cT, cT>(T1, 1.0 / static_cast<double>(count), baseDim, odims.get(),
baseDim, false);
// Index to proper offsets
T1 = createSubArray<cT>(T1, index);
if (getInfo(signal).isComplex() || getInfo(filter).isComplex()) {
return getHandle(cast<T>(T1));
} else {
return getHandle(cast<T>(real<convT>(T1)));
}
}
template<typename T>
inline af_array fftconvolve(const af_array &s, const af_array &f,
const bool expand, AF_BATCH_KIND kind,
const int baseDim) {
if (kind == AF_BATCH_DIFF) {
return fftconvolve_fallback<T>(s, f, expand, baseDim);
} else {
return getHandle(fftconvolve<T>(getArray<T>(s), castArray<T>(f), expand,
kind, baseDim));
}
}
AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims,
const int baseDim) {
dim_t sn = sDims.ndims();
dim_t fn = fDims.ndims();
if (sn == baseDim && fn == baseDim) { return AF_BATCH_NONE; }
if (sn == baseDim && (fn > baseDim && fn <= AF_MAX_DIMS)) {
return AF_BATCH_RHS;
}
if ((sn > baseDim && sn <= AF_MAX_DIMS) && fn == baseDim) {
return AF_BATCH_LHS;
} else if ((sn > baseDim && sn <= AF_MAX_DIMS) &&
(fn > baseDim && fn <= AF_MAX_DIMS)) {
bool doesDimensionsMatch = true;
bool isInterleaved = true;
for (dim_t i = baseDim; i < AF_MAX_DIMS; i++) {
doesDimensionsMatch &= (sDims[i] == fDims[i]);
isInterleaved &=
(sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
}
if (doesDimensionsMatch) { return AF_BATCH_SAME; }
return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
} else {
return AF_BATCH_UNSUPPORTED;
}
}
af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
const bool expand, const int baseDim) {
try {
const ArrayInfo &sInfo = getInfo(signal);
const ArrayInfo &fInfo = getInfo(filter);
af_dtype signalType = sInfo.getType();
const dim4 &sdims = sInfo.dims();
const dim4 &fdims = fInfo.dims();
AF_BATCH_KIND convBT = identifyBatchKind(sdims, fdims, baseDim);
ARG_ASSERT(1, (convBT != AF_BATCH_UNSUPPORTED));
af_array output;
switch (signalType) {
case f64:
output = fftconvolve<double>(signal, filter, expand, convBT,
baseDim);
break;
case f32:
output =
fftconvolve<float>(signal, filter, expand, convBT, baseDim);
break;
case u32:
output =
fftconvolve<uint>(signal, filter, expand, convBT, baseDim);
break;
case s32:
output =
fftconvolve<int>(signal, filter, expand, convBT, baseDim);
break;
case u64:
output =
fftconvolve<uintl>(signal, filter, expand, convBT, baseDim);
break;
case s64:
output =
fftconvolve<intl>(signal, filter, expand, convBT, baseDim);
break;
case u16:
output = fftconvolve<ushort>(signal, filter, expand, convBT,
baseDim);
break;
case s16:
output =
fftconvolve<short>(signal, filter, expand, convBT, baseDim);
break;
case u8:
output =
fftconvolve<uchar>(signal, filter, expand, convBT, baseDim);
break;
case s8:
output =
fftconvolve<schar>(signal, filter, expand, convBT, baseDim);
break;
case b8:
output =
fftconvolve<char>(signal, filter, expand, convBT, baseDim);
break;
case c32:
output = fftconvolve_fallback<cfloat>(signal, filter, expand,
baseDim);
break;
case c64:
output = fftconvolve_fallback<cdouble>(signal, filter, expand,
baseDim);
break;
default: TYPE_ERROR(1, signalType);
}
swap(*out, output);
}
CATCHALL;
return AF_SUCCESS;
}
af_err af_fft_convolve1(af_array *out, const af_array signal,
const af_array filter, const af_conv_mode mode) {
return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 1);
}
af_err af_fft_convolve2(af_array *out, const af_array signal,
const af_array filter, const af_conv_mode mode) {
try {
if (getInfo(signal).dims().ndims() < 2 &&
getInfo(filter).dims().ndims() < 2) {
return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 1);
}
return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 2);
}
CATCHALL;
}
af_err af_fft_convolve3(af_array *out, const af_array signal,
const af_array filter, const af_conv_mode mode) {
try {
if (getInfo(signal).dims().ndims() < 3 &&
getInfo(filter).dims().ndims() < 3) {
return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 2);
}
return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 3);
}
CATCHALL;
}