cavis/libnd4j/include/ops/declarable/helpers/cpu/s_t_b.cpp

493 lines
19 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Yurii Shyrma (iuriish@yahoo.com)
// @author raver119@gmail.com
//
#include <ops/declarable/helpers/s_t_b.h>
namespace nd4j {
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void batchToSpace_(const NDArray& input, NDArray& output, const uint cropBottom, const uint cropTop, const uint cropLeft, const uint cropRight) {
// input [bS, H * blockSize, W * blockSize, iC]
// output [bS, H * blockSize - cropBottom - cropTop, W * blockSize - cropLeft - cropRight, iC]
// if (cropTop = cropBottom = cropRight = cropLeft = 0) shapes are the same
// else:
// oH -> [cropBottom, iH - cropTop]
// oW -> [cropLeft, iH - cropRight]
// xLen > zLen
const T* x = input.bufferAsT<T>();
T* z = output.bufferAsT<T>();
const int rank = 4;
const Nd4jLong* xShapeInfo = input.getShapeInfo();
const Nd4jLong* zShapeInfo = output.getShapeInfo();
const uint bS = xShapeInfo[1];
const uint iH = xShapeInfo[2];
const uint iW = xShapeInfo[3];
const uint iC = xShapeInfo[4];
// loop through output array
PRAGMA_OMP_PARALLEL_FOR_SIMD_ARGS(collapse(4))
for (uint b = 0; b < bS; ++b) {
for (uint h = cropBottom; h < iH - cropTop; ++h) {
for (uint w = cropLeft; w < iW - cropRight; ++w) {
for (uint c = 0; c < iC; ++c) {
const Nd4jLong xOffset = b * xShapeInfo[5] + h * xShapeInfo[6] + w * xShapeInfo[7] + c * xShapeInfo[8];
const Nd4jLong zOffset = b * zShapeInfo[5] + (h - cropBottom) * zShapeInfo[6] + (w - cropLeft) * zShapeInfo[7] + c * zShapeInfo[8];
z[zOffset] = x[xOffset];
}
}
}
}
}
BUILD_SINGLE_TEMPLATE(template void batchToSpace_, (const NDArray& input, NDArray& output, const uint cropBottom, const uint cropTop, const uint cropLeft, const uint cropRight), LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
void batchToSpace(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const uint cropBottom, const uint cropTop, const uint cropLeft, const uint cropRight, const uint blockSize) {
// [bS*blockSize*blockSize, H/blockSize, W/blockSize, iC] is rearranged/permuted to [bS, oH, oW, iC]
// oH = H - cropTop - cropBottom
// oW = W - cropLeft - cropRight
NDArray inputRearranged0 = input.reshape(input.ordering(), {blockSize, blockSize, output.sizeAt(0), input.sizeAt(1), input.sizeAt(2), input.sizeAt(3)});
inputRearranged0.permutei({2, 3,0, 4,1, 5});
if(input.lengthOf() == output.lengthOf())
output.assign(inputRearranged0);
else {
NDArray inputRearranged1 = inputRearranged0.reshape(input.ordering(), {output.sizeAt(0), input.sizeAt(1) * blockSize, input.sizeAt(2) * blockSize, input.sizeAt(3)});
BUILD_SINGLE_SELECTOR(input.dataType(), batchToSpace_, (inputRearranged1, output, cropBottom, cropTop, cropLeft, cropRight), LIBND4J_TYPES);
}
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void batchToSpaceND_(const NDArray& input, const NDArray& crop, NDArray& output, const uint numOfSpatialDims) {
// input [bS, H * blockShape[0], W * blockShape[1], iC]
// output [bS, H * blockShape[0] - cropBottom - cropTop, W * blockShape[1] - cropLeft - cropRight, iC]
// if (cropTop = cropBottom = cropRight = cropLeft = 0) shapes are the same
// else:
// oH -> [cropBottom, iH - cropTop]
// oW -> [cropLeft, iH - cropRight]
// xLen >= zLen
const T* x = input.bufferAsT<T>();
T* z = output.bufferAsT<T>();
const int rank = input.rankOf();
const Nd4jLong zLen = output.lengthOf();
std::vector<Nd4jLong> coords(rank);
// loop through input array
PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(guided) firstprivate(coords))
for (Nd4jLong i = 0; i < zLen; ++i) {
shape::index2coords(i, output.getShapeInfo(), coords.data());
const auto zOffset = shape::getOffset(output.getShapeInfo(), coords.data());
// evaluate spatial coordinates for x
for(uint j = 1; j <= numOfSpatialDims; ++j)
coords[j] += crop.e<uint>(j - 1, 0); // add crop left
z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords.data())];
}
}
BUILD_SINGLE_TEMPLATE(template void batchToSpaceND_, (const NDArray& input, const NDArray& crop, NDArray& output, const uint numOfSpatialDims), LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
void batchToSpaceND(nd4j::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& crop, NDArray& output) {
// 4D example, numOfSpatialDims = 2 - two spatial dimensions
// [bS*blockShape[0]*blockShape[1], iH, iW, iC] is rearranged/permuted to [bS, iH*blockShape[0] - cropTop - cropBottom, iW*blockShape[1] - cropLeft - cropRight, iC]
const uint rank = input.rankOf();
const uint numOfSpatialDims = blockShape.sizeAt(0);
//*** construct reshaping std::vector for first reshape of input array ***//
std::vector<Nd4jLong> temp(numOfSpatialDims + rank);
int i;
for(i = 0; i < numOfSpatialDims; ++i)
temp[i] = blockShape.e<Nd4jLong>(i);
temp[i++] = output.sizeAt(0);
for(int j = 1; j < rank; ++i, ++j)
temp[i] = input.sizeAt(j);
NDArray inputRearranged0 = input.reshape(input.ordering(), temp);
//*** construct permuting std::vector for permutation of input array ***//
temp[0] = numOfSpatialDims;
for(i = 1; i <= numOfSpatialDims; ++i) {
temp[2*i - 1] = numOfSpatialDims + i;
temp[2*i] = i - 1;
}
for(i = 2 * numOfSpatialDims + 1; i < temp.size(); ++i)
temp[i] = i;
inputRearranged0.permutei(temp);
if(input.lengthOf() == output.lengthOf()) {
output.assign(inputRearranged0);
}
else {
//*** construct reshaping std::vector for second reshape of input array ***//
temp.resize(rank);
temp[0] = output.sizeAt(0);
for(i = 1; i < rank; ++i)
temp[i] = (i <= numOfSpatialDims) ? input.sizeAt(i) * blockShape.e<Nd4jLong>(i - 1) : input.sizeAt(i);
NDArray inputRearranged1 = inputRearranged0.reshape(input.ordering(), temp);
BUILD_SINGLE_SELECTOR(input.dataType(), batchToSpaceND_, (inputRearranged1, crop, output, numOfSpatialDims), LIBND4J_TYPES);
}
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void spaceToBatch_(const NDArray& input, NDArray& output, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight) {
// input [bS, H * blockSize - padBottom - padTop, W * blockSize - padLeft - padRight, iC]
// output [bS, H * blockSize, W * blockSize, iC]
// if (padTop = padBottom = padRight = padLeft = 0) shapes are the same
// else:
// iH -> [padBottom, oH - padTop]
// iW -> [padLeft, oW - padRight]
// zLen > xLen
const T* x = input.bufferAsT<T>();
T* z = output.bufferAsT<T>();
const int rank = 4;
const Nd4jLong* xShapeInfo = input.getShapeInfo();
const Nd4jLong* zShapeInfo = output.getShapeInfo();
const uint bS = zShapeInfo[1];
const uint oH = zShapeInfo[2];
const uint oW = zShapeInfo[3];
const uint iC = zShapeInfo[4];
// loop through output array
PRAGMA_OMP_PARALLEL_FOR_SIMD_ARGS(collapse(4))
for (uint b = 0; b < bS; ++b) {
for (uint h = 0; h < oH; ++h) {
for (uint w = 0; w < oW; ++w) {
for (uint c = 0; c < iC; ++c) {
const Nd4jLong zOffset = b * zShapeInfo[5] + h * zShapeInfo[6] + w * zShapeInfo[7] + c * zShapeInfo[8];
if(h >= padBottom && h < oH - padTop && w >= padLeft && w < oW - padRight) {
const Nd4jLong xOffset = b * xShapeInfo[5] + (h - padBottom) * xShapeInfo[6] + (w - padLeft) * xShapeInfo[7] + c * xShapeInfo[8];
z[zOffset] = x[xOffset];
}
else
z[zOffset] = 0.f;
}
}
}
}
}
BUILD_SINGLE_TEMPLATE(template void spaceToBatch_, (const NDArray& input, NDArray& output, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight), LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
void spaceToBatch(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight, const uint blockSize) {
// [bS, iH, iW, iC] is rearranged/permuted to [bS*blockSize*blockSize, (iH + padBottom + padTop)/blockSize, (iW + padLeft + padRight)/blockSize, iC]
NDArray outputRearranged0 = output.reshape(output.ordering(), {blockSize, blockSize, input.sizeAt(0), output.sizeAt(1), output.sizeAt(2), output.sizeAt(3)});
outputRearranged0.permutei({2, 3,0, 4,1, 5});
if(input.lengthOf() == output.lengthOf()) {
outputRearranged0.assign(input);
}
else {
NDArray outputRearranged1 = outputRearranged0.reshape(output.ordering(), {input.sizeAt(0), output.sizeAt(1) * blockSize, output.sizeAt(2) * blockSize, output.sizeAt(3)});
BUILD_SINGLE_SELECTOR(input.dataType(), spaceToBatch_, (input, outputRearranged1, padBottom, padTop, padLeft, padRight), LIBND4J_TYPES);
if(output.getBuffer() != outputRearranged1.getBuffer())
outputRearranged0.assign(outputRearranged1);
}
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void spaceToBatchND_(const NDArray& input, const NDArray& padding, NDArray& output, const uint numOfSpatialDims) {
// 4D example
// input [bS, H * blockShape[0] - padBottom - padTop, W * blockShape[1] - padLeft - padRight, iC]
// output [bS, H * blockShape[0], W * blockShape[1], iC]
// if (padTop = padBottom = padRight = padLeft = 0) shapes are the same
// else:
// iH -> [padBottom, oH - padTop]
// iW -> [padLeft, oW - padRight]
// zLen > xLen
const T* x = input.bufferAsT<T>();
T* z = output.bufferAsT<T>();
const int rank = input.rankOf();
const Nd4jLong zLen = output.lengthOf();
std::vector<Nd4jLong> coords(rank);
// loop through output array
PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(guided) firstprivate(coords))
for (Nd4jLong i = 0; i < zLen; ++i) {
shape::index2coords(i, output.getShapeInfo(), coords.data());
const auto zOffset = shape::getOffset(output.getShapeInfo(), coords.data());
bool within = true;
for(uint j = 1; j <= numOfSpatialDims; ++j) {
const auto padLeft = padding.e<uint>(j - 1, 0);
const auto padRight = padding.e<uint>(j - 1, 1);
within &= (coords[j] >= padLeft && coords[j] < output.sizeAt(j) - padRight);
if(!within)
break;
coords[j] -= padLeft; // get coordinates for x
}
if(within)
z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords.data())];
else
z[zOffset] = 0.f;
}
}
BUILD_SINGLE_TEMPLATE(template void spaceToBatchND_, (const NDArray& input, const NDArray& padding, NDArray& output, const uint numOfSpatialDims), LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
void spaceToBatchND(nd4j::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& padding, NDArray& output ) {
// 4D example with two spatial dimensions
// [bS, iH, iW, iC] is rearranged/permuted to [bS*blockShape[0]*blockShape[1], (iH + padBottom + padTop)/blockShape[0], (iW + padLeft + padRight)/blockShape[1], iC]
const uint rank = input.rankOf();
const uint numOfSpatialDims = blockShape.sizeAt(0);
//*** construct reshaping std::vector for first reshape of output array ***//
std::vector<Nd4jLong> temp(numOfSpatialDims + rank);
int i;
for(i = 0; i < numOfSpatialDims; ++i)
temp[i] = blockShape.e<Nd4jLong>(i);
temp[i++] = input.sizeAt(0);
for(int j = 1; j < rank; ++i, ++j)
temp[i] = output.sizeAt(j);
NDArray outputRearranged0 = output.reshape(output.ordering(), temp);
//*** construct permuting std::vector for permutation of output array ***//
temp[0] = numOfSpatialDims;
for(i = 1; i <= numOfSpatialDims; ++i) {
temp[2*i - 1] = numOfSpatialDims + i;
temp[2*i] = i - 1;
}
for(i = 2 * numOfSpatialDims + 1; i < temp.size(); ++i)
temp[i] = i;
outputRearranged0.permutei(temp);
// ****** //
if(input.lengthOf() == output.lengthOf()) {
outputRearranged0.assign(input);
}
else {
//*** construct reshaping std::vector for second reshape of output array ***//
temp.resize(rank);
temp[0] = input.sizeAt(0);
for(i = 1; i < rank; ++i)
temp[i] = (i <= numOfSpatialDims) ? output.sizeAt(i) * blockShape.e<Nd4jLong>(i - 1) : output.sizeAt(i);
NDArray outputRearranged1 = outputRearranged0.reshape(output.ordering(), temp);
BUILD_SINGLE_SELECTOR(input.dataType(), spaceToBatchND_, (input, padding, outputRearranged1, numOfSpatialDims), LIBND4J_TYPES);
if(output.getBuffer() != outputRearranged1.getBuffer())
outputRearranged0.assign(outputRearranged1);
}
}
/*
template <int N, bool B2S>
struct SpaceToBatchHelper {
template <typename T>
static void run(T *ptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, T *ptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides) {
for (int batch_pos = 0; batch_pos < batch_shape[0]; ++batch_pos) {
const int space_pos = batch_pos * block_shape[0] + block_offsets[0] - pad_start[0];
if (space_pos >= 0 && space_pos < space_shape[0]) {
SpaceToBatchHelper<N - 1, B2S>::run(ptrSpace + space_pos * space_strides[0], space_shape + 1, space_strides + 1, block_shape + 1, pad_start + 1, block_offsets + 1, ptrBatch, batch_shape + 1, batch_strides + 1);
} else {
if (!B2S)
for (int i = 0; i < batch_strides[0]; i++)
ptrBatch[i] = (T) 0.f;
}
ptrBatch += batch_strides[0];
}
}
};
template <bool B2S>
struct SpaceToBatchHelper<0, B2S> {
template <typename T>
static void run(T *ptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, T *ptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides) {
int str = batch_strides[-1];
for (int i = 0; i < str; i++)
if (B2S)
ptrSpace[i] = ptrBatch[i];
else
ptrBatch[i] = ptrSpace[i];
}
};
template <typename T, int NUM_BLOCK_DIMS, bool B2S>
void _execute(nd4j::LaunchContext * context, void *vptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *vptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides) {
auto ptrSpace = reinterpret_cast<T *>(vptrSpace);
auto ptrBatch = reinterpret_cast<T *>(vptrBatch);
SpaceToBatchHelper<NUM_BLOCK_DIMS, B2S>::run(ptrSpace, space_shape, space_strides, block_shape, pad_start, block_offsets, ptrBatch, batch_shape, batch_strides);
};
Nd4jStatus _spaceToBatch(nd4j::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector<Nd4jLong> &internal_input_shape, std::vector<Nd4jLong> &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *paddings) {
auto in = input->reshape('c', internal_input_shape);
auto out = output->reshape('c', internal_output_shape);
switch (internal_block_dims) {
case 1:
_prepare<1, false>(context, &in, &out, block_shape, paddings);
break;
case 2:
_prepare<2, false>(context, &in, &out, block_shape, paddings);
break;
case 3:
_prepare<3, false>(context, &in, &out, block_shape, paddings);
break;
case 4:
_prepare<4, false>(context, &in, &out, block_shape, paddings);
break;
default: {
return Status::THROW("SpaceToBatch: Wrong number of internal_block_dims");
}
}
return Status::OK();
}
Nd4jStatus _batchToSpace(nd4j::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector<Nd4jLong> &internal_input_shape, std::vector<Nd4jLong> &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *crops) {
auto in = input->reshape('c', internal_input_shape);
auto out = output->reshape('c', internal_output_shape);
switch (internal_block_dims) {
case 1:
_prepare<1, true>(context, &in, &out, block_shape, crops);
break;
case 2:
_prepare<2, true>(context, &in, &out, block_shape, crops);
break;
case 3:
_prepare<3, true>(context, &in, &out, block_shape, crops);
break;
case 4:
_prepare<4, true>(context, &in, &out, block_shape, crops);
break;
default: {
return Status::THROW("BatchToSpace: Wrong number of internal_block_dims");
}
}
return Status::OK();
}
#define STB_DIM (0, 1),\
(1, 2),\
(2, 3),\
(3, 4)
#define STB_BOOL (0, false),\
(1, true)
BUILD_TRIPLE_TEMPLATE(template void _execute, (nd4j::LaunchContext * context, void *ptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *ptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides), LIBND4J_TYPES, STB_DIM, STB_BOOL);
#undef STB_BOOL
#undef STB_DIM
*/
}
}
}