wehub-resource-sync
433 lines
25 KiB
C++
433 lines
25 KiB
C++
/* ******************************************************************************
|
|
*
|
|
*
|
|
* 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.
|
|
*
|
|
* See the NOTICE file distributed with this work for additional
|
|
* information regarding copyright ownership.
|
|
* 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), created on 05.09.2018
|
|
//
|
|
|
|
#include <system/op_boilerplate.h>
|
|
#if NOT_EXCLUDED(OP_deconv3d)
|
|
|
|
#include <helpers/MmulHelper.h>
|
|
#include <ops/declarable/CustomOperations.h>
|
|
#include <ops/declarable/helpers/addBias.h>
|
|
#include <ops/declarable/helpers/convolutions.h>
|
|
|
|
namespace sd {
|
|
namespace ops {
|
|
|
|
CUSTOM_OP_IMPL(deconv3d, 2, 1, false, 0, 13) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
|
|
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 5, 0,
|
|
"CUSTOM DECONV3D OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
|
|
REQUIRE_TRUE(weights->rankOf() == 5, 0,
|
|
"CUSTOM DECONV3D OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
|
|
|
|
LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) depth
|
|
LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1)); // filter(kernel) height
|
|
LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(weights->sizeAt(2)); // filter(kernel) width
|
|
LongType sD = INT_ARG(3); // strides depth
|
|
LongType sH = INT_ARG(4); // strides height
|
|
LongType sW = INT_ARG(5); // strides width
|
|
LongType pD = INT_ARG(6); // paddings depth
|
|
LongType pH = INT_ARG(7); // paddings height
|
|
LongType pW = INT_ARG(8); // paddings width
|
|
LongType dD = INT_ARG(9); // dilations depth
|
|
LongType dH = INT_ARG(10); // dilations height
|
|
LongType dW = INT_ARG(11); // dilations width
|
|
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
|
|
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
|
|
int wFormat = block.getIArguments()->size() > 14
|
|
? INT_ARG(14)
|
|
: 0; // 0 - [kD, kH, kW, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC]
|
|
|
|
|
|
LongType bS, iC, iD, iH, iW, oC, oD, oH,
|
|
oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
|
|
LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW,
|
|
indIOioC, indIOioD, indWoC, indWiC, indWkD);
|
|
|
|
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
|
|
"CUSTOM DECONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
|
if (bias)
|
|
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
|
|
"CUSTOM DECONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i "
|
|
"instead !",
|
|
oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
|
|
std::vector<LongType> outputPerm = {0, 4, 1, 2, 3};
|
|
if (!isNCDHW) output = output->permute(outputPerm, false, false); // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW]
|
|
|
|
std::vector<LongType> colPermut;
|
|
if (1 == wFormat)
|
|
colPermut = {1, 2, 3, 4, 0, 5, 6, 7};
|
|
else
|
|
colPermut = {2, 3, 4, 1, 0, 5, 6, 7};
|
|
|
|
if (isSameMode) // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not
|
|
// deconv) forward pass
|
|
ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
|
|
|
|
std::vector<sd::LongType> columnsShape = {bS, oC, kD, kH, kW, iD, iH, iW};
|
|
NDArray columns(input->ordering(),columnsShape, input->dataType(), block.launchContext());
|
|
|
|
//----- calculation of output -----//
|
|
// [kD, kH, kW, oC, iC] x [bS, iD, iH, iW, iC] = [kD, kH, kW, oC, bS, iD, iH, iW]
|
|
// [iC, oC, kD, kH, kW] x [bS, iD, iH, iW, iC] = [oC, kD, kH, kW, bS, iD, iH, iW]
|
|
// [iC, kD, kH, kW, oC] x [bS, iD, iH, iW, iC] = [kD, kH, kW, oC, bS, iD, iH, iW]
|
|
std::vector<LongType> indWiCShape = {indWiC};
|
|
std::vector<LongType> indIOioCShape = {indIOioC};
|
|
sd::MmulHelper::tensorDot(weights, input, &columns, indWiCShape, indIOioCShape,
|
|
colPermut); // [bS, oC, kD, kH, kW, iD, iH, iW] -> [kD, kH, kW, oC, bS, iD, iH, iW]
|
|
|
|
ConvolutionUtils::col2vol(block, columns, *output, sD, sH, sW, pD, pH, pW, dD, dH,
|
|
dW); // [bS, oC, kD, kH, kW, iD, iH, iW] is de-convoluted to [bS, oC, oD, oH, oW]
|
|
|
|
//----- add biases if required -----//
|
|
if (bias)
|
|
helpers::addBias(block, *output, *bias, *output, true);
|
|
|
|
//if (!isNCDHW) delete output;
|
|
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
DECLARE_TYPES(deconv3d) {
|
|
getOpDescriptor()
|
|
->setAllowedInputTypes(0, sd::DataType::ANY)
|
|
->setAllowedInputTypes(1, {ALL_FLOATS})
|
|
->setAllowedInputTypes(2, {ALL_FLOATS})
|
|
->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
DECLARE_SHAPE_FN(deconv3d) {
|
|
auto inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NDCHW)
|
|
auto weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
|
|
auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
|
|
|
|
const sd::LongType rank = 5;
|
|
REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0,
|
|
"CUSTOM DECONV3D OP: rank of input array must be equal to %i, but got %i instead !", rank,
|
|
shape::rank(inputShapeInfo));
|
|
REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0,
|
|
"CUSTOM DECONV3D OP: rank of weights array must be equal to %i, but got %i instead !", rank,
|
|
shape::rank(weightsShapeInfo));
|
|
|
|
LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0))); // filter(kernel) depth
|
|
LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1))); // filter(kernel) height
|
|
LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(2))); // filter(kernel) width
|
|
LongType sD = INT_ARG(3); // strides depth
|
|
LongType sH = INT_ARG(4); // strides height
|
|
LongType sW = INT_ARG(5); // strides width
|
|
LongType pD = INT_ARG(6); // paddings depth
|
|
LongType pH = INT_ARG(7); // paddings height
|
|
LongType pW = INT_ARG(8); // paddings width
|
|
LongType dD = INT_ARG(9); // dilations depth
|
|
LongType dH = INT_ARG(10); // dilations height
|
|
LongType dW = INT_ARG(11); // dilations width
|
|
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
|
|
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
|
|
int wFormat = block.getIArguments()->size() > 14
|
|
? INT_ARG(14)
|
|
: 0; // 0 - [kD, kH, kW, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC]
|
|
|
|
LongType indIOioC, indIiD, indWoC(0 == wFormat ? 3 : (1 == wFormat ? 1 : 4));
|
|
if (!isNCDHW) {
|
|
indIOioC = 4;
|
|
indIiD = 1;
|
|
} else {
|
|
indIOioC = 1;
|
|
indIiD = 2;
|
|
}
|
|
|
|
const LongType bS = inputShapeInfo[1]; // batch size
|
|
const LongType iD = inputShapeInfo[indIiD + 1]; // input depth
|
|
const LongType iH = inputShapeInfo[indIiD + 2]; // input height
|
|
const LongType iW = inputShapeInfo[indIiD + 3]; // input width
|
|
const LongType iC = inputShapeInfo[indIOioC + 1]; // input channels
|
|
const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
|
|
|
|
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(shape::shapeEquals(5, expectedWeightsShape.data(), shape::rank(weightsShapeInfo),
|
|
shape::shapeOf(weightsShapeInfo)),
|
|
0, "CUSTOM DECONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
|
|
ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
|
|
if (biasShapeInfo)
|
|
REQUIRE_TRUE(shape::rank(biasShapeInfo) <= 2 && oC == shape::length(biasShapeInfo), 0,
|
|
"CUSTOM DECONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i "
|
|
"instead !",
|
|
oC, shape::rank(biasShapeInfo), shape::length(biasShapeInfo));
|
|
|
|
LongType oD, oH, oW; // output depth, height, width
|
|
ConvolutionUtils::calcOutSizeDeconv3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW,
|
|
isSameMode);
|
|
|
|
|
|
|
|
std::vector<sd::LongType> outputShape;
|
|
if (isNCDHW) {
|
|
outputShape = {bS,oC,oD,oH,oW};
|
|
} else {
|
|
outputShape = {bS,oD,oH,oW,oC};
|
|
}
|
|
|
|
ShapeDescriptor *shapeDescriptor = new ShapeDescriptor(ArrayOptions::dataType(inputShapeInfo), shape::order(inputShapeInfo),
|
|
outputShape);
|
|
auto outputShapeInfo = ConstantShapeHelper::getInstance().createShapeInfo(shapeDescriptor);
|
|
delete shapeDescriptor;
|
|
return SHAPELIST(outputShapeInfo);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
CUSTOM_OP_IMPL(deconv3d_bp, 3, 2, false, 0, 13) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
auto gradO = block.width() > 3
|
|
? INPUT_VARIABLE(3)
|
|
: INPUT_VARIABLE(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), gradI
|
|
auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
|
|
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 5, 0,
|
|
"CUSTOM DECONV3D_BP OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
|
|
REQUIRE_TRUE(weights->rankOf() == 5, 0,
|
|
"CUSTOM DECONV3D_BP OP: rank of weights array must be equal to 5 , but got %i instead !",
|
|
weights->rankOf());
|
|
REQUIRE_TRUE(
|
|
gradO->rankOf() == 5, 0,
|
|
"CUSTOM DECONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to 5, but got %i instead !",
|
|
gradO->rankOf());
|
|
|
|
LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) depth
|
|
LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1)); // filter(kernel) height
|
|
LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(weights->sizeAt(2)); // filter(kernel) width
|
|
LongType sD = INT_ARG(3); // strides depth
|
|
LongType sH = INT_ARG(4); // strides height
|
|
LongType sW = INT_ARG(5); // strides width
|
|
LongType pD = INT_ARG(6); // paddings depth
|
|
LongType pH = INT_ARG(7); // paddings height
|
|
LongType pW = INT_ARG(8); // paddings width
|
|
LongType dD = INT_ARG(9); // dilations depth
|
|
LongType dH = INT_ARG(10); // dilations height
|
|
LongType dW = INT_ARG(11); // dilations width
|
|
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
|
|
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
|
|
int wFormat = block.getIArguments()->size() > 14
|
|
? INT_ARG(14)
|
|
: 0; // 0 - [kD, kH, kW, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC]
|
|
|
|
LongType bS, iC, iD, iH, iW, oC, oD, oH,
|
|
oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
|
|
LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW,
|
|
indIOioC, indIOioD, indWoC, indWiC, indWkD);
|
|
|
|
LongType trueoD, trueoH, trueoW; // true output height, width
|
|
ConvolutionUtils::calcOutSizeDeconv3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH,
|
|
iW, isSameMode);
|
|
|
|
std::vector<sd::LongType> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx(
|
|
{bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2});
|
|
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
|
|
"CUSTOM DECONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got "
|
|
"%s instead !",
|
|
ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
|
|
"CUSTOM DECONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
|
if (bias)
|
|
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
|
|
"CUSTOM DECONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, "
|
|
"%i instead !",
|
|
oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
if (isSameMode) // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not
|
|
// deconv) forward pass
|
|
ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
|
|
|
|
// ----- calculation of gradI -> pass it through conv3d_ff ----- //
|
|
sd::ops::conv3dnew conv3d;
|
|
const sd::Status status =
|
|
conv3d.execute({gradO, weights}, {gradI}, {},
|
|
{kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, !isNCDHW, wFormat}, {});
|
|
if (status != sd::Status::OK) return status;
|
|
|
|
// -----prepare permutation arrays and axes for dot product ----- //
|
|
std::vector<LongType> inputAxesForDot;
|
|
|
|
if (!isNCDHW) {
|
|
std::vector<LongType> grad0Permute = {0,4,1,2,3};
|
|
gradO = gradO->permute(grad0Permute, false, false); // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW]
|
|
inputAxesForDot = {0, 1, 2, 3}; // bS, iD, iH, iW
|
|
} else
|
|
inputAxesForDot = {0, 2, 3, 4}; // bS, iD, iH, iW
|
|
|
|
std::vector<LongType> gradWAxes; // empty for wFormat = 1
|
|
if (0 == wFormat)
|
|
gradWAxes = {4, 3, 0, 1, 2};
|
|
else if (2 == wFormat)
|
|
gradWAxes = {0, 4, 1, 2, 3};
|
|
|
|
// ----- calculation of gradW ----- //
|
|
auto columns = NDArrayFactory::create(input->ordering(), {bS, oC, kD, kH, kW, iD, iH, iW}, input->dataType(),
|
|
block.launchContext());
|
|
ConvolutionUtils::vol2col(block, gradO, columns, sD, sH, sW, pD, pH, pW, dD, dH,
|
|
dW); // [bS, oC, oD, oH, oW] is deconvoluted to [bS, oC, kD, kH, kW, iD, iH, iW]
|
|
|
|
std::vector<LongType> mulDims = {0,5,6,7};
|
|
MmulHelper::tensorDot(input, columns, gradW, inputAxesForDot, mulDims,
|
|
gradWAxes); // [bS, iC, iD, iH, iW]/[bS, iD, iH, iW, iC] x [bS, oC, kD, kH, kW, iD, iH, iW] =
|
|
// [iC, oC, kD, kH, kW]
|
|
|
|
// ----- calculation of gradB ----- //
|
|
if (gradB) {
|
|
std::vector<LongType> biasShape = {gradB->lengthOf()};
|
|
if (gradB->rankOf() == 2) gradB = gradB->reshape(gradB->ordering(), biasShape, false);
|
|
std::vector<sd::LongType> dims = {{0, 2, 3, 4}};
|
|
gradO->reduceAlongDimension(reduce::Sum, gradB, &dims); // sum over bS, oD, oH, oW
|
|
if (gradB != OUTPUT_VARIABLE(2)) delete gradB;
|
|
}
|
|
|
|
if (!isNCDHW) delete gradO;
|
|
|
|
delete columns;
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
DECLARE_TYPES(deconv3d_bp) {
|
|
getOpDescriptor()
|
|
->setAllowedInputTypes(0, sd::DataType::ANY)
|
|
->setAllowedInputTypes(1, {ALL_FLOATS})
|
|
->setAllowedInputTypes(2, {ALL_FLOATS})
|
|
->setAllowedInputTypes(3, {ALL_FLOATS})
|
|
->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
DECLARE_SHAPE_FN(deconv3d_bp) {
|
|
auto inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
auto weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
|
|
auto biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
|
|
auto gradOShapeInfo =
|
|
block.width() > 3
|
|
? inputShape->at(3)
|
|
: inputShape->at(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
|
|
|
|
const int rank = 5;
|
|
REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0,
|
|
"CUSTOM DECONV3D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank,
|
|
shape::rank(inputShapeInfo));
|
|
REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0,
|
|
"CUSTOM DECONV3D_BP OP: rank of weights array must be equal to %i , but got %i instead !", rank,
|
|
shape::rank(weightsShapeInfo));
|
|
REQUIRE_TRUE(
|
|
shape::rank(gradOShapeInfo) == rank, 0,
|
|
"CUSTOM DECONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !",
|
|
rank, shape::rank(gradOShapeInfo));
|
|
|
|
LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0))); // filter(kernel) depth
|
|
LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1))); // filter(kernel) height
|
|
LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(2))); // filter(kernel) width
|
|
LongType sD = INT_ARG(3); // strides depth
|
|
LongType sH = INT_ARG(4); // strides height
|
|
LongType sW = INT_ARG(5); // strides width
|
|
LongType pD = INT_ARG(6); // paddings depth
|
|
LongType pH = INT_ARG(7); // paddings height
|
|
LongType pW = INT_ARG(8); // paddings width
|
|
LongType dD = INT_ARG(9); // dilations depth
|
|
LongType dH = INT_ARG(10); // dilations height
|
|
LongType dW = INT_ARG(11); // dilations width
|
|
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
|
|
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
|
|
int wFormat = block.getIArguments()->size() > 14
|
|
? INT_ARG(14)
|
|
: 0; // 0 - [kD, kH, kW, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC]
|
|
|
|
LongType indIOioC, indIiD, indWoC(0 == wFormat ? 3 : (1 == wFormat ? 1 : 4));
|
|
if (!isNCDHW) {
|
|
indIOioC = 4;
|
|
indIiD = 1;
|
|
} else {
|
|
indIOioC = 1;
|
|
indIiD = 2;
|
|
}
|
|
|
|
const LongType bS = inputShapeInfo[1]; // batch size
|
|
const LongType iD = inputShapeInfo[indIiD + 1]; // input depth
|
|
const LongType iH = inputShapeInfo[indIiD + 2]; // input height
|
|
const LongType iW = inputShapeInfo[indIiD + 3]; // input width
|
|
const LongType iC = inputShapeInfo[indIOioC + 1]; // input channels
|
|
const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
|
|
|
|
LongType trueoD, trueoH, trueoW; // true output depth, height, width
|
|
ConvolutionUtils::calcOutSizeDeconv3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH,
|
|
iW, isSameMode);
|
|
|
|
std::vector<sd::LongType> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx(
|
|
{bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIiD, indIiD + 1, indIiD + 2});
|
|
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(
|
|
shape::shapeEquals(5, expectedGradOShape.data(), shape::rank(gradOShapeInfo), shape::shapeOf(gradOShapeInfo)), 0,
|
|
"CUSTOM DECONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead "
|
|
"!",
|
|
ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
|
|
REQUIRE_TRUE(shape::shapeEquals(5, expectedWeightsShape.data(), shape::rank(weightsShapeInfo),
|
|
shape::shapeOf(weightsShapeInfo)),
|
|
0, "CUSTOM DECONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
|
|
ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
|
|
if (biasShapeInfo)
|
|
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0,
|
|
"CUSTOM DECONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, "
|
|
"%i instead !",
|
|
oC, biasShapeInfo[0], shape::length(biasShapeInfo));
|
|
|
|
auto gradIShapeInfo =
|
|
ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
auto gradWShapeInfo =
|
|
ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
|
|
auto shapes = SHAPELIST(CONSTANT(gradIShapeInfo), CONSTANT(gradWShapeInfo));
|
|
|
|
if (biasShapeInfo != nullptr) {
|
|
auto gradBShapeInfo =
|
|
ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
shapes->push_back(CONSTANT(gradBShapeInfo));
|
|
}
|
|
|
|
return shapes;
|
|
}
|
|
|
|
} // namespace ops
|
|
} // namespace sd
|
|
|
|
#endif
|