libnd4j/include/ops/declarable/generic/nn/convo/conv2d.cpp
/* ******************************************************************************
*
*
* 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, created on 06.03.2018
//
#ifndef LIBND4J_CONVO_OPS_H
#define LIBND4J_CONVO_OPS_H
#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_conv2d)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/OpRegistrator.h>
#include <ops/declarable/helpers/convolutions.h>
#include <system/op_boilerplate.h>
#include <memory>
namespace sd {
namespace ops {
CUSTOM_OP_IMPL(conv2d, 2, 1, false, 0, 9) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
auto output = OUTPUT_NULLIFIED(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
LongType sH = INT_ARG(2); // strides height
LongType sW = INT_ARG(3); // strides width
LongType pH = INT_ARG(4); // paddings height
LongType pW = INT_ARG(5); // paddings width
LongType dH = INT_ARG(6); // dilations height
LongType dW = INT_ARG(7); // dilations width
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
int wFormat = block.getIArguments()->size() > 10
? INT_ARG(10)
: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) height
LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1)); // filter(kernel) width
LongType bS, iC, iH, iW, oC, oH,
oW; // batch size, input channels, input height/width, output channels, output height/width;
LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC,
indIiH, indWiC, indWoC, indWkH, indOoH);
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
"CUSTOM CONV2D 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 CONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !",
oC, bias->rankOf(), bias->lengthOf());
ConvolutionUtils::conv2d(block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW,
wFormat);
return sd::Status::OK;
}
DECLARE_SHAPE_FN(conv2d) {
auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
// output [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
LongType sH = INT_ARG(2); // strides height
LongType sW = INT_ARG(3); // strides width
LongType pH = INT_ARG(4); // paddings height
LongType pW = INT_ARG(5); // paddings width
LongType dH = INT_ARG(6); // dilations height
LongType dW = INT_ARG(7); // dilations width
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
int wFormat = block.getIArguments()->size() > 10
? INT_ARG(10)
: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0))); // filter(kernel) height
LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1))); // filter(kernel) width
const int rank = 4; // 4
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0,
"CUSTOM CONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank,
inputShapeInfo[0]);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0,
"CUSTOM CONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank,
weightsShapeInfo[0]);
LongType indIOioC, indIiH, indWoC(0 == wFormat ? 3 : 0);
if (!isNCHW) {
indIOioC = 3;
indIiH = 1;
} else {
indIOioC = 1;
indIiH = 2;
}
const LongType bS = inputShapeInfo[1]; // batch size
const LongType iH = inputShapeInfo[indIiH + 1]; // input height
const LongType iW = inputShapeInfo[indIiH + 2]; // 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, kH, kW, iC, oC);
REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0,
"CUSTOM CONV2D 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 CONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !",
oC, biasShapeInfo[0], shape::length(biasShapeInfo));
sd::LongType* outputShapeInfo = nullptr;
ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), sd::LongType);
LongType oH, oW; // output height, width
ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
outputShapeInfo[0] = rank;
outputShapeInfo[1] = bS;
if (isNCHW) {
outputShapeInfo[2] = oC;
outputShapeInfo[3] = oH;
outputShapeInfo[4] = oW;
} else {
outputShapeInfo[2] = oH;
outputShapeInfo[3] = oW;
outputShapeInfo[4] = oC;
}
ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(inputShapeInfo));
return SHAPELIST(CONSTANT(outputShapeInfo));
}
DECLARE_TYPES(conv2d) {
getOpDescriptor()
->setAllowedInputTypes(0, sd::DataType::ANY)
->setAllowedInputTypes(1, {ALL_FLOATS})
->setAllowedInputTypes(2, {ALL_FLOATS})
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_TYPES(conv2d_bp) {
getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(conv2d_bp, 3, 2, false, 0, 9) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
auto gradO = block.width() > 3
? INPUT_VARIABLE(3)
: INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
auto gradI = OUTPUT_NULLIFIED(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
auto gradW = OUTPUT_NULLIFIED(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
auto gradB = block.width() > 3 ? OUTPUT_NULLIFIED(2) : nullptr; // [oC]
LongType kH = INT_ARG(0); // filter(kernel) height
LongType kW = INT_ARG(1); // filter(kernel) width
LongType sH = INT_ARG(2); // strides height
LongType sW = INT_ARG(3); // strides width
LongType pH = INT_ARG(4); // paddings height
LongType pW = INT_ARG(5); // paddings width
LongType dH = INT_ARG(6); // dilations height
LongType dW = INT_ARG(7); // dilations width
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
int wFormat = block.getIArguments()->size() > 10
? INT_ARG(10)
: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
REQUIRE_TRUE(input->rankOf() == 4, 0,
"CUSTOM CONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 4, 0,
"CUSTOM CONV2D_BP OP: rank of weights array must be equal to 4, but got %i instead !",
weights->rankOf());
REQUIRE_TRUE(
gradO->rankOf() == 4, 0,
"CUSTOM CONV2D_BP OP: rank of output's gradients (next epsilon) array must be equal to 4, but got %i instead !",
gradO->rankOf());
LongType bS, iC, iH, iW, oC, oH,
oW; // batch size, input channels, input height/width, output channels, output height/width;
LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
indIiH, indWiC, indWoC, indWkH, indOoH);
LongType trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::vector<sd::LongType> expectedGradOShape =
ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
REQUIRE_TRUE(
gradO->isSameShape(expectedGradOShape), 0,
"CUSTOM CONV2D_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 CONV2D_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 CONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, "
"%i instead !",
oC, bias->rankOf(), bias->lengthOf());
ConvolutionUtils::conv2dBP(block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW,
isSameMode, isNCHW, wFormat);
return sd::Status::OK;
}
DECLARE_SHAPE_FN(conv2d_bp) {
auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
auto biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
auto gradOShapeInfo = block.width() > 3
? inputShape->at(3)
: inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
const LongType rank = 4;
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0,
"CUSTOM CONV2D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank,
inputShapeInfo[0]);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0,
"CUSTOM CONV2D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank,
weightsShapeInfo[0]);
REQUIRE_TRUE(
gradOShapeInfo[0] == rank, 0,
"CUSTOM CONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !",
rank, gradOShapeInfo[0]);
const LongType kH = INT_ARG(0); // filter(kernel) height
const LongType kW = INT_ARG(1); // filter(kernel) width
const LongType sH = INT_ARG(2); // strides height
const LongType sW = INT_ARG(3); // strides width
const LongType pH = INT_ARG(4); // paddings height
const LongType pW = INT_ARG(5); // paddings width
const LongType dH = INT_ARG(6); // dilations height
const LongType dW = INT_ARG(7); // dilations width
const int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
const int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
const int wFormat = block.getIArguments()->size() > 10
? INT_ARG(10)
: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
LongType indIOioC, indIiH, indOoH, indWoC(0 == wFormat ? 3 : 0);
if (!isNCHW) {
indIOioC = 3;
indIiH = 1;
indOoH = 1;
} else {
indIOioC = 1;
indIiH = 2;
indOoH = 2;
}
const LongType bS = inputShapeInfo[1]; // batch size
const LongType iH = inputShapeInfo[indIiH + 1]; // input height
const LongType iW = inputShapeInfo[indIiH + 2]; // input width
const LongType iC = inputShapeInfo[indIOioC + 1]; // input channels
const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
LongType trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::vector<sd::LongType> expectedGradOShape =
ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
REQUIRE_TRUE(
ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0,
"CUSTOM CONV2D_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(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0,
"CUSTOM CONV2D_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 CONV2D_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());
if (biasShapeInfo) {
auto gradBshapeInfo =
ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
}
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo));
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(conv2d_input_bp, 3, 1, false, 0, 9) {
auto gradIShape = INPUT_VARIABLE(0); // [4]
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
auto gradO = INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
auto gradI = OUTPUT_NULLIFIED(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
LongType kH = INT_ARG(0); // filter(kernel) height
LongType kW = INT_ARG(1); // filter(kernel) width
LongType sH = INT_ARG(2); // strides height
LongType sW = INT_ARG(3); // strides width
LongType pH = INT_ARG(4); // paddings height
LongType pW = INT_ARG(5); // paddings width
LongType dH = INT_ARG(6); // dilations height
LongType dW = INT_ARG(7); // dilations width
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
int wFormat = block.getIArguments()->size() > 10
? INT_ARG(10)
: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
const int rank = gradO->rankOf();
REQUIRE_TRUE(weights->rankOf() == rank, 0,
"CUSTOM CONV2D_INPUT_BP OP: rank of weights array must be equal to 4, but got %i instead !",
weights->rankOf());
REQUIRE_TRUE(gradIShape->rankOf() == 1, 0,
"CUSTOM CONV2D_INPUT_BP OP: rank of array with output shape must be equal to 1, but got %i instead !",
gradIShape->rankOf());
REQUIRE_TRUE(gradIShape->lengthOf() == rank, 0,
"CUSTOM CONV2D_INPUT_BP OP: length of array with output shape must be equal to 4, but got %i instead !",
gradIShape->lengthOf());
// create empty conv2d input array
std::vector<sd::LongType> gradIShapeAsVector(rank);
for (int i = 0; i < rank; ++i) gradIShapeAsVector[i] = gradIShape->e<sd::LongType>(i);
NDArray input(gradO->ordering(), gradIShapeAsVector, gradO->dataType(), block.launchContext());
LongType bS, iC, iH, iW, oC, oH,
oW; // batch size, input channels, input height/width, output channels, output height/width;
LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
indIiH, indWiC, indWoC, indWkH, indOoH);
LongType trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::vector<sd::LongType> expectedGradOShape =
ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
"CUSTOM CONV2D_INPUT_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 CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
ConvolutionUtils::conv2dBP(block, &input, weights, nullptr, gradO, gradI, nullptr, nullptr, kH, kW, sH, sW, pH, pW,
dH, dW, isSameMode, isNCHW, wFormat);
return sd::Status::OK;
}
DECLARE_TYPES(conv2d_input_bp) {
getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(conv2d_input_bp) {
auto gradIShapeShapeInfo = inputShape->at(0); // [4]
auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
auto gradOShapeInfo = inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
const LongType rank = 4;
REQUIRE_TRUE(gradIShapeShapeInfo[0] == 1, 0,
"CUSTOM CONV2D_INPUT_BP OP: rank of array with output shape must be equal to %i, but got %i instead !",
1, gradIShapeShapeInfo[0]);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0,
"CUSTOM CONV2D_INPUT_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank,
weightsShapeInfo[0]);
REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0,
"CUSTOM CONV2D_INPUT_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got "
"%i instead !",
rank, gradOShapeInfo[0]);
const LongType kH = INT_ARG(0); // filter(kernel) height
const LongType kW = INT_ARG(1); // filter(kernel) width
const LongType sH = INT_ARG(2); // strides height
const LongType sW = INT_ARG(3); // strides width
const LongType pH = INT_ARG(4); // paddings height
const LongType pW = INT_ARG(5); // paddings width
const LongType dH = INT_ARG(6); // dilations height
const LongType dW = INT_ARG(7); // dilations width
const int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
const int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
const int wFormat = block.getIArguments()->size() > 10
? INT_ARG(10)
: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
int indIOioC, indIiH, indWoC(0 == wFormat ? 3 : 0), indOoH;
if (!isNCHW) {
indIOioC = 3;
indIiH = 1;
indOoH = 1;
} else {
indIOioC = 1;
indIiH = 2;
indOoH = 2;
}
std::vector<sd::LongType> gradIShape = INPUT_VARIABLE(0)->template asVectorT<sd::LongType>();
const LongType bS = gradIShape[0]; // batch size
const LongType iH = gradIShape[indIiH]; // input height
const LongType iW = gradIShape[indIiH + 1]; // input width
const LongType iC = gradIShape[indIOioC]; // input channels
const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
LongType trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::vector<sd::LongType> expectedGradOShape =
ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
REQUIRE_TRUE(ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0,
"CUSTOM CONV2D_INPUT_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(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0,
"CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
sd::LongType* gradIshapeInfo(nullptr);
ALLOCATE(gradIshapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), sd::LongType);
gradIshapeInfo[0] = rank;
gradIshapeInfo[1] = bS;
if (isNCHW) {
gradIshapeInfo[2] = iC;
gradIshapeInfo[3] = iH;
gradIshapeInfo[4] = iW;
} else {
gradIshapeInfo[2] = iH;
gradIshapeInfo[3] = iW;
gradIshapeInfo[4] = iC;
}
ShapeUtils::updateStridesAndType(gradIshapeInfo, gradOShapeInfo, shape::order(gradOShapeInfo));
return SHAPELIST(CONSTANT(gradIshapeInfo));
}
} // namespace ops
} // namespace sd
#endif
#endif // LIBND4J_CONVO_OPS_H