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Shyrma casual conv1d (#90) 

* - add causal mode of padding to convolutions

Signed-off-by: Yurii <iuriish@yahoo.com>

* - add additional tests for causal conv1d

Signed-off-by: Yurii <iuriish@yahoo.com>

* - add causal mode for cuda conv kernels

Signed-off-by: Yurii <iuriish@yahoo.com>

* Java side of Conv1D changes

Signed-off-by: raver119 <raver119@gmail.com>

* Add Conv1DDerivative op

Signed-off-by: Alex Black <blacka101@gmail.com>

* Causal Conv1D gradient checks

Signed-off-by: Alex Black <blacka101@gmail.com>

* Tweaks

Signed-off-by: Alex Black <blacka101@gmail.com>

* - add causal padding mode to conv2d_bp

Signed-off-by: Yurii <iuriish@yahoo.com>

* More thorough causal conv1d tests

Signed-off-by: Alex Black <blacka101@gmail.com>
master
Yurii Shyrma 2019-11-29 13:14:30 +02:00 committed by raver119
parent 5e07998e59
commit d19eeaec52
15 changed files with 695 additions and 219 deletions
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44
libnd4j/include/ops/declarable/generic/nn/convo/conv1d.cpp
View File

@ -31,7 +31,7 @@ namespace ops {
CUSTOM_OP_IMPL(conv1d, 2, 1, false, 0, 4) { CUSTOM_OP_IMPL(conv1d, 2, 1, false, 0, 5) {
auto input = INPUT_VARIABLE(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW) auto input = INPUT_VARIABLE(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
auto weights = INPUT_VARIABLE(1); // [kW, iC, oC] always auto weights = INPUT_VARIABLE(1); // [kW, iC, oC] always
@ -42,8 +42,9 @@ CUSTOM_OP_IMPL(conv1d, 2, 1, false, 0, 4) {
int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) width int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) width
int sW = INT_ARG(1); // strides width int sW = INT_ARG(1); // strides width
int pW = INT_ARG(2); // paddings width int pW = INT_ARG(2); // paddings width
int isSameMode = INT_ARG(3); // 0-VALID, 1-SAME int dW = INT_ARG(3); // dilations width
int isNCW = block.getIArguments()->size() > 4 ? !INT_ARG(4) : 1; // INT_ARG(4): 0-NCW, 1-NWC int paddingMode = INT_ARG(4); // 0-VALID, 1-SAME, 2-CAUSAL
int isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 0-NCW, 1-NWC
const int rank = 3; const int rank = 3;
REQUIRE_TRUE(input->rankOf() == rank, 0, "CUSTOM CONV1D OP: rank of input array must be equal to %i, but got %i instead !", rank, input->rankOf()); REQUIRE_TRUE(input->rankOf() == rank, 0, "CUSTOM CONV1D OP: rank of input array must be equal to %i, but got %i instead !", rank, input->rankOf());
@ -81,7 +82,12 @@ CUSTOM_OP_IMPL(conv1d, 2, 1, false, 0, 4) {
auto outputReshaped = output ->reshape(output->ordering(), reshapeForOutput); auto outputReshaped = output ->reshape(output->ordering(), reshapeForOutput);
auto weightsReshaped = weights->reshape(weights->ordering(), {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}); // [kW, iC, oC] -> [1, kW, iC, oC] auto weightsReshaped = weights->reshape(weights->ordering(), {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}); // [kW, iC, oC] -> [1, kW, iC, oC]
ConvolutionUtils::conv2d(block, &inputReshaped, &weightsReshaped, bias, &outputReshaped, 1,kW, 1,sW, 0,pW, 1,1, isSameMode, isNCW); nd4j::ops::conv2d conv2d;
const Nd4jStatus status = conv2d.execute({&inputReshaped, &weightsReshaped, bias}, {&outputReshaped}, {}, {1,kW, 1,sW, 0,pW, 1,dW, paddingMode, !isNCW}, {});
if (status != ND4J_STATUS_OK)
return status;
// ConvolutionUtils::conv2d(block, &inputReshaped, &weightsReshaped, bias, &outputReshaped, 1,kW, 1,sW, 0,pW, 1,dW, paddingMode, isNCW);
return Status::OK(); return Status::OK();
} }
@ -96,8 +102,9 @@ DECLARE_SHAPE_FN(conv1d) {
int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0)); // filter(kernel) width int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0)); // filter(kernel) width
int sW = INT_ARG(1); // strides width int sW = INT_ARG(1); // strides width
int pW = INT_ARG(2); // paddings width int pW = INT_ARG(2); // paddings width
int isSameMode = INT_ARG(3); // 0-VALID, 1-SAME int dW = INT_ARG(3); // dilations width
int isNCW = block.getIArguments()->size() > 4 ? !INT_ARG(4) : 1; // INT_ARG(4): 1-NWC, 0-NCW int paddingMode = INT_ARG(4); // 0-VALID, 1-SAME
int isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW
int indIOioC, indIiW, indWoC(2); int indIOioC, indIiW, indWoC(2);
if(!isNCW) { if(!isNCW) {
@ -122,7 +129,7 @@ DECLARE_SHAPE_FN(conv1d) {
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV1D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV1D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
int oH, oW; // output height, width int oH, oW; // output height, width
ConvolutionUtils::calcOutSizePool2D(oH,oW, 1,kW, 1,sW, 0,pW, 1,1, 1,iW, isSameMode); ConvolutionUtils::calcOutSizePool2D(oH,oW, 1,kW, 1,sW, 0,pW, 1,dW, 1,iW, paddingMode);
Nd4jLong* outputShapeInfo = nullptr; Nd4jLong* outputShapeInfo = nullptr;
ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong);
@ -153,7 +160,7 @@ DECLARE_TYPES(conv1d) {
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 4) { CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 5) {
auto input = INPUT_VARIABLE(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW) auto input = INPUT_VARIABLE(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
auto weights = INPUT_VARIABLE(1); // [kW, iC, oC] always auto weights = INPUT_VARIABLE(1); // [kW, iC, oC] always
@ -167,8 +174,9 @@ CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 4) {
int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) width int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) width
int sW = INT_ARG(1); // strides width int sW = INT_ARG(1); // strides width
int pW = INT_ARG(2); // paddings width int pW = INT_ARG(2); // paddings width
int isSameMode = INT_ARG(3); // 0-VALID, 1-SAME int dW = INT_ARG(3); // dilations width
int isNCW = block.getIArguments()->size() > 4 ? !INT_ARG(4) : 1; // INT_ARG(4): 1-NWC, 0-NCW int paddingMode = INT_ARG(4); // 0-VALID, 1-SAME, 2-CAUSAL
int isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW
const int rank = 3; const int rank = 3;
REQUIRE_TRUE(input->rankOf() == rank, 0, "CUSTOM CONV1D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, input->rankOf()); REQUIRE_TRUE(input->rankOf() == rank, 0, "CUSTOM CONV1D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, input->rankOf());
@ -188,7 +196,7 @@ CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 4) {
const int oC = weights->sizeAt(indWoC); // output channels const int oC = weights->sizeAt(indWoC); // output channels
int trueoH, trueoW; // true output height, width int trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH,trueoW, 1,kW, 1,sW, 0,pW, 1,1, 1,iW, isSameMode); ConvolutionUtils::calcOutSizePool2D(trueoH,trueoW, 1,kW, 1,sW, 0,pW, 1,dW, 1,iW, paddingMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW, 0,indIOioC,indIiW})); std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW, 0,indIOioC,indIiW}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kW, iC, oC}); std::string expectedWeightsShape = ShapeUtils::shapeAsString({kW, iC, oC});
@ -213,7 +221,12 @@ CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 4) {
auto weightsReshaped = weights->reshape(weights->ordering(),{1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}); // [kW, iC, oC] -> [1, kW, iC, oC] auto weightsReshaped = weights->reshape(weights->ordering(),{1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}); // [kW, iC, oC] -> [1, kW, iC, oC]
auto gradWReshaped = gradW ->reshape(gradW->ordering(), {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}); // [kW, iC, oC] -> [1, kW, iC, oC] auto gradWReshaped = gradW ->reshape(gradW->ordering(), {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}); // [kW, iC, oC] -> [1, kW, iC, oC]
ConvolutionUtils::conv2dBP(block, &inputReshaped, &weightsReshaped, bias, &gradOReshaped, &gradIReshaped, &gradWReshaped, gradB, 1,kW, 1,sW, 0,pW, 1,1, isSameMode, isNCW); nd4j::ops::conv2d_bp conv2dBP;
const Nd4jStatus status = conv2dBP.execute({&inputReshaped, &weightsReshaped, bias, &gradOReshaped}, {&gradIReshaped, &gradWReshaped, gradB}, {}, {1,kW, 1,sW, 0,pW, 1,dW, paddingMode, !isNCW}, {});
if (status != ND4J_STATUS_OK)
return status;
// ConvolutionUtils::conv2dBP(block, &inputReshaped, &weightsReshaped, bias, &gradOReshaped, &gradIReshaped, &gradWReshaped, gradB, 1,kW, 1,sW, 0,pW, 1,dW, paddingMode, isNCW);
return Status::OK(); return Status::OK();
} }
@ -234,8 +247,9 @@ DECLARE_SHAPE_FN(conv1d_bp) {
int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) width int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) width
int sW = INT_ARG(1); // strides width int sW = INT_ARG(1); // strides width
int pW = INT_ARG(2); // paddings width int pW = INT_ARG(2); // paddings width
int isSameMode = INT_ARG(3); // 0-VALID, 1-SAME int dW = INT_ARG(3); // dilations width
int isNCW = block.getIArguments()->size() > 4 ? !INT_ARG(4) : 1; // INT_ARG(4): 1-NWC, 0-NCW int paddingMode = INT_ARG(4); // 0-VALID, 1-SAME
int isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW
int indIOioC, indIiW, indWoC(2); int indIOioC, indIiW, indWoC(2);
if(!isNCW) { if(!isNCW) {
@ -251,7 +265,7 @@ DECLARE_SHAPE_FN(conv1d_bp) {
const int oC = weightsShapeInfo[indWoC+1]; // output channels const int oC = weightsShapeInfo[indWoC+1]; // output channels
int trueoH, trueoW; // true output height, width int trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH,trueoW, 1,kW, 1,sW, 0,pW, 1,1, 1,iW, isSameMode); ConvolutionUtils::calcOutSizePool2D(trueoH,trueoW, 1,kW, 1,sW, 0,pW, 1,dW, 1,iW, paddingMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW, 0,indIOioC,indIiW})); std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW, 0,indIOioC,indIiW}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kW, iC, oC}); std::string expectedWeightsShape = ShapeUtils::shapeAsString({kW, iC, oC});

24
libnd4j/include/ops/declarable/generic/nn/convo/conv3d.cpp
View File

@ -51,20 +51,20 @@ CUSTOM_OP_IMPL(conv3dnew, 2, 1, false, 0, 13) {
int dD = INT_ARG(9); // dilations depth int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID int paddingMode = 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 isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width; int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
REQUIRE_TRUE(paddingMode < 2, 0, "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC}); std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
if (bias) if (bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
if(isSameMode) // SAME ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW, paddingMode);
ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
nd4j_debug("MKL-DNN is not used for conv3dnew!\n", 0); nd4j_debug("MKL-DNN is not used for conv3dnew!\n", 0);
@ -116,10 +116,11 @@ DECLARE_SHAPE_FN(conv3dnew) {
int dD = INT_ARG(9); // dilations depth int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID; int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID;
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
const int rank = 5; const int rank = 5;
REQUIRE_TRUE(paddingMode < 2, 0, "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV3D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo); REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV3D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV3D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo); REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV3D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo);
@ -144,7 +145,7 @@ DECLARE_SHAPE_FN(conv3dnew) {
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
int oD, oH, oW; // output depth, height, width int oD, oH, oW; // output depth, height, width
ConvolutionUtils::calcOutSizePool3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode); ConvolutionUtils::calcOutSizePool3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, paddingMode);
Nd4jLong* outputShapeInfo = nullptr; Nd4jLong* outputShapeInfo = nullptr;
ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo), Nd4jLong); ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo), Nd4jLong);
@ -197,7 +198,7 @@ CUSTOM_OP_IMPL(conv3dnew_bp, 3, 2, false, 0, 13) {
int dD = INT_ARG(9); // dilations depth int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID
int isNDHWC = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW int isNDHWC = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width; int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
@ -205,8 +206,9 @@ CUSTOM_OP_IMPL(conv3dnew_bp, 3, 2, false, 0, 13) {
ConvolutionUtils::getSizesAndIndexesConv3d(isNDHWC, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); ConvolutionUtils::getSizesAndIndexesConv3d(isNDHWC, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
int trueoD, trueoH, trueoW; // true output depth/height/width int trueoD, trueoH, trueoW; // true output depth/height/width
ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode); ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, paddingMode);
REQUIRE_TRUE(paddingMode < 2, 0, "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2})); std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC}); std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
@ -214,8 +216,7 @@ CUSTOM_OP_IMPL(conv3dnew_bp, 3, 2, false, 0, 13) {
if(bias) if(bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV3D_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) // SAME ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW, paddingMode);
ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
nd4j_debug("MKL-DNN is not used for conv3dnew_bp!\n", 0); nd4j_debug("MKL-DNN is not used for conv3dnew_bp!\n", 0);
@ -285,10 +286,11 @@ DECLARE_SHAPE_FN(conv3dnew_bp) {
int dD = INT_ARG(9); // dilations depth int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID
int isNDHWC = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW int isNDHWC = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
const int rank = 5; const int rank = 5;
REQUIRE_TRUE(paddingMode < 2, 0, "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo); REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo); REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo);
REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo); REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo);
@ -309,7 +311,7 @@ DECLARE_SHAPE_FN(conv3dnew_bp) {
int oC = weightsShapeInfo[indWoC+1]; // output channels int oC = weightsShapeInfo[indWoC+1]; // output channels
int trueoD, trueoH, trueoW; // true output depth/height/width int trueoD, trueoH, trueoW; // true output depth/height/width
ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode); ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, paddingMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIiD,indIiD+1,indIiD+2})); std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIiD,indIiD+1,indIiD+2}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC}); std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});

32
libnd4j/include/ops/declarable/headers/convo.h
View File

@ -28,28 +28,28 @@ namespace nd4j {
/** /**
* 1D temporal convolution implementation * 1D temporal convolution implementation
* Expected input: * Expected input:
* x: 3D array * x: 3D array
* weight: 3D Array * weight: 3D Array
* bias: optional vector * bias: optional vector
* *
* Int args: * Int args:
* 0: kernel * 0: kernel
* 1: stride * 1: stride
* 2: padding * 2: padding
*/ */
#if NOT_EXCLUDED(OP_conv1d) #if NOT_EXCLUDED(OP_conv1d)
DECLARE_CUSTOM_OP(conv1d, 2, 1, false, 0, 4); DECLARE_CUSTOM_OP(conv1d, 2, 1, false, 0, 5);
DECLARE_CUSTOM_OP(conv1d_bp, 3, 2, false, 0, 4); DECLARE_CUSTOM_OP(conv1d_bp, 3, 2, false, 0, 5);
#endif #endif
/** /**
* 2D convolution implementation * 2D convolution implementation
* Expected input: * Expected input:
* x: 4D array * x: 4D array
* weight: 4D Array * weight: 4D Array
* bias: optional vector, length of outputChannels * bias: optional vector, length of outputChannels
* *
* IntArgs: * IntArgs:
* 0: kernel height * 0: kernel height
* 1: kernel width * 1: kernel width
@ -83,7 +83,7 @@ namespace nd4j {
/** /**
* 2D deconvolution implementation * 2D deconvolution implementation
* *
* IntArgs: * IntArgs:
* 0: kernel height * 0: kernel height
* 1: kernel width * 1: kernel width
@ -102,7 +102,7 @@ namespace nd4j {
/** /**
* 3D deconvolution implementation * 3D deconvolution implementation
* *
* IntArgs: * IntArgs:
* 0: filter(kernel) depth * 0: filter(kernel) depth
* 1: filter(kernel) height * 1: filter(kernel) height
@ -190,7 +190,7 @@ namespace nd4j {
/** /**
* This op implements im2col algorithm, widely used in convolution neural networks * This op implements im2col algorithm, widely used in convolution neural networks
* Input: 4D input expected * Input: 4D input expected
* *
* Int args: * Int args:
* 0: kernel height * 0: kernel height
* 1: kernel width * 1: kernel width
@ -210,7 +210,7 @@ namespace nd4j {
/** /**
* This op implements col2im algorithm, widely used in convolution neural networks * This op implements col2im algorithm, widely used in convolution neural networks
* Input: 6D input expected (like output of im2col op) * Input: 6D input expected (like output of im2col op)
* *
* Int args: * Int args:
* 0: stride height * 0: stride height
* 1: stride width * 1: stride width
@ -227,7 +227,7 @@ namespace nd4j {
/** /**
* Expected input: 4D array * Expected input: 4D array
* *
* IntArgs: * IntArgs:
* 0: scale factor for rows (height) * 0: scale factor for rows (height)
* 1: scale factor for columns (width) * 1: scale factor for columns (width)
@ -240,7 +240,7 @@ namespace nd4j {
/** /**
* Expected input: 4D array * Expected input: 4D array
* *
* IntArgs: * IntArgs:
* 0: scale factor for depth * 0: scale factor for depth
* 1: scale factor for rows (height) * 1: scale factor for rows (height)
@ -249,13 +249,13 @@ namespace nd4j {
*/ */
#if NOT_EXCLUDED(OP_upsampling3d) #if NOT_EXCLUDED(OP_upsampling3d)
DECLARE_CUSTOM_OP(upsampling3d, 1, 1, false, 0, 3); DECLARE_CUSTOM_OP(upsampling3d, 1, 1, false, 0, 3);
DECLARE_CUSTOM_OP(upsampling3d_bp, 2, 1, false, 0, 0); DECLARE_CUSTOM_OP(upsampling3d_bp, 2, 1, false, 0, 0);
#endif #endif
/** /**
* This op produces binary matrix wrt to target dimension. * This op produces binary matrix wrt to target dimension.
* Maximum value within each TAD is replaced with 1, other values are set to true. * Maximum value within each TAD is replaced with 1, other values are set to true.
* *
* Int args: * Int args:
* 0: axis * 0: axis
*/ */
@ -265,7 +265,7 @@ namespace nd4j {
/** /**
* Dilation2D op * Dilation2D op
* *
* Int args: * Int args:
* 0: isSameMode * 0: isSameMode
*/ */
@ -295,7 +295,7 @@ namespace nd4j {
* Output: * Output:
* 0 - 4D tensor as input * 0 - 4D tensor as input
* 1 - 4D tensor with max value indexes * 1 - 4D tensor with max value indexes
* *
* Int params: * Int params:
* 9 int with 2x4 vectors and 1 bool value * 9 int with 2x4 vectors and 1 bool value
*/ */

151
libnd4j/include/ops/declarable/helpers/convolutions.h
View File

@ -37,79 +37,93 @@ namespace nd4j {
class ConvolutionUtils { class ConvolutionUtils {
public: public:
static inline void calcOutSizePool2D(int& oH, int& oW, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int iH, const int iW, const int isSameMode) { static inline void calcOutSizePool2D(int& oH, int& oW, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int iH, const int iW, const int paddingMode) {
if(isSameMode > 0) {
oH = (int) math::nd4j_ceil<double, double>(iH * 1. / sH); if(paddingMode == 0) { // valid
oW = (int) math::nd4j_ceil<double, double>(iW * 1. / sW);
}
else {
oH = (iH - (kH + (kH-1)*(dH-1)) + 2*pH)/sH + 1; oH = (iH - (kH + (kH-1)*(dH-1)) + 2*pH)/sH + 1;
oW = (iW - (kW + (kW-1)*(dW-1)) + 2*pW)/sW + 1; oW = (iW - (kW + (kW-1)*(dW-1)) + 2*pW)/sW + 1;
} }
else if (paddingMode == 1) { // same
oH = (int) math::nd4j_ceil<double, double>(iH * 1. / sH);
oW = (int) math::nd4j_ceil<double, double>(iW * 1. / sW);
}
else { // causal
oH = (iH - 1) / sH + 1; // 2*pH = (kH-1)*dH
oW = (iW - 1) / sW + 1;
}
} }
static inline void calcOutSizePool3D(int& oD, int& oH, int& oW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int iD, const int iH, const int iW, const int isSameMode) { static inline void calcOutSizePool3D(int& oD, int& oH, int& oW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int iD, const int iH, const int iW, const int paddingMode) {
if(!isSameMode) { // valid
if(paddingMode == 0) { // valid
oD = (iD - (kD + (kD - 1) * (dD - 1)) + 2 * pD) / sD + 1; oD = (iD - (kD + (kD - 1) * (dD - 1)) + 2 * pD) / sD + 1;
oH = (iH - (kH + (kH - 1) * (dH - 1)) + 2 * pH) / sH + 1; oH = (iH - (kH + (kH - 1) * (dH - 1)) + 2 * pH) / sH + 1;
oW = (iW - (kW + (kW - 1) * (dW - 1)) + 2 * pW) / sW + 1; oW = (iW - (kW + (kW - 1) * (dW - 1)) + 2 * pW) / sW + 1;
} }
else { // same else if(paddingMode == 1) { // same
oD = (int) nd4j::math::nd4j_ceil<double, double>(iD * 1. / sD); oD = (int) nd4j::math::nd4j_ceil<double, double>(iD * 1. / sD);
oH = (int) nd4j::math::nd4j_ceil<double, double>(iH * 1. / sH); oH = (int) nd4j::math::nd4j_ceil<double, double>(iH * 1. / sH);
oW = (int) nd4j::math::nd4j_ceil<double, double>(iW * 1. / sW); oW = (int) nd4j::math::nd4j_ceil<double, double>(iW * 1. / sW);
}
else { // causal
oD = (iD - 1) / sD + 1;
oH = (iH - 1) / sH + 1; // 2*pH = (kH-1)*dH
oW = (iW - 1) / sW + 1;
} }
} }
static inline void calcPadding2D(int& pH, int& pW, int oH, int oW, int iH, int iW, int kH, int kW, int sH, int sW, int dH, int dW) { static inline void calcPadding2D(int& pH, int& pW, int oH, int oW, int iH, int iW, int kH, int kW, int sH, int sW, int dH, int dW, const int paddingMode = 1 /* default is same mode*/) {
int eKH, eKW;
if (dH == 1 && dW == 1) {
eKH = kH;
eKW = kW;
} else {
eKH = (kH - 1) * dH + 1;
eKW = (kW - 1) * dW + 1;
}
pH = ((oH - 1) * sH + eKH - iH) / 2; //Note that padBottom is 1 bigger than this if bracketed term is not divisible by 2 if(paddingMode == 0) // valid
pW = ((oW - 1) * sW + eKW - iW) / 2; return;
if(paddingMode == 1) { // same
const int eKH = (kH - 1) * dH + 1;
const int eKW = (kW - 1) * dW + 1;
pH = ((oH - 1) * sH + eKH - iH) / 2; //Note that padBottom is 1 bigger than this if bracketed term is not divisible by 2
pW = ((oW - 1) * sW + eKW - iW) / 2;
}
else { // causal
pH = (kH - 1) * dH;
pW = (kW - 1) * dW;
}
} }
static inline void calcPadding3D(int& pD, int& pH, int& pW, const int oD, const int oH, const int oW, const int iD, const int iH, const int iW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int dD, const int dH, const int dW) { static inline void calcPadding3D(int& pD, int& pH, int& pW, const int oD, const int oH, const int oW, const int iD, const int iH, const int iW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int dD, const int dH, const int dW, const int paddingMode = 1 /* default is same mode*/) {
int eKD, eKH, eKW;
if (dD == 1 && dH == 1 && dW == 1) { if(paddingMode == 0) // valid
eKD = kD; return;
eKH = kH;
eKW = kW; if(paddingMode == 1) { // same
} else {
eKD = (kD - 1) * dD + 1; const int eKD = (kD - 1) * dD + 1;
eKH = (kH - 1) * dH + 1; const int eKH = (kH - 1) * dH + 1;
eKW = (kW - 1) * dW + 1; const int eKW = (kW - 1) * dW + 1;
pD = ((oD - 1) * sD + eKD - iD) / 2;
pH = ((oH - 1) * sH + eKH - iH) / 2; //Note that padBottom is 1 bigger than this if bracketed term is not divisible by 2
pW = ((oW - 1) * sW + eKW - iW) / 2;
}
else { // causal
pD = (kD - 1) * dD;
pH = (kH - 1) * dH;
pW = (kW - 1) * dW;
} }
pD = ((oD - 1) * sD + eKD - iD) / 2; // Note that padBottom is 1 bigger than this if bracketed term is not divisible by 2
pH = ((oH - 1) * sH + eKH - iH) / 2;
pW = ((oW - 1) * sW + eKW - iW) / 2;
} }
// calculation of output height and width in 2D deconvolution procedure // calculation of output height and width in 2D deconvolution procedure
static inline void calcOutSizeDeconv2D(int& oH, int& oW, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int iH, const int iW, const int isSameMode) { static inline void calcOutSizeDeconv2D(int& oH, int& oW, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int iH, const int iW, const int paddingMode) {
if (isSameMode) {
if (paddingMode) {
oH = sH * iH; oH = sH * iH;
oW = sW * iW; oW = sW * iW;
} }
else { else {
int ekH, ekW; const int ekH = (kH - 1) * dH + 1;
if (dH == 1 && dW == 1) { const int ekW = (kW - 1) * dW + 1;
ekH = kH;
ekW = kW;
} else {
ekH = (kH - 1) * dH + 1;
ekW = (kW - 1) * dW + 1;
}
oH = sH * (iH - 1) + ekH - 2 * pH; oH = sH * (iH - 1) + ekH - 2 * pH;
oW = sW * (iW - 1) + ekW - 2 * pW; oW = sW * (iW - 1) + ekW - 2 * pW;
@ -117,24 +131,19 @@ namespace nd4j {
} }
// calculation of output height and width in 3D deconvolution procedure // calculation of output height and width in 3D deconvolution procedure
static inline void calcOutSizeDeconv3D(int& oD, int& oH, int& oW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int iD, const int iH, const int iW, const int isSameMode) { static inline void calcOutSizeDeconv3D(int& oD, int& oH, int& oW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int iD, const int iH, const int iW, const int paddingMode) {
if (isSameMode) {
if (paddingMode) {
oD = sD * iD; oD = sD * iD;
oH = sH * iH; oH = sH * iH;
oW = sW * iW; oW = sW * iW;
} }
else { else {
int ekD, ekH, ekW;
if (dD == 1 && dH == 1 && dW == 1) { const int ekD = (kD - 1) * dD + 1;
ekD = kD; const int ekH = (kH - 1) * dH + 1;
ekH = kH; const int ekW = (kW - 1) * dW + 1;
ekW = kW;
}
else {
ekD = (kD - 1) * dD + 1;
ekH = (kH - 1) * dH + 1;
ekW = (kW - 1) * dW + 1;
}
oD = sD * (iD - 1) + ekD - 2 * pD; oD = sD * (iD - 1) + ekD - 2 * pD;
oH = sH * (iH - 1) + ekH - 2 * pH; oH = sH * (iH - 1) + ekH - 2 * pH;
oW = sW * (iW - 1) + ekW - 2 * pW; oW = sW * (iW - 1) + ekW - 2 * pW;
@ -194,10 +203,10 @@ namespace nd4j {
} }
// static inline void calcPaddingAndDilationForConv2DMKL(const int iH, const int iW, const int oH, const int oW, const int kH, const int kW, const int sH, const int sW, const int isSameMode, int& pH, int& pW, int& dH, int& dW) { // static inline void calcPaddingAndDilationForConv2DMKL(const int iH, const int iW, const int oH, const int oW, const int kH, const int kW, const int sH, const int sW, const int paddingMode, int& pH, int& pW, int& dH, int& dW) {
// if(kH != 1) { // if(kH != 1) {
// if(isSameMode) { // if(paddingMode) {
// pH = (oH - 1) * sH - iH + kH - pH; // pH = (oH - 1) * sH - iH + kH - pH;
// dH = dH - 1; // dH = dH - 1;
// } // }
@ -205,7 +214,7 @@ namespace nd4j {
// dH = (iH + 2*pH - (oH - 1) * sH - kH) / (kH - 1); // dH = (iH + 2*pH - (oH - 1) * sH - kH) / (kH - 1);
// } // }
// if(kW != 1) { // if(kW != 1) {
// if(isSameMode) { // if(paddingMode) {
// pW = (oW - 1) * sW - iW + kW - pW; // pW = (oW - 1) * sW - iW + kW - pW;
// dW = dW - 1; // dW = dW - 1;
// } // }
@ -214,10 +223,10 @@ namespace nd4j {
// } // }
// } // }
// static inline void calcPaddingAndDilationForConv3DMKL(const int iD, const int iH, const int iW, const int oD, const int oH, const int oW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int isSameMode, int& pD, int& pH, int& pW, int& dD, int& dH, int& dW) { // static inline void calcPaddingAndDilationForConv3DMKL(const int iD, const int iH, const int iW, const int oD, const int oH, const int oW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int paddingMode, int& pD, int& pH, int& pW, int& dD, int& dH, int& dW) {
// if(kD != 1) { // if(kD != 1) {
// if(isSameMode) { // if(paddingMode) {
// pD = (oD - 1) * sD - iD + kD - pD; // pD = (oD - 1) * sD - iD + kD - pD;
// dD = dD - 1; // dD = dD - 1;
// } // }
@ -225,7 +234,7 @@ namespace nd4j {
// dD = (iD + 2*pD - (oD - 1) * sD - kD) / (kD - 1); // dD = (iD + 2*pD - (oD - 1) * sD - kD) / (kD - 1);
// } // }
// if(kH != 1) { // if(kH != 1) {
// if(isSameMode) { // if(paddingMode) {
// pH = (oH - 1) * sH - iH + kH - pH; // pH = (oH - 1) * sH - iH + kH - pH;
// dH = dH - 1; // dH = dH - 1;
// } // }
@ -233,7 +242,7 @@ namespace nd4j {
// dH = (iH + 2*pH - (oH - 1) * sH - kH) / (kH - 1); // dH = (iH + 2*pH - (oH - 1) * sH - kH) / (kH - 1);
// } // }
// if(kW != 1) { // if(kW != 1) {
// if(isSameMode) { // if(paddingMode) {
// pW = (oW - 1) * sW - iW + kW - pW; // pW = (oW - 1) * sW - iW + kW - pW;
// dW = dW - 1; // dW = dW - 1;
// } // }
@ -242,19 +251,19 @@ namespace nd4j {
// } // }
// } // }
static void conv2d(nd4j::graph::Context &context, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW); static void conv2d(nd4j::graph::Context &context, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW);
// static void conv2d(nd4j::graph::Context & block, const std::vector<NDArray*>& inArrs, NDArray* output, const std::vector<int>& intArgs); // static void conv2d(nd4j::graph::Context & block, const std::vector<NDArray*>& inArrs, NDArray* output, const std::vector<int>& intArgs);
// static void conv2dBP(nd4j::graph::Context & block, const std::vector<NDArray*>& inArrs, const std::vector<NDArray*>& outArrs, const std::vector<int>& intArgs); // static void conv2dBP(nd4j::graph::Context & block, const std::vector<NDArray*>& inArrs, const std::vector<NDArray*>& outArrs, const std::vector<int>& intArgs);
static void conv2dBP(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW); static void conv2dBP(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW);
static void depthwiseConv2d(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW); static void depthwiseConv2d(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW);
static void depthwiseConv2dBP(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW); static void depthwiseConv2dBP(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW);
static void sconv2d(nd4j::graph::Context & block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW); static void sconv2d(nd4j::graph::Context & block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW);
static void vol2col(nd4j::graph::Context & block, const NDArray& vol, NDArray& col, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW); static void vol2col(nd4j::graph::Context & block, const NDArray& vol, NDArray& col, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW);

60
libnd4j/include/ops/declarable/helpers/cpu/convolutions.cpp
View File

@ -258,7 +258,7 @@ namespace nd4j {
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
template <typename X, typename Y> template <typename X, typename Y>
static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
// weights [kH, kW, iC, oC] always // weights [kH, kW, iC, oC] always
@ -273,15 +273,14 @@ namespace nd4j {
// pW paddings width // pW paddings width
// dH dilations height // dH dilations height
// dW dilations width // dW dilations width
// isSameMode 0-VALID, 1-SAME // paddingMode 0-VALID, 1-SAME
// isNCHW 1-NCHW, 0-NHWC // isNCHW 1-NCHW, 0-NHWC
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
if(isSameMode) // SAME ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
nd4j_debug("MKL-DNN is not used for conv2d!\n", 0); nd4j_debug("MKL-DNN is not used for conv2d!\n", 0);
@ -320,7 +319,7 @@ namespace nd4j {
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
template <typename X, typename Y> template <typename X, typename Y>
static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
// weights [kH, kW, iC, oC] always // weights [kH, kW, iC, oC] always
@ -339,15 +338,14 @@ namespace nd4j {
// pW paddings width // pW paddings width
// dH dilations height // dH dilations height
// dW dilations width // dW dilations width
// isSameMode 0-VALID, 1-SAME // paddingMode 0-VALID, 1-SAME
// isNCHW 0-NHWC, 1-NCHW // isNCHW 0-NHWC, 1-NCHW
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
if(isSameMode) // SAME ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
nd4j_debug("MKL-DNN is not used for conv2d_bp!\n", 0); nd4j_debug("MKL-DNN is not used for conv2d_bp!\n", 0);
@ -393,7 +391,7 @@ namespace nd4j {
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
template <typename X, typename Y> template <typename X, typename Y>
static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
// weights [kH, kW, iC, mC] always // weights [kH, kW, iC, mC] always
@ -408,7 +406,7 @@ namespace nd4j {
// pW paddings width // pW paddings width
// dH dilations height // dH dilations height
// dW dilations width // dW dilations width
// isSameMode 0-VALID, 1-SAME // paddingMode 0-VALID, 1-SAME
// isNCHW 0-NCHW, 1-NHWC // isNCHW 0-NCHW, 1-NHWC
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width
@ -430,7 +428,7 @@ namespace nd4j {
modifOutput = {{1,0,3,4,2},{iC, bS*oH*oW, mC}}; // [bS,iC,mC,oH,oW] -> [iC,bS,oH,oW,mC] -> [iC,bS*oH*oW,mC] modifOutput = {{1,0,3,4,2},{iC, bS*oH*oW, mC}}; // [bS,iC,mC,oH,oW] -> [iC,bS,oH,oW,mC] -> [iC,bS*oH*oW,mC]
} }
if(isSameMode) // SAME if(paddingMode == 1) // SAME
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext()); NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext());
@ -449,7 +447,7 @@ namespace nd4j {
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
template <typename X, typename Y> template <typename X, typename Y>
static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
// input [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW) // input [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
// weights [kH, kW, iC, mC] always // weights [kH, kW, iC, mC] always
@ -467,7 +465,7 @@ namespace nd4j {
// pW paddings width // pW paddings width
// dH dilations height // dH dilations height
// dW dilations width // dW dilations width
// isSameMode 0-VALID, 1-SAME // paddingMode 0-VALID, 1-SAME
// isNCHW 0-NHWC, 1-NCHW // isNCHW 0-NHWC, 1-NCHW
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width
@ -492,7 +490,7 @@ namespace nd4j {
modifGradO2 = {{1,0,2,3},{iC, mC, bS*oH*oW}}; // [bS,iC*mC,oH,oW] -> [iC*mC,bS,oH,oW] -> [iC,mC,bS*oH*oW] modifGradO2 = {{1,0,2,3},{iC, mC, bS*oH*oW}}; // [bS,iC*mC,oH,oW] -> [iC*mC,bS,oH,oW] -> [iC,mC,bS*oH*oW]
} }
if(isSameMode) // SAME if(paddingMode == 1) // SAME
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext()); NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext());
@ -526,7 +524,7 @@ namespace nd4j {
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
template <typename X, typename Y> template <typename X, typename Y>
static void sconv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { static void sconv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
// weightsDepth [kH, kW, iC, mC] always // weightsDepth [kH, kW, iC, mC] always
@ -542,8 +540,8 @@ namespace nd4j {
// pW paddings width // pW paddings width
// dH dilations height // dH dilations height
// dW dilations width // dW dilations width
// isSameMode 0-VALID, 1-SAME // paddingMode 0-VALID, 1-SAME
// isNCHW 1-NCHW, 0-NHWC // isNCHW 1-NCHW, 0-NHWC
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier, output channels, output height/width int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier, output channels, output height/width
int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
@ -555,11 +553,11 @@ namespace nd4j {
outputDepth = new NDArray(output->ordering(), !isNCHW ? std::vector<Nd4jLong>({bS, oH, oW, iC*mC}) : std::vector<Nd4jLong>({bS, iC*mC, oH, oW}), input->dataType(), input->getContext()); outputDepth = new NDArray(output->ordering(), !isNCHW ? std::vector<Nd4jLong>({bS, oH, oW, iC*mC}) : std::vector<Nd4jLong>({bS, iC*mC, oH, oW}), input->dataType(), input->getContext());
// ----- perform depthwise convolution (if weightsPoint is absent then oC = iC*mC) ----- // // ----- perform depthwise convolution (if weightsPoint is absent then oC = iC*mC) ----- //
ConvolutionUtils::depthwiseConv2d(block, input, weightsDepth, weightsPoint ? nullptr : bias, outputDepth, kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, isNCHW); ConvolutionUtils::depthwiseConv2d(block, input, weightsDepth, weightsPoint ? nullptr : bias, outputDepth, kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, isNCHW);
// ----- perform pointwise convolution (oH = iH, oW = iW) ----- // // ----- perform pointwise convolution (oH = iH, oW = iW) ----- //
if (weightsPoint) { if (weightsPoint) {
ConvolutionUtils::conv2d(block, outputDepth, weightsPoint, bias, output, 1,1, 1,1, 0,0, 1,1, isSameMode, isNCHW); // in this case oH=iH, oW=iW ConvolutionUtils::conv2d(block, outputDepth, weightsPoint, bias, output, 1,1, 1,1, 0,0, 1,1, paddingMode, isNCHW); // in this case oH=iH, oW=iW
delete outputDepth; delete outputDepth;
} }
} }
@ -1774,20 +1772,20 @@ namespace nd4j {
void ConvolutionUtils::conv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { void ConvolutionUtils::conv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES);
} }
void ConvolutionUtils::conv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { void ConvolutionUtils::conv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2dBP_, (block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2dBP_, (block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES);
} }
void ConvolutionUtils::depthwiseConv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { void ConvolutionUtils::depthwiseConv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES);
} }
void ConvolutionUtils::depthwiseConv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { void ConvolutionUtils::depthwiseConv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2dBP_, (input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2dBP_, (input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES);
} }
void ConvolutionUtils::sconv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { void ConvolutionUtils::sconv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), sconv2d_, (block, input, weightsDepth, weightsPoint, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), sconv2d_, (block, input, weightsDepth, weightsPoint, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES);
} }
void ConvolutionUtils::vol2col(nd4j::graph::Context& block, const NDArray& volume, NDArray& columns, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) { void ConvolutionUtils::vol2col(nd4j::graph::Context& block, const NDArray& volume, NDArray& columns, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
BUILD_SINGLE_SELECTOR(volume.dataType(), vol2col_, (volume, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR(volume.dataType(), vol2col_, (volume, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW), FLOAT_TYPES);

54
libnd4j/include/ops/declarable/helpers/cuda/convolutions.cu
View File

@ -217,7 +217,7 @@ void ConvolutionUtils::col2vol(nd4j::graph::Context& block, const NDArray& col,
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
template <typename X, typename Y> template <typename X, typename Y>
static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
// weights [kH, kW, iC, oC] always // weights [kH, kW, iC, oC] always
@ -232,15 +232,14 @@ static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDA
// pW paddings width // pW paddings width
// dH dilations height // dH dilations height
// dW dilations width // dW dilations width
// isSameMode 0-VALID, 1-SAME // paddingMode 0-VALID, 1-SAME
// isNCHW 1-NCHW, 0-NHWC // isNCHW 1-NCHW, 0-NHWC
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
if(isSameMode) // SAME ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
std::vector<int> permutForOutput; std::vector<int> permutForOutput;
@ -276,13 +275,13 @@ static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDA
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
void ConvolutionUtils::conv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { void ConvolutionUtils::conv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES);
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
template <typename X, typename Y> template <typename X, typename Y>
static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
// weights [kH, kW, iC, mC] always // weights [kH, kW, iC, mC] always
@ -297,7 +296,7 @@ static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input,
// pW paddings width // pW paddings width
// dH dilations height // dH dilations height
// dW dilations width // dW dilations width
// isSameMode 0-VALID, 1-SAME // paddingMode 0-VALID, 1-SAME
// isNCHW 0-NCHW, 1-NHWC // isNCHW 0-NCHW, 1-NHWC
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width
@ -319,7 +318,7 @@ static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input,
modifOutput = {{1,0,3,4,2},{iC, bS*oH*oW, mC}}; // [bS,iC,mC,oH,oW] -> [iC,bS,oH,oW,mC] -> [iC,bS*oH*oW,mC] modifOutput = {{1,0,3,4,2},{iC, bS*oH*oW, mC}}; // [bS,iC,mC,oH,oW] -> [iC,bS,oH,oW,mC] -> [iC,bS*oH*oW,mC]
} }
if(isSameMode) // SAME if(paddingMode == 1) // SAME
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext()); NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext());
@ -337,13 +336,13 @@ static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input,
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
void ConvolutionUtils::depthwiseConv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { void ConvolutionUtils::depthwiseConv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES);
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
template <typename X, typename Y> template <typename X, typename Y>
static void sconv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { static void sconv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
// weightsDepth [kH, kW, iC, mC] always // weightsDepth [kH, kW, iC, mC] always
@ -359,7 +358,7 @@ static void sconv2d_(nd4j::graph::Context& block, const NDArray* input, const ND
// pW paddings width // pW paddings width
// dH dilations height // dH dilations height
// dW dilations width // dW dilations width
// isSameMode 0-VALID, 1-SAME // paddingMode 0-VALID, 1-SAME
// isNCHW 1-NCHW, 0-NHWC // isNCHW 1-NCHW, 0-NHWC
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier, output channels, output height/width int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier, output channels, output height/width
@ -372,18 +371,18 @@ static void sconv2d_(nd4j::graph::Context& block, const NDArray* input, const ND
outputDepth = new NDArray(output->ordering(), !isNCHW ? std::vector<Nd4jLong>({bS, oH, oW, iC*mC}) : std::vector<Nd4jLong>({bS, iC*mC, oH, oW}), input->dataType(), input->getContext()); outputDepth = new NDArray(output->ordering(), !isNCHW ? std::vector<Nd4jLong>({bS, oH, oW, iC*mC}) : std::vector<Nd4jLong>({bS, iC*mC, oH, oW}), input->dataType(), input->getContext());
// ----- perform depthwise convolution (if weightsPoint is absent then oC = iC*mC) ----- // // ----- perform depthwise convolution (if weightsPoint is absent then oC = iC*mC) ----- //
ConvolutionUtils::depthwiseConv2d(block, input, weightsDepth, weightsPoint ? nullptr : bias, outputDepth, kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, isNCHW); ConvolutionUtils::depthwiseConv2d(block, input, weightsDepth, weightsPoint ? nullptr : bias, outputDepth, kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, isNCHW);
// ----- perform pointwise convolution (oH = iH, oW = iW) ----- // // ----- perform pointwise convolution (oH = iH, oW = iW) ----- //
if (weightsPoint) { if (weightsPoint) {
ConvolutionUtils::conv2d(block, outputDepth, weightsPoint, bias, output, 1,1, 1,1, 0,0, 1,1, isSameMode, isNCHW); // in this case oH=iH, oW=iW ConvolutionUtils::conv2d(block, outputDepth, weightsPoint, bias, output, 1,1, 1,1, 0,0, 1,1, paddingMode, isNCHW); // in this case oH=iH, oW=iW
delete outputDepth; delete outputDepth;
} }
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
void ConvolutionUtils::sconv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { void ConvolutionUtils::sconv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), sconv2d_, (block, input, weightsDepth, weightsPoint, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), sconv2d_, (block, input, weightsDepth, weightsPoint, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES);
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
@ -1177,7 +1176,7 @@ void ConvolutionUtils::pooling3dBP(nd4j::graph::Context& block, const NDArray& i
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
template <typename X, typename Y> template <typename X, typename Y>
static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
// weights [kH, kW, iC, oC] always // weights [kH, kW, iC, oC] always
@ -1196,15 +1195,14 @@ static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const N
// pW paddings width // pW paddings width
// dH dilations height // dH dilations height
// dW dilations width // dW dilations width
// isSameMode 0-VALID, 1-SAME // paddingMode 0-VALID, 1-SAME
// isNCHW 0-NHWC, 1-NCHW // isNCHW 0-NHWC, 1-NCHW
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
if(isSameMode) // SAME ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
std::vector<int> gradOaxesForDot; std::vector<int> gradOaxesForDot;
@ -1247,13 +1245,13 @@ static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const N
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
void ConvolutionUtils::conv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { void ConvolutionUtils::conv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2dBP_, (block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2dBP_, (block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES);
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
template <typename X, typename Y> template <typename X, typename Y>
static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
// input [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW) // input [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
// weights [kH, kW, iC, mC] always // weights [kH, kW, iC, mC] always
@ -1271,7 +1269,7 @@ static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, con
// pW paddings width // pW paddings width
// dH dilations height // dH dilations height
// dW dilations width // dW dilations width
// isSameMode 0-VALID, 1-SAME // paddingMode 0-VALID, 1-SAME
// isNCHW 0-NHWC, 1-NCHW // isNCHW 0-NHWC, 1-NCHW
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width
@ -1296,7 +1294,7 @@ static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, con
modifGradO2 = {{1,0,2,3},{iC, mC, bS*oH*oW}}; // [bS,iC*mC,oH,oW] -> [iC*mC,bS,oH,oW] -> [iC,mC,bS*oH*oW] modifGradO2 = {{1,0,2,3},{iC, mC, bS*oH*oW}}; // [bS,iC*mC,oH,oW] -> [iC*mC,bS,oH,oW] -> [iC,mC,bS*oH*oW]
} }
if(isSameMode) // SAME if(paddingMode == 1) // SAME
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext()); NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext());
@ -1328,8 +1326,8 @@ static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, con
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
void ConvolutionUtils::depthwiseConv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) { void ConvolutionUtils::depthwiseConv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) {
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2dBP_, (input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2dBP_, (input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES);
} }

220
libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp
View File

@ -31,6 +31,7 @@
#include <ops/declarable/helpers/convolutions.h> #include <ops/declarable/helpers/convolutions.h>
#include <ops/declarable/helpers/col2im.h> #include <ops/declarable/helpers/col2im.h>
#include <PointersManager.h> #include <PointersManager.h>
#include <GradCheck.h>
#ifdef HAVE_MKLDNN #ifdef HAVE_MKLDNN
#include <ops/declarable/platform/mkldnn/mkldnnUtils.h> #include <ops/declarable/platform/mkldnn/mkldnnUtils.h>
@ -771,7 +772,7 @@ TYPED_TEST(TypedConvolutionTests1, Test_Conv1D_ff_1) {
bias.linspace(1); bias.linspace(1);
nd4j::ops::conv1d op; nd4j::ops::conv1d op;
auto result_FF = op.execute({&input, &weights, &bias}, {}, {2, 1, 0, 0}); auto result_FF = op.execute({&input, &weights, &bias}, {}, {2, 1, 0, 1, 0, 0});
ASSERT_EQ(ND4J_STATUS_OK, result_FF->status()); ASSERT_EQ(ND4J_STATUS_OK, result_FF->status());
@ -785,7 +786,7 @@ TYPED_TEST(TypedConvolutionTests1, Test_Conv1D_ff_1) {
auto epsilonNxt = z->dup(); auto epsilonNxt = z->dup();
epsilonNxt->linspace(1); epsilonNxt->linspace(1);
auto result_BP = op_bp.execute({&input, &weights, &bias, epsilonNxt}, {}, {2, 1, 0, 0}); auto result_BP = op_bp.execute({&input, &weights, &bias, epsilonNxt}, {}, {2, 1, 0, 1, 0, 0});
ASSERT_EQ(ND4J_STATUS_OK, result_BP->status()); ASSERT_EQ(ND4J_STATUS_OK, result_BP->status());
auto eps = result_BP->at(0); auto eps = result_BP->at(0);
@ -813,7 +814,7 @@ TYPED_TEST(TypedConvolutionTests1, Test_Conv1D_ff_2) {
input.linspace(1); input.linspace(1);
nd4j::ops::conv1d op; nd4j::ops::conv1d op;
auto result = op.execute({&input, &weights}, {}, {2, 1, 0, 1}); auto result = op.execute({&input, &weights}, {}, {2, 1, 0, 1, 1,0});
ASSERT_EQ(ND4J_STATUS_OK, result->status()); ASSERT_EQ(ND4J_STATUS_OK, result->status());
@ -822,6 +823,219 @@ TYPED_TEST(TypedConvolutionTests1, Test_Conv1D_ff_2) {
delete result; delete result;
} }
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, conv1d_causal_1) {
int bS=2, iW=3, iC=4,oC=3, kW=2, sW=1, pW=0, dW=1;
int oW = (iW-1)/sW + 1;
int paddingMode = 2; // CAUSAL
int dataFormat = 1; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iW, iC});
NDArray weights('c', {kW, iC, oC});
NDArray bias('c', {oC}, {-1,-2,-3});
NDArray expOutput('c', {bS, oW, oC}, {18. , 18. , 18. , 53. , 55.6, 58.2, 89.8, 95.6, 101.4, 102. , 106.8, 111.6, 163.4, 175.6, 187.8, 200.2, 215.6, 231.});
input.linspace(1., 1.);
weights.linspace(0.1, 0.1);
nd4j::ops::conv1d op;
auto results = op.execute({&input, &weights, &bias}, {}, {kW, sW, pW, dW, paddingMode, dataFormat});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, conv1d_causal_2) {
int bS=2, iW=16, iC=3,oC=4, kW=2, sW=2, pW=0, dW=1;
int oW = (iW-1)/sW + 1;
int paddingMode = 2; // CAUSAL
int dataFormat = 1; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iW, iC});
NDArray weights('c', {kW, iC, oC});
NDArray bias('c', {oC}, {-1,-2,-3,-4});
NDArray expOutput('c', {bS, oW, oC}, { 10. , 9.6, 9.2, 8.8, 48.9, 51.8, 54.7, 57.6, 88.5, 95. , 101.5, 108. , 128.1, 138.2, 148.3, 158.4,
167.7, 181.4, 195.1, 208.8, 207.3, 224.6, 241.9, 259.2, 246.9, 267.8, 288.7, 309.6, 286.5, 311. , 335.5, 360. ,
254.8, 268.8, 282.8, 296.8, 365.7, 397.4, 429.1, 460.8, 405.3, 440.6, 475.9, 511.2, 444.9, 483.8, 522.7, 561.6,
484.5, 527. , 569.5, 612. , 524.1, 570.2, 616.3, 662.4, 563.7, 613.4, 663.1, 712.8, 603.3, 656.6, 709.9, 763.2});
input.linspace(1., 1.);
weights.linspace(0.1, 0.1);
nd4j::ops::conv1d op;
auto results = op.execute({&input, &weights, &bias}, {}, {kW, sW, pW, dW, paddingMode, dataFormat});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, conv1d_causal_3) {
int bS=2, iW=16, iC=3,oC=4, kW=3, sW=3, pW=0, dW=1;
int oW = (iW-1)/sW + 1;
int paddingMode = 2; // CAUSAL
int dataFormat = 1; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iW, iC});
NDArray weights('c', {kW, iC, oC});
NDArray bias('c', {oC}, {-1,-2,-3,-4});
NDArray expOutput('c', {bS, oW, oC}, {17.2, 16.8, 16.4, 16.,145.4, 151.6, 157.8, 164.,283.1, 297.4, 311.7, 326., 420.8, 443.2, 465.6, 488.,
558.5, 589., 619.5, 650.,696.2001, 734.8, 773.4, 812., 434.8, 448.8, 462.8, 476.8, 879.8, 929.2, 978.6, 1028.,
1017.5, 1075., 1132.5, 1190.,1155.2001, 1220.8, 1286.4, 1352.,1292.8999, 1366.6, 1440.3, 1514., 1430.6001, 1512.4, 1594.2, 1676.});
input.linspace(1., 1.);
weights.linspace(0.1, 0.1);
nd4j::ops::conv1d op;
auto results = op.execute({&input, &weights, &bias}, {}, {kW, sW, pW, dW, paddingMode, dataFormat});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, conv1d_causal_4) {
int bS=2, iW=8, iC=3,oC=4, kW=3, sW=1, pW=0, dW=3;
int oW = (iW-1)/sW + 1;
int paddingMode = 2; // CAUSAL
int dataFormat = 1; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iW, iC});
NDArray weights('c', {kW, iC, oC});
NDArray bias('c', {oC}, {-1,-2,-3,-4});
NDArray expOutput('c', {bS, oW, oC}, {17.2, 16.8, 16.4, 16. ,43.3, 43.8, 44.3, 44.8,69.4, 70.8, 72.2, 73.6,106.5, 109.4, 112.3, 115.2,147.9, 152.6, 157.3, 162. ,189.3, 195.8, 202.3,
208.8,234.5, 243.4, 252.3, 261.2,280.4, 292. , 303.6, 315.2, 226. , 232.8, 239.6, 246.4, 252.1, 259.8, 267.5, 275.2,278.2, 286.8, 295.4, 304. ,437.7,
455. , 472.3, 489.6,479.1, 498.2, 517.3, 536.4,520.5, 541.4, 562.3, 583.2, 601.7, 632.2, 662.7, 693.2, 647.6, 680.8, 714. , 747.2});
input.linspace(1., 1.);
weights.linspace(0.1, 0.1);
nd4j::ops::conv1d op;
auto results = op.execute({&input, &weights, &bias}, {}, {kW, sW, pW, dW, paddingMode, dataFormat});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, conv1d_causal_5) {
int bS=2, iW=8, iC=3,oC=4, kW=3, sW=1, pW=0, dW=3;
int oW = (iW-1)/sW + 1;
int paddingMode = 2; // CAUSAL
int dataFormat = 0; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iC, iW});
NDArray weights('c', {kW, iC, oC});
NDArray bias('c', {oC}, {-1,-2,-3,-4});
NDArray expOutput('c', {bS, oC, oW}, { 83.7, 92.4, 101.1, 162.1, 175.9, 189.7, 223.4, 238.7,85.4, 94.4, 103.4, 167.4, 181.8, 196.2, 233.2, 249.4,87.1, 96.4, 105.7, 172.7, 187.7, 202.7, 243. , 260.1,
88.8, 98.4, 108. , 178. , 193.6, 209.2, 252.8, 270.8, 292.5, 301.2, 309.9, 493.3, 507.1, 520.9, 590.6, 605.9, 301.4, 310.4, 319.4, 513. , 527.4, 541.8, 622. , 638.2,
310.3, 319.6, 328.9, 532.7, 547.7, 562.7, 653.4, 670.5, 319.2, 328.8, 338.4, 552.4, 568. , 583.6, 684.8, 702.8});
input.linspace(1., 1.);
weights.linspace(0.1, 0.1);
nd4j::ops::conv1d op;
auto results = op.execute({&input, &weights, &bias}, {}, {kW, sW, pW, dW, paddingMode, dataFormat});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, conv1d_causal_6) {
int bS=2, iW=16, iC=3,oC=4, kW=3, sW=3, pW=0, dW=1;
int oW = (iW-1)/sW + 1;
int paddingMode = 2; // CAUSAL
int dataFormat = 0; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iC, iW});
NDArray weights('c', {kW, iC, oC});
NDArray bias('c', {oC}, {-1,-2,-3,-4});
NDArray expOutput('c', {bS, oC, oW}, {159.7,335.3,381.2,427.1,473. ,518.9,163.8,351.4,400. ,448.6,497.2,545.8,167.9,367.5,418.8,470.1,521.4,572.7,172. ,383.6,437.6,491.6,545.6,599.6,
577.3, 1069.7, 1115.6, 1161.5, 1207.4, 1253.3,595.8, 1129. , 1177.6, 1226.2, 1274.8, 1323.4,614.3, 1188.3, 1239.6, 1290.9, 1342.2, 1393.5,
632.8, 1247.6, 1301.6, 1355.6, 1409.6, 1463.6});
input.linspace(1., 1.);
weights.linspace(0.1, 0.1);
nd4j::ops::conv1d op;
auto results = op.execute({&input, &weights, &bias}, {}, {kW, sW, pW, dW, paddingMode, dataFormat});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, conv1d_causal_bp_1) {
int bS=2, iW=3, iC=4,oC=3, kW=2, sW=1, pW=0, dW=1;
int oW = (iW-1)/sW + 1;
int paddingMode = 2; // CAUSAL
int dataFormat = 1; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iW, iC});
NDArray weights('c', {kW, iC, oC});
NDArray bias('c', {oC}, {-1,-2,-3});
NDArray gradO('c', {bS, oW, oC});
input.linspace(1., 1.);
weights.linspace(0.1, 0.1);
gradO.linspace(-1.5, 0.1);
const OpArgsHolder argsHolderFF({&input, &weights, &bias}, {}, {kW, sW, pW, dW, paddingMode, dataFormat});
const OpArgsHolder argsHolderBP({&input, &weights, &bias, &gradO}, {}, {kW, sW, pW, dW, paddingMode, dataFormat});
nd4j::ops::conv1d opFF;
nd4j::ops::conv1d_bp opBP;
const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP);
ASSERT_TRUE(isGradCorrect);
}
TEST_F(ConvolutionTests1, Test_Dilation2D_1) { TEST_F(ConvolutionTests1, Test_Dilation2D_1) {
auto input = NDArrayFactory::create<double>('c', {2, 6, 6, 3}); auto input = NDArrayFactory::create<double>('c', {2, 6, 6, 3});
auto weights = NDArrayFactory::create<double>('c', {3, 2, 3}); auto weights = NDArrayFactory::create<double>('c', {3, 2, 3});

2
libnd4j/tests_cpu/layers_tests/ParityOpsTests.cpp
View File

@ -908,7 +908,7 @@ TEST_F(ParityOpsTests, scatterMax_test4) {
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 10, 10, 10, 5, 6, 7, 8}); auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 10, 10, 10, 5, 6, 7, 8});
nd4j::ops::scatter_max op; nd4j::ops::scatter_max op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {}); auto result = op.execute({&matrix, &idc, &updates}, {}, {true});
ASSERT_EQ(ND4J_STATUS_OK, result->status()); ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0); auto z = result->at(0);

34
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv1D.java
View File

@ -29,12 +29,11 @@ import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp; import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv1DConfig; import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv1DConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.PaddingMode;
import org.nd4j.linalg.util.ArrayUtil; import org.nd4j.linalg.util.ArrayUtil;
import java.lang.reflect.Field; import java.lang.reflect.Field;
import java.util.Collections; import java.util.*;
import java.util.List;
import java.util.Map;
/** /**
@ -79,7 +78,8 @@ public class Conv1D extends DynamicCustomOp {
addIArgument(config.getK(), addIArgument(config.getK(),
config.getS(), config.getS(),
config.getP(), config.getP(),
ArrayUtil.fromBoolean(config.isSameMode()), config.getD(),
config.getPaddingMode().ordinal(),
ArrayUtil.fromBoolean(config.isNWC())); ArrayUtil.fromBoolean(config.isNWC()));
} }
@ -95,10 +95,12 @@ public class Conv1D extends DynamicCustomOp {
public Object getValue(Field property) { public Object getValue(Field property) {
if (config == null && !iArguments.isEmpty()) { if (config == null && !iArguments.isEmpty()) {
config = Conv1DConfig.builder() config = Conv1DConfig.builder()
.s(iArguments.get(0)) .k(iArguments.get(0))
.p(iArguments.get(1)) .s(iArguments.get(1))
.isSameMode(iArguments.get(2) == 1) .p(iArguments.get(2))
.dataFormat(iArguments.get(3) == 1 ? Conv1DConfig.NCW : Conv1DConfig.NWC) .d(iArguments.get(3))
.paddingMode(PaddingMode.values()[iArguments.get(4).intValue()])
.dataFormat(iArguments.get(5) == 1 ? Conv1DConfig.NCW : Conv1DConfig.NWC)
.build(); .build();
} }
@ -125,16 +127,20 @@ public class Conv1D extends DynamicCustomOp {
return "conv1d"; return "conv1d";
} }
@Override
public String onnxName() {
throw new NoOpNameFoundException("No ONNX op name found for: " + getClass().getName());
}
@Override @Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){ public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
int n = args().length; int n = args().length;
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes); Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0)); return Collections.singletonList(inputDataTypes.get(0));
} }
@Override
public List<SDVariable> doDiff(List<SDVariable> grads){
List<SDVariable> args = new ArrayList<>();
Collections.addAll(args, args());
args.add(grads.get(0));
Conv1DDerivative gradFn = new Conv1DDerivative(sameDiff, args.toArray(new SDVariable[0]), config);
return Arrays.asList(gradFn.outputVariables());
}
} }

152
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv1DDerivative.java Normal file
View File

@ -0,0 +1,152 @@
/* ******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* 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
******************************************************************************/
package org.nd4j.linalg.api.ops.impl.layers.convolution;
import lombok.Builder;
import lombok.Getter;
import lombok.NoArgsConstructor;
import lombok.NonNull;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv1DConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.PaddingMode;
import org.nd4j.linalg.util.ArrayUtil;
import java.lang.reflect.Field;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* Conv1D Backprop operation
*
* @author Alex Black
*/
@Slf4j
@Getter
@NoArgsConstructor
public class Conv1DDerivative extends DynamicCustomOp {
protected Conv1DConfig config;
private static final String INVALID_CONFIGURATION = "Invalid Conv1D configuration : s = %s p = %s ";
public Conv1DDerivative(@NonNull SameDiff sameDiff,
@NonNull SDVariable[] inputs,
@NonNull Conv1DConfig config) {
super(sameDiff, inputs);
initConfig(config);
}
public Conv1DDerivative(@NonNull SameDiff sd, @NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, SDVariable gradOut, @NonNull Conv1DConfig config){
this(sd, wrapFilterNull(input, weights, bias, gradOut), config);
}
public Conv1DDerivative(INDArray[] inputs, INDArray[] outputs, Conv1DConfig config){
super(inputs, outputs);
initConfig(config);
}
public Conv1DDerivative(@NonNull INDArray input, @NonNull INDArray weights, INDArray bias, @NonNull INDArray gradOut, INDArray output, @NonNull Conv1DConfig config){
this(wrapFilterNull(input, weights, bias, gradOut), wrapOrNull(output), config);
}
private void initConfig(Conv1DConfig config){
this.config = config;
Preconditions.checkState(config.getS() >= 1 && config.getP() >= 0, INVALID_CONFIGURATION, config.getS(), config.getP());
addArgs();
}
protected void addArgs() {
if (config == null)
config = Conv1DConfig.builder().build();
addIArgument(config.getK(),
config.getS(),
config.getP(),
config.getD(),
config.getPaddingMode().ordinal(),
ArrayUtil.fromBoolean(config.isNWC()));
}
@Override
public long[] iArgs() {
if (iArguments.size() == 0)
addArgs();
return super.iArgs();
}
@Override
public Object getValue(Field property) {
if (config == null && !iArguments.isEmpty()) {
config = Conv1DConfig.builder()
.k(iArguments.get(0))
.s(iArguments.get(1))
.p(iArguments.get(2))
.d(iArguments.get(3))
.paddingMode(PaddingMode.values()[iArguments.get(4).intValue()])
.dataFormat(iArguments.get(5) == 1 ? Conv1DConfig.NCW : Conv1DConfig.NWC)
.build();
}
return config.getValue(property);
}
@Override
public Map<String, Object> propertiesForFunction() {
return config.toProperties();
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "config";
}
@Override
public String opName() {
return "conv1d_bp";
}
@Override
public int getNumOutputs(){
if(args().length == 4){
return 3; //Includes bias
} else {
return 2; //No bias - only input + weight grads
}
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
int n = args().length;
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
return new ArrayList<>(inputDataTypes.subList(0, inputDataTypes.size()-1)); //All except gradient input variable
}
}

23
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/config/Conv1DConfig.java
View File

@ -21,6 +21,7 @@ import java.util.Map;
import lombok.Builder; import lombok.Builder;
import lombok.Data; import lombok.Data;
import lombok.NoArgsConstructor; import lombok.NoArgsConstructor;
import lombok.NonNull;
import org.nd4j.base.Preconditions; import org.nd4j.base.Preconditions;
import org.nd4j.linalg.util.ConvConfigUtil; import org.nd4j.linalg.util.ConvConfigUtil;
@ -38,15 +39,28 @@ public class Conv1DConfig extends BaseConvolutionConfig {
@Builder.Default @Builder.Default
private long p = 0; // padding private long p = 0; // padding
@Builder.Default @Builder.Default
private long d = 1; // dilation
@Builder.Default
private String dataFormat = NCW; private String dataFormat = NCW;
private boolean isSameMode; private PaddingMode paddingMode;
public Conv1DConfig(long k, long s, long p, long d, String dataFormat, @NonNull PaddingMode paddingMode) {
this.k = k;
this.s = s;
this.p = p;
this.d = d;
this.dataFormat = dataFormat;
this.paddingMode = paddingMode;
validate();
}
public Conv1DConfig(long k, long s, long p, String dataFormat, boolean isSameMode) { public Conv1DConfig(long k, long s, long p, String dataFormat, boolean isSameMode) {
this.k = k; this.k = k;
this.s = s; this.s = s;
this.p = p; this.p = p;
this.dataFormat = dataFormat; this.dataFormat = dataFormat;
this.isSameMode = isSameMode; this.paddingMode = isSameMode ? PaddingMode.SAME : PaddingMode.VALID;
validate(); validate();
} }
@ -71,14 +85,15 @@ public class Conv1DConfig extends BaseConvolutionConfig {
ret.put("k", k); ret.put("k", k);
ret.put("s", s); ret.put("s", s);
ret.put("p", p); ret.put("p", p);
ret.put("isSameMode", isSameMode); ret.put("d", d);
ret.put("isSameMode", paddingMode);
ret.put("dataFormat", dataFormat); ret.put("dataFormat", dataFormat);
return ret; return ret;
} }
@Override @Override
protected void validate() { protected void validate() {
ConvConfigUtil.validate1D(k, s, p); ConvConfigUtil.validate1D(k, s, p, d);
Preconditions.checkArgument(dataFormat != null, "Data format can't be null"); Preconditions.checkArgument(dataFormat != null, "Data format can't be null");
} }

24
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/config/PaddingMode.java Normal file
View File

@ -0,0 +1,24 @@
/*******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* 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
******************************************************************************/
package org.nd4j.linalg.api.ops.impl.layers.convolution.config;
public enum PaddingMode {
VALID,
SAME,
CAUSAL
}

4
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/util/ConvConfigUtil.java
View File

@ -76,11 +76,13 @@ public class ConvConfigUtil {
/** /**
* Validate a 1D convolution's Kernel, Stride, and Padding * Validate a 1D convolution's Kernel, Stride, and Padding
*/ */
public static void validate1D(long k, long s, long p){ public static void validate1D(long k, long s, long p, long d){
Preconditions.checkArgument(k != 0, "Kernel can not be 0"); Preconditions.checkArgument(k != 0, "Kernel can not be 0");
Preconditions.checkArgument(s > 0, "Stride can not be negative or 0, got: %s", s); Preconditions.checkArgument(s > 0, "Stride can not be negative or 0, got: %s", s);
Preconditions.checkArgument(d > 0, "Dilation can not be negative or 0, got: %s", s);
Preconditions.checkArgument(p >= 0, "Padding can not be negative, got: %s", p); Preconditions.checkArgument(p >= 0, "Padding can not be negative, got: %s", p);
} }

28
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/nativeblas/Nd4jCpu.java
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@ -14592,11 +14592,11 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
/** /**
* 1D temporal convolution implementation * 1D temporal convolution implementation
* Expected input: * Expected input:
* x: 3D array * x: 3D array
* weight: 3D Array * weight: 3D Array
* bias: optional vector * bias: optional vector
* *
* Int args: * Int args:
* 0: kernel * 0: kernel
* 1: stride * 1: stride
@ -14637,11 +14637,11 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
/** /**
* 2D convolution implementation * 2D convolution implementation
* Expected input: * Expected input:
* x: 4D array * x: 4D array
* weight: 4D Array * weight: 4D Array
* bias: optional vector, length of outputChannels * bias: optional vector, length of outputChannels
* *
* IntArgs: * IntArgs:
* 0: kernel height * 0: kernel height
* 1: kernel width * 1: kernel width
@ -14745,7 +14745,7 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
/** /**
* 2D deconvolution implementation * 2D deconvolution implementation
* *
* IntArgs: * IntArgs:
* 0: kernel height * 0: kernel height
* 1: kernel width * 1: kernel width
@ -14792,7 +14792,7 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
/** /**
* 3D deconvolution implementation * 3D deconvolution implementation
* *
* IntArgs: * IntArgs:
* 0: filter(kernel) depth * 0: filter(kernel) depth
* 1: filter(kernel) height * 1: filter(kernel) height
@ -14992,7 +14992,7 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
/** /**
* This op implements im2col algorithm, widely used in convolution neural networks * This op implements im2col algorithm, widely used in convolution neural networks
* Input: 4D input expected * Input: 4D input expected
* *
* Int args: * Int args:
* 0: kernel height * 0: kernel height
* 1: kernel width * 1: kernel width
@ -15040,7 +15040,7 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
/** /**
* This op implements col2im algorithm, widely used in convolution neural networks * This op implements col2im algorithm, widely used in convolution neural networks
* Input: 6D input expected (like output of im2col op) * Input: 6D input expected (like output of im2col op)
* *
* Int args: * Int args:
* 0: stride height * 0: stride height
* 1: stride width * 1: stride width
@ -15071,7 +15071,7 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
/** /**
* Expected input: 4D array * Expected input: 4D array
* *
* IntArgs: * IntArgs:
* 0: scale factor for rows (height) * 0: scale factor for rows (height)
* 1: scale factor for columns (width) * 1: scale factor for columns (width)
@ -15112,7 +15112,7 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
/** /**
* Expected input: 4D array * Expected input: 4D array
* *
* IntArgs: * IntArgs:
* 0: scale factor for depth * 0: scale factor for depth
* 1: scale factor for rows (height) * 1: scale factor for rows (height)
@ -15149,13 +15149,13 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
public upsampling3d_bp() { super((Pointer)null); allocate(); } public upsampling3d_bp() { super((Pointer)null); allocate(); }
private native void allocate(); private native void allocate();
public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block); public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block);
} }
// #endif // #endif
/** /**
* This op produces binary matrix wrt to target dimension. * This op produces binary matrix wrt to target dimension.
* Maximum value within each TAD is replaced with 1, other values are set to true. * Maximum value within each TAD is replaced with 1, other values are set to true.
* *
* Int args: * Int args:
* 0: axis * 0: axis
*/ */
@ -15179,7 +15179,7 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
/** /**
* Dilation2D op * Dilation2D op
* *
* Int args: * Int args:
* 0: isSameMode * 0: isSameMode
*/ */
@ -15307,7 +15307,7 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
* Output: * Output:
* 0 - 4D tensor as input * 0 - 4D tensor as input
* 1 - 4D tensor with max value indexes * 1 - 4D tensor with max value indexes
* *
* Int params: * Int params:
* 9 int with 2x4 vectors and 1 bool value * 9 int with 2x4 vectors and 1 bool value
*/ */

62
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/LayerOpValidation.java
View File

@ -39,14 +39,7 @@ import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.convolution.AvgPooling2D; import org.nd4j.linalg.api.ops.impl.layers.convolution.AvgPooling2D;
import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2D; import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2D;
import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2DDerivative; import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2DDerivative;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv1DConfig; import org.nd4j.linalg.api.ops.impl.layers.convolution.config.*;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv3DConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.DeConv2DConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.DeConv3DConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.LocalResponseNormalizationConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling2DConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling3DConfig;
import org.nd4j.linalg.api.ops.impl.transforms.custom.LayerNorm; import org.nd4j.linalg.api.ops.impl.transforms.custom.LayerNorm;
import org.nd4j.linalg.api.ops.impl.transforms.custom.Standardize; import org.nd4j.linalg.api.ops.impl.transforms.custom.Standardize;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
@ -944,7 +937,7 @@ public class LayerOpValidation extends BaseOpValidation {
Conv1DConfig conv1DConfig = Conv1DConfig.builder() Conv1DConfig conv1DConfig = Conv1DConfig.builder()
.k(k).p(0).s(1) .k(k).p(0).s(1)
.isSameMode(false) .paddingMode(PaddingMode.VALID)
.build(); .build();
SDVariable out = sd.cnn().conv1d(in, w, conv1DConfig); SDVariable out = sd.cnn().conv1d(in, w, conv1DConfig);
@ -960,6 +953,55 @@ public class LayerOpValidation extends BaseOpValidation {
assertNull(err); assertNull(err);
} }
@Test
public void testConv1dCausal() {
Nd4j.getRandom().setSeed(12345);
int nIn = 3;
int nOut = 4;
int mb = 2;
for( int k : new int[]{2, 3}) {
for (int sz : new int[]{3, 4, 5}) {
for (int s : new int[]{1, 2}) {
for (int d : new int[]{1, 2}) {
for (boolean ncw : new boolean[]{true, false}) {
SameDiff sd = SameDiff.create();
INDArray wArr = Nd4j.rand(DataType.DOUBLE, k, nIn, nOut);
INDArray inArr = Nd4j.rand(DataType.DOUBLE, (ncw ? new long[]{mb, nIn, sz} : new long[]{mb, sz, nIn}));
INDArray bArr = Nd4j.rand(DataType.DOUBLE, nOut);
SDVariable in = sd.var("in", inArr);
SDVariable w = sd.var("W", wArr);
SDVariable b = sd.var("b", bArr);
Conv1DConfig conv1DConfig = Conv1DConfig.builder()
.dataFormat(ncw ? Conv1DConfig.NCW : Conv1DConfig.NWC)
.k(k).p(0).s(s).d(d)
.paddingMode(PaddingMode.CAUSAL)
.build();
SDVariable out = sd.cnn().conv1d(in, w, b, conv1DConfig);
SDVariable loss = sd.nn().tanh(out).std(true).rename("loss");
sd.setLossVariables("loss");
String name = "k=" + k + ", sz=" + sz + ", ncw=" + ncw;
System.out.println(name);
TestCase tc = new TestCase(sd).testName(name).gradientCheck(true);
String err = OpValidation
.validate(tc);
assertNull(err);
}
}
}
}
}
}
@Test @Test
public void testConv1dForward(){ public void testConv1dForward(){
int nIn = 2; int nIn = 2;
@ -1254,7 +1296,7 @@ public class LayerOpValidation extends BaseOpValidation {
Conv1DConfig conv1DConfig = Conv1DConfig.builder() Conv1DConfig conv1DConfig = Conv1DConfig.builder()
.k(k).p(-1).s(0) .k(k).p(-1).s(0)
.isSameMode(false) .paddingMode(PaddingMode.VALID)
.build(); .build();
SDVariable out = sd.cnn().conv1d(in, w, conv1DConfig); SDVariable out = sd.cnn().conv1d(in, w, conv1DConfig);