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Commit 9171010f authored by Olivier BICHLER's avatar Olivier BICHLER
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Merged with main

parents 2ddb1201 79cd2ca3
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with 180 additions and 151 deletions
include/aidge/graphRegex/GraphParser.hpp
+ 5
− 3
View file @ 9171010f
......@@ -12,15 +12,17 @@ namespace Aidge{
/**
* @brief this class uses the lexer to create an AST according to a set of gramer rules
*/
class GraphParser{
class GraphParser {
public:
public:
/**
* @brief AST graph creation function
* @param gRegexExpressions String representing the logical fuction to be performed
*/
GraphParser(const std::string gRegexExpressions);
~GraphParser() noexcept;
/**
* @brief AST graph creation function
* @return The AST tree
......@@ -35,7 +37,7 @@ class GraphParser{
const std::string getQuery();
private:
private:
/**
* @brief restart at the start of the ConditionalExpressions for LEXER and restart mCurrentToken
*/
......
include/aidge/nodeTester/ConditionalParser.hpp
+ 3
− 1
View file @ 9171010f
......@@ -29,7 +29,7 @@ using ASTNodeCh = std::vector<std::shared_ptr<AstNode<ConditionalTokenTypes>>>;
/**
* @brief this class uses the lexer to create an AST according to a set of gramer rules
*/
class ConditionalParser{
class ConditionalParser {
public:
/**
......@@ -38,6 +38,8 @@ class ConditionalParser{
*/
ConditionalParser(const std::string ConditionalExpressions);
~ConditionalParser() noexcept;
/**
* @brief AST graph creation function
* @return The AST tree
......
include/aidge/operator/Add.hpp
+ 1
− 1
View file @ 9171010f
......@@ -47,7 +47,7 @@ public:
Add_Op(const Add_Op& op)
: OperatorTensor(op)
{
mImpl = op.mImpl ? Registrar<Add_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<Add_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/AvgPooling.hpp
+ 39
− 35
View file @ 9171010f
......@@ -60,7 +60,7 @@ public:
: OperatorTensor(op),
Attributes_(op)
{
mImpl = op.mImpl ? Registrar<AvgPooling_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<AvgPooling_Op<DIM>>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......@@ -94,40 +94,44 @@ public:
}
// std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const override {
// if (outputIdx != 0) {
// AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
// }
// if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
// // Offset
// const auto outputIdxDims = mOutput->getCoord(firstIdx);
// std::vector<DimSize_t> inputIdxDims = outputIdxDims;
// for (DimIdx_t i = 0; i < (DIM+2); ++i) {
// if (((outputDims[i] + outputIdxDims[i]) > mOutput->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
// AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
// }
// }
// // padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
// // Width
// std::vector<DimSize_t> inputDims;
// inputDims.push_back(outputDims[0]); // same batch value
// inputDims.push_back(outputDims[1]); // same channel value
// for (DimIdx_t i = 0; i < DIM; ++i) {
// inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
// * this->template getAttr<AvgPoolingAttr::StrideDims>()[static_cast<std::size_t>(i)]
// + 1
// + (this->template getAttr<AvgPoolingAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1));
// inputIdxDims[2+i] *= this->template getAttr<AvgPoolingAttr::StrideDims>()[static_cast<std::size_t>(i)];
// }
// std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res = std::vector<std::pair<std::size_t, std::vector<DimSize_t>>>();
// res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInput->getIdx(inputIdxDims), inputDims));
// return res;
// }
// AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
// }
std::vector<std::pair<std::size_t, std::vector<DimSize_t>>>
computeReceptiveField(const std::size_t firstIdx,
const std::vector<DimSize_t>& outputDims,
const IOIndex_t outputIdx = 0) const override final
{
if (outputIdx != 0) {
AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
}
if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
// Offset
const auto outputIdxDims = mOutputs[0]->getCoord(firstIdx);
std::vector<DimSize_t> inputIdxDims = outputIdxDims;
for (DimIdx_t i = 0; i < (DIM+2); ++i) {
if (((outputDims[i] + outputIdxDims[i]) > mOutputs[0]->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
}
}
// padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
// Width
std::vector<DimSize_t> inputDims;
inputDims.push_back(outputDims[0]); // same batch value
inputDims.push_back(outputDims[1]); // same channel value
for (DimIdx_t i = 0; i < DIM; ++i) {
inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
* this->template getAttr<AvgPoolingAttr::StrideDims>()[static_cast<std::size_t>(i)]
+ 1
+ (this->template getAttr<AvgPoolingAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1));
inputIdxDims[2+i] *= this->template getAttr<AvgPoolingAttr::StrideDims>()[static_cast<std::size_t>(i)];
}
std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res;
res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[0]->getIdx(inputIdxDims), inputDims));
return res;
}
AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
}
void setBackend(const std::string &name, int device = 0) override {
......
include/aidge/operator/BatchNorm.hpp
+ 1
− 1
View file @ 9171010f
......@@ -54,7 +54,7 @@ public:
: OperatorTensor(op),
Attributes_(op)
{
mImpl = op.mImpl ? Registrar<BatchNorm_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<BatchNorm_Op<DIM>>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/Concat.hpp
+ 1
− 1
View file @ 9171010f
......@@ -55,7 +55,7 @@ public:
: OperatorTensor(op),
Attributes_(op)
{
mImpl = op.mImpl ? Registrar<Concat_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<Concat_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/Conv.hpp
+ 55
− 53
View file @ 9171010f
......@@ -65,7 +65,7 @@ public:
: OperatorTensor(op),
Attributes_(op)
{
mImpl = op.mImpl ? Registrar<Conv_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<Conv_Op<DIM>>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......@@ -77,9 +77,9 @@ public:
}
// Data operator[](const char* inputName) override final {
// std::shared_ptr<Tensor> in = (strcmp(inputName, "data")) ? mInputs[0] :
// (strcmp(inputName, "weight") ? mInputs[1] :
// (strcmp(inputName, "bias") ? mInputs[2] :
// std::shared_ptr<Tensor> in = (strcmp(inputName, "data")) ? getInput(0) :
// (strcmp(inputName, "weight") ? getInput(1) :
// (strcmp(inputName, "bias") ? getInput(2) :
// nullptr));
// assert((in!=nullptr) && "No such parameter");
// return *in;
......@@ -119,55 +119,57 @@ public:
}
// std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const override {
// if (outputIdx != 0) {
// AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
// }
// if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
// // Offset
// const auto outputIdxDims = mOutput->getCoord(firstIdx);
// auto inputIdxDims = outputIdxDims; // batch idx is the same
// inputIdxDims[1] = 0; // each channel is used so start with the first one
// for (DimIdx_t i = 0; i < (DIM+2); ++i) {
// if (((outputDims[i] + outputIdxDims[i]) > mOutput->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
// AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
// }
// }
// // padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
// // Input
// // same batch value, every input channel is used
// std::vector<DimSize_t> inputDims{outputDims[0], mInputs[0]->dims()[1]};
// for (DimIdx_t i = 0; i < DIM; ++i) {
// inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
// * this->template getAttr<ConvAttr::StrideDims>()[static_cast<std::size_t>(i)]
// + 1
// + (this->template getAttr<ConvAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1)
// * this->template getAttr<ConvAttr::DilationDims>()[static_cast<std::size_t>(i)]);
// inputIdxDims[2+i] *= this->template getAttr<ConvAttr::StrideDims>()[static_cast<std::size_t>(i)];
// }
// // Weight
// // same output value, every input channel is used
// std::vector<DimSize_t> weightDims{outputDims[0], mInputs[0]->dims()[1]};
// weightDims.insert(weightDims.end(), this->template getAttr<ConvAttr::KernelDims>()[0], this->template getAttr<ConvAttr::KernelDims>()[static_cast<std::size_t>(DIM)]);
// std::vector<DimSize_t> weightIdxDims = std::vector<DimSize_t>(DIM+2, 0);
// weightIdxDims[0] = outputIdxDims[1];
// // Bias
// const std::vector<DimSize_t> biasDims{outputDims[0]};
// const std::vector<DimSize_t> biasIdxDims{outputIdxDims[1]};
// // Result
// std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res;
// res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[0]->getIdx(inputIdxDims), inputDims));
// res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[1]->getIdx(weightIdxDims), weightDims));
// res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[2]->getIdx(biasIdxDims), biasDims));
// return res;
// }
// AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
// }
std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const override {
if (outputIdx != 0) {
AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
}
if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
// Offset
const auto outputIdxDims = mOutputs[0]->getCoord(firstIdx);
auto inputIdxDims = outputIdxDims; // batch idx is the same
inputIdxDims[1] = 0; // each channel is used so start with the first one
for (DimIdx_t i = 0; i < (DIM+2); ++i) {
if (((outputDims[i] + outputIdxDims[i]) > mOutputs[0]->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
}
}
// padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
// Input
// same batch value, every input channel is used
std::vector<DimSize_t> inputDims{outputDims[0], getInput(0)->dims()[1]};
for (DimIdx_t i = 0; i < DIM; ++i) {
inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
* this->template getAttr<ConvAttr::StrideDims>()[static_cast<std::size_t>(i)]
+ 1
+ (this->template getAttr<ConvAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1)
* this->template getAttr<ConvAttr::DilationDims>()[static_cast<std::size_t>(i)]);
inputIdxDims[2+i] *= this->template getAttr<ConvAttr::StrideDims>()[static_cast<std::size_t>(i)];
}
// Weight
// same output value, every input channel is used
std::vector<DimSize_t> weightDims{outputDims[1], getInput(0)->dims()[1]};
for (std::size_t i = 0; i < DIM; ++i) {
weightDims.push_back(this->template getAttr<ConvAttr::KernelDims>()[i]);
}
std::vector<DimSize_t> weightIdxDims = std::vector<DimSize_t>(DIM+2, 0);
weightIdxDims[0] = outputIdxDims[1];
// Bias
const std::vector<DimSize_t> biasDims{outputDims[1]}; // the number of output channel
const std::vector<DimSize_t> biasIdxDims{outputIdxDims[1]};
// Result
std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res;
res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(0)->getIdx(inputIdxDims), inputDims));
res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(1)->getIdx(weightIdxDims), weightDims));
res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(2)->getIdx(biasIdxDims), biasDims));
return res;
}
AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
}
void setBackend(const std::string &name, int device = 0) override {
mImpl = Registrar<Conv_Op<DIM>>::create(name)(*this);
......
include/aidge/operator/ConvDepthWise.hpp
+ 50
− 36
View file @ 9171010f
......@@ -67,7 +67,7 @@ public:
: OperatorTensor(op),
Attributes_(op)
{
mImpl = op.mImpl ? Registrar<ConvDepthWise_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<ConvDepthWise_Op<DIM>>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......@@ -115,41 +115,55 @@ public:
}
}
// std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const override {
// if (outputIdx != 0) {
// AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
// }
// if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
// // Offset
// const auto outputIdxDims = mOutput->getCoord(firstIdx);
// auto inputIdxDims = outputIdxDims; // batch idx is the same
// for (DimIdx_t i = 0; i < (DIM+2); ++i) {
// if (((outputDims[i] + outputIdxDims[i]) > mOutput->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
// AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
// }
// }
// // padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
// // Width
// std::vector<DimSize_t> inputDims;
// inputDims.push_back(outputDims[0]); // same batch value
// inputDims.push_back(outputDims[1]); // same channel value
// for (DimIdx_t i = 0; i < DIM; ++i) {
// inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
// * this->template getAttr<ConvDepthWiseAttr::StrideDims>()[static_cast<std::size_t>(i)]
// + 1
// + (this->template getAttr<ConvDepthWiseAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1)
// * this->template getAttr<ConvDepthWiseAttr::DilationDims>()[static_cast<std::size_t>(i)]);
// inputIdxDims[2+i] *= this->template getAttr<ConvDepthWiseAttr::StrideDims>()[static_cast<std::size_t>(i)];
// }
// std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res = std::vector<std::pair<std::size_t, std::vector<DimSize_t>>>();
// res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[0]->getIdx(inputIdxDims), inputDims));
// return res;
// }
// AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
// }
std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const override {
if (outputIdx != 0) {
AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
}
if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
// Offset
const auto outputIdxDims = mOutputs[0]->getCoord(firstIdx);
auto inputIdxDims = outputIdxDims; // batch idx is the same
for (DimIdx_t i = 0; i < (DIM+2); ++i) {
if (((outputDims[i] + outputIdxDims[i]) > mOutputs[0]->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
}
}
// padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
// Input
// same batch value
std::vector<DimSize_t> inputDims{outputDims[0], outputDims[1]};
for (DimIdx_t i = 0; i < DIM; ++i) {
inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
* this->template getAttr<ConvDepthWiseAttr::StrideDims>()[static_cast<std::size_t>(i)]
+ 1
+ (this->template getAttr<ConvDepthWiseAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1)
* this->template getAttr<ConvDepthWiseAttr::DilationDims>()[static_cast<std::size_t>(i)]);
inputIdxDims[2+i] *= this->template getAttr<ConvDepthWiseAttr::StrideDims>()[static_cast<std::size_t>(i)];
}
// Weight
std::vector<DimSize_t> weightDims{outputDims[1], 1};
for (std::size_t i = 0; i < DIM; ++i) {
weightDims.push_back(this->template getAttr<ConvDepthWiseAttr::KernelDims>()[i]);
}
std::vector<DimSize_t> weightIdxDims = std::vector<DimSize_t>(DIM+2, 0);
weightIdxDims[0] = outputIdxDims[1];
// Bias
const std::vector<DimSize_t> biasDims{outputDims[1]}; // the number of output channel
const std::vector<DimSize_t> biasIdxDims{outputIdxDims[1]};
// Result
std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res;
res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(0)->getIdx(inputIdxDims), inputDims));
res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(1)->getIdx(weightIdxDims), weightDims));
res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(2)->getIdx(biasIdxDims), biasDims));
return res;
}
AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
}
void setBackend(const std::string &name, int device = 0) override {
mImpl = Registrar<ConvDepthWise_Op<DIM>>::create(name)(*this);
......
include/aidge/operator/Div.hpp
+ 1
− 1
View file @ 9171010f
......@@ -40,7 +40,7 @@ public:
Div_Op(const Div_Op& op)
: OperatorTensor(op)
{
mImpl = op.mImpl ? Registrar<Div_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<Div_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/FC.hpp
+ 1
− 1
View file @ 9171010f
......@@ -57,7 +57,7 @@ public:
: OperatorTensor(op),
Attributes_(op)
{
mImpl = op.mImpl ? Registrar<FC_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<FC_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/LeakyReLU.hpp
+ 1
− 1
View file @ 9171010f
......@@ -54,7 +54,7 @@ public:
: OperatorTensor(op),
Attributes_(op)
{
mImpl = op.mImpl ? Registrar<LeakyReLU_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<LeakyReLU_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/MatMul.hpp
+ 1
− 1
View file @ 9171010f
......@@ -56,7 +56,7 @@ public:
: OperatorTensor(op),
Attributes_(op)
{
mImpl = op.mImpl ? Registrar<MatMul_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<MatMul_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/MaxPooling.hpp
+ 1
− 1
View file @ 9171010f
......@@ -64,7 +64,7 @@ public:
: OperatorTensor(op),
Attributes_(op)
{
mImpl = op.mImpl ? Registrar<MaxPooling_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<MaxPooling_Op<DIM>>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/Mul.hpp
+ 1
− 1
View file @ 9171010f
......@@ -43,7 +43,7 @@ public:
Mul_Op(const Mul_Op& op)
: OperatorTensor(op)
{
mImpl = op.mImpl ? Registrar<Mul_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<Mul_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/Operator.hpp
+ 0
− 9
View file @ 9171010f
......@@ -74,15 +74,6 @@ public:
virtual std::shared_ptr<Operator> clone() const = 0;
virtual void associateInput(const IOIndex_t inputIdx, const std::shared_ptr<Data>& data) = 0;
/**
* @brief For a given output feature area, compute the associated receptive
* field for each data input.
* @param firstIdx First index of the output feature.
* @param outputDims Size of output feature.
* @param outputIdx Index of the output. Default 0.
* @return std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> For each dataInput Tensor of the Operator, the first index and dimensions of the feature area.
*/
// virtual std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const;
/**
* @brief Set the specified input by performing a deep copy of the given data.
......
include/aidge/operator/OperatorTensor.hpp
+ 12
− 1
View file @ 9171010f
......@@ -56,7 +56,8 @@ public:
mInputs(std::vector<std::shared_ptr<Tensor>>(other.nbInputs(), nullptr)),
mOutputs(std::vector<std::shared_ptr<Tensor>>(other.nbOutputs())) {
for (std::size_t i = 0; i < static_cast<std::size_t>(nbOutputs()); ++i) {
mOutputs[i] = std::make_shared<Tensor>(*(other.getOutput(i)));
mOutputs[i] = std::make_shared<Tensor>();
// mOutputs[i] = std::make_shared<Tensor>(*(other.getOutput(i)));
// datatype already copied
}
}
......@@ -90,6 +91,16 @@ public:
///////////////////////////////////////////////////
// Tensor dimensions
/**
* @brief For a given output feature area, compute the associated receptive
* field for each data input.
* @param firstIdx First index of the output feature.
* @param outputDims Size of output feature.
* @param outputIdx Index of the output. Default 0.
* @return std::vector<std::pair<std::size_t, std::vector<DimSize_t>>>
* For each dataInput Tensor of the Operator, the first index and dimensions of the feature area.
*/
virtual std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const;
virtual void computeOutputDims();
virtual bool outputDimsForwarded() const;
///////////////////////////////////////////////////
......
include/aidge/operator/Pow.hpp
+ 1
− 1
View file @ 9171010f
......@@ -40,7 +40,7 @@ public:
Pow_Op(const Pow_Op& op)
: OperatorTensor(op)
{
mImpl = op.mImpl ? Registrar<Pow_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<Pow_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/Producer.hpp
+ 4
− 1
View file @ 9171010f
......@@ -51,7 +51,10 @@ public:
Producer_Op(const Producer_Op& op)
: OperatorTensor(op)
{
mImpl = op.mImpl ? Registrar<Producer_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
for (std::size_t i = 0; i < static_cast<std::size_t>(nbOutputs()); ++i) {
mOutputs[i] = std::make_shared<Tensor>(*(op.getOutput(i)));
}
mImpl = op.mImpl ? Registrar<Producer_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/ReLU.hpp
+ 1
− 1
View file @ 9171010f
......@@ -39,7 +39,7 @@ public:
ReLU_Op(const ReLU_Op& op)
: OperatorTensor(op)
{
mImpl = op.mImpl ? Registrar<ReLU_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<ReLU_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
include/aidge/operator/Scaling.hpp
+ 1
− 1
View file @ 9171010f
......@@ -55,7 +55,7 @@ public:
: OperatorTensor(op),
Attributes_(op)
{
mImpl = op.mImpl ? Registrar<Scaling_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
mImpl = op.mImpl ? Registrar<Scaling_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
......
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