diff --git a/include/aidge/operator/Add.hpp b/include/aidge/operator/Add.hpp
index b5e37f9bc52d4a74dabf68022235feadc384748f..0c285402929ab7b071d732180891de1b738dc4a8 100644
--- a/include/aidge/operator/Add.hpp
+++ b/include/aidge/operator/Add.hpp
@@ -19,29 +19,25 @@
#include <vector>
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/utils/Types.h"
+#include "aidge/utils/ErrorHandling.hpp"
namespace Aidge {
-class Add_Op : public Operator,
+class Add_Op : public OperatorTensor,
public Registrable<Add_Op, std::string, std::unique_ptr<OperatorImpl>(const Add_Op&)> {
-private:
- // FIXME: change accessibility
- std::vector<std::shared_ptr<Tensor>> mInputs;
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "Add";
Add_Op(const IOIndex_t nbIn)
- : Operator(Type),
- mInputs(std::vector<std::shared_ptr<Tensor>>(nbIn, std::make_shared<Tensor>()))
+ : OperatorTensor(Type, nbIn, 0, 1)
{
- assert(nbIn > 0 && "Add should have at least one input");
- setDatatype(DataType::Float32);
+ if (nbIn == 0) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Add operator should have at least one input.");
+ }
}
/**
@@ -49,14 +45,9 @@ public:
* @param op Operator to copy.
*/
Add_Op(const Add_Op& op)
- : Operator(Type),
- mInputs(std::vector<std::shared_ptr<Tensor>>(op.nbInputs())),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op)
{
- // cpy-ctor
- assert(op.nbInputs() > 0 && "Add should have at least one input");
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Add_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Add_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -76,88 +67,25 @@ public:
// return *in;
// }
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(static_cast<std::size_t>(inputIdx) < nbInputs() && "wrong inputIdx for Add operator.");
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
-
- mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- }
-
- void computeOutputDims() override final {
- if (!mInputs[0]->empty()) {
- const auto expectedDims = mInputs[0]->dims();
- std::size_t nonEmptyInputTensor = 1;
- for (; nonEmptyInputTensor < nbInputs() && (!mInputs[nonEmptyInputTensor]->empty()); ++nonEmptyInputTensor) {
- assert(expectedDims == mInputs[nonEmptyInputTensor]->dims());
- }
- if (nonEmptyInputTensor == nbInputs()) {
- mOutput->resize(expectedDims);
- }
- }
- }
-
- bool outputDimsForwarded() const override final {
- std::size_t forwarded = 0;
- for (; forwarded < nbInputs() && (!mInputs[forwarded]->empty()); ++forwarded) {}
- return ((forwarded==nbInputs()) && !(mOutput->empty()));
- }
// void checkDims() const override final {
// assert(outputDimsForwarded());
// for (const auto& in : mInputs) {
- // assert(in->dims() == mOutput->dims());
+ // assert(in->dims() == mOutputs[0]->dims());
// }
// }
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(static_cast<std::size_t>(inputIdx) < nbInputs() && "wrong inputIdx for Add operator.");
- return *(mInputs[inputIdx].get());
- }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert(static_cast<std::size_t>(inputIdx) < nbInputs() && "wrong inputIdx for Add operator.");
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "Add Operators has only 1 outputs");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(static_cast<std::size_t>(inputIdx) < nbInputs() && "wrong inputIdx for Add operator.");
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
void setBackend(const std::string& name) override {
mImpl = Registrar<Add_Op>::create(name)(*this);
- mOutput->setBackend(name);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
for (std::size_t i = 0; i < nbInputs(); ++i) {
- mInputs[i]->setBackend(name);
+ getInput(i)->setBackend(name);
}
}
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
-
- // FIXME: temporary workaround
- for (std::size_t i = 0; i < nbInputs(); ++i) {
- mInputs[i]->setDatatype(datatype);
- }
- }
-
- inline IOIndex_t nbInputs() const noexcept override final { return mInputs.size(); }
- inline IOIndex_t nbDataInputs() const noexcept override final { return mInputs.size(); }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
-
static const std::vector<std::string> getInputsName(){
return {"data_input_0", "data_input_n"};
}
diff --git a/include/aidge/operator/AvgPooling.hpp b/include/aidge/operator/AvgPooling.hpp
index 994239bc14c477691a39724c2778578023967da0..603fa94ce7e23021dfb9ec027d30ab0fb08aa37e 100644
--- a/include/aidge/operator/AvgPooling.hpp
+++ b/include/aidge/operator/AvgPooling.hpp
@@ -50,22 +50,17 @@ public:
const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1))
: OperatorTensor(Type, 1, 0, 1),
Attributes_(attr<AvgPoolingAttr::StrideDims>(stride_dims),
- attr<AvgPoolingAttr::KernelDims>(kernel_dims)) {
- setDataType(DataType::Float32);
- }
+ attr<AvgPoolingAttr::KernelDims>(kernel_dims)) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
AvgPooling_Op(const AvgPooling_Op<DIM>& op)
- : OperatorTensor(Type, 1, 0, 1),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- setDataType(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<AvgPooling_Op<DIM>>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<AvgPooling_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -78,18 +73,23 @@ public:
void computeOutputDims() override final {
- if (!*mInputs[0]->empty()) {
- std::array<DimSize_t, DIM + 2> outputDims = {};
+ // check inputs have been associated
+ if (!getInput(0)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+ }
+ if (!(getInput(0)->empty())) {
+ std::array<DimSize_t, DIM + 2> outputDims;
+ const std::array<DimSize_t, DIM + 2> inputDims(getInput(0)->dims<DIM+2>());
+ outputDims[0] = inputDims[0];
+ outputDims[1] = inputDims[1];
for (std::size_t dim = 0; dim < this->template getAttr<AvgPoolingAttr::KernelDims>().size() ; ++dim) {
outputDims[dim+2] = 1 + static_cast<DimSize_t>(
- std::floor(static_cast<float>(*mInputs[0]->dims()[dim+2] -
+ std::floor(static_cast<float>(inputDims[dim+2] -
this->template getAttr<AvgPoolingAttr::KernelDims>()[dim]) /
static_cast<float>(this->template getAttr<AvgPoolingAttr::StrideDims>()[dim])));
}
- outputDims[1] = *mInputs[0]->dims()[1];
- outputDims[0] = *mInputs[0]->dims()[0];
- mOutputs[0]->resize(outputDims);
+ getOutput(0)->resize(outputDims);
}
}
@@ -132,10 +132,10 @@ public:
void setBackend(const std::string &name) override {
mImpl = Registrar<AvgPooling_Op<DIM>>::create(name)(*this);
- mOutput->setBackend(name);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInput->setBackend(name);
+ getInput(0)->setBackend(name);
}
static const std::vector<std::string> getInputsName(){
diff --git a/include/aidge/operator/BatchNorm.hpp b/include/aidge/operator/BatchNorm.hpp
index da7360c8ba3816cdfe1d2d00f80b08808a80f961..09a9bb9efac81431673ef3449f717fbcb9af5108 100644
--- a/include/aidge/operator/BatchNorm.hpp
+++ b/include/aidge/operator/BatchNorm.hpp
@@ -19,27 +19,20 @@
#include "aidge/utils/Types.h"
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/operator/Producer.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
namespace Aidge {
-enum class BatchNormAttr { Epsilon, Momentum };
+enum class BatchNormAttr { Epsilon, Momentum };
template <DimIdx_t DIM>
-class BatchNorm_Op : public Operator,
+class BatchNorm_Op : public OperatorTensor,
public Registrable<BatchNorm_Op<DIM>, std::string, std::unique_ptr<OperatorImpl>(const BatchNorm_Op<DIM> &)>,
public StaticAttributes<BatchNormAttr, float, float> {
public:
- // FIXME: change accessibility
- std::array<std::shared_ptr<Tensor>, 5> mInputs = {std::make_shared<Tensor>(), std::make_shared<Tensor>(),
- std::make_shared<Tensor>(), std::make_shared<Tensor>(),
- std::make_shared<Tensor>()};
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
- public:
static constexpr const char *Type = "BatchNorm";
BatchNorm_Op() = delete;
@@ -49,25 +42,19 @@ public:
using attr = typename Attributes_::template attr<e>;
constexpr BatchNorm_Op(float epsilon, float momentum)
- : Operator(Type),
+ : OperatorTensor(Type, 1, 4, 1),
Attributes_(attr<BatchNormAttr::Epsilon>(epsilon),
- attr<BatchNormAttr::Momentum>(momentum)),
- mOutput(std::make_shared<Tensor>()) {
- setDatatype(DataType::Float32);
- }
+ attr<BatchNormAttr::Momentum>(momentum)) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
BatchNorm_Op(const BatchNorm_Op<DIM>& op)
- : Operator(Type),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<BatchNorm_Op<DIM>>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<BatchNorm_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -87,83 +74,41 @@ public:
// return *in;
// }
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx < 5 && "operators supports only 5 inputs");
- assert(strcmp(data->type(), Tensor::Type) == 0 && "input data must be of Tensor type");
-
- mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- }
void computeOutputDims() override final {
- if (!mInputs[0]->empty()) {
- for (std::size_t i = nbDataInputs(); i < nbInputs(); ++i) {
- if(mInputs[i]->size() != mInputs[0]->dims()[1]) {
- mInputs[i]->resize(std::array<DimSize_t, 1>({mInputs[0]->dims()[1]}));
+ // check inputs have been associated
+ bool associated = true;
+ for (IOIndex_t i = 0; i < nbInputs(); ++i) {
+ associated &= !(getInput(i)->empty());
+ }
+ if (associated) {
+ const DimSize_t nbChannels = getInput(0)->dims()[1];
+ for (std::size_t i = nbData(); i < nbInputs(); ++i) {
+ if(getInput(i)->size() != nbChannels) {
+ // /!\ Input size should be handled BEFORE calling this function
+ // This should raise an error
+ getInput(i)->resize(std::array<DimSize_t, 1>({getInput(0)->dims()[1]}));
}
}
- mOutput->resize(mInputs[0]->dims());
+ mOutputs[0]->resize(getInput(0)->dims());
}
}
- bool outputDimsForwarded() const override final { return !(mOutput->empty()); }
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 5 && "operators supports only 5 inputs");
- return *(mInputs[inputIdx].get()); }
-
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 5 && "BatchNorm Operators supports only 5 inputs");
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "BatchNorm Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 5 && "operators supports only 5 inputs");
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
-
void setBackend(const std::string &name) override {
mImpl = Registrar<BatchNorm_Op<DIM>>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInputs[1]->setBackend(name);
- mInputs[2]->setBackend(name);
- mInputs[3]->setBackend(name);
- mInputs[4]->setBackend(name);
- }
-
- void setDatatype(const DataType &datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInputs[1]->setDatatype(datatype);
- mInputs[2]->setDatatype(datatype);
- mInputs[3]->setDatatype(datatype);
- mInputs[4]->setDatatype(datatype);
+ getInput(1)->setBackend(name);
+ getInput(2)->setBackend(name);
+ getInput(3)->setBackend(name);
+ getInput(4)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 5; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
- static const std::vector<std::string> getInputsName(){
+ static const std::vector<std::string> getInputsName() {
return {"data_input", "scale", "shift", "mean", "variance"};
}
- static const std::vector<std::string> getOutputsName(){
+ static const std::vector<std::string> getOutputsName() {
return {"data_output"};
}
};
@@ -187,4 +132,4 @@ template <>
const char *const EnumStrings<Aidge::BatchNormAttr>::data[] = { "Epsilon", "Momentum" };
}
-#endif //AIDGE_CORE_OPERATOR_BATCHNORM_H_
+#endif //AIDGE_CORE_OPERATOR_BATCHNORM_H_
\ No newline at end of file
diff --git a/include/aidge/operator/Concat.hpp b/include/aidge/operator/Concat.hpp
index 31b99370d12a16020ea7c6f9d35c08d9f616f10f..7f97eee0078235db6a17593850d38fa6cd3df753 100644
--- a/include/aidge/operator/Concat.hpp
+++ b/include/aidge/operator/Concat.hpp
@@ -19,7 +19,7 @@
#include <vector>
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/utils/StaticAttributes.hpp"
@@ -28,14 +28,9 @@
namespace Aidge {
enum class ConcatAttr { Axis };
-class Concat_Op : public Operator,
+class Concat_Op : public OperatorTensor,
public Registrable<Concat_Op, std::string, std::unique_ptr<OperatorImpl>(const Concat_Op&)>,
public StaticAttributes<ConcatAttr, DimSize_t> {
-private:
- // FIXME: change accessibility
- std::vector<std::shared_ptr<Tensor>> mInputs;
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "Concat";
@@ -44,12 +39,12 @@ public:
using attr = typename Attributes_::template attr<e>;
Concat_Op(const IOIndex_t nbIn, const DimSize_t axis)
- : Operator(Type),
- mInputs(std::vector<std::shared_ptr<Tensor>>(nbIn, std::make_shared<Tensor>())),
+ : OperatorTensor(Type, nbIn, 0, 1),
Attributes_(attr<ConcatAttr::Axis>(axis))
{
- assert(nbIn > 0 && "Concat should have at least one input");
- setDatatype(DataType::Float32);
+ if (nbIn == 0) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Add operator should have at least one input.");
+ }
}
/**
@@ -57,15 +52,10 @@ public:
* @param op Operator to copy.
*/
Concat_Op(const Concat_Op& op)
- : Operator(Type),
- Attributes_(op),
- mInputs(std::vector<std::shared_ptr<Tensor>>(op.nbInputs(), std::make_shared<Tensor>())),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- assert(op.nbInputs() > 0 && "Concat should have at least one input");
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Concat_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Concat_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -85,90 +75,32 @@ public:
// return *in;
// }
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(static_cast<std::size_t>(inputIdx) < nbInputs() && "wrong inputIdx for Concat operator.");
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
-
- mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- }
void computeOutputDims() override final {
- bool computable = !(mInputs[0]->empty()) && (getAttr<ConcatAttr::Axis>() < mInputs[0]->nbDims());
- for (const auto& input : mInputs) {
- computable &= !(input->empty());
- computable &= (input->nbDims() == mInputs[0]->nbDims());
- }
// Every input is non-empty with the same number of dimensions
- if (computable) {
- auto outputDims = mInputs[0]->dims();
-
- for (std::size_t i = 1; i < nbInputs(); ++i) {
- outputDims[getAttr<ConcatAttr::Axis>()] += mInputs[i]->dims()[getAttr<ConcatAttr::Axis>()];
+ bool associated = (getInput(0) != nullptr);
+ associated &= !(getInput(0)->empty()) && (getAttr<ConcatAttr::Axis>() < getInput(0)->nbDims()); // do not compute anything if no input
+ auto outputDims = getInput(0)->dims();
+ const auto firstInputNbDims = getInput(0) -> nbDims();
+ for (IOIndex_t i = 1; i < nbInputs(); ++i) {
+ if (!getInput(i)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+ }
+ associated &= (getInput(i)->nbDims() == firstInputNbDims);
+ for (DimSize_t dim = 0; dim < firstInputNbDims; ++dim) {
+ if (dim == getAttr<ConcatAttr::Axis>()) {
+ outputDims[dim] += getInput(i)->dims()[dim];
+ }
+ else {
+ associated &= (getInput(i)->dims()[dim] == outputDims[dim]);
+ }
}
- mOutput->resize(outputDims);
- }
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
- // void checkDims() const override final {
- // assert(outputDimsForwarded());
- // for (const auto& in : mInputs) {
- // assert(in->dims() == mOutput->dims());
- // }
- // }
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(static_cast<std::size_t>(inputIdx) < nbInputs() && "wrong inputIdx for Concat operator.");
- return *(mInputs[inputIdx].get());
- }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert(static_cast<std::size_t>(inputIdx) < nbInputs() && "wrong inputIdx for Concat operator.");
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "Concat Operators has only 1 outputs");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(static_cast<std::size_t>(inputIdx) < nbInputs() && "wrong inputIdx for Concat operator.");
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
-
- void setBackend(const std::string& name) override {
- mImpl = Registrar<Concat_Op>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- for (std::size_t i = 0; i < nbInputs(); ++i) {
- mInputs[i]->setBackend(name);
}
- }
-
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
-
- // FIXME: temporary workaround
- for (std::size_t i = 0; i < nbInputs(); ++i) {
- mInputs[i]->setDatatype(datatype);
+ if (associated) {
+ getOutput(0)->resize(outputDims);
}
}
- inline IOIndex_t nbInputs() const noexcept override final { return mInputs.size(); }
- inline IOIndex_t nbDataInputs() const noexcept override final { return mInputs.size(); }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
-
static const std::vector<std::string> getInputsName(){
return {"data_input_0", "data_input_n"};
}
diff --git a/include/aidge/operator/Conv.hpp b/include/aidge/operator/Conv.hpp
index 5bce05413f344a7b69f9fcb1b31e989e6fbbe73a..6c6e64db146b671e09e4549712e2a1e87c2c212a 100644
--- a/include/aidge/operator/Conv.hpp
+++ b/include/aidge/operator/Conv.hpp
@@ -45,32 +45,27 @@ public:
template <ConvAttr e>
using attr = typename Attributes_::template attr<e>;
- constexpr Conv_Op(DimSize_t in_channels,
- DimSize_t out_channels,
- const std::array<DimSize_t, DIM> &kernel_dims,
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1))
+ constexpr Conv_Op(DimSize_t inChannels,
+ DimSize_t outChannels,
+ const std::array<DimSize_t, DIM> &kernelDims,
+ const std::array<DimSize_t, DIM> &strideDims = create_array<DimSize_t,DIM>(1),
+ const std::array<DimSize_t, DIM> &dilationDims = create_array<DimSize_t,DIM>(1))
: OperatorTensor(Type, 1, 2, 1),
- Attributes_(attr<ConvAttr::StrideDims>(stride_dims),
- attr<ConvAttr::DilationDims>(dilation_dims),
- attr<ConvAttr::InChannels>(in_channels),
- attr<ConvAttr::OutChannels>(out_channels),
- attr<ConvAttr::KernelDims>(kernel_dims)) {
- setDataType(DataType::Float32);
- }
+ Attributes_(attr<ConvAttr::StrideDims>(strideDims),
+ attr<ConvAttr::DilationDims>(dilationDims),
+ attr<ConvAttr::InChannels>(inChannels),
+ attr<ConvAttr::OutChannels>(outChannels),
+ attr<ConvAttr::KernelDims>(kernelDims)) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
Conv_Op(const Conv_Op<DIM>& op)
- : OperatorTensor(Type, 1, 2, 1),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- setDataType(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Conv_Op<DIM>>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Conv_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -95,8 +90,17 @@ public:
// }
void computeOutputDims() override final {
- if (!mInputs[0]->empty()) {
- std::array<DimSize_t, DIM + 2> outputDims = {};
+ // check inputs have been associated
+ bool associated = true;
+ for (IOIndex_t i = 0; i < 3; ++i) {
+ if (!getInput(i)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+ }
+ associated &= !(getInput(i)->empty());
+ }
+ if (associated) {
+ std::array<DimSize_t, DIM + 2> outputDims{};
+ const std::array<DimSize_t, DIM + 2> inputDims(getInput(0)->dims<DIM+2>());
for (std::size_t dim = 0; dim < this->template getAttr<ConvAttr::KernelDims>().size() ; ++dim) {
const DimSize_t kernelExtent = this->template getAttr<ConvAttr::DilationDims>()[dim] *
@@ -104,13 +108,13 @@ public:
1;
outputDims[dim+2] = 1 + static_cast<DimSize_t>(
- floor(static_cast<float>(mInputs[0]->dims()[dim+2] - kernelExtent) /
+ floor(static_cast<float>(inputDims[dim+2] - kernelExtent) /
static_cast<float>(this->template getAttr<ConvAttr::StrideDims>()[dim])));
}
outputDims[1] = this->template getAttr<ConvAttr::OutChannels>();
- outputDims[0] = mInputs[0]->dims()[0];
- mOutput->resize(outputDims);
+ outputDims[0] = inputDims[0];
+ mOutputs[0]->resize(outputDims);
}
}
@@ -167,11 +171,11 @@ public:
void setBackend(const std::string &name) override {
mImpl = Registrar<Conv_Op<DIM>>::create(name)(*this);
- mOutput->setBackend(name);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInputs[1]->setBackend(name);
- mInputs[2]->setBackend(name);
+ getInput(1)->setBackend(name);
+ getInput(2)->setBackend(name);
}
static const std::vector<std::string> getInputsName(){
@@ -183,32 +187,32 @@ public:
};
template <std::array<DimSize_t, 1>::size_type DIM>
-inline std::shared_ptr<Node> Conv(DimSize_t in_channels,
- DimSize_t out_channels,
- const std::array<DimSize_t, DIM> &kernel_dims,
+inline std::shared_ptr<Node> Conv(DimSize_t inChannels,
+ DimSize_t outChannels,
+ const std::array<DimSize_t, DIM> &kernelDims,
const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1)) {
+ const std::array<DimSize_t, DIM> &strideDims = create_array<DimSize_t,DIM>(1),
+ const std::array<DimSize_t, DIM> &dilationDims = create_array<DimSize_t,DIM>(1)) {
// FIXME: properly handle default w&b initialization in every cases
static_assert(DIM<=MaxDim,"Too many kernel dimensions required by Conv, not supported");
- auto conv = std::make_shared<Node>(std::make_shared<Conv_Op<static_cast<DimIdx_t>(DIM)>>(in_channels, out_channels, kernel_dims, stride_dims, dilation_dims), name);
+ auto conv = std::make_shared<Node>(std::make_shared<Conv_Op<static_cast<DimIdx_t>(DIM)>>(inChannels, outChannels, kernelDims, strideDims, dilationDims), name);
// addProducer(conv, 1, append(append(kernel_dims, in_channels), out_channels), "w");
- addProducer(conv, 1, append(out_channels, append(in_channels, kernel_dims)), "w");
- addProducer(conv, 2, std::array<DimSize_t, 1>({out_channels}), "b");
+ addProducer(conv, 1, append(outChannels, append(inChannels, kernelDims)), "w");
+ addProducer(conv, 2, std::array<DimSize_t, 1>({outChannels}), "b");
return conv;
}
// helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
template <DimSize_t DIM>
inline std::shared_ptr<Node> Conv(
- DimSize_t in_channels,
- DimSize_t out_channels,
- DimSize_t const (&kernel_dims)[DIM],
+ DimSize_t inChannels,
+ DimSize_t outChannels,
+ DimSize_t const (&kernelDims)[DIM],
const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1)) {
+ const std::array<DimSize_t, DIM> &strideDims = create_array<DimSize_t,DIM>(1),
+ const std::array<DimSize_t, DIM> &dilationDims = create_array<DimSize_t,DIM>(1)) {
static_assert(DIM<=MaxDim,"Too many kernel dimensions required by Conv, not supported");
- return Conv(in_channels, out_channels, to_array(kernel_dims), name, stride_dims, dilation_dims);
+ return Conv(inChannels, outChannels, to_array(kernelDims), name, strideDims, dilationDims);
}
} // namespace Aidge
@@ -223,4 +227,4 @@ const char *const EnumStrings<Aidge::ConvAttr>::data[] = {
};
}
-#endif /* AIDGE_CORE_OPERATOR_CONV_H_ */
+#endif /* AIDGE_CORE_OPERATOR_CONV_H_ */
\ No newline at end of file
diff --git a/include/aidge/operator/ConvDepthWise.hpp b/include/aidge/operator/ConvDepthWise.hpp
index f58f435ac0a3aebfecb069955c6769d2f4dd353a..c2b0431182e07c1e90f8108b8f8aad9a5266b24a 100644
--- a/include/aidge/operator/ConvDepthWise.hpp
+++ b/include/aidge/operator/ConvDepthWise.hpp
@@ -19,7 +19,7 @@
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/operator/Producer.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
@@ -29,20 +29,14 @@ namespace Aidge {
enum class ConvDepthWiseAttr { StrideDims, DilationDims, Channels, KernelDims };
template <DimIdx_t DIM>
-class ConvDepthWise_Op : public Operator,
+class ConvDepthWise_Op : public OperatorTensor,
public Registrable<ConvDepthWise_Op<DIM>, std::string, std::unique_ptr<OperatorImpl>(const ConvDepthWise_Op<DIM> &)>,
public StaticAttributes<ConvDepthWiseAttr,
std::array<DimSize_t, DIM>,
std::array<DimSize_t, DIM>,
DimSize_t,
std::array<DimSize_t, DIM>> {
- public:
- // FIXME: change accessibility
- std::array<std::shared_ptr<Tensor>, 3> mInputs = {std::make_shared<Tensor>(), std::make_shared<Tensor>(),
- std::make_shared<Tensor>()};
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
- public:
+public:
static constexpr const char *Type = "ConvDepthWise";
ConvDepthWise_Op() = delete;
@@ -58,26 +52,21 @@ class ConvDepthWise_Op : public Operator,
constexpr ConvDepthWise_Op(const std::array<DimSize_t, DIM> &kernel_dims,
const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1))
- : Operator(Type),
+ : OperatorTensor(Type, 1, 2, 1),
Attributes_(attr<ConvDepthWiseAttr::StrideDims>(stride_dims),
attr<ConvDepthWiseAttr::DilationDims>(dilation_dims),
attr<ConvDepthWiseAttr::Channels>(0),
- attr<ConvDepthWiseAttr::KernelDims>(kernel_dims)) {
- setDatatype(DataType::Float32);
- }
+ attr<ConvDepthWiseAttr::KernelDims>(kernel_dims)) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
ConvDepthWise_Op(const ConvDepthWise_Op<DIM>& op)
- : Operator(Type),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<ConvDepthWise_Op<DIM>>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<ConvDepthWise_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -88,16 +77,20 @@ class ConvDepthWise_Op : public Operator,
return std::make_shared<ConvDepthWise_Op<DIM>>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx < 3 && "operators supports only 3 inputs");
- assert(strcmp(data->type(), Tensor::Type) == 0 && "input data must be of Tensor type");
-
- mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- }
void computeOutputDims() override final {
- if (!mInputs[0]->empty()) {
+ // check inputs have been associated
+ // TODO : add a check of inputs dimensions ?
+ bool associated = true;
+ for (IOIndex_t i = 0; i < 3; ++i) {
+ if (!getInput(i)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+ }
+ associated &= !(getInput(i)->empty());
+ }
+ if (associated) {
std::array<DimSize_t, DIM + 2> outputDims = {};
+ const std::array<DimSize_t, DIM + 2> inputDims(getInput(0)->dims<DIM+2>());
for (std::size_t dim = 0; dim < this->template getAttr<ConvDepthWiseAttr::KernelDims>().size() ; ++dim) {
const DimSize_t kernelExtent = this->template getAttr<ConvDepthWiseAttr::DilationDims>()[dim] *
@@ -105,10 +98,10 @@ class ConvDepthWise_Op : public Operator,
1;
outputDims[dim+2] = 1 + static_cast<DimSize_t>(
- floor(static_cast<float>(mInputs[0]->dims()[dim+2] - kernelExtent) /
+ floor(static_cast<float>(inputDims[dim+2] - kernelExtent) /
static_cast<float>(this->template getAttr<ConvDepthWiseAttr::StrideDims>()[dim])));
}
- this->template getAttr<ConvDepthWiseAttr::Channels>() = mInputs[0]->dims()[1];
+ this->template getAttr<ConvDepthWiseAttr::Channels>() = inputDims[1];
// std::array<DimSize_t, DIM+2> weightDims = append(mInputs[0]->dims()[1],append(1, this->template getAttr<ConvDepthWiseAttr::KernelDims>()));
// if (mInputs[1]->empty()) {
// mInputs[1]->resize(weightDims);
@@ -116,14 +109,12 @@ class ConvDepthWise_Op : public Operator,
// if (mInputs[2]->empty()) {
// mInputs[2]->resize({mInputs[0]->dims()[1]});
// }
- outputDims[1] = mInputs[0]->dims()[1];
- outputDims[0] = mInputs[0]->dims()[0];
- mOutput->resize(outputDims);
+ outputDims[1] = inputDims[1];
+ outputDims[0] = inputDims[0];
+ mOutputs[0]->resize(outputDims);
}
}
- bool outputDimsForwarded() const override final { return !(mOutput->empty()); }
-
// 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.");
@@ -160,57 +151,15 @@ class ConvDepthWise_Op : public Operator,
// AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
// }
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 3 && "operators supports only 3 inputs");
- return *(mInputs[inputIdx].get());
- }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 3 && "ConvDepthWise Operators supports only 3 inputs");
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "ConvDepthWise Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 3 && "operators supports only 3 inputs");
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
-
-
void setBackend(const std::string &name) override {
mImpl = Registrar<ConvDepthWise_Op<DIM>>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInputs[1]->setBackend(name);
- mInputs[2]->setBackend(name);
- }
-
- void setDatatype(const DataType &datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInputs[0]->setDatatype(datatype);
- mInputs[1]->setDatatype(datatype);
- mInputs[2]->setDatatype(datatype);
+ getInput(1)->setBackend(name);
+ getInput(2)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 3; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input", "weight", "bias"};
}
@@ -220,27 +169,29 @@ class ConvDepthWise_Op : public Operator,
};
template <std::array<DimSize_t, 1>::size_type DIM>
-inline std::shared_ptr<Node> ConvDepthWise(const std::array<DimSize_t, DIM> &kernel_dims,
+inline std::shared_ptr<Node> ConvDepthWise(const DimSize_t nbChannels,
+ const std::array<DimSize_t, DIM> &kernelDims,
const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1)) {
+ const std::array<DimSize_t, DIM> &strideDims = create_array<DimSize_t,DIM>(1),
+ const std::array<DimSize_t, DIM> &dilationDims = create_array<DimSize_t,DIM>(1)) {
// FIXME: properly handle default w&b initialization in every cases
static_assert(DIM<=MaxDim,"Too many kernel dimensions required by ConvDepthWise, not supported");
- auto convDW = std::make_shared<Node>(std::make_shared<ConvDepthWise_Op<static_cast<DimIdx_t>(DIM)>>(kernel_dims, stride_dims, dilation_dims), name);
- addProducer(convDW, 1, std::array<DimSize_t,0>({}), "w");
- addProducer(convDW, 2, std::array<DimSize_t,0>({}), "b");
+ auto convDW = std::make_shared<Node>(std::make_shared<ConvDepthWise_Op<static_cast<DimIdx_t>(DIM)>>(nbChannels, kernelDims, strideDims, dilationDims), name);
+ addProducer(convDW, 1, append(nbChannels, append(1, kernelDims)), "w");
+ addProducer(convDW, 2, std::array<DimSize_t, 1>({nbChannels}), "b");
return convDW;
}
// helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
template <DimSize_t DIM>
inline std::shared_ptr<Node> ConvDepthWise(
- DimSize_t const (&kernel_dims)[DIM],
+ const DimSize_t nbChannels,
+ DimSize_t const (&kernelDims)[DIM],
const std::string& name = "",
- const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
- const std::array<DimSize_t, DIM> &dilation_dims = create_array<DimSize_t,DIM>(1)) {
+ const std::array<DimSize_t, DIM> &strideDims = create_array<DimSize_t,DIM>(1),
+ const std::array<DimSize_t, DIM> &dilationDims = create_array<DimSize_t,DIM>(1)) {
static_assert(DIM<=MaxDim,"Too many kernel dimensions required by ConvDepthWise, not supported");
- return ConvDepthWise(to_array(kernel_dims), name, stride_dims, dilation_dims);
+ return ConvDepthWise(nbChannels, to_array(kernelDims), name, strideDims, dilationDims);
}
} // namespace Aidge
diff --git a/include/aidge/operator/Div.hpp b/include/aidge/operator/Div.hpp
index 4213f979cf9d675f523a228095edc5606f9412ee..b4acd79e49cfd1fb6466f9546240068ab566a004 100644
--- a/include/aidge/operator/Div.hpp
+++ b/include/aidge/operator/Div.hpp
@@ -17,42 +17,31 @@
#include <vector>
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/data/Tensor.hpp"
-#include "aidge/data/Data.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/utils/Types.h"
+#include "aidge/utils/ErrorHandling.hpp"
namespace Aidge {
-class Div_Op : public Operator,
+class Div_Op : public OperatorTensor,
public Registrable<Div_Op, std::string, std::unique_ptr<OperatorImpl>(const Div_Op&)> {
-public:
- // FIXME: change accessibility
- std::array<std::shared_ptr<Tensor>, 2> mInputs = {std::make_shared<Tensor>(), std::make_shared<Tensor>()};
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
public:
static constexpr const char* Type = "Div";
- Div_Op()
- : Operator(Type)
- {
- setDatatype(DataType::Float32);
- }
+ Div_Op() : OperatorTensor(Type, 2, 0, 1) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
Div_Op(const Div_Op& op)
- : Operator(Type),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Div_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Div_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -63,73 +52,15 @@ public:
return std::make_shared<Div_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx < 2 && "operator supports only 2 inputs");
- (void) inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
- mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- }
-
- void computeOutputDims() override final {
- if (!mInputs[0]->empty())
- mOutput->resize(mInputs[0]->dims());
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(static_cast<std::size_t>(inputIdx) < 2 && "wrong inputIdx for Add operator.");
- return *(mInputs[inputIdx].get());
- }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx < 2) && "Div Operator has 2 inputs");
- (void) inputIdx; // avoid unused warning
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "Div Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 2 && "operator supports only 2 inputs");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
-
void setBackend(const std::string& name) override {
mImpl = Registrar<Div_Op>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInputs[0]->setBackend(name);
- mInputs[1]->setBackend(name);
- }
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInputs[0]->setDatatype(datatype);
- mInputs[1]->setDatatype(datatype);
+ getInput(0)->setBackend(name);
+ getInput(1)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 2; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 2; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input"};
}
diff --git a/include/aidge/operator/FC.hpp b/include/aidge/operator/FC.hpp
index b949527c51b9330077dd3bd8f8b4bf1f1b9d719c..4cece292cb322c0a58f96380eb0f0083771d3c19 100644
--- a/include/aidge/operator/FC.hpp
+++ b/include/aidge/operator/FC.hpp
@@ -21,7 +21,7 @@
#include "aidge/utils/Types.h"
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/operator/Producer.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
@@ -29,16 +29,11 @@
namespace Aidge {
enum class FCAttr { OutChannels, NoBias };
-class FC_Op : public Operator,
+class FC_Op : public OperatorTensor,
public Registrable<FC_Op,
std::string,
std::unique_ptr<OperatorImpl>(const FC_Op &)>,
public StaticAttributes<FCAttr, DimSize_t, bool> {
-public:
- // FIXME: change accessibility
- std::array<std::shared_ptr<Tensor>, 3> mInputs = {std::make_shared<Tensor>(), std::make_shared<Tensor>(), std::make_shared<Tensor>()};
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "FC";
@@ -48,26 +43,21 @@ public:
template <FCAttr e> using attr = typename Attributes_::template attr<e>;
FC_Op(DimSize_t out_channels, bool noBias)
- : Operator(Type),
- Attributes_(
- attr<FCAttr::OutChannels>(out_channels),
- attr<FCAttr::NoBias>(noBias))
- {
- setDatatype(DataType::Float32);
- }
+ : OperatorTensor(Type, 1, 2, 1),
+ Attributes_(
+ attr<FCAttr::OutChannels>(out_channels),
+ attr<FCAttr::NoBias>(noBias))
+ {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
FC_Op(const FC_Op& op)
- : Operator(Type),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<FC_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<FC_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -78,7 +68,7 @@ public:
return std::make_shared<FC_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
+ void associateInput(const IOIndex_t inputIdx, const std::shared_ptr<Data>& data) override final {
assert(inputIdx < 3 && "operators supports only 3 inputs");
assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
if (inputIdx == 2) {
@@ -86,78 +76,35 @@ public:
assert(std::dynamic_pointer_cast<Tensor>(data)->nbDims() == 1);
}
mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- if (inputIdx == 0 && mInputs[0]->nbDims() == 1)
- mInputs[inputIdx]->resize(std::array<DimSize_t, 2>({1, mInputs[inputIdx]->size()}));
+ if (inputIdx == 0 && getInput(0)->nbDims() == 1)
+ mInputs[inputIdx]->resize(std::array<DimSize_t, 2>({1, getInput(inputIdx)->size()}));
}
void computeOutputDims() override final {
- if (!mInputs[0]->empty()) {
- // <in_features**, out_channels>
- std::array<DimSize_t, 2> weightDims = {this->template getAttr<FCAttr::OutChannels>(), static_cast<DimSize_t>(mInputs[0]->sizeM1())};
- // <out_channels, batch>
- std::array<DimSize_t, 2> outputDims = {mInputs[0]->dims()[0], this->template getAttr<FCAttr::OutChannels>()};
-
- mInputs[1]->resize(weightDims);
- mOutput->resize(outputDims);
+ bool associated = true;
+ for (IOIndex_t i = 0; i < nbInputs(); ++i) {
+ if (!getInput(i)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+ }
+ associated &= !(getInput(i)->empty());
+ }
+ if (associated) {
+ // <batch, OutChannels>
+ mOutputs[0]->resize({getInput(0)->dims()[0], this->template getAttr<FCAttr::OutChannels>()});
}
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 3 && "operators supports only 3 inputs");
- return *(mInputs[inputIdx].get()); }
- inline Tensor& output(const IOIndex_t /*inputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 3 && "FC Operators supports only 3 inputs");
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "FC Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 3 && "operators supports only 3 inputs");
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
}
void setBackend(const std::string& name) override {
mImpl = Registrar<FC_Op>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInputs[0]->setBackend(name);
- mInputs[1]->setBackend(name);
- mInputs[2]->setBackend(name);
- }
-
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInputs[0]->setDatatype(datatype);
- mInputs[1]->setDatatype(datatype);
- mInputs[2]->setDatatype(datatype);
+ getInput(0)->setBackend(name);
+ getInput(1)->setBackend(name);
+ getInput(2)->setBackend(name);
}
-
- inline IOIndex_t nbInputs() const noexcept override final { return 3; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input", "weight", "bias"};
}
@@ -166,11 +113,11 @@ public:
}
};
-inline std::shared_ptr<Node> FC(DimSize_t out_channels, bool noBias = false, const std::string& name = "") {
+inline std::shared_ptr<Node> FC(DimSize_t inChannels, DimSize_t outChannels, bool noBias = false, const std::string& name = "") {
// FIXME: properly handle default w&b initialization in every cases
- auto fc = std::make_shared<Node>(std::make_shared<FC_Op>(out_channels, noBias), name);
- addProducer(fc, 1, std::array<DimSize_t, 2>({out_channels, 1}), "w");
- addProducer(fc, 2, (noBias ? std::array<DimSize_t, 1>({0}) : std::array<DimSize_t, 1>({out_channels})), "b"); // already sets bias dims
+ auto fc = std::make_shared<Node>(std::make_shared<FC_Op>(outChannels, noBias), name);
+ addProducer(fc, 1, std::array<DimSize_t, 2>({outChannels, inChannels}), "w");
+ addProducer(fc, 2, (noBias ? std::array<DimSize_t, 1>({0}) : std::array<DimSize_t, 1>({outChannels})), "b"); // already sets bias dims
return fc;
}
} // namespace Aidge
@@ -181,4 +128,4 @@ const char *const EnumStrings<Aidge::FCAttr>::data[] = {"OutChannels",
"NoBias"};
}
-#endif /* AIDGE_CORE_OPERATOR_FC_H_ */
+#endif /* AIDGE_CORE_OPERATOR_FC_H_ */
\ No newline at end of file
diff --git a/include/aidge/operator/GenericOperator.hpp b/include/aidge/operator/GenericOperator.hpp
index 55ccbf1516fa79663d57e1e44bc4017bc5c8b843..505c5344990453c8f4ab84fa3893e75b216d7a54 100644
--- a/include/aidge/operator/GenericOperator.hpp
+++ b/include/aidge/operator/GenericOperator.hpp
@@ -19,7 +19,7 @@
#include <cstring>
#include "aidge/graph/Node.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/utils/DynamicAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
@@ -27,50 +27,26 @@
namespace Aidge {
class GenericOperator_Op
- : public Operator,
+ : public OperatorTensor,
public Registrable<GenericOperator_Op, std::string, std::unique_ptr<OperatorImpl>(std::shared_ptr<GenericOperator_Op>)>,
public DynamicAttributes {
- private:
+private:
using ComputeDimsFunc = std::function<std::vector<std::vector<size_t>>(const std::vector<std::vector<size_t>>&)>;
- IOIndex_t mNbDataIn;
- IOIndex_t mNbIn;
- IOIndex_t mNbOut;
- std::vector<std::shared_ptr<Tensor>> mInputs;
- std::vector<std::shared_ptr<Tensor>> mOutputs;
ComputeDimsFunc mComputeOutputDims;
- public:
- GenericOperator_Op(const char *type, IOIndex_t nbDataIn, IOIndex_t nbIn, IOIndex_t nbOut)
- : Operator(type), mNbDataIn(nbDataIn), mNbIn(nbIn), mNbOut(nbOut)
- {
- mInputs = std::vector<std::shared_ptr<Tensor>>(nbIn);
- for (std::size_t i = 0; i < nbIn; ++i) {
- mInputs[i] = std::make_shared<Tensor>();
- }
- mOutputs = std::vector<std::shared_ptr<Tensor>>(nbOut);
- for (std::size_t i = 0; i < nbOut; ++i) {
- mOutputs[i] = std::make_shared<Tensor>();
- }
- }
+public:
+ GenericOperator_Op(const char *type, IOIndex_t nbData, IOIndex_t nbParam, IOIndex_t nbOut)
+ : OperatorTensor(type, nbData, nbParam, nbOut)
+ {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
GenericOperator_Op(const GenericOperator_Op& op)
- : Operator(op.type().c_str()), mNbDataIn(op.mNbDataIn), mNbIn(op.mNbIn), mNbOut(op.mNbOut)
- {
- // cpy-ctor
- mInputs = std::vector<std::shared_ptr<Tensor>>(mNbIn);
- for (std::size_t i = 0; i < mNbIn; ++i) {
- mInputs[i] = std::make_shared<Tensor>();
- }
- mOutputs = std::vector<std::shared_ptr<Tensor>>(mNbOut);
- for (std::size_t i = 0; i < mNbOut; ++i) {
- mOutputs[i] = std::make_shared<Tensor>(*op.mOutputs[i]);
- }
- }
+ : OperatorTensor(op)
+ {}
/**
* @brief Clone the operator using its copy-constructor.
@@ -87,28 +63,19 @@ class GenericOperator_Op
mComputeOutputDims = func;
}
- // Override Virtual Opertor methods
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx < mNbIn && "operators supports only x inputs");
-
- if (strcmp(data->type(), Tensor::Type) == 0) {
- // TODO: associate input only if of type Tensor, otherwise do nothing
- mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- }
- }
void computeOutputDims() override final {
if (mComputeOutputDims) {
- std::vector<std::vector<size_t>> inputsDims(mNbIn, std::vector<size_t>());
- for (std::size_t i = 0; i < mNbIn; ++i) {
- if (mInputs[i]) {
- inputsDims[i] = mInputs[i]->dims();
+ std::vector<std::vector<size_t>> inputsDims(nbInputs(), std::vector<size_t>());
+ for (std::size_t i = 0; i < nbInputs(); ++i) {
+ if (getInput(i)) {
+ inputsDims[i] = getInput(i)->dims();
}
}
const auto& outputsDims = mComputeOutputDims(inputsDims);
- assert(outputsDims.size() == mNbOut && "The provided ComputeDimsFunc function returns the wrong number of outputs");
- for (std::size_t i = 0; i < mNbOut; ++i) {
+ assert(outputsDims.size() == nbOutputs() && "The provided ComputeDimsFunc function returns the wrong number of outputs");
+ for (std::size_t i = 0; i < nbOutputs(); ++i) {
mOutputs[i]->resize(outputsDims[i]);
}
}
@@ -127,47 +94,11 @@ class GenericOperator_Op
}
}
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx < mNbIn) && "input index out of range for this instance of GenericOperator");
- printf("Info: using getRawInput() on a GenericOperator.\n");
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert((inputIdx < mNbIn) && "input index out of range for this instance of GenericOperator");
- printf("Info: using input() on a GenericOperator.\n");
- return *mInputs[inputIdx];
- }
-
-
- std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx < mNbIn) && "input index out of range for this instance of GenericOperator");
- printf("Info: using getInput() on a GenericOperator.\n");
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx < mNbOut) && "output index out of range for this instance of GenericOperator");
- printf("Info: using getOutput() on a GenericOperator.\n");
- return mOutputs[outputIdx];
- }
-
-
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx < mNbOut) && "output index out of range for this instance of GenericOperator");
- printf("Info: using getRawOutput() on a GenericOperator.\n");
- return std::static_pointer_cast<Data>(mOutputs[outputIdx]);
- }
-
- Tensor& output(const IOIndex_t outputIdx) const override final {
- assert((outputIdx < mNbOut) && "output index out of range for this instance of GenericOperator");
- printf("Info: using output() on a GenericOperator.\n");
- return *mOutputs[outputIdx];
- }
~GenericOperator_Op() = default;
void setBackend(const std::string & /*name*/) override { printf("setBackend: not available yet.\n"); }
- void setDatatype(const DataType & /*datatype*/) override { printf("setDatatype: not available yet.\n"); }
+ void setDataType(const DataType& /*datatype*/) const override { printf("setDataType: not available yet.\n"); }
void forward() override final {
if(mImpl){
mImpl->forward();
@@ -182,9 +113,6 @@ class GenericOperator_Op
printf("backward: No implementation is linked.\n");
}
}
- inline IOIndex_t nbInputs() const noexcept override final { return mNbIn; };
- inline IOIndex_t nbDataInputs() const noexcept override final { return mNbDataIn; };
- inline IOIndex_t nbOutputs() const noexcept override final { return mNbOut; };
};
/**
@@ -197,9 +125,9 @@ class GenericOperator_Op
* @param name (optional) name of the Operator.
* @return std::shared_ptr<Node> Node associated with the Generic Operator.
*/
-inline std::shared_ptr<Node> GenericOperator(const char *type, IOIndex_t nbDataIn, IOIndex_t nbIn, IOIndex_t nbOut,
+inline std::shared_ptr<Node> GenericOperator(const char *type, IOIndex_t nbData, IOIndex_t nbParam, IOIndex_t nbOut,
const std::string& name = "") {
- return std::make_shared<Node>(std::make_shared<GenericOperator_Op>(type, nbDataIn, nbIn, nbOut), name);
+ return std::make_shared<Node>(std::make_shared<GenericOperator_Op>(type, nbData, nbParam, nbOut), name);
}
} // namespace Aidge
diff --git a/include/aidge/operator/LeakyReLU.hpp b/include/aidge/operator/LeakyReLU.hpp
index bcdcbc7cabd8eda46a7c0c4930f317e562fb46a0..800c8c61d876b6f33cce1af3365179b7eb14b68d 100644
--- a/include/aidge/operator/LeakyReLU.hpp
+++ b/include/aidge/operator/LeakyReLU.hpp
@@ -17,7 +17,7 @@
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/data/Data.hpp"
@@ -29,14 +29,9 @@ enum class LeakyReLUAttr {
NegativeSlope
};
-class LeakyReLU_Op : public Operator,
+class LeakyReLU_Op : public OperatorTensor,
public Registrable<LeakyReLU_Op, std::string, std::unique_ptr<OperatorImpl>(const LeakyReLU_Op&)>,
public StaticAttributes<LeakyReLUAttr, float> {
-public:
- // FIXME: change accessibility
- std::shared_ptr<Tensor> mInput = std::make_shared<Tensor>();
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "LeakyReLU";
@@ -46,25 +41,20 @@ public:
template <LeakyReLUAttr e> using attr = typename Attributes_::template attr<e>;
LeakyReLU_Op(float negativeSlope)
- : Operator(Type),
- Attributes_(
- attr<LeakyReLUAttr::NegativeSlope>(negativeSlope))
- {
- setDatatype(DataType::Float32);
- }
+ : OperatorTensor(Type, 1, 0, 1),
+ Attributes_(
+ attr<LeakyReLUAttr::NegativeSlope>(negativeSlope))
+ {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
LeakyReLU_Op(const LeakyReLU_Op& op)
- : Operator(Type),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<LeakyReLU_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<LeakyReLU_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -75,69 +65,17 @@ public:
return std::make_shared<LeakyReLU_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void) inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
- mInput = std::dynamic_pointer_cast<Tensor>(data);
- }
-
- void computeOutputDims() override final {
- if (!mInput->empty())
- mOutput->resize(mInput->dims());
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t /*inputIdx*/) const override final { return *(mInput.get()); }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx == 0) && "LeakyReLU Operator has only 1 input");
- (void) inputIdx; // avoid unused warning
- return mInput;
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "LeakyReLU Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInput);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
void setBackend(const std::string& name) override {
mImpl = Registrar<LeakyReLU_Op>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInput->setBackend(name);
- }
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInput->setDatatype(datatype);
+ getInput(0)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
- static const std::vector<std::string> getInputsName(){
+ static const std::vector<std::string> getInputsName(){
return {"data_input"};
}
static const std::vector<std::string> getOutputsName(){
diff --git a/include/aidge/operator/MatMul.hpp b/include/aidge/operator/MatMul.hpp
index eed1ec04535aa5896aa3d01a27d8023d37a42183..23c12d45802e25f29891c48164acfb2d3ad137ac 100644
--- a/include/aidge/operator/MatMul.hpp
+++ b/include/aidge/operator/MatMul.hpp
@@ -21,7 +21,7 @@
#include "aidge/utils/Types.h"
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/operator/Producer.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
@@ -29,15 +29,11 @@
namespace Aidge {
enum class MatMulAttr { OutChannels };
-class MatMul_Op : public Operator,
+class MatMul_Op : public OperatorTensor,
public Registrable<MatMul_Op,
std::string,
std::unique_ptr<OperatorImpl>(const MatMul_Op &)>,
public StaticAttributes<MatMulAttr, DimSize_t> {
-public:
- std::array<std::shared_ptr<Tensor>, 2> mInputs = {std::make_shared<Tensor>(), std::make_shared<Tensor>()};
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "MatMul";
@@ -47,25 +43,20 @@ public:
template <MatMulAttr e> using attr = typename Attributes_::template attr<e>;
MatMul_Op(DimSize_t out_channels)
- : Operator(Type),
+ : OperatorTensor(Type, 1, 1, 1),
Attributes_(
attr<MatMulAttr::OutChannels>(out_channels))
- {
- setDatatype(DataType::Float32);
- }
+ {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
MatMul_Op(const MatMul_Op& op)
- : Operator(Type),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<MatMul_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<MatMul_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -76,78 +67,31 @@ public:
return std::make_shared<MatMul_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx < 2 && "operators supports only 2 inputs");
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
- mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- }
void computeOutputDims() override final {
- if (!mInputs[0]->empty()) {
- // <in_features**, out_channels>
- std::array<DimSize_t, 2> weightDims = {this->template getAttr<MatMulAttr::OutChannels>(), static_cast<DimSize_t>(mInputs[0]->sizeM1())};
- // <out_channels, batch>
- std::array<DimSize_t, 2> outputDims = {mInputs[0]->dims()[0], this->template getAttr<MatMulAttr::OutChannels>()};
-
- mInputs[1]->resize(weightDims);
- mOutput->resize(outputDims);
+ bool associated = true;
+ for (IOIndex_t i = 0; i < nbInputs(); ++i) {
+ if (!getInput(i)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+ }
+ associated &= !(getInput(i)->empty());
+ }
+ if (associated) {
+ // <batch, OutChannels>
+ mOutputs[0]->resize({getInput(0)->dims()[0], this->template getAttr<MatMulAttr::OutChannels>()});
}
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 2 && "operators supports only 2 inputs");
- return *(mInputs[inputIdx].get()); }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 2 && "MatMul Operators has 2 inputs");
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "MatMul Operators has 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 2 && "operators supports only 2 inputs");
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
}
void setBackend(const std::string& name) override {
mImpl = Registrar<MatMul_Op>::create(name)(*this);
- mOutput->setBackend(name);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInputs[0]->setBackend(name);
- mInputs[1]->setBackend(name);
+ getInput(0)->setBackend(name);
+ getInput(1)->setBackend(name);
}
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
-
- // FIXME: temporary workaround
- mInputs[0]->setDatatype(datatype);
- mInputs[1]->setDatatype(datatype);
- }
-
-
- inline IOIndex_t nbInputs() const noexcept override final { return 2; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input", "weight"};
}
@@ -156,10 +100,10 @@ public:
}
};
-inline std::shared_ptr<Node> MatMul(DimSize_t out_channels, const std::string& name = "") {
+inline std::shared_ptr<Node> MatMul(DimSize_t inChannels, DimSize_t outChannels, const std::string& name = "") {
// FIXME: properly handle default w initialization in every cases
- auto matmul = std::make_shared<Node>(std::make_shared<MatMul_Op>(out_channels), name);
- addProducer(matmul, 1, std::array<DimSize_t, 2>({out_channels, 1}), "w");
+ auto matmul = std::make_shared<Node>(std::make_shared<MatMul_Op>(outChannels), name);
+ addProducer(matmul, 1, std::array<DimSize_t, 2>({outChannels, inChannels}), "w");
return matmul;
}
} // namespace Aidge
diff --git a/include/aidge/operator/MaxPooling.hpp b/include/aidge/operator/MaxPooling.hpp
index bcf47f13cc34132f668ea1ffcb2c91ed6f06f44d..45aec34c1a6577896ca17aae6d055efc2c31f671 100644
--- a/include/aidge/operator/MaxPooling.hpp
+++ b/include/aidge/operator/MaxPooling.hpp
@@ -19,7 +19,7 @@
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/operator/Producer.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
@@ -29,17 +29,12 @@ namespace Aidge {
enum class MaxPoolingAttr { StrideDims, KernelDims, CeilMode };
template <DimIdx_t DIM>
-class MaxPooling_Op : public Operator,
+class MaxPooling_Op : public OperatorTensor,
public Registrable<MaxPooling_Op<DIM>, std::string, std::unique_ptr<OperatorImpl>(const MaxPooling_Op<DIM> &)>,
public StaticAttributes<MaxPoolingAttr,
std::array<DimSize_t, DIM>,
std::array<DimSize_t, DIM>,
bool> {
-private:
- // FIXME: change accessibility
- std::shared_ptr<Tensor> mInput = std::make_shared<Tensor>();
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char *Type = "MaxPooling";
@@ -55,26 +50,21 @@ public:
constexpr MaxPooling_Op(const std::array<DimSize_t, DIM> &kernel_dims,
const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
bool ceil_mode = false)
- : Operator(Type),
+ : OperatorTensor(Type, 1, 0, 1),
Attributes_(attr<MaxPoolingAttr::StrideDims>(stride_dims),
attr<MaxPoolingAttr::KernelDims>(kernel_dims),
- attr<MaxPoolingAttr::CeilMode>(ceil_mode)),
- mOutput(std::make_shared<Tensor>()) {
- setDatatype(DataType::Float32);
- }
+ attr<MaxPoolingAttr::CeilMode>(ceil_mode))
+ {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
MaxPooling_Op(const MaxPooling_Op<DIM>& op)
- : Operator(Type),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<MaxPooling_Op<DIM>>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<MaxPooling_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -85,17 +75,14 @@ public:
return std::make_shared<MaxPooling_Op<DIM>>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx < 1 && "operators supports only 3 inputs");
- (void) inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type) == 0 && "input data must be of Tensor type");
-
- mInput = std::dynamic_pointer_cast<Tensor>(data);
- }
void computeOutputDims() override final {
- if (!mInput->empty()) {
- std::array<DimSize_t, DIM + 2> outputDims = {};
+ if (!getInput(0)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+ }
+ if (!(getInput(0)->empty())) {
+ std::array<DimSize_t, DIM + 2> outputDims{};
+ const std::array<DimSize_t, DIM + 2> inputDims(getInput(0)->dims<DIM+2>());
std::function<float(float)> roundingFunction;
if (this->template getAttr<MaxPoolingAttr::CeilMode>()) {
@@ -106,69 +93,25 @@ public:
for (std::size_t dim = 0; dim < this->template getAttr<MaxPoolingAttr::KernelDims>().size() ; ++dim) {
outputDims[dim+2] = 1 + static_cast<DimSize_t>(
- roundingFunction(static_cast<float>(mInput->dims()[dim+2] -
+ roundingFunction(static_cast<float>(inputDims[dim+2] -
this->template getAttr<MaxPoolingAttr::KernelDims>()[dim]) /
static_cast<float>(this->template getAttr<MaxPoolingAttr::StrideDims>()[dim])));
}
- outputDims[1] = mInput->dims()[1];
- outputDims[0] = mInput->dims()[0];
- mOutput->resize(outputDims);
+ outputDims[1] = inputDims[1];
+ outputDims[0] = inputDims[0];
+ mOutputs[0]->resize(outputDims);
}
}
- bool outputDimsForwarded() const override final { return !(mOutput->empty()); }
-
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "operators supports only 1 inputs");
- (void) inputIdx; // avoid unused warning
- return *(mInput.get());
- }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "MaxPooling Operators supports only 1 inputs");
- (void) inputIdx; // avoid unused warning
- return mInput;
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "MaxPooling Operators has only 1 outputs");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "operators supports only 1 inputs");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInput);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
void setBackend(const std::string &name) override {
mImpl = Registrar<MaxPooling_Op<DIM>>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInput->setBackend(name);
- }
-
- void setDatatype(const DataType &datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInput->setDatatype(datatype);
+ getInput(0)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input"};
}
diff --git a/include/aidge/operator/MetaOperator.hpp b/include/aidge/operator/MetaOperator.hpp
index 72058dfcba6e811a01a22e261208741879638cad..5775dd24d7d8b59b2ea945bcd15a38ddfdb71cd0 100644
--- a/include/aidge/operator/MetaOperator.hpp
+++ b/include/aidge/operator/MetaOperator.hpp
@@ -12,26 +12,24 @@
#ifndef AIDGE_CORE_OPERATOR_METAOPERATOR_H_
#define AIDGE_CORE_OPERATOR_METAOPERATOR_H_
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/graph/GraphView.hpp"
#include "aidge/graph/OpArgs.hpp"
#include "aidge/scheduler/Scheduler.hpp"
namespace Aidge {
-class MetaOperator_Op : public Operator,
+class MetaOperator_Op : public OperatorTensor,
public Registrable<MetaOperator_Op, std::array<std::string, 2>, std::unique_ptr<OperatorImpl>(const MetaOperator_Op &)> {
public:
- std::vector<std::shared_ptr<Tensor>> mInputs;
- std::vector<std::shared_ptr<Tensor>> mOutputs; // These are shared with micro-graph outputs tensors
-
+ // outputs shared with micro-graph output Tensors
// Micro-graph handling:
std::shared_ptr<GraphView> mGraph; // Meta operator micro-graph
std::shared_ptr<SequentialScheduler> mScheduler;
// Need to store an ordored list of input/output operators for the micro-graph,
// because input/output nodes in a GraphView are unordered.
// TODO: refactor GraphView to handle ordered input/output?
- std::vector<std::pair<std::shared_ptr<Operator>, IOIndex_t>> mInputOps;
- std::vector<std::pair<std::shared_ptr<Operator>, IOIndex_t>> mOutputOps;
+ std::vector<std::pair<std::shared_ptr<OperatorTensor>, IOIndex_t>> mInputOps;
+ std::vector<std::pair<std::shared_ptr<OperatorTensor>, IOIndex_t>> mOutputOps;
public:
MetaOperator_Op(const char *type, const std::shared_ptr<GraphView>& graph,
@@ -43,11 +41,9 @@ public:
* @param op Operator to copy.
*/
MetaOperator_Op(const MetaOperator_Op& op)
- : Operator(op.type().c_str()),
+ : OperatorTensor(op),
mGraph(op.mGraph->clone())
- {
- // cpy-ctor
- }
+ {}
/**
* @brief Clone the operator using its copy-constructor.
@@ -65,7 +61,7 @@ public:
return mScheduler;
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
+ void associateInput(const IOIndex_t inputIdx, const std::shared_ptr<Data>& data) override final {
assert(strcmp(data->type(), Tensor::Type) == 0 && "input data must be of Tensor type");
const auto& inputOp = mInputOps[inputIdx];
@@ -86,38 +82,6 @@ public:
}
}
- bool outputDimsForwarded() const override final { return !(mOutputs[0]->empty()); }
-
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < mInputs.size() && "inputIdx out of range");
- return *(mInputs[inputIdx].get());
- }
-
- inline Tensor& output(const IOIndex_t outputIdx) const override final {
- assert(outputIdx < mOutputs.size() && "outputIdx out of range");
- return *(mOutputs[outputIdx].get());
- }
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < mInputs.size() && "inputIdx out of range");
- return mInputs[inputIdx];
- }
-
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx < mOutputs.size() && "outputIdx out of range");
- return mOutputs[outputIdx];
- }
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < mInputs.size() && "inputIdx out of range");
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
-
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx < mOutputs.size() && "outputIdx out of range");
- return std::static_pointer_cast<Data>(mOutputs[outputIdx]);
- }
void setBackend(const std::string &name) override {
if (Registrar<MetaOperator_Op>::exists({name, type()})) {
@@ -131,17 +95,13 @@ public:
mGraph->setBackend(name);
}
- void setDatatype(const DataType &datatype) override {
+ void setDataType(const DataType &datatype) const override {
// The micro-graph should always be set to the right data type, since it
// shares input/output tensors.
// Input/output tensors data type are updated here.
- mGraph->setDatatype(datatype);
+ mGraph->setDataType(datatype);
}
- inline IOIndex_t nbInputs() const noexcept override final { return mGraph->inputs().size(); }
- inline IOIndex_t nbDataInputs() const noexcept override final { return mGraph->dataInputs().size(); }
- inline IOIndex_t nbOutputs() const noexcept override final { return mGraph->outputs().size(); }
-
NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override;
NbElts_t getNbConsumedData(IOIndex_t inputIdx) const override;
NbElts_t getNbProducedData(IOIndex_t outputIdx) const override;
diff --git a/include/aidge/operator/Mul.hpp b/include/aidge/operator/Mul.hpp
index 4ea79fe52622b22f8ea8fbd9191d50d45e26acac..f1537f5b2b62e24dfc3a46a337fe6fd657a2b982 100644
--- a/include/aidge/operator/Mul.hpp
+++ b/include/aidge/operator/Mul.hpp
@@ -12,47 +12,38 @@
#ifndef AIDGE_CORE_OPERATOR_MUL_H_
#define AIDGE_CORE_OPERATOR_MUL_H_
-#include <cassert>
#include <memory>
+#include <string>
#include <vector>
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/data/Tensor.hpp"
-#include "aidge/data/Data.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/utils/Types.h"
namespace Aidge {
-class Mul_Op : public Operator,
+/**
+ * @brief Tensor element-wise multiplication.
+ */
+class Mul_Op : public OperatorTensor,
public Registrable<Mul_Op, std::string, std::unique_ptr<OperatorImpl>(const Mul_Op&)> {
-public:
- // FIXME: change accessibility
- std::array<std::shared_ptr<Tensor>, 2> mInputs = {std::make_shared<Tensor>(), std::make_shared<Tensor>()};
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "Mul";
- Mul_Op()
- : Operator(Type)
- {
- setDatatype(DataType::Float32);
- }
+ Mul_Op() : OperatorTensor(Type, 2, 0, 1) {}
/**
- * @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
+ * @brief Copy-constructor. Copy the operator attributes and its output tensor(s),
+ * but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
Mul_Op(const Mul_Op& op)
- : Operator(Type),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Mul_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Mul_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -63,73 +54,16 @@ public:
return std::make_shared<Mul_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx < 2 && "operator supports only 2 inputs");
- (void) inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
- mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- }
-
- void computeOutputDims() override final {
- if (!mInputs[0]->empty())
- mOutput->resize(mInputs[0]->dims());
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(static_cast<std::size_t>(inputIdx) < 2 && "wrong inputIdx for Add operator.");
- return *(mInputs[inputIdx].get());
- }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx < 2) && "Mul Operator has 2 inputs");
- (void) inputIdx; // avoid unused warning
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "Mul Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 2 && "operator supports only 2 inputs");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
void setBackend(const std::string& name) override {
mImpl = Registrar<Mul_Op>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInputs[0]->setBackend(name);
- mInputs[1]->setBackend(name);
- }
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInputs[0]->setDatatype(datatype);
- mInputs[1]->setDatatype(datatype);
+ getInput(0)->setBackend(name);
+ getInput(1)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 2; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 2; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input"};
}
@@ -141,6 +75,6 @@ public:
inline std::shared_ptr<Node> Mul(const std::string& name = "") {
return std::make_shared<Node>(std::make_shared<Mul_Op>(), name);
}
-}
+} // namespace Aidge
-#endif /* AIDGE_CORE_OPERATOR_MUL_H_ */
+#endif /* AIDGE_CORE_OPERATOR_MUL_H_ */
\ No newline at end of file
diff --git a/include/aidge/operator/Pad.hpp b/include/aidge/operator/Pad.hpp
index cbebb16e1e24501b0ea371fb45211047f6e2b5e7..3fa6fa097d60c30a694d05e8097d1b3685241227 100644
--- a/include/aidge/operator/Pad.hpp
+++ b/include/aidge/operator/Pad.hpp
@@ -19,7 +19,7 @@
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/operator/Producer.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
@@ -30,17 +30,12 @@ enum class PadAttr { BeginEndBorders, BorderType, BorderValue };
enum class PadBorderType { Constant, Edge, Reflect, Wrap };
template <DimIdx_t DIM>
-class Pad_Op : public Operator,
+class Pad_Op : public OperatorTensor,
public Registrable<Pad_Op<DIM>, std::string, std::unique_ptr<OperatorImpl>(const Pad_Op<DIM> &)>,
public StaticAttributes<PadAttr,
std::array<DimSize_t, 2*DIM>,
PadBorderType,
double> {
-private:
- // FIXME: change accessibility
- std::shared_ptr<Tensor> mInput = std::make_shared<Tensor>();
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char *Type = "Pad";
@@ -56,25 +51,19 @@ public:
constexpr Pad_Op(const std::array<DimSize_t, 2*DIM> &beginEndTuples,
const PadBorderType &borderType = PadBorderType::Constant,
double borderValue = 0.0)
- : Operator(Type),
+ : OperatorTensor(Type, 1, 0, 1),
Attributes_(attr<PadAttr::BeginEndBorders>(beginEndTuples),
attr<PadAttr::BorderType>(borderType),
- attr<PadAttr::BorderValue>(borderValue)) {
- setDatatype(DataType::Float32);
- }
+ attr<PadAttr::BorderValue>(borderValue)) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
Pad_Op(const Pad_Op& op)
- : Operator(Type),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
- {
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- }
+ : OperatorTensor(op),
+ Attributes_(op)
+ {}
/**
* @brief Clone the operator using its copy-constructor.
@@ -84,82 +73,38 @@ public:
return std::make_shared<Pad_Op<DIM>>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx < 1 && "operators supports only 3 inputs");
- (void) inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type) == 0 && "input data must be of Tensor type");
-
- mInput = std::dynamic_pointer_cast<Tensor>(data);
- }
void computeOutputDims() override final {
- if (!mInput->empty()) {
- std::array<DimSize_t, DIM + 2> outputDims = {};
+ bool associated = true;
+ for (IOIndex_t i = 0; i < nbInputs(); ++i) {
+ if (!getInput(i)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+ }
+ associated &= !(getInput(i)->empty());
+ }
+ if (associated) {
+ std::array<DimSize_t, DIM + 2> outputDims{};
+ const std::array<DimSize_t, DIM + 2> inputDims = getInput(0)->dims<DIM+2>();
for (std::size_t dim = 0; dim < DIM; ++dim) {
outputDims[dim+2] = this->template getAttr<PadAttr::BeginEndBorders>()[2*dim]
- + mInput->dims()[dim+2]
+ + inputDims[dim+2]
+ this->template getAttr<PadAttr::BeginEndBorders>()[2*dim+1];
}
- outputDims[1] = mInput->dims()[1];
- outputDims[0] = mInput->dims()[0];
- mOutput->resize(outputDims);
+ outputDims[1] = inputDims[1];
+ outputDims[0] = inputDims[0];
+ mOutputs[0]->resize(outputDims);
}
}
- bool outputDimsForwarded() const override final { return !(mOutput->empty()); }
-
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "operators supports only 1 inputs");
- (void) inputIdx; // avoid unused warning
- return *(mInput.get());
- }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "Pad Operators supports only 1 inputs");
- (void) inputIdx; // avoid unused warning
- return mInput;
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "Pad Operators has only 1 outputs");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "operators supports only 1 inputs");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInput);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
-
void setBackend(const std::string &name) override {
mImpl = Registrar<Pad_Op<DIM>>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInput->setBackend(name);
- }
-
- void setDatatype(const DataType &datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInput->setDatatype(datatype);
+ getInput(0)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input"};
}
diff --git a/include/aidge/operator/Pow.hpp b/include/aidge/operator/Pow.hpp
index 732cf36b4ef7e7640648c542191acd02d0875a4f..0ab73441fb87720512a96f4dbd934e8405a60fe4 100644
--- a/include/aidge/operator/Pow.hpp
+++ b/include/aidge/operator/Pow.hpp
@@ -17,7 +17,7 @@
#include <vector>
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/data/Data.hpp"
@@ -26,33 +26,21 @@
namespace Aidge {
-class Pow_Op : public Operator,
+class Pow_Op : public OperatorTensor,
public Registrable<Pow_Op, std::string, std::unique_ptr<OperatorImpl>(const Pow_Op&)> {
-public:
- // FIXME: change accessibility
- std::array<std::shared_ptr<Tensor>, 2> mInputs = {std::make_shared<Tensor>(), std::make_shared<Tensor>()};
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "Pow";
- Pow_Op()
- : Operator(Type)
- {
- setDatatype(DataType::Float32);
- }
+ Pow_Op() : OperatorTensor(Type, 2, 0, 1) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
Pow_Op(const Pow_Op& op)
- : Operator(Type),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Pow_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Pow_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -63,73 +51,16 @@ public:
return std::make_shared<Pow_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx < 2 && "operator supports only 2 inputs");
- (void) inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
- mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- }
-
- void computeOutputDims() override final {
- if (!mInputs[0]->empty())
- mOutput->resize(mInputs[0]->dims());
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(static_cast<std::size_t>(inputIdx) < 2 && "wrong inputIdx for Add operator.");
- return *(mInputs[inputIdx].get());
- }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx < 2) && "Pow Operator has 2 inputs");
- (void) inputIdx; // avoid unused warning
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "Pow Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 2 && "operator supports only 2 inputs");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
void setBackend(const std::string& name) override {
mImpl = Registrar<Pow_Op>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInputs[0]->setBackend(name);
- mInputs[1]->setBackend(name);
- }
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInputs[0]->setDatatype(datatype);
- mInputs[1]->setDatatype(datatype);
+ getInput(0)->setBackend(name);
+ getInput(1)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 2; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 2; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input"};
}
@@ -141,6 +72,6 @@ public:
inline std::shared_ptr<Node> Pow(const std::string& name = "") {
return std::make_shared<Node>(std::make_shared<Pow_Op>(), name);
}
-}
+} // namespace Aidge
-#endif /* AIDGE_CORE_OPERATOR_POW_H_ */
+#endif /* AIDGE_CORE_OPERATOR_POW_H_ */
\ No newline at end of file
diff --git a/include/aidge/operator/Producer.hpp b/include/aidge/operator/Producer.hpp
index d747b340618cc7e321f2cfc2ed9169798e5d77e9..0b63e5a32afe2fa21cbec30855468fbef85881d2 100644
--- a/include/aidge/operator/Producer.hpp
+++ b/include/aidge/operator/Producer.hpp
@@ -18,49 +18,40 @@
#include "aidge/utils/Types.h"
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
namespace Aidge {
class Producer_Op
- : public Operator,
+ : public OperatorTensor,
public Registrable<Producer_Op, std::string, std::unique_ptr<OperatorImpl>(
const Producer_Op &)> {
-private:
- std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "Producer";
template <std::size_t DIM>
Producer_Op(const std::array<DimSize_t, DIM>& dims)
- : Operator(Type)
+ : OperatorTensor(Type, 0, 0, 1)
{
- //ctor
- setDatatype(DataType::Float32);
- mOutput->resize(dims);
+ mOutputs[0]->resize(dims);
}
Producer_Op(const std::shared_ptr<Tensor> tensor)
- : Operator(Type),
- mOutput(tensor)
+ : OperatorTensor(Type, 0, 0, 1)
{
- setDatatype(tensor->dataType());
+ mOutputs[0] = tensor; // copy the pointer of the Tensor
}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
- * @param op Operator to copy.
+ * @param op OperatorTensor to copy.
*/
Producer_Op(const Producer_Op& op)
- : Operator(Type),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Producer_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Producer_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -71,8 +62,8 @@ public:
return std::make_shared<Producer_Op>(*this);
}
- void associateInput(const IOIndex_t /*inputIdx*/, std::shared_ptr<Data> /*data*/) override final {
- assert(false && "Producer operator takes no input");
+ void associateInput(const IOIndex_t /*inputIdx*/, const std::shared_ptr<Data>& /*data*/) override final {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Producer operator takes no input.");
}
/**
@@ -81,8 +72,8 @@ public:
*
* @param newOutput Tensor containing the values to copy
*/
- void setOutputTensor(const Tensor& newOutput) {
- *mOutput = newOutput;
+ void setOutput(const std::shared_ptr<Tensor>& newOutput) {
+ mOutputs[0] = newOutput;
}
void computeOutputDims() override final {}
@@ -90,48 +81,13 @@ public:
bool outputDimsForwarded() const override final {return true;}
- [[noreturn]] inline Tensor& input(const IOIndex_t /*inputIdx*/) const override final {
- assert(false);
- exit(-1);
- }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t /*inputIdx*/) const override final {
- assert(false && "Producer Operator has no input");
- return nullptr;
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "Producer Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t /*inputIdx*/) const override final {
- assert(false && "Producer operator takes no input");
- return nullptr;
- }
-
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
- inline const std::vector<DimSize_t> dims() const noexcept { return mOutput->dims(); }
+ inline const std::vector<DimSize_t> dims() const noexcept { return mOutputs[0]->dims(); }
void setBackend(const std::string& name) override {
mImpl = Registrar<Producer_Op>::create(name)(*this);
- mOutput->setBackend(name);
- }
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 0; };
- inline IOIndex_t nbDataInputs() const noexcept override final { return 0; };
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; };
static const std::vector<std::string> getInputsName(){
return {};
}
@@ -181,4 +137,4 @@ void addProducer(std::shared_ptr<Node>& otherNode, const IOIndex_t inputIdx, Dim
}
} // namespace Aidge
-#endif /* AIDGE_CORE_OPERATOR_PRODUCER_H_ */
+#endif /* AIDGE_CORE_OPERATOR_PRODUCER_H_ */
\ No newline at end of file
diff --git a/include/aidge/operator/ReLU.hpp b/include/aidge/operator/ReLU.hpp
index 52f13f1c5ce1d0b7a0d4ccaa4d7fe9927bcc3e53..3444c25fc2e1572e78a1377b3273580f494ac8f9 100644
--- a/include/aidge/operator/ReLU.hpp
+++ b/include/aidge/operator/ReLU.hpp
@@ -17,42 +17,29 @@
#include <vector>
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/data/Tensor.hpp"
-#include "aidge/data/Data.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/utils/Types.h"
namespace Aidge {
-class ReLU_Op : public Operator,
+class ReLU_Op : public OperatorTensor,
public Registrable<ReLU_Op, std::string, std::unique_ptr<OperatorImpl>(const ReLU_Op&)> {
-public:
- // FIXME: change accessibility
- std::shared_ptr<Tensor> mInput = std::make_shared<Tensor>();
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "ReLU";
- ReLU_Op()
- : Operator(Type)
- {
- setDatatype(DataType::Float32);
- }
+ ReLU_Op() : OperatorTensor(Type, 1, 0, 1) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
ReLU_Op(const ReLU_Op& op)
- : Operator(Type),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<ReLU_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<ReLU_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -63,68 +50,15 @@ public:
return std::make_shared<ReLU_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void) inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
- mInput = std::dynamic_pointer_cast<Tensor>(data);
- }
-
- void computeOutputDims() override final {
- if (!mInput->empty())
- mOutput->resize(mInput->dims());
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t /*inputIdx*/) const override final { return *(mInput.get()); }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx == 0) && "ReLU Operator has only 1 input");
- (void) inputIdx; // avoid unused warning
- return mInput;
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "ReLU Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInput);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
void setBackend(const std::string& name) override {
mImpl = Registrar<ReLU_Op>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInput->setBackend(name);
- }
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInput->setDatatype(datatype);
+ getInput(0)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input"};
}
@@ -138,4 +72,4 @@ inline std::shared_ptr<Node> ReLU(const std::string& name = "") {
}
}
-#endif /* AIDGE_CORE_OPERATOR_RELU_H_ */
+#endif /* AIDGE_CORE_OPERATOR_RELU_H_ */
\ No newline at end of file
diff --git a/include/aidge/operator/Scaling.hpp b/include/aidge/operator/Scaling.hpp
index 40bc397fa71ca6802c7a5a00394af7850fc73a12..fd6d6bcfccc36829671538e1f2e31b13644e3938 100644
--- a/include/aidge/operator/Scaling.hpp
+++ b/include/aidge/operator/Scaling.hpp
@@ -15,14 +15,11 @@
#include <vector>
#include <memory>
-
-
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/data/Tensor.hpp"
-#include "aidge/data/Data.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/utils/Types.h"
@@ -31,14 +28,9 @@ enum class ScalingAttr {
scalingFactor, quantizedNbBits, isOutputUnsigned
};
-class Scaling_Op : public Operator,
+class Scaling_Op : public OperatorTensor,
public Registrable<Scaling_Op, std::string, std::unique_ptr<OperatorImpl>(const Scaling_Op&)>,
public StaticAttributes<ScalingAttr, float, size_t, bool> {
-public:
- // FIXME: change accessibility
- std::shared_ptr<Tensor> mInput = std::make_shared<Tensor>();
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "Scaling";
@@ -48,27 +40,22 @@ public:
template <ScalingAttr e> using attr = typename Attributes_::template attr<e>;
Scaling_Op(float scalingFactor, std::size_t nbBits, bool isOutputUnsigned)
- : Operator(Type),
- Attributes_(
- attr<ScalingAttr::scalingFactor>(scalingFactor),
- attr<ScalingAttr::quantizedNbBits>(nbBits),
- attr<ScalingAttr::isOutputUnsigned>(isOutputUnsigned)) {
-
- setDatatype(DataType::Float32);
- }
+ : OperatorTensor(Type, 1, 0, 1),
+ Attributes_(
+ attr<ScalingAttr::scalingFactor>(scalingFactor),
+ attr<ScalingAttr::quantizedNbBits>(nbBits),
+ attr<ScalingAttr::isOutputUnsigned>(isOutputUnsigned))
+ {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
Scaling_Op(const Scaling_Op& op)
- : Operator(Type),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Scaling_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Scaling_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -79,79 +66,17 @@ public:
return std::make_shared<Scaling_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
- (void) inputIdx; //avoid unused warning
- mInput = std::dynamic_pointer_cast<Tensor>(data);
- }
-
- void computeOutputDims() override final {
- if (!mInput->empty())
- mOutput->resize(mInput->dims());
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert((inputIdx == 0) && "Scaling Operator has only 1 input");
- (void) inputIdx; // avoid unused warning
- return *(mInput.get());
- }
- inline Tensor& output(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "Scaling Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return *(mOutput.get());
- }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx == 0) && "Scaling Operator has only 1 input");
- (void) inputIdx; // avoid unused warning
- return mInput;
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "Scaling Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInput);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning;
- return mOutput;
- }
-
-
void setBackend(const std::string& name) override {
mImpl = Registrar<Scaling_Op>::create(name)(*this);
- mOutput->setBackend(name);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInput->setBackend(name);
+ mInputs[0]->setBackend(name);
}
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
- // FIXME: temporary workaround
- mInput->setDatatype(datatype);
- }
-
- inline IOIndex_t nbInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
- static const std::vector<std::string> getInputsName(){
+ static const std::vector<std::string> getInputsName() {
return {"data_input"};
}
- static const std::vector<std::string> getOutputsName(){
+ static const std::vector<std::string> getOutputsName() {
return {"data_output"};
}
};
@@ -164,8 +89,7 @@ inline std::shared_ptr<Node> Scaling(float scalingFactor = 1.0f, const std::stri
inline std::shared_ptr<Node> Scaling(float scalingFactor = 1.0f, std::size_t quantizedNbBits=8, bool isOutputUnsigned=true, const std::string& name = "") {
return std::make_shared<Node>(std::make_shared<Scaling_Op>(scalingFactor,quantizedNbBits, isOutputUnsigned), name);
}
-
-}
+} // namespace Aidge
namespace {
template <>
@@ -173,4 +97,4 @@ const char* const EnumStrings<Aidge::ScalingAttr>::data[]
= {"scalingFactor", "quantizedNbBits", "isOutputUnsigned"};
}
-#endif /* __AIDGE_CORE_OPERATOR_RELU_H__ */
+#endif /* __AIDGE_CORE_OPERATOR_RELU_H__ */
\ No newline at end of file
diff --git a/include/aidge/operator/Slice.hpp b/include/aidge/operator/Slice.hpp
index 6b1077c8d383b1fd4c1ebe6f64a4cad8135c594e..f12afafabc534dff6d6a8d0eed2ba16021b92cc1 100644
--- a/include/aidge/operator/Slice.hpp
+++ b/include/aidge/operator/Slice.hpp
@@ -16,10 +16,9 @@
#include <vector>
#include "aidge/backend/OperatorImpl.hpp"
-#include "aidge/data/Data.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Types.h"
@@ -29,14 +28,9 @@ enum class SliceAttr { Beginning, SliceDims };
template <DimIdx_t DIM>
class Slice_Op
- : public Operator,
+ : public OperatorTensor,
public Registrable<Slice_Op<DIM>, std::string, std::unique_ptr<OperatorImpl>(const Slice_Op<DIM> &)>,
public StaticAttributes<SliceAttr, std::size_t, std::array<DimSize_t, DIM>> {
-public:
- // FIXME: change accessibility
- std::shared_ptr<Tensor> mInput = std::make_shared<Tensor>();
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char *Type = "Slice";
@@ -47,12 +41,10 @@ public:
using attr = typename Attributes_::template attr<e>;
Slice_Op(std::size_t beginningPos, std::array<DimSize_t, DIM> sliceDims)
- : Operator(Type),
+ : OperatorTensor(Type, 1, 0, 1),
Attributes_(attr<SliceAttr::Beginning>(beginningPos),
attr<SliceAttr::SliceDims>(sliceDims))
- {
- setDatatype(DataType::Float32);
- }
+ {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its
@@ -60,13 +52,10 @@ public:
* @param op Operator to copy.
*/
Slice_Op(const Slice_Op &op)
- : Operator(Type),
- Attributes_(op),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op),
+ Attributes_(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Slice_Op<DIM>>::create(mOutput->getImpl()->backend())(*this)
+ mImpl = op.mImpl ? Registrar<Slice_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this)
: nullptr;
}
@@ -77,91 +66,49 @@ public:
*/
std::shared_ptr<Operator> clone() const override { return std::make_shared<Slice_Op>(*this); }
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void)inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type) == 0 && "input data must be of Tensor type");
- mInput = std::dynamic_pointer_cast<Tensor>(data);
- }
-
void computeOutputDims() override final {
- if (!mInput->empty()) {
- // Check input dimensions is compatible with slice dimensions
- if (mInput->nbDims() != DIM) {
- printf("Error: input and slice dimensions are not the same size.\n");
- exit(-1);
- }
- std::array<DimSize_t, DIM> outputDims;
-
- // Check that the sliced Tensor is actually part of the input Tensor
- // For a 5*5 tensor ('x') and a 3*3 slice kernel ('o'):
- // xxxxx xxxxx
- // xxxxx xxxxx
- // xxooo --> ok xxxoo --> out of bound
- // xxooo xxxoo
- // xxooo xxxoo
- std::vector<std::size_t> beginningCoords = mInput->getCoord(this->template getAttr<SliceAttr::Beginning>());
- for (std::size_t i = 0; i < DIM; ++i) {
- if (beginningCoords[i] + this->template getAttr<SliceAttr::SliceDims>()[i] > mInput->dims()[i]) {
- printf("ROI of Slice operator out of bounds");
- exit(-1);
- } else {
- outputDims[i] = this->template getAttr<SliceAttr::SliceDims>()[i];
- }
+ if (!getInput(0) || (getInput(0)->empty())) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
+ }
+ // Check input dimensions is compatible with slice dimensions
+ if (getInput(0)->nbDims() != DIM) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Error: input and slice dimensions are not the same size.");
+ }
+ std::array<DimSize_t, DIM> outputDims;
+ const std::array<DimSize_t, DIM> inputDims = getInput(0)->dims<DIM>();
+
+ // Check that the sliced Tensor is actually part of the input Tensor
+ // For a 5*5 tensor ('x') and a 3*3 slice kernel ('o'):
+ // xxxxx xxxxx
+ // xxxxx xxxxx
+ // xxooo --> ok xxxoo --> out of bound
+ // xxooo xxxoo
+ // xxooo xxxoo
+ std::vector<std::size_t> beginningCoords = mInputs[0]->getCoord(this->template getAttr<SliceAttr::Beginning>());
+ for (std::size_t i = 0; i < DIM; ++i) {
+ if (beginningCoords[i] + this->template getAttr<SliceAttr::SliceDims>()[i] > inputDims[i]) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "ROI of Slice operator out of bounds");
+ } else {
+ outputDims[i] = this->template getAttr<SliceAttr::SliceDims>()[i];
}
-
- mOutput->resize(outputDims);
}
- }
-
- bool outputDimsForwarded() const override final { return !(mOutput->empty()); }
-
- inline Tensor &input(const IOIndex_t /*inputIdx*/) const override final {
- return *(mInput.get());
- }
- inline Tensor &output(const IOIndex_t /*outputIdx*/) const override final {
- return *(mOutput.get());
- }
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx == 0) && "Slice Operator has only 1 input");
- (void)inputIdx; // avoid unused warning
- return mInput;
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "Slice Operator has only 1 output");
- (void)outputIdx; // avoid unused warning
- return mOutput;
- }
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void)inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInput);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void)outputIdx; // avoid unused warning
- return mOutput;
+ mOutputs[0]->resize(outputDims);
}
void setBackend(const std::string &name) {
mImpl = Registrar<Slice_Op>::create(name)(*this);
- mOutput->setBackend(name);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInput->setBackend(name);
+ getInput(0)->setBackend(name);
}
- void setDatatype(const DataType &datatype) {
- mOutput->setDatatype(datatype);
- // FIXME: temporary workaround
- mInput->setDatatype(datatype);
+ static const std::vector<std::string> getInputsName(){
+ return {"data_input"};
+ }
+ static const std::vector<std::string> getOutputsName(){
+ return {"data_output"};
}
-
- inline IOIndex_t nbInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
};
template <std::size_t DIM>
diff --git a/include/aidge/operator/Softmax.hpp b/include/aidge/operator/Softmax.hpp
index ba6132a5ee00325d0f7de57db117a169d42352e9..cc19cb8210af516f349de124f65cdd55308609fb 100644
--- a/include/aidge/operator/Softmax.hpp
+++ b/include/aidge/operator/Softmax.hpp
@@ -17,7 +17,7 @@
#include <vector>
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/data/Data.hpp"
@@ -26,33 +26,21 @@
namespace Aidge {
-class Softmax_Op : public Operator,
+class Softmax_Op : public OperatorTensor,
public Registrable<Softmax_Op, std::string, std::unique_ptr<OperatorImpl>(const Softmax_Op&)> {
-public:
- // FIXME: change accessibility
- std::shared_ptr<Tensor> mInput = std::make_shared<Tensor>();
- const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
-
public:
static constexpr const char* Type = "Softmax";
- Softmax_Op()
- : Operator(Type)
- {
- setDatatype(DataType::Float32);
- }
+ Softmax_Op() : OperatorTensor(Type, 1, 0, 1) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
Softmax_Op(const Softmax_Op& op)
- : Operator(Type),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Softmax_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Softmax_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -63,68 +51,14 @@ public:
return std::make_shared<Softmax_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void) inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
- mInput = std::dynamic_pointer_cast<Tensor>(data);
- }
-
- void computeOutputDims() override final {
- if (!mInput->empty())
- mOutput->resize(mInput->dims());
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t /*inputIdx*/) const override final { return *(mInput.get()); }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx == 0) && "Softmax Operator has only 1 input");
- (void) inputIdx; // avoid unused warning
- return mInput;
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "Softmax Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInput);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
-
void setBackend(const std::string& name) override {
mImpl = Registrar<Softmax_Op>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInput->setBackend(name);
- }
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInput->setDatatype(datatype);
+ getInput(0)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input"};
}
diff --git a/include/aidge/operator/Sqrt.hpp b/include/aidge/operator/Sqrt.hpp
index 90b2ae6a8ae1311aef14e4eba4d3563a28a3d18e..a4069b59bbe7e7586d02b71a39d811d9bf972b77 100644
--- a/include/aidge/operator/Sqrt.hpp
+++ b/include/aidge/operator/Sqrt.hpp
@@ -17,7 +17,7 @@
#include <vector>
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/data/Data.hpp"
@@ -26,7 +26,7 @@
namespace Aidge {
-class Sqrt_Op : public Operator,
+class Sqrt_Op : public OperatorTensor,
public Registrable<Sqrt_Op, std::string, std::unique_ptr<OperatorImpl>(const Sqrt_Op&)> {
public:
// FIXME: change accessibility
@@ -36,23 +36,16 @@ public:
public:
static constexpr const char* Type = "Sqrt";
- Sqrt_Op()
- : Operator(Type)
- {
- setDatatype(DataType::Float32);
- }
+ Sqrt_Op() : OperatorTensor(Type, 1, 0, 1) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
Sqrt_Op(const Sqrt_Op& op)
- : Operator(Type),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Sqrt_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Sqrt_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -63,68 +56,14 @@ public:
return std::make_shared<Sqrt_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void) inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
- mInput = std::dynamic_pointer_cast<Tensor>(data);
- }
-
- void computeOutputDims() override final {
- if (!mInput->empty())
- mOutput->resize(mInput->dims());
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t /*inputIdx*/) const override final { return *(mInput.get()); }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx == 0) && "Sqrt Operator has only 1 input");
- (void) inputIdx; // avoid unused warning
- return mInput;
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "Sqrt Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx == 0 && "operator supports only 1 input");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInput);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
-
void setBackend(const std::string& name) override {
mImpl = Registrar<Sqrt_Op>::create(name)(*this);
- mOutput->setBackend(name);
-
- // FIXME: temporary workaround
- mInput->setBackend(name);
- }
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInput->setDatatype(datatype);
+ getInput(0)->setBackend(name);
}
- inline IOIndex_t nbInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input"};
}
diff --git a/include/aidge/operator/Sub.hpp b/include/aidge/operator/Sub.hpp
index 451cba08f58e7a580576531ce2a97c92fb9be3ae..3a826bd0f6b1ad89800c5ea70cc412a681a074b6 100644
--- a/include/aidge/operator/Sub.hpp
+++ b/include/aidge/operator/Sub.hpp
@@ -17,7 +17,7 @@
#include <vector>
#include "aidge/utils/Registrar.hpp"
-#include "aidge/operator/Operator.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/data/Data.hpp"
@@ -26,7 +26,7 @@
namespace Aidge {
-class Sub_Op : public Operator,
+class Sub_Op : public OperatorTensor,
public Registrable<Sub_Op, std::string, std::unique_ptr<OperatorImpl>(const Sub_Op&)> {
public:
// FIXME: change accessibility
@@ -36,23 +36,16 @@ public:
public:
static constexpr const char* Type = "Sub";
- Sub_Op()
- : Operator(Type)
- {
- setDatatype(DataType::Float32);
- }
+ Sub_Op() : OperatorTensor(Type, 2, 0, 1) {}
/**
* @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
* @param op Operator to copy.
*/
Sub_Op(const Sub_Op& op)
- : Operator(Type),
- mOutput(std::make_shared<Tensor>(*op.mOutput))
+ : OperatorTensor(op)
{
- // cpy-ctor
- setDatatype(op.mOutput->dataType());
- mImpl = op.mImpl ? Registrar<Sub_Op>::create(mOutput->getImpl()->backend())(*this) : nullptr;
+ mImpl = op.mImpl ? Registrar<Sub_Op>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
}
/**
@@ -63,73 +56,16 @@ public:
return std::make_shared<Sub_Op>(*this);
}
- void associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
- assert(inputIdx < 2 && "operator supports only 2 inputs");
- (void) inputIdx; // avoid unused warning
- assert(strcmp(data->type(), Tensor::Type)==0 && "input data must be of Tensor type");
- mInputs[inputIdx] = std::dynamic_pointer_cast<Tensor>(data);
- }
-
- void computeOutputDims() override final {
- if (!mInputs[0]->empty())
- mOutput->resize(mInputs[0]->dims());
- }
-
- bool outputDimsForwarded() const override final {
- return !(mOutput->empty());
- }
-
-
- inline Tensor& input(const IOIndex_t inputIdx) const override final {
- assert(static_cast<std::size_t>(inputIdx) < 2 && "wrong inputIdx for Add operator.");
- return *(mInputs[inputIdx].get());
- }
- inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
-
-
- inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
- assert((inputIdx < 2) && "Sub Operator has 2 inputs");
- (void) inputIdx; // avoid unused warning
- return mInputs[inputIdx];
- }
- inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
- assert((outputIdx == 0) && "Sub Operator has only 1 output");
- (void) outputIdx; // avoid unused warning
- return mOutput;
- }
-
-
- std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
- assert(inputIdx < 2 && "operator supports only 2 inputs");
- (void) inputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mInputs[inputIdx]);
- }
- std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
- assert(outputIdx == 0 && "operator supports only 1 output");
- (void) outputIdx; // avoid unused warning
- return std::static_pointer_cast<Data>(mOutput);
- }
-
void setBackend(const std::string& name) override {
mImpl = Registrar<Sub_Op>::create(name)(*this);
- mOutput->setBackend(name);
+ mOutputs[0]->setBackend(name);
// FIXME: temporary workaround
- mInputs[0]->setBackend(name);
- mInputs[1]->setBackend(name);
+ getInput(0)->setBackend(name);
+ getInput(1)->setBackend(name);
}
- void setDatatype(const DataType& datatype) override {
- mOutput->setDatatype(datatype);
- // FIXME: temporary workaround
- mInputs[0]->setDatatype(datatype);
- mInputs[1]->setDatatype(datatype);
- }
-
- inline IOIndex_t nbInputs() const noexcept override final { return 2; }
- inline IOIndex_t nbDataInputs() const noexcept override final { return 2; }
- inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
static const std::vector<std::string> getInputsName(){
return {"data_input"};
}
@@ -141,6 +77,6 @@ public:
inline std::shared_ptr<Node> Sub(const std::string& name = "") {
return std::make_shared<Node>(std::make_shared<Sub_Op>(), name);
}
-}
+} // namespace Aidge
-#endif /* AIDGE_CORE_OPERATOR_SUB_H_ */
+#endif /* AIDGE_CORE_OPERATOR_SUB_H_ */
\ No newline at end of file