Merge branch 'better_inputs_to_attr' into 'dev'

Replace swich case with refCastFrom()

See merge request eclipse/aidge/aidge_core!123

include/aidge/backend/cpu/data/TensorImpl.hpp

@@ -125,9 +125,9 @@ static Registrar<Tensor> registrarTensorImpl_cpu_Float32(
 static Registrar<Tensor> registrarTensorImpl_cpu_Float16(
         {"cpu", DataType::Float16}, Aidge::TensorImpl_cpu<half_float::half>::create);
 static Registrar<Tensor> registrarTensorImpl_cpu_Int64(
-        {"cpu", DataType::Int64}, Aidge::TensorImpl_cpu<long>::create);
+        {"cpu", DataType::Int64}, Aidge::TensorImpl_cpu<int64_t>::create);
 static Registrar<Tensor> registrarTensorImpl_cpu_Int32(
-        {"cpu", DataType::Int32}, Aidge::TensorImpl_cpu<int>::create);
+        {"cpu", DataType::Int32}, Aidge::TensorImpl_cpu<int32_t>::create);
 static Registrar<Tensor> registrarTensorImpl_cpu_Int16(
         {"cpu", DataType::Int16}, Aidge::TensorImpl_cpu<int16_t>::create);
 static Registrar<Tensor> registrarTensorImpl_cpu_UInt16(

include/aidge/operator/Gather.hpp

@@ -79,6 +79,7 @@ public:
         return std::make_shared<Gather_Op>(*this);
     }
 
+    bool dimsForwarded() const override final;
     bool forwardDims(bool allowDataDependency = false) override final;
 
     void setBackend(const std::string& name, DeviceIdx_t device = 0) override;

include/aidge/operator/Reshape.hpp

@@ -75,6 +75,7 @@ public:
         return std::make_shared<Reshape_Op>(*this);
     }
 
+    bool dimsForwarded() const override final;
     bool forwardDims(bool allowDataDependency = false) override final;
 
     void setBackend(const std::string& name, DeviceIdx_t device = 0) override final;

include/aidge/operator/Slice.hpp

@@ -78,6 +78,7 @@ public:
      */
     std::shared_ptr<Operator> clone() const override { return std::make_shared<Slice_Op>(*this); }
 
+    bool dimsForwarded() const override final;
     bool forwardDims(bool allowDataDependency = false) override final;
 
     void setBackend(const std::string &name, DeviceIdx_t device = 0) override;

src/operator/Gather.cpp

@@ -51,64 +51,61 @@ void Aidge::Gather_OpImpl::forward() {
 
 const std::string Aidge::Gather_Op::Type = "Gather";
 
-bool Aidge::Gather_Op::forwardDims(bool /*allowDataDependency*/) {
+bool Aidge::Gather_Op::dimsForwarded() const {
+    if (getInput(1) && !getInput(1)->empty()) {
+        // output dims are data dependent
+        return false;
+    }
+
+    return OperatorTensor::dimsForwarded();
+}
+
+bool Aidge::Gather_Op::forwardDims(bool allowDataDependency) {
     // check data input has been associated
     if (!getInput(0)) {
         AIDGE_THROW_OR_ABORT(std::runtime_error, "{}: input #0 should be associated with a Tensor", type());
     }
 
-    if (!getInput(0)->empty()) {
-        if (this->template getAttr<GatherAttr::Indices>().empty())
-        {
-            if(getInput(1)->empty()) {
-                AIDGE_THROW_OR_ABORT(std::runtime_error, "{}: Either indices input or attribute must be provided", type());
-            }
-            this->template getAttr<GatherAttr::GatheredShape>() = getInput(1)->dims();
-            this->template getAttr<GatherAttr::Indices>().clear(); // If both are provided input would override attrs
-            this->template getAttr<GatherAttr::Indices>().reserve(getInput(1)->size());
-            switch (mInputs[1]->dataType()) {
-                case DataType::Float64:
-                    std::copy_n(static_cast<double*>(mInputs[1]->getImpl()->rawPtr()),
-                                getInput(1)->size(),
-                                std::back_inserter(this->template getAttr<GatherAttr::Indices>()));
-                    break;
-                case DataType::Float32:
-                    std::copy_n(static_cast<float*>(mInputs[1]->getImpl()->rawPtr()),
-                                getInput(1)->size(),
-                                std::back_inserter(this->template getAttr<GatherAttr::Indices>()));
-                    break;
-                case DataType::Int64:
-                    std::copy_n(static_cast<std::int64_t*>(mInputs[1]->getImpl()->rawPtr()),
-                                getInput(1)->size(),
-                                std::back_inserter(this->template getAttr<GatherAttr::Indices>()));
-                    break;
-                case DataType::Int32:
-                    std::copy_n(static_cast<std::int32_t*>(mInputs[1]->getImpl()->rawPtr()),
-                                getInput(1)->size(),
-                                std::back_inserter(this->template getAttr<GatherAttr::Indices>()));
-                    break;
-                default:
-                    AIDGE_THROW_OR_ABORT(std::runtime_error, "{}: Indices input DataType is not supported.", type());
-                    break;
-            }
+    if (getInput(0)->empty()) {
+        return false;
+    }
+
+    if (getInput(1) && !getInput(1)->empty()) {
+        if (!this->template getAttr<GatherAttr::Indices>().empty()) {
+            Log::notice("Gather_Op: ignoring non-empty Indices attribute because input#1 takes precedence");
         }
-        std::vector<DimSize_t> outDims = getInput(0)->dims();
 
-        std::int8_t axisIdx = this->template getAttr<GatherAttr::Axis>()>=0?
-                              this->template getAttr<GatherAttr::Axis>():
-                              this->template getAttr<GatherAttr::Axis>()+outDims.size();
-        outDims.erase(outDims.begin() + static_cast<std::size_t>(axisIdx));
-        if( !this->template getAttr<GatherAttr::GatheredShape>().empty())
-        {
-            outDims.insert(outDims.begin() + static_cast<std::size_t>(axisIdx),
-                           this->template getAttr<GatherAttr::GatheredShape>().begin(),
-                           this->template getAttr<GatherAttr::GatheredShape>().end());
+        if (!allowDataDependency) {
+            Log::warn("Gather_Op: unable to forwardDims() because output dims are data dependent on input#1");
+            return false;
         }
-        mOutputs[0]->resize(outDims);
-        return true;
+
+        std::shared_ptr<Tensor> fallback;
+        this->template getAttr<GatherAttr::GatheredShape>() = getInput(1)->dims();
+        this->template getAttr<GatherAttr::Indices>().clear(); // If both are provided input would override attrs
+        this->template getAttr<GatherAttr::Indices>().reserve(getInput(1)->size());
+        const auto& indices = mInputs[1]->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+        std::copy_n(static_cast<int64_t*>(indices.getImpl()->hostPtr()),
+                    indices.size(),
+                    std::back_inserter(this->template getAttr<GatherAttr::Indices>()));
     }
 
-    return false;
+    AIDGE_ASSERT(!this->template getAttr<GatherAttr::Indices>().empty(), "Missing input#1 or Indices attribute");
+
+    std::vector<DimSize_t> outDims = getInput(0)->dims();
+
+    std::int8_t axisIdx = this->template getAttr<GatherAttr::Axis>()>=0?
+                            this->template getAttr<GatherAttr::Axis>():
+                            this->template getAttr<GatherAttr::Axis>()+outDims.size();
+    outDims.erase(outDims.begin() + static_cast<std::size_t>(axisIdx));
+    if( !this->template getAttr<GatherAttr::GatheredShape>().empty())
+    {
+        outDims.insert(outDims.begin() + static_cast<std::size_t>(axisIdx),
+                        this->template getAttr<GatherAttr::GatheredShape>().begin(),
+                        this->template getAttr<GatherAttr::GatheredShape>().end());
+    }
+    mOutputs[0]->resize(outDims);
+    return true;
 }
 
 void Aidge::Gather_Op::setBackend(const std::string& name, Aidge::DeviceIdx_t device) {

src/operator/OperatorTensor.cpp

@@ -182,7 +182,8 @@ void Aidge::OperatorTensor::setDataType(const DataType& dataType) const {
 
 void Aidge::OperatorTensor::forward() {
     if (!dimsForwarded()) {
-        forwardDims();
+        // Allow data dependent forwardDims at this point (data is available)
+        forwardDims(true);
     }
 
     Operator::forward();

src/operator/Reshape.cpp

@@ -30,85 +30,77 @@ void Aidge::Reshape_OpImpl::forward() {
 
 const std::string Aidge::Reshape_Op::Type = "Reshape";
 
-bool Aidge::Reshape_Op::forwardDims(bool /*allowDataDependency*/) {
+bool Aidge::Reshape_Op::dimsForwarded() const {
+    if (getInput(1) && !getInput(1)->empty()) {
+        // output dims are data dependent
+        return false;
+    }
+
+    return OperatorTensor::dimsForwarded();
+}
+
+bool Aidge::Reshape_Op::forwardDims(bool allowDataDependency) {
     // check input has been associated
     if (!getInput(0)) {
         AIDGE_THROW_OR_ABORT(std::runtime_error, "{}: input #0 should be associated with a Tensor", type());
     }
-    if (!getInput(0)->empty()) {
-        std::vector<DimSize_t> outDims;
-        // variables to handle a negative dimension
-        bool foundNegativeDimension = false;
-        std::size_t outSize = 1;
-        DimIdx_t negativeIndex = 0;
-
-        // Fill shape attr if empty
-        if (this->template getAttr<ReshapeAttr::Shape>().empty()) {
-            if (!getInput(1)) {
-                AIDGE_THROW_OR_ABORT(std::runtime_error, "{}: input #1 should be associated with a Tensor", type());
-            }
-            if(!getInput(1)->empty()) {
-                this->template getAttr<ReshapeAttr::Shape>().clear(); // If both are provided input would override attrs
-                this->template getAttr<ReshapeAttr::Shape>().reserve(getInput(1)->size());
-                switch (mInputs[1]->dataType()) {
-                    case DataType::Float64:
-                        std::copy_n(static_cast<double*>(mInputs[1]->getImpl()->rawPtr()),
-                                    getInput(1)->size(),
-                                    std::back_inserter(this->template getAttr<ReshapeAttr::Shape>()));
-                        break;
-                    case DataType::Float32:
-                        std::copy_n(static_cast<float*>(mInputs[1]->getImpl()->rawPtr()),
-                                    getInput(1)->size(),
-                                    std::back_inserter(this->template getAttr<ReshapeAttr::Shape>()));
-                        break;
-                    case DataType::Int64:
-                        std::copy_n(static_cast<std::int64_t*>(mInputs[1]->getImpl()->rawPtr()),
-                                    getInput(1)->size(),
-                                    std::back_inserter(this->template getAttr<ReshapeAttr::Shape>()));
-                        break;
-                    case DataType::Int32:
-                        std::copy_n(static_cast<std::int32_t*>(mInputs[1]->getImpl()->rawPtr()),
-                                    getInput(1)->size(),
-                                    std::back_inserter(this->template getAttr<ReshapeAttr::Shape>()));
-                        break;
-                    default:
-                        AIDGE_THROW_OR_ABORT(std::runtime_error, "Shape input DataType is not supported.");
-                        break;
-                }
-            }
-            else {
-                AIDGE_THROW_OR_ABORT(std::runtime_error, "Shape attribute or Input is needed");
-            }
+
+    if (getInput(0)->empty()) {
+        return false;
+    }
+
+    if (getInput(1) && !getInput(1)->empty()) {
+        if (!this->template getAttr<ReshapeAttr::Shape>().empty()) {
+            Log::notice("Reshape_Op: ignoring non-empty Shape attribute because input#1 takes precedence");
         }
 
-        for(std::size_t i = 0; i < this->template getAttr<ReshapeAttr::Shape>().size(); ++i)
-        {
-            std::int64_t dimSize = this->template getAttr<ReshapeAttr::Shape>()[i];
-            if (dimSize < 0) {
-                if (foundNegativeDimension) {
-                    AIDGE_THROW_OR_ABORT(std::runtime_error, "Found more than one negative dimension in Reshape Operator.");
-                }
-                foundNegativeDimension = true;
-                dimSize = 1;
-                negativeIndex = static_cast<DimIdx_t>(i);
-            }
-            else if (dimSize == 0)
-            {
-                dimSize = getInput(0) -> dims()[i];
-            }
-            outDims.push_back(static_cast<DimSize_t>(dimSize));
-            outSize *= static_cast<DimSize_t>(dimSize);
+        if (!allowDataDependency) {
+            Log::warn("Reshape_Op: unable to forwardDims() because output dims are data dependent on input#1");
+            return false;
         }
 
-        if (foundNegativeDimension) {
-            outDims[negativeIndex] = (getInput(0) -> size()) / outSize;
+        std::shared_ptr<Tensor> fallback;
+        this->template getAttr<ReshapeAttr::Shape>().clear(); // If both are provided input would override attrs
+        this->template getAttr<ReshapeAttr::Shape>().reserve(getInput(1)->size());
+        const auto& shape = mInputs[1]->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+        std::copy_n(static_cast<int64_t*>(shape.getImpl()->hostPtr()),
+                    shape.size(),
+                    std::back_inserter(this->template getAttr<ReshapeAttr::Shape>()));
+    }
+
+    AIDGE_ASSERT(!this->template getAttr<ReshapeAttr::Shape>().empty(), "Missing input#1 or Shape attribute");
+
+    std::vector<DimSize_t> outDims;
+    // variables to handle a negative dimension
+    bool foundNegativeDimension = false;
+    std::size_t outSize = 1;
+    DimIdx_t negativeIndex = 0;
+
+    for(std::size_t i = 0; i < this->template getAttr<ReshapeAttr::Shape>().size(); ++i)
+    {
+        int64_t dimSize = this->template getAttr<ReshapeAttr::Shape>()[i];
+        if (dimSize < 0) {
+            if (foundNegativeDimension) {
+                AIDGE_THROW_OR_ABORT(std::runtime_error, "Found more than one negative dimension in Reshape Operator.");
+            }
+            foundNegativeDimension = true;
+            dimSize = 1;
+            negativeIndex = static_cast<DimIdx_t>(i);
         }
+        else if (dimSize == 0)
+        {
+            dimSize = getInput(0) -> dims()[i];
+        }
+        outDims.push_back(static_cast<DimSize_t>(dimSize));
+        outSize *= static_cast<DimSize_t>(dimSize);
+    }
 
-        mOutputs[0]->resize(outDims);
-        return true;
+    if (foundNegativeDimension) {
+        outDims[negativeIndex] = (getInput(0) -> size()) / outSize;
     }
 
-    return false;
+    mOutputs[0]->resize(outDims);
+    return true;
 }
 
 void Aidge::Reshape_Op::setBackend(const std::string& name, Aidge::DeviceIdx_t device) {

src/operator/Slice.cpp

@@ -36,33 +36,24 @@ void Aidge::Slice_OpImpl::forward() {
                  (op.template getAttr<SliceAttr::Starts>().size() == op.template getAttr<SliceAttr::Axes>().size()),
                  "start, end and axes arguments should be the same size.");
 
-    const std::size_t nbDims = op.getInput(0)->nbDims();
-
-    const std::vector<std::size_t>& inputDims = op.getInput(0)->dims();
-    auto outputDims = op.getInput(0)->dims();
+    const auto nbDims = op.getInput(0)->nbDims();
+    const auto& inputDims = op.getInput(0)->dims();
+    const auto& outputDims = op.getOutput(0)->dims();
 
     // compute index of the output's first element
-    // compute output dimension at the same time (may change between two forward calls)
     std::size_t beginning = 0;
     const std::size_t nbAxes = op.template getAttr<SliceAttr::Axes>().size();
     for (std::size_t i = 0; i < nbAxes; ++i) {
         // For each slice operation get the params and cast them to size_t
-        DimIdx_t axis = op.template getAttr<SliceAttr::Axes>()[i] >= 0 ?
+        const DimIdx_t axis = op.template getAttr<SliceAttr::Axes>()[i] >= 0 ?
                             static_cast<DimIdx_t>(op.template getAttr<SliceAttr::Axes>()[i]) :
                             static_cast<DimIdx_t>(op.template getAttr<SliceAttr::Axes>()[i] + static_cast<DimIdx_t>(inputDims.size()));
-        DimSize_t start = op.template getAttr<SliceAttr::Starts>()[i] >= 0 ?
+        const DimSize_t start = op.template getAttr<SliceAttr::Starts>()[i] >= 0 ?
                             static_cast<DimSize_t>(op.template getAttr<SliceAttr::Starts>()[i]) :
                             static_cast<DimSize_t>(op.template getAttr<SliceAttr::Starts>()[i] + static_cast<DimSize_t>(inputDims[axis]));
-        DimSize_t end = op.template getAttr<SliceAttr::Ends>()[i] >= 0 ?
-                        static_cast<DimSize_t>(op.template getAttr<SliceAttr::Ends>()[i]) :
-                        static_cast<DimSize_t>(op.template getAttr<SliceAttr::Ends>()[i] + static_cast<DimSize_t>(inputDims[axis]));
         const std::size_t stridePostAxis = std::accumulate(inputDims.cbegin()+axis+1, inputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
         beginning += start * stridePostAxis;
-        const std::size_t sliceLength = end - start;
-        outputDims[axis] = sliceLength;
     }
-    op.getOutput(0)->resize(outputDims);
-
 
     // for inputDims = {4,5,5,3} & outputDims = {3,2,2,1}: substractDims = {1,5,5,3}
     std::vector<std::size_t> substractedDims = std::vector<std::size_t>(nbDims);
@@ -111,104 +102,113 @@ void Aidge::Slice_OpImpl::forward() {
 
 const std::string Aidge::Slice_Op::Type = "Slice";
 
-bool Aidge::Slice_Op::forwardDims(bool /*allowDataDependency*/) {
+bool Aidge::Slice_Op::dimsForwarded() const {
+    if ((getInput(1) && !getInput(1)->empty())
+        || (getInput(2) && !getInput(2)->empty())
+        || (getInput(3) && !getInput(3)->empty()))
+    {
+        // output dims are data dependent
+        return false;
+    }
+
+    return OperatorTensor::dimsForwarded();
+}
+
+bool Aidge::Slice_Op::forwardDims(bool allowDataDependency) {
     // check inputs have been associated
     if (!getInput(0)) {
         AIDGE_THROW_OR_ABORT(std::runtime_error, "{}: input #0 should be associated with a Tensor", type());
     }
 
-    if(!getInput(0)->empty())
-    {
-        if(this->template getAttr<SliceAttr::Starts>().empty() || this->template getAttr<SliceAttr::Ends>().empty() || this->template getAttr<SliceAttr::Axes>().empty())
-        {
-            if(getInput(1)->empty() || getInput(2)->empty() || getInput(3)->empty()) {
-                AIDGE_THROW_OR_ABORT(std::runtime_error, "{}: Starts, Ends and Axes must be provided either as input or attributes", type());
-            }
+    if (getInput(0)->empty()) {
+        return false;
+    }
 
-            AIDGE_ASSERT((mInputs[1]->dataType() == mInputs[2]->dataType()) && (mInputs[1]->dataType() == mInputs[3]->dataType()), "Slice inputs must have the same dataType.");
-
-            this->template getAttr<SliceAttr::Starts>().clear(); // If both are provided input would override attrs
-            this->template getAttr<SliceAttr::Starts>().reserve(getInput(1)->size());
-            this->template getAttr<SliceAttr::Ends>().clear();
-            this->template getAttr<SliceAttr::Ends>().reserve(getInput(1)->size());
-            this->template getAttr<SliceAttr::Axes>().clear();
-            this->template getAttr<SliceAttr::Axes>().reserve(getInput(1)->size());
-            switch (mInputs[1]->dataType()) {
-                case DataType::Float64:
-                    std::copy_n(static_cast<double*>(mInputs[1]->getImpl()->rawPtr()),
-                                getInput(1)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Starts>()));
-                    std::copy_n(static_cast<double*>(mInputs[2]->getImpl()->rawPtr()),
-                                getInput(2)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Ends>()));
-                    std::copy_n(static_cast<double*>(mInputs[3]->getImpl()->rawPtr()),
-                                getInput(3)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Axes>()));
-                    break;
-                case DataType::Float32:
-                    std::copy_n(static_cast<float*>(mInputs[1]->getImpl()->rawPtr()),
-                                getInput(1)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Starts>()));
-                    std::copy_n(static_cast<float*>(mInputs[2]->getImpl()->rawPtr()),
-                                getInput(2)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Ends>()));
-                    std::copy_n(static_cast<float*>(mInputs[3]->getImpl()->rawPtr()),
-                                getInput(3)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Axes>()));
-                    break;
-                case DataType::Int64:
-                    std::copy_n(static_cast<std::int64_t*>(mInputs[1]->getImpl()->rawPtr()),
-                                getInput(1)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Starts>()));
-                    std::copy_n(static_cast<std::int64_t*>(mInputs[2]->getImpl()->rawPtr()),
-                                getInput(2)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Ends>()));
-                    std::copy_n(static_cast<std::int64_t*>(mInputs[3]->getImpl()->rawPtr()),
-                                getInput(3)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Axes>()));
-                    break;
-                case DataType::Int32:
-                    std::copy_n(static_cast<std::int32_t*>(mInputs[1]->getImpl()->rawPtr()),
-                                getInput(1)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Starts>()));
-                    std::copy_n(static_cast<std::int32_t*>(mInputs[2]->getImpl()->rawPtr()),
-                                getInput(2)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Ends>()));
-                    std::copy_n(static_cast<std::int32_t*>(mInputs[3]->getImpl()->rawPtr()),
-                                getInput(3)->size(),
-                                std::back_inserter(this->template getAttr<SliceAttr::Axes>()));                                
-                    break;
-                default:
-                    AIDGE_THROW_OR_ABORT(std::runtime_error, "{}: Indices input DataType is not supported.", type());
-                    break;
-            }
+    std::shared_ptr<Tensor> fallback;
+
+    if (getInput(1) && !getInput(1)->empty()) {
+        if (!this->template getAttr<SliceAttr::Starts>().empty()) {
+            Log::notice("Slice_Op: ignoring non-empty Starts attribute because input#1 takes precedence");
         }
 
-        DimSize_t nbAxes = this->template getAttr<SliceAttr::Axes>().size();
-        std::vector<DimSize_t> outDims = getInput(0)->dims();
-        for (std::size_t i = 0; i < nbAxes; ++i) {
-            DimIdx_t axis = this->template getAttr<SliceAttr::Axes>()[i] >= 0 ?
-                            static_cast<DimIdx_t>(this->template getAttr<SliceAttr::Axes>()[i]) :
-                            static_cast<DimIdx_t>(this->template getAttr<SliceAttr::Axes>()[i] + static_cast<DimIdx_t>(getInput(0)->nbDims()));
-            DimSize_t start = this->template getAttr<SliceAttr::Starts>()[i] >= 0 ?
-                              static_cast<DimSize_t>(this->template getAttr<SliceAttr::Starts>()[i]) :
-                              static_cast<DimSize_t>(this->template getAttr<SliceAttr::Starts>()[i] + static_cast<DimSize_t>(getInput(0)->dims()[axis]));
-            DimSize_t end = this->template getAttr<SliceAttr::Ends>()[i] >= 0 ?
-                            static_cast<DimSize_t>(this->template getAttr<SliceAttr::Ends>()[i]) :
-                            static_cast<DimSize_t>(this->template getAttr<SliceAttr::Ends>()[i] + static_cast<DimSize_t>(getInput(0)->dims()[axis]));
-
-            const std::size_t sliceLength = end - start;
-            // Check if slice length is valid
-            if (sliceLength > getInput(0)->dims()[axis])
-            {
-                AIDGE_THROW_OR_ABORT(std::runtime_error, "ROI of Slice operator out of bounds");
-            }
-            outDims[axis] = sliceLength;
+        if (!allowDataDependency) {
+            Log::warn("Slice_Op: unable to forwardDims() because output dims are data dependent on input#1");
+            return false;
+        }
+
+        this->template getAttr<SliceAttr::Starts>().clear(); // If both are provided input would override attrs
+        this->template getAttr<SliceAttr::Starts>().reserve(getInput(1)->size());
+        const auto& starts = getInput(1)->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+        std::copy_n(static_cast<int64_t*>(starts.getImpl()->hostPtr()),
+                    starts.size(),
+                    std::back_inserter(this->template getAttr<SliceAttr::Starts>()));
+    }
+
+    AIDGE_ASSERT(!this->template getAttr<SliceAttr::Starts>().empty(), "Missing input#1 or Starts attribute");
+
+    if (getInput(2) && !getInput(2)->empty()) {
+        if (!this->template getAttr<SliceAttr::Ends>().empty()) {
+            Log::notice("Slice_Op: ignoring non-empty Ends attribute because input#2 takes precedence");
+        }
+
+        if (!allowDataDependency) {
+            Log::warn("Slice_Op: unable to forwardDims() because output dims are data dependent on input#2");
+            return false;
+        }
+
+        this->template getAttr<SliceAttr::Ends>().clear(); // If both are provided input would override attrs
+        this->template getAttr<SliceAttr::Ends>().reserve(getInput(2)->size());
+        const auto& ends = getInput(2)->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+        std::copy_n(static_cast<int64_t*>(ends.getImpl()->hostPtr()),
+                    ends.size(),
+                    std::back_inserter(this->template getAttr<SliceAttr::Ends>()));
+    }
+
+    AIDGE_ASSERT(!this->template getAttr<SliceAttr::Ends>().empty(), "Missing input#2 or Ends attribute");
+
+    if (getInput(3) && !getInput(3)->empty()) {
+        if (!this->template getAttr<SliceAttr::Axes>().empty()) {
+            Log::notice("Slice_Op: ignoring non-empty Axes attribute because input#3 takes precedence");
+        }
+
+        if (!allowDataDependency) {
+            Log::warn("Slice_Op: unable to forwardDims() because output dims are data dependent on input#3");
+            return false;
+        }
+
+        this->template getAttr<SliceAttr::Axes>().clear(); // If both are provided input would override attrs
+        this->template getAttr<SliceAttr::Axes>().reserve(getInput(3)->size());
+        const auto& axes = getInput(3)->refCastFrom(fallback, NativeType<int8_t>::type, "cpu");
+        std::copy_n(static_cast<int8_t*>(axes.getImpl()->hostPtr()),
+                    axes.size(),
+                    std::back_inserter(this->template getAttr<SliceAttr::Axes>()));
+    }
+
+    AIDGE_ASSERT(!this->template getAttr<SliceAttr::Axes>().empty(), "Missing input#3 or Axes attribute");
+
+    const DimSize_t nbAxes = this->template getAttr<SliceAttr::Axes>().size();
+    std::vector<DimSize_t> outDims = getInput(0)->dims();
+    for (std::size_t i = 0; i < nbAxes; ++i) {
+        const DimIdx_t axis = this->template getAttr<SliceAttr::Axes>()[i] >= 0 ?
+                        static_cast<DimIdx_t>(this->template getAttr<SliceAttr::Axes>()[i]) :
+                        static_cast<DimIdx_t>(this->template getAttr<SliceAttr::Axes>()[i] + static_cast<DimIdx_t>(getInput(0)->nbDims()));
+        const DimSize_t start = this->template getAttr<SliceAttr::Starts>()[i] >= 0 ?
+                            static_cast<DimSize_t>(this->template getAttr<SliceAttr::Starts>()[i]) :
+                            static_cast<DimSize_t>(this->template getAttr<SliceAttr::Starts>()[i] + static_cast<DimSize_t>(getInput(0)->dims()[axis]));
+        const DimSize_t end = this->template getAttr<SliceAttr::Ends>()[i] >= 0 ?
+                        static_cast<DimSize_t>(this->template getAttr<SliceAttr::Ends>()[i]) :
+                        static_cast<DimSize_t>(this->template getAttr<SliceAttr::Ends>()[i] + static_cast<DimSize_t>(getInput(0)->dims()[axis]));
+
+        const std::size_t sliceLength = end - start;
+        // Check if slice length is valid
+        if (sliceLength > getInput(0)->dims()[axis])
+        {
+            AIDGE_THROW_OR_ABORT(std::runtime_error, "ROI of Slice operator out of bounds");
         }
-        mOutputs[0]->resize(outDims);
-        return true;
+        outDims[axis] = sliceLength;
     }
-    return false;
+    mOutputs[0]->resize(outDims);
+    return true;
 }
 
 void Aidge::Slice_Op::setBackend(const std::string& name, Aidge::DeviceIdx_t device) {