From dd21faee98903a0d08accfe0ada3144847fc2d96 Mon Sep 17 00:00:00 2001
From: NAUD Maxence <maxence.naud@cea.fr>
Date: Fri, 24 Nov 2023 14:24:01 +0000
Subject: [PATCH] [WIP] uncomment 'computeReceptiveField()' member function
---
include/aidge/operator/AvgPooling.hpp | 72 +++++++++--------
include/aidge/operator/Conv.hpp | 98 +++++++++++------------
include/aidge/operator/Operator.hpp | 9 ---
include/aidge/operator/OperatorTensor.hpp | 10 +++
src/operator/Operator.cpp | 23 ------
src/operator/OperatorTensor.cpp | 25 ++++++
unit_tests/operator/Test_Conv_Op.cpp | 95 +++++++++++-----------
7 files changed, 172 insertions(+), 160 deletions(-)
diff --git a/include/aidge/operator/AvgPooling.hpp b/include/aidge/operator/AvgPooling.hpp
index f0f9f6c54..ca2980821 100644
--- a/include/aidge/operator/AvgPooling.hpp
+++ b/include/aidge/operator/AvgPooling.hpp
@@ -94,40 +94,44 @@ public:
}
- // std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const override {
- // if (outputIdx != 0) {
- // AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
- // }
- // if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
- // // Offset
- // const auto outputIdxDims = mOutput->getCoord(firstIdx);
- // std::vector<DimSize_t> inputIdxDims = outputIdxDims;
-
- // for (DimIdx_t i = 0; i < (DIM+2); ++i) {
- // if (((outputDims[i] + outputIdxDims[i]) > mOutput->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
- // AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
- // }
- // }
-
- // // padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
- // // Width
- // std::vector<DimSize_t> inputDims;
- // inputDims.push_back(outputDims[0]); // same batch value
- // inputDims.push_back(outputDims[1]); // same channel value
-
- // for (DimIdx_t i = 0; i < DIM; ++i) {
- // inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
- // * this->template getAttr<AvgPoolingAttr::StrideDims>()[static_cast<std::size_t>(i)]
- // + 1
- // + (this->template getAttr<AvgPoolingAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1));
- // inputIdxDims[2+i] *= this->template getAttr<AvgPoolingAttr::StrideDims>()[static_cast<std::size_t>(i)];
- // }
- // std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res = std::vector<std::pair<std::size_t, std::vector<DimSize_t>>>();
- // res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInput->getIdx(inputIdxDims), inputDims));
- // return res;
- // }
- // AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
- // }
+ std::vector<std::pair<std::size_t, std::vector<DimSize_t>>>
+ computeReceptiveField(const std::size_t firstIdx,
+ const std::vector<DimSize_t>& outputDims,
+ const IOIndex_t outputIdx = 0) const override final
+ {
+ if (outputIdx != 0) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
+ }
+ if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
+ // Offset
+ const auto outputIdxDims = mOutputs[0]->getCoord(firstIdx);
+ std::vector<DimSize_t> inputIdxDims = outputIdxDims;
+
+ for (DimIdx_t i = 0; i < (DIM+2); ++i) {
+ if (((outputDims[i] + outputIdxDims[i]) > mOutputs[0]->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
+ }
+ }
+
+ // padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
+ // Width
+ std::vector<DimSize_t> inputDims;
+ inputDims.push_back(outputDims[0]); // same batch value
+ inputDims.push_back(outputDims[1]); // same channel value
+
+ for (DimIdx_t i = 0; i < DIM; ++i) {
+ inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
+ * this->template getAttr<AvgPoolingAttr::StrideDims>()[static_cast<std::size_t>(i)]
+ + 1
+ + (this->template getAttr<AvgPoolingAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1));
+ inputIdxDims[2+i] *= this->template getAttr<AvgPoolingAttr::StrideDims>()[static_cast<std::size_t>(i)];
+ }
+ std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res;
+ res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[0]->getIdx(inputIdxDims), inputDims));
+ return res;
+ }
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
+ }
void setBackend(const std::string &name) override {
diff --git a/include/aidge/operator/Conv.hpp b/include/aidge/operator/Conv.hpp
index 4f0fb1ea2..5fbd1c052 100644
--- a/include/aidge/operator/Conv.hpp
+++ b/include/aidge/operator/Conv.hpp
@@ -119,55 +119,55 @@ public:
}
-// std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const override {
- // if (outputIdx != 0) {
- // AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
- // }
- // if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
- // // Offset
- // const auto outputIdxDims = mOutput->getCoord(firstIdx);
- // auto inputIdxDims = outputIdxDims; // batch idx is the same
- // inputIdxDims[1] = 0; // each channel is used so start with the first one
-
- // for (DimIdx_t i = 0; i < (DIM+2); ++i) {
- // if (((outputDims[i] + outputIdxDims[i]) > mOutput->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
- // AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
- // }
- // }
-
- // // padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
- // // Input
- // // same batch value, every input channel is used
- // std::vector<DimSize_t> inputDims{outputDims[0], mInputs[0]->dims()[1]};
- // for (DimIdx_t i = 0; i < DIM; ++i) {
- // inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
- // * this->template getAttr<ConvAttr::StrideDims>()[static_cast<std::size_t>(i)]
- // + 1
- // + (this->template getAttr<ConvAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1)
- // * this->template getAttr<ConvAttr::DilationDims>()[static_cast<std::size_t>(i)]);
- // inputIdxDims[2+i] *= this->template getAttr<ConvAttr::StrideDims>()[static_cast<std::size_t>(i)];
- // }
-
- // // Weight
- // // same output value, every input channel is used
- // std::vector<DimSize_t> weightDims{outputDims[0], mInputs[0]->dims()[1]};
- // weightDims.insert(weightDims.end(), this->template getAttr<ConvAttr::KernelDims>()[0], this->template getAttr<ConvAttr::KernelDims>()[static_cast<std::size_t>(DIM)]);
- // std::vector<DimSize_t> weightIdxDims = std::vector<DimSize_t>(DIM+2, 0);
- // weightIdxDims[0] = outputIdxDims[1];
-
- // // Bias
- // const std::vector<DimSize_t> biasDims{outputDims[0]};
- // const std::vector<DimSize_t> biasIdxDims{outputIdxDims[1]};
-
- // // Result
- // std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res;
- // res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[0]->getIdx(inputIdxDims), inputDims));
- // res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[1]->getIdx(weightIdxDims), weightDims));
- // res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[2]->getIdx(biasIdxDims), biasDims));
- // return res;
- // }
- // AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
- // }
+std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const override {
+ if (outputIdx != 0) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
+ }
+ if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
+ // Offset
+ const auto outputIdxDims = mOutput->getCoord(firstIdx);
+ auto inputIdxDims = outputIdxDims; // batch idx is the same
+ inputIdxDims[1] = 0; // each channel is used so start with the first one
+
+ for (DimIdx_t i = 0; i < (DIM+2); ++i) {
+ if (((outputDims[i] + outputIdxDims[i]) > mOutput->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
+ }
+ }
+
+ // padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
+ // Input
+ // same batch value, every input channel is used
+ std::vector<DimSize_t> inputDims{outputDims[0], mInputs[0]->dims()[1]};
+ for (DimIdx_t i = 0; i < DIM; ++i) {
+ inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
+ * this->template getAttr<ConvAttr::StrideDims>()[static_cast<std::size_t>(i)]
+ + 1
+ + (this->template getAttr<ConvAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1)
+ * this->template getAttr<ConvAttr::DilationDims>()[static_cast<std::size_t>(i)]);
+ inputIdxDims[2+i] *= this->template getAttr<ConvAttr::StrideDims>()[static_cast<std::size_t>(i)];
+ }
+
+ // Weight
+ // same output value, every input channel is used
+ std::vector<DimSize_t> weightDims{outputDims[0], mInputs[0]->dims()[1]};
+ weightDims.insert(weightDims.end(), this->template getAttr<ConvAttr::KernelDims>()[0], this->template getAttr<ConvAttr::KernelDims>()[static_cast<std::size_t>(DIM)]);
+ std::vector<DimSize_t> weightIdxDims = std::vector<DimSize_t>(DIM+2, 0);
+ weightIdxDims[0] = outputIdxDims[1];
+
+ // Bias
+ const std::vector<DimSize_t> biasDims{outputDims[0]};
+ const std::vector<DimSize_t> biasIdxDims{outputIdxDims[1]};
+
+ // Result
+ std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res;
+ res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[0]->getIdx(inputIdxDims), inputDims));
+ res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[1]->getIdx(weightIdxDims), weightDims));
+ res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[2]->getIdx(biasIdxDims), biasDims));
+ return res;
+ }
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
+ }
void setBackend(const std::string &name) override {
mImpl = Registrar<Conv_Op<DIM>>::create(name)(*this);
diff --git a/include/aidge/operator/Operator.hpp b/include/aidge/operator/Operator.hpp
index b0f8435bd..1f4cdd23f 100644
--- a/include/aidge/operator/Operator.hpp
+++ b/include/aidge/operator/Operator.hpp
@@ -74,15 +74,6 @@ public:
virtual std::shared_ptr<Operator> clone() const = 0;
virtual void associateInput(const IOIndex_t inputIdx, const std::shared_ptr<Data>& data) = 0;
- /**
- * @brief For a given output feature area, compute the associated receptive
- * field for each data input.
- * @param firstIdx First index of the output feature.
- * @param outputDims Size of output feature.
- * @param outputIdx Index of the output. Default 0.
- * @return std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> For each dataInput Tensor of the Operator, the first index and dimensions of the feature area.
- */
- // virtual std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const;
/**
* @brief Set the specified input by performing a deep copy of the given data.
diff --git a/include/aidge/operator/OperatorTensor.hpp b/include/aidge/operator/OperatorTensor.hpp
index a55d7ac28..a490b09d0 100644
--- a/include/aidge/operator/OperatorTensor.hpp
+++ b/include/aidge/operator/OperatorTensor.hpp
@@ -90,6 +90,16 @@ public:
///////////////////////////////////////////////////
// Tensor dimensions
+ /**
+ * @brief For a given output feature area, compute the associated receptive
+ * field for each data input.
+ * @param firstIdx First index of the output feature.
+ * @param outputDims Size of output feature.
+ * @param outputIdx Index of the output. Default 0.
+ * @return std::vector<std::pair<std::size_t, std::vector<DimSize_t>>>
+ * For each dataInput Tensor of the Operator, the first index and dimensions of the feature area.
+ */
+ virtual std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const;
virtual void computeOutputDims();
virtual bool outputDimsForwarded() const;
///////////////////////////////////////////////////
diff --git a/src/operator/Operator.cpp b/src/operator/Operator.cpp
index eb94db87d..4adc57f55 100644
--- a/src/operator/Operator.cpp
+++ b/src/operator/Operator.cpp
@@ -31,29 +31,6 @@ Aidge::Operator::~Operator() noexcept = default;
// IMPLEMENTATION
///////////////////////////////////////////////////////
-// std::vector<std::pair<std::size_t, std::vector<Aidge::DimSize_t>>> Aidge::Operator::computeReceptiveField(
-// const std::size_t firstIdx, const std::vector<Aidge::DimSize_t>& outputDims, const Aidge::IOIndex_t outputIdx) const
-// {
-// static_cast<void>(outputIdx);
-// if (outputIdx >= nbOutputs()) {
-// AIDGE_THROW_OR_ABORT(std::runtime_error, "Operator output index out of range.");
-// }
-// if (nbInputs() != nbDataInputs()) {
-// AIDGE_THROW_OR_ABORT(std::runtime_error, "Operator has attributes. Must be handled in an overrided function.");
-// }
-// if (!outputDimsForwarded() || getOutput(0)->nbDims() != outputDims.size()) {
-// AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
-// }
-// const auto outputIdxDims = getOutput(0)->getCoord(firstIdx);
-// for (DimIdx_t i = 0; i < outputDims.size(); ++i) {
-// if (((outputDims[i] + outputIdxDims[i]) > getOutput(0)->dims()[i]) || (outputDims[i] == 0)) {
-// AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
-// }
-// }
-// // return the same Tensor description as given in function parameter for each data input
-// return std::vector<std::pair<std::size_t, std::vector<Aidge::DimSize_t>>>(nbDataInputs(),std::pair<std::size_t, std::vector<Aidge::DimSize_t>>(firstIdx, outputDims));
-// }
-
Aidge::NbElts_t Aidge::Operator::getNbRequiredData(const Aidge::IOIndex_t inputIdx) const {
return mImpl->getNbRequiredData(inputIdx);
}
diff --git a/src/operator/OperatorTensor.cpp b/src/operator/OperatorTensor.cpp
index 1d16e9064..1237fdc0b 100644
--- a/src/operator/OperatorTensor.cpp
+++ b/src/operator/OperatorTensor.cpp
@@ -88,6 +88,31 @@ const std::shared_ptr<Aidge::Tensor>& Aidge::OperatorTensor::getOutput(const Aid
}
+std::vector<std::pair<std::size_t, std::vector<Aidge::DimSize_t>>> Aidge::OperatorTensor::computeReceptiveField(
+ const std::size_t firstIdx,
+ const std::vector<Aidge::DimSize_t>& outputDims,
+ const Aidge::IOIndex_t outputIdx) const
+{
+ static_cast<void>(outputIdx);
+ if (outputIdx >= nbOutputs()) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Operator output index out of range.");
+ }
+ if (nbInputs() != nbData()) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Operator has attributes. Must be handled in an overrided function.");
+ }
+ if (!outputDimsForwarded() || getOutput(0)->nbDims() != outputDims.size()) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
+ }
+ const auto outputIdxDims = getOutput(0)->getCoord(firstIdx);
+ for (DimIdx_t i = 0; i < outputDims.size(); ++i) {
+ if (((outputDims[i] + outputIdxDims[i]) > getOutput(0)->dims()[i]) || (outputDims[i] == 0)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
+ }
+ }
+ // return the same Tensor description as given in function parameter for each data input
+ return std::vector<std::pair<std::size_t, std::vector<Aidge::DimSize_t>>>(nbData(),std::pair<std::size_t, std::vector<Aidge::DimSize_t>>(firstIdx, outputDims));
+}
+
void Aidge::OperatorTensor::computeOutputDims() {
// check inputs have been associated
bool associated = (nbInputs() > 0); // do not compute anything if no input
diff --git a/unit_tests/operator/Test_Conv_Op.cpp b/unit_tests/operator/Test_Conv_Op.cpp
index ac667ec5a..1a543ae64 100644
--- a/unit_tests/operator/Test_Conv_Op.cpp
+++ b/unit_tests/operator/Test_Conv_Op.cpp
@@ -22,58 +22,63 @@
#include "aidge/utils/Types.h"
namespace Aidge {
-// TEST_CASE("[core/operator] Conv_Op(computeReceptiveField)", "[Operator][computeReceptiveField][Conv]") {
-// auto dataProvider = Producer({16, 3, 224, 224}, "dataProvider");
-// auto conv1 = Conv(3, 32, {5, 5}, "conv1"); // output dims: {16, 32, 220, 220}
-// auto conv2 = Conv(32, 64, {3, 3}, "conv2"); // output dims: {16, 64, 218, 218}
-// auto conv3 = Conv(64, 10, {2, 2}, "conv3", {2,2}); // output dims: {16, 10, 109, 109}
-// auto conv4 = Conv(10, 10, {1, 1}, "conv4"); // output dims: {16, 10, 109, 109}
+TEST_CASE("[core/operator] Conv_Op(computeReceptiveField)", "[Operator][computeReceptiveField][Conv]") {
+ auto dataProvider = Producer({16, 3, 224, 224}, "dataProvider");
+ auto conv1 = Conv(3, 32, {5, 5}, "conv1"); // output dims: {16, 32, 220, 220}
+ auto conv2 = Conv(32, 64, {3, 3}, "conv2"); // output dims: {16, 64, 218, 218}
+ auto conv3 = Conv(64, 10, {2, 2}, "conv3", {2,2}); // output dims: {16, 10, 109, 109}
+ auto conv4 = Conv(10, 10, {1, 1}, "conv4"); // output dims: {16, 10, 109, 109}
-// auto g = std::make_shared<GraphView>("TestGraph");
+ auto g = std::make_shared<GraphView>("TestGraph");
-// dataProvider->addChild(conv1, 0);
-// g->add(conv1);
-// g->addChild(conv2, conv1, 0);
-// g->addChild(conv3, conv2, 0);
-// g->addChild(conv4, conv3, 0);
+ dataProvider->addChild(conv1, 0);
+ g->add(conv1);
+ g->addChild(conv2, conv1, 0);
+ g->addChild(conv3, conv2, 0);
+ g->addChild(conv4, conv3, 0);
-// g->forwardDims();
+ g->forwardDims();
-// SECTION("Check individual receptive fields") {
-// auto res1 = conv1->getOperator()->computeReceptiveField(0, {16,32,10,10});
-// auto res2 = conv2->getOperator()->computeReceptiveField(conv2->getOperator()->output(0).getIdx({3,20,100,28}), {4,20,30,40});
-// auto res3 = conv3->getOperator()->computeReceptiveField(0, {1,1,109,109});
-// auto res4 = conv4->getOperator()->computeReceptiveField(conv4->getOperator()->output(0).getIdx({5,0,108,108}), {10,10,1,1});
+ auto op1 = std::dynamic_pointer_cast<OperatorTensor>(conv1 -> getOperator());
+ auto op2 = std::dynamic_pointer_cast<OperatorTensor>(conv2 -> getOperator());
+ auto op3 = std::dynamic_pointer_cast<OperatorTensor>(conv3 -> getOperator());
+ auto op4 = std::dynamic_pointer_cast<OperatorTensor>(conv4 -> getOperator());
-// REQUIRE(((res1[0].first == 0) && (res1[0].second == std::vector<DimSize_t>({16, 3, 14, 14}))));
-// REQUIRE(((res2[0].first == conv2->getOperator()->input(0).getIdx({3,0,100,28})) && (res2[0].second == std::vector<DimSize_t>({4, 32, 32, 42}))));
-// REQUIRE(((res3[0].first == 0) && (res3[0].second == std::vector<DimSize_t>({1, 64, 218, 218}))));
-// REQUIRE(((res4[0].first == conv4->getOperator()->input(0).getIdx({5, 0, 108, 108})) && (res4[0].second == std::vector<DimSize_t>({10, 10, 1, 1}))));
-// }
+ SECTION("Check individual receptive fields") {
+ auto res1 = op1 -> computeReceptiveField(0, {16,32,10,10});
+ auto res2 = op2 -> computeReceptiveField(op2 -> getOutput(0)->getIdx({3,20,100,28}), {4,20,30,40});
+ auto res3 = op3 -> computeReceptiveField(0, {1,1,109,109});
+ auto res4 = op4 -> computeReceptiveField(op4 -> getOutput(0)->getIdx({5,0,108,108}), {10,10,1,1});
-// SECTION("Check receptive field propagation") {
-// // input: first-{5, 0, 50, 50} dims-{1, 1, 1, 1}
-// auto res4 = conv4->getOperator()->computeReceptiveField(conv4->getOperator()->output(0).getIdx({5,0,50,50}), {1,1,1,1});
-// // conv4 RF: first-{5, 0, 50, 50} dims-{1, 10, 1, 1}
-// auto res3 = conv3->getOperator()->computeReceptiveField(res4[0].first, res4[0].second);
-// // conv3 RF: first-{5, 0, 100, 100} dims-{1, 64, 2, 2}
-// auto res2 = conv2->getOperator()->computeReceptiveField(res3[0].first, res3[0].second);
-// // conv2 RF: first-{5, 0, 100, 100} dims-{1, 32, 4, 4}
-// auto res1 = conv1->getOperator()->computeReceptiveField(res2[0].first, res2[0].second);
-// // conv1 RF: first-{5, 0, 100, 100} dims-{1, 3, 8, 8}
+ REQUIRE(((res1[0].first == 0) && (res1[0].second == std::vector<DimSize_t>({16, 3, 14, 14}))));
+ REQUIRE(((res2[0].first == op2->input(0).getIdx({3,0,100,28})) && (res2[0].second == std::vector<DimSize_t>({4, 32, 32, 42}))));
+ REQUIRE(((res3[0].first == 0) && (res3[0].second == std::vector<DimSize_t>({1, 64, 218, 218}))));
+ REQUIRE(((res4[0].first == op4->input(0).getIdx({5, 0, 108, 108})) && (res4[0].second == std::vector<DimSize_t>({10, 10, 1, 1}))));
+ }
-// REQUIRE(((res1[0].first == conv1->getOperator()->input(0).getIdx({5, 0, 100, 100})) && (res1[0].second == std::vector<DimSize_t>({1, 3, 8, 8}))));
+ SECTION("Check receptive field propagation") {
+ // input: first-{5, 0, 50, 50} dims-{1, 1, 1, 1}
+ auto res4 = op4->computeReceptiveField(op4->getOutput(0)->getIdx({5,0,50,50}), {1,1,1,1});
+ // conv4 RF: first-{5, 0, 50, 50} dims-{1, 10, 1, 1}
+ auto res3 = op3->computeReceptiveField(res4[0].first, res4[0].second);
+ // conv3 RF: first-{5, 0, 100, 100} dims-{1, 64, 2, 2}
+ auto res2 = op2->computeReceptiveField(res3[0].first, res3[0].second);
+ // conv2 RF: first-{5, 0, 100, 100} dims-{1, 32, 4, 4}
+ auto res1 = op1->computeReceptiveField(res2[0].first, res2[0].second);
+ // conv1 RF: first-{5, 0, 100, 100} dims-{1, 3, 8, 8}
+ REQUIRE(((res1[0].first == op1->input(0).getIdx({5, 0, 100, 100})) && (res1[0].second == std::vector<DimSize_t>({1, 3, 8, 8}))));
-// // std::cout << "conv1: {";
-// // std::cout << conv1->getOperator()->input(0).getCoord(res1[0].first)[0] << ", "
-// // << conv1->getOperator()->input(0).getCoord(res1[0].first)[1] << ", "
-// // << conv1->getOperator()->input(0).getCoord(res1[0].first)[2] << ", "
-// // << conv1->getOperator()->input(0).getCoord(res1[0].first)[3] << "} - {";
-// // std::cout << res1[0].second[0] << ", "
-// // << res1[0].second[1] << ", "
-// // << res1[0].second[2] << ", "
-// // << res1[0].second[3] << "}" << std::endl;
-// }
-// }
+
+ // std::cout << "conv1: {";
+ // std::cout << op1->input(0).getCoord(res1[0].first)[0] << ", "
+ // << op1->input(0).getCoord(res1[0].first)[1] << ", "
+ // << op1->input(0).getCoord(res1[0].first)[2] << ", "
+ // << op1->input(0).getCoord(res1[0].first)[3] << "} - {";
+ // std::cout << res1[0].second[0] << ", "
+ // << res1[0].second[1] << ", "
+ // << res1[0].second[2] << ", "
+ // << res1[0].second[3] << "}" << std::endl;
+ }
+}
} // namespace Aidge
\ No newline at end of file
--
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