From 1c8db798d9763bfe07911a1f83fccb78cefe7b4b Mon Sep 17 00:00:00 2001
From: NAUD Maxence <maxence.naud@cea.fr>
Date: Mon, 27 Nov 2023 13:22:55 +0000
Subject: [PATCH] [Fix] Conv and ConDepthWise computeReceptiveField() memeber
function
---
include/aidge/operator/Conv.hpp | 28 ++++----
include/aidge/operator/ConvDepthWise.hpp | 85 ++++++++++++++----------
2 files changed, 65 insertions(+), 48 deletions(-)
diff --git a/include/aidge/operator/Conv.hpp b/include/aidge/operator/Conv.hpp
index 5fbd1c052..6d81352ce 100644
--- a/include/aidge/operator/Conv.hpp
+++ b/include/aidge/operator/Conv.hpp
@@ -77,9 +77,9 @@ public:
}
// Data operator[](const char* inputName) override final {
- // std::shared_ptr<Tensor> in = (strcmp(inputName, "data")) ? mInputs[0] :
- // (strcmp(inputName, "weight") ? mInputs[1] :
- // (strcmp(inputName, "bias") ? mInputs[2] :
+ // std::shared_ptr<Tensor> in = (strcmp(inputName, "data")) ? getInput(0) :
+ // (strcmp(inputName, "weight") ? getInput(1) :
+ // (strcmp(inputName, "bias") ? getInput(2) :
// nullptr));
// assert((in!=nullptr) && "No such parameter");
// return *in;
@@ -125,12 +125,12 @@ std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveFiel
}
if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
// Offset
- const auto outputIdxDims = mOutput->getCoord(firstIdx);
+ const auto outputIdxDims = mOutputs[0]->getCoord(firstIdx);
auto inputIdxDims = outputIdxDims; // batch idx is the same
inputIdxDims[1] = 0; // each channel is used so start with the first one
for (DimIdx_t i = 0; i < (DIM+2); ++i) {
- if (((outputDims[i] + outputIdxDims[i]) > mOutput->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
+ 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]);
}
}
@@ -138,8 +138,8 @@ std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveFiel
// 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) {
+ std::vector<DimSize_t> inputDims{outputDims[0], getInput(0)->dims()[1]};
+ for (DimIdx_t i = 0; i < DIM; ++i) {
inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
* this->template getAttr<ConvAttr::StrideDims>()[static_cast<std::size_t>(i)]
+ 1
@@ -150,20 +150,22 @@ std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveFiel
// 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> weightDims{outputDims[1], getInput(0)->dims()[1]};
+ for (std::size_t i = 0; i < DIM; ++i) {
+ weightDims.push_back(this->template getAttr<ConvAttr::KernelDims>()[i]);
+ }
std::vector<DimSize_t> weightIdxDims = std::vector<DimSize_t>(DIM+2, 0);
weightIdxDims[0] = outputIdxDims[1];
// Bias
- const std::vector<DimSize_t> biasDims{outputDims[0]};
+ const std::vector<DimSize_t> biasDims{outputDims[1]}; // the number of output channel
const std::vector<DimSize_t> biasIdxDims{outputIdxDims[1]};
// Result
std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res;
- res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(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));
+ res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(0)->getIdx(inputIdxDims), inputDims));
+ res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(1)->getIdx(weightIdxDims), weightDims));
+ res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(2)->getIdx(biasIdxDims), biasDims));
return res;
}
AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
diff --git a/include/aidge/operator/ConvDepthWise.hpp b/include/aidge/operator/ConvDepthWise.hpp
index ca6401e0e..d351e41dd 100644
--- a/include/aidge/operator/ConvDepthWise.hpp
+++ b/include/aidge/operator/ConvDepthWise.hpp
@@ -115,41 +115,56 @@ public:
}
}
- // std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const override {
- // if (outputIdx != 0) {
- // AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
- // }
- // if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
- // // Offset
- // const auto outputIdxDims = mOutput->getCoord(firstIdx);
- // auto inputIdxDims = outputIdxDims; // batch idx is the same
-
- // for (DimIdx_t i = 0; i < (DIM+2); ++i) {
- // if (((outputDims[i] + outputIdxDims[i]) > mOutput->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
- // AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
- // }
- // }
-
- // // padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
- // // Width
- // std::vector<DimSize_t> inputDims;
- // inputDims.push_back(outputDims[0]); // same batch value
- // inputDims.push_back(outputDims[1]); // same channel value
-
- // for (DimIdx_t i = 0; i < DIM; ++i) {
- // inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
- // * this->template getAttr<ConvDepthWiseAttr::StrideDims>()[static_cast<std::size_t>(i)]
- // + 1
- // + (this->template getAttr<ConvDepthWiseAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1)
- // * this->template getAttr<ConvDepthWiseAttr::DilationDims>()[static_cast<std::size_t>(i)]);
- // inputIdxDims[2+i] *= this->template getAttr<ConvDepthWiseAttr::StrideDims>()[static_cast<std::size_t>(i)];
- // }
- // std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res = std::vector<std::pair<std::size_t, std::vector<DimSize_t>>>();
- // res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(mInputs[0]->getIdx(inputIdxDims), inputDims));
- // return res;
- // }
- // AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
- // }
+ std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> computeReceptiveField(const std::size_t firstIdx, const std::vector<DimSize_t>& outputDims, const IOIndex_t outputIdx = 0) const override {
+ if (outputIdx != 0) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Conv_Op Operator has got only one output Tensor.");
+ }
+ if ((outputDims.size() == (DIM+2)) && outputDimsForwarded()) {
+ // Offset
+ const auto outputIdxDims = mOutputs[0]->getCoord(firstIdx);
+ auto inputIdxDims = outputIdxDims; // batch idx is the same
+ inputIdxDims[1] = 0; // each channel is used so start with the first one
+
+ for (DimIdx_t i = 0; i < (DIM+2); ++i) {
+ if (((outputDims[i] + outputIdxDims[i]) > mOutputs[0]->template dims<DIM+2>()[i]) || (outputDims[i] == 0)) {
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range for dimension %lu (%lu + %lu)", static_cast<std::size_t>(i), outputIdxDims[i], outputDims[i]);
+ }
+ }
+
+ // padding is not a parameter of Conv_Op. It is handled in Pad_Op Operator
+ // Input
+ // same batch value
+ std::vector<DimSize_t> inputDims{outputDims[0], outputDims[1]};
+ for (DimIdx_t i = 0; i < DIM; ++i) {
+ inputDims.push_back((outputDims[2+static_cast<std::size_t>(i)] - 1)
+ * this->template getAttr<ConvDepthWiseAttr::StrideDims>()[static_cast<std::size_t>(i)]
+ + 1
+ + (this->template getAttr<ConvDepthWiseAttr::KernelDims>()[static_cast<std::size_t>(i)] - 1)
+ * this->template getAttr<ConvDepthWiseAttr::DilationDims>()[static_cast<std::size_t>(i)]);
+ inputIdxDims[2+i] *= this->template getAttr<ConvDepthWiseAttr::StrideDims>()[static_cast<std::size_t>(i)];
+ }
+
+ // Weight
+ std::vector<DimSize_t> weightDims{outputDims[1], 1};
+ for (std::size_t i = 0; i < DIM; ++i) {
+ weightDims.push_back(this->template getAttr<ConvDepthWiseAttr::KernelDims>()[i]);
+ }
+ std::vector<DimSize_t> weightIdxDims = std::vector<DimSize_t>(DIM+2, 0);
+ weightIdxDims[0] = outputIdxDims[1];
+
+ // Bias
+ const std::vector<DimSize_t> biasDims{outputDims[1]}; // the number of output channel
+ const std::vector<DimSize_t> biasIdxDims{outputIdxDims[1]};
+
+ // Result
+ std::vector<std::pair<std::size_t, std::vector<DimSize_t>>> res;
+ res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(0)->getIdx(inputIdxDims), inputDims));
+ res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(1)->getIdx(weightIdxDims), weightDims));
+ res.push_back(std::pair<std::size_t, std::vector<DimSize_t>>(getInput(2)->getIdx(biasIdxDims), biasDims));
+ return res;
+ }
+ AIDGE_THROW_OR_ABORT(std::runtime_error, "Given outputDim out of range or output dim not forwarded yet.");
+ }
void setBackend(const std::string &name) override {
mImpl = Registrar<ConvDepthWise_Op<DIM>>::create(name)(*this);
--
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