diff --git a/include/aidge/data/Tensor.hpp b/include/aidge/data/Tensor.hpp
index 7aa2ed52b95e11598a2975558212b00a85dac598..785caaa0e8959ba34d438913a4c0e5bad3df0f86 100644
--- a/include/aidge/data/Tensor.hpp
+++ b/include/aidge/data/Tensor.hpp
@@ -462,12 +462,33 @@ public:
* data is copy-transposed.
*/
void setDataFormat(const DataFormat df, bool copyTrans = true) {
- if (mImpl && copyTrans && (dataFormat() != df) && df != DataFormat::Default && dataFormat() != DataFormat::Default) {
- copyTranspose(*this, getDataFormatTranspose(dataFormat(), df));
+ if (!copyTrans || df == dataFormat()) {
+ mDataFormat = df;
+ return;
}
+
+ const auto transpose = getDataFormatTranspose(dataFormat(), df);
+
+ if (mImpl) {
+ copyTranspose(*this, transpose);
+ } else {
+ std::vector<DimSize_t> newDims;
+ for (std::size_t i = 0; i < dims().size(); ++i) {
+ newDims.push_back(dims()[transpose[i]]);
+ }
+
+ std::vector<std::size_t> newStrides(dims().size(), 1);
+ for (size_t i = 0; i < dims().size(); ++i) {
+ for (size_t j = i + 1; j < dims().size(); ++j) {
+ newStrides[i] *= newDims[j];
+ }
+ }
+ mDims = std::move(newDims);
+ mStrides = std::move(newStrides);
+ }
+
mDataFormat = df;
}
-
/**
* @brief Get the Impl object
* @return constexpr const std::shared_ptr<TensorImpl>&
diff --git a/include/aidge/operator/Conv.hpp b/include/aidge/operator/Conv.hpp
index 135ff8860706d245ae6095322d6cf017456cc2e1..f9c9109282cb90dadfa9b26d6f830faf9fdecd7c 100644
--- a/include/aidge/operator/Conv.hpp
+++ b/include/aidge/operator/Conv.hpp
@@ -172,6 +172,11 @@ public:
if (!getInput(1)) {
AIDGE_THROW_OR_ABORT(std::runtime_error, "Convolution operator has no weight Tensor associated so no specific number of input channel imposed.");
}
+
+ // check format
+ if(getInput(1)->dataFormat()==Aidge::DataFormat::NHWC)
+ return getInput(1)->template dims<DIM+2>()[DIM+1];
+ // default format is NCHW
return getInput(1)->template dims<DIM+2>()[1];
}
@@ -184,6 +189,7 @@ public:
if (!getInput(1)) {
AIDGE_THROW_OR_ABORT(std::runtime_error, "Convolution operator has no weight Tensor associated so no specific number of output channel imposed.");
}
+ // first weight dimension for both NCHW (Cout,Cin,H,W) and NHWC (Cout,H,W,Cin) data format
return getInput(1)->template dims<DIM+2>()[0];
}
diff --git a/src/operator/Conv.cpp b/src/operator/Conv.cpp
index 836c47645c20ff23539b836af8593cddfbb48498..2077cab52f613780e77bba80efacb41d06a7f3cf 100644
--- a/src/operator/Conv.cpp
+++ b/src/operator/Conv.cpp
@@ -40,42 +40,57 @@ Aidge::Conv_Op<DIM>::Conv_Op(const Aidge::Conv_Op<DIM>& op)
template <Aidge::DimIdx_t DIM>
bool Aidge::Conv_Op<DIM>::forwardDims(bool /*allowDataDependency*/) {
- if (inputsAssociated()) {
- // first check weight since it defines inChannels and outChannels
- AIDGE_ASSERT((getInput(1)->nbDims() == (DIM+2)),
- "Wrong weight Tensor dimension: {} for Conv{}D operator. Expected number of dimensions is {}.", getInput(1)->nbDims(), DIM, DIM+2);
- // check data
+ if (!inputsAssociated())
+ return false;
+ // first check weight since it defines inChannels and outChannels
+ if(getInput(0)->dataFormat() == Aidge::DataFormat::NHWC){
AIDGE_ASSERT((getInput(0)->nbDims() == (DIM+2)) &&
- (getInput(0)->template dims<DIM+2>()[1] == inChannels()),
- "Wrong input size ({}) for Conv operator. Expected dims are [x, {}, {}].", getInput(0)->dims(), inChannels(), fmt::join(std::vector<std::string>(DIM, "x"), ", "));
- // check optional bias
- if(getInput(2))
- AIDGE_ASSERT((getInput(2)->nbDims() == (1)) &&
- (getInput(2)->template dims<1>()[0] == outChannels()),
- "Wrong bias size ({}) for Conv operator. Expected dims are [{}].", getInput(2)->dims(), outChannels());
-
- std::array<DimSize_t, DIM + 2> outputDims{};
- const std::array<DimSize_t, DIM + 2> inputDims(getInput(0)->template dims<DIM+2>());
-
- for (std::size_t dim = 0; dim < mAttributes->template getAttr<ConvAttr::KernelDims>().size() ; ++dim) {
- const DimSize_t kernelExtent = mAttributes->template getAttr<ConvAttr::DilationDims>()[dim] *
- (mAttributes->template getAttr<ConvAttr::KernelDims>()[dim] - 1) +
- 1;
-
- outputDims[dim+2] = 1 + static_cast<DimSize_t>(
- floor(static_cast<float>(inputDims[dim+2] - kernelExtent) /
- static_cast<float>(mAttributes->template getAttr<ConvAttr::StrideDims>()[dim])));
- }
+ (getInput(0)->template dims<DIM+2>()[DIM+1] == inChannels()),
+ "Wrong input size ({}) for Conv operator. Expected dims are [x, {}, {}].", getInput(0)->dims(), fmt::join(std::vector<std::string>(DIM, "x"), ", "), inChannels());
+ }
+ else{ //For dataFormat in NCHW or Default Format
+ AIDGE_ASSERT((getInput(0)->nbDims() == (DIM+2)) &&
+ (getInput(0)->template dims<DIM+2>()[1] == inChannels()),
+ "Wrong input size ({}) for Conv operator. Expected dims are [x, {}, {}].", getInput(0)->dims(), inChannels(), fmt::join(std::vector<std::string>(DIM, "x"), ", "));
+ }
- outputDims[1] = outChannels();
- outputDims[0] = inputDims[0];
- mOutputs[0]->resize(outputDims);
- return true;
+ AIDGE_ASSERT((getInput(1)->nbDims() == (DIM+2)),
+ "Wrong weight Tensor dimension: {} for Conv{}D operator. Expected number of dimensions is {}.", getInput(1)->nbDims(), DIM, DIM+2);
+
+ if(getInput(2))
+ AIDGE_ASSERT((getInput(2)->nbDims() == (1)) &&
+ (getInput(2)->template dims<1>()[0] == outChannels()),
+ "Wrong bias size ({}) for Conv operator. Expected dims are [{}].", getInput(2)->dims(), outChannels());
+
+ const std::array<DimSize_t, DIM + 2> inputDims(getInput(0)->template dims<DIM+2>());
+ std::array<DimSize_t, DIM + 2> outputDims{};
+
+
+ unsigned int in_dims_index = (getInput(0)->dataFormat() == Aidge::DataFormat::NHWC) ? 1 : 2;
+ unsigned int out_dims_index = (getOutput(0)->dataFormat() == Aidge::DataFormat::NHWC) ? 1 : 2;
+
+ for (std::size_t dim = 0; dim < mAttributes->template getAttr<ConvAttr::KernelDims>().size(); ++dim) {
+ const DimSize_t kernelExtent = mAttributes->template getAttr<ConvAttr::DilationDims>()[dim] *
+ (mAttributes->template getAttr<ConvAttr::KernelDims>()[dim] - 1) +
+ 1;
+
+ outputDims[dim + out_dims_index] = 1 + static_cast<DimSize_t>(
+ floor(static_cast<float>(inputDims[dim + in_dims_index] - kernelExtent) /
+ static_cast<float>(mAttributes->template getAttr<ConvAttr::StrideDims>()[dim]))
+ );
}
- return false;
-}
+ if(getOutput(0)->dataFormat() == Aidge::DataFormat::NHWC)
+ outputDims[DIM+1] = outChannels();
+ else
+ outputDims[1] = outChannels();
+ outputDims[0] = inputDims[0];
+ mOutputs[0]->resize(outputDims);
+ return true;
+
+
+}
template <Aidge::DimIdx_t DIM>
std::vector<std::pair<std::vector<Aidge::DimSize_t>, std::vector<Aidge::DimSize_t>>>
diff --git a/unit_tests/operator/Test_Conv_Op.cpp b/unit_tests/operator/Test_Conv_Op.cpp
index bc24fc8081d78dedf853450ff648b6d91b47c1dc..de33ddd5a7613cde16b96b23722f6d2ab412f373 100644
--- a/unit_tests/operator/Test_Conv_Op.cpp
+++ b/unit_tests/operator/Test_Conv_Op.cpp
@@ -22,6 +22,89 @@
#include "aidge/utils/Types.h"
namespace Aidge {
+TEST_CASE("[core/operator] Conv_Op(ForwardDims) ", "[Operator][ForwardDims][Conv]") {
+ SECTION("I:NCHW O:NCHW W:NCHW"){
+ std::shared_ptr<Tensor> input = std::make_shared<Tensor>(std::vector<std::size_t>({16,3,224,450}));
+ std::shared_ptr<Tensor> weight = std::make_shared<Tensor>(std::vector<std::size_t>({4,3,3,4})); // Out_ch, In_ch_h,W,H
+
+ const std::vector<std::size_t> expectedOutputDims({16,4,222,447});
+ auto conv1 = Conv_Op<2>(std::array<size_t, 2>{3, 4});
+
+ //Set DataFormat
+ conv1.getOutput(0)->setDataFormat(Aidge::DataFormat::NCHW);
+ input->setDataFormat(Aidge::DataFormat::NCHW);
+ weight->setDataFormat(Aidge::DataFormat::NCHW);
+
+ //Set inputs
+ conv1.setInput(1,weight);
+ conv1.setInput(0,input);
+
+ REQUIRE(conv1.forwardDims());
+ REQUIRE(conv1.getOutput(0)->dims() == expectedOutputDims);
+ }
+ SECTION("I:NCHW O:NCHW W:NHWC") {
+ std::shared_ptr<Tensor> input = std::make_shared<Tensor>(std::vector<std::size_t>({16, 3, 224, 450}));
+ std::shared_ptr<Tensor> weight = std::make_shared<Tensor>(std::vector<std::size_t>({4, 3, 3, 4})); // Out_ch, H, W, In_ch
+
+ const std::vector<std::size_t> expectedOutputDims({16, 4, 222, 447});
+ auto conv1 = Conv_Op<2>(std::array<size_t, 2>{3, 4});
+
+ // Set DataFormat
+ conv1.getOutput(0)->setDataFormat(Aidge::DataFormat::NCHW);
+ input->setDataFormat(Aidge::DataFormat::NCHW);
+ weight->setDataFormat(Aidge::DataFormat::NHWC); // NHWC weight format
+
+ // Set inputs
+ conv1.setInput(1, weight);
+ conv1.setInput(0, input);
+
+ REQUIRE(conv1.forwardDims());
+ REQUIRE(conv1.getOutput(0)->dims() == expectedOutputDims);
+ }
+
+ SECTION("I:NHWC O:NHWC W:NCHW") {
+ std::shared_ptr<Tensor> input = std::make_shared<Tensor>(std::vector<std::size_t>({16, 3, 224, 450}));
+ std::shared_ptr<Tensor> weight = std::make_shared<Tensor>(std::vector<std::size_t>({4, 3, 3, 4})); // Out_ch, In_ch, H, W
+
+ const std::vector<std::size_t> expectedOutputDims({16, 222, 447, 4});
+ auto conv1 = Conv_Op<2>(std::array<size_t, 2>{3, 4});
+
+ // Set DataFormat
+ conv1.getOutput(0)->setDataFormat(Aidge::DataFormat::NHWC);
+ input->setDataFormat(Aidge::DataFormat::NHWC);
+ weight->setDataFormat(Aidge::DataFormat::NCHW); // NCHW weight format
+
+ // Set inputs
+ conv1.setInput(1, weight);
+ conv1.setInput(0, input);
+
+ REQUIRE(conv1.forwardDims());
+ REQUIRE(conv1.getOutput(0)->dims() == expectedOutputDims);
+ }
+
+ SECTION("I:NHWC O:NHWC W:NHWC") {
+ std::shared_ptr<Tensor> input = std::make_shared<Tensor>(std::vector<std::size_t>({16, 3,224, 450}));
+ std::shared_ptr<Tensor> weight = std::make_shared<Tensor>(std::vector<std::size_t>({4, 3, 3, 4})); // (Out_ch, H, W, In_ch)
+
+ const std::vector<std::size_t> expectedOutputDims({16, 222, 447, 4});
+ auto conv1 = Conv_Op<2>(std::array<size_t, 2>{3, 4});
+
+ // Set DataFormat
+ conv1.getOutput(0)->setDataFormat(Aidge::DataFormat::NHWC);
+ input->setDataFormat(Aidge::DataFormat::NHWC);
+ weight->setDataFormat(Aidge::DataFormat::NHWC);
+
+ // Set inputs
+ conv1.setInput(1, weight);
+ conv1.setInput(0, input);
+
+ REQUIRE(conv1.forwardDims());
+ REQUIRE(conv1.getOutput(0)->dims() == expectedOutputDims);
+ }
+
+}
+
+
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}