diff --git a/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp b/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
index e764eecfd6746585a7526b8dc7c2a7295c242285..f772ed77cb8d543cfa43df35502784cb6309a5ec 100644
--- a/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
@@ -1108,13 +1108,12 @@ void ConvImpl3D_cpu_forward_kernel(const array<DimSize_t, 3> &strideDims,
////////////////////////////////////////////////////////////////////////
// COMPUTATION
- for (DimSize_t batch = 0; batch < iDims[0];
- ++batch, oOffset[0] += oStride[0], iOffset[0] += iStride[0]) {
-
- oOffset[1] = oOffset[0];
- kOffset[0] = 0;
- for (DimSize_t oChannel = 0; oChannel < oDims[1];
- ++oChannel, oOffset[1] += oStride[1], kOffset[0] += kStride[0]) {
+ for (DimSize_t batch = 0; batch < iDims[0]; ++batch) {
+ oOffset[0] = batch * oStride[0];
+ iOffset[0] = batch * iStride[0];
+ for (DimSize_t oChannel = 0; oChannel < oDims[1]; ++oChannel) {
+ oOffset[1] = oChannel * oStride[1] + oOffset[0];
+ kOffset[0] = oChannel * kStride[0];
// Filling given channel with corresponding bias value
if (biases != nullptr) {
@@ -1124,43 +1123,33 @@ void ConvImpl3D_cpu_forward_kernel(const array<DimSize_t, 3> &strideDims,
biasVal);
}
- iOffset[1] = iOffset[0];
- kOffset[1] = kOffset[0];
- for (DimSize_t iChannel = 0; iChannel < iDims[1]; ++iChannel,
- iOffset[1] += iStride[1],
- kOffset[1] += kStride[1]) {
-
- iOffset[2] = iOffset[1];
- oOffset[2] = oOffset[1];
- for (DimSize_t oX = 0; oX < oDims[2]; ++oX,
- iOffset[2] += strideDims[0] * iStride[2],
- oOffset[2] += oStride[2]) {
-
- iOffset[3] = iOffset[2];
- oOffset[3] = oOffset[2];
- for (DimSize_t oY = 0; oY < oDims[3]; ++oY,
- iOffset[3] += strideDims[1] * iStride[3],
- oOffset[3] += oStride[3]) {
-
- for (DimSize_t iZ = 0, oZ = 0; oZ < oDims[4];
- ++oZ, iZ += strideDims[2]) {
+ for (DimSize_t iChannel = 0; iChannel < iDims[1]; ++iChannel) {
+ iOffset[1] = iChannel * iStride[1] + iOffset[0];
+ kOffset[1] = iChannel * kStride[1] + kOffset[0];
+
+ for (DimSize_t oX = 0; oX < oDims[2]; ++oX) {
+ iOffset[2] = oX * strideDims[0] * iStride[2] + iOffset[1];
+ oOffset[2] = oX * oStride[2] + oOffset[1];
+
+ for (DimSize_t oY = 0; oY < oDims[3]; ++oY) {
+ iOffset[3] =
+ oY * strideDims[1] * iStride[3] + iOffset[2];
+ oOffset[3] = oY * oStride[3] + oOffset[2];
+
+ for (DimSize_t oZ = 0; oZ < oDims[4]; ++oZ) {
auto oIdx = oOffset[3] + oZ;
- auto iIdx = iOffset[3] + iZ;
-
- kOffset[2] = kOffset[1];
- kDilOffset[0] = 0;
- for (DimSize_t kX = 0; kX < kDims[0]; ++kX,
- kOffset[2] += kStride[2],
- kDilOffset[0] += dilationDims[0] *
- iStride[2]) {
-
- kOffset[3] = kOffset[2];
- kDilOffset[1] = kDilOffset[0];
- for (DimSize_t kY = 0; kY < kDims[1];
- ++kY,
- kOffset[3] += kStride[3],
- kDilOffset[1] +=
- dilationDims[1] * iStride[3]) {
+ auto iIdx = iOffset[3] + oZ * strideDims[2];
+
+ for (DimSize_t kX = 0; kX < kDims[0]; ++kX) {
+ kOffset[2] = kX * kStride[2] + kOffset[1];
+ kDilOffset[0] =
+ kX * dilationDims[0] * iStride[2];
+
+ for (DimSize_t kY = 0; kY < kDims[1]; ++kY) {
+ kOffset[3] = kY * kStride[3] + kOffset[2];
+ kDilOffset[1] =
+ kY * dilationDims[1] * iStride[3] +
+ kDilOffset[0];
for (DimSize_t kZ = 0; kZ < kDims[2];
++kZ) {
@@ -1194,8 +1183,8 @@ void ConvImpl3D_cpu_forward_kernel(const array<DimSize_t, 3> &strideDims,
* for each weight
* for each output
* multiply the weight with the associated value
- * @note kernel & stride are passed as single integers as they are just arrays
- * of length 1
+ * @note kernel & stride are passed as single integers as they are just
+ * arrays of length 1
* @note reminder that kernel dimensions are
* {outChannels, inChannels, {kernelDims}}
* <=> {oDims[1], iDims[1], kernelDim}
@@ -1238,54 +1227,36 @@ void conv3DBackwardInput(const array<DimSize_t, 3> &stride,
for (DimSize_t batch = 0; batch < iDims[0];
++batch, iOffset[0] += iStrides[0], oOffset[0] += oStrides[0]) {
- kOffset[0] = 0;
- oOffset[1] = oOffset[0];
- for (DimSize_t oChannel = 0; oChannel < oDims[1]; oChannel++,
- oOffset[1] += oStrides[1],
- kOffset[0] += kStrides[0]) {
-
- iOffset[1] = iOffset[0];
- kOffset[1] = kOffset[0];
- for (DimSize_t iChannel = 0; iChannel < iDims[1]; ++iChannel,
- iOffset[1] += iStrides[1],
- kOffset[1] += kStrides[1]) {
-
- oOffset[2] = oOffset[1];
- iOffset[2] = iOffset[1];
- DimSize_t iX = 0;
- for (DimSize_t oX = 0; oX < oDims[2]; ++oX,
- iX += stride[0],
- oOffset[2] += oStrides[2],
- iOffset[2] += stride[0] * iStrides[2]) {
-
- DimSize_t iY = 0;
- oOffset[3] = oOffset[2];
- iOffset[3] = iOffset[2];
- for (DimSize_t oY = 0; oY < oDims[3]; ++oY,
- iY += stride[1],
- oOffset[3] += oStrides[3],
- iOffset[3] += stride[1] * iStrides[3]) {
-
- DimSize_t iZ = 0;
- for (DimSize_t oZ = 0; oZ < oDims[4];
- ++oZ, iZ += stride[2]) {
+ for (DimSize_t oChannel = 0; oChannel < oDims[1]; oChannel++) {
+ oOffset[1] = oChannel * oStrides[1] + oOffset[0];
+ kOffset[0] = oChannel * kStrides[0];
+
+ for (DimSize_t iChannel = 0; iChannel < iDims[1]; ++iChannel) {
+ iOffset[1] = iChannel * iStrides[1] + iOffset[0];
+ kOffset[1] = iChannel * kStrides[1] + kOffset[0];
+
+ for (DimSize_t oX = 0; oX < oDims[2]; ++oX) {
+ oOffset[2] = oX * oStrides[2] + oOffset[1];
+ iOffset[2] = oX * stride[0] * iStrides[2] + iOffset[1];
+
+ for (DimSize_t oY = 0; oY < oDims[3]; ++oY) {
+ oOffset[3] = oY * oStrides[3] + oOffset[2];
+ iOffset[3] = oY * stride[1] * iStrides[3] + iOffset[2];
+
+ for (DimSize_t oZ = 0; oZ < oDims[4]; ++oZ) {
auto oIdx = oOffset[3] + oZ;
- auto iIdx = iOffset[3] + iZ;
-
- iDilkernelOffset[0] = 0;
- kOffset[2] = kOffset[1];
- for (DimSize_t kX = 0; kX < kDims[0]; ++kX,
- iDilkernelOffset[0] += dilation[0] *
- iStrides[2],
- kOffset[2] += kStrides[2]) {
-
- kOffset[3] = kOffset[2];
- iDilkernelOffset[1] = iDilkernelOffset[0];
- for (DimSize_t kY = 0; kY < kDims[1];
- ++kY,
- kOffset[3] += kStrides[3],
- iDilkernelOffset[1] +=
- dilation[1] * iStrides[3]) {
+ auto iIdx = iOffset[3] + oZ * stride[2];
+
+ for (DimSize_t kX = 0; kX < kDims[0]; ++kX) {
+ kOffset[2] = kX * kStrides[2] + kOffset[1];
+ iDilkernelOffset[0] =
+ kX * dilation[0] * iStrides[2];
+
+ for (DimSize_t kY = 0; kY < kDims[1]; ++kY) {
+ kOffset[3] = kY * kStrides[3] + kOffset[2];
+ iDilkernelOffset[1] =
+ kY * dilation[1] * iStrides[3] +
+ iDilkernelOffset[0];
for (DimSize_t kZ = 0; kZ < kDims[2];
++kZ) {
@@ -1325,9 +1296,10 @@ void conv3DBackwardInput(const array<DimSize_t, 3> &stride,
* @param[in] oDims output data dimmensions
* @param[in] oStrides nb element in each dimension of output tensor
* @param[in] oGrad gradients of output data
- * @param[in] kDim dimensions of kernel (not taking in count In/OutChannels)
- * @param[in] kStrides nb element in each dimension of kernel tensor (taking in
- * count In/OutChannels)
+ * @param[in] kDim dimensions of kernel (not taking in count
+ * In/OutChannels)
+ * @param[in] kStrides nb element in each dimension of kernel tensor
+ * (taking in count In/OutChannels)
* @param[in] stride attribute of the convolution operator
* @param[in] dilation attribute of the convolution operator
* @param[inout] weightsGrad gradients of the kernel weights
@@ -1444,17 +1416,14 @@ static void conv3DBackwardBias(const array<DimSize_t, 5> &oDims,
for (DimSize_t batchIdx = 0; batchIdx < oDims[0]; ++batchIdx) {
oOffsets[0] = batchIdx * oStrides[0];
- oOffsets[1] = oOffsets[0];
- for (DimSize_t oChannel = 0; oChannel < oDims[1];
- ++oChannel, oOffsets[1] += oStrides[1]) {
+ for (DimSize_t oChannel = 0; oChannel < oDims[1]; ++oChannel) {
+ oOffsets[1] = oChannel * oStrides[1] + oOffsets[0];
- oOffsets[2] = oOffsets[1];
- for (DimSize_t oX = 0; oX < oDims[2];
- ++oX, oOffsets[2] += oStrides[2]) {
+ for (DimSize_t oX = 0; oX < oDims[2]; ++oX) {
+ oOffsets[2] = oX * oStrides[2] + oOffsets[1];
- oOffsets[3] = oOffsets[2];
- for (DimSize_t oY = 0; oY < oDims[3];
- ++oY, oOffsets[3] += oStrides[3]) {
+ for (DimSize_t oY = 0; oY < oDims[3]; ++oY) {
+ oOffsets[3] = oY * oStrides[3] + oOffsets[2];
for (DimSize_t oZ = 0; oZ < oDims[4]; ++oZ) {
biasesGrad[oChannel] += oGrad[oOffsets[3] + oZ];
}
diff --git a/include/aidge/backend/cpu/operator/ConvTransposeImpl.hpp b/include/aidge/backend/cpu/operator/ConvTransposeImpl.hpp
index 7604a96a18e7be44f4c2e8970a0b60b1c4ad918b..d47636ef8112f2905583e92be4ccbd9710102bde 100644
--- a/include/aidge/backend/cpu/operator/ConvTransposeImpl.hpp
+++ b/include/aidge/backend/cpu/operator/ConvTransposeImpl.hpp
@@ -51,9 +51,24 @@ using ConvTransposeImpl2D_cpu =
const void *,
void *)>;
+using ConvTranspose3D_Op = ConvTranspose_Op<3>;
+using ConvTransposeImpl3D_cpu =
+ OperatorImpl_cpu<ConvTranspose3D_Op,
+ void(const array<DimSize_t, 3> &,
+ const array<DimSize_t, 3> &,
+ const array<DimSize_t, 3> &,
+ const array<DimSize_t, 5> &,
+ const array<DimSize_t, 5> &,
+ const void *,
+ const void *,
+ const void *,
+ void *)>;
+
// Implementation entry point registration to Operator
REGISTRAR(ConvTranspose1D_Op, "cpu", ConvTransposeImpl1D_cpu::create);
REGISTRAR(ConvTranspose2D_Op, "cpu", ConvTransposeImpl2D_cpu::create);
+REGISTRAR(ConvTranspose3D_Op, "cpu", ConvTransposeImpl3D_cpu::create);
+
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_CONVTRANSPOSEIMPL_H_ */
diff --git a/include/aidge/backend/cpu/operator/ConvTransposeImpl_kernels.hpp b/include/aidge/backend/cpu/operator/ConvTransposeImpl_kernels.hpp
index e11dd2625ae1645a8e7c5482b1635b85fb475b06..a734add2acb612d86ceaa1d09514da5a727c7ce4 100644
--- a/include/aidge/backend/cpu/operator/ConvTransposeImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvTransposeImpl_kernels.hpp
@@ -300,6 +300,149 @@ REGISTRAR(
ConvTransposeImpl2D_cpu_forward_kernel<double, double, double, double>,
nullptr});
+////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////
+// 3D
+////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////
+
+/**
+ * @brief performs forward bias operation for convtranspose operator
+ *
+ * @tparam B Bias data type.
+ * @tparam O Output data type.
+ * @param[in] bias bias values
+ * @param[in] oDims dimensions of the output
+ * @param[in] oStrides nb of elements contained per dimension of the output
+ * @param[out] output
+ */
+template <class B, class O>
+static void convTranspose3DForwardBias(const B *biases,
+ const array<DimSize_t, 5> &oDims,
+ const array<DimSize_t, 4> &oStrides,
+ O *output) {
+ array<DimSize_t, 2> outOffsets{0, 0};
+
+ for (DimSize_t batch = 0; batch < oDims[0]; ++batch) {
+ outOffsets[0] = batch * oStrides[0];
+
+ for (DimSize_t outCh = 0; outCh < oDims[1]; ++outCh) {
+ outOffsets[1] = outCh * oStrides[1] + outOffsets[0];
+ // If bias = nullptr, set B(0)
+ B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
+ std::fill(output + outOffsets[1],
+ (output + outOffsets[1]) + oStrides[1],
+ biasVal);
+ }
+ }
+}
+
+/**
+ * @brief forward kernel for convtranspose
+ * @note ConvTranspose forward is simply convolution backward kernel.
+ * Check convolution functions for more in-depth details on how the
+ subfunctions are built.
+ * @tparam I Input data type.
+ * @tparam W Weight data type.
+ * @tparam B Bias data type.
+ * @tparam O Output data type.
+ * @param[in] stride stride parameter of the convTranspose operator
+ * @param[in] dilation dilation parameter of the convTranspose operator
+ * @param[in] inputDims input dimensions
+ * @param[in] outputDims output tensor dimensions
+ * @param[in] oStrides nb of elements contained per dimension of the output
+ * @param[in] input_ values
+ * @param[in] weight_ values
+ * @param[in] biases_ values
+ * @param[out] output
+ */
+template <class I, class W, class B, class O>
+void ConvTransposeImpl3D_cpu_forward_kernel(
+ const array<DimSize_t, 3> &stride,
+ const array<DimSize_t, 3> &dilation,
+ const array<DimSize_t, 3> &kernelDims,
+ const array<DimSize_t, 5> &inputDims,
+ const array<DimSize_t, 5> &outputDims,
+ const void *input_,
+ const void *weights_,
+ const void *biases_,
+ void *output_) {
+
+ auto input = static_cast<const I *>(input_);
+ auto weights = static_cast<const W *>(weights_);
+ auto output = static_cast<O *>(output_);
+
+ // {channel_stride, dim0_stride, dim1_stride}
+ const array<DimSize_t, 4> inputStrides{
+ inputDims[1] * inputDims[2] * inputDims[3] * inputDims[4],
+ inputDims[2] * inputDims[3] * inputDims[4],
+ inputDims[3] * inputDims[4],
+ inputDims[4]};
+
+ // {channel_stride, dim0_stride, dim1_stride}
+ const array<DimSize_t, 4> outputStrides{
+ outputDims[1] * outputDims[2] * outputDims[3] * outputDims[4],
+ outputDims[2] * outputDims[3] * outputDims[4],
+ outputDims[3] * outputDims[4],
+ outputDims[4]};
+
+ // NOTE: kernel dims = {inChannels, outChannels, kernelDims[0],
+ // kernelDims[1]}
+ const array<DimSize_t, 4> kernelStrides{
+ outputDims[1] * kernelDims[0] * kernelDims[1] * kernelDims[2],
+ kernelDims[0] * kernelDims[1] * kernelDims[2],
+ kernelDims[1] * kernelDims[2],
+ kernelDims[2]};
+
+ if (biases_ != nullptr) {
+ auto biases = static_cast<const B *>(biases_);
+ convTranspose3DForwardBias(biases, outputDims, outputStrides, output);
+ }
+
+ conv3DBackwardInput(stride,
+ dilation,
+ kernelDims,
+ kernelStrides,
+ weights,
+ inputDims,
+ inputStrides,
+ input,
+ outputDims,
+ outputStrides,
+ output);
+}
+
+REGISTRAR(ConvTransposeImpl3D_cpu,
+ {{DataType::Any, DataFormat::NCHW},
+ {DataType::Int32, DataFormat::NCHW}},
+ {ProdConso::inPlaceModel,
+ ConvTransposeImpl3D_cpu_forward_kernel<std::int32_t,
+ std::int32_t,
+ std::int32_t,
+ std::int32_t>,
+ nullptr});
+REGISTRAR(ConvTransposeImpl3D_cpu,
+ {{DataType::Any, DataFormat::NCHW},
+ {DataType::Float16, DataFormat::NCHW}},
+ {ProdConso::inPlaceModel,
+ ConvTransposeImpl3D_cpu_forward_kernel<half_float::half,
+ half_float::half,
+ half_float::half,
+ half_float::half>,
+ nullptr});
+REGISTRAR(ConvTransposeImpl3D_cpu,
+ {{DataType::Any, DataFormat::NCHW},
+ {DataType::Float32, DataFormat::NCHW}},
+ {ProdConso::inPlaceModel,
+ ConvTransposeImpl3D_cpu_forward_kernel<float, float, float, float>,
+ nullptr});
+REGISTRAR(
+ ConvTransposeImpl3D_cpu,
+ {{DataType::Any, DataFormat::NCHW}, {DataType::Float64, DataFormat::NCHW}},
+ {ProdConso::inPlaceModel,
+ ConvTransposeImpl3D_cpu_forward_kernel<double, double, double, double>,
+ nullptr});
+
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_CONVTRANSPOSEIMPL_KERNELS_H_ */
diff --git a/src/operator/ConvTransposeImpl.cpp b/src/operator/ConvTransposeImpl.cpp
index d1135cc92dd3c68746b9dcf80739f4f65acdad2e..4f6a8f62be6cc14303419ce6cfa89b3065b01569 100644
--- a/src/operator/ConvTransposeImpl.cpp
+++ b/src/operator/ConvTransposeImpl.cpp
@@ -89,3 +89,42 @@ template <> void Aidge::ConvTransposeImpl2D_cpu::backward() {
"Backward not yet implemented for Conv_Op<2> on backend cpu");
}
+template <> void Aidge::ConvTransposeImpl3D_cpu::forward() {
+ const auto &op = static_cast<const ConvTranspose_Op<3> &>(mOp);
+
+ AIDGE_ASSERT(op.getInput(0), "{}: missing data input (#0).", op.type());
+ AIDGE_ASSERT(op.getInput(1), "{}: missing bias input (#1).", op.type());
+ AIDGE_ASSERT(op.getInput(2), "{}: missing weight input (#1).", op.type());
+
+ std::shared_ptr<Tensor> inputDataFallback, inputWeightFallback,
+ inputBiasFallback;
+ const auto &inputData =
+ op.getInput(0)->refCastFrom(inputDataFallback, *op.getOutput(0));
+ const auto &inputWeight =
+ op.getInput(1)->refCastFrom(inputWeightFallback, *op.getOutput(0));
+ const auto &inputBias =
+ (op.getInput(2))
+ ? op.getInput(2)->refCastFrom(inputBiasFallback, *op.getOutput(0))
+ : Tensor();
+
+ // Call kernel
+ const auto impl = Registrar<ConvTransposeImpl3D_cpu>::create(
+ getBestMatch(getRequiredSpec()));
+
+ impl.forward(op.strideDims(),
+ op.dilationDims(),
+ op.kernelDims(),
+ op.getInput(0)->template dims<5>(),
+ op.getOutput(0)->template dims<5>(),
+ inputData.getImpl()->hostPtr(),
+ inputWeight.getImpl()->hostPtr(),
+ op.getInput(2) ? inputBias.getImpl()->hostPtr() : nullptr,
+ op.getOutput(0)->getImpl()->rawPtr());
+}
+
+template <> void Aidge::ConvTransposeImpl3D_cpu::backward() {
+ AIDGE_THROW_OR_ABORT(
+ std::runtime_error,
+ "Backward not yet implemented for Conv_Op<2> on backend cpu");
+}
+
diff --git a/unit_tests/operator/Test_ConvImpl.cpp b/unit_tests/operator/Test_ConvImpl.cpp
index a47b315bd89ee7e9f054311dc88c04767e518c0a..7b14546351e1eb61af1063a5b98aa16fcebdd029 100644
--- a/unit_tests/operator/Test_ConvImpl.cpp
+++ b/unit_tests/operator/Test_ConvImpl.cpp
@@ -21,8 +21,8 @@
#include "aidge/filler/Filler.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/operator/Conv.hpp"
-#include "aidge/utils/TensorUtils.hpp"
#include "aidge/operator/Pad.hpp"
+#include "aidge/utils/TensorUtils.hpp"
namespace Aidge {
diff --git a/unit_tests/operator/Test_ConvTranspose.cpp b/unit_tests/operator/Test_ConvTranspose.cpp
index 6e889e809e0a05d551829bd15fda9cc651068465..7bb87835a3d9210b7f2f6bce682df60657d049a7 100644
--- a/unit_tests/operator/Test_ConvTranspose.cpp
+++ b/unit_tests/operator/Test_ConvTranspose.cpp
@@ -2293,6 +2293,1332 @@ TEST_CASE("[cpu/operator] ConvTranspose(forward)", "[ConvTranspose][CPU]") {
CHECK(approxEq<float, float>(*op->getOutput(0), *expectedOutput));
}
}
+ SECTION("3D") {
+ constexpr DimSize_t DIM = 3;
+ SECTION("Big test to ensure kernel capabilities") {
+ constexpr DimSize_t batchSize = 1;
+ constexpr DimSize_t inChannels = 3;
+ constexpr DimSize_t outChannels = 2;
+
+ constexpr std::array<DimSize_t, DIM> kernelSize{1, 2, 3};
+
+ constexpr std::array<DimSize_t, DIM> inDataSize{4, 4, 5};
+ constexpr std::array<DimSize_t, DIM> outDataSize{4, 10, 15};
+
+ constexpr std::array<DimSize_t, DIM> stride{1, 2, 3};
+ constexpr std::array<DimSize_t, DIM> dilation{2, 3, 1};
+
+ auto input = std::make_shared<Tensor>(Array5D<float,
+ batchSize,
+ inChannels,
+ inDataSize[0],
+ inDataSize[1],
+ inDataSize[2]>(
+ {{{{{{1., 2., 3., 4., 5.},
+ {6., 7., 8., 9., 10.},
+ {11., 12., 13., 14., 15.},
+ {16., 17., 18., 19., 20.}},
+
+ {{21., 22., 23., 24., 25.},
+ {26., 27., 28., 29., 30.},
+ {31., 32., 33., 34., 35.},
+ {36., 37., 38., 39., 40.}},
+
+ {{41., 42., 43., 44., 45.},
+ {46., 47., 48., 49., 50.},
+ {51., 52., 53., 54., 55.},
+ {56., 57., 58., 59., 60.}},
+
+ {{61., 62., 63., 64., 65.},
+ {66., 67., 68., 69., 70.},
+ {71., 72., 73., 74., 75.},
+ {76., 77., 78., 79., 80.}}},
+
+ {{{81., 82., 83., 84., 85.},
+ {86., 87., 88., 89., 90.},
+ {91., 92., 93., 94., 95.},
+ {96., 97., 98., 99., 100.}},
+
+ {{101., 102., 103., 104., 105.},
+ {106., 107., 108., 109., 110.},
+ {111., 112., 113., 114., 115.},
+ {116., 117., 118., 119., 120.}},
+
+ {{121., 122., 123., 124., 125.},
+ {126., 127., 128., 129., 130.},
+ {131., 132., 133., 134., 135.},
+ {136., 137., 138., 139., 140.}},
+
+ {{141., 142., 143., 144., 145.},
+ {146., 147., 148., 149., 150.},
+ {151., 152., 153., 154., 155.},
+ {156., 157., 158., 159., 160.}}},
+
+ {{{161., 162., 163., 164., 165.},
+ {166., 167., 168., 169., 170.},
+ {171., 172., 173., 174., 175.},
+ {176., 177., 178., 179., 180.}},
+
+ {{181., 182., 183., 184., 185.},
+ {186., 187., 188., 189., 190.},
+ {191., 192., 193., 194., 195.},
+ {196., 197., 198., 199., 200.}},
+
+ {{201., 202., 203., 204., 205.},
+ {206., 207., 208., 209., 210.},
+ {211., 212., 213., 214., 215.},
+ {216., 217., 218., 219., 220.}},
+
+ {{221., 222., 223., 224., 225.},
+ {226., 227., 228., 229., 230.},
+ {231., 232., 233., 234., 235.},
+ {236., 237., 238., 239., 240.}}}}}}));
+
+ auto weights = std::make_shared<Tensor>(Array5D<float,
+ inChannels,
+ outChannels,
+ kernelSize[0],
+ kernelSize[1],
+ kernelSize[2]>(
+ {{{{{{0.1, 0.2, 0.3}, {0.4, 0.5, 0.6}}},
+
+ {{{0.7, 0.8, 0.9}, {1.0, 1.1, 1.2}}}},
+
+ {{{{1.3, 1.4, 1.5}, {1.6, 1.7, 1.8}}},
+
+ {{{1.9, 2.0, 2.1}, {2.2, 2.3, 2.4}}}}}}));
+
+ auto biases = std::make_shared<Tensor>(
+ Array1D<float, outChannels>({{0.01, 0.02}}));
+
+ auto op = setupTestConvTranspose<DIM>(batchSize,
+ inChannels,
+ outChannels,
+ kernelSize,
+ inDataSize,
+ stride,
+ dilation,
+ input,
+ weights,
+ biases);
+
+ REQUIRE_NOTHROW(op->forward());
+
+ auto expectedOutput = std::make_shared<Tensor>(
+ Array5D<float,
+ batchSize,
+ outChannels,
+ outDataSize[0],
+ outDataSize[1],
+ outDataSize[2]>({{{{{{507.910034,
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}
} // namespace Aidge