diff --git a/include/aidge/backend/cpu/operator/AddImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/AddImpl_forward_kernels.hpp
index 490598599aedf24b26865ce6a1ddb3fe32044b1b..221e36dcfac44e21d1b1a35674ca21403b4b57ab 100644
--- a/include/aidge/backend/cpu/operator/AddImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/AddImpl_forward_kernels.hpp
@@ -20,7 +20,7 @@ namespace Aidge {
template <class I1, class O>
void AddImpl1I_cpu_forward_kernel(const std::size_t inputLength, const void* input1_, void* output_) {
- // FIXME: missing Add parameters as arguments
+ // FIXME: missing Add attributes as arguments
const I1* input1 = static_cast<const I1*>(input1_);
O* output = static_cast<O*>(output_);
@@ -32,7 +32,7 @@ void AddImpl1I_cpu_forward_kernel(const std::size_t inputLength, const void* inp
template <class I1, class I2, class O>
void AddImpl2I_cpu_forward_kernel(const std::size_t inputLength, const void* input1_, const void* input2_,
void* output_) {
- // FIXME: missing Add parameters as arguments
+ // FIXME: missing Add attributes as arguments
const I1* input1 = static_cast<const I1*>(input1_);
const I2* input2 = static_cast<const I2*>(input2_);
O* output = static_cast<O*>(output_);
@@ -45,7 +45,7 @@ void AddImpl2I_cpu_forward_kernel(const std::size_t inputLength, const void* inp
template <class I1, class I2, class I3, class O>
void AddImpl3I_cpu_forward_kernel(const std::size_t inputLength, const void* input1_, const void* input2_,
const void* input3_, void* output_) {
- // FIXME: missing Add parameters as arguments
+ // FIXME: missing Add attributes as arguments
const I1* input1 = static_cast<const I1*>(input1_);
const I2* input2 = static_cast<const I2*>(input2_);
const I3* input3 = static_cast<const I3*>(input3_);
diff --git a/include/aidge/backend/cpu/operator/AvgPoolingImpl.hpp b/include/aidge/backend/cpu/operator/AvgPoolingImpl.hpp
index 8373cb84a550efd8741a2dbc04c1e94ad37fe611..cfbcadfe6b719369618955a14c4cde5733ef6773 100644
--- a/include/aidge/backend/cpu/operator/AvgPoolingImpl.hpp
+++ b/include/aidge/backend/cpu/operator/AvgPoolingImpl.hpp
@@ -29,11 +29,11 @@ namespace Aidge {
class AvgPoolingImpl2DForward_cpu
: public Registrable<AvgPoolingImpl2DForward_cpu,
std::tuple<DataType, DataType>,
- void(const AvgPooling_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *, void *)> {};
+ void(const AvgPooling_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *, void *)> {};
class AvgPoolingImpl2DBackward_cpu
: public Registrable<AvgPoolingImpl2DBackward_cpu,
std::tuple<DataType, DataType>,
- void(const AvgPooling_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *, void *)> {};
+ void(const AvgPooling_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *, void *)> {};
class AvgPoolingImpl2D_cpu : public OperatorImpl {
private:
diff --git a/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp
index 776e020f1a20056db345c8e845fd73bb31b4138b..60b4923bdc18674da52be9bd07d9947fb9790f0d 100644
--- a/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/AvgPoolingImpl_forward_kernels.hpp
@@ -26,51 +26,51 @@ namespace Aidge {
* @brief Forward kernel for 2D AvgPoolingolution on CPU backend.
* @tparam I Input data type.
* @tparam O Output data type.
- * @param params tuple of Parameters from the Operator
+ * @param params tuple of Attributes from the Operator
* @param dims Array of input dimensions.
* @param input_ const input Tensor.
* @param output_ Output Tensor.
*/
template <class I, class O>
-void AvgPoolingImpl2D_cpu_forward_kernel(const AvgPooling_Op<2>::Parameters ¶ms,
+void AvgPoolingImpl2D_cpu_forward_kernel(const AvgPooling_Op<2>::Attrs &attrs,
const std::array<DimSize_t, 4> &dims,
const void *input_,
void *output_) {
- // FIXME: missing convolution parameters as arguments
+ // FIXME: missing convolution attributes as arguments
const I *input = static_cast<const I *>(input_);
O *output = static_cast<O *>(output_);
// output H size
const std::size_t oxSize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<2>(params)[0] + std::get<2>(params)[2] - std::get<1>(params)[0] + std::get<0>(params)[0]) /
- static_cast<float>(std::get<0>(params)[0])));
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<2>(attrs)[0] + std::get<2>(attrs)[2] - std::get<1>(attrs)[0] + std::get<0>(attrs)[0]) /
+ static_cast<float>(std::get<0>(attrs)[0])));
// output W size
const std::size_t oySize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<2>(params)[1] + std::get<2>(params)[3] - std::get<1>(params)[1] + std::get<0>(params)[1]) /
- static_cast<float>(std::get<0>(params)[1])));
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<2>(attrs)[1] + std::get<2>(attrs)[3] - std::get<1>(attrs)[1] + std::get<0>(attrs)[1])/
+ static_cast<float>(std::get<0>(attrs)[1])));
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
// input (batch, ch, Xin, Yin)
// weight (outCh, ch, kernelX, kernelY)
- // does not take Dilation parameter into account
+ // does not take Dilation attribute into account
using signedsize = std::make_signed<std::size_t>::type;
for (std::size_t batch = 0; batch < dims[0]; ++batch) {
for (std::size_t ch = 0; ch < dims[1]; ++ch) {
const std::size_t oIndex = (ch + batch*dims[1]) * oxSize * oySize;
const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
- const signedsize difx = static_cast<signedsize>(std::get<2>(params)[0] - ox * std::get<0>(params)[0]);
+ const signedsize difx = static_cast<signedsize>(std::get<2>(attrs)[0] - ox * std::get<0>(attrs)[0]);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
- const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<1>(params)[0] ? std::get<1>(params)[0] : dims[2] + difx);
+ const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<1>(attrs)[0] ? std::get<1>(attrs)[0] : dims[2] + difx);
for (std::size_t oy = 0; oy < oySize; ++oy) {
- const signedsize dify = static_cast<signedsize>(std::get<2>(params)[1] - oy * std::get<0>(params)[1]);
+ const signedsize dify = static_cast<signedsize>(std::get<2>(attrs)[1] - oy * std::get<0>(attrs)[1]);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
- const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<1>(params)[1] ? std::get<1>(params)[1] : dims[3] + dify);
+ const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<1>(attrs)[1] ? std::get<1>(attrs)[1] : dims[3] + dify);
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
- const std::size_t ix = ox * std::get<0>(params)[0];
- const std::size_t iy = oy * std::get<0>(params)[1];
+ const std::size_t ix = ox * std::get<0>(attrs)[0];
+ const std::size_t iy = oy * std::get<0>(attrs)[1];
if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
output[oIndexFull] += static_cast<O>(
diff --git a/include/aidge/backend/cpu/operator/BatchNormImpl.hpp b/include/aidge/backend/cpu/operator/BatchNormImpl.hpp
index d9f25b4a8e38510f82fc5afe9ed4b656197a47d5..30557f6cbba05829b3cc9e17364ae4d933a568cf 100644
--- a/include/aidge/backend/cpu/operator/BatchNormImpl.hpp
+++ b/include/aidge/backend/cpu/operator/BatchNormImpl.hpp
@@ -29,7 +29,7 @@ namespace Aidge {
class BatchNormImpl2DForward_cpu
: public Registrable<BatchNormImpl2DForward_cpu,
std::tuple<DataType, DataType, DataType>,
- void(const BatchNorm_Op<2>::Parameters &,
+ void(const BatchNorm_Op<2>::Attrs &,
const std::array<DimSize_t, 4> &,
const void *,
const void *,
@@ -41,7 +41,7 @@ class BatchNormImpl2DForward_cpu
class BatchNormImpl2DBackward_cpu
: public Registrable<BatchNormImpl2DBackward_cpu,
std::tuple<DataType, DataType, DataType>,
- void(const BatchNorm_Op<2>::Parameters &,
+ void(const BatchNorm_Op<2>::Attrs &,
const std::array<DimSize_t, 4> &,
const void *,
const void *,
diff --git a/include/aidge/backend/cpu/operator/BatchNormImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/BatchNormImpl_forward_kernels.hpp
index eedb80bde60d65b53bac70cc33ca83eb4f0121e7..486829e782ae2173332a7efa6646bb7bba322252 100644
--- a/include/aidge/backend/cpu/operator/BatchNormImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/BatchNormImpl_forward_kernels.hpp
@@ -27,7 +27,7 @@ namespace Aidge {
* @tparam W Weight data type.
* @tparam B Bias data type.
* @tparam O Output data type.
- * @param params tuple of Parameters from the Operator
+ * @param params tuple of Attributes from the Operator
* @param dims Array of input dimensions.
* @param input_ const input Tensor.
* @param scale_ const scale Tensor.
@@ -37,9 +37,9 @@ namespace Aidge {
* @param output_ Output Tensor.
*/
template <class I, class P, class O>
-void BatchNormImpl2D_cpu_forward_kernel(const BatchNorm_Op<2>::Parameters ¶ms, const std::array<DimSize_t, 4> &dims,
+void BatchNormImpl2D_cpu_forward_kernel(const BatchNorm_Op<2>::Attrs &attrs, const std::array<DimSize_t, 4> &dims,
const void *input_, const void *scale_, const void *shift_, void *batchMean_, void *batchVar_, void *output_, const bool freeze) {
- // FIXME: missing convolution parameters as arguments
+ // FIXME: missing convolution attributes as arguments
const I *input = static_cast<const I *>(input_);
const P *scale = static_cast<const P *>(scale_);
const P *shift = static_cast<const P *>(shift_);
@@ -52,12 +52,12 @@ void BatchNormImpl2D_cpu_forward_kernel(const BatchNorm_Op<2>::Parameters ¶m
const DimSize_t featureMapSize = dims[2]*dims[3];
- if ((freeze == true) || (std::get<1>(params) == 0.0f)) {
+ if ((freeze == true) || (std::get<1>(attrs) == 0.0f)) {
for (std::size_t batch = 0; batch < nbBatch; ++batch) {
for (std::size_t ch = 0; ch < nbChannels; ++ch) {
const std::size_t ioIndex = (ch + batch*nbChannels) * featureMapSize;
std::fill(output + ioIndex, output + ioIndex + featureMapSize, shift[ch]);
- const P var = std::sqrt(batchVar[ch] + static_cast<P>(std::get<0>(params)));
+ const P var = std::sqrt(batchVar[ch] + static_cast<P>(std::get<0>(attrs)));
for (std::size_t feature = 0; feature<featureMapSize; ++feature) {
output[ioIndex + feature] += scale[ch] * (input[ioIndex + feature]-batchMean[ch]) / var;
@@ -81,10 +81,10 @@ void BatchNormImpl2D_cpu_forward_kernel(const BatchNorm_Op<2>::Parameters ¶m
const I inputMean = sum / static_cast<I>(nbDataPerChannel);
const I inputVar = sumSquare / static_cast<I>(nbDataPerChannel) - inputMean*inputMean;
- batchMean[ch] = batchMean[ch]*(1-std::get<1>(params)) + inputMean*std::get<1>(params);
- batchVar[ch] = batchVar[ch]*(1-std::get<1>(params)) + inputVar*(static_cast<I>(nbDataPerChannel)/static_cast<I>(nbDataPerChannel-1))*std::get<1>(params);
+ batchMean[ch] = batchMean[ch]*(1-std::get<1>(attrs)) + inputMean*std::get<1>(attrs);
+ batchVar[ch] = batchVar[ch]*(1-std::get<1>(attrs)) + inputVar*(static_cast<I>(nbDataPerChannel)/static_cast<I>(nbDataPerChannel-1))*std::get<1>(attrs);
- const P var = std::sqrt(inputVar + static_cast<P>(std::get<0>(params)));
+ const P var = std::sqrt(inputVar + static_cast<P>(std::get<0>(attrs)));
for (std::size_t batch = 0; batch < nbBatch; ++batch) {
const std::size_t ioIndex = (ch + batch*nbChannels) * featureMapSize;
for (std::size_t feature = 0; feature<featureMapSize; ++feature) {
diff --git a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp
index 0d21c676d797b2fc4e95c4aea47674c8fca5eef4..2826b635590c5d19f34c8e4beee20fc8dba2183b 100644
--- a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp
+++ b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp
@@ -29,12 +29,12 @@ namespace Aidge {
class ConvDepthWiseImpl2DForward_cpu
: public Registrable<ConvDepthWiseImpl2DForward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
- void(const ConvDepthWise_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *,
+ void(const ConvDepthWise_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *,
const void *, const void *, void *)> {};
class ConvDepthWiseImpl2DBackward_cpu
: public Registrable<ConvDepthWiseImpl2DBackward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
- void(const ConvDepthWise_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *,
+ void(const ConvDepthWise_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *,
const void *, const void *, void *)> {};
class ConvDepthWiseImpl2D_cpu : public OperatorImpl {
diff --git a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp
index ee2d82e00376c5a2cc5a075565e35eb8885c021e..669bdbc898528b0f96a59dd3c6f8e438ae1291e4 100644
--- a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp
@@ -27,7 +27,7 @@ namespace Aidge {
* @tparam W Weight data type.
* @tparam B Bias data type.
* @tparam O Output data type.
- * @param params tuple of Parameters from the Operator
+ * @param params tuple of Attributes from the Operator
* @param dims Array of input dimensions.
* @param input_ const input Tensor.
* @param weights_ const weight Tensor.
@@ -35,9 +35,9 @@ namespace Aidge {
* @param output_ Output Tensor.
*/
template <class I, class W, class B, class O>
-void ConvDepthWiseImpl2D_cpu_forward_kernel(const ConvDepthWise_Op<2>::Parameters ¶ms, const std::array<DimSize_t, 4> &dims,
+void ConvDepthWiseImpl2D_cpu_forward_kernel(const ConvDepthWise_Op<2>::Attrs &attrs, const std::array<DimSize_t, 4> &dims,
const void *input_, const void *weights_, const void *biases_, void *output_) {
- // FIXME: missing convolution parameters as arguments
+ // FIXME: missing convolution attributes as arguments
const I *input = static_cast<const I *>(input_);
const W *weights = static_cast<const W *>(weights_);
const B *biases = static_cast<const B *>(biases_);
@@ -46,52 +46,52 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const ConvDepthWise_Op<2>::Parameter
// output H size
const std::size_t oxSize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<4>(params)[0] + std::get<4>(params)[2] - std::get<3>(params)[0] + std::get<0>(params)[0]) /
- static_cast<float>(std::get<0>(params)[0])));
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<4>(attrs)[0] + std::get<4>(attrs)[2] - std::get<3>(attrs)[0] + std::get<0>(attrs)[0]) /
+ static_cast<float>(std::get<0>(attrs)[0])));
// output W size
const std::size_t oySize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<4>(params)[1] + std::get<4>(params)[3] - std::get<3>(params)[1] + std::get<0>(params)[1]) /
- static_cast<float>(std::get<0>(params)[1])));
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<4>(attrs)[1] + std::get<4>(attrs)[3] - std::get<3>(attrs)[1] + std::get<0>(attrs)[1]) /
+ static_cast<float>(std::get<0>(attrs)[1])));
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
// input (batch, ch, Xin, Yin)
// weight (outCh, ch, kernelX, kernelY)
- // does not take Dilation parameter into account
+ // does not take Dilation attribute into account
using signedsize = std::make_signed<std::size_t>::type;
for (std::size_t batch = 0; batch < dims[0]; ++batch) {
- for (std::size_t ch = 0; ch < std::get<2>(params); ++ch) {
- const std::size_t oIndex = (ch + batch*std::get<2>(params)) * oxSize * oySize;
+ for (std::size_t ch = 0; ch < std::get<2>(attrs); ++ch) {
+ const std::size_t oIndex = (ch + batch*std::get<2>(attrs)) * oxSize * oySize;
B biasVal = (biases != nullptr) ? biases[ch] : B(0);
std::fill(output + oIndex, output+(oIndex+oxSize*oySize), biasVal);
const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
- const std::size_t wIndex = ch * std::get<3>(params)[0] * std::get<3>(params)[1];
+ const std::size_t wIndex = ch * std::get<3>(attrs)[0] * std::get<3>(attrs)[1];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
- const signedsize difx = static_cast<signedsize>(std::get<4>(params)[0] - ox * std::get<0>(params)[0]);
+ const signedsize difx = static_cast<signedsize>(std::get<4>(attrs)[0] - ox * std::get<0>(attrs)[0]);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
- const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<3>(params)[0] ? std::get<3>(params)[0] : dims[2] + difx);
+ const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<3>(attrs)[0] ? std::get<3>(attrs)[0] : dims[2] + difx);
for (std::size_t oy = 0; oy < oySize; ++oy) {
- const signedsize dify = static_cast<signedsize>(std::get<4>(params)[1] - oy * std::get<0>(params)[1]);
+ const signedsize dify = static_cast<signedsize>(std::get<4>(attrs)[1] - oy * std::get<0>(attrs)[1]);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
- const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<3>(params)[1] ? std::get<3>(params)[1] : dims[3] + dify);
+ const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<3>(attrs)[1] ? std::get<3>(attrs)[1] : dims[3] + dify);
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
- const signedsize ix = static_cast<signedsize>(ox * std::get<0>(params)[0]) - std::get<4>(params)[0];
- const signedsize iy = static_cast<signedsize>(oy * std::get<0>(params)[1]) - std::get<4>(params)[1];
+ const signedsize ix = static_cast<signedsize>(ox * std::get<0>(attrs)[0]) - std::get<4>(attrs)[0];
+ const signedsize iy = static_cast<signedsize>(oy * std::get<0>(attrs)[1]) - std::get<4>(attrs)[1];
if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
- output[oIndexFull] += (weights[wIndex + 0*std::get<3>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 0*std::get<3>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 0*std::get<3>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+2)] +
- weights[wIndex + 1*std::get<3>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 1*std::get<3>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 1*std::get<3>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+2)] +
- weights[wIndex + 2*std::get<3>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 2*std::get<3>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 2*std::get<3>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+2)]);
+ output[oIndexFull] += (weights[wIndex + 0*std::get<3>(attrs)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+0)] +
+ weights[wIndex + 0*std::get<3>(attrs)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+1)] +
+ weights[wIndex + 0*std::get<3>(attrs)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+2)] +
+ weights[wIndex + 1*std::get<3>(attrs)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+0)] +
+ weights[wIndex + 1*std::get<3>(attrs)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+1)] +
+ weights[wIndex + 1*std::get<3>(attrs)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+2)] +
+ weights[wIndex + 2*std::get<3>(attrs)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+0)] +
+ weights[wIndex + 2*std::get<3>(attrs)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+1)] +
+ weights[wIndex + 2*std::get<3>(attrs)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+2)]);
} else {
for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
for (std::size_t sy = syMin; sy < syMax; ++sy) {
- output[oIndexFull] += weights[wIndex + sx*std::get<3>(params)[1] + sy] *
+ output[oIndexFull] += weights[wIndex + sx*std::get<3>(attrs)[1] + sy] *
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))*dims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(sy))];
}
}
diff --git a/include/aidge/backend/cpu/operator/ConvImpl.hpp b/include/aidge/backend/cpu/operator/ConvImpl.hpp
index 1f3dffe43b966bc37887f267cc56760a899476f9..b9411fe0f1ac079d9857cc8f2178fc98fadc3a77 100644
--- a/include/aidge/backend/cpu/operator/ConvImpl.hpp
+++ b/include/aidge/backend/cpu/operator/ConvImpl.hpp
@@ -29,12 +29,12 @@ namespace Aidge {
class ConvImpl2DForward_cpu
: public Registrable<ConvImpl2DForward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
- void(const Conv_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *,
+ void(const Conv_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *,
const void *, const void *, void *)> {};
class ConvImpl2DBackward_cpu
: public Registrable<ConvImpl2DBackward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
- void(const Conv_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *,
+ void(const Conv_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *,
const void *, const void *, void *)> {};
class ConvImpl2D_cpu : public OperatorImpl {
diff --git a/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
index bc2f10099f42cba91be8d089b66dc176fdeb7c10..9d4d6dfdfcc114e47e478089c4d5a42c2bee0f28 100644
--- a/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
@@ -27,7 +27,7 @@ namespace Aidge {
* @tparam W Weight data type.
* @tparam B Bias data type.
* @tparam O Output data type.
- * @param params tuple of Parameters from the Operator
+ * @param params tuple of Attributes from the Operator
* @param dims Array of input dimensions.
* @param input_ const input Tensor.
* @param weights_ const weight Tensor.
@@ -35,9 +35,9 @@ namespace Aidge {
* @param output_ Output Tensor.
*/
template <class I, class W, class B, class O>
-void ConvImpl2D_cpu_forward_kernel(const Conv_Op<2>::Parameters ¶ms, const std::array<DimSize_t, 4> &dims,
+void ConvImpl2D_cpu_forward_kernel(const Conv_Op<2>::Attrs &attrs, const std::array<DimSize_t, 4> &dims,
const void *input_, const void *weights_, const void *biases_, void *output_) {
- // FIXME: missing convolution parameters as arguments
+ // FIXME: missing convolution attributes as arguments
const I *input = static_cast<const I *>(input_);
const W *weights = static_cast<const W *>(weights_);
const B *biases = static_cast<const B *>(biases_);
@@ -45,34 +45,34 @@ void ConvImpl2D_cpu_forward_kernel(const Conv_Op<2>::Parameters ¶ms, const s
/*
// output H size
const std::size_t oxSize =
- static_cast<std::size_t>(static_cast<float>(dims[0] - std::get<4>(params)[0] + std::get<0>(params)[0]) /
- static_cast<float>(std::get<0>(params)[0]));
+ static_cast<std::size_t>(static_cast<float>(dims[0] - std::get<4>(attrs)[0] + std::get<0>(attrs)[0]) /
+ static_cast<float>(std::get<0>(attrs)[0]));
// output W size
const std::size_t oySize =
- static_cast<std::size_t>(static_cast<float>(dims[1] - std::get<4>(params)[1] + std::get<0>(params)[1]) /
- static_cast<float>(std::get<0>(params)[1]));
+ static_cast<std::size_t>(static_cast<float>(dims[1] - std::get<4>(attrs)[1] + std::get<0>(attrs)[1]) /
+ static_cast<float>(std::get<0>(attrs)[1]));
// TODO: kernel computation
// output (Xout, Yout, outCh, batch)
// input (Xin, Yin, inCh, batch)
// weight (kernelX, kernelY, inCh, outCh)
- // does not take Dilation parameter into account
+ // does not take Dilation attribute into account
for (std::size_t ox = 0; ox < oxSize; ++ox) {
for (std::size_t oy = 0; oy < oySize; ++oy) {
- const std::size_t ix = ox * std::get<0>(params)[0];
- const std::size_t iy = oy * std::get<0>(params)[1];
+ const std::size_t ix = ox * std::get<0>(attrs)[0];
+ const std::size_t iy = oy * std::get<0>(attrs)[1];
- for (std::size_t outCh = 0; outCh < std::get<3>(params); ++outCh) {
- const std::size_t oIndex = dims[3] * (outCh + std::get<3>(params) * (oy + oySize * ox));
+ for (std::size_t outCh = 0; outCh < std::get<3>(attrs); ++outCh) {
+ const std::size_t oIndex = dims[3] * (outCh + std::get<3>(attrs) * (oy + oySize * ox));
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
for (std::size_t batch = 0; batch < dims[3]; ++batch) {
output[oIndex + batch] = biasVal;
}
for (std::size_t inCh = 0; inCh < dims[2]; ++inCh) {
- for (std::size_t sx = 0; sx < std::get<4>(params)[0]; ++sx) {
- for (std::size_t sy = 0; sy < std::get<4>(params)[1]; ++sy) {
+ for (std::size_t sx = 0; sx < std::get<4>(attrs)[0]; ++sx) {
+ for (std::size_t sy = 0; sy < std::get<4>(attrs)[1]; ++sy) {
const std::size_t wIndex =
- outCh + std::get<3>(params) * (inCh + dims[2] * (sy + std::get<4>(params)[1] * sx));
+ outCh + std::get<3>(attrs) * (inCh + dims[2] * (sy + std::get<4>(attrs)[1] * sx));
std::size_t iIndex = dims[3] * (inCh + dims[2] * ((iy + sy) + dims[1] * (ix + sx)));
for (std::size_t batch = 0; batch < dims[3]; ++batch) {
output[oIndex + batch] += weights[wIndex] * input[iIndex + batch];
@@ -88,53 +88,53 @@ void ConvImpl2D_cpu_forward_kernel(const Conv_Op<2>::Parameters ¶ms, const s
// output H size
const std::size_t oxSize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<5>(params)[0] + std::get<5>(params)[2] - std::get<4>(params)[0] + std::get<0>(params)[0]) /
- static_cast<float>(std::get<0>(params)[0])));
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<5>(attrs)[0] + std::get<5>(attrs)[2] - std::get<4>(attrs)[0] + std::get<0>(attrs)[0]) /
+ static_cast<float>(std::get<0>(attrs)[0])));
// output W size
const std::size_t oySize =
- static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<5>(params)[1] + std::get<5>(params)[3] - std::get<4>(params)[1] + std::get<0>(params)[1]) /
- static_cast<float>(std::get<0>(params)[1])));
+ static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<5>(attrs)[1] + std::get<5>(attrs)[3] - std::get<4>(attrs)[1] + std::get<0>(attrs)[1]) /
+ static_cast<float>(std::get<0>(attrs)[1])));
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
// input (batch, inCh, Xin, Yin)
// weight (outCh, inCh, kernelX, kernelY)
- // does not take Dilation parameter into account
+ // does not take Dilation attribute into account
using signedsize = std::make_signed<std::size_t>::type;
for (std::size_t batch = 0; batch < dims[0]; ++batch) {
- for (std::size_t outCh = 0; outCh < std::get<3>(params); ++outCh) {
- const std::size_t oIndex = (outCh + batch*std::get<3>(params)) * oxSize * oySize;
+ for (std::size_t outCh = 0; outCh < std::get<3>(attrs); ++outCh) {
+ const std::size_t oIndex = (outCh + batch*std::get<3>(attrs)) * oxSize * oySize;
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
std::fill(output + oIndex, output+(oIndex+oxSize*oySize), biasVal);
for (std::size_t inCh = 0; inCh < dims[1]; ++inCh) {
const std::size_t iIndex = (inCh + batch*dims[1]) * dims[2] * dims[3];
- const std::size_t wIndex = (inCh + outCh*dims[1]) * std::get<4>(params)[0] * std::get<4>(params)[1];
+ const std::size_t wIndex = (inCh + outCh*dims[1]) * std::get<4>(attrs)[0] * std::get<4>(attrs)[1];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
- const signedsize difx = static_cast<signedsize>(std::get<5>(params)[0] - ox * std::get<0>(params)[0]);
+ const signedsize difx = static_cast<signedsize>(std::get<5>(attrs)[0] - ox * std::get<0>(attrs)[0]);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
- const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<4>(params)[0] ? std::get<4>(params)[0] : dims[2] + difx);
+ const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<4>(attrs)[0] ? std::get<4>(attrs)[0] : dims[2] + difx);
for (std::size_t oy = 0; oy < oySize; ++oy) {
- const signedsize dify = static_cast<signedsize>(std::get<5>(params)[1] - oy * std::get<0>(params)[1]);
+ const signedsize dify = static_cast<signedsize>(std::get<5>(attrs)[1] - oy * std::get<0>(attrs)[1]);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
- const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<4>(params)[1] ? std::get<4>(params)[1] : dims[3] + dify);
+ const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<4>(attrs)[1] ? std::get<4>(attrs)[1] : dims[3] + dify);
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
- const signedsize ix = static_cast<signedsize>(ox * std::get<0>(params)[0]) - std::get<5>(params)[0];
- const signedsize iy = static_cast<signedsize>(oy * std::get<0>(params)[1]) - std::get<5>(params)[1];
+ const signedsize ix = static_cast<signedsize>(ox * std::get<0>(attrs)[0]) - std::get<5>(attrs)[0];
+ const signedsize iy = static_cast<signedsize>(oy * std::get<0>(attrs)[1]) - std::get<5>(attrs)[1];
if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
- output[oIndexFull] += (weights[wIndex + 0*std::get<4>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 0*std::get<4>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 0*std::get<4>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+2)] +
- weights[wIndex + 1*std::get<4>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 1*std::get<4>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 1*std::get<4>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+2)] +
- weights[wIndex + 2*std::get<4>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 2*std::get<4>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 2*std::get<4>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+2)]);
+ output[oIndexFull] += (weights[wIndex + 0*std::get<4>(attrs)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+0)] +
+ weights[wIndex + 0*std::get<4>(attrs)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+1)] +
+ weights[wIndex + 0*std::get<4>(attrs)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+2)] +
+ weights[wIndex + 1*std::get<4>(attrs)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+0)] +
+ weights[wIndex + 1*std::get<4>(attrs)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+1)] +
+ weights[wIndex + 1*std::get<4>(attrs)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+2)] +
+ weights[wIndex + 2*std::get<4>(attrs)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+0)] +
+ weights[wIndex + 2*std::get<4>(attrs)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+1)] +
+ weights[wIndex + 2*std::get<4>(attrs)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+2)]);
} else {
for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
for (std::size_t sy = syMin; sy < syMax; ++sy) {
- output[oIndexFull] += weights[wIndex + sx*std::get<4>(params)[1] + sy] *
+ output[oIndexFull] += weights[wIndex + sx*std::get<4>(attrs)[1] + sy] *
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))*dims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(sy))];
}
}
diff --git a/include/aidge/backend/cpu/operator/FCImpl.hpp b/include/aidge/backend/cpu/operator/FCImpl.hpp
index c69cc0b08a58877108c78d6f12c29e9089c2f665..1dfa40439dbba9cdd4fe3436fea30f771678c1ff 100644
--- a/include/aidge/backend/cpu/operator/FCImpl.hpp
+++ b/include/aidge/backend/cpu/operator/FCImpl.hpp
@@ -26,11 +26,11 @@ namespace Aidge {
// compute kernel registry for forward and backward
class FCImplForward_cpu : public Registrable<FCImplForward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
- void(const FC_Op::Parameters &, const DimSize_t, const DimSize_t,
+ void(const FC_Op::Attrs &, const DimSize_t, const DimSize_t,
const void *, const void *, const void *, void *)> {};
class FCImplBackward_cpu : public Registrable<FCImplBackward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
- void(const FC_Op::Parameters &, const DimSize_t, const DimSize_t,
+ void(const FC_Op::Attrs &, const DimSize_t, const DimSize_t,
const void *, const void *, const void *, void *)> {};
class FCImpl_cpu : public OperatorImpl {
diff --git a/include/aidge/backend/cpu/operator/FCImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/FCImpl_forward_kernels.hpp
index d6acb7dfea3415a8d67384745e16ecdd8bf06324..91e2558a7ef1079cbc9fb11f78fab53ef4246149 100644
--- a/include/aidge/backend/cpu/operator/FCImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/FCImpl_forward_kernels.hpp
@@ -19,17 +19,17 @@
namespace Aidge {
// template <class I, class W, class B, class O>
-// void FCImpl_cpu_forward_kernel(const FC_Op::Parameters& params, const std::array<DimSize_t, 4>& dims,
+// void FCImpl_cpu_forward_kernel(const FC_Op::Attrs& attrs, const std::array<DimSize_t, 4>& dims,
// const void* input_, const void* weights_, const void* biases_, void* output_) {
-// // FIXME: missing FC parameters as arguments
+// // FIXME: missing FC attributes as arguments
// const I* input = static_cast<const I*>(input_);
// const W* weights = static_cast<const W*>(weights_);
// const B* biases = static_cast<const B*>(biases_);
// O* output = static_cast<O*>(output_);
-// for (std::size_t outIdx = 0; outIdx < std::get<0>(params); ++outIdx) {
+// for (std::size_t outIdx = 0; outIdx < std::get<0>(attrs); ++outIdx) {
// std::size_t oIndex = outIdx * dims[3];
-// const B bias = std::get<1>(params) ? B(0) : biases[outIdx];
+// const B bias = std::get<1>(attrs) ? B(0) : biases[outIdx];
// for (std::size_t batch = 0; batch < dims[3]; ++batch) {
// output[oIndex + batch] = bias;
// }
@@ -39,10 +39,10 @@ namespace Aidge {
// for (std::size_t iy = 0; iy < dims[1]; ++iy) {
// for (std::size_t inCh = 0; inCh < dims[2]; ++inCh) {
// const std::size_t iIndex = dims[3] * (inCh + dims[2] * (iy + dims[1] * ix));
-// for (std::size_t outCh = 0; outCh < std::get<0>(params); ++outCh) {
+// for (std::size_t outCh = 0; outCh < std::get<0>(attrs); ++outCh) {
// const std::size_t oIndex = dims[3] * outCh;
-// const std::size_t wIndex = (inCh + dims[2] * (iy + dims[1] * ix)) * std::get<0>(params) +
-// outCh; // (iIndex*std::get<0>(params) + oIndex)/dims[3];
+// const std::size_t wIndex = (inCh + dims[2] * (iy + dims[1] * ix)) * std::get<0>(attrs) +
+// outCh; // (iIndex*std::get<0>(attrs) + oIndex)/dims[3];
// for (std::size_t batch = 0; batch < dims[3]; ++batch) {
// output[oIndex + batch] += weights[wIndex] * input[iIndex + batch];
// }
@@ -53,9 +53,9 @@ namespace Aidge {
// }
// template <class I, class W, class B, class O>
-// void FCImpl_cpu_forward_kernel(const FC_Op::Parameters& params, const std::array<DimSize_t, 2>& dims,
+// void FCImpl_cpu_forward_kernel(const FC_Op::Attrs& attrs, const std::array<DimSize_t, 2>& dims,
// const void* input_, const void* weights_, const void* biases_, void* output_) {
-// // FIXME: missing FC parameters as arguments
+// // FIXME: missing FC attributes as arguments
// const I* input = static_cast<const I*>(input_);
// const W* weights = static_cast<const W*>(weights_);
// const B* biases = static_cast<const B*>(biases_);
@@ -63,9 +63,9 @@ namespace Aidge {
// // let's have I.dims() = [N, C, H, W] instead of [H, W, C, N]
-// for (std::size_t outIdx = 0; outIdx < std::get<0>(params); ++outIdx) {
+// for (std::size_t outIdx = 0; outIdx < std::get<0>(attrs); ++outIdx) {
// std::size_t oIndex = outIdx * dims[0];
-// const B bias = std::get<1>(params) ? B(0) : biases[outIdx];
+// const B bias = std::get<1>(attrs) ? B(0) : biases[outIdx];
// for (std::size_t batch = 0; batch < dims[0]; ++batch) {
// output[oIndex + batch] = bias;
// }
@@ -74,8 +74,8 @@ namespace Aidge {
// for (std::size_t batch = 0; batch < dims[0]; ++batch) {
// const std::size_t oIndex = dims[1] * batch;
// for (std::size_t i = 0; i < dims[1]; ++i) {
-// for (std::size_t outCh = 0; outCh < std::get<0>(params); ++outCh) {
-// std::size_t wIndex = i * std::get<0>(params) + outCh; // (iIndex*std::get<0>(params) + oIndex)/dims[3];
+// for (std::size_t outCh = 0; outCh < std::get<0>(attrs); ++outCh) {
+// std::size_t wIndex = i * std::get<0>(attrs) + outCh; // (iIndex*std::get<0>(attrs) + oIndex)/dims[3];
// output[oIndex + outCh] += weights[wIndex] * input[i + batch];
// }
// }
@@ -83,29 +83,29 @@ namespace Aidge {
// }
template <class I, class W, class B, class O>
-void FCImpl_cpu_forward_kernel(const FC_Op::Parameters& params, const DimSize_t batchSize, const DimSize_t oneInputSize,
+void FCImpl_cpu_forward_kernel(const FC_Op::Attrs& attrs, const DimSize_t batchSize, const DimSize_t oneInputSize,
const void* input_, const void* weights_, const void* biases_, void* output_) {
- // FIXME: missing FC parameters as arguments
+ // FIXME: missing FC attributes as arguments
const I* input = static_cast<const I*>(input_);
const W* weights = static_cast<const W*>(weights_);
const B* biases = static_cast<const B*>(biases_);
O* output = static_cast<O*>(output_);
- if (std::get<1>(params)) {
- std::fill(output, output+(batchSize*std::get<0>(params)), B(0));
+ if (std::get<1>(attrs)) {
+ std::fill(output, output+(batchSize*std::get<0>(attrs)), B(0));
}
else {
for (std::size_t batch = 0; batch < batchSize; ++batch) {
- std::copy(biases, biases+std::get<0>(params), output+(batch*std::get<0>(params)));
+ std::copy(biases, biases+std::get<0>(attrs), output+(batch*std::get<0>(attrs)));
}
}
for (std::size_t batch = 0; batch < batchSize; ++batch) {
- for (std::size_t out = 0; out < std::get<0>(params); ++out) {
- output[out + batch*std::get<0>(params)] = std::inner_product(input + batch*oneInputSize,
+ for (std::size_t out = 0; out < std::get<0>(attrs); ++out) {
+ output[out + batch*std::get<0>(attrs)] = std::inner_product(input + batch*oneInputSize,
input + (batch + 1)*oneInputSize,
weights + out*oneInputSize,
- output[out + batch*std::get<0>(params)]);
+ output[out + batch*std::get<0>(attrs)]);
}
}
}
diff --git a/include/aidge/backend/cpu/operator/LeakyReLUImpl.hpp b/include/aidge/backend/cpu/operator/LeakyReLUImpl.hpp
index abe167bea16de01f861beb9701f747d39f265d9d..386ef999fddbda184edee88723d213f53ff62ded 100644
--- a/include/aidge/backend/cpu/operator/LeakyReLUImpl.hpp
+++ b/include/aidge/backend/cpu/operator/LeakyReLUImpl.hpp
@@ -24,10 +24,10 @@ namespace Aidge {
// compute kernel registry for forward and backward
class LeakyReLUImplForward_cpu
- : public Registrable<LeakyReLUImplForward_cpu, std::tuple<DataType, DataType>, void(const LeakyReLU_Op::Parameters&, std::size_t, const void*, void*)> {
+ : public Registrable<LeakyReLUImplForward_cpu, std::tuple<DataType, DataType>, void(const LeakyReLU_Op::Attrs&, std::size_t, const void*, void*)> {
};
class LeakyReLUImplBackward_cpu
- : public Registrable<LeakyReLUImplBackward_cpu, std::tuple<DataType, DataType>, void(const LeakyReLU_Op::Parameters&, std::size_t, const void*, void*)> {
+ : public Registrable<LeakyReLUImplBackward_cpu, std::tuple<DataType, DataType>, void(const LeakyReLU_Op::Attrs&, std::size_t, const void*, void*)> {
};
class LeakyReLUImpl_cpu : public OperatorImpl {
diff --git a/include/aidge/backend/cpu/operator/LeakyReLUImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/LeakyReLUImpl_forward_kernels.hpp
index ff9a8ac6a8f968f244429b330401d794f16fac01..761b9579c3c3dc187e4b0fac24812fa77f916e65 100644
--- a/include/aidge/backend/cpu/operator/LeakyReLUImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/LeakyReLUImpl_forward_kernels.hpp
@@ -18,14 +18,14 @@
namespace Aidge {
template <class I, class O>
-void LeakyReLUImpl_cpu_forward_kernel(const LeakyReLU_Op::Parameters& params,
+void LeakyReLUImpl_cpu_forward_kernel(const LeakyReLU_Op::Attrs& attrs,
std::size_t inputLenght,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
- I negativeSlope = static_cast<I>(std::get<0>(params));
+ I negativeSlope = static_cast<I>(std::get<0>(attrs));
for (std::size_t i = 0; i < inputLenght; ++i) {
output[i] = input[i] >= 0 ? input[i] : input[i] * negativeSlope;
diff --git a/include/aidge/backend/cpu/operator/ScalingImpl.hpp b/include/aidge/backend/cpu/operator/ScalingImpl.hpp
index 6e75b6f42d565a481021bdbba17ee0e637f4707e..37549349b9f5ffbf443d976135db05b4cec209b7 100644
--- a/include/aidge/backend/cpu/operator/ScalingImpl.hpp
+++ b/include/aidge/backend/cpu/operator/ScalingImpl.hpp
@@ -18,16 +18,17 @@
#include "aidge/utils/Types.h"
#include <memory>
#include <vector>
+#include <array>
namespace Aidge {
// class Scaling_Op;
// compute kernel registry for forward and backward
class ScalingImplForward_cpu
- : public Registrable<ScalingImplForward_cpu, std::tuple<DataType, DataType>, void(const Scaling_Op::Parameters&, std::size_t, const void*, void*)> {
+ : public Registrable<ScalingImplForward_cpu, std::tuple<DataType, DataType>, void(const Scaling_Op::Attrs&, std::size_t, const void*, void*)> {
};
class ScalingImplBackward_cpu
- : public Registrable<ScalingImplBackward_cpu, std::tuple<DataType, DataType>, void(const Scaling_Op::Parameters&, std::size_t, const void*, void*)> {
+ : public Registrable<ScalingImplBackward_cpu, std::tuple<DataType, DataType>, void(const Scaling_Op::Attrs&, std::size_t, const void*, void*)> {
};
class ScalingImpl_cpu : public OperatorImpl {
diff --git a/include/aidge/backend/cpu/operator/ScalingImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ScalingImpl_forward_kernels.hpp
index c5b06290ee04ecf9759f418cd26d83e889fcc84e..8fe13bce3a4c470d77b083603d3b889a46fda71f 100644
--- a/include/aidge/backend/cpu/operator/ScalingImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ScalingImpl_forward_kernels.hpp
@@ -18,14 +18,14 @@
namespace Aidge {
template <class I, class O>
-void ScalingImpl_cpu_forward_kernel(const Scaling_Op::Parameters& params,
+void ScalingImpl_cpu_forward_kernel(const Scaling_Op::Attrs& attrs,
std::size_t inputLenght,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
- I scalingFactor = static_cast<I>(std::get<0>(params));
+ const I& scalingFactor = static_cast<const I&>(std::get<0>(attrs));
for (std::size_t i = 0; i < inputLenght; ++i) {
output[i] = input[i] * scalingFactor;
diff --git a/src/operator/AvgPoolingImpl.cpp b/src/operator/AvgPoolingImpl.cpp
index 6c434a5c38853a1dee66db5be95b6b1bfdde8162..b1f82bbb4323a402d698d772966409e1a8f7224b 100644
--- a/src/operator/AvgPoolingImpl.cpp
+++ b/src/operator/AvgPoolingImpl.cpp
@@ -70,7 +70,7 @@ void Aidge::AvgPoolingImpl2D_cpu::forward() {
Registrar<AvgPoolingImpl2DForward_cpu>::create({mOp.getInput(0)->dataType(), mOp.getOutput(0)->dataType()});
// Call kernel
- kernelFunc(mOp.getParams(),
+ kernelFunc(mOp.getStaticAttributes(),
mOp.getInput(0)->dims<4>(),
mOp.getInput(0)->getImpl()->rawPtr(),
mOp.getOutput(0)->getImpl()->rawPtr());
diff --git a/src/operator/BatchNormImpl.cpp b/src/operator/BatchNormImpl.cpp
index a0d4d032ded9ede1b2dba307aa967af330167d25..90ee2b7a2361166109568e317a1788137150a8d1 100644
--- a/src/operator/BatchNormImpl.cpp
+++ b/src/operator/BatchNormImpl.cpp
@@ -76,7 +76,7 @@ void Aidge::BatchNormImpl2D_cpu::forward() {
mOp.getOutput(0)->dataType()});
// Call kernel
- kernelFunc(mOp.getParams(),
+ kernelFunc(mOp.getStaticAttributes(),
mOp.getInput(0)->dims<4>(),
mOp.getInput(0)->getImpl()->rawPtr(),
mOp.getInput(1)->getImpl()->rawPtr(),
diff --git a/src/operator/ConvDepthWiseImpl.cpp b/src/operator/ConvDepthWiseImpl.cpp
index 3e920cf68366b82bce8df29c8aea0c838e6a1364..7801f64ef46ced22d95af47b8b0e8cc9888a81da 100644
--- a/src/operator/ConvDepthWiseImpl.cpp
+++ b/src/operator/ConvDepthWiseImpl.cpp
@@ -77,7 +77,7 @@ void Aidge::ConvDepthWiseImpl2D_cpu::forward() {
mOp.getInput(2)->dataType(), mOp.getOutput(0)->dataType()});
// Call kernel
- kernelFunc(mOp.getParams(), std::static_pointer_cast<Tensor>(mOp.getInput(0))->dims<4>(),
+ kernelFunc(mOp.getStaticAttributes(), std::static_pointer_cast<Tensor>(mOp.getInput(0))->dims<4>(),
mOp.getInput(0)->getImpl()->rawPtr(), mOp.getInput(1)->getImpl()->rawPtr(),
mOp.getInput(2)->getImpl()->rawPtr(), mOp.getOutput(0)->getImpl()->rawPtr());
}
diff --git a/src/operator/ConvImpl.cpp b/src/operator/ConvImpl.cpp
index b4ddf80929923a9c2c5998ac8614ebb0d3afe000..edab4432fd5792f27ea158f265641855532d6d0b 100644
--- a/src/operator/ConvImpl.cpp
+++ b/src/operator/ConvImpl.cpp
@@ -75,7 +75,7 @@ void Aidge::ConvImpl2D_cpu::forward() {
mOp.getInput(2)->dataType(), mOp.getOutput(0)->dataType()});
// Call kernel
- kernelFunc(mOp.getParams(), std::static_pointer_cast<Tensor>(mOp.getInput(0))->dims<4>(),
+ kernelFunc(mOp.getStaticAttributes(), std::static_pointer_cast<Tensor>(mOp.getInput(0))->dims<4>(),
mOp.getInput(0)->getImpl()->rawPtr(), mOp.getInput(1)->getImpl()->rawPtr(),
mOp.getInput(2)->getImpl()->rawPtr(), mOp.getOutput(0)->getImpl()->rawPtr());
diff --git a/src/operator/FCImpl.cpp b/src/operator/FCImpl.cpp
index 086902be0ab1c2027a8c62c143bc27921e5e9e1b..3cf1ccf6e951ea05521ef67c99a3e628e0f620f5 100644
--- a/src/operator/FCImpl.cpp
+++ b/src/operator/FCImpl.cpp
@@ -98,7 +98,7 @@ void Aidge::FCImpl_cpu::forward()
// Call kernel
// if (mOp.getInput(0)->nbDims() == 4) {
// kernelFunc(
- // mOp.getParams(),
+ // mOp.getStaticAttributes(),
// std::static_pointer_cast<Tensor>(mOp.getInput(0))->dims<4>(),
// mOp.getInput(0)->getImpl()->rawPtr(),
// mOp.mInputs[1]->getImpl()->rawPtr(),
@@ -107,7 +107,7 @@ void Aidge::FCImpl_cpu::forward()
// }
// else
kernelFunc(
- mOp.getParams(),
+ mOp.getStaticAttributes(),
mOp.getInput(0)->dims()[0],
mOp.getInput(0)->sizeM1(),
mOp.getInput(0)->getImpl()->rawPtr(),
diff --git a/src/operator/LeakyReLUImpl.cpp b/src/operator/LeakyReLUImpl.cpp
index f6a44d381081c7c7f1dcbbf02d91212168cc07aa..316d3641bb960ed8850a94f40186b77cc8522b58 100644
--- a/src/operator/LeakyReLUImpl.cpp
+++ b/src/operator/LeakyReLUImpl.cpp
@@ -65,7 +65,7 @@ void Aidge::LeakyReLUImpl_cpu::forward() {
mOp.getOutput(0)->dataType()});
// Call kernel
- kernelFunc(mOp.getParams(),
+ kernelFunc(mOp.getStaticAttributes(),
std::static_pointer_cast<Tensor>(mOp.getInput(0))->size(),
mOp.getInput(0)->getImpl()->rawPtr(),
mOp.getOutput(0)->getImpl()->rawPtr());
diff --git a/src/operator/ScalingImpl.cpp b/src/operator/ScalingImpl.cpp
index c6a96f3bc8ea865da1c31ddfadff67c1e8556ad5..84cd6ee33a8316a24bae472c74c039dabe0afba3 100644
--- a/src/operator/ScalingImpl.cpp
+++ b/src/operator/ScalingImpl.cpp
@@ -68,7 +68,7 @@ void Aidge::ScalingImpl_cpu::forward() {
mOp.getOutput(0)->dataType()});
// Call kernel
- kernelFunc(mOp.getParams(),
+ kernelFunc(mOp.getStaticAttributes(),
std::static_pointer_cast<Tensor>(mOp.getInput(0))->size(),
mOp.getInput(0)->getImpl()->rawPtr(),
mOp.getOutput(0)->getImpl()->rawPtr());
diff --git a/unit_tests/operator/Test_LeakyReLUImpl.cpp b/unit_tests/operator/Test_LeakyReLUImpl.cpp
index 7096962e196c2ace4abf2b0b14aca8dfa37d3441..d5bd91ff75404a7b928c8919c64e06315b78206f 100644
--- a/unit_tests/operator/Test_LeakyReLUImpl.cpp
+++ b/unit_tests/operator/Test_LeakyReLUImpl.cpp
@@ -153,7 +153,7 @@ TEST_CASE("[cpu/operator] LeakyReLU(forward)") {
REQUIRE(*myLeakyReLU->getOperator()->getOutput(0) == *expectedOutput);
}
- SECTION("Test construction parameter: negative_slop") {
+ SECTION("Test construction attribute: negative_slop") {
std::shared_ptr<Tensor> input0 = std::make_shared<Tensor>(Array1D<float,10> {
{0.0f, 1.0f, 2.0f,-3.0f, 4.0f,-5.0f,-6.0f, 7.0f, 8.0f, 9.0f}
});