diff --git a/include/aidge/backend/cpu/operator/AbsImpl_kernels.hpp b/include/aidge/backend/cpu/operator/AbsImpl_kernels.hpp
index 16e5f9dee26a6f8b760e14a1ad66a40d8f0f7e93..e6474cf2cca459601f8a7a564ce45742e74f01b5 100644
--- a/include/aidge/backend/cpu/operator/AbsImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/AbsImpl_kernels.hpp
@@ -20,14 +20,14 @@
namespace Aidge {
template <class I, class O>
-void AbsImpl_cpu_forward_kernel(std::size_t inputLenght,
+void AbsImpl_cpu_forward_kernel(std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
output[i] = std::abs(input[i]);
}
}
diff --git a/include/aidge/backend/cpu/operator/AtanImpl_kernels.hpp b/include/aidge/backend/cpu/operator/AtanImpl_kernels.hpp
index 2a786339503354514416705b61cfedfcc0b7c321..141e5b60ec3680c6c66ef6552704a57a41a8cf27 100644
--- a/include/aidge/backend/cpu/operator/AtanImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/AtanImpl_kernels.hpp
@@ -20,20 +20,20 @@
namespace Aidge {
template <class I, class O>
-void AtanImpl_cpu_forward_kernel(std::size_t inputLenght,
+void AtanImpl_cpu_forward_kernel(std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
- for (size_t i = 0; i < inputLenght; ++i) {
+ for (size_t i = 0; i < inputLength; ++i) {
output[i] = static_cast<O>(atan(input[i]));
}
}
template <class O, class GI, class GO>
-void AtanImpl_cpu_backward_kernel(const std::size_t inputLenght,
+void AtanImpl_cpu_backward_kernel(const std::size_t inputLength,
const void* output_, const void* grad_output_,
void* grad_input_) {
const O* output = static_cast<const O*>(output_);
@@ -41,7 +41,7 @@ void AtanImpl_cpu_backward_kernel(const std::size_t inputLenght,
GI* grad_input = static_cast<GI*>(grad_input_);
// Apply the derivative of atan for each element in the input array
- for (size_t i = 0; i < inputLenght; ++i) {
+ for (size_t i = 0; i < inputLength; ++i) {
// dx = dy * (1 / (1 + x^2))
grad_input[i] = grad_output[i] * static_cast<O>(1.0 / (1.0 + output[i] * output[i]));
}
diff --git a/include/aidge/backend/cpu/operator/ErfImpl_kernels.hpp b/include/aidge/backend/cpu/operator/ErfImpl_kernels.hpp
index 02041f55ce9a1b2476db575b40340b1bb6517ce1..709f4a6ff208aa384478f3787710fdb010835bdf 100644
--- a/include/aidge/backend/cpu/operator/ErfImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ErfImpl_kernels.hpp
@@ -20,14 +20,14 @@
namespace Aidge {
template <class I, class O>
-void ErfImpl_cpu_forward_kernel(std::size_t inputLenght,
+void ErfImpl_cpu_forward_kernel(std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
output[i] = std::erf(input[i]);
}
}
diff --git a/include/aidge/backend/cpu/operator/HeavisideImpl_kernels.hpp b/include/aidge/backend/cpu/operator/HeavisideImpl_kernels.hpp
index 3fd6ca7de348ff18e75b2a88281d4db980b58774..06d7fff8776d342da0467ad7f9d6759f45202151 100644
--- a/include/aidge/backend/cpu/operator/HeavisideImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/HeavisideImpl_kernels.hpp
@@ -23,14 +23,14 @@
namespace Aidge {
template <class I, class O>
-void HeavisideImplCpuForwardKernel(std::size_t inputLenght,
+void HeavisideImplCpuForwardKernel(std::size_t inputLength,
const void *input_,
void *output_,
const float value) {
const I *input = static_cast<const I *>(input_);
O *output = static_cast<O *>(output_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
output[i] = (input[i] > 0) ? 1 : (input[i] == 0 ? value : 0);
}
}
diff --git a/include/aidge/backend/cpu/operator/LeakyReLUImpl_kernels.hpp b/include/aidge/backend/cpu/operator/LeakyReLUImpl_kernels.hpp
index bc856f703aee8ba422887d43cb96db2132fc4603..7afd8298329a285ce72106dbc766546076cfb37e 100644
--- a/include/aidge/backend/cpu/operator/LeakyReLUImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/LeakyReLUImpl_kernels.hpp
@@ -19,7 +19,7 @@
namespace Aidge {
template <class I, class O>
void LeakyReLUImpl_cpu_forward_kernel(const float negativeSlope_,
- std::size_t inputLenght,
+ std::size_t inputLength,
const void* input_,
void* output_) {
@@ -27,14 +27,14 @@ void LeakyReLUImpl_cpu_forward_kernel(const float negativeSlope_,
O* output = static_cast<O*>(output_);
const I negativeSlope = static_cast<const I>(negativeSlope_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
output[i] = (input[i] >= 0) ? input[i] : input[i] * negativeSlope;
}
}
template <class I, class O>
void LeakyReLUImpl_cpu_backward_kernel(const float negativeSlope_,
- std::size_t inputLenght,
+ std::size_t inputLength,
const void* input_,
void* output_) {
@@ -42,7 +42,7 @@ void LeakyReLUImpl_cpu_backward_kernel(const float negativeSlope_,
O* output = static_cast<O*>(output_);
const I negativeSlope = static_cast<const I>(negativeSlope_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
output[i] = (input[i] > 0) ? input[i] : negativeSlope*input[i];
}
}
diff --git a/include/aidge/backend/cpu/operator/LnImpl_kernels.hpp b/include/aidge/backend/cpu/operator/LnImpl_kernels.hpp
index b30b05bb806de08d4e70c67e66979fb3138980df..ee2864b6a650214bf1705a158b0ec3e88a7b7b16 100755
--- a/include/aidge/backend/cpu/operator/LnImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/LnImpl_kernels.hpp
@@ -18,7 +18,7 @@
namespace Aidge {
template <class I, class O>
-void LnImpl_cpu_forward_kernel(std::size_t inputLenght,
+void LnImpl_cpu_forward_kernel(std::size_t inputLength,
const void* input_,
void* output_) {
@@ -26,8 +26,8 @@ void LnImpl_cpu_forward_kernel(std::size_t inputLenght,
O* output = static_cast<O*>(output_);
const float eps = 1.0e-20f;
-//#pragma omp parallel for if (inputLenght > 1024)
- for (std::size_t i = 0; i < inputLenght; ++i) {
+//#pragma omp parallel for if (inputLength > 1024)
+ for (std::size_t i = 0; i < inputLength; ++i) {
if (input[i] > I(eps)) {
output[i] = std::log(input[i]);
} else {
@@ -37,7 +37,7 @@ void LnImpl_cpu_forward_kernel(std::size_t inputLenght,
}
template <class I, class GI, class GO>
-void LnImpl_cpu_backward_kernel(const std::size_t inputLenght,
+void LnImpl_cpu_backward_kernel(const std::size_t inputLength,
const void* input_, const void* grad_output_,
void* grad_input_) {
@@ -46,7 +46,7 @@ void LnImpl_cpu_backward_kernel(const std::size_t inputLenght,
GI* grad_input = static_cast<GI*>(grad_input_);
const float eps = 1.0e-20f;
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
if (input[i] > I(eps)) {
grad_input[i] = grad_output[i] / input[i];
} else {
diff --git a/include/aidge/backend/cpu/operator/ReLUImpl_kernels.hpp b/include/aidge/backend/cpu/operator/ReLUImpl_kernels.hpp
index e39e9b7decd91e392c5db7e9e9bc4ed0f366829d..bb5d7cc35c18db29eb734b5f868b311d3a734bdc 100644
--- a/include/aidge/backend/cpu/operator/ReLUImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ReLUImpl_kernels.hpp
@@ -26,27 +26,27 @@
namespace Aidge {
// Kernels
template <class I, class O>
-void ReLUImpl_cpu_forward_kernel(std::size_t inputLenght,
+void ReLUImpl_cpu_forward_kernel(std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
-//#pragma omp parallel for if (inputLenght > 1024)
- for (std::size_t i = 0; i < inputLenght; ++i) {
+//#pragma omp parallel for if (inputLength > 1024)
+ for (std::size_t i = 0; i < inputLength; ++i) {
output[i] = (input[i] > 0) ? input[i] : 0;
}
}
template <class I, class GI, class GO>
-void ReLUImpl_cpu_backward_kernel(const std::size_t inputLenght,
+void ReLUImpl_cpu_backward_kernel(const std::size_t inputLength,
const void* input_, const void* grad_output_,
void* grad_input_) {
const I* input = static_cast<const I*>(input_);
const GO* grad_output = static_cast<const GO*>(grad_output_);
GI* grad_input = static_cast<GI*>(grad_input_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
grad_input[i] = (input[i] > 0) ? grad_output[i] : 0;
}
}
diff --git a/include/aidge/backend/cpu/operator/RoundImpl_kernels.hpp b/include/aidge/backend/cpu/operator/RoundImpl_kernels.hpp
index ba9c63bc3618ba81e238d7721147c894b54cf832..7ac4319b2b10241dda2617db7df40a7947eb17ff 100644
--- a/include/aidge/backend/cpu/operator/RoundImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/RoundImpl_kernels.hpp
@@ -21,14 +21,14 @@
namespace Aidge {
template <class I, class O>
-void RoundImpl_cpu_forward_kernel(const std::size_t inputLenght,
+void RoundImpl_cpu_forward_kernel(const std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
//std::round would not work since it doesn't follow the halves rules (See ONNX Round)
output[i] = static_cast<O>(std::nearbyint(static_cast<float>(input[i])));
}
diff --git a/include/aidge/backend/cpu/operator/ScalingImpl_kernels.hpp b/include/aidge/backend/cpu/operator/ScalingImpl_kernels.hpp
index c758c9cf39e76bb370c6d03c28e3a670c280eefc..f9ca00b73193c9dbd54d286125da1f084ae25587 100644
--- a/include/aidge/backend/cpu/operator/ScalingImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ScalingImpl_kernels.hpp
@@ -76,14 +76,14 @@ template <class I, class O>
void ScalingImpl_cpu_forward_kernel(const float scalingFactor,
const std::size_t quantizedNbBits,
const bool isOutputUnsigned,
- std::size_t inputLenght,
+ std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
output[i] = static_cast<O>(input[i] * static_cast<I>(scalingFactor));
if(quantizedNbBits > 0) {
diff --git a/include/aidge/backend/cpu/operator/SigmoidImpl_kernels.hpp b/include/aidge/backend/cpu/operator/SigmoidImpl_kernels.hpp
index dfd71ce0a878efbeb779f3a67ad4ccc762bb8363..83ad4575f1aae1cee48a0ba33f2455204b26ac9a 100644
--- a/include/aidge/backend/cpu/operator/SigmoidImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/SigmoidImpl_kernels.hpp
@@ -18,15 +18,15 @@
namespace Aidge {
template <class I, class O>
-void SigmoidImpl_cpu_forward_kernel(std::size_t inputLenght,
+void SigmoidImpl_cpu_forward_kernel(std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
-//#pragma omp parallel for if (inputLenght > 1024)
- for (std::size_t i = 0; i < inputLenght; ++i) {
+//#pragma omp parallel for if (inputLength > 1024)
+ for (std::size_t i = 0; i < inputLength; ++i) {
if (input[i] > I(0)) {
output[i] = O(1) / (O(1) + std::exp(-input[i]));
} else {
@@ -36,13 +36,13 @@ void SigmoidImpl_cpu_forward_kernel(std::size_t inputLenght,
}
template <class O, class GI, class GO>
-void SigmoidImpl_cpu_backward_kernel(const std::size_t inputLenght,
+void SigmoidImpl_cpu_backward_kernel(const std::size_t inputLength,
const void* output_, const void* grad_output_,
void* grad_input_) {
const O* output = static_cast<const O*>(output_);
const GO* grad_output = static_cast<const GO*>(grad_output_);
GI* grad_input = static_cast<GI*>(grad_input_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
grad_input[i] = output[i] * (O(1) - output[i]) * grad_output[i];
}
}
diff --git a/include/aidge/backend/cpu/operator/SqrtImpl_kernels.hpp b/include/aidge/backend/cpu/operator/SqrtImpl_kernels.hpp
index 0464119cad60742bc58c79da984b30776bc7932f..1ce1ef9b675ac6924e5e543f2bdfa4a79e8a8b30 100644
--- a/include/aidge/backend/cpu/operator/SqrtImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/SqrtImpl_kernels.hpp
@@ -21,27 +21,27 @@
namespace Aidge {
template <class I, class O>
-void SqrtImpl_cpu_forward_kernel(const std::size_t inputLenght,
+void SqrtImpl_cpu_forward_kernel(const std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
output[i] = static_cast<O>(std::sqrt(static_cast<float>(input[i])));
}
}
template <class I, class O>
-void SqrtImpl_cpu_backward_kernel(const std::size_t inputLenght,
+void SqrtImpl_cpu_backward_kernel(const std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
output[i] = static_cast<O>(0.5/(std::sqrt(static_cast<float>(input[i]))));
}
}
diff --git a/include/aidge/backend/cpu/operator/TanhImpl_kernels.hpp b/include/aidge/backend/cpu/operator/TanhImpl_kernels.hpp
index fdcac210484b11f2220dcc2a6813efed503d1913..49cfe9cbe97320e53b982e388a19e23370423d26 100644
--- a/include/aidge/backend/cpu/operator/TanhImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/TanhImpl_kernels.hpp
@@ -18,27 +18,27 @@
namespace Aidge {
template <class I, class O>
-void TanhImpl_cpu_forward_kernel(std::size_t inputLenght,
+void TanhImpl_cpu_forward_kernel(std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
-//#pragma omp parallel for if (inputLenght > 1024)
- for (std::size_t i = 0; i < inputLenght; ++i) {
+//#pragma omp parallel for if (inputLength > 1024)
+ for (std::size_t i = 0; i < inputLength; ++i) {
output[i] = std::tanh(input[i]);
}
}
template <class O, class GI, class GO>
-void TanhImpl_cpu_backward_kernel(const std::size_t inputLenght,
+void TanhImpl_cpu_backward_kernel(const std::size_t inputLength,
const void* output_, const void* grad_output_,
void* grad_input_) {
const O* output = static_cast<const O*>(output_);
const GO* grad_output = static_cast<const GO*>(grad_output_);
GI* grad_input = static_cast<GI*>(grad_input_);
- for (std::size_t i = 0; i < inputLenght; ++i) {
+ for (std::size_t i = 0; i < inputLength; ++i) {
grad_input[i] = (O(1) - output[i] * output[i]) * grad_output[i];
}
}