diff --git a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp
index c39cf9cccb41905653c508b985cf27253e20f686..28ed8969aa415ab4151d038869594376480eba43 100644
--- a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp
@@ -150,42 +150,93 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
// weight (outCh, ch, kernelX, kernelY)
// does not take Dilation attribute into account
using signedsize = std::make_signed<std::size_t>::type;
- for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
- for (std::size_t ch = 0; ch < inputDims[1]; ++ch) {
- const std::size_t oIndex = (ch + batch*inputDims[1]) * 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*inputDims[1]) * inputDims[2] * inputDims[3];
- const std::size_t wIndex = ch * kernelDims[0] * kernelDims[1];
- for (std::size_t ox = 0; ox < oxSize; ++ox) {
- // const signedsize difx = static_cast<signedsize>(- ox * strideDims[0]);
- // const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
- // const std::size_t sxMax = (static_cast<signedsize>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : inputDims[2] + difx);
- const std::size_t sxMin = 0;
- const std::size_t sxMax = dilated_kernel_x;
- for (std::size_t oy = 0; oy < oySize; ++oy) {
- // const signedsize dify = static_cast<signedsize>(- oy * strideDims[1]);
- // const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
- // const std::size_t syMax = (static_cast<signedsize>(inputDims[3]) + dify) < 0 ? 0 : ((inputDims[3] + dify) > kernelDims[1] ? kernelDims[1] : inputDims[3] + dify);
- const std::size_t syMin = 0;
- const std::size_t syMax = dilated_kernel_y;
- const std::size_t oIndexFull = oIndex + ox*oySize + oy;
- const signedsize ix = static_cast<signedsize>(ox * strideDims[0]);
- const signedsize iy = static_cast<signedsize>(oy * strideDims[1]);
-
- if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
- output[oIndexFull] += (weights[wIndex + 0*kernelDims[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 0*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 0*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+2)] +
- weights[wIndex + 1*kernelDims[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 1*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 1*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+2)] +
- weights[wIndex + 2*kernelDims[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 2*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 2*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+2)]);
- } else {
- for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
- for (std::size_t sy = syMin; sy*dilationDims[1] < syMax; ++sy) {
+ const std::size_t outChannels_s = oxSize * oySize;
+
+ if (dilated_kernel_x ==3 && dilated_kernel_y == 3) {
+ for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
+ for (std::size_t ch = 0; ch < inputDims[1]; ++ch) {
+
+ B biasVal = (biases != nullptr) ? biases[ch] : B(0);
+
+ std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
+ const std::size_t wIndex = ch * 9;
+
+ if (strideDims[0] == 1 && strideDims[1]==1) {
+ for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex-=inputDims[3]) {
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] = biasVal + weights[wIndex+0]*input[iIndex+oy]+weights[wIndex+1]*input[iIndex+oy+1]+weights[wIndex+2]*input[iIndex+oy+2];
+ }
+ iIndex+=inputDims[3];
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+3]*input[iIndex+oy]+weights[wIndex+4]*input[iIndex+oy+1]+weights[wIndex+5]*input[iIndex+oy+2];
+ }
+ iIndex+=inputDims[3];
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+6]*input[iIndex+oy]+weights[wIndex+7]*input[iIndex+oy+1]+weights[wIndex+8]*input[iIndex+oy+2];
+ }
+ }
+ } else {
+ for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex-=strideDims[0]*inputDims[3]) {
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+0]*input[iIndex+oy]+weights[wIndex+1]*input[iIndex+oy+strideDims[0]]+weights[wIndex+2]*input[iIndex+oy+strideDims[0]*2];
+ }
+ iIndex+=strideDims[0]*inputDims[3];
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+3]*input[iIndex+oy]+weights[wIndex+4]*input[iIndex+oy+strideDims[0]]+weights[wIndex+5]*input[iIndex+oy+strideDims[0]*2];
+ }
+ iIndex+=strideDims[0]*inputDims[3];
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+6]*input[iIndex+oy]+weights[wIndex+7]*input[iIndex+oy+strideDims[0]]+weights[wIndex+8]*input[iIndex+oy+strideDims[0]*2];
+ }
+ }
+ }
+ output += outChannels_s;
+ }
+ }
+ } else if (dilated_kernel_x == 1 && dilated_kernel_y == 1) {
+ std::size_t index = 0;
+ for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
+ for (std::size_t ch = 0; ch < inputDims[1]; ++ch) {
+
+ B biasVal = (biases != nullptr) ? biases[ch] : B(0);
+
+ const std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
+ const std::size_t wIndex = ch;
+
+ if (strideDims[0] == 1 && strideDims[1] == 1) {
+ for (; index < iIndex + oxSize*oySize; ++index) {
+ output[index] = biasVal + weights[wIndex] * input[index];
+ }
+ } else {
+ std::size_t oIndex = (ch + batch*inputDims[1]) * oxSize * oySize;
+ for (std::size_t ox = 0; ox < oxSize; ++ox, oIndex+=oySize) {
+ index = iIndex + strideDims[0]*inputDims[3];
+ for (std::size_t oy = 0, iy = 0; oy < oySize; ++oy, iy+=strideDims[1]) {
+ output[oIndex + oy] += weights[wIndex]*input[index+iy];
+ }
+ }
+ }
+ }
+ }
+ } else {
+ for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
+ for (std::size_t ch = 0; ch < inputDims[1]; ++ch) {
+
+ B biasVal = (biases != nullptr) ? biases[ch] : B(0);
+ std::fill(output, output+outChannels_s, biasVal);
+
+ const std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
+ const std::size_t wIndex = ch * kernelDims[0] * kernelDims[1];
+
+ for (std::size_t ox = 0; ox < oxSize; ++ox) {
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+
+ const std::size_t oIndexFull = ox*oySize + oy;
+ const signedsize ix = static_cast<signedsize>(ox * strideDims[0]);
+ const signedsize iy = static_cast<signedsize>(oy * strideDims[1]);
+
+ for (std::size_t sx = 0; sx*dilationDims[0] < dilated_kernel_x; ++sx) {
+ for (std::size_t sy = 0; sy*dilationDims[1] < dilated_kernel_y; ++sy) {
output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + sy] *
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx*dilationDims[0]))*inputDims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(sy*dilationDims[1]))];
}
@@ -193,10 +244,12 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
}
}
}
+ output += outChannels_s;
}
}
}
+
// Kernels registration to implementation entry point
REGISTRAR(ConvDepthWiseImpl2D_cpu,
{{DataType::Any, DataFormat::NCHW}, {DataType::Float32, DataFormat::NCHW}},
diff --git a/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp b/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
index e800c252676ec5247a776abf458f808289b278c8..b4abac19143d8222cf632757f1c9d4a532cb3661 100644
--- a/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
@@ -141,15 +141,15 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
O *output = static_cast<O *>(output_);
// output H size
+ const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
const std::size_t oxSize =
- static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - dilationDims[0]*(kernelDims[0] - 1) - 1 + strideDims[0]) /
+ static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - dilated_kernel_x + strideDims[0]) /
static_cast<float>(strideDims[0])));
- const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
// output W size
+ const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
const std::size_t oySize =
- static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[3] - dilationDims[1]*(kernelDims[1] - 1) - 1 + strideDims[1]) /
+ static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[3] - dilated_kernel_y + strideDims[1]) /
static_cast<float>(strideDims[1])));
- const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
// TODO: kernel computation
@@ -157,57 +157,108 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
// input (batch, inCh, Xin, Yin)
// weight (outCh, inCh, kernelX, kernelY)
// does not take Dilation attribute into account
+ const std::size_t outChannels_s = oxSize * oySize;
using signedsize = std::make_signed<std::size_t>::type;
- for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
- for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
- const std::size_t oIndex = (outCh + batch*outChannels) * oxSize * oySize;
- // If bias = nullptr, set B(0)
- B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
- std::fill(output + oIndex, output+(oIndex+oxSize*oySize), biasVal);
- for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
- const std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
- const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0] * kernelDims[1];
- for (std::size_t ox = 0; ox < oxSize; ++ox) {
- // const signedsize difx = static_cast<signedsize>(- ox * strideDims[0]);
- // const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
- // const std::size_t sxMax = (static_cast<signedsize>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : inputDims[2] + difx);
- const std::size_t sxMin = 0;
- const std::size_t sxMax = dilated_kernel_x;
- for (std::size_t oy = 0; oy < oySize; ++oy) {
- // const signedsize dify = static_cast<signedsize>(- oy * strideDims[1]);
- // const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
- // const std::size_t syMax = (static_cast<signedsize>(inputDims[3]) + dify) < 0 ? 0 : ((inputDims[3] + dify) > kernelDims[1] ? kernelDims[1] : inputDims[3] + dify);
- const std::size_t syMin = 0;
- const std::size_t syMax = dilated_kernel_y;
- const std::size_t oIndexFull = oIndex + ox*oySize + oy;
- const signedsize ix = static_cast<signedsize>(ox * strideDims[0]);
- const signedsize iy = static_cast<signedsize>(oy * strideDims[1]);
-
- if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
- output[oIndexFull] += (weights[wIndex + 0*kernelDims[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 0*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 0*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+2)] +
- weights[wIndex + 1*kernelDims[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 1*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 1*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+2)] +
- weights[wIndex + 2*kernelDims[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+0)] +
- weights[wIndex + 2*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
- weights[wIndex + 2*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+2)]);
- } else {
- for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
- for (std::size_t sy = syMin; sy*dilationDims[1] < syMax; ++sy) {
- output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + sy] *
- input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx*dilationDims[0]))*inputDims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(sy*dilationDims[1]))];
+
+ if (dilated_kernel_x == 3 && dilated_kernel_y == 3) {
+ for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
+ for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
+ // If bias = nullptr, set B(0)
+ B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
+ std::fill(output, output+outChannels_s, biasVal);
+ for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
+ std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
+ const std::size_t wIndex = (inCh + outCh*inputDims[1]) * 9;
+ if (strideDims[0] == 1 && strideDims[1]==1) {
+ for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex-=inputDims[3]) {
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+0]*input[iIndex+oy]+weights[wIndex+1]*input[iIndex+oy+1]+weights[wIndex+2]*input[iIndex+oy+2];
+ }
+ iIndex+=inputDims[3];
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+3]*input[iIndex+oy]+weights[wIndex+4]*input[iIndex+oy+1]+weights[wIndex+5]*input[iIndex+oy+2];
+ }
+ iIndex+=inputDims[3];
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+6]*input[iIndex+oy]+weights[wIndex+7]*input[iIndex+oy+1]+weights[wIndex+8]*input[iIndex+oy+2];
+ }
+ }
+ } else {
+ for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex-=strideDims[0]*inputDims[3]) {
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+0]*input[iIndex+oy]+weights[wIndex+1]*input[iIndex+oy+strideDims[0]]+weights[wIndex+2]*input[iIndex+oy+strideDims[0]*2];
+ }
+ iIndex+=strideDims[0]*inputDims[3];
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+3]*input[iIndex+oy]+weights[wIndex+4]*input[iIndex+oy+strideDims[0]]+weights[wIndex+5]*input[iIndex+oy+strideDims[0]*2];
+ }
+ iIndex+=strideDims[0]*inputDims[3];
+ for (std::size_t oy = 0; oy < oySize; ++oy) {
+ output[oIndex + oy] += weights[wIndex+6]*input[iIndex+oy]+weights[wIndex+7]*input[iIndex+oy+strideDims[0]]+weights[wIndex+8]*input[iIndex+oy+strideDims[0]*2];
+ }
+ }
+ }
+ }
+ output += outChannels_s;
+ }
+ }
+ } else if (dilated_kernel_x == 1 && dilated_kernel_y == 1) {
+ for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
+ for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
+ // If bias = nullptr, set B(0)
+ B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
+ std::fill(output, output+outChannels_s, biasVal);
+ for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
+ std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
+ const std::size_t wIndex = (inCh + outCh*inputDims[1]);
+ if (strideDims[0] == 1 && strideDims[1] == 1) {
+ for (std::size_t oIndex = 0; oIndex < oxSize*oySize; ++oIndex, ++iIndex) {
+ output[oIndex] += weights[wIndex] * input[iIndex];
+ }
+ } else {
+ for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex+=inputDims[3]*strideDims[0]) {
+ for (std::size_t oy = 0, iy = 0; oy < oySize; ++oy, iy+=strideDims[1]) {
+ output[oIndex + oy] += weights[wIndex+0]*input[iIndex+iy];
+ }
+ }
+ }
+ }
+ output += outChannels_s;
+ }
+ }
+ } else {
+ for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
+ for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
+ // If bias = nullptr, set B(0)
+ B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
+ std::fill(output, output+outChannels_s, biasVal);
+ for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
+ std::size_t iIndex_channel = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
+ const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0] * kernelDims[1];
+
+ // loop over each ouput line
+ for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex_channel+=inputDims[3]*strideDims[0]) {
+ // loop over associated input line
+ for (std::size_t ky = 0, ix = 0; ky < kernelDims[0]; ++ky, ix += inputDims[3]*dilationDims[0]) {
+ // loop over the entire line
+ for (std::size_t oy = 0, iy = 0; oy < oySize; ++oy, iy+=strideDims[1]) {
+ const std::size_t iIndex = iIndex_channel + ix + iy;
+ // loop over elements assosicated with one output
+ for (std::size_t kx = 0; kx < kernelDims[0]; ++kx) {
+ output[oIndex + oy] += weights[wIndex+kernelDims[0]*ky+kx]*input[iIndex+kx*dilationDims[1]];
}
}
}
}
}
+ output += outChannels_s;
}
}
}
}
+
+
// Kernels registration to implementation entry point
REGISTRAR(ConvImpl2D_cpu,
{{DataType::Any, DataFormat::NCHW}, {DataType::Float32, DataFormat::NCHW}},
diff --git a/unit_tests/operator/Test_ConvDepthWiseImpl.cpp b/unit_tests/operator/Test_ConvDepthWiseImpl.cpp
index e4e46de91bfbc38f41520f1edfc7e99d197e5c83..f1594ef5a21070803a7b86861eac513708ec03a2 100644
--- a/unit_tests/operator/Test_ConvDepthWiseImpl.cpp
+++ b/unit_tests/operator/Test_ConvDepthWiseImpl.cpp
@@ -11,144 +11,219 @@
#include <catch2/catch_test_macros.hpp>
#include <memory>
+#include <vector>
+#include "aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp"
+#include "aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/operator/ConvDepthWise.hpp"
-
-#include "aidge/backend/cpu.hpp"
+#include "aidge/utils/TensorUtils.hpp"
using namespace Aidge;
TEST_CASE("[cpu/operator] ConvDepthWise(forward)", "[ConvDepthWise][CPU]") {
- std::shared_ptr<Node> myCDW = ConvDepthWise(4, {3,3}, "mycdw");
- auto op = std::static_pointer_cast<OperatorTensor>(myCDW -> getOperator());
- std::shared_ptr<Tensor> myWeights = std::make_shared<Tensor>(Array4D<int,4,1,3,3> {
- {
- {{
- { 0, 1, 2},
- { 3, 4, 5},
- { 6, 7, 8}
-
- }},
- {{
- { 27, 28, 29},
- { 30, 31, 32},
- { 33, 34, 35}
-
- }},
- {{
- { 54, 55, 56},
- { 57, 58, 59},
- { 60, 61, 62}
- }},
- {{
- { 81, 82, 83},
- { 84, 85, 86},
- { 87, 88, 89}
- }}
- }
- });
- std::shared_ptr<Tensor> myBias = std::make_shared<Tensor>(Array1D<int,4> {{7,0,9,0}});
- std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,4,5,5> { //NCHW
- {
- {
- {{ 0, 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}}
- },
+ SECTION("k[3,3]") {
+ std::shared_ptr<Node> myCDW = ConvDepthWise(4, {3,3}, "mycdw");
+ auto op = std::static_pointer_cast<OperatorTensor>(myCDW -> getOperator());
+ std::shared_ptr<Tensor> myWeights = std::make_shared<Tensor>(Array4D<int,4,1,3,3> {
{
- {{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}}
+ {{
+ { 0, 1, 2},
+ { 3, 4, 5},
+ { 6, 7, 8}
+
+ }},
+ {{
+ { 27, 28, 29},
+ { 30, 31, 32},
+ { 33, 34, 35}
+
+ }},
+ {{
+ { 54, 55, 56},
+ { 57, 58, 59},
+ { 60, 61, 62}
+ }},
+ {{
+ { 81, 82, 83},
+ { 84, 85, 86},
+ { 87, 88, 89}
+ }}
}
- }
- });
- std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,4,3,3> {
- {
+ });
+ std::shared_ptr<Tensor> myBias = std::make_shared<Tensor>(Array1D<int,4> {{7,0,9,0}});
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,4,5,5> { //NCHW
{
- {{ 319, 355, 391},
- { 499, 535, 571},
- { 679, 715, 751}},
-
- {{ 8745, 9024, 9303},
- { 10140, 10419, 10698},
- { 11535, 11814, 12093}},
-
- {{ 29337, 29859, 30381},
- { 31947, 32469, 32991},
- { 34557, 35079, 35601}},
-
- {{ 62061, 62826, 63591},
- { 65886, 66651, 67416},
- { 69711, 70476, 71241}}
- },
+ {
+ {{ 0, 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}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,4,3,3> {
{
- {{ 3919, 3955, 3991},
- { 4099, 4135, 4171},
- { 4279, 4315, 4351}},
-
- {{ 36645, 36924, 37203},
- { 38040, 38319, 38598},
- { 39435, 39714, 39993}},
-
- {{ 81537, 82059, 82581},
- { 84147, 84669, 85191},
- { 86757, 87279, 87801}},
-
- {{138561, 139326, 140091},
- {142386, 143151, 143916},
- {146211, 146976, 147741}}
+ {
+ {{ 319, 355, 391},
+ { 499, 535, 571},
+ { 679, 715, 751}},
+
+ {{ 8745, 9024, 9303},
+ { 10140, 10419, 10698},
+ { 11535, 11814, 12093}},
+
+ {{ 29337, 29859, 30381},
+ { 31947, 32469, 32991},
+ { 34557, 35079, 35601}},
+
+ {{ 62061, 62826, 63591},
+ { 65886, 66651, 67416},
+ { 69711, 70476, 71241}}
+ },
+ {
+ {{ 3919, 3955, 3991},
+ { 4099, 4135, 4171},
+ { 4279, 4315, 4351}},
+
+ {{ 36645, 36924, 37203},
+ { 38040, 38319, 38598},
+ { 39435, 39714, 39993}},
+
+ {{ 81537, 82059, 82581},
+ { 84147, 84669, 85191},
+ { 86757, 87279, 87801}},
+
+ {{138561, 139326, 140091},
+ {142386, 143151, 143916},
+ {146211, 146976, 147741}}
+ }
}
- }
- });
- op -> associateInput(0, myInput);
- op -> associateInput(1, myWeights);
- op -> associateInput(2, myBias);
- op->setDataType(DataType::Int32);
- op->setBackend("cpu");
- myCDW -> forward();
- op -> getOutput(0) -> print();
- REQUIRE(*(op -> getOutput(0)) == *myOutput);
-
- // std::cout << static_cast<Tensor>((*op)["weight"])[0][0][0][0] << std::endl;
+ });
+ op -> associateInput(0, myInput);
+ op -> associateInput(1, myWeights);
+ op -> associateInput(2, myBias);
+ op->setDataType(DataType::Int32);
+ op->setBackend("cpu");
+ myCDW -> forward();
+ op -> getOutput(0) -> print();
+ REQUIRE(*(op -> getOutput(0)) == *myOutput);
+ }
+ SECTION("point-wise") {
+ ConvDepthWise_Op<2> conv_op = ConvDepthWise_Op<2>({1,1});
+ std::shared_ptr<Tensor> weights = std::make_shared<Tensor>(std::vector<std::size_t>({3,1,1,1}));
+ weights -> setBackend("cpu");
+ std::shared_ptr<Tensor> biases = std::make_shared<Tensor>(std::vector<std::size_t>({3}));
+ biases -> setBackend("cpu");
+ std::shared_ptr<Tensor> input = std::make_shared<Tensor>(std::vector<std::size_t>({2,3,5,5}));
+ input -> setBackend("cpu");
+ std::shared_ptr<Tensor> expected_output = std::make_shared<Tensor>(std::vector<std::size_t>({2,3,5,5}));
+ expected_output -> setBackend("cpu");
+
+ float weighst_array[3] {-0.0045, -0.4223, -0.9452};
+ weights->getImpl()->setRawPtr(weighst_array, 3);
+
+ float biases_array[3] {-0.8595, 0.7062, -0.0062};
+ biases->getImpl()->setRawPtr(biases_array, 3);
+
+ float input_array[2*3*5*5] {
+ 0.6581, 0.2509, 0.2660, 0.8270, 0.8040, 0.3147, 0.5028, 0.2591, 0.8585,
+ 0.7762, 0.9972, 0.0305, 0.1202, 0.2682, 0.9306, 0.7927, 0.1494, 0.0678,
+ 0.5550, 0.4132, 0.4742, 0.6199, 0.1802, 0.6350, 0.2539, 0.5594, 0.0143,
+ 0.8656, 0.7105, 0.1420, 0.2464, 0.7883, 0.5715, 0.7642, 0.5492, 0.6628,
+ 0.4922, 0.7941, 0.8421, 0.7914, 0.0237, 0.8081, 0.0174, 0.6018, 0.7402,
+ 0.3770, 0.8786, 0.3651, 0.5355, 0.4267, 0.4457, 0.6756, 0.9631, 0.0145,
+ 0.4470, 0.5202, 0.2675, 0.5815, 0.3487, 0.3457, 0.7179, 0.0518, 0.1520,
+ 0.0573, 0.9219, 0.3615, 0.0866, 0.5237, 0.4725, 0.2565, 0.8726, 0.6434,
+ 0.6875, 0.2919, 0.3355, 0.1886, 0.1749, 0.0785, 0.4091, 0.1907, 0.4664,
+ 0.2738, 0.4784, 0.7807, 0.0687, 0.3091, 0.4557, 0.2277, 0.2424, 0.8691,
+ 0.1893, 0.2918, 0.5691, 0.1926, 0.2866, 0.0097, 0.5445, 0.5085, 0.1110,
+ 0.7099, 0.8927, 0.6182, 0.2538, 0.8694, 0.7872, 0.3196, 0.0710, 0.2888,
+ 0.0403, 0.1670, 0.6840, 0.7323, 0.4861, 0.3390, 0.1096, 0.5070, 0.3872,
+ 0.7473, 0.6224, 0.6910, 0.7530, 0.0149, 0.0866, 0.9022, 0.5027, 0.3849,
+ 0.5255, 0.1977, 0.0570, 0.9581, 0.5461, 0.4623, 0.0101, 0.2362, 0.5922,
+ 0.8398, 0.1497, 0.5160, 0.2862, 0.5931, 0.9728, 0.1353, 0.7790, 0.9137,
+ 0.9351, 0.4036, 0.7638, 0.3873, 0.0494, 0.7450};
+ input->getImpl()->setRawPtr(input_array, 2*3*5*5);
+
+ float expected_output_array[2*3*5*5] {
+ -0.8624, -0.8606, -0.8607, -0.8632, -0.8631, -0.8609, -0.8617, -0.8606,
+ -0.8633, -0.8629, -0.8639, -0.8596, -0.8600, -0.8607, -0.8636, -0.8630,
+ -0.8601, -0.8598, -0.8620, -0.8613, -0.8616, -0.8622, -0.8603, -0.8623,
+ -0.8606, 0.4700, 0.7002, 0.3407, 0.4062, 0.6463, 0.6022, 0.3733,
+ 0.4649, 0.3835, 0.4743, 0.4263, 0.4984, 0.3709, 0.3506, 0.3720,
+ 0.6962, 0.3650, 0.6989, 0.4521, 0.3936, 0.5470, 0.3352, 0.5520,
+ 0.4801, 0.5260, -0.4274, -0.6447, -0.9165, -0.0199, -0.4287, -0.4979,
+ -0.2590, -0.5559, -0.3358, -0.3329, -0.6847, -0.0552, -0.1499, -0.0603,
+ -0.8776, -0.3479, -0.0881, -0.5011, -0.4528, -0.2486, -0.8309, -0.6143,
+ -0.6561, -0.2821, -0.3233, -0.8603, -0.8603, -0.8598, -0.8613, -0.8603,
+ -0.8616, -0.8607, -0.8616, -0.8630, -0.8598, -0.8609, -0.8615, -0.8605,
+ -0.8606, -0.8634, -0.8603, -0.8608, -0.8620, -0.8603, -0.8608, -0.8595,
+ -0.8619, -0.8617, -0.8600, -0.8626, 0.3292, 0.4451, 0.5991, 0.3390,
+ 0.3738, 0.5712, 0.6762, 0.5843, 0.6892, 0.6357, 0.4174, 0.3969,
+ 0.5009, 0.5631, 0.6599, 0.4921, 0.5427, 0.3906, 0.4434, 0.4144,
+ 0.3882, 0.6999, 0.6697, 0.3252, 0.4939, -0.3700, -0.5029, -0.1931,
+ -0.0601, -0.9118, -0.5224, -0.4432, -0.0157, -0.2294, -0.5660, -0.7999,
+ -0.1477, -0.4939, -0.2767, -0.5668, -0.9257, -0.1341, -0.7425, -0.8698,
+ -0.8900, -0.3877, -0.7282, -0.3722, -0.0529, -0.7103};
+ expected_output->getImpl()->setRawPtr(expected_output_array, 2*3*5*5);
+
+ conv_op.associateInput(0, input);
+ conv_op.associateInput(1, weights);
+ conv_op.associateInput(2, biases);
+
+ conv_op.setBackend("cpu");
+ conv_op.setDataType(DataType::Float32);
+ conv_op.forwardDims();
+
+ conv_op.forward();
+
+ conv_op.getOutput(0)->print();
+
+ REQUIRE(approxEq<float>(*(conv_op.getOutput(0)),*expected_output, 1e-3f, 1e-4f));
+ }
}
\ No newline at end of file
diff --git a/unit_tests/operator/Test_ConvImpl.cpp b/unit_tests/operator/Test_ConvImpl.cpp
index b52085139294021de2fe9d72e173ad74db028ea3..e48d69c89eb0d6d52a834b3f32a41d8621fdd42b 100644
--- a/unit_tests/operator/Test_ConvImpl.cpp
+++ b/unit_tests/operator/Test_ConvImpl.cpp
@@ -15,6 +15,7 @@
#include "aidge/data/Tensor.hpp"
#include "aidge/operator/Conv.hpp"
+#include "aidge/utils/TensorUtils.hpp"
#include "aidge/backend/cpu.hpp"
@@ -153,7 +154,7 @@ TEST_CASE("[cpu/operator] Conv(forward)", "[Conv][CPU]") {
op->setDataType(DataType::Int32);
op->setBackend("cpu");
myConv->forward();
- // op->getOutput(0)->print();
+ op->getOutput(0)->print();
REQUIRE(*(op->getOutput(0)) == *myOutput);
}
SECTION("Point-wise") {
@@ -251,4 +252,147 @@ TEST_CASE("[cpu/operator] Conv(forward)", "[Conv][CPU]") {
REQUIRE(std::abs(resPtr[i]-expectedPtr[i]) < 0.00001);
}
}
+ SECTION("Strided and dilated Conv") {
+ std::shared_ptr<Node> myConv = Conv(3,4,{3,3}, "myconv", {3,3},{2,2});
+ auto op = std::static_pointer_cast<OperatorTensor>(myConv -> getOperator());
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<float,2,3,8,8> {
+ {{{
+ {0.0107F, 0.5076F, 0.2293F, 0.0486F, 0.7375F, 0.2637F, 0.9615F, 0.9138F},
+ {0.0678F, 0.5604F, 0.1940F, 0.0287F, 0.1029F, 0.2059F, 0.5058F, 0.9885F},
+ {0.9904F, 0.2890F, 0.4606F, 0.1055F, 0.9028F, 0.1654F, 0.6499F, 0.4775F},
+ {0.9499F, 0.4695F, 0.1713F, 0.0731F, 0.4913F, 0.8921F, 0.1782F, 0.1111F},
+ {0.2479F, 0.4669F, 0.1078F, 0.6153F, 0.0299F, 0.6484F, 0.2397F, 0.1814F},
+ {0.3779F, 0.9032F, 0.5651F, 0.3896F, 0.8439F, 0.6404F, 0.3813F, 0.0841F},
+ {0.5566F, 0.8950F, 0.1226F, 0.8881F, 0.9870F, 0.6256F, 0.6387F, 0.0628F},
+ {0.2857F, 0.0579F, 0.6247F, 0.1286F, 0.0951F, 0.1268F, 0.9510F, 0.3789F}},
+
+ {{0.7648F, 0.5340F, 0.1024F, 0.4098F, 0.9958F, 0.7941F, 0.1190F, 0.7328F},
+ {0.4532F, 0.6598F, 0.9146F, 0.1690F, 0.6041F, 0.7230F, 0.5719F, 0.9282F},
+ {0.2862F, 0.2329F, 0.7302F, 0.6717F, 0.1983F, 0.1876F, 0.4561F, 0.2126F},
+ {0.7849F, 0.0239F, 0.7977F, 0.5935F, 0.9958F, 0.4703F, 0.4612F, 0.1627F},
+ {0.6393F, 0.3544F, 0.8643F, 0.5039F, 0.8087F, 0.6521F, 0.5086F, 0.9331F},
+ {0.7749F, 0.9798F, 0.6820F, 0.7869F, 0.5144F, 0.2941F, 0.8137F, 0.4561F},
+ {0.6505F, 0.3974F, 0.6909F, 0.7019F, 0.2729F, 0.4240F, 0.0162F, 0.1536F},
+ {0.3529F, 0.8821F, 0.1812F, 0.3426F, 0.3472F, 0.0300F, 0.8841F, 0.8088F}},
+
+ {{0.5099F, 0.3323F, 0.1488F, 0.3424F, 0.1494F, 0.6225F, 0.8103F, 0.5995F},
+ {0.9198F, 0.5635F, 0.8908F, 0.9378F, 0.6689F, 0.3176F, 0.3755F, 0.3883F},
+ {0.0626F, 0.5309F, 0.0307F, 0.3955F, 0.2794F, 0.1420F, 0.4758F, 0.7558F},
+ {0.6154F, 0.5280F, 0.2318F, 0.3832F, 0.4435F, 0.3490F, 0.4043F, 0.5872F},
+ {0.3705F, 0.3848F, 0.2182F, 0.8332F, 0.4559F, 0.5310F, 0.4611F, 0.4236F},
+ {0.6141F, 0.8103F, 0.2260F, 0.9907F, 0.5615F, 0.4520F, 0.6949F, 0.0175F},
+ {0.3969F, 0.5021F, 0.0970F, 0.9937F, 0.9270F, 0.4302F, 0.2868F, 0.3891F},
+ {0.8693F, 0.5170F, 0.5348F, 0.2676F, 0.9769F, 0.3356F, 0.9427F, 0.3908F}}
+ },
+ {
+ {{0.4803F, 0.5223F, 0.6395F, 0.8402F, 0.4442F, 0.6377F, 0.7852F, 0.9063F},
+ {0.0361F, 0.0470F, 0.3104F, 0.6921F, 0.0543F, 0.4490F, 0.9541F, 0.7395F},
+ {0.3832F, 0.3828F, 0.2236F, 0.2068F, 0.4369F, 0.7443F, 0.6952F, 0.6394F},
+ {0.5309F, 0.8483F, 0.1991F, 0.9756F, 0.8969F, 0.7284F, 0.4657F, 0.5486F},
+ {0.8839F, 0.3260F, 0.6892F, 0.4074F, 0.9473F, 0.5526F, 0.4147F, 0.4786F},
+ {0.9674F, 0.0952F, 0.8379F, 0.2163F, 0.9420F, 0.4046F, 0.1339F, 0.5234F},
+ {0.4213F, 0.8392F, 0.3184F, 0.4576F, 0.9349F, 0.8267F, 0.0931F, 0.8009F},
+ {0.5570F, 0.5871F, 0.4175F, 0.5465F, 0.6679F, 0.9224F, 0.0049F, 0.9421F}},
+
+ {{0.3739F, 0.6230F, 0.7613F, 0.1337F, 0.8527F, 0.0557F, 0.6424F, 0.8463F},
+ {0.7179F, 0.5638F, 0.2457F, 0.4579F, 0.0487F, 0.8693F, 0.8216F, 0.0415F},
+ {0.1724F, 0.5108F, 0.9103F, 0.0850F, 0.0080F, 0.8927F, 0.7706F, 0.3600F},
+ {0.7751F, 0.8828F, 0.7872F, 0.4541F, 0.3181F, 0.1855F, 0.2486F, 0.0033F},
+ {0.5558F, 0.3500F, 0.6034F, 0.1763F, 0.7418F, 0.5190F, 0.5147F, 0.4090F},
+ {0.4476F, 0.1249F, 0.8116F, 0.9091F, 0.1738F, 0.6150F, 0.3285F, 0.3133F},
+ {0.5657F, 0.4447F, 0.5049F, 0.3425F, 0.7443F, 0.2718F, 0.2466F, 0.5586F},
+ {0.3684F, 0.7616F, 0.5165F, 0.9621F, 0.2864F, 0.7747F, 0.8110F, 0.7045F}},
+
+ {{0.4570F, 0.4577F, 0.0373F, 0.6084F, 0.4632F, 0.3472F, 0.9917F, 0.2011F},
+ {0.7921F, 0.2202F, 0.9525F, 0.7274F, 0.3357F, 0.0076F, 0.5786F, 0.3034F},
+ {0.6510F, 0.0798F, 0.2757F, 0.1738F, 0.3046F, 0.2197F, 0.3872F, 0.5650F},
+ {0.1532F, 0.3204F, 0.6094F, 0.3287F, 0.8903F, 0.9773F, 0.7950F, 0.2845F},
+ {0.2482F, 0.3395F, 0.8795F, 0.4325F, 0.1395F, 0.2457F, 0.2968F, 0.5424F},
+ {0.8636F, 0.7426F, 0.2151F, 0.6900F, 0.3938F, 0.0062F, 0.4980F, 0.4098F},
+ {0.8026F, 0.0464F, 0.2662F, 0.7835F, 0.8444F, 0.0688F, 0.8796F, 0.7625F},
+ {0.2764F, 0.5341F, 0.1773F, 0.6671F, 0.7555F, 0.5235F, 0.7142F, 0.9423F}}}}
+ });
+ std::shared_ptr<Tensor> myBias = std::make_shared<Tensor>(Array1D<float,4> {{ 0.1902F, -0.1789F, -0.0314F, -0.0589F}});
+ std::shared_ptr<Tensor> myWeights = std::make_shared<Tensor>(Array4D<float,4,3,3,3> { //NCHW
+ {
+ {
+ {{ 0.0039F, 0.1098F, -0.0834F},
+ {-0.0890F, 0.0725F, -0.1178F},
+ { 0.1056F, -0.0924F, -0.0574F}},
+ {{ 0.0070F, -0.0730F, -0.0674F},
+ {-0.0380F, -0.1025F, -0.0085F},
+ {-0.1451F, -0.0656F, 0.1137F}},
+ {{ 0.1020F, 0.1025F, -0.0678F},
+ { 0.0028F, 0.1512F, -0.0871F},
+ { 0.1563F, -0.1446F, -0.1636F}}
+ },
+ {
+ {{ 0.1472F, 0.0025F, -0.0281F},
+ { 0.0350F, 0.0296F, -0.1711F},
+ {-0.1197F, -0.1198F, -0.1130F}},
+ {{-0.1492F, 0.1554F, -0.1044F},
+ { 0.1203F, -0.1596F, 0.0589F},
+ {-0.0436F, -0.1876F, -0.0816F}},
+ {{ 0.1572F, -0.0982F, 0.1293F},
+ { 0.1358F, 0.1559F, 0.1322F},
+ { 0.0296F, -0.0354F, -0.0632F}}
+ },
+ {
+ {{-0.0941F, -0.0479F, 0.0908F},
+ {-0.1319F, -0.1333F, 0.1223F},
+ {-0.1098F, 0.1924F, 0.1075F}},
+ {{ 0.1796F, 0.0213F, 0.0626F},
+ { 0.0275F, 0.1883F, -0.0818F},
+ { 0.0363F, 0.0684F, 0.1094F}},
+ {{ 0.1131F, 0.1258F, -0.0558F},
+ { 0.1498F, 0.0322F, -0.0186F},
+ {-0.1801F, -0.0358F, 0.1727F}}
+ },
+ {
+ {{-0.1500F, -0.0554F, -0.0994F},
+ {-0.0818F, -0.1223F, 0.1365F},
+ { 0.1281F, 0.1507F, -0.0890F}},
+ {{-0.0444F, -0.1071F, -0.1632F},
+ { 0.0757F, -0.1235F, 0.0408F},
+ { 0.0401F, -0.1914F, 0.1772F}},
+ {{-0.0714F, 0.1582F, -0.0065F},
+ {-0.0119F, 0.1375F, -0.0727F},
+ {-0.1532F, -0.1826F, -0.0417F}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<float,2,4,2,2> {
+ {
+ {
+ {{-0.2174F, -0.0778F},
+ {-0.2584F, 0.2303F}},
+ {{-0.7686F, -0.3879F},
+ {-0.1775F, 0.0119F}},
+ {{ 0.5180F, 0.5087F},
+ { 0.5398F, 0.3476F}},
+ {{-0.5258F, -0.3128F},
+ {-0.6673F, -0.1827F}}
+ },
+ {
+ {{-0.1902F, -0.0467F},
+ {-0.3327F, -0.1701F}},
+ {{-0.5505F, -0.4875F},
+ {-0.4119F, -0.5726F}},
+ {{ 0.5777F, 0.4428F},
+ { 0.6121F, 0.7221F}},
+ {{-0.6009F, -0.6335F},
+ {-0.5159F, -0.3353F}}
+ }
+ }
+ });
+ op->associateInput(0,myInput);
+ op->associateInput(1,myWeights);
+ op->associateInput(2,myBias);
+ op->setDataType(DataType::Float32);
+ op->setBackend("cpu");
+ op->forwardDims();
+ myConv->forward();
+ op->getOutput(0)->print();
+ REQUIRE(approxEq<float>(*(op->getOutput(0)),*myOutput, 1e-3f, 1e-4f));
+ }
}
\ No newline at end of file