Skip to content
Snippets Groups Projects
Commit 9349f51e authored by Maxence Naud's avatar Maxence Naud Committed by Maxence Naud
Browse files

[Upd] Conv[DW] 2D kernels

parent e7eec22f
No related branches found
No related tags found
1 merge request!101Upd 2D Conv[DepthWise] kernels
Hide whitespace changes
Inline Side-by-side
include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp
+ 65
− 27
View file @ 9349f51e
...@@ -150,30 +150,24 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri ...@@ -150,30 +150,24 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
// weight (outCh, ch, kernelX, kernelY) // weight (outCh, ch, kernelX, kernelY)
// does not take Dilation attribute into account // does not take Dilation attribute into account
using signedsize = std::make_signed<std::size_t>::type; using signedsize = std::make_signed<std::size_t>::type;
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) { const std::size_t outChannels_s = oxSize * oySize;
for (std::size_t ch = 0; ch < inputDims[1]; ++ch) {
const std::size_t oIndex = (ch + batch*inputDims[1]) * oxSize * oySize; if (dilated_kernel_x ==3 && dilated_kernel_y == 3) {
B biasVal = (biases != nullptr) ? biases[ch] : B(0); for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
std::fill(output + oIndex, output+(oIndex+oxSize*oySize), biasVal); for (std::size_t ch = 0; ch < inputDims[1]; ++ch) {
const std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = ch * kernelDims[0] * kernelDims[1]; B biasVal = (biases != nullptr) ? biases[ch] : B(0);
for (std::size_t ox = 0; ox < oxSize; ++ox) { std::fill(output, output + outChannels_s, biasVal);
// 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 iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
// 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 wIndex = ch * 9;
const std::size_t sxMin = 0;
const std::size_t sxMax = dilated_kernel_x; for (std::size_t ox = 0; ox < oxSize; ++ox) {
for (std::size_t oy = 0; oy < oySize; ++oy) { for (std::size_t oy = 0; oy < oySize; ++oy) {
// const signedsize dify = static_cast<signedsize>(- oy * strideDims[1]); const std::size_t oIndexFull = ox*oySize + oy;
// const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0))); const signedsize ix = static_cast<signedsize>(ox * strideDims[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 signedsize iy = static_cast<signedsize>(oy * strideDims[1]);
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)] + 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] + 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 + 0*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+2)] +
...@@ -183,9 +177,51 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri ...@@ -183,9 +177,51 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
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] + 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] + 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)]); 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 += 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 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;
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]);
output[oIndexFull] += weights[wIndex] * input[iIndex + static_cast<std::size_t>(ix)*inputDims[3] + static_cast<std::size_t>(iy)];
}
}
}
output += outChannels_s;
}
} 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] * 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]))]; 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 +229,12 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri ...@@ -193,10 +229,12 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
} }
} }
} }
output += outChannels_s;
} }
} }
} }
// Kernels registration to implementation entry point // Kernels registration to implementation entry point
REGISTRAR(ConvDepthWiseImpl2D_cpu, REGISTRAR(ConvDepthWiseImpl2D_cpu,
{{DataType::Any, DataFormat::NCHW}, {DataType::Float32, DataFormat::NCHW}}, {{DataType::Any, DataFormat::NCHW}, {DataType::Float32, DataFormat::NCHW}},
......
include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
+ 73
− 32
View file @ 9349f51e
...@@ -141,15 +141,15 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims, ...@@ -141,15 +141,15 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
O *output = static_cast<O *>(output_); O *output = static_cast<O *>(output_);
// output H size // output H size
const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
const std::size_t oxSize = 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]))); static_cast<float>(strideDims[0])));
const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
// output W size // output W size
const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
const std::size_t oySize = 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]))); static_cast<float>(strideDims[1])));
const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
// TODO: kernel computation // TODO: kernel computation
...@@ -158,51 +158,92 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims, ...@@ -158,51 +158,92 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
// weight (outCh, inCh, kernelX, kernelY) // weight (outCh, inCh, kernelX, kernelY)
// does not take Dilation attribute into account // does not take Dilation attribute into account
const std::size_t outChannels_s = oxSize * oySize; 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) { if (dilated_kernel_x == 3 && dilated_kernel_y == 3) {
for (std::size_t outCh = 0; outCh < outChannels; ++outCh) { for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
// If bias = nullptr, set B(0) for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
B biasVal = (biases != nullptr) ? biases[outCh] : B(0); // If bias = nullptr, set B(0)
std::fill(output, output+outChannels_s, biasVal); 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) {
const std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = (inCh + outCh*inputDims[1]) * 9;
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 inCh = 0; inCh < inputDims[1]; ++inCh) { 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)] +
const std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3]; weights[wIndex + 0*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0] * kernelDims[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)] +
for (std::size_t ox = 0; ox < oxSize; ++ox) { 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)]);
}
}
}
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) {
const std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = (inCh + outCh*inputDims[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]);
output[oIndexFull] += weights[wIndex] * input[iIndex + static_cast<std::size_t>(ix)*inputDims[3] + static_cast<std::size_t>(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) {
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) {
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 oy = 0; oy < oySize; ++oy) {
const std::size_t oIndexFull = ox*oySize + oy;
const size_t ix = ox * strideDims[0];
const size_t iy = oy * strideDims[1];
if (kernelDims[0] == 3 && kernelDims[1] == 3 && dilationDims[0] == 1 && dilationDims[1] == 1) {
output[oIndexFull] += (weights[wIndex] * input[iIndex + static_cast<std::size_t>(ix)*inputDims[3] + static_cast<std::size_t>(iy)] +
weights[wIndex + 1] * input[iIndex + static_cast<std::size_t>(ix)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 2] * input[iIndex + static_cast<std::size_t>(ix)*inputDims[3] + static_cast<std::size_t>(iy+2)] +
weights[wIndex + kernelDims[1]] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy)] +
weights[wIndex + kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 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]] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy)] +
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 = 0; sx*dilationDims[0] < dilated_kernel_x; ++sx) { 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) { for (std::size_t sy = 0; sy*dilationDims[1] < dilated_kernel_y; ++sy) {
output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + sy] * output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + sy] *
input[iIndex + (ix + (sx*dilationDims[0]))*inputDims[3] + (iy + (sy*dilationDims[1]))]; 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]))];
} }
} }
} }
} }
} }
output += outChannels_s;
} }
output += outChannels_s;
} }
} }
} }
// Kernels registration to implementation entry point // Kernels registration to implementation entry point
REGISTRAR(ConvImpl2D_cpu, REGISTRAR(ConvImpl2D_cpu,
{{DataType::Any, DataFormat::NCHW}, {DataType::Float32, DataFormat::NCHW}}, {{DataType::Any, DataFormat::NCHW}, {DataType::Float32, DataFormat::NCHW}},
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment

Copyright © Eclipse Foundation, Inc. All Rights Reserved.     Privacy Policy | Terms of Use | Copyright Agent