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Rename dim2d to shape2d

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This commit is contained in:
Matúš Námešný
2024-05-27 21:14:51 +02:00
parent 07d505a0e5
commit df47a31f36
22 changed files with 120 additions and 120 deletions
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4
examples/alexnet/alexnet.cpp
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@@ -32,7 +32,7 @@ readAndNormalizeImage(const std::string &imagePath, int width, int height) {
}
CUDANet::Model *createModel(
const dim2d inputSize,
const shape2d inputSize,
const int inputChannels,
const int outputSize
) {
@@ -112,7 +112,7 @@ int main(int argc, const char *const argv[]) {
std::string modelWeightsPath = argv[1];
std::string imagePath = argv[2];
const dim2d inputSize = {227, 227};
const shape2d inputSize = {227, 227};
const int inputChannels = 3;
const int outputSize = 1000;

24
examples/inception_v3/inception_v3.cpp
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@@ -14,12 +14,12 @@ int main(int argc, const char *const argv[]) {
class BasicConv2d : public CUDANet::Module {
public:
BasicConv2d(
const dim2d inputSize,
const shape2d inputSize,
const int inputChannels,
const int outputChannels,
const dim2d kernelSize,
const dim2d stride,
const dim2d padding,
const shape2d kernelSize,
const shape2d stride,
const shape2d padding,
const std::string &prefix
) {
// Create the convolution layer
@@ -28,7 +28,7 @@ class BasicConv2d : public CUDANet::Module {
padding, CUDANet::Layers::ActivationType::NONE
);
dim2d batchNormSize = conv->getOutputDims();
shape2d batchNormSize = conv->getOutputDims();
batchNorm = new CUDANet::Layers::BatchNorm2d(
batchNormSize, outputChannels, 1e-3f,
@@ -44,7 +44,7 @@ class BasicConv2d : public CUDANet::Module {
return batchNorm->forward(d_output);
}
dim2d getOutputDims() {
shape2d getOutputDims() {
return batchNorm->getOutputDims();
}
@@ -56,7 +56,7 @@ class BasicConv2d : public CUDANet::Module {
class InceptionA : public CUDANet::Module {
public:
InceptionA(
const dim2d inputSize,
const shape2d inputSize,
const int inputChannels,
const int poolFeatures,
const std::string &prefix
@@ -144,7 +144,7 @@ class InceptionA : public CUDANet::Module {
}
private:
dim2d inputSize;
shape2d inputSize;
int inputChannels;
int poolFeatures;
@@ -168,7 +168,7 @@ class InceptionA : public CUDANet::Module {
class InceptionB : public CUDANet::Module {
public:
InceptionB(
const dim2d inputSize,
const shape2d inputSize,
const int inputChannels,
const std::string &prefix
)
@@ -227,7 +227,7 @@ class InceptionB : public CUDANet::Module {
}
private:
dim2d inputSize;
shape2d inputSize;
int inputChannels;
BasicConv2d *branch3x3;
@@ -245,7 +245,7 @@ class InceptionB : public CUDANet::Module {
class InceptionC : public CUDANet::Module {
public:
InceptionC(
const dim2d inputSize,
const shape2d inputSize,
const int inputChannels,
const int nChannels_7x7,
const std::string &prefix
@@ -338,7 +338,7 @@ class InceptionC : public CUDANet::Module {
}
private:
dim2d inputSize;
shape2d inputSize;
int inputChannels;
BasicConv2d *branch1x1;

10
include/kernels/convolution.cuh
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@@ -25,13 +25,13 @@ __global__ void convolution(
const float* __restrict__ d_kernel,
const float* __restrict__ d_bias,
float* __restrict__ d_output,
const dim2d inputSize,
const shape2d inputSize,
const int nChannels,
const dim2d paddingSize,
const dim2d kernelSize,
const dim2d stride,
const shape2d paddingSize,
const shape2d kernelSize,
const shape2d stride,
const int nFilters,
const dim2d outputSize
const shape2d outputSize
);
} // namespace CUDANet::Kernels

20
include/kernels/pooling.cuh
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@@ -9,23 +9,23 @@ namespace CUDANet::Kernels {
__global__ void max_pooling(
const float* __restrict__ d_input,
float* __restrict__ d_output,
const dim2d inputSize,
const dim2d outputSize,
const shape2d inputSize,
const shape2d outputSize,
const int nChannels,
const dim2d poolingSize,
const dim2d stride,
const dim2d padding
const shape2d poolingSize,
const shape2d stride,
const shape2d padding
);
__global__ void avg_pooling(
const float* __restrict__ d_input,
float* __restrict__ d_output,
const dim2d inputSize,
const dim2d outputSize,
const shape2d inputSize,
const shape2d outputSize,
const int nChannels,
const dim2d poolingSize,
const dim2d stride,
const dim2d padding
const shape2d poolingSize,
const shape2d stride,
const shape2d padding
);
} // namespace CUDANet::Kernels

20
include/layers/avg_pooling.cuh
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@@ -9,11 +9,11 @@ namespace CUDANet::Layers {
class AvgPooling2d : public SequentialLayer, public TwoDLayer {
public:
AvgPooling2d(
dim2d inputSize,
shape2d inputSize,
int nChannels,
dim2d poolingSize,
dim2d stride,
dim2d padding,
shape2d poolingSize,
shape2d stride,
shape2d padding,
ActivationType activationType
);
~AvgPooling2d();
@@ -34,16 +34,16 @@ class AvgPooling2d : public SequentialLayer, public TwoDLayer {
*/
int getInputSize();
dim2d getOutputDims();
shape2d getOutputDims();
private:
dim2d inputSize;
shape2d inputSize;
int nChannels;
dim2d poolingSize;
dim2d stride;
dim2d padding;
shape2d poolingSize;
shape2d stride;
shape2d padding;
dim2d outputSize;
shape2d outputSize;
float* d_output;

6
include/layers/batch_norm.cuh
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@@ -10,7 +10,7 @@ namespace CUDANet::Layers {
class BatchNorm2d : public WeightedLayer, public TwoDLayer {
public:
BatchNorm2d(dim2d inputSize, int inputChannels, float epsilon, ActivationType activationType);
BatchNorm2d(shape2d inputSize, int inputChannels, float epsilon, ActivationType activationType);
~BatchNorm2d();
@@ -64,11 +64,11 @@ class BatchNorm2d : public WeightedLayer, public TwoDLayer {
*/
int getInputSize();
dim2d getOutputDims();
shape2d getOutputDims();
private:
dim2d inputSize;
shape2d inputSize;
int inputChannels;
int gridSize;

22
include/layers/conv2d.cuh
View File

@@ -28,12 +28,12 @@ class Conv2d : public WeightedLayer, public TwoDLayer {
* 'SOFTMAX' or 'NONE')
*/
Conv2d(
dim2d inputSize,
shape2d inputSize,
int inputChannels,
dim2d kernelSize,
dim2d stride,
shape2d kernelSize,
shape2d stride,
int numFilters,
dim2d paddingSize,
shape2d paddingSize,
ActivationType activationType
);
@@ -98,24 +98,24 @@ class Conv2d : public WeightedLayer, public TwoDLayer {
*
* @return int
*/
dim2d getPaddingSize() {
shape2d getPaddingSize() {
return paddingSize;
}
dim2d getOutputDims();
shape2d getOutputDims();
private:
// Inputs
dim2d inputSize;
shape2d inputSize;
int inputChannels;
// Outputs
dim2d outputSize;
shape2d outputSize;
// Kernel
dim2d kernelSize;
dim2d stride;
dim2d paddingSize;
shape2d kernelSize;
shape2d stride;
shape2d paddingSize;
int numFilters;
// Kernels

4
include/layers/layer.cuh
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@@ -7,7 +7,7 @@
#define CUDANET_SAME_PADDING(inputSize, kernelSize, stride) \
((stride - 1) * inputSize - stride + kernelSize) / 2;
typedef std::pair<int, int> dim2d;
typedef std::pair<int, int> shape2d;
namespace CUDANet::Layers {
@@ -15,7 +15,7 @@ namespace CUDANet::Layers {
class TwoDLayer {
public:
virtual dim2d getOutputDims() = 0;
virtual shape2d getOutputDims() = 0;
};

20
include/layers/max_pooling.cuh
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@@ -9,11 +9,11 @@ namespace CUDANet::Layers {
class MaxPooling2d : public SequentialLayer, public TwoDLayer {
public:
MaxPooling2d(
dim2d inputSize,
shape2d inputSize,
int nChannels,
dim2d poolingSize,
dim2d stride,
dim2d padding,
shape2d poolingSize,
shape2d stride,
shape2d padding,
ActivationType activationType
);
~MaxPooling2d();
@@ -34,16 +34,16 @@ class MaxPooling2d : public SequentialLayer, public TwoDLayer {
*/
int getInputSize();
dim2d getOutputDims();
shape2d getOutputDims();
private:
dim2d inputSize;
shape2d inputSize;
int nChannels;
dim2d poolingSize;
dim2d stride;
dim2d padding;
shape2d poolingSize;
shape2d stride;
shape2d padding;
dim2d outputSize;
shape2d outputSize;
float* d_output;

4
include/model/model.hpp
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@@ -26,7 +26,7 @@ struct TensorInfo {
class Model {
public:
Model(const dim2d inputSize, const int inputChannels, const int outputSize);
Model(const shape2d inputSize, const int inputChannels, const int outputSize);
Model(const Model& other);
~Model();
@@ -43,7 +43,7 @@ class Model {
Layers::Input* inputLayer;
Layers::Output* outputLayer;
dim2d inputSize;
shape2d inputSize;
int inputChannels;
int outputSize;

10
src/kernels/convolution.cu
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@@ -9,13 +9,13 @@ __global__ void Kernels::convolution(
const float* __restrict__ d_kernel,
const float* __restrict__ d_bias,
float* __restrict__ d_output,
const dim2d inputSize,
const shape2d inputSize,
const int nChannels,
const dim2d paddingSize,
const dim2d kernelSize,
const dim2d stride,
const shape2d paddingSize,
const shape2d kernelSize,
const shape2d stride,
const int nFilters,
const dim2d outputSize
const shape2d outputSize
) {
int j = blockDim.x * blockIdx.x + threadIdx.x;
int i = blockDim.y * blockIdx.y + threadIdx.y;

20
src/kernels/pooling.cu
View File

@@ -7,12 +7,12 @@ using namespace CUDANet;
__global__ void Kernels::max_pooling(
const float* __restrict__ d_input,
float* __restrict__ d_output,
const dim2d inputSize,
const dim2d outputSize,
const shape2d inputSize,
const shape2d outputSize,
const int nChannels,
const dim2d poolingSize,
const dim2d stride,
const dim2d padding
const shape2d poolingSize,
const shape2d stride,
const shape2d padding
) {
int j = blockDim.x * blockIdx.x + threadIdx.x;
int i = blockDim.y * blockIdx.y + threadIdx.y;
@@ -48,12 +48,12 @@ __global__ void Kernels::max_pooling(
__global__ void Kernels::avg_pooling(
const float* __restrict__ d_input,
float* __restrict__ d_output,
const dim2d inputSize,
const dim2d outputSize,
const shape2d inputSize,
const shape2d outputSize,
const int nChannels,
const dim2d poolingSize,
const dim2d stride,
const dim2d padding
const shape2d poolingSize,
const shape2d stride,
const shape2d padding
) {
int j = blockDim.x * blockIdx.x + threadIdx.x;
int i = blockDim.y * blockIdx.y + threadIdx.y;

10
src/layers/avg_pooling.cu
View File

@@ -5,11 +5,11 @@
using namespace CUDANet::Layers;
AvgPooling2d::AvgPooling2d(
dim2d inputSize,
shape2d inputSize,
int nChannels,
dim2d poolingSize,
dim2d stride,
dim2d padding,
shape2d poolingSize,
shape2d stride,
shape2d padding,
ActivationType activationType
)
: inputSize(inputSize),
@@ -66,6 +66,6 @@ int AvgPooling2d::getInputSize() {
return inputSize.first * inputSize.second * nChannels;
}
dim2d AvgPooling2d::getOutputDims() {
shape2d AvgPooling2d::getOutputDims() {
return outputSize;
}

4
src/layers/batch_norm.cu
View File

@@ -10,7 +10,7 @@
using namespace CUDANet::Layers;
BatchNorm2d::BatchNorm2d(
dim2d inputSize,
shape2d inputSize,
int inputChannels,
float epsilon,
ActivationType activationType
@@ -128,7 +128,7 @@ int BatchNorm2d::getOutputSize() {
return inputSize.first * inputSize.second * inputChannels;
}
dim2d BatchNorm2d::getOutputDims() {
shape2d BatchNorm2d::getOutputDims() {
return inputSize;
}

10
src/layers/conv2d.cu
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@@ -12,12 +12,12 @@
using namespace CUDANet::Layers;
Conv2d::Conv2d(
dim2d inputSize,
shape2d inputSize,
int inputChannels,
dim2d kernelSize,
dim2d stride,
shape2d kernelSize,
shape2d stride,
int numFilters,
dim2d paddingSize,
shape2d paddingSize,
ActivationType activationType
)
: inputSize(inputSize),
@@ -139,6 +139,6 @@ int Conv2d::getInputSize() {
return inputSize.first * inputSize.second * inputChannels;
}
dim2d Conv2d::getOutputDims() {
shape2d Conv2d::getOutputDims() {
return outputSize;
}

10
src/layers/max_pooling.cu
View File

@@ -5,11 +5,11 @@
using namespace CUDANet::Layers;
MaxPooling2d::MaxPooling2d(
dim2d inputSize,
shape2d inputSize,
int nChannels,
dim2d poolingSize,
dim2d stride,
dim2d padding,
shape2d poolingSize,
shape2d stride,
shape2d padding,
ActivationType activationType
)
: inputSize(inputSize),
@@ -70,6 +70,6 @@ int MaxPooling2d::getInputSize() {
return inputSize.first * inputSize.second * nChannels;
}
dim2d MaxPooling2d::getOutputDims() {
shape2d MaxPooling2d::getOutputDims() {
return outputSize;
}

2
src/model/model.cpp
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@@ -11,7 +11,7 @@
using namespace CUDANet;
Model::Model(const dim2d inputSize, const int inputChannels, const int outputSize)
Model::Model(const shape2d inputSize, const int inputChannels, const int outputSize)
: inputSize(inputSize),
inputChannels(inputChannels),
outputSize(outputSize),

8
test/layers/test_avg_pooling.cu
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@@ -7,11 +7,11 @@
class AvgPoolingLayerTest : public ::testing::Test {
protected:
dim2d inputSize;
shape2d inputSize;
int nChannels;
dim2d poolingSize;
dim2d stride;
dim2d padding;
shape2d poolingSize;
shape2d stride;
shape2d padding;
std::vector<float> input;
std::vector<float> expected;

2
test/layers/test_batch_norm.cu
View File

@@ -8,7 +8,7 @@
class BatchNormLayerTest : public ::testing::Test {
protected:
dim2d inputSize;
shape2d inputSize;
int nChannels;
std::vector<float> weights;
std::vector<float> biases;

8
test/layers/test_conv2d.cu
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@@ -7,12 +7,12 @@
class Conv2dTest : public ::testing::Test {
protected:
dim2d inputSize;
shape2d inputSize;
int inputChannels;
dim2d kernelSize;
dim2d stride;
shape2d kernelSize;
shape2d stride;
int numFilters;
dim2d paddingSize;
shape2d paddingSize;
CUDANet::Layers::ActivationType activationType;
std::vector<float> input;
std::vector<float> kernels;

8
test/layers/test_max_pooling.cu
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@@ -7,11 +7,11 @@
class MaxPoolingLayerTest : public ::testing::Test {
protected:
dim2d inputSize;
shape2d inputSize;
int nChannels;
dim2d poolingSize;
dim2d stride;
dim2d padding;
shape2d poolingSize;
shape2d stride;
shape2d padding;
std::vector<float> input;
std::vector<float> expected;

14
test/model/test_model.cu
View File

@@ -10,21 +10,21 @@ class ModelTest : public ::testing::Test {
CUDANet::Model *commonTestSetup(
bool setWeights = true,
dim2d inputSize = {6, 6},
shape2d inputSize = {6, 6},
int inputChannels = 2,
int outputSize = 3,
dim2d kernelSize = {3, 3},
dim2d stride = {1, 1},
shape2d kernelSize = {3, 3},
shape2d stride = {1, 1},
int numFilters = 2,
dim2d poolingSize = {2, 2},
dim2d poolingStride = {2, 2}
shape2d poolingSize = {2, 2},
shape2d poolingStride = {2, 2}
) {
CUDANet::Model *model =
new CUDANet::Model(inputSize, inputChannels, outputSize);
dim2d paddingSize = {0, 0};
shape2d paddingSize = {0, 0};
// Conv2d
CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d(
@@ -38,7 +38,7 @@ class ModelTest : public ::testing::Test {
model->addLayer("conv1", conv2d);
// maxpool2d
dim2d poolingInput = {
shape2d poolingInput = {
inputSize.first - kernelSize.first + 1,
inputSize.second - kernelSize.second + 1
};