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Rework padding size setting

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This commit is contained in:
Matúš Námešný
2024-04-20 16:31:28 +02:00
parent dbaab5652e
commit ecf7416f8e
5 changed files with 44 additions and 59 deletions
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4
include/layers/conv2d.cuh
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@@ -23,7 +23,7 @@ class Conv2d : public WeightedLayer {
* @param kernelSize Width and height of the convolution kernel * @param kernelSize Width and height of the convolution kernel
* @param stride Convolution stride * @param stride Convolution stride
* @param numFilters Number of output filters * @param numFilters Number of output filters
* @param padding Padding type ('SAME' or 'VALID') * @param paddingSize Padding size
* @param activationType Activation function type ('RELU', 'SIGMOID', * @param activationType Activation function type ('RELU', 'SIGMOID',
* 'SOFTMAX' or 'NONE') * 'SOFTMAX' or 'NONE')
*/ */
@@ -33,7 +33,7 @@ class Conv2d : public WeightedLayer {
int kernelSize, int kernelSize,
int stride, int stride,
int numFilters, int numFilters,
Padding padding, int paddingSize,
ActivationType activationType ActivationType activationType
); );

26
include/layers/layer.cuh
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@@ -4,32 +4,26 @@
#include <vector> #include <vector>
#define CUDANET_SAME_PADDING(inputSize, kernelSize, stride) ((stride - 1) * inputSize - stride + kernelSize) / 2;
namespace CUDANet::Layers { namespace CUDANet::Layers {
/**
* @brief Padding types
*
* SAME: Zero padding such that the output size is the same as the input
* VALID: No padding
*
*/
enum Padding { SAME, VALID };
/** /**
* @brief Basic Sequential Layer * @brief Basic Sequential Layer
* *
*/ */
class SequentialLayer { class SequentialLayer {
public: public:
/** /**
* @brief Destroy the Sequential Layer * @brief Destroy the Sequential Layer
* *
*/ */
virtual ~SequentialLayer() {}; virtual ~SequentialLayer(){};
/** /**
* @brief Forward propagation virtual function * @brief Forward propagation virtual function
* *
* @param input Device pointer to the input * @param input Device pointer to the input
* @return float* Device pointer to the output * @return float* Device pointer to the output
*/ */
@@ -45,7 +39,7 @@ class WeightedLayer : public SequentialLayer {
* @brief Destroy the ILayer object * @brief Destroy the ILayer object
* *
*/ */
virtual ~WeightedLayer() {}; virtual ~WeightedLayer(){};
/** /**
* @brief Virtual function for forward pass * @brief Virtual function for forward pass
@@ -64,7 +58,7 @@ class WeightedLayer : public SequentialLayer {
/** /**
* @brief Virtual function for getting weights * @brief Virtual function for getting weights
* *
*/ */
virtual std::vector<float> getWeights() = 0; virtual std::vector<float> getWeights() = 0;
@@ -77,7 +71,7 @@ class WeightedLayer : public SequentialLayer {
/** /**
* @brief Virtual function for getting biases * @brief Virtual function for getting biases
* *
*/ */
virtual std::vector<float> getBiases() = 0; virtual std::vector<float> getBiases() = 0;

20
src/layers/conv2d.cu
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@@ -12,29 +12,17 @@ Conv2d::Conv2d(
int kernelSize, int kernelSize,
int stride, int stride,
int numFilters, int numFilters,
Padding padding, int paddingSize,
ActivationType activationType ActivationType activationType
) )
: inputSize(inputSize), : inputSize(inputSize),
inputChannels(inputChannels), inputChannels(inputChannels),
kernelSize(kernelSize), kernelSize(kernelSize),
stride(stride), stride(stride),
numFilters(numFilters) { numFilters(numFilters),
paddingSize(paddingSize) {
switch (padding) {
case SAME:
outputSize = inputSize;
paddingSize = ((stride - 1) * inputSize - stride + kernelSize) / 2;
break;
case VALID: outputSize = (inputSize - kernelSize + 2 * paddingSize) / stride + 1;
paddingSize = 0;
outputSize = (inputSize - kernelSize) / stride + 1;
break;
default:
break;
}
activation = Activation( activation = Activation(
activationType, outputSize * outputSize * numFilters activationType, outputSize * outputSize * numFilters

49
test/layers/test_conv2d.cu
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@@ -13,7 +13,7 @@ class Conv2dTest : public ::testing::Test {
int kernelSize, int kernelSize,
int stride, int stride,
int numFilters, int numFilters,
CUDANet::Layers::Padding padding, int paddingSize,
CUDANet::Layers::ActivationType activationType, CUDANet::Layers::ActivationType activationType,
std::vector<float>& input, std::vector<float>& input,
float* kernels, float* kernels,
@@ -21,8 +21,8 @@ class Conv2dTest : public ::testing::Test {
) { ) {
// Create Conv2d layer // Create Conv2d layer
CUDANet::Layers::Conv2d conv2d( CUDANet::Layers::Conv2d conv2d(
inputSize, inputChannels, kernelSize, stride, numFilters, padding, inputSize, inputChannels, kernelSize, stride, numFilters,
activationType paddingSize, activationType
); );
conv2d.setWeights(kernels); conv2d.setWeights(kernels);
@@ -54,12 +54,13 @@ class Conv2dTest : public ::testing::Test {
}; };
TEST_F(Conv2dTest, SimpleTest) { TEST_F(Conv2dTest, SimpleTest) {
int inputSize = 4; int inputSize = 4;
int inputChannels = 1; int inputChannels = 1;
int kernelSize = 2; int kernelSize = 2;
int stride = 1; int stride = 1;
int numFilters = 1; int numFilters = 1;
CUDANet::Layers::Padding padding = CUDANet::Layers::Padding::VALID; int paddingSize = 0;
CUDANet::Layers::ActivationType activationType = CUDANet::Layers::ActivationType activationType =
CUDANet::Layers::ActivationType::NONE; CUDANet::Layers::ActivationType::NONE;
@@ -77,7 +78,7 @@ TEST_F(Conv2dTest, SimpleTest) {
float* d_output; float* d_output;
CUDANet::Layers::Conv2d conv2d = commonTestSetup( CUDANet::Layers::Conv2d conv2d = commonTestSetup(
inputSize, inputChannels, kernelSize, stride, numFilters, padding, inputSize, inputChannels, kernelSize, stride, numFilters, paddingSize,
activationType, input, kernels.data(), d_input activationType, input, kernels.data(), d_input
); );
@@ -104,12 +105,12 @@ TEST_F(Conv2dTest, SimpleTest) {
} }
TEST_F(Conv2dTest, PaddedTest) { TEST_F(Conv2dTest, PaddedTest) {
int inputSize = 5; int inputSize = 5;
int inputChannels = 3; int inputChannels = 3;
int kernelSize = 3; int kernelSize = 3;
int stride = 1; int stride = 1;
int numFilters = 2; int numFilters = 2;
CUDANet::Layers::Padding padding = CUDANet::Layers::Padding::SAME; int paddingSize = CUDANET_SAME_PADDING(inputSize, kernelSize, stride);
CUDANet::Layers::ActivationType activationType = CUDANet::Layers::ActivationType activationType =
CUDANet::Layers::ActivationType::NONE; CUDANet::Layers::ActivationType::NONE;
@@ -167,7 +168,7 @@ TEST_F(Conv2dTest, PaddedTest) {
float* d_output; float* d_output;
CUDANet::Layers::Conv2d conv2d = commonTestSetup( CUDANet::Layers::Conv2d conv2d = commonTestSetup(
inputSize, inputChannels, kernelSize, stride, numFilters, padding, inputSize, inputChannels, kernelSize, stride, numFilters, paddingSize,
activationType, input, kernels.data(), d_input activationType, input, kernels.data(), d_input
); );
@@ -206,12 +207,12 @@ TEST_F(Conv2dTest, PaddedTest) {
} }
TEST_F(Conv2dTest, StridedPaddedConvolution) { TEST_F(Conv2dTest, StridedPaddedConvolution) {
int inputSize = 5; int inputSize = 5;
int inputChannels = 2; int inputChannels = 2;
int kernelSize = 3; int kernelSize = 3;
int stride = 2; int stride = 2;
int numFilters = 2; int numFilters = 2;
CUDANet::Layers::Padding padding = CUDANet::Layers::Padding::SAME; int paddingSize = CUDANET_SAME_PADDING(inputSize, kernelSize, stride);
CUDANet::Layers::ActivationType activationType = CUDANet::Layers::ActivationType activationType =
CUDANet::Layers::ActivationType::RELU; CUDANet::Layers::ActivationType::RELU;
@@ -254,7 +255,7 @@ TEST_F(Conv2dTest, StridedPaddedConvolution) {
float* d_output; float* d_output;
CUDANet::Layers::Conv2d conv2d = commonTestSetup( CUDANet::Layers::Conv2d conv2d = commonTestSetup(
inputSize, inputChannels, kernelSize, stride, numFilters, padding, inputSize, inputChannels, kernelSize, stride, numFilters, paddingSize,
activationType, input, kernels.data(), d_input activationType, input, kernels.data(), d_input
); );

4
test/model/test_model.cu
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@@ -24,10 +24,12 @@ class ModelTest : public ::testing::Test {
CUDANet::Model *model = CUDANet::Model *model =
new CUDANet::Model(inputSize, inputChannels, outputSize); new CUDANet::Model(inputSize, inputChannels, outputSize);
int paddingSize = 0;
// Conv2d // Conv2d
CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d( CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d(
inputSize, inputChannels, kernelSize, stride, numFilters, inputSize, inputChannels, kernelSize, stride, numFilters,
CUDANet::Layers::Padding::VALID, paddingSize,
CUDANet::Layers::ActivationType::NONE CUDANet::Layers::ActivationType::NONE
); );