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Fix alexnet

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This commit is contained in:
Matúš Námešný
2024-05-26 19:15:46 +02:00
parent c09e978308
commit 4a60e0142c
1 changed files with 12 additions and 12 deletions
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24
examples/alexnet/alexnet.cpp
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@@ -32,7 +32,7 @@ readAndNormalizeImage(const std::string &imagePath, int width, int height) {
} }
CUDANet::Model *createModel( CUDANet::Model *createModel(
const int inputSize, const dim2d inputSize,
const int inputChannels, const int inputChannels,
const int outputSize const int outputSize
) { ) {
@@ -41,44 +41,44 @@ CUDANet::Model *createModel(
// Block 1 // Block 1
CUDANet::Layers::Conv2d *conv1 = new CUDANet::Layers::Conv2d( CUDANet::Layers::Conv2d *conv1 = new CUDANet::Layers::Conv2d(
inputSize, inputChannels, 11, 4, 64, 2, inputSize, inputChannels, {11, 11}, {4, 4}, 64, {2, 2},
CUDANet::Layers::ActivationType::RELU CUDANet::Layers::ActivationType::RELU
); );
model->addLayer("features.0", conv1); // Match pytorch naming model->addLayer("features.0", conv1); // Match pytorch naming
CUDANet::Layers::MaxPooling2d *pool1 = new CUDANet::Layers::MaxPooling2d( CUDANet::Layers::MaxPooling2d *pool1 = new CUDANet::Layers::MaxPooling2d(
56, 64, 3, 2, CUDANet::Layers::ActivationType::NONE {56, 56}, 64, {3, 3}, {2, 2}, {0, 0}, CUDANet::Layers::ActivationType::NONE
); );
model->addLayer("pool1", pool1); model->addLayer("pool1", pool1);
// Block 2 // Block 2
CUDANet::Layers::Conv2d *conv2 = new CUDANet::Layers::Conv2d( CUDANet::Layers::Conv2d *conv2 = new CUDANet::Layers::Conv2d(
27, 64, 5, 1, 192, 2, CUDANet::Layers::ActivationType::RELU {27, 27}, 64, {5, 5}, {1, 1}, 192, {2, 2}, CUDANet::Layers::ActivationType::RELU
); );
model->addLayer("features.3", conv2); model->addLayer("features.3", conv2);
CUDANet::Layers::MaxPooling2d *pool2 = new CUDANet::Layers::MaxPooling2d( CUDANet::Layers::MaxPooling2d *pool2 = new CUDANet::Layers::MaxPooling2d(
27, 192, 3, 2, CUDANet::Layers::ActivationType::NONE {27, 27}, 192, {3, 3}, {2, 2}, {0, 0}, CUDANet::Layers::ActivationType::NONE
); );
model->addLayer("pool2", pool2); model->addLayer("pool2", pool2);
// Block 3 // Block 3
CUDANet::Layers::Conv2d *conv3 = new CUDANet::Layers::Conv2d( CUDANet::Layers::Conv2d *conv3 = new CUDANet::Layers::Conv2d(
13, 192, 3, 1, 384, 1, CUDANet::Layers::ActivationType::RELU {13, 13}, 192, {3, 3}, {1, 1}, 384, {1, 1}, CUDANet::Layers::ActivationType::RELU
); );
model->addLayer("features.6", conv3); model->addLayer("features.6", conv3);
// Block 4 // Block 4
CUDANet::Layers::Conv2d *conv4 = new CUDANet::Layers::Conv2d( CUDANet::Layers::Conv2d *conv4 = new CUDANet::Layers::Conv2d(
13, 384, 3, 1, 256, 1, CUDANet::Layers::ActivationType::RELU {13, 13}, 384, {3, 3}, {1, 1}, 256, {1, 1}, CUDANet::Layers::ActivationType::RELU
); );
model->addLayer("features.8", conv4); model->addLayer("features.8", conv4);
// Block 5 // Block 5
CUDANet::Layers::Conv2d *conv5 = new CUDANet::Layers::Conv2d( CUDANet::Layers::Conv2d *conv5 = new CUDANet::Layers::Conv2d(
13, 256, 3, 1, 256, 1, CUDANet::Layers::ActivationType::RELU {13, 13}, 256, {3, 3}, {1, 1}, 256, {1, 1}, CUDANet::Layers::ActivationType::RELU
); );
model->addLayer("features.10", conv5); model->addLayer("features.10", conv5);
CUDANet::Layers::MaxPooling2d *pool5 = new CUDANet::Layers::MaxPooling2d( CUDANet::Layers::MaxPooling2d *pool5 = new CUDANet::Layers::MaxPooling2d(
13, 256, 3, 2, CUDANet::Layers::ActivationType::NONE {13, 13}, 256, {3, 3}, {2, 2}, {0, 0}, CUDANet::Layers::ActivationType::NONE
); );
model->addLayer("pool5", pool5); model->addLayer("pool5", pool5);
@@ -112,7 +112,7 @@ int main(int argc, const char *const argv[]) {
std::string modelWeightsPath = argv[1]; std::string modelWeightsPath = argv[1];
std::string imagePath = argv[2]; std::string imagePath = argv[2];
const int inputSize = 227; const dim2d inputSize = {227, 227};
const int inputChannels = 3; const int inputChannels = 3;
const int outputSize = 1000; const int outputSize = 1000;
@@ -124,10 +124,10 @@ int main(int argc, const char *const argv[]) {
// Read and normalize the image // Read and normalize the image
std::vector<float> imageData = std::vector<float> imageData =
readAndNormalizeImage(imagePath, inputSize, inputSize); readAndNormalizeImage(imagePath, inputSize.first, inputSize.second);
// Print the size of the image data // Print the size of the image data
float *output = model->predict(imageData.data()); const float *output = model->predict(imageData.data());
// Get max index // Get max index
int maxIndex = 0; int maxIndex = 0;