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Add Alexnet README

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Matúš Námešný
2024-04-22 21:59:08 +02:00
parent 9a9b034ce5
commit 157d4c736b
2 changed files with 53 additions and 19 deletions
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44
examples/alexnet/main.cpp
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@@ -1,14 +1,14 @@
#include <conv2d.cuh>
#include <dense.cuh>
#include <iostream>
#include <max_pooling.cuh>
#include <model.hpp>
#include <opencv2/opencv.hpp>
#include <string>
#include <vector>
#include <opencv2/opencv.hpp>
#include <model.hpp>
#include <conv2d.cuh>
#include <max_pooling.cuh>
#include <dense.cuh>
std::vector<float> readAndNormalizeImage(const std::string& imagePath, int width, int height) {
std::vector<float>
readAndNormalizeImage(const std::string &imagePath, int width, int height) {
// Read the image using OpenCV
cv::Mat image = cv::imread(imagePath, cv::IMREAD_COLOR);
@@ -35,15 +35,20 @@ std::vector<float> readAndNormalizeImage(const std::string& imagePath, int width
return imageData;
}
CUDANet::Model* createModel(const int inputSize, const int inputChannels, const int outputSize) {
CUDANet::Model *createModel(
const int inputSize,
const int inputChannels,
const int outputSize
) {
CUDANet::Model *model =
new CUDANet::Model(inputSize, inputChannels, outputSize);
// Block 1
CUDANet::Layers::Conv2d *conv1 = new CUDANet::Layers::Conv2d(
inputSize, inputChannels, 11, 4, 64, 2, CUDANet::Layers::ActivationType::RELU
inputSize, inputChannels, 11, 4, 64, 2,
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(
56, 64, 3, 2, CUDANet::Layers::ActivationType::NONE
);
@@ -100,20 +105,20 @@ CUDANet::Model* createModel(const int inputSize, const int inputChannels, const
return model;
}
int main(int argc, const char* const argv[]) {
int main(int argc, const char *const argv[]) {
if (argc != 3) {
std::cerr << "Usage: " << argv[0] << "<model_weights_path> <image_path>" << std::endl;
return 1; // Return error code indicating incorrect usage
std::cerr << "Usage: " << argv[0] << "<model_weights_path> <image_path>"
<< std::endl;
return 1; // Return error code indicating incorrect usage
}
// Path to the image file
std::string modelWeightsPath = argv[1];
std::string imagePath = argv[2];
std::string imagePath = argv[2];
const int inputSize = 227;
const int inputSize = 227;
const int inputChannels = 3;
const int outputSize = 1000;
const int outputSize = 1000;
CUDANet::Model *model = createModel(inputSize, inputChannels, outputSize);
@@ -122,10 +127,11 @@ int main(int argc, const char* const argv[]) {
model->loadWeights(modelWeightsPath);
// Read and normalize the image
std::vector<float> imageData = readAndNormalizeImage(imagePath, inputSize, inputSize);
std::vector<float> imageData =
readAndNormalizeImage(imagePath, inputSize, inputSize);
// Print the size of the image data
float* output = model->predict(imageData.data());
float *output = model->predict(imageData.data());
// Get max index
int maxIndex = 0;