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Fix weigh bias parsing and better error logging

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This commit is contained in:
Matúš Námešný
2024-04-20 18:36:53 +02:00
parent ecf7416f8e
commit d08567a563
6 changed files with 93 additions and 11 deletions
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75
examples/alexnet/main.cpp
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@@ -5,6 +5,8 @@
#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) {
// Read the image using OpenCV
@@ -30,15 +32,63 @@ CUDANet::Model* createModel(const int inputSize, const int inputChannels, const
CUDANet::Model *model =
new CUDANet::Model(inputSize, inputChannels, outputSize);
// AlexNet
// Block 1
CUDANet::Layers::Conv2d *conv1 = new CUDANet::Layers::Conv2d(
inputSize, inputChannels, 11, 4, 96, CUDANet::Layers::Padding::SAME, CUDANet::Layers::ActivationType::RELU
inputSize, inputChannels, 11, 4, 64, 2, CUDANet::Layers::ActivationType::RELU
);
model->addLayer("conv1", conv1);
CUDANet::Layers::MaxPooling *pool1 = new CUDANet::Layers::MaxPooling(
3, 3, 2
)
model->addLayer("features.0", conv1); // Match pytorch naming
CUDANet::Layers::MaxPooling2D *pool1 = new CUDANet::Layers::MaxPooling2D(
56, 64, 3, 2, CUDANet::Layers::ActivationType::NONE
);
model->addLayer("pool1", pool1);
// Block 2
CUDANet::Layers::Conv2d *conv2 = new CUDANet::Layers::Conv2d(
27, 64, 5, 1, 192, 2, CUDANet::Layers::ActivationType::RELU
);
model->addLayer("features.3", conv2);
CUDANet::Layers::MaxPooling2D *pool2 = new CUDANet::Layers::MaxPooling2D(
27, 192, 3, 2, CUDANet::Layers::ActivationType::NONE
);
model->addLayer("pool2", pool2);
// Block 3
CUDANet::Layers::Conv2d *conv3 = new CUDANet::Layers::Conv2d(
13, 192, 3, 1, 384, 1, CUDANet::Layers::ActivationType::RELU
);
model->addLayer("features.6", conv3);
// Block 4
CUDANet::Layers::Conv2d *conv4 = new CUDANet::Layers::Conv2d(
13, 384, 3, 1, 256, 1, CUDANet::Layers::ActivationType::RELU
);
model->addLayer("features.8", conv4);
// Block 5
CUDANet::Layers::Conv2d *conv5 = new CUDANet::Layers::Conv2d(
13, 256, 3, 1, 256, 1, CUDANet::Layers::ActivationType::RELU
);
model->addLayer("features.10", conv5);
CUDANet::Layers::MaxPooling2D *pool5 = new CUDANet::Layers::MaxPooling2D(
13, 256, 3, 2, CUDANet::Layers::ActivationType::NONE
);
model->addLayer("pool5", pool5);
// Classifier
CUDANet::Layers::Dense *dense1 = new CUDANet::Layers::Dense(
6 * 6 * 256, 4096, CUDANet::Layers::ActivationType::RELU
);
model->addLayer("classifier.1", dense1);
CUDANet::Layers::Dense *dense2 = new CUDANet::Layers::Dense(
4096, 4096, CUDANet::Layers::ActivationType::RELU
);
model->addLayer("classifier.4", dense2);
CUDANet::Layers::Dense *dense3 = new CUDANet::Layers::Dense(
4096, 1000, CUDANet::Layers::ActivationType::NONE
);
model->addLayer("classifier.6", dense3);
return model;
}
@@ -59,13 +109,22 @@ int main(int argc, const char* const argv[]) {
const int outputSize = 1000;
CUDANet::Model *model = createModel(inputSize, inputChannels, outputSize);
model->loadWeights(modelWeightsPath);
// Read and normalize the image
std::vector<float> imageData = readAndNormalizeImage(imagePath, inputSize, inputSize);
// Print the size of the image data
std::cout << "Size of image data: " << imageData.size() << std::endl;
float* output = model->predict(imageData.data());
// Get max index
int maxIndex = 0;
for (int i = 0; i < outputSize; i++) {
if (output[i] > output[maxIndex]) {
maxIndex = i;
}
}
std::cout << "Prediction: " << maxIndex << std::endl;
return 0;
}