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Start implementing Inception v3
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48
examples/inception_v3/inception_v3.cpp
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48
examples/inception_v3/inception_v3.cpp
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#include <cudanet.cuh>
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#include <iostream>
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int main(int argc, const char *const argv[]) {
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if (argc != 3) {
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std::cerr << "Usage: " << argv[0] << "<model_weights_path> <image_path>"
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<< std::endl;
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return 1; // Return error code indicating incorrect usage
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}
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std::cout << "Loading model..." << std::endl;
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}
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class BasicConv2d : public CUDANet::Module {
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public:
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BasicConv2d(
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const int inputSize,
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const int inputChannels,
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const int outputChannels,
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const int kernelSize,
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const int stride,
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const int padding,
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const std::string& prefix
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)
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: inputSize(inputSize),
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inputChannels(inputChannels),
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outputChannels(outputChannels) {
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// Create the convolution layer
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CUDANet::Layers::Conv2d *conv = new CUDANet::Layers::Conv2d(
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inputSize, inputChannels, kernelSize, stride, outputChannels, padding, CUDANet::Layers::ActivationType::NONE
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);
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int batchNormSize = conv->getOutputSize();
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CUDANet::Layers::BatchNorm *batchNorm =
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new CUDANet::Layers::BatchNorm(
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batchNormSize, outputChannels, 1e-3f, CUDANet::Layers::ActivationType::RELU
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);
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addLayer(prefix + ".conv", conv);
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addLayer(prefix + ".bn", batchNorm);
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}
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private:
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int inputSize;
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int inputChannels;
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int outputChannels;
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};
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17
examples/inception_v3/inception_v3.py
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examples/inception_v3/inception_v3.py
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import torch
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import torchvision
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import sys
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from torchsummary import summary
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inception = torchvision.models.inception_v3(weights=torchvision.models.Inception_V3_Weights.DEFAULT)
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inception.eval()
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sys.path.append('../../tools') # Ugly hack
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from utils import export_model_weights, print_model_parameters
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print_model_parameters(inception) # print layer names and number of parameters
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inception.cuda()
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summary(inception, (3, 299, 299))
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