├── .gitignore
├── LearningLibTorch
    ├── LearningLibTorch.sln
    └── LearningLibTorch
    │   ├── DeepLearning.cpp
    │   ├── LearningLibTorch.vcxproj
    │   ├── LearningLibTorch.vcxproj.filters
    │   └── LearningLibTorch.vcxproj.user
├── README.md
└── data
    ├── t10k-images-idx3-ubyte
    ├── t10k-labels-idx1-ubyte
    ├── train-images-idx3-ubyte
    └── train-labels-idx1-ubyte


/.gitignore:
--------------------------------------------------------------------------------
 1 | # Prerequisites
 2 | *.d
 3 | 
 4 | # Compiled Object files
 5 | *.slo
 6 | *.lo
 7 | *.o
 8 | *.obj
 9 | 
10 | # Precompiled Headers
11 | *.gch
12 | *.pch
13 | 
14 | # Compiled Dynamic libraries
15 | *.so
16 | *.dylib
17 | *.dll
18 | 
19 | # Fortran module files
20 | *.mod
21 | *.smod
22 | 
23 | # Compiled Static libraries
24 | *.lai
25 | *.la
26 | *.a
27 | *.lib
28 | 
29 | # Executables
30 | *.exe
31 | *.out
32 | *.app
33 | 
34 | **/x64/**
35 | */.vs/*
36 | 


--------------------------------------------------------------------------------
/LearningLibTorch/LearningLibTorch.sln:
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 7 | EndProject
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10 | 		Debug|x64 = Debug|x64
11 | 		Debug|x86 = Debug|x86
12 | 		Release|x64 = Release|x64
13 | 		Release|x86 = Release|x86
14 | 	EndGlobalSection
15 | 	GlobalSection(ProjectConfigurationPlatforms) = postSolution
16 | 		{05769FE2-11F1-476C-BAA2-2F04EC03339B}.Debug|x64.ActiveCfg = Debug|x64
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18 | 		{05769FE2-11F1-476C-BAA2-2F04EC03339B}.Debug|x86.ActiveCfg = Debug|Win32
19 | 		{05769FE2-11F1-476C-BAA2-2F04EC03339B}.Debug|x86.Build.0 = Debug|Win32
20 | 		{05769FE2-11F1-476C-BAA2-2F04EC03339B}.Release|x64.ActiveCfg = Release|x64
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--------------------------------------------------------------------------------
/LearningLibTorch/LearningLibTorch/DeepLearning.cpp:
--------------------------------------------------------------------------------
  1 | #include <torch/torch.h>
  2 | 
  3 | #include <cstddef>
  4 | #include <cstdio>
  5 | #include <iostream>
  6 | #include <string>
  7 | #include <vector>
  8 | #include <string>
  9 | 
 10 | // Where to find the MNIST dataset.
 11 | const std::string kDataRoot = R"(..\..\data)";
 12 | 
 13 | // The batch size for training.
 14 | const int64_t kTrainBatchSize = 64;
 15 | 
 16 | // The batch size for testing.
 17 | const int64_t kTestBatchSize = 1000;
 18 | 
 19 | // The number of epochs to train.
 20 | const int64_t kNumberOfEpochs = 10;
 21 | 
 22 | // After how many batches to log a new update with the loss value.
 23 | const int64_t kLogInterval = 10;
 24 | 
 25 | struct Net : torch::nn::Module {
 26 |     Net()
 27 |         : conv1(torch::nn::Conv2dOptions(1, 10, /*kernel_size=*/5)),
 28 |         conv2(torch::nn::Conv2dOptions(10, 20, /*kernel_size=*/5)),
 29 |         fc1(320, 50),
 30 |         fc2(50, 10) {
 31 |         register_module("conv1", conv1);
 32 |         register_module("conv2", conv2);
 33 |         register_module("conv2_drop", conv2_drop);
 34 |         register_module("fc1", fc1);
 35 |         register_module("fc2", fc2);
 36 |     }
 37 | 
 38 |     torch::Tensor forward(torch::Tensor x) {
 39 |         x = torch::relu(torch::max_pool2d(conv1->forward(x), 2));
 40 |         x = torch::relu(
 41 |             torch::max_pool2d(conv2_drop->forward(conv2->forward(x)), 2));
 42 |         x = x.view({ -1, 320 });
 43 |         x = torch::relu(fc1->forward(x));
 44 |         x = torch::dropout(x, /*p=*/0.5, /*training=*/is_training());
 45 |         x = fc2->forward(x);
 46 |         return torch::log_softmax(x, /*dim=*/1);
 47 |     }
 48 | 
 49 |     torch::nn::Conv2d conv1;
 50 |     torch::nn::Conv2d conv2;
 51 |     torch::nn::Dropout2d conv2_drop;
 52 |     torch::nn::Linear fc1;
 53 |     torch::nn::Linear fc2;
 54 | };
 55 | 
 56 | template <typename DataLoader>
 57 | void train(
 58 |     size_t epoch,
 59 |     Net& model,
 60 |     torch::Device device,
 61 |     DataLoader& data_loader,
 62 |     torch::optim::Optimizer& optimizer,
 63 |     size_t dataset_size) {
 64 |     model.train();
 65 |     size_t batch_idx = 0;
 66 |     for (auto& batch : data_loader) {
 67 |         auto data = batch.data.to(device), targets = batch.target.to(device);
 68 |         optimizer.zero_grad();
 69 |         auto output = model.forward(data);
 70 |         auto loss = torch::nll_loss(output, targets);
 71 |         AT_ASSERT(!std::isnan(loss.template item<float>()));
 72 |         loss.backward();
 73 |         optimizer.step();
 74 | 
 75 |         if (batch_idx++ % kLogInterval == 0) {
 76 |             std::printf(
 77 |                 "\rTrain Epoch: %ld [%5ld/%5ld] Loss: %.4f",
 78 |                 epoch,
 79 |                 batch_idx * batch.data.size(0),
 80 |                 dataset_size,
 81 |                 loss.template item<float>());
 82 |         }
 83 |     }
 84 | }
 85 | 
 86 | template <typename DataLoader>
 87 | void test(
 88 |     Net& model,
 89 |     torch::Device device,
 90 |     DataLoader& data_loader,
 91 |     size_t dataset_size) {
 92 |     torch::NoGradGuard no_grad;
 93 |     model.eval();
 94 |     double test_loss = 0;
 95 |     int32_t correct = 0;
 96 |     for (const auto& batch : data_loader) {
 97 |         auto data = batch.data.to(device), targets = batch.target.to(device);
 98 |         auto output = model.forward(data);
 99 |         test_loss += torch::nll_loss(
100 |             output,
101 |             targets,
102 |             /*weight=*/{},
103 |             torch::Reduction::Sum)
104 |             .template item<float>();
105 |         auto pred = output.argmax(1);
106 |         correct += pred.eq(targets).sum().template item<int64_t>();
107 |     }
108 | 
109 |     test_loss /= dataset_size;
110 |     std::printf(
111 |         "\nTest set: Average loss: %.4f | Accuracy: %.3f\n",
112 |         test_loss,
113 |         static_cast<double>(correct) / dataset_size);
114 | }
115 | 
116 | auto main() -> int {
117 |     torch::manual_seed(1);
118 | 
119 |     torch::DeviceType device_type;
120 |     if (torch::cuda::is_available()) {
121 |         std::cout << "CUDA available! Training on GPU." << std::endl;
122 |         device_type = torch::kCUDA;
123 |     }
124 |     else {
125 |         std::cout << "Training on CPU." << std::endl;
126 |         device_type = torch::kCPU;
127 |     }
128 |     torch::Device device(device_type);
129 | 
130 |     Net model;
131 |     model.to(device);
132 | 
133 |     auto train_dataset = torch::data::datasets::MNIST(kDataRoot)
134 |         .map(torch::data::transforms::Normalize<>(0.1307, 0.3081))
135 |         .map(torch::data::transforms::Stack<>());
136 |     const size_t train_dataset_size = train_dataset.size().value();
137 |     auto train_loader =
138 |         torch::data::make_data_loader<torch::data::samplers::SequentialSampler>(
139 |             std::move(train_dataset), kTrainBatchSize);
140 | 
141 |     auto test_dataset = torch::data::datasets::MNIST(
142 |         kDataRoot, torch::data::datasets::MNIST::Mode::kTest)
143 |         .map(torch::data::transforms::Normalize<>(0.1307, 0.3081))
144 |         .map(torch::data::transforms::Stack<>());
145 |     const size_t test_dataset_size = test_dataset.size().value();
146 |     auto test_loader =
147 |         torch::data::make_data_loader(std::move(test_dataset), kTestBatchSize);
148 | 
149 |     torch::optim::SGD optimizer(
150 |         model.parameters(), torch::optim::SGDOptions(0.01).momentum(0.5));
151 | 
152 |     for (size_t epoch = 1; epoch <= kNumberOfEpochs; ++epoch) {
153 |         train(epoch, model, device, *train_loader, optimizer, train_dataset_size);
154 |         test(model, device, *test_loader, test_dataset_size);
155 |     }
156 | }


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/README.md:
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1 | # libtorch-mnist-visual-studio
2 | This repository contains a visual studio project for training a classifier on the mnist dataset using the libtorch c++ wrapper.
3 | 
4 | The tutorial can be found here: 
5 | 
6 | https://expoundai.wordpress.com/2020/10/13/setting-up-a-cpp-project-in-visual-studio-2019-with-libtorch-1-6/
7 | 


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