Change Cifar10 to CIFAR10

Author: mfl28

tutorials/intermediate/deep_residual_network/include/cifar10.h

@@ -7,26 +7,26 @@
 #include <cstddef>
 #include <string>
 
-// Cifar10 dataset
+// CIFAR10 dataset
 // based on: https://github.com/pytorch/pytorch/blob/master/torch/csrc/api/include/torch/data/datasets/mnist.h.
-class Cifar10 : public torch::data::datasets::Dataset<Cifar10> {
+class CIFAR10 : public torch::data::datasets::Dataset<CIFAR10> {
  public:
     // The mode in which the dataset is loaded
     enum Mode { kTrain, kTest };
 
-    // Loads the Cifar10 dataset from the `root` path.
+    // Loads the CIFAR10 dataset from the `root` path.
     //
     // The supplied `root` path should contain the *content* of the unzipped
-    // Cifar10 dataset (binary version), available from http://www.cs.toronto.edu/~kriz/cifar.html.
-    explicit Cifar10(const std::string& root, Mode mode = Mode::kTrain);
+    // CIFAR10 dataset (binary version), available from http://www.cs.toronto.edu/~kriz/cifar.html.
+    explicit CIFAR10(const std::string& root, Mode mode = Mode::kTrain);
 
     // Returns the `Example` at the given `index`.
     torch::data::Example<> get(size_t index) override;
 
     // Returns the size of the dataset.
     torch::optional<size_t> size() const override;
 
-    // Returns true if this is the training subset of Cifar10.
+    // Returns true if this is the training subset of CIFAR10.
     bool is_train() const noexcept;
 
     // Returns all images stacked into a single tensor.

tutorials/intermediate/deep_residual_network/src/cifar10.cpp

@@ -2,7 +2,7 @@
 #include "cifar10.h"
 
 namespace {
-// Cifar10 dataset description can be found at https://www.cs.toronto.edu/~kriz/cifar.html.
+// CIFAR10 dataset description can be found at https://www.cs.toronto.edu/~kriz/cifar.html.
 constexpr uint32_t kTrainSize = 50000;
 constexpr uint32_t kTestSize = 10000;
 constexpr uint32_t kSizePerBatch = 10000;
@@ -70,29 +70,29 @@ std::pair<torch::Tensor, torch::Tensor> read_data(const std::string& root, bool
 }
 }  // namespace
 
-Cifar10::Cifar10(const std::string& root, Mode mode) : mode_(mode) {
+CIFAR10::CIFAR10(const std::string& root, Mode mode) : mode_(mode) {
     auto data = read_data(root, mode == Mode::kTrain);
 
     images_ = std::move(data.first);
     targets_ = std::move(data.second);
 }
 
-torch::data::Example<> Cifar10::get(size_t index) {
+torch::data::Example<> CIFAR10::get(size_t index) {
     return {images_[index], targets_[index]};
 }
 
-torch::optional<size_t> Cifar10::size() const {
+torch::optional<size_t> CIFAR10::size() const {
     return images_.size(0);
 }
 
-bool Cifar10::is_train() const noexcept {
+bool CIFAR10::is_train() const noexcept {
     return mode_ == Mode::kTrain;
 }
 
-const torch::Tensor& Cifar10::images() const {
+const torch::Tensor& CIFAR10::images() const {
     return images_;
 }
 
-const torch::Tensor& Cifar10::targets() const {
+const torch::Tensor& CIFAR10::targets() const {
     return targets_;
 }

tutorials/intermediate/deep_residual_network/src/main.cpp

@@ -28,8 +28,8 @@ int main() {
 
     const std::string CIFAR_data_path = "../../../../tutorials/intermediate/deep_residual_network/data/";
 
-    // Cifar10 custom dataset
-    auto train_dataset = Cifar10(CIFAR_data_path)
+    // CIFAR10 custom dataset
+    auto train_dataset = CIFAR10(CIFAR_data_path)
         .map(ConstantPad(4))
         .map(RandomHorizontalFlip())
         .map(RandomCrop({32, 32}))
@@ -38,7 +38,7 @@ int main() {
     // Number of samples in the training set
     auto num_train_samples = train_dataset.size().value();
 
-    auto test_dataset = Cifar10(CIFAR_data_path, Cifar10::Mode::kTest)
+    auto test_dataset = CIFAR10(CIFAR_data_path, CIFAR10::Mode::kTest)
         .map(torch::data::transforms::Stack<>());
 
     // Number of samples in the testset