Define all C++ model constructors explicit (#2944)

* Making all model constructors explicit.

* formatting.

torchvision/csrc/models/alexnet.h

@@ -11,7 +11,7 @@ namespace models {
 struct VISION_API AlexNetImpl : torch::nn::Module {
   torch::nn::Sequential features{nullptr}, classifier{nullptr};
 
-  AlexNetImpl(int64_t num_classes = 1000);
+  explicit AlexNetImpl(int64_t num_classes = 1000);
 
   torch::Tensor forward(torch::Tensor x);
 };

torchvision/csrc/models/densenet.h

@@ -23,7 +23,7 @@ struct VISION_API DenseNetImpl : torch::nn::Module {
   torch::nn::Sequential features{nullptr};
   torch::nn::Linear classifier{nullptr};
 
-  DenseNetImpl(
+  explicit DenseNetImpl(
       int64_t num_classes = 1000,
       int64_t growth_rate = 32,
       const std::vector<int64_t>& block_config = {6, 12, 24, 16},
@@ -35,7 +35,7 @@ struct VISION_API DenseNetImpl : torch::nn::Module {
 };
 
 struct VISION_API DenseNet121Impl : DenseNetImpl {
-  DenseNet121Impl(
+  explicit DenseNet121Impl(
       int64_t num_classes = 1000,
       int64_t growth_rate = 32,
       const std::vector<int64_t>& block_config = {6, 12, 24, 16},
@@ -45,7 +45,7 @@ struct VISION_API DenseNet121Impl : DenseNetImpl {
 };
 
 struct VISION_API DenseNet169Impl : DenseNetImpl {
-  DenseNet169Impl(
+  explicit DenseNet169Impl(
       int64_t num_classes = 1000,
       int64_t growth_rate = 32,
       const std::vector<int64_t>& block_config = {6, 12, 32, 32},
@@ -55,7 +55,7 @@ struct VISION_API DenseNet169Impl : DenseNetImpl {
 };
 
 struct VISION_API DenseNet201Impl : DenseNetImpl {
-  DenseNet201Impl(
+  explicit DenseNet201Impl(
       int64_t num_classes = 1000,
       int64_t growth_rate = 32,
       const std::vector<int64_t>& block_config = {6, 12, 48, 32},
@@ -65,7 +65,7 @@ struct VISION_API DenseNet201Impl : DenseNetImpl {
 };
 
 struct VISION_API DenseNet161Impl : DenseNetImpl {
-  DenseNet161Impl(
+  explicit DenseNet161Impl(
       int64_t num_classes = 1000,
       int64_t growth_rate = 48,
       const std::vector<int64_t>& block_config = {6, 12, 36, 24},

torchvision/csrc/models/googlenet.h

@@ -12,7 +12,7 @@ struct VISION_API BasicConv2dImpl : torch::nn::Module {
   torch::nn::Conv2d conv{nullptr};
   torch::nn::BatchNorm2d bn{nullptr};
 
-  BasicConv2dImpl(torch::nn::Conv2dOptions options);
+  explicit BasicConv2dImpl(torch::nn::Conv2dOptions options);
 
   torch::Tensor forward(torch::Tensor x);
 };
@@ -71,7 +71,7 @@ struct VISION_API GoogLeNetImpl : torch::nn::Module {
   torch::nn::Dropout dropout{nullptr};
   torch::nn::Linear fc{nullptr};
 
-  GoogLeNetImpl(
+  explicit GoogLeNetImpl(
       int64_t num_classes = 1000,
       bool aux_logits = true,
       bool transform_input = false,

torchvision/csrc/models/inception.h

@@ -11,7 +11,9 @@ struct VISION_API BasicConv2dImpl : torch::nn::Module {
   torch::nn::Conv2d conv{nullptr};
   torch::nn::BatchNorm2d bn{nullptr};
 
-  BasicConv2dImpl(torch::nn::Conv2dOptions options, double std_dev = 0.1);
+  explicit BasicConv2dImpl(
+      torch::nn::Conv2dOptions options,
+      double std_dev = 0.1);
 
   torch::Tensor forward(torch::Tensor x);
 };
@@ -30,7 +32,7 @@ struct VISION_API InceptionAImpl : torch::nn::Module {
 struct VISION_API InceptionBImpl : torch::nn::Module {
   BasicConv2d branch3x3, branch3x3dbl_1, branch3x3dbl_2, branch3x3dbl_3;
 
-  InceptionBImpl(int64_t in_channels);
+  explicit InceptionBImpl(int64_t in_channels);
 
   torch::Tensor forward(const torch::Tensor& x);
 };
@@ -50,7 +52,7 @@ struct VISION_API InceptionDImpl : torch::nn::Module {
   BasicConv2d branch3x3_1, branch3x3_2, branch7x7x3_1, branch7x7x3_2,
       branch7x7x3_3, branch7x7x3_4;
 
-  InceptionDImpl(int64_t in_channels);
+  explicit InceptionDImpl(int64_t in_channels);
 
   torch::Tensor forward(const torch::Tensor& x);
 };
@@ -60,7 +62,7 @@ struct VISION_API InceptionEImpl : torch::nn::Module {
       branch3x3dbl_1, branch3x3dbl_2, branch3x3dbl_3a, branch3x3dbl_3b,
       branch_pool;
 
-  InceptionEImpl(int64_t in_channels);
+  explicit InceptionEImpl(int64_t in_channels);
 
   torch::Tensor forward(const torch::Tensor& x);
 };
@@ -110,7 +112,7 @@ struct VISION_API InceptionV3Impl : torch::nn::Module {
 
   _inceptionimpl::InceptionAux AuxLogits{nullptr};
 
-  InceptionV3Impl(
+  explicit InceptionV3Impl(
       int64_t num_classes = 1000,
       bool aux_logits = true,
       bool transform_input = false);

torchvision/csrc/models/mnasnet.h

@@ -11,25 +11,28 @@ struct VISION_API MNASNetImpl : torch::nn::Module {
 
   void _initialize_weights();
 
-  MNASNetImpl(double alpha, int64_t num_classes = 1000, double dropout = .2);
+  explicit MNASNetImpl(
+      double alpha,
+      int64_t num_classes = 1000,
+      double dropout = .2);
 
   torch::Tensor forward(torch::Tensor x);
 };
 
 struct MNASNet0_5Impl : MNASNetImpl {
-  MNASNet0_5Impl(int64_t num_classes = 1000, double dropout = .2);
+  explicit MNASNet0_5Impl(int64_t num_classes = 1000, double dropout = .2);
 };
 
 struct MNASNet0_75Impl : MNASNetImpl {
-  MNASNet0_75Impl(int64_t num_classes = 1000, double dropout = .2);
+  explicit MNASNet0_75Impl(int64_t num_classes = 1000, double dropout = .2);
 };
 
 struct MNASNet1_0Impl : MNASNetImpl {
-  MNASNet1_0Impl(int64_t num_classes = 1000, double dropout = .2);
+  explicit MNASNet1_0Impl(int64_t num_classes = 1000, double dropout = .2);
 };
 
 struct MNASNet1_3Impl : MNASNetImpl {
-  MNASNet1_3Impl(int64_t num_classes = 1000, double dropout = .2);
+  explicit MNASNet1_3Impl(int64_t num_classes = 1000, double dropout = .2);
 };
 
 TORCH_MODULE(MNASNet);

torchvision/csrc/models/mobilenet.h

@@ -10,7 +10,7 @@ struct VISION_API MobileNetV2Impl : torch::nn::Module {
   int64_t last_channel;
   torch::nn::Sequential features, classifier;
 
-  MobileNetV2Impl(
+  explicit MobileNetV2Impl(
       int64_t num_classes = 1000,
       double width_mult = 1.0,
       std::vector<std::vector<int64_t>> inverted_residual_settings = {},

torchvision/csrc/models/resnet.h

@@ -80,7 +80,7 @@ struct ResNetImpl : torch::nn::Module {
       int64_t blocks,
       int64_t stride = 1);
 
-  ResNetImpl(
+  explicit ResNetImpl(
       const std::vector<int>& layers,
       int64_t num_classes = 1000,
       bool zero_init_residual = false,
@@ -186,45 +186,55 @@ torch::Tensor ResNetImpl<Block>::forward(torch::Tensor x) {
 }
 
 struct VISION_API ResNet18Impl : ResNetImpl<_resnetimpl::BasicBlock> {
-  ResNet18Impl(int64_t num_classes = 1000, bool zero_init_residual = false);
+  explicit ResNet18Impl(
+      int64_t num_classes = 1000,
+      bool zero_init_residual = false);
 };
 
 struct VISION_API ResNet34Impl : ResNetImpl<_resnetimpl::BasicBlock> {
-  ResNet34Impl(int64_t num_classes = 1000, bool zero_init_residual = false);
+  explicit ResNet34Impl(
+      int64_t num_classes = 1000,
+      bool zero_init_residual = false);
 };
 
 struct VISION_API ResNet50Impl : ResNetImpl<_resnetimpl::Bottleneck> {
-  ResNet50Impl(int64_t num_classes = 1000, bool zero_init_residual = false);
+  explicit ResNet50Impl(
+      int64_t num_classes = 1000,
+      bool zero_init_residual = false);
 };
 
 struct VISION_API ResNet101Impl : ResNetImpl<_resnetimpl::Bottleneck> {
-  ResNet101Impl(int64_t num_classes = 1000, bool zero_init_residual = false);
+  explicit ResNet101Impl(
+      int64_t num_classes = 1000,
+      bool zero_init_residual = false);
 };
 
 struct VISION_API ResNet152Impl : ResNetImpl<_resnetimpl::Bottleneck> {
-  ResNet152Impl(int64_t num_classes = 1000, bool zero_init_residual = false);
+  explicit ResNet152Impl(
+      int64_t num_classes = 1000,
+      bool zero_init_residual = false);
 };
 
 struct VISION_API ResNext50_32x4dImpl : ResNetImpl<_resnetimpl::Bottleneck> {
-  ResNext50_32x4dImpl(
+  explicit ResNext50_32x4dImpl(
       int64_t num_classes = 1000,
       bool zero_init_residual = false);
 };
 
 struct VISION_API ResNext101_32x8dImpl : ResNetImpl<_resnetimpl::Bottleneck> {
-  ResNext101_32x8dImpl(
+  explicit ResNext101_32x8dImpl(
       int64_t num_classes = 1000,
       bool zero_init_residual = false);
 };
 
 struct VISION_API WideResNet50_2Impl : ResNetImpl<_resnetimpl::Bottleneck> {
-  WideResNet50_2Impl(
+  explicit WideResNet50_2Impl(
       int64_t num_classes = 1000,
       bool zero_init_residual = false);
 };
 
 struct VISION_API WideResNet101_2Impl : ResNetImpl<_resnetimpl::Bottleneck> {
-  WideResNet101_2Impl(
+  explicit WideResNet101_2Impl(
       int64_t num_classes = 1000,
       bool zero_init_residual = false);
 };

torchvision/csrc/models/shufflenetv2.h

@@ -21,19 +21,19 @@ struct VISION_API ShuffleNetV2Impl : torch::nn::Module {
 };
 
 struct VISION_API ShuffleNetV2_x0_5Impl : ShuffleNetV2Impl {
-  ShuffleNetV2_x0_5Impl(int64_t num_classes = 1000);
+  explicit ShuffleNetV2_x0_5Impl(int64_t num_classes = 1000);
 };
 
 struct VISION_API ShuffleNetV2_x1_0Impl : ShuffleNetV2Impl {
-  ShuffleNetV2_x1_0Impl(int64_t num_classes = 1000);
+  explicit ShuffleNetV2_x1_0Impl(int64_t num_classes = 1000);
 };
 
 struct VISION_API ShuffleNetV2_x1_5Impl : ShuffleNetV2Impl {
-  ShuffleNetV2_x1_5Impl(int64_t num_classes = 1000);
+  explicit ShuffleNetV2_x1_5Impl(int64_t num_classes = 1000);
 };
 
 struct VISION_API ShuffleNetV2_x2_0Impl : ShuffleNetV2Impl {
-  ShuffleNetV2_x2_0Impl(int64_t num_classes = 1000);
+  explicit ShuffleNetV2_x2_0Impl(int64_t num_classes = 1000);
 };
 
 TORCH_MODULE(ShuffleNetV2);

torchvision/csrc/models/squeezenet.h

@@ -10,7 +10,7 @@ struct VISION_API SqueezeNetImpl : torch::nn::Module {
   int64_t num_classes;
   torch::nn::Sequential features{nullptr}, classifier{nullptr};
 
-  SqueezeNetImpl(double version = 1.0, int64_t num_classes = 1000);
+  explicit SqueezeNetImpl(double version = 1.0, int64_t num_classes = 1000);
 
   torch::Tensor forward(torch::Tensor x);
 };
@@ -19,7 +19,7 @@ struct VISION_API SqueezeNetImpl : torch::nn::Module {
 // accuracy with 50x fewer parameters and <0.5MB model size"
 // <https://arxiv.org/abs/1602.07360> paper.
 struct VISION_API SqueezeNet1_0Impl : SqueezeNetImpl {
-  SqueezeNet1_0Impl(int64_t num_classes = 1000);
+  explicit SqueezeNet1_0Impl(int64_t num_classes = 1000);
 };
 
 // SqueezeNet 1.1 model from the official SqueezeNet repo
@@ -27,7 +27,7 @@ struct VISION_API SqueezeNet1_0Impl : SqueezeNetImpl {
 // SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
 // than SqueezeNet 1.0, without sacrificing accuracy.
 struct VISION_API SqueezeNet1_1Impl : SqueezeNetImpl {
-  SqueezeNet1_1Impl(int64_t num_classes = 1000);
+  explicit SqueezeNet1_1Impl(int64_t num_classes = 1000);
 };
 
 TORCH_MODULE(SqueezeNet);

torchvision/csrc/models/vgg.h

@@ -11,7 +11,7 @@ struct VISION_API VGGImpl : torch::nn::Module {
 
   void _initialize_weights();
 
-  VGGImpl(
+  explicit VGGImpl(
       const torch::nn::Sequential& features,
       int64_t num_classes = 1000,
       bool initialize_weights = true);
@@ -21,42 +21,58 @@ struct VISION_API VGGImpl : torch::nn::Module {
 
 // VGG 11-layer model (configuration "A")
 struct VISION_API VGG11Impl : VGGImpl {
-  VGG11Impl(int64_t num_classes = 1000, bool initialize_weights = true);
+  explicit VGG11Impl(
+      int64_t num_classes = 1000,
+      bool initialize_weights = true);
 };
 
 // VGG 13-layer model (configuration "B")
 struct VISION_API VGG13Impl : VGGImpl {
-  VGG13Impl(int64_t num_classes = 1000, bool initialize_weights = true);
+  explicit VGG13Impl(
+      int64_t num_classes = 1000,
+      bool initialize_weights = true);
 };
 
 // VGG 16-layer model (configuration "D")
 struct VISION_API VGG16Impl : VGGImpl {
-  VGG16Impl(int64_t num_classes = 1000, bool initialize_weights = true);
+  explicit VGG16Impl(
+      int64_t num_classes = 1000,
+      bool initialize_weights = true);
 };
 
 // VGG 19-layer model (configuration "E")
 struct VISION_API VGG19Impl : VGGImpl {
-  VGG19Impl(int64_t num_classes = 1000, bool initialize_weights = true);
+  explicit VGG19Impl(
+      int64_t num_classes = 1000,
+      bool initialize_weights = true);
 };
 
 // VGG 11-layer model (configuration "A") with batch normalization
 struct VISION_API VGG11BNImpl : VGGImpl {
-  VGG11BNImpl(int64_t num_classes = 1000, bool initialize_weights = true);
+  explicit VGG11BNImpl(
+      int64_t num_classes = 1000,
+      bool initialize_weights = true);
 };
 
 // VGG 13-layer model (configuration "B") with batch normalization
 struct VISION_API VGG13BNImpl : VGGImpl {
-  VGG13BNImpl(int64_t num_classes = 1000, bool initialize_weights = true);
+  explicit VGG13BNImpl(
+      int64_t num_classes = 1000,
+      bool initialize_weights = true);
 };
 
 // VGG 16-layer model (configuration "D") with batch normalization
 struct VISION_API VGG16BNImpl : VGGImpl {
-  VGG16BNImpl(int64_t num_classes = 1000, bool initialize_weights = true);
+  explicit VGG16BNImpl(
+      int64_t num_classes = 1000,
+      bool initialize_weights = true);
 };
 
 // VGG 19-layer model (configuration 'E') with batch normalization
 struct VISION_API VGG19BNImpl : VGGImpl {
-  VGG19BNImpl(int64_t num_classes = 1000, bool initialize_weights = true);
+  explicit VGG19BNImpl(
+      int64_t num_classes = 1000,
+      bool initialize_weights = true);
 };
 
 TORCH_MODULE(VGG);