Deprecate the C++ vision::models namespace (#4375)

* Add deprecation warnings on vision::models

* Change the C++ example.

* Chage readme.

* Update deprecation warning.

README.rst

@@ -106,9 +106,9 @@ otherwise, add the include and library paths in the environment variables ``TORC
 .. _libjpeg: http://ijg.org/
 .. _libjpeg-turbo: https://libjpeg-turbo.org/
 
-C++ API
-=======
-TorchVision also offers a C++ API that contains C++ equivalent of python models.
+Using the models on C++
+=======================
+TorchVision provides an example project for how to use the models on C++ using JIT Script.
 
 Installation From source:
 

examples/cpp/hello_world/main.cpp

@@ -1,25 +1,44 @@
 #include <iostream>
+#include <torch/script.h>
 #include <torch/torch.h>
 #include <torchvision/vision.h>
-#include <torchvision/models/resnet.h>
 
-int main()
-{
-  auto model = vision::models::ResNet18();
-  model->eval();
+int main() {
+  torch::DeviceType device_type;
+  device_type = torch::kCPU;
 
-  // Create a random input tensor and run it through the model.
-  auto in = torch::rand({1, 3, 10, 10});
-  auto out = model->forward(in);
+  torch::jit::script::Module model;
+  try {
+    std::cout << "Loading model\n";
+    // Deserialize the ScriptModule from a file using torch::jit::load().
+    model = torch::jit::load("resnet18.pt");
+    std::cout << "Model loaded\n";
+  } catch (const torch::Error& e) {
+    std::cout << "error loading the model\n";
+    return -1;
+  } catch (const std::exception& e) {
+    std::cout << "Other error: " << e.what() << "\n";
+    return -1;
+  }
 
-  std::cout << out.sizes();
+  // TorchScript models require a List[IValue] as input
+  std::vector<torch::jit::IValue> inputs;
+
+  // Create a random input tensor and run it through the model.
+  inputs.push_back(torch::rand({1, 3, 10, 10}));
+  auto out = model.forward(inputs);
+  std::cout << out << "\n";
 
   if (torch::cuda::is_available()) {
     // Move model and inputs to GPU
-    model->to(torch::kCUDA);
-    auto gpu_in = in.to(torch::kCUDA);
-    auto gpu_out = model->forward(gpu_in);
+    model.to(torch::kCUDA);
+
+    // Add GPU inputs
+    inputs.clear();
+    torch::TensorOptions options = torch::TensorOptions{torch::kCUDA};
+    inputs.push_back(torch::rand({1, 3, 10, 10}, options));
 
-    std::cout << gpu_out.sizes();
+    auto gpu_out = model.forward(inputs);
+    std::cout << gpu_out << "\n";
   }
 }

examples/cpp/hello_world/trace_model.py

@@ -0,0 +1,13 @@
+import os.path as osp
+
+import torch
+import torchvision
+
+HERE = osp.dirname(osp.abspath(__file__))
+ASSETS = osp.dirname(osp.dirname(HERE))
+
+model = torchvision.models.resnet18(pretrained=False)
+model.eval()
+
+traced_model = torch.jit.script(model)
+traced_model.save("resnet18.pt")

packaging/build_cmake.sh

@@ -98,13 +98,18 @@ fi
 # Compile and run the CPP example
 popd
 cd examples/cpp/hello_world
-
 mkdir build
+
+# Trace model
+python trace_model.py
+cp resnet18.pt build
+
 cd build
 cmake .. -DTorch_DIR=$TORCH_PATH/share/cmake/Torch
 
 if [[ "$OSTYPE" == "msys" ]]; then
     "$script_dir/windows/internal/vc_env_helper.bat" "$script_dir/windows/internal/build_cpp_example.bat" $PARALLELISM
+    mv resnet18.pt Release
     cd Release
 else
     make -j$PARALLELISM

torchvision/csrc/models/alexnet.cpp

@@ -32,6 +32,8 @@ AlexNetImpl::AlexNetImpl(int64_t num_classes) {
 
   register_module("features", features);
   register_module("classifier", classifier);
+
+  modelsimpl::deprecation_warning();
 }
 
 torch::Tensor AlexNetImpl::forward(torch::Tensor x) {

torchvision/csrc/models/densenet.cpp

@@ -142,6 +142,8 @@ DenseNetImpl::DenseNetImpl(
     } else if (auto M = dynamic_cast<torch::nn::LinearImpl*>(module.get()))
       torch::nn::init::constant_(M->bias, 0);
   }
+
+  modelsimpl::deprecation_warning();
 }
 
 torch::Tensor DenseNetImpl::forward(torch::Tensor x) {

torchvision/csrc/models/googlenet.cpp

@@ -1,5 +1,7 @@
 #include "googlenet.h"
 
+#include "modelsimpl.h"
+
 namespace vision {
 namespace models {
 
@@ -143,6 +145,8 @@ GoogLeNetImpl::GoogLeNetImpl(
 
   if (init_weights)
     _initialize_weights();
+
+  modelsimpl::deprecation_warning();
 }
 
 void GoogLeNetImpl::_initialize_weights() {

torchvision/csrc/models/inception.cpp

@@ -1,5 +1,7 @@
 #include "inception.h"
 
+#include "modelsimpl.h"
+
 namespace vision {
 namespace models {
 
@@ -297,6 +299,8 @@ InceptionV3Impl::InceptionV3Impl(
   register_module("Mixed_7b", Mixed_7b);
   register_module("Mixed_7c", Mixed_7c);
   register_module("fc", fc);
+
+  modelsimpl::deprecation_warning();
 }
 
 InceptionV3Output InceptionV3Impl::forward(torch::Tensor x) {

torchvision/csrc/models/mnasnet.cpp

@@ -158,6 +158,8 @@ MNASNetImpl::MNASNetImpl(double alpha, int64_t num_classes, double dropout) {
   register_module("classifier", classifier);
 
   _initialize_weights();
+
+  modelsimpl::deprecation_warning();
 }
 
 torch::Tensor MNASNetImpl::forward(torch::Tensor x) {

torchvision/csrc/models/mobilenet.cpp

@@ -146,6 +146,8 @@ MobileNetV2Impl::MobileNetV2Impl(
       torch::nn::init::zeros_(M->bias);
     }
   }
+
+  modelsimpl::deprecation_warning();
 }
 
 torch::Tensor MobileNetV2Impl::forward(at::Tensor x) {

torchvision/csrc/models/modelsimpl.h

@@ -34,6 +34,13 @@ inline bool double_compare(double a, double b) {
   return double(std::abs(a - b)) < std::numeric_limits<double>::epsilon();
 };
 
+inline void deprecation_warning() {
+  TORCH_WARN_ONCE(
+      "The vision::models namespace is not actively maintained, use at "
+      "your own discretion. We recommend using Torch Script instead: "
+      "https://pytorch.org/tutorials/advanced/cpp_export.html");
+}
+
 } // namespace modelsimpl
 } // namespace models
 } // namespace vision

torchvision/csrc/models/resnet.h

@@ -2,6 +2,7 @@
 
 #include <torch/nn.h>
 #include "../macros.h"
+#include "modelsimpl.h"
 
 namespace vision {
 namespace models {
@@ -164,6 +165,8 @@ ResNetImpl<Block>::ResNetImpl(
       else if (auto* M = dynamic_cast<_resnetimpl::BasicBlock*>(module.get()))
         torch::nn::init::constant_(M->bn2->weight, 0);
     }
+
+  modelsimpl::deprecation_warning();
 }
 
 template <typename Block>

torchvision/csrc/models/shufflenetv2.cpp

@@ -146,6 +146,8 @@ ShuffleNetV2Impl::ShuffleNetV2Impl(
   register_module("stage4", stage4);
   register_module("conv2", conv5);
   register_module("fc", fc);
+
+  modelsimpl::deprecation_warning();
 }
 
 torch::Tensor ShuffleNetV2Impl::forward(torch::Tensor x) {

torchvision/csrc/models/squeezenet.cpp

@@ -93,6 +93,8 @@ SqueezeNetImpl::SqueezeNetImpl(double version, int64_t num_classes)
       if (M->options.bias())
         torch::nn::init::constant_(M->bias, 0);
     }
+
+  modelsimpl::deprecation_warning();
 }
 
 torch::Tensor SqueezeNetImpl::forward(torch::Tensor x) {

torchvision/csrc/models/vgg.cpp

@@ -69,6 +69,8 @@ VGGImpl::VGGImpl(
 
   if (initialize_weights)
     _initialize_weights();
+
+  modelsimpl::deprecation_warning();
 }
 
 torch::Tensor VGGImpl::forward(torch::Tensor x) {