codeformat

PhysicsTools/PyTorch/test/create_simple_dnn.py

@@ -0,0 +1,20 @@
+import torch
+
+class MyModule(torch.nn.Module):
+    def __init__(self, N, M):
+        super(MyModule, self).__init__()
+        self.weight = torch.nn.Parameter(torch.ones(N, M))
+        self.bias = torch.nn.Parameter(torch.ones(N))
+
+    def forward(self, input):
+          return torch.sum(torch.nn.functional.elu(self.weight.mv(input) + self.bias))
+
+
+module = MyModule(10, 10)
+x = torch.ones(10)
+
+tm = torch.jit.trace(module.eval(), x)
+
+print(tm.graph)
+
+tm.save("simple_dnn.pt")

PhysicsTools/PyTorch/test/testBase.h

@@ -21,7 +21,6 @@ class testBasePyTorch : public CppUnit::TestFixture {
   std::string cmsswPath(std::string path);
 
   virtual void test() = 0;
-
 };
 
 void testBasePyTorch::setUp() {

PhysicsTools/PyTorch/test/testBaseCUDA.h

@@ -34,7 +34,6 @@ class testBasePyTorchCUDA : public CppUnit::TestFixture {
   std::string cmsswPath(std::string path);
 
   virtual void test() = 0;
-
 };
 
 void testBasePyTorchCUDA::setUp() {

PhysicsTools/PyTorch/test/testTorchSimpleDnn.cc

@@ -4,7 +4,6 @@
 #include <memory>
 #include <vector>
 
-
 class testSimpleDNN : public testBasePyTorch {
   CPPUNIT_TEST_SUITE(testSimpleDNN);
   CPPUNIT_TEST(test);
@@ -24,25 +23,20 @@ void testSimpleDNN::test() {
     // Deserialize the ScriptModule from a file using torch::jit::load().
     module = torch::jit::load(model_path);
     module.to(device);
-  }
-  catch (const c10::Error& e) {
-
+  } catch (const c10::Error& e) {
     std::cerr << "error loading the model\n" << e.what() << std::endl;
   }
   // Create a vector of inputs.
   std::vector<torch::jit::IValue> inputs;
   inputs.push_back(torch::ones(10, device));
 
-
   // Execute the model and turn its output into a tensor.
   at::Tensor output = module.forward(inputs).toTensor();
-  std::cout << "output: "<< output << '\n';
+  std::cout << "output: " << output << '\n';
   CPPUNIT_ASSERT(output.item<float_t>() == 110.);
   std::cout << "ok\n";
 }
 
-
-
 // int main(int argc, const char* argv[]) {
 //   std::cout << "Running model on CPU" << std::endl;
 //   torch::Device cpu(torch::kCPU);
@@ -51,6 +45,6 @@ void testSimpleDNN::test() {
 //   std::cout << "Running model on CUDA" << std::endl;
 //   torch::Device cuda(torch::kCUDA);
 //   runModel("/data/user/dvalsecc/simple_dnn.pt", cuda);
-  
+
 //   return 0;
 // }

PhysicsTools/PyTorch/test/testTorchSimpleDnnCUDA.cc

@@ -4,7 +4,6 @@
 #include <memory>
 #include <vector>
 
-
 class testSimpleDNNCUDA : public testBasePyTorchCUDA {
   CPPUNIT_TEST_SUITE(testSimpleDNNCUDA);
   CPPUNIT_TEST(test);
@@ -41,34 +40,27 @@ process.add_(cms.Service('CUDAService'))
 
   std::cout << "Testing CUDA backend" << std::endl;
 
-
-
   std::string model_path = testPath_ + "/simple_dnn.pt";
-  torch::Device device(torch::kCUDA );
+  torch::Device device(torch::kCUDA);
   torch::jit::script::Module module;
   try {
     // Deserialize the ScriptModule from a file using torch::jit::load().
     module = torch::jit::load(model_path);
     module.to(device);
-  }
-  catch (const c10::Error& e) {
-
+  } catch (const c10::Error& e) {
     std::cerr << "error loading the model\n" << e.what() << std::endl;
   }
   // Create a vector of inputs.
   std::vector<torch::jit::IValue> inputs;
   inputs.push_back(torch::ones(10, device));
 
-
   // Execute the model and turn its output into a tensor.
   at::Tensor output = module.forward(inputs).toTensor();
-  std::cout << "output: "<< output << '\n';
+  std::cout << "output: " << output << '\n';
   CPPUNIT_ASSERT(output.item<float_t>() == 110.);
   std::cout << "ok\n";
 }
 
-
-
 // int main(int argc, const char* argv[]) {
 //   std::cout << "Running model on CPU" << std::endl;
 //   torch::Device cpu(torch::kCPU);
@@ -77,6 +69,6 @@ process.add_(cms.Service('CUDAService'))
 //   std::cout << "Running model on CUDA" << std::endl;
 //   torch::Device cuda(torch::kCUDA);
 //   runModel("/data/user/dvalsecc/simple_dnn.pt", cuda);
-  
+
 //   return 0;
 // }

PhysicsTools/PyTorch/test/testTorchSimpleDnnCuda.cc

@@ -4,7 +4,6 @@
 #include <memory>
 #include <vector>
 
-
 class testSimpleDNNCUDA : public testBasePyTorchCUDA {
   CPPUNIT_TEST_SUITE(testSimpleDNNCUDA);
   CPPUNIT_TEST(test);
@@ -41,34 +40,27 @@ process.add_(cms.Service('CUDAService'))
 
   std::cout << "Testing CUDA backend" << std::endl;
 
-
-
   std::string model_path = testPath_ + "/simple_dnn.pt";
-  torch::Device device(torch::kCUDA );
+  torch::Device device(torch::kCUDA);
   torch::jit::script::Module module;
   try {
     // Deserialize the ScriptModule from a file using torch::jit::load().
     module = torch::jit::load(model_path);
     module.to(device);
-  }
-  catch (const c10::Error& e) {
-
+  } catch (const c10::Error& e) {
     std::cerr << "error loading the model\n" << e.what() << std::endl;
   }
   // Create a vector of inputs.
   std::vector<torch::jit::IValue> inputs;
   inputs.push_back(torch::ones(10, device));
 
-
   // Execute the model and turn its output into a tensor.
   at::Tensor output = module.forward(inputs).toTensor();
-  std::cout << "output: "<< output << '\n';
+  std::cout << "output: " << output << '\n';
   CPPUNIT_ASSERT(output.item<float_t>() == 110.);
   std::cout << "ok\n";
 }
 
-
-
 // int main(int argc, const char* argv[]) {
 //   std::cout << "Running model on CPU" << std::endl;
 //   torch::Device cpu(torch::kCPU);
@@ -77,6 +69,6 @@ process.add_(cms.Service('CUDAService'))
 //   std::cout << "Running model on CUDA" << std::endl;
 //   torch::Device cuda(torch::kCUDA);
 //   runModel("/data/user/dvalsecc/simple_dnn.pt", cuda);
-  
+
 //   return 0;
 // }