Improved C++ code

Author: ankane

ext/torch/device.cpp

@@ -1,6 +1,9 @@
+#include <string>
+
 #include <torch/torch.h>
 
 #include <rice/rice.hpp>
+#include <rice/stl.hpp>
 
 #include "utils.h"
 

ext/torch/ext.cpp

@@ -17,8 +17,7 @@ void init_ivalue(Rice::Module& m, Rice::Class& rb_cIValue);
 void init_random(Rice::Module& m);
 
 extern "C"
-void Init_ext()
-{
+void Init_ext() {
   auto m = Rice::define_module("Torch");
 
   // need to define certain classes up front to keep Rice happy

ext/torch/ivalue.cpp

@@ -1,3 +1,5 @@
+#include <utility>
+
 #include <torch/torch.h>
 
 #include <rice/rice.hpp>

ext/torch/nn.cpp

@@ -1,3 +1,5 @@
+#include <utility>
+
 #include <torch/torch.h>
 
 #include <rice/rice.hpp>

ext/torch/ruby_arg_parser.cpp

@@ -1,5 +1,10 @@
 // adapted from PyTorch - python_arg_parser.cpp
 
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
 #include "ruby_arg_parser.h"
 
 VALUE THPGeneratorClass = Qnil;
@@ -99,7 +104,7 @@ FunctionParameter::FunctionParameter(const std::string& fmt, bool keyword_only)
   ruby_name = THPUtils_internSymbol(name);
   auto np_compat_it = numpy_compatibility_arg_names.find(name);
   if (np_compat_it != numpy_compatibility_arg_names.end()) {
-    for (const auto& str: np_compat_it->second) {
+    for (const auto& str : np_compat_it->second) {
       numpy_python_names.push_back(THPUtils_internSymbol(str));
     }
   }
@@ -190,8 +195,7 @@ static bool is_int_or_symint_list(VALUE obj, int broadcast_size) {
 }
 
 // argnum is needed for raising the TypeError, it's used in the error message.
-auto FunctionParameter::check(VALUE obj, int argnum) -> bool
-{
+auto FunctionParameter::check(VALUE obj, int argnum) -> bool {
   switch (type_) {
     case ParameterType::TENSOR: {
       if (THPVariable_Check(obj)) {

ext/torch/tensor.cpp

@@ -1,3 +1,6 @@
+#include <string>
+#include <vector>
+
 #include <torch/torch.h>
 
 #include <rice/rice.hpp>
@@ -7,7 +10,8 @@
 #include "templates.h"
 #include "utils.h"
 
-using namespace Rice;
+using Rice::Array;
+using Rice::Object;
 using torch::indexing::TensorIndex;
 
 template<typename T>
@@ -75,8 +79,7 @@ std::vector<TensorIndex> index_vector(Array a) {
 // https://github.com/pytorch/pytorch/commit/2e5bfa9824f549be69a28e4705a72b4cf8a4c519
 // TODO add support for inputs argument
 // _backward
-static VALUE tensor__backward(int argc, VALUE* argv, VALUE self_)
-{
+static VALUE tensor__backward(int argc, VALUE* argv, VALUE self_) {
   HANDLE_TH_ERRORS
   Tensor& self = Rice::detail::From_Ruby<Tensor&>().convert(self_);
   static RubyArgParser parser({
@@ -197,7 +200,7 @@ void init_tensor(Rice::Module& m, Rice::Class& c, Rice::Class& rb_cTensorOptions
     .define_method(
       "_dtype",
       [](Tensor& self) {
-        return (int) at::typeMetaToScalarType(self.dtype());
+        return static_cast<int>(at::typeMetaToScalarType(self.dtype()));
       })
     .define_method(
       "_type",

ext/torch/torch.cpp

@@ -1,9 +1,11 @@
+#include <fstream>
+#include <string>
+#include <vector>
+
 #include <torch/torch.h>
 
 #include <rice/rice.hpp>
 
-#include <fstream>
-
 #include "torch_functions.h"
 #include "templates.h"
 #include "utils.h"