refactor: apply linting

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

Author: narendasan

core/conversion/conversionctx/ConversionCtx.cpp

@@ -55,7 +55,8 @@ ConversionCtx::ConversionCtx(BuilderSettings build_settings)
   }
 
   builder = make_trt(nvinfer1::createInferBuilder(logger));
-  net = make_trt(builder->createNetworkV2(1U << static_cast<uint32_t>(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH)));
+  net = make_trt(
+      builder->createNetworkV2(1U << static_cast<uint32_t>(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH)));
 
   LOG_DEBUG(build_settings);
   cfg = make_trt(builder->createBuilderConfig());
@@ -165,7 +166,7 @@ std::string ConversionCtx::SerializeEngine() {
   }
   auto serialized_network = engine->serialize();
   engine->destroy();
-#endif  
+#endif
   auto engine_str = std::string((const char*)serialized_network->data(), serialized_network->size());
   return engine_str;
 }

core/conversion/converters/Weights.h

@@ -1,7 +1,7 @@
 #pragma once
 
-#include "core/util/prelude.h"
 #include "core/conversion/conversionctx/ConversionCtx.h"
+#include "core/util/prelude.h"
 
 namespace trtorch {
 namespace core {

core/conversion/converters/impl/interpolate.cpp

@@ -114,7 +114,7 @@ void resize_layer_size(
   if (align_corners) {
     resize_layer->setCoordinateTransformation(nvinfer1::ResizeCoordinateTransformation::kALIGN_CORNERS);
   }
-#endif  
+#endif
   auto layer_output = ctx->AssociateValueAndTensor(n->outputs()[0], resize_layer->getOutput(0));
 
   LOG_DEBUG("Output tensor shape: " << layer_output->getDimensions());

core/conversion/converters/impl/quantization.cpp

@@ -8,7 +8,7 @@ namespace conversion {
 namespace converters {
 namespace impl {
 namespace {
-  
+
 #if NV_TENSORRT_MAJOR > 7
 // clang-format off
 auto quantization_registrations TRTORCH_UNUSED = RegisterNodeConversionPatterns()

core/runtime/runtime.h

@@ -74,7 +74,6 @@ class DeviceList {
   std::string dump_list();
 };
 
-  
 DeviceList get_available_device_list();
 const std::unordered_map<std::string, std::string>& get_dla_supported_SMs();
 

core/util/trt_util.h

@@ -9,27 +9,27 @@
 namespace nvinfer1 {
 
 #if NV_TENSORRT_MAJOR < 8
-  
+
 #define TRT_ENGINE_CAPABILITY_STANDARD nvinfer1::EngineCapability::kDEFAULT
 #define TRT_ENGINE_CAPABILITY_SAFETY nvinfer1::EngineCapability::kSAFE_GPU
 #define TRT_ENGINE_CAPABILITY_DLA_STANDALONE nvinfer1::EngineCapability::kSAFE_DLA
-  
+
 template <class T>
 std::shared_ptr<T> make_trt(T* p) {
-  return std::shared_ptr<T>(p, [](T* p){p->destroy();});
+  return std::shared_ptr<T>(p, [](T* p) { p->destroy(); });
 }
- 
+
 #else
- 
+
 #define TRT_ENGINE_CAPABILITY_STANDARD nvinfer1::EngineCapability::kSTANDARD
 #define TRT_ENGINE_CAPABILITY_SAFETY nvinfer1::EngineCapability::kSAFETY
 #define TRT_ENGINE_CAPABILITY_DLA_STANDALONE nvinfer1::EngineCapability::kDLA_STANDALONE
- 
+
 template <class T>
 std::shared_ptr<T> make_trt(T* p) {
   return std::shared_ptr<T>(p);
 }
- 
+
 #endif
 
 inline std::ostream& operator<<(std::ostream& os, const nvinfer1::TensorFormat& format) {

py/trtorch/csrc/tensorrt_backend.cpp

@@ -65,26 +65,30 @@ c10::impl::GenericList TensorRTBackend::execute(c10::IValue handle, c10::impl::G
 }
 
 namespace {
-  c10::IValue preprocess(const torch::jit::Module& mod,
-			 const c10::Dict<c10::IValue,
-			 // this API changed between 1.9 and 1.10
+c10::IValue preprocess(
+    const torch::jit::Module& mod,
+    const c10::Dict<
+        c10::IValue,
+// this API changed between 1.9 and 1.10
 #if TORCH_VERSION_MAJOR < 2 && TORCH_VERSION_MINOR < 10
-			 c10::IValue>& method_compile_spec
-# else  
-			 c10::IValue>& method_compile_spec, const torch::jit::BackendDebugHandleGenerator& generate_debug_handles
+        c10::IValue>& method_compile_spec
+#else
+        c10::IValue>& method_compile_spec,
+    const torch::jit::BackendDebugHandleGenerator& generate_debug_handles
 #endif
-			 ) {
-    for (auto it = method_compile_spec.begin(), end = method_compile_spec.end(); it != end; ++it) {
-      TRTORCH_CHECK(
-		    core::CheckMethodOperatorSupport(mod, it->key().toStringRef()),
-		    "Method " << it->key().toStringRef() << "cannot be compiled by TRTorch");
-    }
-    return mod._ivalue();
-  };
-  
-  static const std::string trt("tensorrt");
-  static auto reg = torch::jit::backend<TensorRTBackend>(trt);
-  static auto preproc_reg = torch::jit::backend_preprocess_register(trt, torch::jit::detail::BackendPreprocessFunction(preprocess));
+) {
+  for (auto it = method_compile_spec.begin(), end = method_compile_spec.end(); it != end; ++it) {
+    TRTORCH_CHECK(
+        core::CheckMethodOperatorSupport(mod, it->key().toStringRef()),
+        "Method " << it->key().toStringRef() << "cannot be compiled by TRTorch");
+  }
+  return mod._ivalue();
+};
+
+static const std::string trt("tensorrt");
+static auto reg = torch::jit::backend<TensorRTBackend>(trt);
+static auto preproc_reg =
+    torch::jit::backend_preprocess_register(trt, torch::jit::detail::BackendPreprocessFunction(preprocess));
 } // namespace
 
 } // namespace backend