Fix C++ linnt (#1971)

torchvision/csrc/ROIAlign.h

@@ -36,7 +36,13 @@ at::Tensor ROIAlign_forward(
 #endif
   }
   return ROIAlign_forward_cpu(
-      input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned);
+      input,
+      rois,
+      spatial_scale,
+      pooled_height,
+      pooled_width,
+      sampling_ratio,
+      aligned);
 }
 
 at::Tensor ROIAlign_backward(
@@ -137,8 +143,13 @@ class ROIAlignFunction : public torch::autograd::Function<ROIAlignFunction> {
         input_shape[3],
         ctx->saved_data["sampling_ratio"].toInt(),
         ctx->saved_data["aligned"].toBool());
-    return {
-        grad_in, Variable(), Variable(), Variable(), Variable(), Variable(), Variable()};
+    return {grad_in,
+            Variable(),
+            Variable(),
+            Variable(),
+            Variable(),
+            Variable(),
+            Variable()};
   }
 };
 

torchvision/csrc/models/mnasnet.cpp

@@ -107,10 +107,7 @@ void MNASNetImpl::_initialize_weights() {
   for (auto& module : modules(/*include_self=*/false)) {
     if (auto M = dynamic_cast<torch::nn::Conv2dImpl*>(module.get()))
       torch::nn::init::kaiming_normal_(
-          M->weight,
-          0,
-          torch::kFanOut,
-          torch::kReLU);
+          M->weight, 0, torch::kFanOut, torch::kReLU);
     else if (auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
       torch::nn::init::ones_(M->weight);
       torch::nn::init::zeros_(M->bias);

torchvision/csrc/models/mobilenet.cpp

@@ -134,11 +134,11 @@ MobileNetV2Impl::MobileNetV2Impl(
 
   for (auto& module : modules(/*include_self=*/false)) {
     if (auto M = dynamic_cast<torch::nn::Conv2dImpl*>(module.get())) {
-      torch::nn::init::kaiming_normal_(
-          M->weight, 0, torch::kFanOut);
+      torch::nn::init::kaiming_normal_(M->weight, 0, torch::kFanOut);
       if (M->options.bias())
         torch::nn::init::zeros_(M->bias);
-    } else if (auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
+    } else if (
+        auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
       torch::nn::init::ones_(M->weight);
       torch::nn::init::zeros_(M->bias);
     } else if (auto M = dynamic_cast<torch::nn::LinearImpl*>(module.get())) {

torchvision/csrc/models/vgg.cpp

@@ -38,7 +38,8 @@ void VGGImpl::_initialize_weights() {
           torch::kFanOut,
           torch::kReLU);
       torch::nn::init::constant_(M->bias, 0);
-    } else if (auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
+    } else if (
+        auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
       torch::nn::init::constant_(M->weight, 1);
       torch::nn::init::constant_(M->bias, 0);
     } else if (auto M = dynamic_cast<torch::nn::LinearImpl*>(module.get())) {