diff --git a/torchvision/csrc/DeformConv.h b/torchvision/csrc/DeformConv.h
index 13e799538d2..fd3c858326f 100644
--- a/torchvision/csrc/DeformConv.h
+++ b/torchvision/csrc/DeformConv.h
@@ -88,21 +88,15 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor> DeformConv2d_backward
       offset_groups);
 }
 
-using namespace at;
-using torch::Tensor;
-using torch::autograd::AutogradContext;
-using torch::autograd::Variable;
-using torch::autograd::variable_list;
-
 class DeformConv2dFunction
     : public torch::autograd::Function<DeformConv2dFunction> {
  public:
-  static variable_list forward(
-      AutogradContext* ctx,
-      Variable input,
-      Variable weight,
-      Variable offset,
-      Variable bias,
+  static torch::autograd::variable_list forward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::Variable input,
+      torch::autograd::Variable weight,
+      torch::autograd::Variable offset,
+      torch::autograd::Variable bias,
       int64_t stride_h,
       int64_t stride_w,
       int64_t pad_h,
@@ -137,9 +131,9 @@ class DeformConv2dFunction
     };
   }
 
-  static variable_list backward(
-      AutogradContext* ctx,
-      variable_list grad_output) {
+  static torch::autograd::variable_list backward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::variable_list grad_output) {
     auto saved = ctx->get_saved_variables();
     auto input = saved[0];
     auto weight = saved[1];
@@ -176,14 +170,14 @@ class DeformConv2dFunction
         grad_weight,
         grad_offset,
         grad_bias,
-        Variable(),
-        Variable(),
-        Variable(),
-        Variable(),
-        Variable(),
-        Variable(),
-        Variable(),
-        Variable(),
+        torch::autograd::Variable(),
+        torch::autograd::Variable(),
+        torch::autograd::Variable(),
+        torch::autograd::Variable(),
+        torch::autograd::Variable(),
+        torch::autograd::Variable(),
+        torch::autograd::Variable(),
+        torch::autograd::Variable(),
     };
   }
 };
diff --git a/torchvision/csrc/PSROIAlign.h b/torchvision/csrc/PSROIAlign.h
index f1d30ba24b5..a6fab275031 100644
--- a/torchvision/csrc/PSROIAlign.h
+++ b/torchvision/csrc/PSROIAlign.h
@@ -79,19 +79,13 @@ at::Tensor PSROIAlign_backward(
       width);
 }
 
-using namespace at;
-using torch::Tensor;
-using torch::autograd::AutogradContext;
-using torch::autograd::Variable;
-using torch::autograd::variable_list;
-
 class PSROIAlignFunction
     : public torch::autograd::Function<PSROIAlignFunction> {
  public:
-  static variable_list forward(
-      AutogradContext* ctx,
-      Variable input,
-      Variable rois,
+  static torch::autograd::variable_list forward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::Variable input,
+      torch::autograd::Variable rois,
       const double spatial_scale,
       const int64_t pooled_height,
       const int64_t pooled_width,
@@ -115,9 +109,9 @@ class PSROIAlignFunction
     return {output, channel_mapping};
   }
 
-  static variable_list backward(
-      AutogradContext* ctx,
-      variable_list grad_output) {
+  static torch::autograd::variable_list backward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::variable_list grad_output) {
     // Use data saved in forward
     auto saved = ctx->get_saved_variables();
     auto rois = saved[0];
@@ -135,19 +129,23 @@ class PSROIAlignFunction
         input_shape[1],
         input_shape[2],
         input_shape[3]);
-    return {
-        grad_in, Variable(), Variable(), Variable(), Variable(), Variable()};
+    return {grad_in,
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable()};
   }
 };
 
-std::tuple<Tensor, Tensor> ps_roi_align(
-    const Tensor& input,
-    const Tensor& rois,
+std::tuple<at::Tensor, at::Tensor> ps_roi_align(
+    const at::Tensor& input,
+    const at::Tensor& rois,
     const double spatial_scale,
     const int64_t pooled_height,
     const int64_t pooled_width,
     const int64_t sampling_ratio) {
   auto result = PSROIAlignFunction::apply(
       input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
-  return std::tuple<Tensor, Tensor>(result[0], result[1]);
+  return std::tuple<at::Tensor, at::Tensor>(result[0], result[1]);
 }
diff --git a/torchvision/csrc/PSROIPool.h b/torchvision/csrc/PSROIPool.h
index 3f530804400..851acff2092 100644
--- a/torchvision/csrc/PSROIPool.h
+++ b/torchvision/csrc/PSROIPool.h
@@ -68,18 +68,12 @@ at::Tensor PSROIPool_backward(
       width);
 }
 
-using namespace at;
-using torch::Tensor;
-using torch::autograd::AutogradContext;
-using torch::autograd::Variable;
-using torch::autograd::variable_list;
-
 class PSROIPoolFunction : public torch::autograd::Function<PSROIPoolFunction> {
  public:
-  static variable_list forward(
-      AutogradContext* ctx,
-      Variable input,
-      Variable rois,
+  static torch::autograd::variable_list forward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::Variable input,
+      torch::autograd::Variable rois,
       const double spatial_scale,
       const int64_t pooled_height,
       const int64_t pooled_width) {
@@ -96,9 +90,9 @@ class PSROIPoolFunction : public torch::autograd::Function<PSROIPoolFunction> {
     return {output, channel_mapping};
   }
 
-  static variable_list backward(
-      AutogradContext* ctx,
-      variable_list grad_output) {
+  static torch::autograd::variable_list backward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::variable_list grad_output) {
     // Use data saved in forward
     auto saved = ctx->get_saved_variables();
     auto rois = saved[0];
@@ -115,17 +109,21 @@ class PSROIPoolFunction : public torch::autograd::Function<PSROIPoolFunction> {
         input_shape[1],
         input_shape[2],
         input_shape[3]);
-    return {grad_in, Variable(), Variable(), Variable(), Variable()};
+    return {grad_in,
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable()};
   }
 };
 
-std::tuple<Tensor, Tensor> ps_roi_pool(
-    const Tensor& input,
-    const Tensor& rois,
+std::tuple<at::Tensor, at::Tensor> ps_roi_pool(
+    const at::Tensor& input,
+    const at::Tensor& rois,
     const double spatial_scale,
     const int64_t pooled_height,
     const int64_t pooled_width) {
   auto result = PSROIPoolFunction::apply(
       input, rois, spatial_scale, pooled_height, pooled_width);
-  return std::tuple<Tensor, Tensor>(result[0], result[1]);
+  return std::tuple<at::Tensor, at::Tensor>(result[0], result[1]);
 }
diff --git a/torchvision/csrc/ROIAlign.h b/torchvision/csrc/ROIAlign.h
index 54a05c2a9d4..78dcb101dce 100644
--- a/torchvision/csrc/ROIAlign.h
+++ b/torchvision/csrc/ROIAlign.h
@@ -89,18 +89,12 @@ at::Tensor ROIAlign_backward(
       aligned);
 }
 
-using namespace at;
-using torch::Tensor;
-using torch::autograd::AutogradContext;
-using torch::autograd::Variable;
-using torch::autograd::variable_list;
-
 class ROIAlignFunction : public torch::autograd::Function<ROIAlignFunction> {
  public:
-  static variable_list forward(
-      AutogradContext* ctx,
-      Variable input,
-      Variable rois,
+  static torch::autograd::variable_list forward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::Variable input,
+      torch::autograd::Variable rois,
       const double spatial_scale,
       const int64_t pooled_height,
       const int64_t pooled_width,
@@ -124,9 +118,9 @@ class ROIAlignFunction : public torch::autograd::Function<ROIAlignFunction> {
     return {result};
   }
 
-  static variable_list backward(
-      AutogradContext* ctx,
-      variable_list grad_output) {
+  static torch::autograd::variable_list backward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::variable_list grad_output) {
     // Use data saved in forward
     auto saved = ctx->get_saved_variables();
     auto rois = saved[0];
@@ -144,18 +138,18 @@ class ROIAlignFunction : public torch::autograd::Function<ROIAlignFunction> {
         ctx->saved_data["sampling_ratio"].toInt(),
         ctx->saved_data["aligned"].toBool());
     return {grad_in,
-            Variable(),
-            Variable(),
-            Variable(),
-            Variable(),
-            Variable(),
-            Variable()};
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable()};
   }
 };
 
-Tensor roi_align(
-    const Tensor& input,
-    const Tensor& rois,
+at::Tensor roi_align(
+    const at::Tensor& input,
+    const at::Tensor& rois,
     const double spatial_scale,
     const int64_t pooled_height,
     const int64_t pooled_width,
diff --git a/torchvision/csrc/ROIPool.h b/torchvision/csrc/ROIPool.h
index 70f0c191906..2bf38ef6208 100644
--- a/torchvision/csrc/ROIPool.h
+++ b/torchvision/csrc/ROIPool.h
@@ -68,18 +68,12 @@ at::Tensor ROIPool_backward(
       width);
 }
 
-using namespace at;
-using torch::Tensor;
-using torch::autograd::AutogradContext;
-using torch::autograd::Variable;
-using torch::autograd::variable_list;
-
 class ROIPoolFunction : public torch::autograd::Function<ROIPoolFunction> {
  public:
-  static variable_list forward(
-      AutogradContext* ctx,
-      Variable input,
-      Variable rois,
+  static torch::autograd::variable_list forward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::Variable input,
+      torch::autograd::Variable rois,
       const double spatial_scale,
       const int64_t pooled_height,
       const int64_t pooled_width) {
@@ -96,9 +90,9 @@ class ROIPoolFunction : public torch::autograd::Function<ROIPoolFunction> {
     return {output, argmax};
   }
 
-  static variable_list backward(
-      AutogradContext* ctx,
-      variable_list grad_output) {
+  static torch::autograd::variable_list backward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::variable_list grad_output) {
     // Use data saved in forward
     auto saved = ctx->get_saved_variables();
     auto rois = saved[0];
@@ -115,17 +109,21 @@ class ROIPoolFunction : public torch::autograd::Function<ROIPoolFunction> {
         input_shape[1],
         input_shape[2],
         input_shape[3]);
-    return {grad_in, Variable(), Variable(), Variable(), Variable()};
+    return {grad_in,
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable(),
+            torch::autograd::Variable()};
   }
 };
 
-std::tuple<Tensor, Tensor> roi_pool(
-    const Tensor& input,
-    const Tensor& rois,
+std::tuple<at::Tensor, at::Tensor> roi_pool(
+    const at::Tensor& input,
+    const at::Tensor& rois,
     const double spatial_scale,
     const int64_t pooled_height,
     const int64_t pooled_width) {
   auto result = ROIPoolFunction::apply(
       input, rois, spatial_scale, pooled_height, pooled_width);
-  return std::tuple<Tensor, Tensor>(result[0], result[1]);
+  return std::tuple<at::Tensor, at::Tensor>(result[0], result[1]);
 }
diff --git a/torchvision/csrc/empty_tensor_op.h b/torchvision/csrc/empty_tensor_op.h
index 435ed82133c..64a1c44f600 100644
--- a/torchvision/csrc/empty_tensor_op.h
+++ b/torchvision/csrc/empty_tensor_op.h
@@ -5,26 +5,20 @@
 // Python bindings for the C++ frontend (includes Python.h).
 #include <torch/python.h>
 
-using namespace at;
-using torch::Tensor;
-using torch::autograd::AutogradContext;
-using torch::autograd::Variable;
-using torch::autograd::variable_list;
-
 class NewEmptyTensorOp : public torch::autograd::Function<NewEmptyTensorOp> {
  public:
-  static variable_list forward(
-      AutogradContext* ctx,
-      Variable input,
+  static torch::autograd::variable_list forward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::Variable input,
       c10::List<int64_t> new_shape) {
     ctx->saved_data["shape"] = input.sizes();
     std::vector<int64_t> shape(new_shape.begin(), new_shape.end());
-    return {input.new_empty(shape, TensorOptions())};
+    return {input.new_empty(shape, at::TensorOptions())};
   }
 
-  static variable_list backward(
-      AutogradContext* ctx,
-      variable_list grad_output) {
+  static torch::autograd::variable_list backward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::variable_list grad_output) {
     // Use data saved in forward
     auto shape = ctx->saved_data["shape"].toIntList();
     auto out = forward(ctx, grad_output[0], shape);
@@ -32,6 +26,6 @@ class NewEmptyTensorOp : public torch::autograd::Function<NewEmptyTensorOp> {
   }
 };
 
-Tensor new_empty_tensor(const Tensor& input, c10::List<int64_t> shape) {
+at::Tensor new_empty_tensor(const at::Tensor& input, c10::List<int64_t> shape) {
   return NewEmptyTensorOp::apply(input, shape)[0];
 }