diff --git a/torchvision/csrc/cpu/ROIAlign_cpu.cpp b/torchvision/csrc/cpu/ROIAlign_cpu.cpp
index f421f09ebe8..3850b2833ab 100644
--- a/torchvision/csrc/cpu/ROIAlign_cpu.cpp
+++ b/torchvision/csrc/cpu/ROIAlign_cpu.cpp
@@ -231,8 +231,8 @@ at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,
   auto height = input.size(2);
   auto width = input.size(3);
 
-  at::Tensor output = input.type().tensor({num_rois, channels, pooled_height, pooled_width});
-
+  at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type());
+  
   auto output_size = num_rois * pooled_height * pooled_width * channels;
 
   if (output.numel() == 0)
diff --git a/torchvision/csrc/cpu/ROIPool_cpu.cpp b/torchvision/csrc/cpu/ROIPool_cpu.cpp
index 5500a63f4ca..8ae35930533 100644
--- a/torchvision/csrc/cpu/ROIPool_cpu.cpp
+++ b/torchvision/csrc/cpu/ROIPool_cpu.cpp
@@ -16,8 +16,8 @@ std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cpu(const at::Tensor &input,
     int input_height = input.size(2);
     int input_width = input.size(3);
 
-    at::Tensor output = input.type().tensor({num_rois, channels, pooled_height, pooled_width});
-    at::Tensor argmax = input.type().toScalarType(at::kInt).tensor({num_rois, channels, pooled_height, pooled_width}).zero_();
+    at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type());
+    at::Tensor argmax = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type().toScalarType(at::kInt));
 
     // define accessors for indexing
     auto input_a = input.accessor<float, 4>();
diff --git a/torchvision/csrc/cpu/nms_cpu.cpp b/torchvision/csrc/cpu/nms_cpu.cpp
index aa4b9b53256..fa543b5f00c 100644
--- a/torchvision/csrc/cpu/nms_cpu.cpp
+++ b/torchvision/csrc/cpu/nms_cpu.cpp
@@ -1,6 +1,5 @@
 #include "cpu/vision.h"
 
-
 template <typename scalar_t>
 at::Tensor nms_cpu_kernel(const at::Tensor& dets,
                           const at::Tensor& scores,
@@ -10,7 +9,7 @@ at::Tensor nms_cpu_kernel(const at::Tensor& dets,
   AT_ASSERTM(dets.type() == scores.type(), "dets should have the same type as scores");
 
   if (dets.numel() == 0)
-    return torch::CPU(at::kLong).tensor();
+    return at::empty({0}, at::device(at::kCPU).dtype(at::kLong));
 
   auto x1_t = dets.select(1, 0).contiguous();
   auto y1_t = dets.select(1, 1).contiguous();
@@ -22,7 +21,7 @@ at::Tensor nms_cpu_kernel(const at::Tensor& dets,
   auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
 
   auto ndets = dets.size(0);
-  at::Tensor suppressed_t = at::zeros(torch::CPU(at::kByte), {ndets});
+  at::Tensor suppressed_t = at::zeros({ndets}, at::device(at::kCPU).dtype(at::kByte));
 
   auto suppressed = suppressed_t.data<uint8_t>();
   auto order = order_t.data<int64_t>();
@@ -66,7 +65,7 @@ at::Tensor nms_cpu(const at::Tensor& dets,
                const at::Tensor& scores,
                const float threshold) {
 
-  auto result = dets.type().tensor();
+  auto result = at::empty({0}, dets.type());
 
   AT_DISPATCH_FLOATING_TYPES(dets.type(), "nms", [&] {
     result = nms_cpu_kernel<scalar_t>(dets, scores, threshold);
diff --git a/torchvision/csrc/cuda/ROIAlign_cuda.cu b/torchvision/csrc/cuda/ROIAlign_cuda.cu
index e8408c4ee06..9cc5ae28934 100644
--- a/torchvision/csrc/cuda/ROIAlign_cuda.cu
+++ b/torchvision/csrc/cuda/ROIAlign_cuda.cu
@@ -267,7 +267,7 @@ at::Tensor ROIAlign_forward_cuda(const at::Tensor& input,
   auto height = input.size(2);
   auto width = input.size(3);
 
-  at::Tensor output = input.type().tensor({num_rois, channels, pooled_height, pooled_width});
+  at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.options());
 
   auto output_size = num_rois * pooled_height * pooled_width * channels;
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
@@ -313,7 +313,7 @@ at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad,
   AT_ASSERTM(rois.type().is_cuda(), "rois must be a CUDA tensor");
 
   auto num_rois = rois.size(0);
-  at::Tensor grad_input = grad.type().tensor({batch_size, channels, height, width}).zero_();
+  at::Tensor grad_input = at::zeros({batch_size, channels, height, width}, grad.options());
 
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
diff --git a/torchvision/csrc/cuda/ROIPool_cuda.cu b/torchvision/csrc/cuda/ROIPool_cuda.cu
index 29d9c9c9319..2ba8dc33e25 100644
--- a/torchvision/csrc/cuda/ROIPool_cuda.cu
+++ b/torchvision/csrc/cuda/ROIPool_cuda.cu
@@ -116,8 +116,8 @@ std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cuda(const at::Tensor& input,
   auto height = input.size(2);
   auto width = input.size(3);
 
-  at::Tensor output = input.type().tensor({num_rois, channels, pooled_height, pooled_width});
-  at::Tensor argmax = input.type().toScalarType(at::kInt).tensor({num_rois, channels, pooled_height, pooled_width}).zero_();
+  at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type());
+  at::Tensor argmax = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type().toScalarType(at::kInt));
 
   auto output_size = num_rois * pooled_height * pooled_width * channels;
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
diff --git a/torchvision/layers/roi_pool.py b/torchvision/layers/roi_pool.py
index f232d0cc160..b6d288e95e2 100644
--- a/torchvision/layers/roi_pool.py
+++ b/torchvision/layers/roi_pool.py
@@ -11,14 +11,14 @@
 
 class _ROIPool(Function):
     @staticmethod
-    def forward(ctx, input, roi, output_size, spatial_scale):
+    def forward(ctx, input, rois, output_size, spatial_scale):
         ctx.output_size = _pair(output_size)
         ctx.spatial_scale = spatial_scale
         ctx.input_shape = input.size()
         output, argmax = _C.roi_pool_forward(
-            input, roi, spatial_scale,
+            input, rois, spatial_scale,
             output_size[0], output_size[1])
-        ctx.save_for_backward(roi, argmax)
+        ctx.save_for_backward(rois, argmax)
         return output
 
     @staticmethod