diff --git a/CHANGELOG.md b/CHANGELOG.md
index e3371a3db7..863d0d577b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -20,8 +20,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed an issue with forward hooks not being removed after model summary ([#2298](https://github.com/PyTorchLightning/pytorch-lightning/pull/2298))
 
-- Fixed `average_precision` metric ([#2319](https://github.com/PyTorchLightning/pytorch-lightning/pull/2319))
+- Fixed ROC metric for CUDA tensors ([#2304](https://github.com/PyTorchLightning/pytorch-lightning/pull/2304))
 
+- Fixed `average_precision` metric ([#2319](https://github.com/PyTorchLightning/pytorch-lightning/pull/2319))
 
 ## [0.8.1] - 2020-06-19
 
diff --git a/pytorch_lightning/metrics/functional/classification.py b/pytorch_lightning/metrics/functional/classification.py
index c5d3613640..4e23898738 100644
--- a/pytorch_lightning/metrics/functional/classification.py
+++ b/pytorch_lightning/metrics/functional/classification.py
@@ -3,6 +3,7 @@ from functools import wraps
 from typing import Optional, Tuple, Callable
 
 import torch
+from torch.nn import functional as F
 
 from pytorch_lightning.metrics.functional.reduction import reduce
 from pytorch_lightning.utilities import rank_zero_warn
@@ -500,8 +501,7 @@ def _binary_clf_curve(
     # the indices associated with the distinct values. We also
     # concatenate a value for the end of the curve.
     distinct_value_indices = torch.where(pred[1:] - pred[:-1])[0]
-    threshold_idxs = torch.cat([distinct_value_indices,
-                                torch.tensor([target.size(0) - 1])])
+    threshold_idxs = F.pad(distinct_value_indices, (0, 1), value=target.size(0) - 1)
 
     target = (target == pos_label).to(torch.long)
     tps = torch.cumsum(target * weight, dim=0)[threshold_idxs]
diff --git a/tests/metrics/functional/test_classification.py b/tests/metrics/functional/test_classification.py
index 6a06ef4a4a..6aab85fa7b 100644
--- a/tests/metrics/functional/test_classification.py
+++ b/tests/metrics/functional/test_classification.py
@@ -45,25 +45,30 @@ from pytorch_lightning.metrics.functional.classification import (
 ])
 def test_against_sklearn(sklearn_metric, torch_metric):
     """Compare PL metrics to sklearn version."""
-    pred = torch.randint(10, (500,))
-    target = torch.randint(10, (500,))
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+    pred = torch.randint(10, (500,), device=device)
+    target = torch.randint(10, (500,), device=device)
 
     assert torch.allclose(
-        torch.tensor(sklearn_metric(target, pred), dtype=torch.float),
+        torch.tensor(sklearn_metric(target.cpu().detach().numpy(),
+                                    pred.cpu().detach().numpy()), dtype=torch.float, device=device),
         torch_metric(pred, target))
 
-    pred = torch.randint(10, (200,))
-    target = torch.randint(5, (200,))
+    pred = torch.randint(10, (200,), device=device)
+    target = torch.randint(5, (200,), device=device)
 
     assert torch.allclose(
-        torch.tensor(sklearn_metric(target, pred), dtype=torch.float),
+        torch.tensor(sklearn_metric(target.cpu().detach().numpy(),
+                                    pred.cpu().detach().numpy()), dtype=torch.float, device=device),
         torch_metric(pred, target))
 
-    pred = torch.randint(5, (200,))
-    target = torch.randint(10, (200,))
+    pred = torch.randint(5, (200,), device=device)
+    target = torch.randint(10, (200,), device=device)
 
     assert torch.allclose(
-        torch.tensor(sklearn_metric(target, pred), dtype=torch.float),
+        torch.tensor(sklearn_metric(target.cpu().detach().numpy(),
+                                    pred.cpu().detach().numpy()), dtype=torch.float, device=device),
         torch_metric(pred, target))