Fixed device when used in _gen_affine_grid and _perspective_grid (#2813)

torchvision/transforms/functional_tensor.py

@@ -929,8 +929,10 @@ def _gen_affine_grid(
 
     d = 0.5
     base_grid = torch.empty(1, oh, ow, 3, dtype=theta.dtype, device=theta.device)
-    base_grid[..., 0].copy_(torch.linspace(-ow * 0.5 + d, ow * 0.5 + d - 1, steps=ow))
-    base_grid[..., 1].copy_(torch.linspace(-oh * 0.5 + d, oh * 0.5 + d - 1, steps=oh).unsqueeze_(-1))
+    x_grid = torch.linspace(-ow * 0.5 + d, ow * 0.5 + d - 1, steps=ow, device=theta.device)
+    base_grid[..., 0].copy_(x_grid)
+    y_grid = torch.linspace(-oh * 0.5 + d, oh * 0.5 + d - 1, steps=oh, device=theta.device).unsqueeze_(-1)
+    base_grid[..., 1].copy_(y_grid)
     base_grid[..., 2].fill_(1)
 
     rescaled_theta = theta.transpose(1, 2) / torch.tensor([0.5 * w, 0.5 * h], dtype=theta.dtype, device=theta.device)
@@ -1065,8 +1067,10 @@ def _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype,
 
     d = 0.5
     base_grid = torch.empty(1, oh, ow, 3, dtype=dtype, device=device)
-    base_grid[..., 0].copy_(torch.linspace(d, ow * 1.0 + d - 1.0, steps=ow))
-    base_grid[..., 1].copy_(torch.linspace(d, oh * 1.0 + d - 1.0, steps=oh).unsqueeze_(-1))
+    x_grid = torch.linspace(d, ow * 1.0 + d - 1.0, steps=ow, device=device)
+    base_grid[..., 0].copy_(x_grid)
+    y_grid = torch.linspace(d, oh * 1.0 + d - 1.0, steps=oh, device=device).unsqueeze_(-1)
+    base_grid[..., 1].copy_(y_grid)
     base_grid[..., 2].fill_(1)
 
     rescaled_theta1 = theta1.transpose(1, 2) / torch.tensor([0.5 * ow, 0.5 * oh], dtype=dtype, device=device)