fix negative dim in scatter_mean

csrc/scatter.cpp

@@ -71,6 +71,7 @@ public:
                                Variable index, int64_t dim,
                                torch::optional<Variable> optional_out,
                                torch::optional<int64_t> dim_size) {
+    dim = dim < 0 ? src.dim() + dim : dim;
     ctx->saved_data["dim"] = dim;
     ctx->saved_data["src_shape"] = src.sizes();
 

test/test_broadcasting.py

+from itertools import product
+
 import pytest
 import torch
-from torch_scatter import scatter_add
+from torch_scatter import scatter
 
-from .utils import devices
+from .utils import reductions, devices
 
 
-@pytest.mark.parametrize('device', devices)
-def test_broadcasting(device):
+@pytest.mark.parametrize('reduce,device', product(reductions, devices))
+def test_broadcasting(reduce, device):
     B, C, H, W = (4, 3, 8, 8)
 
+    src = torch.randn((B, C, H, W), device=device)
+    index = torch.randint(0, H, (H, )).to(device, torch.long)
+    out = scatter(src, index, dim=2, dim_size=H, reduce=reduce)
+    assert out.size() == (B, C, H, W)
+
     src = torch.randn((B, C, H, W), device=device)
     index = torch.randint(0, H, (B, 1, H, W)).to(device, torch.long)
-    out = scatter_add(src, index, dim=2, dim_size=H)
+    out = scatter(src, index, dim=2, dim_size=H, reduce=reduce)
     assert out.size() == (B, C, H, W)
 
     src = torch.randn((B, C, H, W), device=device)
     index = torch.randint(0, H, (H, )).to(device, torch.long)
-    out = scatter_add(src, index, dim=2, dim_size=H)
+    out = scatter(src, index, dim=2, dim_size=H, reduce=reduce)
     assert out.size() == (B, C, H, W)