Merge branch 'master' of github.com:rusty1s/pytorch_scatter

.coveragerc

@@ -5,3 +5,4 @@ exclude_lines =
     pragma: no cover
     cuda
     backward
+    raise

.travis.yml

@@ -17,13 +17,14 @@ before_install:
   - export CXX="g++-4.9"
 install:
   - pip install numpy
-  - pip install -q torch
+  - pip install -q torch -f https://download.pytorch.org/whl/nightly/cpu/torch.html
   - pip install pycodestyle
   - pip install flake8
   - pip install codecov
   - pip install sphinx
   - pip install sphinx_rtd_theme
 script:
+  - python -c "import torch; print(torch.__version__)"
   - pycodestyle .
   - flake8 .
   - python setup.py install

README.md

@@ -35,7 +35,7 @@ The package consists of the following operations:
 * [**Scatter Min**](https://pytorch-scatter.readthedocs.io/en/latest/functions/min.html)
 * [**Scatter Max**](https://pytorch-scatter.readthedocs.io/en/latest/functions/max.html)
 
-All included operations work on varying data types, are implemented both for CPU and GPU and include a backwards implementation.
+All included operations are broadcastable, work on varying data types, and are implemented both for CPU and GPU with corresponding backward implementations.
 
 ## Installation
 

docs/source/functions/add.rst

@@ -2,5 +2,6 @@ Scatter Add
 ===========
 
 .. automodule:: torch_scatter
+   :noindex:
 
 .. autofunction:: scatter_add

docs/source/functions/div.rst

@@ -2,5 +2,6 @@ Scatter Div
 ===========
 
 .. automodule:: torch_scatter
+   :noindex:
 
 .. autofunction:: scatter_div

docs/source/functions/max.rst

@@ -2,5 +2,6 @@ Scatter Max
 ===========
 
 .. automodule:: torch_scatter
+   :noindex:
 
 .. autofunction:: scatter_max

docs/source/functions/mean.rst

@@ -2,5 +2,6 @@ Scatter Mean
 ============
 
 .. automodule:: torch_scatter
+   :noindex:
 
 .. autofunction:: scatter_mean

docs/source/functions/mul.rst

@@ -2,5 +2,6 @@ Scatter Mul
 ===========
 
 .. automodule:: torch_scatter
+   :noindex:
 
 .. autofunction:: scatter_mul

docs/source/functions/std.rst

@@ -2,5 +2,6 @@ Scatter Std
 ===========
 
 .. automodule:: torch_scatter
+   :noindex:
 
 .. autofunction:: scatter_std

docs/source/functions/sub.rst

@@ -2,5 +2,6 @@ Scatter Sub
 ===========
 
 .. automodule:: torch_scatter
+   :noindex:
 
 .. autofunction:: scatter_sub

docs/source/index.rst

@@ -6,7 +6,7 @@ PyTorch Scatter Documentation
 This package consists of a small extension library of highly optimized sparse update (scatter) operations for the use in `PyTorch <http://pytorch.org/>`_, which are missing in the main package.
 Scatter operations can be roughly described as reduce operations based on a given "group-index" tensor.
 
-All included operations work on varying data types, are implemented both for CPU and GPU and include a backwards implementation.
+All included operations are broadcastable, work on varying data types, and are implemented both for CPU and GPU with corresponding backward implementations.
 
 .. toctree::
    :glob:

test/test_broadcasting.py

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

torch_scatter/add.py

@@ -24,6 +24,8 @@ def scatter_add(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
     :attr:`dim` = `i`, then :attr:`out` must be an n-dimensional tensor with
     size :math:`(x_0, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})`. Moreover, the
     values of :attr:`index` must be between `0` and `out.size(dim) - 1`.
+    Both :attr:`src` and :attr:`index` are broadcasted in case their dimensions
+    do not match.
 
     For one-dimensional tensors, the operation computes
 

torch_scatter/utils/gen.py

+from __future__ import division
+
 from itertools import repeat
 
 import torch
@@ -21,6 +23,18 @@ def gen(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
         else:  # PyTorch has a bug when view is used on zero-element tensors.
             index = src.new_empty(index_size, dtype=torch.long)
 
+    # Broadcasting capabilties: Expand dimensions to match.
+    if src.dim() != index.dim():
+        raise ValueError(
+            ('Number of dimensions of src and index tensor do not match, '
+             'got {} and {}').format(src.dim(), index.dim()))
+
+    expand_size = []
+    for s, i in zip(src.size(), index.size()):
+        expand_size += [-1 if s == i and s != 1 and i != 1 else max(i, s)]
+    src = src.expand(expand_size)
+    index = index.expand_as(src)
+
     # Generate output tensor if not given.
     if out is None:
         out_size = list(src.size())