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Commit e2ae2360 authored by rusty1s's avatar rusty1s
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g++4.9

parent 58a05cff
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Showing with 124 additions and 127 deletions
.travis.yml
View file @ e2ae2360
language: python
sudo: required
dist: trusty
addons:
apt:
sources:
- ubuntu-toolchain-r-test
packages:
- g++-4.9
matrix:
include:
- python: 2.7
......
test/test_backward.py
View file @ e2ae2360
......@@ -14,14 +14,13 @@ indices = [2, 0, 1, 1, 0]
@pytest.mark.parametrize('func,device', product(funcs, devices))
def test_backward(func, device):
pass
# index = torch.tensor(indices, dtype=torch.long, device=device)
# src = torch.rand((index.size(0), 2), dtype=torch.double, device=device)
# src.requires_grad_()
index = torch.tensor(indices, dtype=torch.long, device=device)
src = torch.rand((index.size(0), 2), dtype=torch.double, device=device)
src.requires_grad_()
# op = getattr(torch_scatter, 'scatter_{}'.format(func))
# data = (src, index, 0)
# assert gradcheck(op, data, eps=1e-6, atol=1e-4) is True
op = getattr(torch_scatter, 'scatter_{}'.format(func))
data = (src, index, 0)
assert gradcheck(op, data, eps=1e-6, atol=1e-4) is True
tests = [{
......@@ -45,13 +44,12 @@ tests = [{
@pytest.mark.parametrize('test,dtype,device', product(tests, dtypes, devices))
def test_arg_backward(test, dtype, device):
pass
# src = tensor(test['src'], dtype, device)
# src.requires_grad_()
# index = tensor(test['index'], torch.long, device)
# grad = tensor(test['grad'], dtype, device)
# op = getattr(torch_scatter, 'scatter_{}'.format(test['name']))
# out, _ = op(src, index, test['dim'], fill_value=test['fill_value'])
# out.backward(grad)
# assert src.grad.tolist() == test['expected']
src = tensor(test['src'], dtype, device)
src.requires_grad_()
index = tensor(test['index'], torch.long, device)
grad = tensor(test['grad'], dtype, device)
op = getattr(torch_scatter, 'scatter_{}'.format(test['name']))
out, _ = op(src, index, test['dim'], fill_value=test['fill_value'])
out.backward(grad)
assert src.grad.tolist() == test['expected']
test/test_forward.py
View file @ e2ae2360
......@@ -6,116 +6,109 @@ import torch_scatter
from .utils import dtypes, devices, tensor
tests = [
{
'name': 'add',
'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1,
'fill_value': 0,
'expected': [[0, 0, 4, 3, 3, 0], [2, 4, 4, 0, 0, 0]],
},
{
'name': 'add',
'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
'index': [0, 1, 1, 0],
'dim': 0,
'fill_value': 0,
'expected': [[6, 5], [6, 8]],
},
{
'name': 'sub',
'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1,
'fill_value': 9,
'expected': [[9, 9, 5, 6, 6, 9], [7, 5, 5, 9, 9, 9]],
},
{
'name': 'sub',
'src': [[5, 2], [2, 2], [4, 2], [1, 3]],
'index': [0, 1, 1, 0],
'dim': 0,
'fill_value': 9,
'expected': [[3, 4], [3, 5]],
},
{
'name': 'mul',
'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1,
'fill_value': 1,
'expected': [[1, 1, 4, 3, 2, 0], [0, 4, 3, 1, 1, 1]],
},
{
'name': 'mul',
'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
'index': [0, 1, 1, 0],
'dim': 0,
'fill_value': 1,
'expected': [[5, 6], [8, 15]],
# }, {
# 'name': 'div',
# 'src': [[2, 1, 1, 4, 2], [1, 2, 1, 2, 4]],
# 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
# 'dim': -1,
# 'fill_value': 1,
# 'expected': [[1, 1, 0.25, 0.5, 0.5, 1], [0.5, 0.25, 0.5, 1, 1, 1]],
# }, {
# 'name': 'div',
# 'src': [[4, 2], [2, 1], [4, 2], [1, 2]],
# 'index': [0, 1, 1, 0],
# 'dim': 0,
# 'fill_value': 1,
# 'expected': [[0.25, 0.25], [0.125, 0.5]],
# }, {
# 'name': 'mean',
# 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
# 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
# 'dim': -1,
# 'fill_value': 0,
# 'expected': [[0, 0, 4, 3, 1.5, 0], [1, 4, 2, 0, 0, 0]],
# }, {
# 'name': 'mean',
# 'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
# 'index': [0, 1, 1, 0],
# 'dim': 0,
# 'fill_value': 0,
# 'expected': [[3, 2.5], [3, 4]],
# }, {
# 'name': 'max',
# 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
# 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
# 'dim': -1,
# 'fill_value': 0,
# 'expected': [[0, 0, 4, 3, 2, 0], [2, 4, 3, 0, 0, 0]],
# 'expected_arg': [[-1, -1, 3, 4, 0, 1], [1, 4, 3, -1, -1, -1]],
# }, {
# 'name': 'max',
# 'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
# 'index': [0, 1, 1, 0],
# 'dim': 0,
# 'fill_value': 0,
# 'expected': [[5, 3], [4, 5]],
# 'expected_arg': [[0, 3], [2, 1]],
# }, {
# 'name': 'min',
# 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
# 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
# 'dim': -1,
# 'fill_value': 9,
# 'expected': [[9, 9, 4, 3, 1, 0], [0, 4, 1, 9, 9, 9]],
# 'expected_arg': [[-1, -1, 3, 4, 2, 1], [0, 4, 2, -1, -1, -1]],
# }, {
# 'name': 'min',
# 'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
# 'index': [0, 1, 1, 0],
# 'dim': 0,
# 'fill_value': 9,
# 'expected': [[1, 2], [2, 3]],
# 'expected_arg': [[3, 0], [1, 2]],
}
]
tests = [{
'name': 'add',
'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1,
'fill_value': 0,
'expected': [[0, 0, 4, 3, 3, 0], [2, 4, 4, 0, 0, 0]],
}, {
'name': 'add',
'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
'index': [0, 1, 1, 0],
'dim': 0,
'fill_value': 0,
'expected': [[6, 5], [6, 8]],
}, {
'name': 'sub',
'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1,
'fill_value': 9,
'expected': [[9, 9, 5, 6, 6, 9], [7, 5, 5, 9, 9, 9]],
}, {
'name': 'sub',
'src': [[5, 2], [2, 2], [4, 2], [1, 3]],
'index': [0, 1, 1, 0],
'dim': 0,
'fill_value': 9,
'expected': [[3, 4], [3, 5]],
}, {
'name': 'mul',
'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1,
'fill_value': 1,
'expected': [[1, 1, 4, 3, 2, 0], [0, 4, 3, 1, 1, 1]],
}, {
'name': 'mul',
'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
'index': [0, 1, 1, 0],
'dim': 0,
'fill_value': 1,
'expected': [[5, 6], [8, 15]],
}, {
'name': 'div',
'src': [[2, 1, 1, 4, 2], [1, 2, 1, 2, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1,
'fill_value': 1,
'expected': [[1, 1, 0.25, 0.5, 0.5, 1], [0.5, 0.25, 0.5, 1, 1, 1]],
}, {
'name': 'div',
'src': [[4, 2], [2, 1], [4, 2], [1, 2]],
'index': [0, 1, 1, 0],
'dim': 0,
'fill_value': 1,
'expected': [[0.25, 0.25], [0.125, 0.5]],
}, {
'name': 'mean',
'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1,
'fill_value': 0,
'expected': [[0, 0, 4, 3, 1.5, 0], [1, 4, 2, 0, 0, 0]],
}, {
'name': 'mean',
'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
'index': [0, 1, 1, 0],
'dim': 0,
'fill_value': 0,
'expected': [[3, 2.5], [3, 4]],
}, {
'name': 'max',
'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1,
'fill_value': 0,
'expected': [[0, 0, 4, 3, 2, 0], [2, 4, 3, 0, 0, 0]],
'expected_arg': [[-1, -1, 3, 4, 0, 1], [1, 4, 3, -1, -1, -1]],
}, {
'name': 'max',
'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
'index': [0, 1, 1, 0],
'dim': 0,
'fill_value': 0,
'expected': [[5, 3], [4, 5]],
'expected_arg': [[0, 3], [2, 1]],
}, {
'name': 'min',
'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1,
'fill_value': 9,
'expected': [[9, 9, 4, 3, 1, 0], [0, 4, 1, 9, 9, 9]],
'expected_arg': [[-1, -1, 3, 4, 2, 1], [0, 4, 2, -1, -1, -1]],
}, {
'name': 'min',
'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
'index': [0, 1, 1, 0],
'dim': 0,
'fill_value': 9,
'expected': [[1, 2], [2, 3]],
'expected_arg': [[3, 0], [1, 2]],
}]
@pytest.mark.parametrize('test,dtype,device', product(tests, dtypes, devices))
......
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