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Fix repeat non owning #4084

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Merged
apaszke merged 5 commits into from
Dec 16, 2017
Merged

Fix repeat non owning #4084

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bf37336
Add addiitonal tensor.repeat tests.
maciejkula Dec 7, 2017
d9160f2
Make sure tensor.repeat works for tensors with shared storage.
maciejkula Dec 7, 2017
8139aa6
Change clone to view.
maciejkula Dec 8, 2017
983e676
Clean up tensor.repeat. Add noncontiguous input tests.
maciejkula Dec 9, 2017
0570712
Skip numpy-based repeat tests if numpy not found.
maciejkula Dec 10, 2017
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35 changes: 32 additions & 3 deletions test/test_torch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3873,19 +3873,48 @@ def test_expand(self):
self.assertEqual(torch.randn(()).expand(()), torch.randn(()))

def test_repeat(self):
result = torch.Tensor()
tensor = torch.rand(8, 4)

initial_shape = (8, 4)
tensor = torch.rand(*initial_shape)

size = (3, 1, 1)
torchSize = torch.Size(size)
target = [3, 8, 4]
self.assertEqual(tensor.repeat(*size).size(), target, 'Error in repeat')
self.assertEqual(tensor.repeat(torchSize).size(), target, 'Error in repeat using LongStorage')
self.assertEqual(tensor.repeat(torchSize).size(), target,
'Error in repeat using LongStorage')
result = tensor.repeat(*size)
self.assertEqual(result.size(), target, 'Error in repeat using result')
result = tensor.repeat(torchSize)
self.assertEqual(result.size(), target, 'Error in repeat using result and LongStorage')
self.assertEqual(result.mean(0).view(8, 4), tensor, 'Error in repeat (not equal)')

@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_repeat_tile(self):

initial_shape = (8, 4)

repeats = ((3, 1, 1),
(3, 3, 3),
(1, 2, 1),
(2, 2, 2, 2))

def _generate_noncontiguous_input():

out = np.broadcast_to(np.random.random((1, 4)),
initial_shape)

assert not (out.flags.c_contiguous or out.flags.f_contiguous)

return out

for repeat in repeats:
for tensor in (torch.from_numpy(np.random.random(initial_shape)),
torch.from_numpy(_generate_noncontiguous_input()),):

self.assertEqual(tensor.repeat(*repeat).numpy(),
np.tile(tensor.numpy(), repeat))

def test_is_same_size(self):
t1 = torch.Tensor(3, 4, 9, 10)
t2 = torch.Tensor(3, 4)
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31 changes: 16 additions & 15 deletions torch/tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -266,30 +266,31 @@ def repeat(self, *sizes):
# If args == (torch.Size,), then we need to unpack the tuple
if len(sizes) == 1 and isinstance(sizes[0], torch.Size):
sizes = sizes[0]

repeats = list(sizes)
result = self.new()
src = self.contiguous()

if len(repeats) < src.dim():
if len(repeats) < self.dim():
raise ValueError('Number of dimensions of repeat dims can not be '
'smaller than number of dimensions of tensor')

xtensor = src.new().set_(src)

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xsize = list(xtensor.size())
for i in _range(len(repeats) - src.dim()):
xsize = [1] + xsize
# Add new leading dimensions to the tensor if the
# number of target dimensions is larger than the
# number of source dimensions.
num_new_dimensions = len(repeats) - self.dim()
padded_size = [1] * num_new_dimensions + list(self.size())
target_size = torch.Size([a * b for a, b in zip(padded_size, repeats)])

xtensor = self.new().set_(self)
xtensor = xtensor.expand(padded_size)

size = torch.Size([a * b for a, b in zip(xsize, repeats)])
xtensor.resize_(torch.Size(xsize))
result.resize_(size)
result = self.new()
result.resize_(target_size)
urtensor = result.new(result)
for i in _range(xtensor.dim()):
urtensor = urtensor.unfold(i, xtensor.size(i), xtensor.size(i))
for i in _range(urtensor.dim() - xtensor.dim()):
xsize = [1] + xsize
xtensor.resize_(torch.Size(xsize))
xxtensor = xtensor.expand_as(urtensor)
urtensor.copy_(xxtensor)

urtensor.copy_(xtensor.expand_as(urtensor))

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return result

def masked_copy_(self, *args, **kwargs):
Expand Down