8000 Initial utils implementation + bug fixes by apaszke · Pull Request #12 · pytorch/pytorch · GitHub
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Initial utils implementation + bug fixes #12

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8000 Merged
merged 3 commits into from
Sep 9, 2016
Merged

Initial utils implementation + bug fixes #12

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ee85fe1
Initial utils implementation
apaszke Sep 9, 2016
1703f2a
Add utils tests
apaszke Sep 9, 2016
f646391
Bug fixes and test improvements
apaszke Sep 8, 2016
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4 changes: 4 additions & 0 deletions test/common_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -380,6 +380,10 @@ def __call__(self, test_case):
module = self.constructor(*self.constructor_args)
input = self._get_input()

# Check that these methods don't raise errors
module.__repr__()
str(module)

if self.reference_fn is not None:
out = test_case._forward_criterion(module, input, self.target)
expected_out = self.reference_fn(deepcopy(self._unpack_input(input)),
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1 change: 1 addition & 0 deletions test/run_test.sh
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Expand Up @@ -8,6 +8,7 @@ python test_autograd.py
python test_nn.py
python test_legacy_nn.py
python test_multiprocessing.py
python test_utils.py
if which nvcc >/dev/null 2>&1
then
python test_cuda.py
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92 changes: 78 additions & 14 deletions test/test_legacy_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -152,12 +152,11 @@ def _do_test(self, test_case, module, input):
OldModuleTest(nn.Squeeze,
input_size=(2, 1, 1, 4, 5),
reference_fn=lambda i,_: i.squeeze()),
# TODO: should squeeze work inplace?
# OldModuleTest(nn.Squeeze,
# (1,),
# input_size=(2, 1, 1, 4, 5),
# reference_fn=lambda i,_: i.squeeze(1),
# desc='dim'),
OldModuleTest(nn.Squeeze,
(1,),
input_size=(2, 1, 1, 4, 5),
reference_fn=lambda i,_: i.squeeze(1),
desc='dim'),
OldModuleTest(nn.Unsqueeze,
(1,),
input_size=(2, 4, 5),
Expand Down Expand Up @@ -356,13 +355,12 @@ def _do_test(self, test_case, module, input):
desc='stride_pad'),
OldModuleTest(nn.SpatialAdaptiveMaxPooling,
(4, 4),
input_size=(2, 3, 8, 8)),
input_size=(2, 3, 8, 8),
reference_fn=lambda i,_: nn.SpatialMaxPooling(2, 2).forward(i)),
OldModuleTest(nn.SpatialAdaptiveMaxPooling,
(4, 4),
input_size=(2, 3, 7, 11),
desc='irregular'),
# TODO: enable after implementing MaxPooling
# reference_fn=lambda i,_: nn.SpatialMaxPooling(2, 2).forward(i)),
OldModuleTest(nn.SpatialConvolution,
(3, 4, 3, 3),
input_size=(2, 3, 6, 6)),
Expand All @@ -385,10 +383,9 @@ def _do_test(self, test_case, module, input):
(3, 2, 6, 6, 2, 2, 2, 2, 1, 1),
input_size=(2, 3, 6, 6),
desc='stride_pad'),
# TODO FIX THIS
# OldModuleTest(nn.SpatialCrossMapLRN,
# (3,),
# input_size=(2, 3, 6, 6)),
OldModuleTest(nn.SpatialCrossMapLRN,
(5, 5e-3, 1e-3, 2),
input_size=(2, 3, 6, 6)),
OldModuleTest(nn.SpatialDivisiveNormalization,
(3,),
input_size=(2, 3, 8, 8)),
Expand Down Expand Up @@ -750,6 +747,10 @@ def test_Dropout(self):
gradInput = module.backward(input.clone(), input.clone())
self.assertLess(abs(gradInput.mean() - (1-p)), 0.05)

# Check that these don't raise errors
module.__repr__()
str(module)

def test_SpatialDropout(self):
p = 0.2
b = random.randint(1, 5)
Expand All @@ -764,6 +765,10 @@ def test_SpatialDropout(self):
gradInput = module.backward(input, input)
self.assertLess(abs(gradInput.mean() - (1-p)), 0.05)

# Check that these don't raise errors
module.__repr__()
str(module)

def test_VolumetricDropout(self):
p = 0.2
bsz = random.randint(1,5)
Expand All @@ -779,6 +784,10 @@ def test_VolumetricDropout(self):
gradInput = module.backward(input, input)
self.assertLess(abs(gradInput.mean() - (1-p)), 0.05)

# Check that these don't raise errors
module.__repr__()
str(module)

def test_ReLU_reference(self):
input = torch.randn(10, 20)
module = nn.ReLU()
Expand All @@ -790,7 +799,6 @@ def test_ReLU6_reference(self):
input = torch.randn(10, 20).mul(10)
module = nn.ReLU6()
output = module.forward(input)
# TODO: check elements between 0 and 6
self.assertTrue(output[input.ge(6)].eq(6).all())
self.assertTrue(output[input.lt(0)].eq(0).all())

Expand All @@ -807,6 +815,10 @@ def test_Copy(self):
c.double()
self.assertEqual(torch.typename(output), 'torch.FloatTensor')

# Check that these don't raise errors
c.__repr__()
str(c)

def test_FlattenTable(self):
input = [
torch.rand(1),
Expand Down Expand Up @@ -839,6 +851,10 @@ def test_FlattenTable(self):
self.assertEqual(gradOutput[2], gradInput[1][1][0])
self.assertEqual(gradOutput[3], gradInput[2])

# Check that these don't raise errors
m.__repr__()
str(m)

# More uglyness: FlattenTable doesn't rebuild the table every updateOutput
# call, so we need to make sure that modifications to the input are
# detected correctly (and that the table is correctly rebuilt.
Expand Down Expand Up @@ -877,6 +893,10 @@ def test_Concat(self):
l.weight.fill_(1)
l.bias.fill_(0)

# Check that these don't raise errors
m.__repr__()
str(m)

output = m.forward(input)
output2 = input.sum(1).expand(4, 5).repeatTensor(num_modules, 1)
self.assertEqual(output2, output)
Expand All @@ -896,6 +916,10 @@ def test_Parallel(self):
m.add(nn.View(4, 5, 1))
m.add(nn.View(4, 5, 1))

# Check that these don't raise errors
m.__repr__()
str(m)

output = m.forward(input)
output2 = input.transpose(0, 2).transpose(0, 1)
self.assertEqual(output2, output)
Expand All @@ -914,6 +938,10 @@ def test_ParallelTable(self):
m.add(p)
m.add(nn.JoinTable(2))

# Check that these don't raise errors
p.__repr__()
str(p)

output = m.forward(input)
output2 = input.transpose(0,2).transpose(0,1)
self.assertEqual(output2, output)
Expand All @@ -939,6 +967,10 @@ def test_ConcatTable(self):
module.add(nn.Identity())
module.float()

# Check that these don't raise errors
module.__repr__()
str(module)

output = module.forward(input)
output2 = [input, input, input]
self.assertEqual(output2, output)
Expand Down Expand Up @@ -998,6 +1030,10 @@ def test_DepthConcat(self):
self.assertEqual(output, outputConcat)
self.assertEqual(gradInput, gradInputConcat)

# Check that these don't raise errors
concat.__repr__()
str(concat)

def test_Contiguous(self):
input = torch.randn(10, 10, 10)
noncontig = input[:, 4]
Expand All @@ -1007,6 +1043,10 @@ def test_Contiguous(self):
self.assertEqual(output, noncontig)
self.assertTrue(output.contiguous())

# Check that these don't raise errors
module.__repr__()
str(module)

def test_Index(self):
net = nn.Index(0)

Expand All @@ -1028,6 +1068,10 @@ def test_Index(self):
gradInput = net.backward(input, gradOutput)
self.assertEqual(gradInput[0], torch.Tensor(((2, 4), (0, 0))))

# Check that these don't raise errors
net.__repr__()
str(net)

def test_L1Penalty(self):
weight = 1
m = nn.L1Penalty(weight, False, False)
Expand All @@ -1044,6 +1088,10 @@ def test_L1Penalty(self):
input.lt(0).typeAs(grad).mul_(-1)).mul_(weight)
self.assertEqual(true_grad, grad)

# Check that these don't raise errors
m.__repr__()
str(m)

def test_MaskedSelect(self):
input = torch.randn(4, 5)
mask = torch.ByteTensor(4, 5).bernoulli_()
Expand All @@ -1060,6 +1108,10 @@ def test_MaskedSelect(self):
gradIn = module.backward([input, mask], gradOut)
self.assertEqual(inTarget, gradIn[0])

# Check that these don't raise errors
module.__repr__()
str(module)

def test_MultiCriterion(self):
input = torch.rand(2, 10)
target = torch.Tensor((1, 8))
Expand All @@ -1085,6 +1137,10 @@ def test_MultiCriterion(self):
self.assertEqual(output, output3)
self.assertEqual(gradInput.float(), gradInput3)

# Check that these don't raise errors
mc.__repr__()
str(mc)

# test table input
# TODO: enable when Criterion.clone is ready
# mc.double()
Expand Down Expand Up @@ -1164,6 +1220,10 @@ def test_ParallelCriterion(self):
self.assertEqual(gradInput[1][0], gradInput2[1][0])
self.assertEqual(gradInput[1][1], gradInput2[1][1])

# Check that these don't raise errors
pc.__repr__()
str(pc)

def test_NarrowTable(self):
input = [torch.Tensor(i) for i in range(1, 6)]

Expand All @@ -1175,6 +1235,10 @@ def test_NarrowTable(self):
output = module.forward(input)
self.assertEqual(output, input[2:5])

# Check that these don't raise errors
module.__repr__()
str(module)


if __name__ == '__main__':
prepare_tests()
Expand Down
14 changes: 13 additions & 1 deletion test/test_torch.py
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Expand Up @@ -4,7 +4,7 @@
import torch
import tempfile
import unittest
from itertools import product
from itertools import product, chain
from common import TestCase, iter_indices

SIZE = 100
Expand Down Expand Up @@ -2252,5 +2252,17 @@ def test_from_buffer(self):
self.assertEqual(floats.size(), 1)
self.assertEqual(floats[0], 2.25)

def test_print(self):
for t in torch._tensor_classes:
obj = t(100, 100).fill_(1)
obj.__repr__()
str(obj)
for t in torch._storage_classes:
obj = t(100).fill_(1)
obj.__repr__()
str(obj)


if __name__ == '__main__':
unittest.main()

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