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Make test fixtures more light-weight and thorough #91

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Jan 17, 2022
Merged

Make test fixtures more light-weight and thorough #91

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16 changes: 8 additions & 8 deletions tests/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@ def rng(request):
@pytest.fixture(scope='session', params=[
*product(
[Linear],
prodict(in_features=[16], out_features=[16], bias=[True, False]),
prodict(in_features=[16], out_features=[15], bias=[True, False]),
),
*product(
[Conv1d, Conv2d, Conv3d, ConvTranspose1d, ConvTranspose2d, ConvTranspose3d],
Expand All @@ -36,7 +36,7 @@ def rng(request):
])
def module_linear(rng, request):
module_type, kwargs = request.param
return module_type(**kwargs).eval()
return module_type(**kwargs).to(torch.float64).eval()


@pytest.fixture(scope='session')
Expand All @@ -47,8 +47,8 @@ def module_batchnorm(module_linear):
((Conv3d, ConvTranspose3d), BatchNorm3d),
]
feature_index_map = [
((Linear, ConvTranspose1d, ConvTranspose2d, ConvTranspose3d), 1),
((Conv1d, Conv2d, Conv3d), 0),
((ConvTranspose1d, ConvTranspose2d, ConvTranspose3d), 1),
((Linear, Conv1d, Conv2d, Conv3d), 0),
]

batchnorm_type = None
Expand All @@ -67,12 +67,12 @@ def module_batchnorm(module_linear):
if feature_index is None:
raise RuntimeError('No feature index for linear layer found.')

return batchnorm_type(num_features=module_linear.weight.shape[feature_index]).eval()
return batchnorm_type(num_features=module_linear.weight.shape[feature_index]).to(torch.float64).eval()


@pytest.fixture(scope='session')
def data_input(rng, module_linear):
shape = (16,)
shape = (4,)
setups = [
(Conv1d, 1, 1),
(ConvTranspose1d, 0, 1),
Expand All @@ -86,6 +86,6 @@ def data_input(rng, module_linear):
else:
for module_type, dim, ndims in setups:
if isinstance(module_linear, module_type):
shape += (module_linear.weight.shape[dim],) + (16,) * ndims
shape += (module_linear.weight.shape[dim],) + (4,) * ndims

return torch.empty(*shape).normal_(generator=rng)
return torch.empty(*shape, dtype=torch.float64).normal_(generator=rng)