def test_create_variable(VariableMock):
v = np.arange(0, 3)
with mock.patch("fastai.core.IS_TORCH_04", True):
core.create_variable(v, volatile=True)
assert VariableMock.call_args[1] == {"requires_grad": False}
with mock.patch("fastai.core.IS_TORCH_04", False):
core.create_variable(v, volatile=True)
assert VariableMock.call_args[1] == {"requires_grad": False, "volatile": True}
def test_create_variable(VariableMock):
v = np.arange(0, 3)
with mock.patch("fastai.core.IS_TORCH_04", True):
core.create_variable(v, volatile=True)
assert VariableMock.call_args[1] == {"requires_grad": False}
with mock.patch("fastai.core.IS_TORCH_04", False):
core.create_variable(v, volatile=True)
assert VariableMock.call_args[1] == {"requires_grad": False, "volatile": True}
def test_create_variable_passing_Variable_object():
v = torch.autograd.Variable(core.T(np.arange(0, 3)))
cv = core.create_variable(v, volatile=True)
if core.IS_TORCH_04:
assert (cv == v).all()
else:
assert cv is v
def image_loader(image_name: str, image_size: int):
"""load image, returns cuda tensor"""
loader = get_loader(image_size)
image = Image.open(image_name)
image = loader(image).float()
image = create_variable(image, True, requires_grad=False)
image = image.unsqueeze(0)
return image
def test_create_variable_passing_Variable_object():
v = torch.autograd.Variable(core.T(np.arange(0, 3)))
assert core.create_variable(v, volatile=True) is v
def test_create_variable_passing_Variable_object(): v = torch.autograd.Variable(core.T(np.arange(0, 3))) cv = core.create_variable(v, volatile=True) if core.IS_TORCH_04: assert (cv == v).all() else: assert cv is v
