def test_bias(self):
unet = Unet(
in_features=3,
down=[16, 32, 64],
up=[40, 4],
)
checker = lambda name_weight: 'bias' in name_weight[0]
bias = any(map(checker, unet.named_parameters()))
self.assertTrue(bias)
def test_no_bias(self):
unet = Unet(in_features=3,
down=[16, 32, 64],
up=[40, 4],
setup={
**fat_setup, 'bias': False
})
checker = lambda name_weight: 'bias' not in name_weight[0]
no_bias = all(map(checker, unet.named_parameters()))
self.assertTrue(no_bias)
def test_inequal_output_asymmetric(self):
unet = Unet(in_features=3,
down=[16, 32, 64],
up=[40, 4],
setup=thin_setup)
input = torch.zeros(2, 3, 104, 104)
output = unet(input)
self.assertEqual(torch.Size([2, 4, 64, 64]), output.size())
def test_inequal_output_symmetric(self):
unet = Unet(down=[16, 32, 64],
up=[40, 1],
setup={
**thin_setup, 'gate': AttentionGate
})
input = torch.zeros(2, 1, 104, 104)
output = unet(input)
self.assertEqual(torch.Size([2, 1, 64, 64]), output.size())
def test_inequal_output_asymmetric(self):
unet = Unet(in_features=3,
down=[16, 32, 64],
up=[40, 4],
setup={
**fat_setup, 'checkpointed': True
})
input = torch.zeros(2, 3, 104, 104)
output = unet(input)
self.assertEqual(torch.Size([2, 4, 24, 24]), output.size())
def __init__(
self,
desc_dim=128,
window=8,
setup=DEFAULT_SETUP,
kernel_size=5,
):
super(DISK, self).__init__()
self.desc_dim = desc_dim
self.unet = Unet(
in_features=3, size=kernel_size,
down=[16, 32, 64, 64, 64],
up=[64, 64, 64, desc_dim+1],
setup=setup,
)
self.detector = Detector(window=window)
def test_inequal_output_symmetric(self):
unet = Unet(down=[16, 32, 64], up=[40, 1])
input = torch.zeros(2, 1, 104, 104)
output = unet(input)
self.assertEqual(torch.Size([2, 1, 24, 24]), output.size())