def test_jit(self, device, dtype):
B, C, H, W = 1, 1, 32, 32
patches = torch.rand(B, C, H, W, device=device, dtype=dtype)
laf = torch.tensor([[[[8.0, 0.0, 16.0], [0.0, 8.0, 16.0]]]],
device=device)
laf_estimator = LAFAffNetShapeEstimator(True).to(
device, dtype=patches.dtype).eval()
laf_estimator_jit = torch.jit.script(
LAFAffNetShapeEstimator(True).to(device,
dtype=patches.dtype).eval())
assert_close(laf_estimator(laf, patches),
laf_estimator_jit(laf, patches))
def test_toy(self, device):
aff = LAFAffNetShapeEstimator(True).to(device).eval()
inp = torch.zeros(1, 1, 32, 32, device=device)
inp[:, :, 15:-15, 9:-9] = 1
laf = torch.tensor([[[[20.0, 0.0, 16.0], [0.0, 20.0, 16.0]]]],
device=device)
new_laf = aff(laf, inp)
expected = torch.tensor(
[[[[40.8758, 0.0, 16.0], [-0.3824, 9.7857, 16.0]]]], device=device)
assert_close(new_laf, expected, atol=1e-4, rtol=1e-4)
def test_gradcheck(self, device):
batch_size, channels, height, width = 1, 1, 35, 35
patches = torch.rand(batch_size,
channels,
height,
width,
device=device)
patches = utils.tensor_to_gradcheck_var(patches) # to var
laf = torch.tensor([[[[8.0, 0.0, 16.0], [0.0, 8.0, 16.0]]]],
device=device)
laf = utils.tensor_to_gradcheck_var(laf) # to var
assert gradcheck(
LAFAffNetShapeEstimator(True).to(device, dtype=patches.dtype),
(laf, patches),
raise_exception=True,
rtol=1e-3,
atol=1e-3,
nondet_tol=1e-4,
)
def test_print(self, device):
sift = LAFAffNetShapeEstimator()
sift.__repr__()
def test_shape_batch(self, device):
inp = torch.rand(2, 1, 32, 32, device=device)
laf = torch.rand(2, 5, 2, 3, device=device)
ori = LAFAffNetShapeEstimator().to(device).eval()
out = ori(laf, inp)
assert out.shape == laf.shape
def test_pretrained(self, device):
inp = torch.rand(1, 1, 32, 32, device=device)
laf = torch.rand(1, 1, 2, 3, device=device)
ori = LAFAffNetShapeEstimator(True).to(device).eval()
out = ori(laf, inp)
assert out.shape == laf.shape