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import pytest
import torch
from torch.autograd import gradcheck
import kornia.testing as utils # test utils
from kornia.feature import DeFMO
from kornia.testing import assert_close
class TestDeFMO:
def test_shape(self, device, dtype):
inp = torch.ones(1, 6, 128, 160, device=device, dtype=dtype)
defmo = DeFMO().to(device, dtype)
defmo.eval() # batchnorm with size 1 is not allowed in train mode
out = defmo(inp)
assert out.shape == (1, 24, 4, 128, 160)
def test_shape_batch(self, device, dtype):
inp = torch.ones(2, 6, 128, 160, device=device, dtype=dtype)
defmo = DeFMO().to(device, dtype)
out = defmo(inp)
with torch.no_grad():
assert out.shape == (2, 24, 4, 128, 160)
@pytest.mark.skip("jacobian not well computed")
def test_gradcheck(self, device, dtype):
patches = torch.rand(2, 6, 128, 128, device=device, dtype=dtype)
patches = utils.tensor_to_gradcheck_var(patches) # to var
defmo = DeFMO().to(patches.device, patches.dtype)
assert gradcheck(defmo, (patches,), eps=1e-4, atol=1e-4, raise_exception=True)
@pytest.mark.jit
def test_jit(self, device, dtype):
B, C, H, W = 1, 6, 128, 160
patches = torch.rand(B, C, H, W, device=device, dtype=dtype)
model = DeFMO(True).to(patches.device, patches.dtype).eval()
model_jit = torch.jit.script(DeFMO(True).to(patches.device, patches.dtype).eval())
with torch.no_grad():
assert_close(model(patches), model_jit(patches))
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