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import pytest
import torch
from torch.autograd import gradcheck
import kornia.testing as utils # test utils
from kornia.feature import TFeat
from kornia.testing import assert_close
class TestTFeat:
def test_shape(self, device):
inp = torch.ones(1, 1, 32, 32, device=device)
tfeat = TFeat().to(device)
tfeat.eval() # batchnorm with size 1 is not allowed in train mode
out = tfeat(inp)
assert out.shape == (1, 128)
def test_pretrained(self, device):
inp = torch.ones(1, 1, 32, 32, device=device)
tfeat = TFeat(True).to(device)
tfeat.eval() # batchnorm with size 1 is not allowed in train mode
out = tfeat(inp)
assert out.shape == (1, 128)
def test_shape_batch(self, device):
inp = torch.ones(16, 1, 32, 32, device=device)
tfeat = TFeat().to(device)
out = tfeat(inp)
assert out.shape == (16, 128)
@pytest.mark.skip("jacobian not well computed")
def test_gradcheck(self, device):
patches = torch.rand(2, 1, 32, 32, device=device)
patches = utils.tensor_to_gradcheck_var(patches) # to var
tfeat = TFeat().to(patches.device, patches.dtype)
assert gradcheck(tfeat, (patches,), eps=1e-2, atol=1e-2, raise_exception=True)
@pytest.mark.jit
def test_jit(self, device, dtype):
B, C, H, W = 2, 1, 32, 32
patches = torch.ones(B, C, H, W, device=device, dtype=dtype)
tfeat = TFeat(True).to(patches.device, patches.dtype).eval()
tfeat_jit = torch.jit.script(TFeat(True).to(patches.device, patches.dtype).eval())
assert_close(tfeat_jit(patches), tfeat(patches))
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