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)
model = HardNet().to(patches.device, patches.dtype).eval()
model_jit = torch.jit.script(HardNet().to(patches.device,
patches.dtype).eval())
assert_allclose(model(patches), model_jit(patches))
def test_gradcheck(self, device):
patches = torch.rand(2, 1, 32, 32, device=device)
patches = utils.tensor_to_gradcheck_var(patches) # to var
hardnet = HardNet().to(patches.device, patches.dtype)
assert gradcheck(hardnet, (patches, ),
eps=1e-4,
atol=1e-4,
raise_exception=True)
def test_shape_batch(self, device):
inp = torch.ones(16, 1, 32, 32, device=device)
hardnet = HardNet().to(device)
out = hardnet(inp)
assert out.shape == (16, 128)
def test_shape(self, device):
inp = torch.ones(1, 1, 32, 32, device=device)
hardnet = HardNet().to(device)
hardnet.eval() # batchnorm with size 1 is not allowed in train mode
out = hardnet(inp)
assert out.shape == (1, 128)
