def test_shape(self, input_param, input_data, expected_shape):
net = DynUNet(**input_param)
net.eval()
with torch.no_grad():
result = net(input_data)
self.assertEqual(result.shape, expected_shape)
def test_shape(self, input_param, input_shape, expected_shape):
net = DynUNet(**input_param).to(device)
net.eval()
with torch.no_grad():
result = net(torch.randn(input_shape).to(device))
self.assertEqual(result.shape, expected_shape)
