import torch import refiners.fluxion.layers as fl def test_chain_remove_replace(): chain = fl.Chain( fl.Linear(1, 1), fl.Linear(1, 1), fl.Chain( fl.Linear(1, 1), fl.Linear(1, 1), fl.Chain(fl.Linear(1, 1), fl.Linear(1, 1)), ), fl.Conv2d(1, 1, 1), ) assert len(chain) == 4 assert len(chain.Chain) == 3 chain.remove(chain[-1]) assert len(chain) == 3 assert len(chain.Chain) == 3 assert isinstance(chain.Chain.Chain[1], fl.Linear) chain.Chain.Chain.replace(chain.Chain.Chain[1], fl.Conv2d(1, 1, 1)) assert len(chain) == 3 assert len(chain.Chain) == 3 assert isinstance(chain.Chain.Chain[1], fl.Conv2d) def test_chain_structural_copy(): m = fl.Chain( fl.Sum( fl.Linear(in_features=4, out_features=8), fl.Linear(in_features=4, out_features=8), ), fl.Linear(in_features=8, out_features=12), ) x = torch.randn(7, 4) y = m(x) assert y.shape == (7, 12) m2 = m.structural_copy() assert m.Linear == m2.Linear assert m.Sum.Linear_1 == m2.Sum.Linear_1 assert m.Sum.Linear_2 == m2.Sum.Linear_2 assert m.Sum != m2.Sum assert m != m2 assert m.Sum.parent == m assert m2.Sum.parent == m2 y2 = m2(x) assert y2.shape == (7, 12) torch.equal(y2, y) def test_chain_find(): chain = fl.Chain( fl.Linear(1, 1), ) assert isinstance(chain.find(fl.Linear), fl.Linear) assert chain.find(fl.Conv2d) is None def test_chain_slice() -> None: chain = fl.Chain( fl.Linear(in_features=1, out_features=1), fl.Linear(in_features=1, out_features=1), fl.Linear(in_features=1, out_features=1), fl.Chain( fl.Linear(in_features=1, out_features=1), fl.Linear(in_features=1, out_features=1), ), fl.Linear(in_features=1, out_features=1), ) x = torch.randn(1, 1) sliced_chain = chain[1:4] assert len(chain) == 5 assert len(sliced_chain) == 3 assert chain[:-1](x).shape == (1, 1)