import unittest from types import SimpleNamespace import torch from torch import nn from baselines.hihr.model import ( CLIPTokenPromptLearner, HiHRModel, LorentzManifold, PromptLearner, TextGuidedFusion, ViewPromptMoE, batch_hard_triplet, intra_view_triplet, ) from fastreid.data.samplers import CVIdentitySampler class HiHRReimplementationTest(unittest.TestCase): def test_view_prompt_moe_shapes_and_gradients(self): moe = ViewPromptMoE(dim=8, hidden_dim=16) features = torch.randn(4, 8, requires_grad=True) prompt = torch.randn(4, 8, requires_grad=True) views = torch.tensor([0, 1, 0, 1]) output = moe(features, views, prompt) self.assertEqual(output["features"].shape, (4, 8)) self.assertEqual(output["router_probs"].shape, (4, 3)) self.assertTrue(torch.allclose(output["router_probs"].sum(dim=1), torch.ones(4))) output["features"].sum().backward() self.assertIsNotNone(features.grad) self.assertIsNotNone(moe.view_embed.weight.grad) def test_disabled_hhl_skips_geometry(self): model = HiHRModel.__new__(HiHRModel) nn.Module.__init__(model) model.lambda_entail = 0.0 model.entailment_terms = frozenset( ("proto_child", "prompt_order", "parent_alignment", "child_alignment") ) losses = model.hierarchy_losses( {"parent_g": torch.randn(4, 8, requires_grad=True)}, torch.tensor([0, 0, 1, 1]), ) self.assertEqual(len(losses), 4) self.assertTrue(all(float(loss.detach()) == 0.0 for loss in losses.values())) def test_token_prompt_pools_at_attended_eos(self): class FakeTokenizer: def __call__(self, texts, **kwargs): if texts == "X": return {"input_ids": [7]} token_ids = torch.tensor( [ [1, 7, 7, 7, 7, 9, 99, 0, 0, 0, 0, 0], [1, 7, 7, 7, 7, 9, 7, 7, 7, 7, 9, 99], [1, 7, 7, 7, 7, 9, 7, 7, 7, 7, 9, 99], ] ) attention_mask = token_ids.ne(0).long() return {"input_ids": token_ids, "attention_mask": attention_mask} text_model = nn.Module() text_model.config = SimpleNamespace(hidden_size=8) learner = CLIPTokenPromptLearner( text_model, nn.Linear(8, 8), FakeTokenizer(), num_context=4 ) torch.testing.assert_close(learner.eos_positions, torch.tensor([6, 11, 11])) def test_lorentz_roundtrip_and_constraint(self): manifold = LorentzManifold(1.0) tangent = torch.randn(8, 16) * 0.05 point = manifold.expmap0(tangent) recovered = manifold.logmap0(point) torch.testing.assert_close(recovered, tangent, atol=2e-5, rtol=2e-4) c = manifold.curvature.detach() norm = LorentzManifold.inner(point, point) torch.testing.assert_close(norm, torch.full_like(norm, -1.0 / c), atol=2e-4, rtol=2e-4) def test_distance_is_finite_and_zero_on_diagonal(self): manifold = LorentzManifold(1.0) point = manifold.expmap0(torch.randn(4, 8) * 0.05) distance = manifold.distance(point, point) self.assertTrue(torch.isfinite(distance).all()) self.assertLess(float(distance.detach().max()), 5e-3) def test_entailment_cone_prefers_aligned_child(self): manifold = LorentzManifold(1.0) parent = manifold.expmap0(torch.tensor([[0.5, 0.0]])) aligned = manifold.expmap0(torch.tensor([[2.0, 0.0]])) opposed = manifold.expmap0(torch.tensor([[-2.0, 0.0]])) aligned_loss = manifold.entailment_cone_loss(parent, aligned) opposed_loss = manifold.entailment_cone_loss(parent, opposed) self.assertLess(float(aligned_loss.detach()), float(opposed_loss.detach())) def test_arbitrary_logmap_points_outward(self): manifold = LorentzManifold(1.0) parent = manifold.expmap0(torch.tensor([[0.5, 0.0]])) child = manifold.expmap0(torch.tensor([[2.0, 0.0]])) origin = manifold.origin_like(parent) outward = -manifold.logmap(parent, origin) toward_child = manifold.logmap(parent, child) cosine = LorentzManifold.inner(outward, toward_child) / ( LorentzManifold.inner(outward, outward).sqrt() * LorentzManifold.inner(toward_child, toward_child).sqrt() ) torch.testing.assert_close(cosine, torch.ones_like(cosine), atol=2e-5, rtol=2e-5) def test_prompt_learner_selects_view_query(self): learner = PromptLearner(torch.randn(8), torch.randn(2, 8)) agnostic, aware = learner(torch.tensor([0, 1, 0])) self.assertEqual(tuple(agnostic.shape), (3, 8)) self.assertEqual(tuple(aware.shape), (3, 8)) torch.testing.assert_close(aware[0], aware[2]) def test_tmf_shapes_and_gradients(self): fusion = TextGuidedFusion(16, num_heads=4) cls = torch.randn(3, 3, 16, requires_grad=True) patches = torch.randn(3, 3, 8, 16, requires_grad=True) query = torch.randn(3, 16, requires_grad=True) output, weights = fusion(cls, patches, query) self.assertEqual(tuple(output.shape), (3, 16)) torch.testing.assert_close(weights.sum(dim=1), torch.ones(3)) output.sum().backward() self.assertIsNotNone(query.grad) def test_cross_and_intra_view_triplet(self): features = torch.randn(8, 16, requires_grad=True) labels = torch.tensor([0, 0, 1, 1, 0, 0, 1, 1]) views = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1]) cross = batch_hard_triplet(features, labels) intra = intra_view_triplet(features, labels, views) self.assertTrue(torch.isfinite(cross)) self.assertTrue(torch.isfinite(intra)) (cross + intra).backward() self.assertIsNotNone(features.grad) def test_cross_view_sampler_provides_two_samples_per_view(self): items = [] for pid in range(4): for view in ("Aerial", "Ground"): for index in range(3): items.append((f"{pid}_{view}_{index}.jpg", pid, index, view)) sampler = CVIdentitySampler(items, mini_batch_size=8, num_instances=4, seed=3) iterator = iter(sampler) batch = [next(iterator) for _ in range(8)] counts = {} for index in batch: _, pid, _, view = items[index] counts.setdefault(pid, {"Aerial": 0, "Ground": 0})[view] += 1 self.assertEqual(len(counts), 2) for views in counts.values(): self.assertEqual(views, {"Aerial": 2, "Ground": 2}) if __name__ == "__main__": unittest.main()