""" Tests for the product gallery's matching logic. The embedding model is injected, so these run without torch or a GPU: what is being tested is the retrieval contract - best view wins, several views per SKU count as one answer, and a stranger is rejected rather than named. """ from __future__ import annotations import os import sys import unittest import numpy as np current_dir = os.path.dirname(os.path.abspath(__file__)) project_root = os.path.dirname(current_dir) if os.path.exists(os.path.join(project_root, "stock")): os.chdir(project_root) if current_dir not in sys.path: sys.path.insert(0, current_dir) from gallery import ( # noqa: E402 DINOV2_MODEL, DINOV2_SHA256, GalleryEntry, ProductGallery, catalog_digest, dinov2_weights_path, head_class_set, load_dinov2, ) import gallery as gallery_module # noqa: E402 def a_gallery(views, threshold=0.55): """A gallery with hand-made embeddings: no model, no files.""" skus = sorted({sku for sku, _ in views}) gallery = ProductGallery("does-not-exist", skus, threshold=threshold) embeddings = [] entries = [] for index, (sku, vector) in enumerate(views): vector = np.asarray(vector, dtype=np.float32) vector /= np.linalg.norm(vector) embeddings.append(vector) entries.append(GalleryEntry(sku_id=sku, view=f"{sku}_{index}", embedding=vector)) gallery._entries = entries gallery._matrix = np.stack(embeddings) gallery._available = True return gallery def unit(vector): vector = np.asarray(vector, dtype=np.float32) return (vector / np.linalg.norm(vector)).tolist() class Backbone: """A deterministic stand-in for DINOv2: the prepared crop's channel means, tiled to the width the real backbone returns.""" def __init__(self): self.calls = 0 def __call__(self, tensor): self.calls += 1 return tensor.mean(dim=(2, 3)).repeat(1, gallery_module.DINOV2_EMBED_DIM // 3) def a_query(): """A unit vector in the backbone's space, for match() without a model.""" query = np.zeros(gallery_module.DINOV2_EMBED_DIM, dtype=np.float32) query[0] = 1.0 query[1] = 0.05 return query / np.linalg.norm(query) class UnreachableBackbone: def __call__(self, tensor): raise AssertionError("the gallery loaded a backbone it should have cached") def write_view(path, seed): import cv2 image = np.random.RandomState(seed).randint(0, 256, (24, 32, 3), dtype=np.uint8) cv2.imwrite(path, image) def a_store(directory, names=("sku_a", "sku_a_1", "sku_b")): """A store dir: one JPEG per view plus the stock.csv the catalog is read from.""" with open(os.path.join(directory, "stock.csv"), "w", encoding="utf-8") as handle: handle.write("SKU_ID,product_name\n") for index, name in enumerate(names): write_view(os.path.join(directory, f"{name}.jpeg"), index) def a_stored_gallery(directory, names, backbone, image_size=32): gallery = ProductGallery(directory, list(names), device="cpu", image_size=image_size) gallery._load_model = lambda: backbone return gallery class MatchingTests(unittest.TestCase): def test_the_nearest_view_names_the_product(self): gallery = a_gallery([("sku_a", unit([1, 0, 0])), ("sku_b", unit([0, 1, 0]))]) gallery.embed = lambda crop: np.array([0.9, 0.1, 0.0], dtype=np.float32) match = gallery.match(object()) self.assertFalse(match.rejected) self.assertEqual(match.sku_id, "sku_a") self.assertAlmostEqual(match.similarity, 0.9, places=3) def test_several_views_of_one_sku_are_one_answer(self): # The second view of sku_b is closer than any view of sku_a, but sku_a must # still win on its best view: a SKU is not outvoted by having more photos. gallery = a_gallery([ ("sku_a", unit([1, 0, 0])), ("sku_b", unit([0, 1, 0])), ("sku_b", unit([0.2, 0.98, 0])), ]) gallery.embed = lambda crop: np.array([1.0, 0.05, 0.0], dtype=np.float32) match = gallery.match(object()) self.assertEqual(match.sku_id, "sku_a") self.assertEqual(match.runner_up, "sku_b") def test_a_stranger_is_rejected_not_named(self): gallery = a_gallery([("sku_a", unit([1, 0, 0])), ("sku_b", unit([0, 1, 0]))]) gallery.embed = lambda crop: np.array([0.0, 0.0, 1.0], dtype=np.float32) match = gallery.match(object()) self.assertTrue(match.rejected) self.assertIsNone(match.sku_id) def test_the_threshold_moves_the_rejection_line(self): gallery = a_gallery([("sku_a", unit([1, 0, 0]))], threshold=0.99) gallery.embed = lambda crop: np.array([0.9, 0.4, 0.0], dtype=np.float32) self.assertTrue(gallery.match(object()).rejected) self.assertFalse(gallery.match(object(), threshold=0.5).rejected) def test_margin_separates_a_confident_match_from_a_close_one(self): gallery = a_gallery([("sku_a", unit([1, 0, 0])), ("sku_b", unit([0.9, 0.44, 0]))]) gallery.embed = lambda crop: np.array([1.0, 0.1, 0.0], dtype=np.float32) confident = gallery.match(object()) gallery.embed = lambda crop: np.array([0.95, 0.3, 0.0], dtype=np.float32) close = gallery.match(object()) self.assertGreater(confident.margin, close.margin) def test_the_per_sku_max_does_not_depend_on_entry_order(self): # Three SKUs, several views each, enrolled out of order: a SKU is scored # by its best view, and the answer cannot move because a row moved. gallery = a_gallery([ ("sku_b", unit([0, 1, 0])), ("sku_a", unit([1, 0, 0])), ("sku_c", unit([0, 0, 1])), ("sku_b", unit([0.3, 0.95, 0])), ("sku_a", unit([0.98, 0.2, 0])), ]) gallery.embed = lambda crop: np.array([1.0, 0.05, 0.0], dtype=np.float32) straight = gallery.match(object()) self.assertEqual(straight.sku_id, "sku_a") self.assertEqual(straight.runner_up, "sku_b") for order in ([4, 0, 3, 1, 2], [2, 3, 1, 4, 0]): gallery._entries = [gallery._entries[index] for index in order] gallery._matrix = gallery._matrix[order] shuffled = gallery.match(object()) self.assertEqual(shuffled.sku_id, straight.sku_id) self.assertEqual(shuffled.runner_up, straight.runner_up) self.assertAlmostEqual(shuffled.similarity, straight.similarity, places=6) self.assertAlmostEqual(shuffled.runner_up_similarity, straight.runner_up_similarity, places=6) def test_a_tie_keeps_the_first_enrolled_sku(self): # Exactly equidistant: first appearance decides, as it did before the # per-SKU maximum became a segmented reduction. gallery = a_gallery([("sku_a", unit([1, 0, 0])), ("sku_b", unit([0, 1, 0]))]) gallery.embed = lambda crop: np.array([1.0, 1.0, 0.0], dtype=np.float32) match = gallery.match(object()) self.assertFalse(match.rejected) self.assertEqual(match.sku_id, "sku_a") self.assertEqual(match.runner_up, "sku_b") self.assertAlmostEqual(match.similarity, match.runner_up_similarity, places=6) self.assertAlmostEqual(match.margin, 0.0, places=6) def test_an_unloaded_gallery_rejects_everything(self): gallery = ProductGallery("does-not-exist", ["sku_a"]) match = gallery.match(object()) self.assertTrue(match.rejected) class EnrollmentTests(unittest.TestCase): def test_views_are_found_by_sku_prefix(self): import tempfile with tempfile.TemporaryDirectory() as directory: for name in ("sku_a.jpeg", "sku_a_1.jpeg", "sku_a-2.jpeg", "sku_ab.jpeg", "notes.txt"): open(os.path.join(directory, name), "wb").close() gallery = ProductGallery(directory, ["sku_a", "sku_ab"]) self.assertEqual(len(gallery._views_for("sku_a")), 3, "one view, two extra views, and not sku_ab") self.assertEqual(len(gallery._views_for("sku_ab")), 1) class FeatureCacheTests(unittest.TestCase): """The cached matrix must be this store's, or it must not be used.""" def setUp(self): import hashlib import tempfile self.directory = tempfile.TemporaryDirectory() self.addCleanup(self.directory.cleanup) a_store(self.directory.name) # A cache is only served when the pinned backbone is on disk, so point the # pin at a stand-in file the module's expected digest is told about. The # check itself - hash this file, compare - is what is under test. self.weights = os.path.join(self.directory.name, "dinov2.safetensors") self.write(self.weights, b"not really a checkpoint") os.environ["DORAYAKI_DINOV2_WEIGHTS"] = self.weights self.addCleanup(os.environ.pop, "DORAYAKI_DINOV2_WEIGHTS", None) self.addCleanup(setattr, gallery_module, "DINOV2_SHA256", gallery_module.DINOV2_SHA256) gallery_module.DINOV2_SHA256 = hashlib.sha256(b"not really a checkpoint").hexdigest() def write(self, path, content): with open(path, "wb") as handle: handle.write(content) return path def real_gallery(self): """A gallery with the real _load_model: the server's own readiness path.""" return ProductGallery(self.directory.name, ["sku_a", "sku_b"], device="cpu", image_size=32) def loaded(self, names=("sku_a", "sku_b"), image_size=32): backbone = Backbone() gallery = a_stored_gallery(self.directory.name, names, backbone, image_size=image_size) self.assertTrue(gallery.load()) return gallery, backbone def test_a_second_start_answers_the_same_from_the_cache(self): first, backbone = self.loaded() self.assertEqual(backbone.calls, 1) self.assertEqual(first.view_count(), 3) self.assertTrue(os.path.isfile(first._cache_path())) cached = a_stored_gallery(self.directory.name, ("sku_a", "sku_b"), UnreachableBackbone()) self.assertTrue(cached.load()) self.assertEqual(cached.view_count(), first.view_count()) np.testing.assert_array_equal(cached._matrix, first._matrix) self.assertEqual([entry.view for entry in cached._entries], [entry.view for entry in first._entries]) query = a_query() first.embed = cached.embed = lambda crop: query self.assertEqual(vars(cached.match(object())), vars(first.match(object()))) def test_changed_view_bytes_re_embed(self): first, _ = self.loaded() before = first._matrix.copy() write_view(os.path.join(self.directory.name, "sku_b.jpeg"), seed=99) second, backbone = self.loaded() self.assertEqual(backbone.calls, 1, "a replaced view must not be served from cache") self.assertFalse(np.array_equal(second._matrix, before)) def test_a_reordered_catalog_does_not_borrow_another_mapping(self): self.loaded() swapped, backbone = self.loaded(names=("sku_b", "sku_a")) self.assertEqual(backbone.calls, 1) self.assertEqual([entry.sku_id for entry in swapped._entries], ["sku_b", "sku_a", "sku_a"]) def test_another_image_size_is_never_served(self): small, _ = self.loaded(image_size=32) large, backbone = self.loaded(image_size=64) self.assertEqual(backbone.calls, 1) self.assertNotEqual(small._cache_path(), large._cache_path()) def test_a_corrupt_cache_re_embeds_the_same_matrix(self): first, _ = self.loaded() with open(first._cache_path(), "wb") as handle: handle.write(b"not an npz at all") second, backbone = self.loaded() self.assertEqual(backbone.calls, 1) np.testing.assert_array_equal(second._matrix, first._matrix) def test_a_tampered_cache_row_is_not_served(self): # A current key says nothing about the rows inside the file. Swap them # and the gallery must re-embed, never name a SKU by a moved row. first, _ = self.loaded() path = first._cache_path() with np.load(path, allow_pickle=False) as payload: key = str(payload["key"].item()) skus = [str(value) for value in payload["skus"].tolist()] views = [str(value) for value in payload["views"].tolist()] matrix = payload["matrix"] # The two sku_a views are adjacent, so a label swap has to cross SKUs to # mean anything - and the matrix that stays put is the dangerous one: # a vector that would then be filed under the wrong product. self.assertEqual(list(zip(skus, views)), [("sku_a", "sku_a.jpeg"), ("sku_a", "sku_a_1.jpeg"), ("sku_b", "sku_b.jpeg")]) tampered = { "reordered rows": (skus[::-1], views[::-1], matrix[::-1]), "swapped labels": ([skus[2], skus[1], skus[0]], views, matrix), "another store's row": (skus[1:] + ["other_store"], views + ["other_store.jpeg"], matrix), } for label, (bad_skus, bad_views, bad_matrix) in tampered.items(): np.savez(path, key=key, skus=np.array(bad_skus), views=np.array(bad_views), matrix=bad_matrix) backbone = Backbone() gallery = a_stored_gallery(self.directory.name, ("sku_a", "sku_b"), backbone) self.assertTrue(gallery.load(), label) self.assertEqual(backbone.calls, 1, f"{label} must not be served from cache") np.testing.assert_array_equal(gallery._matrix, first._matrix) self.assertEqual([entry.sku_id for entry in gallery._entries], ["sku_a", "sku_a", "sku_b"]) def test_a_loaded_gallery_reloads_when_its_source_moves(self): gallery, backbone = self.loaded() self.assertTrue(gallery.load()) self.assertEqual(backbone.calls, 1, "an unchanged store must not re-embed") write_view(os.path.join(self.directory.name, "sku_b.jpeg"), seed=99) self.assertTrue(gallery.load()) self.assertEqual(backbone.calls, 2) with open(os.path.join(self.directory.name, "stock.csv"), "a", encoding="utf-8") as handle: handle.write("sku_c,new product\n") self.assertTrue(gallery.load()) self.assertEqual(backbone.calls, 3, "stock.csv is part of what an embedding is pinned to") self.assertEqual(gallery.view_count(), 3) def test_a_valid_cache_needs_the_pinned_backbone_on_disk(self): # The regression: a cache hit never opens load_dinov2, so a deleted or # altered checkpoint used to read as a loaded gallery and then raise on # the first match - after the server had reported itself ready. self.loaded() gallery = self.real_gallery() self.assertTrue(gallery.load(), "the cache serves while the pin holds") os.environ["DORAYAKI_DINOV2_WEIGHTS"] = os.path.join(self.directory.name, "gone.safetensors") missing = self.real_gallery() self.assertFalse(missing.load()) self.assertFalse(missing.available()) os.environ["DORAYAKI_DINOV2_WEIGHTS"] = self.write( os.path.join(self.directory.name, "altered.safetensors"), b"a different checkpoint" ) altered = self.real_gallery() self.assertFalse(altered.load()) self.assertFalse(altered.available()) def test_an_altered_backbone_re_embeds_rather_than_serves(self): # With a backbone that can still load, an altered file must re-embed. self.loaded() self.assertTrue( a_stored_gallery(self.directory.name, ("sku_a", "sku_b"), UnreachableBackbone()).load(), "the cache still serves on the pinned file", ) self.write(self.weights, b"a different checkpoint") backbone = Backbone() rebuilt = a_stored_gallery(self.directory.name, ("sku_a", "sku_b"), backbone) self.assertTrue(rebuilt.load()) self.assertEqual(backbone.calls, 1, "an unpinned cache must not be served") class OfflineWeightsTests(unittest.TestCase): def test_a_missing_weights_file_names_the_path_and_does_not_fetch(self): previous = os.environ.get("DORAYAKI_DINOV2_WEIGHTS") os.environ["DORAYAKI_DINOV2_WEIGHTS"] = os.path.join( "does-not-exist", "dinov2_vits14_lvd142m.safetensors" ) try: with self.assertRaises(FileNotFoundError) as raised: load_dinov2(224) finally: if previous is None: os.environ.pop("DORAYAKI_DINOV2_WEIGHTS", None) else: os.environ["DORAYAKI_DINOV2_WEIGHTS"] = previous message = str(raised.exception) self.assertIn("does-not-exist", message) self.assertIn(DINOV2_SHA256, message) self.assertIn("does not download", message) def test_the_default_path_stays_inside_the_repo(self): previous = os.environ.pop("DORAYAKI_DINOV2_WEIGHTS", None) try: self.assertTrue(dinov2_weights_path().endswith( os.path.join("weights", "dinov2_vits14_lvd142m.safetensors") )) finally: if previous is not None: os.environ["DORAYAKI_DINOV2_WEIGHTS"] = previous def _head_payload(class_ids, torch): projection = torch.nn.Linear(384, 128) prototypes = torch.nn.functional.normalize(torch.randn(len(class_ids), 128), dim=1) return { "class_ids": list(class_ids), "image_size": 518, "base_model": DINOV2_MODEL, "projection": {key: value.detach().cpu() for key, value in projection.state_dict().items()}, "prototypes": prototypes.detach().cpu(), } class HeadPinTests(unittest.TestCase): def test_a_stale_head_cannot_label_this_catalog(self): import tempfile import torch with tempfile.TemporaryDirectory() as directory: with open(os.path.join(directory, "stock.csv"), "w", encoding="utf-8") as handle: handle.write("SKU_ID,product_name\nsku_a,A\nsku_b,B\nsku_c,C\n") gallery = ProductGallery(directory, ["sku_a", "sku_b", "sku_c"]) gallery._available = True path = gallery._head_path subset = ["sku_c", "sku_a"] digest = catalog_digest(directory, subset) foreign = _head_payload(["other_store", "sku_a"], torch) torch.save(foreign, path) self.assertFalse(gallery.load_head()) self.assertIsNone(gallery._head) self.assertIn("catalog_mismatch", gallery.head_reason) self.assertTrue(gallery.available()) legacy = _head_payload(subset, torch) torch.save(legacy, path) self.assertTrue(gallery.load_head()) self.assertEqual(gallery.head_reason, "legacy_unpinned") self.assertEqual(gallery._head["class_ids"], subset) self.assertNotIn("other_store", gallery._head["class_ids"]) stale = _head_payload(subset, torch) stale["catalog_digest"] = "0" * 64 torch.save(stale, path) self.assertFalse(gallery.load_head()) self.assertIsNone(gallery._head) self.assertIn("catalog_mismatch", gallery.head_reason) self.assertTrue(gallery.available()) wrong_backbone = _head_payload(subset, torch) wrong_backbone["catalog_digest"] = digest wrong_backbone["base_model"] = "vit_base_patch14_dinov2.lvd142m" torch.save(wrong_backbone, path) self.assertFalse(gallery.load_head()) self.assertIn("backbone_mismatch", gallery.head_reason) wrong_size = _head_payload(subset, torch) wrong_size["catalog_digest"] = digest wrong_size["image_size"] = 224 torch.save(wrong_size, path) self.assertFalse(gallery.load_head()) self.assertIn("image_size_mismatch", gallery.head_reason) torch.save(["not", "a", "head"], path) self.assertFalse(gallery.load_head()) self.assertTrue(gallery.head_reason.startswith("malformed")) self.assertTrue(gallery.available()) self.assertIsNone(gallery._head) current = _head_payload(subset, torch) current["catalog_digest"] = digest torch.save(current, path) self.assertTrue(gallery.load_head()) self.assertEqual(gallery.head_reason, "ok") self.assertEqual(gallery._head["class_ids"], subset) self.assertIsNone(head_class_set(["sku_a", "sku_a", ""])) class TrainerPinTests(unittest.TestCase): def test_head_training_loads_the_pinned_file_and_keeps_preprocess(self): import finetune_identity import gallery as gallery_module seen = [] class FakeBackbone: def eval(self): return self def to(self, device): return self sentinel = FakeBackbone() def fake_load(image_size): seen.append(image_size) return sentinel # build_model resolves the loader from gallery at call time, so that is # the attribute to replace; finetune_identity has no module-level copy. original = gallery_module.load_dinov2 gallery_module.load_dinov2 = fake_load try: model, device = finetune_identity.build_model(224) finally: gallery_module.load_dinov2 = original self.assertIs(model, sentinel) self.assertIn(device, ("cpu", "mps")) self.assertEqual(seen, [224]) image = np.zeros((10, 20, 3), dtype=np.uint8) self.assertEqual(finetune_identity.prepare(image, 32).shape, gallery_module._prepare(image, 32).shape) self.assertTrue(np.allclose( finetune_identity.prepare(image, 32), gallery_module._prepare(image, 32), )) if __name__ == "__main__": unittest.main()