updated examples

Digital-Dermatology · Mar 23, 2024 · 889adea · 889adea
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diff --git a/assets/pre_trained_models/DINO-ImageNette/checkpoints/model_best.pth b/assets/pre_trained_models/DINO-ImageNette/checkpoints/model_best.pth
diff --git a/assets/pre_trained_models/DINO-OxfordIIITPet/checkpoints/model_best.pth b/assets/pre_trained_models/DINO-OxfordIIITPet/checkpoints/model_best.pth
diff --git a/examples/Investigate_Flowers102.ipynb b/examples/Investigate_Flowers102.ipynb
diff --git a/examples/Investigate_Imagenette.ipynb b/examples/Investigate_Imagenette.ipynb
diff --git a/examples/Investigate_OxfordIIITPet.ipynb b/examples/Investigate_OxfordIIITPet.ipynb
diff --git a/setup.py b/setup.py
@@ -31,7 +31,7 @@ def parse_requirements(filename):
     name=PACKAGE_NAME,
     packages=proj_packages,
     package_dir={PACKAGE_NAME: SOURCE_DIRECTORY},
-    version="0.0.15",
+    version="0.0.16",
     author="Fabian Groeger",
     author_email="fabian.groeger@unibas.ch",
     description="A holistic self-supervised data cleaning strategy to detect irrelevant samples, near duplicates and label errors.",

diff --git a/src/ssl_library b/src/ssl_library
diff --git a/tests/unittests/cleaner/test_selfclean_cleaner.py b/tests/unittests/cleaner/test_selfclean_cleaner.py
@@ -38,6 +38,20 @@ def test_predict(self):
             self.assertIsNotNone(v["scores"])
             self.assertEqual(len(v["indices"]), len(v["scores"]))
 
+    def test_2_predict(self):
+        cleaner = SelfCleanCleaner(memmap=False)
+        cleaner.fit(emb_space=self.emb_space, labels=self.labels)
+        out_dict = cleaner.predict()
+        out_dict = cleaner.predict()
+        for issue_type in ["irrelevants", "near_duplicates", "label_errors"]:
+            v = out_dict.get_issues(issue_type)
+            self.assertIsNotNone(v)
+            self.assertTrue("indices" in v)
+            self.assertTrue("scores" in v)
+            self.assertIsNotNone(v["indices"])
+            self.assertIsNotNone(v["scores"])
+            self.assertEqual(len(v["indices"]), len(v["scores"]))
+
     def test_predict_with_class_labels(self):
         cleaner = SelfCleanCleaner(memmap=False)
         cleaner.fit(