Add DMRL model (#597)

tests/cornac/models/dmrl/test_transformervision.py

+"""
+Tests for the TransformersVisionModality class. In order to run this test please
+insert url_to_beach1, url_to_beach2, url_to_cat in the get_photos method. Use
+your favorite beach and cat photos and check the similarity scores.
+"""
+
+import unittest
+
+# add a checker to make sure all requirements needed in the imports here are really present.
+# if they are missing skip the respective test
+# If a user wants to un these please run: pip install -r cornac/models/dmrl/requirements.txt
+try:
+    import torch
+    import requests
+    from PIL import Image
+    from sentence_transformers import util
+
+    from cornac.models.dmrl.transformer_vision import TransformersVisionModality
+
+    run_dmrl_test_funcs = True
+
+except ImportError:
+    run_dmrl_test_funcs = False
+
+
+def skip_test_in_case_of_missing_reqs(test_func):
+    test_func.__test__ = (
+        run_dmrl_test_funcs  # Mark the test function as (non-)discoverable by unittest
+    )
+    return test_func
+
+
+# Please insert valid urls here to two beach photos and one cat photo
+beach_urls = ["url_to_beach1", "url_to_beach2"]
+cat_url = "url_to_cat"
+
+
+class TestTransformersVisionModality(unittest.TestCase):
+
+    def get_photos(self):
+        for i, url in enumerate(beach_urls):
+            r = requests.get(url)
+            with open(f"beach{i}.jpg", "wb") as f:
+                f.write(r.content)
+
+        r = requests.get(cat_url)
+        with open("cat.jpg", "wb") as f:
+            f.write(r.content)
+
+    @skip_test_in_case_of_missing_reqs
+    def setUp(self):
+        self.get_photos()
+        beach1 = Image.open("beach0.jpg")
+        beach2 = Image.open("beach1.jpg")
+        cat = Image.open("cat.jpg")
+        self.images = [beach1, beach2, cat]
+        self.ids = [0, 1]
+        self.modality = TransformersVisionModality(
+            images=self.images, ids=self.ids, preencode=True
+        )
+
+    @skip_test_in_case_of_missing_reqs
+    @unittest.skipIf(
+        "url_to_beach1" in beach_urls,
+        "Please insert a valid url to download 2 beach and one cat photo",
+    )
+    def test_transform_image_to_tensor(self):
+        """
+        Tests that an image is transformed correctly to a tensor
+        """
+        image_tensor_batch = self.modality.transform_images_to_torch_tensor(self.images)
+        assert isinstance(image_tensor_batch, torch.Tensor)
+        assert image_tensor_batch.shape[0:2] == torch.Size(
+            (3, 3)
+        )  # 3 images with 3 channels each
+        assert image_tensor_batch.shape[2:] == torch.Size(self.modality.image_size)
+
+    @skip_test_in_case_of_missing_reqs
+    @unittest.skipIf(
+        "url_to_beach1" in beach_urls,
+        "Please insert a valid url to download 2 beach and one cat photo",
+    )
+    def test_encode_all_images(self):
+        """
+        Tests that all images are encoded
+        """
+        self.modality._encode_images()
+        assert isinstance(self.modality.features, torch.Tensor)
+        assert self.modality.features.shape[0] == len(self.images)
+        assert self.modality.features.shape[1] == 1000
+
+    @skip_test_in_case_of_missing_reqs
+    @unittest.skipIf(
+        "url_to_beach1" in beach_urls,
+        "Please insert a valid url to download 2 beach and one cat photo",
+    )
+    def test_encoding_quality(self):
+        """
+        Test similiarity in latent space between some images
+        """
+        self.modality._encode_images()
+        beach1_beach2_similarity = util.cos_sim(
+            self.modality.features[0], self.modality.features[1]
+        )
+        assert beach1_beach2_similarity > 0.7
+
+        beach_cat_similarity = util.cos_sim(
+            self.modality.features[0], self.modality.features[2]
+        )
+        assert beach_cat_similarity < 0.1
+
+        assert beach1_beach2_similarity > beach_cat_similarity