change absolute paths to get_path fixture in test_multimodal_search, added get_testdict fixture

misinformation/test/conftest.py

@@ -16,3 +16,12 @@ def set_environ(request):
         mypath + "/../../data/seismic-bonfire-329406-412821a70264.json"
     )
     print(os.environ.get("GOOGLE_APPLICATION_CREDENTIALS"))
+
+
+@pytest.fixture
+def get_testdict(get_path):
+    testdict = {
+        "IMG_2746": {"filename": get_path + "IMG_2746.png"},
+        "IMG_2809": {"filename": get_path + "IMG_2809.png"},
+    }
+    return testdict

misinformation/test/test_multimodal_search.py

@@ -5,22 +5,17 @@ import numpy
 from torch import device, cuda
 import misinformation.multimodal_search as ms
 
-
-testdict = {
-    "IMG_2746": {"filename": "./test/data/IMG_2746.png"},
-    "IMG_2809": {"filename": "./test/data/IMG_2809.png"},
-}
-
 related_error = 1e-2
 gpu_is_not_available = not cuda.is_available()
 
-
 cuda.empty_cache()
 
 
-def test_read_img():
+def test_read_img(get_testdict):
     my_dict = {}
-    test_img = ms.MultimodalSearch.read_img(my_dict, testdict["IMG_2746"]["filename"])
+    test_img = ms.MultimodalSearch.read_img(
+        my_dict, get_testdict["IMG_2746"]["filename"]
+    )
     assert list(numpy.array(test_img)[257][34]) == [70, 66, 63]
 
 
@@ -354,6 +349,8 @@ def test_parsing_images(
     pre_extracted_feature_text,
     pre_simularity,
     pre_sorted,
+    get_path,
+    get_testdict,
     tmp_path,
 ):
     ms.MultimodalSearch.multimodal_device = pre_multimodal_device
@@ -365,7 +362,7 @@ def test_parsing_images(
         _,
         features_image_stacked,
     ) = ms.MultimodalSearch.parsing_images(
-        testdict, pre_model, path_to_saved_tensors=tmp_path
+        get_testdict, pre_model, path_to_saved_tensors=tmp_path
     )
 
     for i, num in zip(range(10), features_image_stacked[0, 10:12].tolist()):
@@ -374,7 +371,7 @@ def test_parsing_images(
             is True
         )
 
-    test_pic = Image.open(testdict["IMG_2746"]["filename"]).convert("RGB")
+    test_pic = Image.open(get_testdict["IMG_2746"]["filename"]).convert("RGB")
     test_querry = (
         "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
     )
@@ -390,10 +387,10 @@ def test_parsing_images(
 
     search_query = [
         {"text_input": test_querry},
-        {"image": testdict["IMG_2746"]["filename"]},
+        {"image": get_testdict["IMG_2746"]["filename"]},
     ]
     multi_features_stacked = ms.MultimodalSearch.querys_processing(
-        testdict, search_query, model, txt_processor, vis_processor, pre_model
+        get_testdict, search_query, model, txt_processor, vis_processor, pre_model
     )
 
     for i, num in zip(range(10), multi_features_stacked[0, 10:12].tolist()):
@@ -410,11 +407,11 @@ def test_parsing_images(
 
     search_query2 = [
         {"text_input": "A bus"},
-        {"image": "../misinformation/test/data/IMG_3758.png"},
+        {"image": get_path + "IMG_3758.png"},
     ]
 
     similarity, sorted_list = ms.MultimodalSearch.multimodal_search(
-        testdict,
+        get_testdict,
         model,
         vis_processor,
         txt_processor,
@@ -448,26 +445,26 @@ def test_parsing_images(
 
 
 @pytest.mark.long
-def test_itm():
+def test_itm(get_path):
     test_my_dict = {
         "IMG_2746": {
-            "filename": "../misinformation/test/data/IMG_2746.png",
+            "filename": get_path + "IMG_2746.png",
             "rank A bus": 1,
             "A bus": 0.15640679001808167,
-            "rank ../misinformation/test/data/IMG_3758.png": 1,
+            "rank " + get_path + "IMG_3758.png": 1,
-            "../misinformation/test/data/IMG_3758.png": 0.7533495426177979,
+            get_path + "IMG_3758.png": 0.7533495426177979,
         },
         "IMG_2809": {
-            "filename": "../misinformation/test/data/IMG_2809.png",
+            "filename": get_path + "IMG_2809.png",
             "rank A bus": 0,
             "A bus": 0.1970970332622528,
-            "rank ../misinformation/test/data/IMG_3758.png": 0,
+            "rank " + get_path + "IMG_3758.png": 0,
-            "../misinformation/test/data/IMG_3758.png": 0.8907483816146851,
+            get_path + "IMG_3758.png": 0.8907483816146851,
         },
     }
     search_query3 = [
         {"text_input": "A bus"},
-        {"image": "../misinformation/test/data/IMG_3758.png"},
+        {"image": get_path + "IMG_3758.png"},
     ]
     image_keys = ["IMG_2746", "IMG_2809"]
     sorted_list = [[1, 0], [1, 0]]
@@ -509,26 +506,26 @@ def test_itm():
 
 
 @pytest.mark.long
-def test_itm_blip2_coco():
+def test_itm_blip2_coco(get_path):
     test_my_dict = {
         "IMG_2746": {
-            "filename": "../misinformation/test/data/IMG_2746.png",
+            "filename": get_path + "IMG_2746.png",
             "rank A bus": 1,
             "A bus": 0.15640679001808167,
-            "rank ../misinformation/test/data/IMG_3758.png": 1,
+            "rank " + get_path + "IMG_3758.png": 1,
-            "../misinformation/test/data/IMG_3758.png": 0.7533495426177979,
+            get_path + "IMG_3758.png": 0.7533495426177979,
         },
         "IMG_2809": {
-            "filename": "../misinformation/test/data/IMG_2809.png",
+            "filename": get_path + "IMG_2809.png",
             "rank A bus": 0,
             "A bus": 0.1970970332622528,
-            "rank ../misinformation/test/data/IMG_3758.png": 0,
+            "rank " + get_path + "IMG_3758.png": 0,
-            "../misinformation/test/data/IMG_3758.png": 0.8907483816146851,
+            get_path + "IMG_3758.png": 0.8907483816146851,
         },
     }
     search_query3 = [
         {"text_input": "A bus"},
-        {"image": "../misinformation/test/data/IMG_3758.png"},
+        {"image": get_path + "IMG_3758.png"},
     ]
     image_keys = ["IMG_2746", "IMG_2809"]
     sorted_list = [[1, 0], [1, 0]]