added test, fixed dependencies

misinformation/test/test_multimodal_search.py

@@ -28,7 +28,7 @@ def test_read_img():
     assert list(numpy.array(test_img)[257][34]) == [70, 66, 63]
 
 
-@pytest.mark.skipif(not cuda.is_available(), reason="model for gpu only")
+@pytest.mark.skipif(gpu_is_not_available, reason="model for gpu only")
 def test_load_feature_extractor_model_blip2():
     my_dict = {}
     multimodal_device = device("cuda" if cuda.is_available() else "cpu")
@@ -46,14 +46,12 @@ def test_load_feature_extractor_model_blip2():
     processed_pic = vis_processor["eval"](test_pic).unsqueeze(0).to(multimodal_device)
     processed_text = txt_processor["eval"](test_querry)
 
-    with no_grad():
-        with cuda.amp.autocast(enabled=(device != device("cpu"))):
-            extracted_feature_img = model.extract_features(
-                {"image": processed_pic, "text_input": ""}, mode="image"
-            )
-            extracted_feature_text = model.extract_features(
-                {"image": "", "text_input": processed_text}, mode="text"
-            )
+    extracted_feature_img = model.extract_features(
+        {"image": processed_pic, "text_input": ""}, mode="image"
+    )
+    extracted_feature_text = model.extract_features(
+        {"image": "", "text_input": processed_text}, mode="text"
+    )
     check_list_processed_pic = [
         -1.0039474964141846,
         -1.0039474964141846,
@@ -122,10 +120,13 @@ def test_load_feature_extractor_model_blip2():
         )
 
     image_paths = [TEST_IMAGE_2, TEST_IMAGE_3]
+
     raw_images, images_tensors = ms.MultimodalSearch.read_and_process_images(
         my_dict, image_paths, vis_processor
     )
 
+    assert list(numpy.array(raw_images[0])[257][34]) == [70, 66, 63]
+
     check_list_images_tensors = [
         -1.0039474964141846,
         -1.0039474964141846,
@@ -657,3 +658,138 @@ def test_load_feature_extractor_model_clip_vitl14_336(multimodal_device):
 
     del model, vis_processor, txt_processor
     cuda.empty_cache()
+
+
+model_type = "blip"
+# model_type = "blip2"
+# model_type = "albef"
+# model_type = "clip_base"
+# model_type = "clip_vitl14"
+# model_type = "clip_vitl14_336"
+
+
+@pytest.mark.parametrize(
+    (
+        "pre_multimodal_device",
+        "pre_model",
+        "pre_proc_pic",
+        "pre_proc_text",
+        "pre_extracted_feature_img",
+        "pre_extracted_feature_text",
+    ),
+    [
+        pytest.param(
+            device("cuda"),
+            "blip2",
+            [
+                -1.0039474964141846,
+                -1.0039474964141846,
+                -0.8433647751808167,
+                -0.6097899675369263,
+                -0.5951915383338928,
+                -0.6243883967399597,
+                -0.6827820539474487,
+                -0.6097899675369263,
+                -0.7119789123535156,
+                -1.0623412132263184,
+            ],
+            "the bird sat on a tree located at the intersection of 23rd and 43rd streets",
+            [
+                0.04566730558872223,
+                -0.042554520070552826,
+                -0.06970272958278656,
+                -0.009771779179573059,
+                0.01446065679192543,
+                0.10173682868480682,
+                0.007092420011758804,
+                -0.020045937970280647,
+                0.12923966348171234,
+                0.006452132016420364,
+            ],
+            [
+                -0.1384204626083374,
+                -0.008662976324558258,
+                0.006269007455557585,
+                0.03151319921016693,
+                0.060558050870895386,
+                -0.03230040520429611,
+                0.015861615538597107,
+                -0.11856459826231003,
+                -0.058296192437410355,
+                0.03699290752410889,
+            ],
+            marks=pytest.mark.skipif(
+                gpu_is_not_available, reason="gpu_is_not_availible"
+            ),
+        ),
+        #        (device("cpu"),"blip"),
+        #        (device("cpu"),"albef"),
+        #        (device("cpu"),"clip_base"),
+        #        (device("cpu"),"clip_vitl14"),
+        #        (device("cpu"),"clip_vitl14_336"),
+        #        pytest.param( device("cuda"),"blip", marks=pytest.mark.skipif(gpu_is_not_available, reason="gpu_is_not_availible"),),
+        #        pytest.param( device("cuda"),"albef", marks=pytest.mark.skipif(gpu_is_not_available, reason="gpu_is_not_availible"),),
+        #        pytest.param( device("cuda"),"clip_base", marks=pytest.mark.skipif(gpu_is_not_available, reason="gpu_is_not_availible"),),
+        #        pytest.param( device("cuda"),"clip_vitl14", marks=pytest.mark.skipif(gpu_is_not_available, reason="gpu_is_not_availible"),),
+        #        pytest.param( device("cuda"),"clip_vitl14_336", marks=pytest.mark.skipif(gpu_is_not_available, reason="gpu_is_not_availible"),),
+    ],
+)
+def test_parsing_images(
+    pre_multimodal_device,
+    pre_model,
+    pre_proc_pic,
+    pre_proc_text,
+    pre_extracted_feature_img,
+    pre_extracted_feature_text,
+):
+    mydict = {
+        "IMG_2746": {"filename": "./test/data/IMG_2746.png"},
+        "IMG_2750": {"filename": "./test/data/IMG_2750.png"},
+    }
+    ms.MultimodalSearch.multimodal_device = pre_multimodal_device
+    (
+        model,
+        vis_processor,
+        txt_processor,
+        image_keys,
+        image_names,
+        features_image_stacked,
+    ) = ms.MultimodalSearch.parsing_images(mydict, pre_model)
+
+    for i, num in zip(range(10), features_image_stacked[0, 10:20].tolist()):
+        assert (
+            math.isclose(num, pre_extracted_feature_img[i], rel_tol=related_error)
+            is True
+        )
+
+    test_pic = Image.open(TEST_IMAGE_2).convert("RGB")
+    test_querry = (
+        "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
+    )
+    processed_pic = (
+        vis_processor["eval"](test_pic).unsqueeze(0).to(pre_multimodal_device)
+    )
+    processed_text = txt_processor["eval"](test_querry)
+
+    for i, num in zip(range(10), processed_pic[0, 0, 0, 25:35].tolist()):
+        assert math.isclose(num, pre_proc_pic[i], rel_tol=related_error) is True
+
+    assert processed_text == pre_proc_text
+
+    search_query = [
+        {
+            "text_input": "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
+        }
+    ]
+    multi_features_stacked = ms.MultimodalSearch.querys_processing(
+        mydict, search_query, model, txt_processor, vis_processor, pre_model
+    )
+
+    for i, num in zip(range(10), multi_features_stacked[0, 10:20].tolist()):
+        assert (
+            math.isclose(num, pre_extracted_feature_text[i], rel_tol=related_error)
+            is True
+        )
+
+    del model, vis_processor, txt_processor
+    cuda.empty_cache()

pyproject.toml

@@ -47,7 +47,7 @@ dependencies = [
     "spacytextblob",
     "textblob",
     "torch",
-    "salesforce-lavis @ git+https://github.com/salesforce/LAVIS.git@main",
+    "salesforce-lavis",
     "bertopic",
     "grpcio",
 ]