fix if-else, added clip ViT-L-14=336 model

misinformation/multimodal_search.py

@@ -15,54 +15,58 @@ class MultimodalSearch(AnalysisMethod):
 
     multimodal_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    def load_feature_extractor_model(device, model_type):
+    def load_feature_extractor_model_blip2(device):
-        if model_type == "blip2":
         model, vis_processors, txt_processors = load_model_and_preprocess(
             name="blip2_feature_extractor",
             model_type="pretrain",
             is_eval=True,
             device=device,
         )
-        elif model_type == "blip":
+        return model, vis_processors, txt_processors
+
+    def load_feature_extractor_model_blip(device):
         model, vis_processors, txt_processors = load_model_and_preprocess(
             name="blip_feature_extractor",
             model_type="base",
             is_eval=True,
             device=device,
         )
-        elif model_type == "albef":
+        return model, vis_processors, txt_processors
+
+    def load_feature_extractor_model_albef(device):
         model, vis_processors, txt_processors = load_model_and_preprocess(
             name="albef_feature_extractor",
             model_type="base",
             is_eval=True,
             device=device,
         )
-        elif model_type == "clip_base":
+        return model, vis_processors, txt_processors
+
+    def load_feature_extractor_model_clip_base(device):
         model, vis_processors, txt_processors = load_model_and_preprocess(
             name="clip_feature_extractor",
             model_type="base",
             is_eval=True,
             device=device,
         )
-        elif model_type == "clip_rn50":
+        return model, vis_processors, txt_processors
-            model, vis_processors, txt_processors = load_model_and_preprocess(
+
-                name="clip_feature_extractor",
+    def load_feature_extractor_model_clip_vitl14(device):
-                model_type="RN50",
-                is_eval=True,
-                device=device,
-            )
-        elif model_type == "clip_vitl14":
         model, vis_processors, txt_processors = load_model_and_preprocess(
             name="clip_feature_extractor",
             model_type="ViT-L-14",
             is_eval=True,
             device=device,
         )
-        else:
+        return model, vis_processors, txt_processors
-            print(
-                "Please, use one of the following models: blip2, blip, albef, clip_base, clip_rn50, clip_vitl14"
-            )
 
+    def load_feature_extractor_model_clip_vitl14_336(device):
+        model, vis_processors, txt_processors = load_model_and_preprocess(
+            name="clip_feature_extractor",
+            model_type="ViT-L-14-336",
+            is_eval=True,
+            device=device,
+        )
         return model, vis_processors, txt_processors
 
     def read_img(filepath):
@@ -81,34 +85,29 @@ class MultimodalSearch(AnalysisMethod):
 
         return raw_images, images_tensors
 
-    def extract_image_features(model, images_tensors, model_type):
+    def extract_image_features_blip2(model, images_tensors):
-        if model_type == "blip2":
         with torch.cuda.amp.autocast(
             enabled=(MultimodalSearch.multimodal_device != torch.device("cpu"))
         ):
             features_image = [
-                    model.extract_features(
+                model.extract_features({"image": ten, "text_input": ""}, mode="image")
-                        {"image": ten, "text_input": ""}, mode="image"
-                    )
                 for ten in images_tensors
             ]
             features_image_stacked = torch.stack(
-                    [
+                [feat.image_embeds_proj[:, 0, :].squeeze(0) for feat in features_image]
-                        feat.image_embeds_proj[:, 0, :].squeeze(0)
-                        for feat in features_image
-                    ]
             )
-        elif model_type in ("clip_base", "clip_rn50", "clip_vitl14"):
+        return features_image_stacked
+
+    def extract_image_features_clip(model, images_tensors):
         features_image = [
             model.extract_features({"image": ten}) for ten in images_tensors
         ]
         features_image_stacked = torch.stack(
-                [
+            [Func.normalize(feat.float(), dim=-1).squeeze(0) for feat in features_image]
-                    Func.normalize(feat.float(), dim=-1).squeeze(0)
-                    for feat in features_image
-                ]
         )
-        else:
+        return features_image_stacked
+
+    def extract_image_features_basic(model, images_tensors):
         features_image = [
             model.extract_features({"image": ten, "text_input": ""}, mode="image")
             for ten in images_tensors
@@ -137,9 +136,9 @@ class MultimodalSearch(AnalysisMethod):
 
         return features_text
 
-    def parsing_images(self, model_type):
+    def parsing_images(self, model_type, path_to_saved_tensors=None):
 
-        if model_type in ("clip_base", "clip_rn50", "clip_vitl14"):
+        if model_type in ("clip_base", "clip_vitl14_336", "clip_vitl14"):
             path_to_lib = lavis.__file__[:-11] + "models/clip_models/"
             url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/lavis/models/clip_models/bpe_simple_vocab_16e6.txt.gz"
             r = requests.get(url, allow_redirects=False)
@@ -148,20 +147,46 @@ class MultimodalSearch(AnalysisMethod):
         image_keys = sorted(self.keys())
         image_names = [self[k]["filename"] for k in image_keys]
 
-        (
+        select_model = {
-            model,
+            "blip2": MultimodalSearch.load_feature_extractor_model_blip2,
-            vis_processors,
+            "blip": MultimodalSearch.load_feature_extractor_model_blip,
-            txt_processors,
+            "albef": MultimodalSearch.load_feature_extractor_model_albef,
-        ) = MultimodalSearch.load_feature_extractor_model(
+            "clip_base": MultimodalSearch.load_feature_extractor_model_clip_base,
-            MultimodalSearch.multimodal_device, model_type
+            "clip_vitl14": MultimodalSearch.load_feature_extractor_model_clip_vitl14,
+            "clip_vitl14_336": MultimodalSearch.load_feature_extractor_model_clip_vitl14_336,
+        }
+
+        select_extract_image_features = {
+            "blip2": MultimodalSearch.extract_image_features_blip2,
+            "blip": MultimodalSearch.extract_image_features_basic,
+            "albef": MultimodalSearch.extract_image_features_basic,
+            "clip_base": MultimodalSearch.extract_image_features_clip,
+            "clip_vitl14": MultimodalSearch.extract_image_features_clip,
+            "clip_vitl14_336": MultimodalSearch.extract_image_features_clip,
+        }
+
+        if model_type in select_model.keys():
+            (model, vis_processors, txt_processors,) = select_model[
+                model_type
+            ](MultimodalSearch.multimodal_device)
+        else:
+            raise SyntaxError(
+                "Please, use one of the following models: blip2, blip, albef, clip_base, clip_vitl14, clip_vitl14_336"
             )
+
         raw_images, images_tensors = MultimodalSearch.read_and_process_images(
             image_names, vis_processors
         )
-        features_image_stacked = MultimodalSearch.extract_image_features(
+        if path_to_saved_tensors is None:
-            model, images_tensors, model_type
+            with torch.no_grad():
+                features_image_stacked = select_extract_image_features[model_type](
+                    model, images_tensors
                 )
             MultimodalSearch.save_tensors(model_type, features_image_stacked)
+        else:
+            features_image_stacked = MultimodalSearch.load_tensors(
+                str(path_to_saved_tensors)
+            )
 
         return (
             model,
@@ -175,6 +200,16 @@ class MultimodalSearch(AnalysisMethod):
     def querys_processing(
         self, search_query, model, txt_processors, vis_processors, model_type
     ):
+
+        select_extract_image_features = {
+            "blip2": MultimodalSearch.extract_image_features_blip2,
+            "blip": MultimodalSearch.extract_image_features_basic,
+            "albef": MultimodalSearch.extract_image_features_basic,
+            "clip_base": MultimodalSearch.extract_image_features_clip,
+            "clip_vitl14": MultimodalSearch.extract_image_features_clip,
+            "clip_vitl14_336": MultimodalSearch.extract_image_features_clip,
+        }
+
         for query in search_query:
             if not (len(query) == 1) and (query in ("image", "text_input")):
                 raise SyntaxError(
@@ -194,10 +229,10 @@ class MultimodalSearch(AnalysisMethod):
                 {"image": images_tensors, "text_input": text_processing}
             )
 
-        if model_type in ("clip_base", "clip_rn50", "clip_vitl14"):
         multi_features_query = []
         for query in multi_sample:
             if query["image"] == "":
+                if model_type in ("clip_base", "clip_vitl14_336", "clip_vitl14"):
                     features = model.extract_features(
                         {"text_input": query["text_input"]}
                     )
@@ -208,17 +243,7 @@ class MultimodalSearch(AnalysisMethod):
                     multi_features_query.append(
                         Func.normalize(features_squeeze, dim=-1)
                     )
-                if query["text_input"] == "":
-                    multi_features_query.append(
-                        MultimodalSearch.extract_image_features(
-                            model, query["image"], model_type
-                        )
-                    )
-
                 else:
-            multi_features_query = []
-            for query in multi_sample:
-                if query["image"] == "":
                     features = model.extract_features(query, mode="text")
                     features_squeeze = (
                         features.text_embeds_proj[:, 0, :]
@@ -228,9 +253,7 @@ class MultimodalSearch(AnalysisMethod):
                     multi_features_query.append(features_squeeze)
             if query["text_input"] == "":
                 multi_features_query.append(
-                        MultimodalSearch.extract_image_features(
+                    select_extract_image_features[model_type](model, query["image"])
-                            model, query["image"], model_type
-                        )
                 )
 
         multi_features_stacked = torch.stack(
@@ -251,11 +274,13 @@ class MultimodalSearch(AnalysisMethod):
     ):
         features_image_stacked.to(MultimodalSearch.multimodal_device)
 
+        with torch.no_grad():
             multi_features_stacked = MultimodalSearch.querys_processing(
                 self, search_query, model, txt_processors, vis_processors, model_type
             )
 
         similarity = features_image_stacked @ multi_features_stacked.t()
+        similarity_soft_max = torch.nn.Softmax(dim=0)(similarity / 0.01)
         sorted_lists = [
             sorted(range(len(similarity)), key=lambda k: similarity[k, i], reverse=True)
             for i in range(len(similarity[0]))
@@ -267,7 +292,7 @@ class MultimodalSearch(AnalysisMethod):
                 self[key]["rank " + list(search_query[q].values())[0]] = places[q][i]
                 self[key][list(search_query[q].values())[0]] = similarity[i][q].item()
 
-        return similarity
+        return similarity, similarity_soft_max
 
     def show_results(self, query):
         if "image" in query.keys():

notebooks/multimodal_search.ipynb

@@ -81,7 +81,7 @@
    "id": "66d6ede4-00bc-4aeb-9a36-e52d7de33fe5",
    "metadata": {},
    "source": [
-    "You can choose one of the following models: blip, blip2, albef, clip_base, clip_rn50, clip_vitl14"
+    "You can choose one of the following models: blip, blip2, albef, clip_base, clip_vitl14, clip_vitl14_336"
    ]
   },
   {
@@ -91,7 +91,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_type = \"blip\""
+    "model_type = \"clip_vitl14_336\""
    ]
   },
   {
@@ -116,17 +116,32 @@
    "id": "9ff8a894-566b-4c4f-acca-21c50b5b1f52",
    "metadata": {},
    "source": [
-    "The of all images `features_image_stacked` was saved in `saved_features_image.pt`. If you run it once for current model and set of images you do not need to repeat it again. Instead you can load this features with the command:"
+    "The tensors of all images `features_image_stacked` was saved in `<Number_of_images>_<model_name>_saved_features_image.pt`. If you run it once for current model and current set of images you do not need to repeat it again. Instead you can load this features with the command:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c40e93f0-6bea-4886-b904-8b46ed6ec819",
+   "id": "56c6d488-f093-4661-835a-5c73a329c874",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# features_image_stacked = ms.MultimodalSearch.load_tensors('saved_features_image.pt')"
+    "# (\n",
+    "#    model,\n",
+    "#    vis_processors,\n",
+    "#    txt_processors,\n",
+    "#    image_keys,\n",
+    "#    image_names,\n",
+    "#    features_image_stacked,\n",
+    "# ) = ms.MultimodalSearch.parsing_images(mydict, model_type,\"18_clip_base_saved_features_image.pt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "309923c1-d6f8-4424-8fca-bde5f3a98b38",
+   "metadata": {},
+   "source": [
+    "Here we already processed our image folder with 18 images with `clip_base` model. So you need just write the name `18_clip_base_saved_features_image.pt` of the saved file that consists of tensors of all images as a 3rd argument to the previous function. "
    ]
   },
   {
@@ -170,7 +185,7 @@
     "    image_keys,\n",
     "    features_image_stacked,\n",
     "    search_query3,\n",
-    ");"
+    ")"
    ]
   },
   {
@@ -206,7 +221,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ms.MultimodalSearch.show_results(mydict, search_query3[0])"
+    "ms.MultimodalSearch.show_results(mydict, search_query3[4])"
    ]
   },
   {