fixed new model in summary.py

misinformation/summary.py

@@ -15,7 +15,7 @@ class SummaryDetector(AnalysisMethod):
 
     summary_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    def load_model_base(self):
+    def load_model_base():
         summary_model, summary_vis_processors, _ = load_model_and_preprocess(
             name="blip_caption",
             model_type="base_coco",
@@ -24,7 +24,7 @@ class SummaryDetector(AnalysisMethod):
         )
         return summary_model, summary_vis_processors
 
-    def load_model_large(self):
+    def load_model_large():
         summary_model, summary_vis_processors, _ = load_model_and_preprocess(
             name="blip_caption",
             model_type="large_coco",
@@ -33,6 +33,14 @@ class SummaryDetector(AnalysisMethod):
         )
         return summary_model, summary_vis_processors
 
+    def load_model(model_type):
+        select_model = {
+            "base": SummaryDetector.load_model_base,
+            "large": SummaryDetector.load_model_large,
+        }
+        summary_model, summary_vis_processors = select_model[model_type]()
+        return summary_model, summary_vis_processors
+
     def set_keys(self) -> dict:
         params = {
             "const_image_summary": None,
@@ -40,13 +48,8 @@ class SummaryDetector(AnalysisMethod):
         }
         return params
 
-    def analyse_image(self, model_type):
+    def analyse_image(self, summary_model, summary_vis_processors):
 
-        select_model = {
-            "base": self.load_model_base,
-            "large": self.load_model_large,
-        }
-        summary_model, summary_vis_processors = select_model[model_type]()
         path = self.subdict["filename"]
         raw_image = Image.open(path).convert("RGB")
         image = (
@@ -73,13 +76,13 @@ class SummaryDetector(AnalysisMethod):
         name="blip_vqa", model_type="vqav2", is_eval=True, device=summary_device
     )
 
-    def analyse_questions(self, model_type, list_of_questions):
+    def analyse_questions(self, list_of_questions):
 
         if len(list_of_questions) > 0:
             path = self.subdict["filename"]
             raw_image = Image.open(path).convert("RGB")
             image = (
-                summary_VQA_vis_processors["eval"](raw_image)
+                self.summary_VQA_vis_processors["eval"](raw_image)
                 .unsqueeze(0)
                 .to(self.summary_device)
             )

notebooks/image_summary.ipynb

@@ -40,7 +40,7 @@
    "outputs": [],
    "source": [
     "images = mutils.find_files(\n",
-    "    path=\"../data/Image_some_text/\",\n",
+    "    path=\"../misinformation/test/data/\",\n",
     "    limit=1000,\n",
     ")"
    ]
@@ -70,6 +70,15 @@
     "## Create captions for images and directly write to csv"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary_model, summary_vis_processors = sm.SummaryDetector.load_model(\"base\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -77,7 +86,9 @@
    "outputs": [],
    "source": [
     "for key in mydict:\n",
-    "    mydict[key] = sm.SummaryDetector(mydict[key]).analyse_image()"
+    "    mydict[key] = sm.SummaryDetector(mydict[key]).analyse_image(\n",
+    "        summary_model, summary_vis_processors\n",
+    "    )"
    ]
   },
   {
@@ -260,7 +271,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.10.8"
   },
   "vscode": {
    "interpreter": {