fixed saving path, added tmp_path (#67)

misinformation/multimodal_search.py

@@ -3,6 +3,7 @@ import torch
 import torch.nn.functional as Func
 import requests
 import lavis
+import os
 from PIL import Image
 from IPython.display import display
 from lavis.models import load_model_and_preprocess
@@ -118,15 +119,27 @@ class MultimodalSearch(AnalysisMethod):
         return features_image_stacked
 
     def save_tensors(
-        self, model_type, features_image_stacked, name="saved_features_image.pt"
+        self,
+        model_type,
+        features_image_stacked,
+        name="saved_features_image.pt",
+        path="./saved_tensors/",
     ):
+        if not os.path.exists(path):
+            os.makedirs(path)
         with open(
-            str(len(features_image_stacked)) + "_" + model_type + "_" + name, "wb"
+            str(path)
+            + str(len(features_image_stacked))
+            + "_"
+            + model_type
+            + "_"
+            + name,
+            "wb",
         ) as f:
             torch.save(features_image_stacked, f)
         return name
 
-    def load_tensors(self, name="saved_features_image.pt"):
+    def load_tensors(self, name):
         features_image_stacked = torch.load(name)
         return features_image_stacked
 
@@ -136,7 +149,12 @@ class MultimodalSearch(AnalysisMethod):
 
         return features_text
 
-    def parsing_images(self, model_type, path_to_saved_tensors=None):
+    def parsing_images(
+        self,
+        model_type,
+        path_to_saved_tensors="./saved_tensors/",
+        path_to_load_tensors=None,
+    ):
         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"
@@ -180,15 +198,17 @@ class MultimodalSearch(AnalysisMethod):
         _, images_tensors = MultimodalSearch.read_and_process_images(
             self, image_names, vis_processors
         )
-        if path_to_saved_tensors is None:
+        if path_to_load_tensors is None:
             with torch.no_grad():
                 features_image_stacked = select_extract_image_features[model_type](
                     self, model, images_tensors
                 )
-            MultimodalSearch.save_tensors(self, model_type, features_image_stacked)
+            MultimodalSearch.save_tensors(
+                self, model_type, features_image_stacked, path=path_to_saved_tensors
+            )
         else:
             features_image_stacked = MultimodalSearch.load_tensors(
-                self, str(path_to_saved_tensors)
+                self, str(path_to_load_tensors)
             )
 
         return (
@@ -303,7 +323,6 @@ class MultimodalSearch(AnalysisMethod):
 
         for q in range(len(search_query)):
             max_val = similarity[sorted_lists[q][0]][q].item()
-            print(max_val)
             for i, key in zip(range(len(image_keys)), sorted_lists[q]):
                 if (
                     i < filter_number_of_images

misinformation/test/test_multimodal_search.py

@@ -354,6 +354,7 @@ def test_parsing_images(
     pre_extracted_feature_text,
     pre_simularity,
     pre_sorted,
+    tmp_path,
 ):
     ms.MultimodalSearch.multimodal_device = pre_multimodal_device
     (
@@ -363,7 +364,9 @@ def test_parsing_images(
         image_keys,
         _,
         features_image_stacked,
-    ) = ms.MultimodalSearch.parsing_images(testdict, pre_model)
+    ) = ms.MultimodalSearch.parsing_images(
+        testdict, pre_model, path_to_saved_tensors=tmp_path
+    )
 
     for i, num in zip(range(10), features_image_stacked[0, 10:12].tolist()):
         assert (

notebooks/multimodal_search.ipynb

@@ -20,7 +20,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "f10ad6c9-b1a0-4043-8c5d-ed660d77be37",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import misinformation\n",
@@ -39,7 +41,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "8d3fe589-ff3c-4575-b8f5-650db85596bc",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "images = misinformation.utils.find_files(\n",
@@ -52,7 +56,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "adf3db21-1f8b-4d44-bbef-ef0acf4623a0",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "mydict = misinformation.utils.initialize_dict(images)"
@@ -62,7 +68,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "d98b6227-886d-41b8-a377-896dd8ab3c2a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "mydict"
@@ -88,7 +96,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "7bbca1f0-d4b0-43cd-8e05-ee39d37c328e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "model_type = \"blip\"\n",
@@ -103,7 +113,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "ca095404-57d0-4f5d-aeb0-38c232252b17",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "(\n",
@@ -113,7 +125,9 @@
     "    image_keys,\n",
     "    image_names,\n",
     "    features_image_stacked,\n",
-    ") = ms.MultimodalSearch.parsing_images(mydict, model_type)"
+    ") = ms.MultimodalSearch.parsing_images(\n",
+    "    mydict, model_type, path_to_saved_tensors=\"./saved_tensors/\"\n",
+    ")"
    ]
   },
   {
@@ -128,17 +142,23 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "56c6d488-f093-4661-835a-5c73a329c874",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# (\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\")"
+    "#     model,\n",
+    "#     vis_processors,\n",
+    "#     txt_processors,\n",
+    "#     image_keys,\n",
+    "#     image_names,\n",
+    "#     features_image_stacked,\n",
+    "# ) = ms.MultimodalSearch.parsing_images(\n",
+    "#     mydict,\n",
+    "#     model_type,\n",
+    "#     path_to_load_tensors=\"./saved_tensors/18_blip_saved_features_image.pt\",\n",
+    "# )"
    ]
   },
   {
@@ -328,7 +348,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,