Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -93,9 +93,21 @@
         "\n",
         "f_add = torch.nn.quantized.FloatFunctional()\n",
         "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TC5lMJrS1HCC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
         "index = 0\n",
         "%cd {home_directory + 'fusion-t2i-generator-data/' + 'vocab'}\n",
-        "\n",
         "vocab_encodings = torch.load('vocab_encodings.pt', weights_only=False)\n",
         "for key in vocab_encodings:\n",
         "  index = index + 1;\n",
@@ -107,10 +119,10 @@
         "for key in torch.load('reference_text_and_image_encodings.pt', weights_only=False):\n",
         "  index = index + 1;\n",
         "#------#\n",
-        "NUM_REFERENCE_ITEMS = index\n"
+        "NUM_REFERENCE_ITEMS = index"
       ],
       "metadata": {
-        "id": "TC5lMJrS1HCC"
+        "id": "Z8x3Y7IsnGlT"
       },
       "execution_count": null,
       "outputs": []
@@ -239,7 +251,7 @@
       "metadata": {
         "id": "XNHz0hfhHRUu"
       },
-      "execution_count": 113,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -550,6 +562,51 @@
       },
       "execution_count": null,
       "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# @title \t⚄ New code (work in progress)\n",
+        "\n",
+        "LIST_SIZE = 1000\n",
+        "SCALE = 0.0043\n",
+        "DIM = 768\n",
+        "\n",
+        "from safetensors.torch import load_file\n",
+        "\n",
+        "def get_most_similiar_items_to(ref , url , num_items):\n",
+        "  vocab =  load_file(url)\n",
+        "\n",
+        "  def similarity(item):\n",
+        "    key = item[0]\n",
+        "    value = item[1]\n",
+        "    tmp = torch.sub(value[1:DIM+1] , torch.ones(DIM) , alpha = value[0].item()).to(dtype=torch.uint8)\n",
+        "    return torch.dot(tmp,ref).item()\n",
+        "  #--------#\n",
+        "\n",
+        "  return dict(sorted(vocab.items(), key=similarity))\n",
+        "#----------#\n",
+        "\n",
+        "ref = torch.rand(DIM)\n",
+        "ref = (1/SCALE) * ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
+        "ref = torch.round(ref).to(dtype=torch.uint8)\n",
+        "\n",
+        "url = '/content/fusion-t2i-generator-data/lyrics_vocab_q0043_41905.safetensors'\n",
+        "test = get_most_similiar_items_to(ref , url , LIST_SIZE)\n",
+        "\n",
+        "index = 0\n",
+        "for key in test:\n",
+        "  print(key)\n",
+        "  index = index + 1\n",
+        "  if index>=10:break\n",
+        "#-----#"
+      ],
+      "metadata": {
+        "id": "PGyLzCmYqCPg"
+      },
+      "execution_count": null,
+      "outputs": []
     }
   ]
 }