diff --git "a/demo.ipynb" "b/demo.ipynb"
deleted file mode 100644--- "a/demo.ipynb"
+++ /dev/null
@@ -1,6434 +0,0 @@
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- },
- "id": "n8Mfa4064fL1",
- "outputId": "ccd3b170-f23c-40bc-bca2-4b07b0624caf"
- },
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Downloading: 0%| | 0.00/523 [00:00"
- ],
- "text/html": [
- ""
- ]
- },
- "metadata": {}
- },
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "(,\n",
- " 'http://127.0.0.1:7860/',\n",
- " 'https://25490.gradio.app')"
- ]
- },
- "metadata": {},
- "execution_count": 9
- }
- ],
- "source": [
- "#!/usr/bin/env python3\n",
- "from doctest import OutputChecker\n",
- "import sys\n",
- "import argparse\n",
- "import torch\n",
- "import re\n",
- "import os\n",
- "import gradio as gr\n",
- "from sentence_transformers import SentenceTransformer, util\n",
- "from sklearn.metrics.pairwise import cosine_similarity\n",
- "from lm_scorer.models.auto import AutoLMScorer as LMScorer\n",
- "from sklearn.metrics.pairwise import cosine_similarity\n",
- "\n",
- "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
- "model = SentenceTransformer('stsb-distilbert-base', device=device)\n",
- "batch_size = 1\n",
- "scorer = LMScorer.from_pretrained('gpt2' , device=device, batch_size=batch_size)\n",
- "\n",
- "\n",
- "def cos_sim(a, b):\n",
- " return np.inner(a, b) / (np.linalg.norm(a) * (np.linalg.norm(b)))\n",
- "\n",
- "\n",
- " \n",
- "def Visual_re_ranker(caption, visual_context_label, visual_context_prob):\n",
- " caption = caption \n",
- " visual_context_label= visual_context_label\n",
- " visual_context_prob = visual_context_prob\n",
- " caption_emb = model.encode(caption, convert_to_tensor=True)\n",
- " visual_context_label_emb = model.encode(visual_context_label, convert_to_tensor=True)\n",
- "\n",
- "\n",
- " sim = cosine_scores = util.pytorch_cos_sim(caption_emb, visual_context_label_emb)\n",
- " sim = sim.cpu().numpy()\n",
- " sim = str(sim)[1:-1]\n",
- " sim = str(sim)[1:-1] \n",
- "\n",
- " LM = scorer.sentence_score(caption, reduce=\"mean\")\n",
- " score = pow(float(LM),pow((1-float(sim))/(1+ float(sim)),1-float(visual_context_prob)))\n",
- " \n",
- "\n",
- " #return {\"LM\": float(LM)/1, \"sim\": float(sim)/1, \"score\": float(score)/1 }\n",
- " return {\"init hypothesis\": float(LM)/1, \"Visual Belief Revision\": float(score)/1 }\n",
- " #return LM, sim, score \n",
- "\n",
- "\n",
- "\n",
- "demo = gr.Interface(\n",
- " fn=Visual_re_ranker,\n",
- " description=\"Demo for Belief Revision based Caption Re-ranker with Visual Semantic Information\",\n",
- " inputs=[gr.Textbox(value=\"a city street filled with traffic at night\") , gr.Textbox(value=\"traffic\"), gr.Textbox(value=\"0.7458009\")],\n",
- " #outputs=[gr.Textbox(value=\"Language Model Score\") , gr.Textbox(value=\"Semantic Similarity Score\"), gr.Textbox(value=\"Belief revision score via visual context\")],\n",
- " outputs=\"label\",\n",
- ")\n",
- "demo.launch()"
- ]
- }
- ]
-}
\ No newline at end of file