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"source": [
"#!pip install gradio\n",
"#!pip install sentence_transformers\n",
"!unzip /content/lm_scorer.zip"
],
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"text": [
"Archive: /content/lm_scorer.zip\n",
" creating: lm_scorer/\n",
" extracting: lm_scorer/__init__.py \n",
" creating: lm_scorer/__pycache__/\n",
" inflating: lm_scorer/__pycache__/__init__.cpython-39.pyc \n",
" inflating: lm_scorer/__pycache__/__init__.cpython-37.pyc \n",
" inflating: lm_scorer/__pycache__/__init__.cpython-36.pyc \n",
" creating: lm_scorer/models/\n",
" inflating: lm_scorer/models/gpt2.py \n",
" inflating: lm_scorer/models/auto.py \n",
" extracting: lm_scorer/models/__init__.py \n",
" creating: lm_scorer/bin/\n",
" inflating: lm_scorer/bin/cli.py \n",
" extracting: lm_scorer/bin/__init__.py \n",
" creating: lm_scorer/models/__pycache__/\n",
" inflating: lm_scorer/models/__pycache__/auto.cpython-37.pyc \n",
" inflating: lm_scorer/models/__pycache__/gpt2.cpython-39.pyc \n",
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]
}
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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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()"
]
}
]
}