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+ } + } + } + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "#!pip install gradio\n", + "#!pip install sentence_transformers\n", + "!unzip /content/lm_scorer.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "x5Fabuh14rW0", + "outputId": "4692aff9-2878-4408-be09-c147ae341e73" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "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", + " inflating: lm_scorer/models/__pycache__/__init__.cpython-39.pyc \n", + " inflating: lm_scorer/models/__pycache__/gpt2.cpython-37.pyc \n", + " inflating: lm_scorer/models/__pycache__/__init__.cpython-37.pyc \n", + " inflating: lm_scorer/models/__pycache__/gpt2.cpython-36.pyc \n", + " inflating: lm_scorer/models/__pycache__/auto.cpython-36.pyc \n", + " inflating: lm_scorer/models/__pycache__/__init__.cpython-36.pyc \n", + " creating: lm_scorer/models/abc/\n", + " inflating: lm_scorer/models/abc/transformers.py \n", + " inflating: lm_scorer/models/abc/batch.py \n", + " inflating: lm_scorer/models/abc/base.py \n", + " extracting: lm_scorer/models/abc/__init__.py \n", + " creating: lm_scorer/models/abc/__pycache__/\n", + " inflating: lm_scorer/models/abc/__pycache__/base.cpython-39.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/batch.cpython-39.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/transformers.cpython-39.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/__init__.cpython-39.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/base.cpython-37.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/batch.cpython-37.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/transformers.cpython-37.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/__init__.cpython-37.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/batch.cpython-36.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/transformers.cpython-36.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/base.cpython-36.pyc \n", + " inflating: lm_scorer/models/abc/__pycache__/__init__.cpython-36.pyc \n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + 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" + ] + }, + "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