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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": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "f7fcb75891c84443bde60a2332b16df9", "7f95f7c660b94b10bf596dc8afa593fe", "2b0fed9839fa43cfa3e72677648d9388", "8d52287f4b37426698501e00c47c0fb4", "4cfe8b93eb01486a84e210e90947032e", "01703d1e508b4027ac79cd938848c550", "b8c483ad741249a39d717fb973c8582d", "8df2491d8da4449caa41c76062135c52", "a1193395cc524070b741ef192734409f", "b1c72a301c9145299911a708f2d0e609", "785b38a66be4485588c4a6624dd14e5c", "1441327258564e08be4564382bb57681", "08038f58010a4170ab96d6b4cb5d3065", "5db6c7f15dcd48f5a061550968a9165b", "a3256310fc6d42b3a8aa0f7712d65dff", "9a0af62ce5ac4f6385f4cb980f15a7dd", "aee86f1e58ca41fab84162e760e92043", "06e380e5bea74827903b417cc1fc8481", "fabb4be1e7fc4fcaa390fc4feb341687", "8df5f22d968540169c5ad9afd7e930fc", "0645fed7d9054114938b832672db3cfb", 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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()" ] } ] }