RESEARCH - Open-I NLMCXR Report Generation.ipynb

{"cells":[{"cell_type":"markdown","source":["# Preprocessing"],"metadata":{"id":"zsFATC8eyx4w"},"id":"zsFATC8eyx4w"},{"cell_type":"markdown","source":["## Installations"],"metadata":{"id":"PFB8JZJEUNiY"},"id":"PFB8JZJEUNiY"},{"cell_type":"code","execution_count":null,"metadata":{"id":"xRjQiuyxSyiQ"},"outputs":[],"source":["!pip install transformers\n","!pip install --upgrade transformers\n","!pip install datasets\n","!pip install threadpoolctl\n","!pip install evaluate\n","!pip install rouge_score\n","!pip install pycocoevalcap\n","!pip install transformers\n","!pip install sentencepiece"],"id":"xRjQiuyxSyiQ"},{"cell_type":"code","source":["import os\n","import tqdm\n","import json\n","import random\n","\n","import matplotlib.pyplot as plt\n","import matplotlib.font_manager as fm\n","from PIL import Image\n","import pandas as pd\n","\n","import torch\n","import numpy as np\n","\n","from xml.dom import minidom\n","\n","from pathlib import Path\n","from google.colab import drive\n","\n","import nltk\n","import evaluate\n","\n","from datasets import Dataset, DatasetDict\n","\n","from transformers import ViTImageProcessor, BioGptTokenizer, TrainingArguments, Trainer\n","from transformers import VisionEncoderDecoderModel, AutoTokenizer, AutoFeatureExtractor\n","from transformers import pipeline\n","from transformers import default_data_collator\n","from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n","from transformers.utils import logging\n","\n","from pycocoevalcap.cider.cider import Cider\n","from pycocoevalcap.spice.spice import Spice"],"metadata":{"id":"vyICBnEyUHl4"},"id":"vyICBnEyUHl4","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Set-Up Torch Device and Working Directory"],"metadata":{"id":"C69YLN44URu_"},"id":"C69YLN44URu_"},{"cell_type":"code","source":["device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n","\n","drive.mount('/content/gdrive')\n","%cd \"gdrive\"\n","%cd \"MyDrive\"\n","%cd \"RESEARCH - Radiological Report Generation (Troy)\""],"metadata":{"id":"s6Ycv0RdUQRY"},"id":"s6Ycv0RdUQRY","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Read the X-Ray Dataset"],"metadata":{"id":"wDL7j5ydUYgu"},"id":"wDL7j5ydUYgu"},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":78521,"status":"ok","timestamp":1678611507502,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"},"user_tz":-480},"id":"uArNbLU0Syil","outputId":"d558cf4f-dfea-40e7-a2d2-532fe08bd905"},"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 3955/3955 [01:00<00:00, 65.67it/s] "]},{"output_type":"stream","name":"stdout","text":["                                             image_path  \\\n","0     RESEARCH - NLMCXR Dataset/NLMCXR_png/CXR3682_I...   \n","1     RESEARCH - NLMCXR Dataset/NLMCXR_png/CXR3699_I...   \n","2     RESEARCH - NLMCXR Dataset/NLMCXR_png/CXR3691_I...   \n","3     RESEARCH - NLMCXR Dataset/NLMCXR_png/CXR3690_I...   \n","4     RESEARCH - NLMCXR Dataset/NLMCXR_png/CXR3692_I...   \n","...                                                 ...   \n","1958  RESEARCH - NLMCXR Dataset/NLMCXR_png/CXR1873_I...   \n","1959  RESEARCH - NLMCXR Dataset/NLMCXR_png/CXR1844_I...   \n","1960  RESEARCH - NLMCXR Dataset/NLMCXR_png/CXR1887_I...   \n","1961  RESEARCH - NLMCXR Dataset/NLMCXR_png/CXR1886_I...   \n","1962  RESEARCH - NLMCXR Dataset/NLMCXR_png/CXR1849_I...   \n","\n","                                                caption  \n","0     FINDINGS:\\nThe lungs are hypoventilated. There...  \n","1     FINDINGS:\\nNormal heart size mediastinal conto...  \n","2     FINDINGS:\\nThe heart is normal in size. The me...  \n","3     FINDINGS:\\nThe heart is again enlarged. Aorta ...  \n","4     FINDINGS:\\nStable cardiomediastinal silhouette...  \n","...                                                 ...  \n","1958  FINDINGS:\\nCardiomediastinal silhouette is wit...  \n","1959  FINDINGS:\\nNormal cardiomediastinal contours. ...  \n","1960  FINDINGS:\\nThe lungs are clear. There is no pl...  \n","1961  FINDINGS:\\nHeart size and mediastinal contour ...  \n","1962  FINDINGS:\\nHeart size is within normal limits....  \n","\n","[1963 rows x 2 columns]\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}],"source":["# Go to directory of dataset\n","files = os.listdir('RESEARCH - NLMCXR Dataset/ecgen-radiology')\n","data = pd.DataFrame(columns=['image_path', 'caption'])\n","\n","for file in tqdm.tqdm(files):\n","    # For each file in the directory:\n","    file = minidom.parse(f'RESEARCH - NLMCXR Dataset/ecgen-radiology/{file}')\n","    num_views = len(file.getElementsByTagName('url'))\n","    for j in range(num_views):\n","        image_path = file.getElementsByTagName('url')[j].firstChild.nodeValue\n","        \n","        # Exclude lateral X-ray reports\n","        if '2001' in image_path:\n","            continue\n","            \n","        image_path = image_path.replace('/hadoop/storage/radiology/extract/', '')\n","        image_path = image_path.replace('.jpg', '.png')\n","        \n","        abstract = file.getElementsByTagName('AbstractText')\n","        if abstract[-1].firstChild and abstract[-2].firstChild:\n","            # Minimal parsing for dataset that will be processed by computer\n","            impression = abstract[-1].firstChild.nodeValue\n","            findings = abstract[-2].firstChild.nodeValue\n","            report_text = f'FINDINGS:\\n{findings}\\nIMPRESSION:\\n{impression}'\n","            data.loc[len(data)] = ['RESEARCH - NLMCXR Dataset/NLMCXR_png/' + image_path, report_text]\n","\n","data = data[data['caption'].apply(len) < 400].reset_index(drop=True)\n","data = data.drop_duplicates('caption').reset_index(drop=True)"],"id":"uArNbLU0Syil"},{"cell_type":"markdown","source":["## Preparation of Tokenizers, Model and Feature Extractors"],"metadata":{"id":"f4pOPOcEVOFu"},"id":"f4pOPOcEVOFu"},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"executionInfo":{"elapsed":64,"status":"ok","timestamp":1678611507507,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"},"user_tz":-480},"id":"p6YJAKrrSyip","outputId":"f1759f8b-411e-495e-d8f6-284fcc2ce6da"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<AxesSubplot:>"]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["def word_count(str):\n","    words = str.split()\n","    return len(words)\n","data['caption'].apply(word_count).hist()"],"id":"p6YJAKrrSyip"},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":232,"referenced_widgets":["31ed0e438e8746abb2482da13effd2a5","747c8a5ff8e844d29a57d712f7a2c5ba","f0331ce14a0e4c7c97819dc79eb3b972","5f86e065a4fd4048bacdcebd40d8a2b7","662f0f963556420f930d4654c80beaab","097098a9ca854971bbbbce27cf5aa7ba","3a75e46d130e40feabc20c7b8e3fcb45","7ef7a9bbd56a43829d63e091aec48baf","b4f151b8f25d41faba9b59356f9c94b5","d00af688287646bb8f692733cbbc0216","6b1b228fb30f4db48cb37f20ba3facad","b182f901c0164d569bb3ddbb83938123","d417bd28e6c842faabe60aa5404b00a1","2db7bf09e551466799e7dc566f849680","fc04bf1fe78f4980ab87a0368b63b604","c1b1028c66ec4e539217e65d2b95e049","9bda55a522994191b94eaf64fead73ad","ec224597fa06445d85baa513293c20da","c39fc5da89804ab2b4f6d152532626a4","0b160e3262e540d8810a93ac2b3bda66","0232bc45b90b4b228a48c538390cc82d","dd733c0eebd24b3e8e6502c34cb42d53","1dd8e856a78447b79ce97babea6f68f8","5f5da5c8a54045f897332c3e81707411","c82b519a65e84187882b500a8e281635","aeaa5f6b6f314291828ba22b864829a3","7dfb500b8eae40e08767bff18596815f","ae4fc586d2144955b2d4438ecf677bac","9ddcb7bdae3247d6b5ae8bf2ad4ace96","cd71bf9356114e5fb4af59426d045e67","3b411b2f48aa40dfa912f9a1434fd7c8","082ad619dd894193885f63aa85338d62","d2d12aeed23340239abbdb2aa676028c","94d3567de3fa480989de6e65cb9006e8","378a055e2b63424fae4dc13970c501dd","0bcfc9c43057487d9a7028571f7477e7","4b50555e5cfd4a5aacb06ae85f58fc3a","1e378af9f98b4555b0dfddfee2feecf9","74fdc5bfb4a849979a486e44061bee7f","e27cf3a816084634a28c1ff9c58b5b2f","70d49ab3fdad45279e3ff4c8c78c83f3","87aeb2d9f667474e9fb8702a29e76d14","53fa155114ae4e6faa1621108e2730ad","8dcffda05a6a4bba9d39cea2ba086da5","746b99c055e5455cb159f35743ea948a","eea6616a02b842d783e5cbe6cfcff488","39f109d7ef834941abf8d4082c1a3d39","361f0f00ba04417884592dcf2e414054","d2ff823d84c84d8981224ade4ced2169","3d1cebeec1504f65b1db6d65270d57a5","cf325a805e2e42918cf45c44df54fb99","bbaaf951503542449c82c3a09d3d1794","7c90c60a0d834e798ade5d4f514eded1","9e26898519ce4696b0a591e57122219f","d369722d7b7043ea88754b7fd965e2d7"]},"executionInfo":{"elapsed":16611,"status":"ok","timestamp":1678611524089,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"},"user_tz":-480},"id":"YXd-lZB2Syiq","outputId":"3c9e1919-6ace-4d43-8f08-198aa447ab48"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/502 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"31ed0e438e8746abb2482da13effd2a5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/346M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b182f901c0164d569bb3ddbb83938123"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1dd8e856a78447b79ce97babea6f68f8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/548M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"94d3567de3fa480989de6e65cb9006e8"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of GPT2LMHeadModel were not initialized from the model checkpoint at gpt2 and are newly initialized: ['h.1.crossattention.masked_bias', 'h.7.crossattention.bias', 'h.8.ln_cross_attn.weight', 'h.3.crossattention.bias', 'h.0.ln_cross_attn.weight', 'h.10.crossattention.c_proj.bias', 'h.4.ln_cross_attn.weight', 'h.2.crossattention.c_attn.weight', 'h.9.crossattention.c_proj.bias', 'h.1.crossattention.q_attn.weight', 'h.10.crossattention.c_proj.weight', 'h.0.crossattention.masked_bias', 'h.3.crossattention.q_attn.weight', 'h.10.crossattention.masked_bias', 'h.3.crossattention.masked_bias', 'h.11.crossattention.masked_bias', 'h.11.crossattention.q_attn.weight', 'h.9.crossattention.bias', 'h.3.crossattention.c_attn.weight', 'h.3.crossattention.c_proj.bias', 'h.5.crossattention.q_attn.weight', 'h.4.crossattention.q_attn.weight', 'h.0.crossattention.c_attn.weight', 'h.1.crossattention.c_attn.weight', 'h.7.ln_cross_attn.weight', 'h.0.crossattention.c_proj.weight', 'h.6.crossattention.bias', 'h.8.crossattention.bias', 'h.2.crossattention.bias', 'h.5.crossattention.masked_bias', 'h.5.crossattention.c_attn.weight', 'h.0.crossattention.q_attn.weight', 'h.2.crossattention.c_proj.bias', 'h.5.crossattention.bias', 'h.6.ln_cross_attn.weight', 'h.9.crossattention.c_attn.weight', 'h.1.ln_cross_attn.weight', 'h.7.crossattention.q_attn.weight', 'h.0.crossattention.bias', 'h.4.crossattention.masked_bias', 'h.11.crossattention.c_proj.weight', 'h.6.crossattention.c_attn.weight', 'h.6.crossattention.q_attn.weight', 'h.8.crossattention.c_proj.weight', 'h.4.crossattention.c_attn.weight', 'h.10.crossattention.bias', 'h.8.crossattention.c_attn.weight', 'h.10.ln_cross_attn.weight', 'h.7.crossattention.c_proj.weight', 'h.1.crossattention.c_proj.weight', 'h.3.ln_cross_attn.weight', 'h.5.crossattention.c_proj.weight', 'h.9.crossattention.c_proj.weight', 'h.11.crossattention.c_attn.weight', 'h.0.crossattention.c_proj.bias', 'h.11.ln_cross_attn.weight', 'h.8.crossattention.c_proj.bias', 'h.2.crossattention.q_attn.weight', 'h.3.crossattention.c_proj.weight', 'h.2.crossattention.c_proj.weight', 'h.7.crossattention.masked_bias', 'h.10.crossattention.c_attn.weight', 'h.2.ln_cross_attn.weight', 'h.6.crossattention.masked_bias', 'h.4.crossattention.c_proj.weight', 'h.9.ln_cross_attn.weight', 'h.7.crossattention.c_proj.bias', 'h.11.crossattention.bias', 'h.8.crossattention.masked_bias', 'h.1.crossattention.c_proj.bias', 'h.6.crossattention.c_proj.bias', 'h.4.crossattention.c_proj.bias', 'h.5.crossattention.c_proj.bias', 'h.4.crossattention.bias', 'h.9.crossattention.q_attn.weight', 'h.5.ln_cross_attn.weight', 'h.6.crossattention.c_proj.weight', 'h.11.crossattention.c_proj.bias', 'h.2.crossattention.masked_bias', 'h.8.crossattention.q_attn.weight', 'h.9.crossattention.masked_bias', 'h.10.crossattention.q_attn.weight', 'h.7.crossattention.c_attn.weight', 'h.1.crossattention.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"746b99c055e5455cb159f35743ea948a"}},"metadata":{}}],"source":["image_encoder_model = \"google/vit-base-patch16-224-in21k\"\n","text_decode_model = \"gpt2\"\n","\n","model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(\n","    image_encoder_model, text_decode_model)"],"id":"YXd-lZB2Syiq"},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":3163,"status":"ok","timestamp":1678611527228,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"},"user_tz":-480},"id":"XZE90yv7Syir","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["3d2b1c4fd5ec463a81dd66b7b31bd0f4","5bb901ce30e2476096e34a5f5a3efaf0","0335c48c482e4fd98c392c33a7fa07df","6fb75206fd6d4a3b85a902fcea4bb33a","e6eb266601264a8d897315934c328c68","b30b754da015446ab19840fec2baef2b","4ac3822057db49ee8f41de89562fe6fb","16edbb99c28c489abffeeac894c435c0","492d3af7cea24fb39f86339b24def1be","f4fcdd20108549e592e38348e818e902","b50217271ff346e0a7c79888cb3a58ef","b0889e6ff3b34a998d3ed239c4e7d3c9","2eafa49c4692472c990e0f3106c6da89","8eb2924ae86546b99549d2e34e9ffe1f","6aa37e2ef064485cbeafd1593cc3a04d","566480202a17460ca7d23e98aad3c419","d45dc9c57ac4451b9ef36ada3af2ec43","93f473fafc9541c58321a14def19c5c6","08ab5398970c45889360eee5fd7138cd","b5679e0545d24e4ca00afc7035f44f43","a86e4e94204240a092265c6c2bf45710","2ad02d5aa8944e15bc06bb03a27fa007","19a9cad6516f49d3aa752a9137fb9a83","e2030512d453420099195d7377ed50d4","263982702ca647498b7a56383726822a","35b035f9d67f4a74b057153cb312d1b0","873be62572a94d7b9f80a2b8da2700bb","64215c2793ad4b22b458c2ee62cb1044","58a02e8ec87d405681e00a7e16a1fee9","7a40c10aaf3b4992acda3f42c3ac16fd","1ac968f71d0d4d6bbf9f8c0a72954870","cfbbe58485ba4d0ba747a18f66cf176b","eb25081319e341239e753b510c322672","8c84aeabbac5421fa8eec92e154718f0","32b458c2ba964e8fbf83d7aafce863c1","5af441c1a83d465fbb644f6b848014b9","67029cdbbed343739be5a07bad7b286a","7a02599dd1354aa0b120fe861477da53","b159e99ea3bb4eed8d5d6daaa023bdab","fd399e24093e4bd685971b50919dc1a8","5e10b72e4beb4321909fa9f0dac63486","81d52245dfb841dfa664ebae9286d687","9d3c743c69fd438d8e4dd6820b462651","fda9cfd1406b4e15bf8a39f491dcb4aa"]},"outputId":"cfea525f-656d-4adc-d323-a44f27d86853"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)rocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3d2b1c4fd5ec463a81dd66b7b31bd0f4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b0889e6ff3b34a998d3ed239c4e7d3c9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"19a9cad6516f49d3aa752a9137fb9a83"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8c84aeabbac5421fa8eec92e154718f0"}},"metadata":{}}],"source":["feature_extractor = ViTImageProcessor.from_pretrained(image_encoder_model)\n","tokenizer = AutoTokenizer.from_pretrained(text_decode_model)"],"id":"XZE90yv7Syir"},{"cell_type":"code","execution_count":7,"metadata":{"id":"E4sJ8_PpSyis","executionInfo":{"status":"ok","timestamp":1678611527229,"user_tz":-480,"elapsed":9,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"outputs":[],"source":["# # GPT2 only has bos/eos tokens but not decoder_start/pad tokens\n","tokenizer.pad_token = tokenizer.unk_token\n","\n","# # update the model config\n","model.eos_token_id = tokenizer.eos_token_id\n","model.config.eos_token_id = tokenizer.eos_token_id\n","\n","model.decoder_start_token_id = tokenizer.bos_token_id\n","model.config.decoder_start_token_id = tokenizer.bos_token_id\n","\n","model.pad_token_id = tokenizer.pad_token_id\n","model.config.pad_token_id = tokenizer.pad_token_id\n","\n","model.max_length = 128\n","model.config.max_length = 128\n","model.config.decoder.max_length = 128\n","\n","model.min_length = 40\n","model.config.min_length = 40\n","model.config.decoder.min_length = 40\n","\n","model.no_repeat_ngram_size = 3\n","model.config.no_repeat_ngram_size = 3\n","model.config.decoder.no_repeat_ngram_size = 3\n","\n","\n","tokenizer.pad_token = tokenizer.eos_token\n","\n","# update the model config\n","model.config.eos_token_id = tokenizer.eos_token_id\n","model.config.decoder_start_token_id = tokenizer.bos_token_id\n","model.config.pad_token_id = tokenizer.pad_token_id"],"id":"E4sJ8_PpSyis"},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17143,"status":"ok","timestamp":1678611544365,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"},"user_tz":-480},"id":"0IEHcKNgSyit","outputId":"d49e6fb6-8fda-4632-f79c-2fcbf470071d","scrolled":true},"outputs":[{"output_type":"execute_result","data":{"text/plain":["('vit-gpt-model/tokenizer_config.json',\n"," 'vit-gpt-model/special_tokens_map.json',\n"," 'vit-gpt-model/vocab.json',\n"," 'vit-gpt-model/merges.txt',\n"," 'vit-gpt-model/added_tokens.json',\n"," 'vit-gpt-model/tokenizer.json')"]},"metadata":{},"execution_count":8}],"source":["# output_dir = \"vit-gpt-model\"\n","output_dir = \"vit-gpt-model\"\n","model.save_pretrained(output_dir)\n","feature_extractor.save_pretrained(output_dir)\n","tokenizer.save_pretrained(output_dir)"],"id":"0IEHcKNgSyit"},{"cell_type":"markdown","source":["## Final processing of dataset"],"metadata":{"id":"LViP1_QYVTO6"},"id":"LViP1_QYVTO6"},{"cell_type":"code","execution_count":9,"metadata":{"id":"hJVVs9BcSyiu","executionInfo":{"status":"ok","timestamp":1678611544366,"user_tz":-480,"elapsed":42,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"outputs":[],"source":["train_dataset = Dataset.from_pandas(data[:654])\n","validation_dataset = Dataset.from_pandas(data[654:1308])\n","test_dataset = Dataset.from_pandas(data[1308:])"],"id":"hJVVs9BcSyiu"},{"cell_type":"code","execution_count":10,"metadata":{"id":"mDMRou3bSyix","executionInfo":{"status":"ok","timestamp":1678611544367,"user_tz":-480,"elapsed":38,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"outputs":[],"source":["ds = DatasetDict()\n","\n","ds['train'] = train_dataset\n","ds['validation'] = validation_dataset\n","ds['test'] = test_dataset"],"id":"mDMRou3bSyix"},{"cell_type":"code","execution_count":11,"metadata":{"id":"uCkabLa_Syiz","executionInfo":{"status":"ok","timestamp":1678611544368,"user_tz":-480,"elapsed":37,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"outputs":[],"source":["from PIL import Image\n","\n","# text preprocessing step\n","def tokenization_fn(captions, max_target_length):\n","    \"\"\"Run tokenization on captions.\"\"\"\n","    labels = tokenizer(captions, \n","                      padding=\"max_length\", \n","                      max_length=max_target_length, return_tensors='pt').input_ids\n","    return labels\n","\n","# image preprocessing step\n","def feature_extraction_fn(image_paths, check_image=True):\n","    \"\"\"\n","    Run feature extraction on images\n","    If `check_image` is `True`, the examples that fails during `Image.open()` will be caught and discarded.\n","    Otherwise, an exception will be thrown.\n","    \"\"\"\n","\n","    model_inputs = {}\n","\n","    if check_image:\n","        images = []\n","        to_keep = []\n","        for image_file in image_paths:\n","            try:\n","                img = Image.open(image_file)\n","                images.append(img)\n","                to_keep.append(True)\n","            except Exception:\n","                to_keep.append(False)\n","    else:\n","        images = [Image.open(image_file) for image_file in image_paths]\n","\n","    encoder_inputs = feature_extractor(images=images, return_tensors=\"pt\")\n","\n","    return encoder_inputs.pixel_values\n","\n","def preprocess_fn(examples, max_target_length, check_image = True):\n","    \"\"\"Run tokenization + image feature extraction\"\"\"\n","    image_paths = examples['image_path']\n","    captions = examples['caption']    \n","    \n","    model_inputs = {}\n","    # This contains image path column\n","    model_inputs['labels'] = tokenization_fn(captions, max_target_length)\n","    model_inputs['pixel_values'] = feature_extraction_fn(image_paths, check_image=check_image)\n","    return model_inputs"],"id":"uCkabLa_Syiz"},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":191,"referenced_widgets":["f893f3a70c2d406392acdfd5323926a0","7d5f106c780944aeb8f9f1d39089bd8e","de3eb188c4e34756815dc1aa7adac528","bcff0f25f90b4bb68dcb2272c35a3322","414d9e76c4554b8b9d693e503dec4ed3","f115550928c54ffdba3955f325516bec","78f06505c0354725abf200673aa4591e","230ae4d4c9d447bea36b07ee55c24958","623eeb8474ab4143b3fe5077bbecc9a3","589c4ff1b3624cf6aa7f5077c0c16a59","e5b1e4a723164527a943b4ad24288a10","979374e76aea4ecebad017545ca90331","120e7b143a5d4f3aac9c2b4057c0a016","47b07747e56c46e48f184fcf4547f269","e85661a451414834a362265280c35cee","59cba19245294a5fa11bb1dbc7797586","1829d630a8734ef8beb27c2c9b07365e","52658d143e3b4708849b42db3b355058","bebadc998a36436f966a3cee9a7cc49a","168907a4172c4853a93c99bc0814ba2a","4abea8cc58964a58be6a29e49ba2372e","15139361c4e34388996abb7f4a7df52b","f314850070644245a1fa477950284ce6","d7ed74a3f1664793822fb727fe8a25fa","03c881b3c18f476bb971bc6d7b21000b","8899c132a0d04dae944ad633e33a9b6d","d1afeed389254a078225448f7c99af7d","422171b7b9c7430e88ca265e3060e58c","441eb4bc2edc46d1b78af905b2459d8e","ed06b0e0635e401e91b1c006690f26bb","00ffb32349d84a71b3adf59f96d67829","fd503cbb9dc848a4a652217adce7b511","864605635e694290a038f16de25ee4bf"]},"executionInfo":{"elapsed":538354,"status":"ok","timestamp":1678612082686,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"},"user_tz":-480},"id":"P0e68e_iSyi0","outputId":"cf220dd1-d504-42ee-fd84-5340ea418cd8"},"outputs":[{"output_type":"stream","name":"stdout","text":[" \u001b[0m\u001b[01;34mimage-captioning-output-BLEU\u001b[0m/\n"," \u001b[01;34mimage-captioning-output-SPICE\u001b[0m/\n","\u001b[01;34m'RESEARCH - Cardiomegaly Classification'\u001b[0m/\n","'RESEARCH - Evaluation Metrics.gdoc'\n","\u001b[01;34m'RESEARCH - NLMCXR Dataset'\u001b[0m/\n","'RESEARCH - Open-I NLMCXR Report Generation.ipynb'\n","'RESEARCH - Related Literature.gdoc'\n","'RESEARCH -    TODO.gdoc'\n","'RESERACH - Literature Review with Code.gdoc'\n"," \u001b[01;34mvit-gpt-model\u001b[0m/\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/654 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f893f3a70c2d406392acdfd5323926a0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/654 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"979374e76aea4ecebad017545ca90331"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/655 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f314850070644245a1fa477950284ce6"}},"metadata":{}}],"source":["%ls\n","processed_dataset = ds.map(\n","    function=preprocess_fn,\n","    batched=True,\n","    fn_kwargs={\"max_target_length\": 128},\n","    remove_columns=ds['train'].column_names\n",")"],"id":"P0e68e_iSyi0"},{"cell_type":"markdown","source":["# Evaluation Functions\n","The following metrics are available:\n","- ROUGE metric\n","- CIDEr metric\n","- BLEU metric\n","- METEOR evaluation\n","- SPICE evaluation"],"metadata":{"id":"IWmHzcn0x8oe"},"id":"IWmHzcn0x8oe"},{"cell_type":"markdown","source":["## Post-processing Text Function"],"metadata":{"id":"dpXYS5asW7cd"},"id":"dpXYS5asW7cd"},{"cell_type":"code","source":["nltk.download(\"punkt\")\n","ignore_pad_token_for_loss = True\n","\n","def postprocess_text(preds, labels):\n","    preds = [pred.strip() for pred in preds] # Get prediction tokens\n","    labels = [label.strip() for label in labels] # Get label tokens\n","\n","    # Decode preds / labels\n","    preds = [\"\\n\".join(nltk.sent_tokenize(pred)) for pred in preds]\n","    labels = [\"\\n\".join(nltk.sent_tokenize(label)) for label in labels]\n","\n","    return preds, labels"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Kz52zTkzRUV","executionInfo":{"status":"ok","timestamp":1678612811864,"user_tz":-480,"elapsed":1803,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}},"outputId":"744875b9-24ef-4f1e-a476-2d87edad3d58"},"id":"0Kz52zTkzRUV","execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data]   Unzipping tokenizers/punkt.zip.\n"]}]},{"cell_type":"markdown","source":["## ROUGE metric"],"metadata":{"id":"xAzk2o6IW9u7"},"id":"xAzk2o6IW9u7"},{"cell_type":"code","source":["def compute_ROUGE_score(eval_preds):\n","    preds, labels = eval_preds\n","    if isinstance(preds, tuple):\n","        preds = preds[0]\n","    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n","    if ignore_pad_token_for_loss:\n","        # Replace -100 in the labels as we can't decode them.\n","        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","\n","    # Some simple post-processing\n","    decoded_preds, decoded_labels = postprocess_text(decoded_preds,\n","                                                    decoded_labels)\n","\n","    # ROUGE metric\n","    rouge_metric = evaluate.load(\"rouge\")\n","    result = rouge_metric.compute(predictions=decoded_preds,\n","                            references=decoded_labels)\n","    \n","    # Average precisions\n","    result[\"precisions\"] = sum(result[\"precisions\"])/len(result[\"precisions\"])\n","  \n","    # Finalize predictions\n","    prediction_lens = [\n","        np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds\n","    ]\n","    result[\"gen_len\"] = np.mean(prediction_lens)\n","    \n","    return result"],"metadata":{"id":"ZG-8pgHRw7Bd","executionInfo":{"status":"ok","timestamp":1678612811865,"user_tz":-480,"elapsed":10,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"id":"ZG-8pgHRw7Bd","execution_count":14,"outputs":[]},{"cell_type":"markdown","source":["## METEOR score"],"metadata":{"id":"ebpU1CkqXAdF"},"id":"ebpU1CkqXAdF"},{"cell_type":"code","source":["def compute_METEOR_score(eval_preds):\n","    preds, labels = eval_preds\n","    if isinstance(preds, tuple):\n","        preds = preds[0]\n","    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n","    if ignore_pad_token_for_loss:\n","        # Replace -100 in the labels as we can't decode them.\n","        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","\n","    # Some simple post-processing\n","    decoded_preds, decoded_labels = postprocess_text(decoded_preds,\n","                                                    decoded_labels)\n","\n","    # METEOR metric\n","    meteor_metric = evaluate.load(\"meteor\")\n","    result = meteor_metric.compute(predictions=decoded_preds,\n","                            references=decoded_labels)\n","    \n","    # Average precisions\n","    result[\"precisions\"] = sum(result[\"precisions\"])/len(result[\"precisions\"])\n","  \n","    # Finalize predictions\n","    prediction_lens = [\n","        np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds\n","    ]\n","    result[\"gen_len\"] = np.mean(prediction_lens)\n","    \n","    return result"],"metadata":{"id":"OmGP4XhEw_Nj","executionInfo":{"status":"ok","timestamp":1678612811866,"user_tz":-480,"elapsed":11,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"id":"OmGP4XhEw_Nj","execution_count":15,"outputs":[]},{"cell_type":"markdown","source":["## BLEU score"],"metadata":{"id":"_Jjm2Z3zXCPt"},"id":"_Jjm2Z3zXCPt"},{"cell_type":"code","source":["def compute_BLEU_score(eval_preds):\n","    preds, labels = eval_preds\n","    if isinstance(preds, tuple):\n","        preds = preds[0]\n","    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n","    if ignore_pad_token_for_loss:\n","        # Replace -100 in the labels as we can't decode them.\n","        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","\n","    # Some simple post-processing\n","    decoded_preds, decoded_labels = postprocess_text(decoded_preds,\n","                                                    decoded_labels)\n","\n","    # BLEU metric\n","    bleu_metric = evaluate.load(\"bleu\")\n","    result = bleu_metric.compute(predictions=decoded_preds,\n","                            references=decoded_labels)\n","    \n","    # Average precisions\n","    result[\"precisions\"] = sum(result[\"precisions\"])/len(result[\"precisions\"])\n","  \n","    # Finalize predictions\n","    prediction_lens = [\n","        np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds\n","    ]\n","    result[\"gen_len\"] = np.mean(prediction_lens)\n","    \n","    return result"],"metadata":{"id":"6n4dkVPtw9SC","executionInfo":{"status":"ok","timestamp":1678612811866,"user_tz":-480,"elapsed":10,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"id":"6n4dkVPtw9SC","execution_count":16,"outputs":[]},{"cell_type":"markdown","source":["## CIDEr Score"],"metadata":{"id":"mDgSNzsCXEIR"},"id":"mDgSNzsCXEIR"},{"cell_type":"code","source":["def compute_CIDER_score(eval_preds):\n","    preds, labels = eval_preds\n","    if isinstance(preds, tuple):\n","        preds = preds[0]\n","    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n","    if ignore_pad_token_for_loss:\n","        # Replace -100 in the labels as we can't decode them.\n","        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","\n","    # Some simple post-processing\n","    decoded_preds, decoded_labels = postprocess_text(decoded_preds,\n","                                                    decoded_labels)\n","\n","    # Create CIDEr metric object\n","    cider_metric = Cider()\n","\n","    # Format decoded to satisfy pycocoevalcap\n","    dict_decoded_preds = {}\n","    dict_decoded_labels = {}\n","    for i in range(len(decoded_preds)):\n","        dict_decoded_preds[i] = [decoded_preds[i]]\n","        dict_decoded_labels[i] = [decoded_labels[i]]\n","    decoded_preds = dict_decoded_preds\n","    decoded_labels = dict_decoded_labels\n","\n","    # Store results into metric; not too reliable but works\n","    result = cider_metric.compute_score(decoded_preds, decoded_labels)\n","    result = {\"CIDEr\": result[0], \"Scores\": result[1]}\n","  \n","    prediction_lens = [\n","        np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds\n","    ]\n","    result[\"gen_len\"] = np.mean(prediction_lens)\n","    return result"],"metadata":{"id":"nOzp4aORxCmC","executionInfo":{"status":"ok","timestamp":1678612811867,"user_tz":-480,"elapsed":11,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"id":"nOzp4aORxCmC","execution_count":17,"outputs":[]},{"cell_type":"markdown","source":["## SPICE metric"],"metadata":{"id":"vbdy_1n6XGDX"},"id":"vbdy_1n6XGDX"},{"cell_type":"code","source":["def compute_SPICE_score(eval_preds):\n","    preds, labels = eval_preds\n","    if isinstance(preds, tuple):\n","        preds = preds[0]\n","    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n","    if ignore_pad_token_for_loss:\n","        # Replace -100 in the labels as we can't decode them.\n","        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","\n","    # Some simple post-processing\n","    decoded_preds, decoded_labels = postprocess_text(decoded_preds,\n","                                                    decoded_labels)\n","\n","    # Create SPICE metric object\n","    spice_metric = Spice()\n","\n","    # Format decoded to satisfy pycocoevalcap\n","    dict_decoded_preds = {}\n","    dict_decoded_labels = {}\n","    for i in range(len(decoded_preds)):\n","        dict_decoded_preds[i] = [decoded_preds[i]]\n","        dict_decoded_labels[i] = [decoded_labels[i]]\n","    decoded_preds = dict_decoded_preds\n","    decoded_labels = dict_decoded_labels\n","\n","    # Store results into metric; not too reliable but works\n","    result = spice_metric.compute_score(decoded_preds, decoded_labels)\n","    result = {\"SPICE\": result[0], \"Scores\": result[1]}\n","  \n","    prediction_lens = [\n","        np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds\n","    ]\n","\n","    result[\"gen_len\"] = np.mean(prediction_lens)\n","    return result"],"metadata":{"id":"dR0ltyDbw2IZ","executionInfo":{"status":"ok","timestamp":1678612811868,"user_tz":-480,"elapsed":11,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"id":"dR0ltyDbw2IZ","execution_count":18,"outputs":[]},{"cell_type":"markdown","source":["# Train on Metric"],"metadata":{"id":"Qy4eitQCySg1"},"id":"Qy4eitQCySg1"},{"cell_type":"code","execution_count":null,"metadata":{"id":"77DkQC2jSyi3"},"outputs":[],"source":["# Set which metric will be used\n","metric_name = \"BLEU\"\n","metrics = {\"BLEU\": compute_BLEU_score, \"SPICE\": compute_SPICE_score, \"CIDER\": compute_CIDER_score, \n","           \"METEOR\": compute_METEOR_score, \"ROUGE\": compute_ROUGE_score}\n","\n","# instantiate trainer\n","training_args = Seq2SeqTrainingArguments(\n","  predict_with_generate=True,\n","  evaluation_strategy=\"epoch\",\n","  per_device_train_batch_size=4,\n","  per_device_eval_batch_size=4,\n","  output_dir=f\"./image-captioning-output-{metric_name}\",\n","  num_train_epochs = 10\n",")\n","\n","# Set-up trainer (metric to use, etc.)\n","trainer = Seq2SeqTrainer(\n","    model=model,\n","    tokenizer=feature_extractor,\n","    args=training_args,\n","    compute_metrics=metrics[metric_name],\n","    train_dataset=processed_dataset['train'],\n","    eval_dataset=processed_dataset['validation'],\n","    data_collator=default_data_collator,\n",")\n","\n","# TRAIN THE MODEL!!!\n","trainer.train()\n","\n","# Miclin (02/08 11:30 PM): Added so that cuda releases dram when done\n","torch.cuda.empty_cache()\n","\n","# Save model into desired directory\n","trainer.save_model(f\"./image-captioning-output-{metric_name}\")\n","tokenizer.save_pretrained(f\"./image-captioning-output-{metric_name}\")"],"id":"77DkQC2jSyi3"},{"cell_type":"markdown","source":["# Open model and sample data (generated vs reference)"],"metadata":{"id":"aaTOOMSSXj8f"},"id":"aaTOOMSSXj8f"},{"cell_type":"code","execution_count":20,"metadata":{"id":"8ctJ7qVaSyi8","scrolled":true,"executionInfo":{"status":"ok","timestamp":1678613675793,"user_tz":-480,"elapsed":8,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"outputs":[],"source":["json_filename = f'./image-captioning-output-{metric_name}/preprocessor_config.json'\n","\n","with open(json_filename) as json_file:\n","    json_decoded = json.load(json_file)\n","\n","if 'image_processor_type' in json_decoded:\n","    json_decoded['feature_extractor_type'] = json_decoded.pop('image_processor_type')\n","\n","with open(json_filename, 'w') as json_file:\n","    json.dump(json_decoded, json_file, indent=2, separators=(',', ': '))\n"],"id":"8ctJ7qVaSyi8"},{"cell_type":"code","execution_count":null,"metadata":{"id":"Mzcl4yk1Sywq"},"outputs":[],"source":["# full dataset trained model can be found at https://huggingface.co/nlpconnect/vit-gpt2-image-captioning\n","image_captioner = pipeline(\"image-to-text\", model=f\"image-captioning-output-{metric_name}\")"],"id":"Mzcl4yk1Sywq"},{"cell_type":"code","execution_count":37,"metadata":{"id":"eoYtiV2eSywr","executionInfo":{"status":"ok","timestamp":1678614290586,"user_tz":-480,"elapsed":326,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}}},"outputs":[],"source":["logging.set_verbosity(40)"],"id":"eoYtiV2eSywr"},{"cell_type":"code","execution_count":39,"metadata":{"id":"kmrPVioHSywr","executionInfo":{"status":"ok","timestamp":1678614337275,"user_tz":-480,"elapsed":12411,"user":{"displayName":"Troy Serapio","userId":"18399633372019105300"}},"colab":{"base_uri":"https://localhost:8080/","height":553},"outputId":"dd19a368-1aaa-4f74-e710-73dcf4eadb8e"},"outputs":[{"output_type":"stream","name":"stdout","text":["(ORIGINAL)\n","\n","FINDINGS:\n","Lungs are clear without focal consolidation, effusion, or pneumothorax. Normal heart size. Bony thorax and soft tissues grossly unremarkable\n","IMPRESSION:\n","Negative for acute cardiopulmonary abnormality.\n","============================\n","\n","(GENERATED)\n","\n","FINDINGS:\n","Heart size within normal limits, stable mediastinal contour, and pulmonary vascularity. No focal consolidation, pleural effusion, or pneumothorax. No acute bony abnormality.\n","IMPRESSION:\n","No acute cardiopulmonary abnormality identified.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 360x360 with 1 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demo():\n","    row_number = random.randint(1308, len(data) - 1)\n","    image = Image.open(data.iloc[row_number].image_path)\n","    original = data.iloc[row_number].caption\n","    fig, ax = plt.subplots(figsize=(5,5))\n","    ax.imshow(image)\n","    generated = image_captioner(image)[0]['generated_text']\n","    fontprop = fm.FontProperties(fname='Roboto-Regular.ttf', size=12)\n","\n","#     ax.text(8, 1, impression, fontproperties=fontprop)\n","    \n","    ax.spines['top'].set_visible(False)\n","    ax.spines['right'].set_visible(False)\n","    ax.spines['bottom'].set_visible(False)\n","    ax.spines['left'].set_visible(False)\n","    ax.get_xaxis().set_ticks([])\n","    ax.get_yaxis().set_ticks([]) \n","    return original, generated\n","\n","original, generated = 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