diff --git a/Meme_recognition.ipynb b/Meme_recognition.ipynb
index b5895a7..a1d7e3c 100644
--- a/Meme_recognition.ipynb
+++ b/Meme_recognition.ipynb
@@ -1 +1 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Meme_recognition.ipynb","provenance":[],"collapsed_sections":["A5-fgPXsyewt","8alL7g6bd_6Y","X3ByzgAFxJxp","m6gS301YB1T1","pFQuZxvx3jeg","zN73WeadNetg","swSaaoA3YgJT","vNwR6t_E9luJ","sshIvyxWh139"],"mount_file_id":"1_pKyZaYx90NMBD1AAOvZ6Cjc063Bq3Mf","authorship_tag":"ABX9TyMEsndyEISiGqZla+w6BnIv"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Import del dataset"],"metadata":{"id":"A5-fgPXsyewt"}},{"cell_type":"code","execution_count":4,"metadata":{"id":"AvIBo58qMPsQ","executionInfo":{"status":"ok","timestamp":1642598721326,"user_tz":-60,"elapsed":366,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"outputs":[],"source":["#Path file\n","percorso = '/content/drive/MyDrive/hateful_memes.zip'"]},{"cell_type":"code","source":["#Unzip the file\n","import zipfile\n","with zipfile.ZipFile(percorso, 'r') as zip_ref:\n","    zip_ref.extractall('.')"],"metadata":{"id":"rAvOAfxfMn9Y","executionInfo":{"status":"ok","timestamp":1642598796096,"user_tz":-60,"elapsed":74261,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["#import json files\n","import sys\n","import os\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","\n","train = pd.read_json('/content/hateful_memes/train.jsonl',lines=True)\n","val = pd.read_json('/content/hateful_memes/dev_seen.jsonl',lines=True)\n","test = pd.read_json('/content/hateful_memes/test_seen.jsonl',lines=True)"],"metadata":{"id":"KCKDKIa3NWCp","executionInfo":{"status":"ok","timestamp":1642598796097,"user_tz":-60,"elapsed":21,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["#Visualize dataset\n","path_base = '/content/hateful_memes'\n","plt.figure(figsize=(10,6))\n","img = plt.imread(path_base + '/' +train['img'][2])\n","plt.imshow(img)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":395},"id":"jnX8XUTBODIs","executionInfo":{"status":"ok","timestamp":1642598797309,"user_tz":-60,"elapsed":1231,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"82ffd9e8-ccf5-48ea-b485-4d76436b227f"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["#Funzione per visualizzare le immagini dato il path\n","def VisualizeImage(path):\n","  path_base = '/content/hateful_memes'\n","  plt.figure(figsize=(10,6))\n","  img = plt.imread(path_base + '/' +path)\n","  plt.imshow(img)"],"metadata":{"id":"BsBttolBvk0A","executionInfo":{"status":"ok","timestamp":1642598797310,"user_tz":-60,"elapsed":6,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":["# Name Entity Recognition"],"metadata":{"id":"8alL7g6bd_6Y"}},{"cell_type":"code","source":["import spacy\n","nlp = spacy.load(\"en_core_web_sm\")"],"metadata":{"id":"67Cm0KVSrytv","executionInfo":{"status":"ok","timestamp":1642598861965,"user_tz":-60,"elapsed":2459,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["def NameEntityRec(text):\n","  doc = nlp(text)\n","  words = []\n","\n","  for token in doc:\n","    if token.pos_ == 'NOUN' or token.pos_ == 'PROPN':\n","      words.append(token.text)  \n","  return words  "],"metadata":{"id":"qtaNc1mXFoNO","executionInfo":{"status":"ok","timestamp":1642598861965,"user_tz":-60,"elapsed":2,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["#Prova per vedere cosa stampa\n","#text = train['text'][4]\n","#doc = nlp(text)\n","#print(text)\n","\n","#words = []\n","\n","#for token in doc:\n","  #print(token, token.pos_)\n","  #if token.pos_ == 'NOUN' or token.pos_ == 'PROPN':\n","    #words.append(token.text)"],"metadata":{"id":"i9h5TmLymUjn","executionInfo":{"status":"ok","timestamp":1642598861966,"user_tz":-60,"elapsed":3,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["#Concateno le parole\n","#string=\"\"\n","#for elem in words:\n","#  string += ' ' + elem "],"metadata":{"id":"5c4J-l6lvV5E","executionInfo":{"status":"ok","timestamp":1642598861966,"user_tz":-60,"elapsed":2,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":["# Object Detection"],"metadata":{"id":"X3ByzgAFxJxp"}},{"cell_type":"code","source":["#Installo dipendenze per Object Detection\n","!pip install tensorflow==2.4.0\n","!pip install keras==2.4.3 numpy==1.19.3 pillow==7.0.0 scipy==1.4.1 h5py==2.10.0 matplotlib==3.3.2 opencv-python keras-resnet==0.2.0\n","!pip install imageai --upgrade"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f9Le9rlyymLc","executionInfo":{"status":"ok","timestamp":1642598882983,"user_tz":-60,"elapsed":10677,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"c223990c-1ac3-45db-f188-2e9116fa8eaa"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: tensorflow==2.4.0 in /usr/local/lib/python3.7/dist-packages (2.4.0)\n","Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (3.17.3)\n","Requirement already satisfied: flatbuffers~=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (1.12)\n","Requirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (0.37.1)\n","Requirement already satisfied: astunparse~=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (1.6.3)\n","Requirement already satisfied: numpy~=1.19.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (1.19.3)\n","Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (0.3.3)\n","Requirement already satisfied: opt-einsum~=3.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (3.3.0)\n","Requirement already satisfied: absl-py~=0.10 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (0.12.0)\n","Requirement already satisfied: wrapt~=1.12.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (1.12.1)\n","Requirement already satisfied: six~=1.15.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (1.15.0)\n","Requirement already satisfied: typing-extensions~=3.7.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (3.7.4.3)\n","Requirement already satisfied: grpcio~=1.32.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (1.32.0)\n","Requirement already satisfied: termcolor~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (1.1.0)\n","Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (1.1.2)\n","Requirement already satisfied: google-pasta~=0.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (0.2.0)\n","Requirement already satisfied: tensorboard~=2.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (2.7.0)\n","Requirement already satisfied: tensorflow-estimator<2.5.0,>=2.4.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (2.4.0)\n","Requirement already satisfied: h5py~=2.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (2.10.0)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.0) (2.23.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.0) (0.4.6)\n","Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.0) (1.35.0)\n","Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.0) (1.8.1)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.0) (1.0.1)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.0) (3.3.6)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.0) (57.4.0)\n","Requirement already satisfied: 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already satisfied: keras-resnet==0.2.0 in /usr/local/lib/python3.7/dist-packages (0.2.0)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from keras==2.4.3) (3.13)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from h5py==2.10.0) (1.15.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.2) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.2) (0.11.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.2) (3.0.6)\n","Requirement already satisfied: certifi>=2020.06.20 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.2) (2021.10.8)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.2) (2.8.2)\n","Requirement already satisfied: imageai in /usr/local/lib/python3.7/dist-packages (2.1.6)\n","Requirement already satisfied: pillow==7.0.0 in /usr/local/lib/python3.7/dist-packages (from imageai) (7.0.0)\n","Requirement already satisfied: h5py==2.10.0 in /usr/local/lib/python3.7/dist-packages (from imageai) (2.10.0)\n","Requirement already satisfied: numpy==1.19.3 in /usr/local/lib/python3.7/dist-packages (from imageai) (1.19.3)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imageai) (4.1.2.30)\n","Requirement already satisfied: keras-resnet==0.2.0 in /usr/local/lib/python3.7/dist-packages (from imageai) (0.2.0)\n","Requirement already satisfied: keras==2.4.3 in /usr/local/lib/python3.7/dist-packages (from imageai) (2.4.3)\n","Requirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.7/dist-packages (from imageai) (1.4.1)\n","Requirement already satisfied: matplotlib==3.3.2 in /usr/local/lib/python3.7/dist-packages (from imageai) (3.3.2)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from h5py==2.10.0->imageai) (1.15.0)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from keras==2.4.3->imageai) (3.13)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.2->imageai) (2.8.2)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.2->imageai) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.2->imageai) (0.11.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.2->imageai) (3.0.6)\n","Requirement already satisfied: certifi>=2020.06.20 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.2->imageai) (2021.10.8)\n"]}]},{"cell_type":"code","source":["#Definisco qui il modello di Object Detection\n","from imageai.Detection import ObjectDetection\n","import os\n","\n","#path_drive_COCO = \"/content/drive/MyDrive/resnet50_coco_best_v2.1.0.h5\"\n","#path_drive_YOLO_tiny = \"/content/drive/MyDrive/yolo-tiny.h5\"\n","path_drive_YOLO = \"/content/drive/MyDrive/yolo.h5\"\n","\n","#execution_path = os.getcwd()\n","\n","detector = ObjectDetection()\n","detector.setModelTypeAsYOLOv3() #Risulta essere veloce e mediamente accurato\n","#detector.setModelTypeAsRetinaNet()\n","#detector.setModelTypeAsTinyYOLOv3()\n","detector.setModelPath(path_drive_YOLO)\n","detector.loadModel()\n"],"metadata":{"id":"Z9saV3-0xJfD","executionInfo":{"status":"ok","timestamp":1642598891267,"user_tz":-60,"elapsed":8293,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["#Prova per vedere i risultati di probabilità\n","#path_img = path_base + '/' +train['img'][50]\n","#print(path_img)\n","#tags = []\n","#detections = detector.detectObjectsFromImage(input_image=path_img, output_image_path=\"/content/output/imgnew1.jpg\")\n","\n","#for eachObject in detections:\n","#    print(eachObject[\"name\"] , \" : \" , eachObject[\"percentage_probability\"] )\n","#    tags.append(eachObject[\"name\"])"],"metadata":{"id":"xpBF1C0jzs4g","executionInfo":{"status":"ok","timestamp":1642598891268,"user_tz":-60,"elapsed":16,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["#Definisco la funzione di Object Recognition\n","def ObjectRecognizer(path_img):\n","  \n","  detections = detector.detectObjectsFromImage(input_image=path_img, output_image_path=\"/content/output/imgnew1.jpg\")\n","  tags = []\n","\n","  for eachObject in detections:\n","    tags.append(eachObject[\"name\"])\n","\n","  return tags #Ritorno i tags con gli elementi trovati"],"metadata":{"id":"rkc8yMfMB_F1","executionInfo":{"status":"ok","timestamp":1642598891268,"user_tz":-60,"elapsed":14,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":["# Generazione Dataframe"],"metadata":{"id":"m6gS301YB1T1"}},{"cell_type":"code","source":["path_of_images = path_base+\"/img\"\n","rows = []\n","n_elem_train = 100\n","\n","#Versione tagliata del train per motivi di tempo\n","train_cut = train.head(n_elem_train)\n","\n","for i in range(len(train_cut)):\n","  print(\"Elementi rimanenti: \", (len(train_cut)-i), end='\\r')\n","  #Creo i tags visivi\n","  tags = ObjectRecognizer(path_base+'/'+train_cut['img'][i])\n","  #Cerco i tags nella frase\n","  ner = NameEntityRec(train_cut['text'][i])\n","\n","  #Creo la matrice\n","  rows.append([train_cut['img'][i], tags, ner, train_cut['text'][i]])\n","\n","#Genero dalla matrice il DataFrame\n","informazioni_ricevute = pd.DataFrame(rows,columns=['img', 'tags', 'ner', 'testo'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PI3AQlgEB1H8","executionInfo":{"status":"ok","timestamp":1642601829208,"user_tz":-60,"elapsed":593907,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"c174beaa-7c20-4f32-92e2-4295ce661797"},"execution_count":43,"outputs":[{"output_type":"stream","name":"stdout","text":[""]}]},{"cell_type":"code","source":["#Visualizzo i risultati del dataframe\n","with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also\n","    print(informazioni_ricevute.head(11))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lnlUpsCBuXq2","executionInfo":{"status":"ok","timestamp":1642594155258,"user_tz":-60,"elapsed":387,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"21defe20-0e87-4732-fdf6-c2de1faf5a62"},"execution_count":149,"outputs":[{"output_type":"stream","name":"stdout","text":["              img                                      tags  \\\n","0   img/42953.png                          [person, person]   \n","1   img/23058.png                     [person, person, tie]   \n","2   img/13894.png                                [cat, cat]   \n","3   img/37408.png                                [dog, dog]   \n","4   img/82403.png                             [person, tie]   \n","5   img/16952.png  [person, person, person, person, person]   \n","6   img/76932.png                                [dog, dog]   \n","7   img/70914.png                                  [person]   \n","8   img/02973.png                                     [dog]   \n","9   img/58306.png                  [person, person, person]   \n","10  img/79351.png                                  [person]   \n","\n","                                                  ner  \\\n","0                                  [character, color]   \n","1                                                [ex]   \n","2                                         [bows, pet]   \n","3                              [squirrels, squirrels]   \n","4                  [chocolate, chip, cookies, hitler]   \n","5                         [go, sports, thing, points]   \n","6                                                  []   \n","7                  [tattoos, health, people, tattoos]   \n","8                                             [chain]   \n","9   [tax, returns, hillary, emails, bleach, bit, d...   \n","10                                         [jew, mad]   \n","\n","                                                testo  \n","0    its their character not their color that matters  \n","1   don't be afraid to love again everyone is not ...  \n","2                            putting bows on your pet  \n","3   i love everything and everybody! except for sq...  \n","4   everybody loves chocolate chip cookies, even h...  \n","5            go sports! do the thing! win the points!  \n","6      fine you're right. now can we fucking drop it?  \n","7   tattoos are bad for your health i know 5 milli...  \n","8         how long can i run? till the chain tightens  \n","9   what is he hiding? we need to see his tax retu...  \n","10                           jew mad? get fuhrerious!  \n"]}]},{"cell_type":"code","source":["#Visualizzo il meme\n","VisualizeImage('img/13894.png')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":378},"id":"glPt50Wcv5N2","executionInfo":{"status":"ok","timestamp":1642597164238,"user_tz":-60,"elapsed":916,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"4266a1dc-ddf5-40d9-edf6-dbdb31c32d30"},"execution_count":159,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Wikidata"],"metadata":{"id":"pFQuZxvx3jeg"}},{"cell_type":"code","source":["#Importiamo le dipendenze\n","import requests\n","import json"],"metadata":{"id":"Hrom9Xrx6aGA","executionInfo":{"status":"ok","timestamp":1642601863751,"user_tz":-60,"elapsed":403,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":44,"outputs":[]},{"cell_type":"code","source":["#Definiamo l'indirizzo base\n","API_WIKI = \"https://wikidata.org/w/api.php\"\n","n_risultati = 1\n","\n","def SearchOnWiki(query):\n","  params ={\n","      'action': 'wbsearchentities',\n","      'format': 'json',\n","      'language': 'en',\n","      'limit' : n_risultati,\n","      'search': query\n","  }\n","\n","  r = requests.get(API_WIKI, params=params)\n","\n","  return r.json()"],"metadata":{"id":"is_v7T9i5lAS","executionInfo":{"status":"ok","timestamp":1642601864239,"user_tz":-60,"elapsed":4,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":45,"outputs":[]},{"cell_type":"code","source":["#Rimuove i duplicati dalla lista\n","def remove_duplicates(lista):\n","  return list(dict.fromkeys(lista))"],"metadata":{"id":"hHrys6Fa-fBc","executionInfo":{"status":"ok","timestamp":1642601864239,"user_tz":-60,"elapsed":4,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":46,"outputs":[]},{"cell_type":"code","source":["#Dato un vettore di tag ritorna un DF con label e descrizione\n","#Viene presa solo la prima descrizione dal WikiData \n","#Nel caso si vogliano ritornare più descrizioni si deve modificare n_risultati\n","def InformationRetrival(vettore_informazioni):\n","  #Rimuovo i duplicati\n","  vettore_informazioni = remove_duplicates(vettore_informazioni)\n","  #Nuova lista di conoscenza\n","  informazioni_da_wiki = []\n","\n","  #Devo gestire il caso in cui il vettore di informazioni\n","  #abbia più elementi all'interno\n","  if len(vettore_informazioni) > 1:\n","    for elem in vettore_informazioni:\n","      json_file = SearchOnWiki(elem)\n","      informazioni_da_wiki.append([json_file['search'][0]['label'], json_file['search'][0]['description']])\n","  #Se il vettore ha un solo elemento\n","  else:\n","    json_file = SearchOnWiki(vettore_informazioni)\n","    informazioni_da_wiki.append([json_file['search'][0]['label'], json_file['search'][0]['description']])\n","\n","  #Genero un pandas dataframe\n","  informazioni_da_wiki_pd = pd.DataFrame(informazioni_da_wiki,columns=['label', 'description'])\n","\n","  return informazioni_da_wiki_pd\n"],"metadata":{"id":"R2idHjWgKNX7","executionInfo":{"status":"ok","timestamp":1642601864240,"user_tz":-60,"elapsed":5,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":47,"outputs":[]},{"cell_type":"markdown","source":["# Estrazione Conoscenza"],"metadata":{"id":"zN73WeadNetg"}},{"cell_type":"code","source":["#Prendiamo ora l'elemento 4 dalla tabella \"informazioni_ricevute\"\n","#che viene generato nella fase di \"Generazione Dataframe\"\n","#ed estraiamo conoscenza per TAG e NER\n","POSIZIONE=10\n","#TAG\n","print(\"Questa è conoscenza ricavata dai tag dell'immagine\")\n","print(InformationRetrival(informazioni_ricevute['tags'][POSIZIONE]))\n","print(\"\\n\")\n","print(\"Questa è conoscenza ricavata dal ner del testo\")\n","print(InformationRetrival(informazioni_ricevute['ner'][POSIZIONE]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pmw3yroWLe58","executionInfo":{"status":"ok","timestamp":1642594130822,"user_tz":-60,"elapsed":1177,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"66cbd9d2-f6c4-46d3-d50f-fa9161035432"},"execution_count":147,"outputs":[{"output_type":"stream","name":"stdout","text":["Questa è conoscenza ricavata dai tag dell'immagine\n","   label                                        description\n","0  human  common name of Homo sapiens, unique extant spe...\n","\n","\n","Questa è conoscenza ricavata dal ner del testo\n","           label                                        description\n","0  Jewish people  ancient nation and ethnoreligious group from t...\n","1     Madagascar  island sovereign state off the coast of Southe...\n"]}]},{"cell_type":"markdown","source":["# Face Analyzer"],"metadata":{"id":"swSaaoA3YgJT"}},{"cell_type":"code","source":["!pip install deepface\n","from deepface import DeepFace"],"metadata":{"id":"WpeV5pxtYfgn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def RilevazioneVolto(path):\n","  \n","  path_deepface = '/content/hateful_memes/' + path\n","  try:\n","    obj = DeepFace.analyze(img_path = path_deepface, actions = ['gender', 'race', 'emotion'], prog_bar=False)\n","  except:\n","    obj = {\n","        \"gender\" : None,\n","        \"dominant_race\"   : None,\n","        \"dominant_emotion\": None\n","    }\n","\n","  return obj"],"metadata":{"id":"z94a5Hg5ccyr","executionInfo":{"status":"ok","timestamp":1642599667332,"user_tz":-60,"elapsed":393,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":["#Genero informazioni a partire dal volto (detection della race)\n","informazioni_volto = []\n","for i in range(len(informazioni_ricevute)):\n","  path = informazioni_ricevute['img'][i]\n","  rilevazione = RilevazioneVolto(path)\n","  informazioni_volto.append([path, rilevazione['gender'],rilevazione['dominant_race'],rilevazione['dominant_emotion']])\n","informazioni_volto_pd = pd.DataFrame(informazioni_volto, columns=['img', 'gender', 'race', 'emotion'])"],"metadata":{"id":"pzP6r9mjZEIK","executionInfo":{"status":"ok","timestamp":1642601968782,"user_tz":-60,"elapsed":82042,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":48,"outputs":[]},{"cell_type":"code","source":["print(informazioni_volto_pd.head(50))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3cwTvj2UjWOP","executionInfo":{"status":"ok","timestamp":1642602003324,"user_tz":-60,"elapsed":415,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"611a71ec-8154-44da-b4d4-c592008bf480"},"execution_count":49,"outputs":[{"output_type":"stream","name":"stdout","text":["              img gender             race   emotion\n","0   img/42953.png   None             None      None\n","1   img/23058.png   None             None      None\n","2   img/13894.png   None             None      None\n","3   img/37408.png    Man            white     happy\n","4   img/82403.png   None             None      None\n","5   img/16952.png    Man            black   neutral\n","6   img/76932.png   None             None      None\n","7   img/70914.png   None             None      None\n","8   img/02973.png   None             None      None\n","9   img/58306.png  Woman            white     angry\n","10  img/79351.png    Man            white     angry\n","11  img/34096.png   None             None      None\n","12  img/25489.png    Man            white      fear\n","13  img/19324.png   None             None      None\n","14  img/79346.png  Woman            white     happy\n","15  img/13426.png  Woman            white     happy\n","16  img/97628.png    Man            black  surprise\n","17  img/14238.png    Man   middle eastern       sad\n","18  img/28936.png   None             None      None\n","19  img/59784.png   None             None      None\n","20  img/02146.png   None             None      None\n","21  img/70146.png   None             None      None\n","22  img/28061.png   None             None      None\n","23  img/97180.png   None             None      None\n","24  img/49752.png    Man            white     happy\n","25  img/25610.png   None             None      None\n","26  img/15872.png  Woman            white      fear\n","27  img/72640.png    Man  latino hispanic       sad\n","28  img/64318.png  Woman            asian   neutral\n","29  img/17956.png   None             None      None\n","30  img/93547.png   None             None      None\n","31  img/10743.png   None             None      None\n","32  img/37091.png   None             None      None\n","33  img/25719.png   None             None      None\n","34  img/76825.png   None             None      None\n","35  img/72598.png   None             None      None\n","36  img/43078.png    Man            white   neutral\n","37  img/51846.png   None             None      None\n","38  img/01569.png    Man            asian     happy\n","39  img/95812.png    Man   middle eastern   neutral\n","40  img/06418.png   None             None      None\n","41  img/53976.png   None             None      None\n","42  img/70193.png    Man            white     angry\n","43  img/60427.png   None             None      None\n","44  img/78395.png   None             None      None\n","45  img/04876.png   None             None      None\n","46  img/92075.png  Woman  latino hispanic     angry\n","47  img/86354.png   None             None      None\n","48  img/74386.png   None             None      None\n","49  img/59613.png   None             None      None\n"]}]},{"cell_type":"code","source":["#Unisco le informazioni del volto e quelle di ner e tag in un unico PD\n","informazioni_ricevute_e_volto = pd.concat([informazioni_ricevute, informazioni_volto_pd], axis=1, join=\"inner\")\n","informazioni_ricevute_e_volto.head(30)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":990},"id":"KnL5Jzggjk-3","executionInfo":{"status":"ok","timestamp":1642602015630,"user_tz":-60,"elapsed":456,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"8ed95ba8-6978-4a15-8327-ee186b14928e"},"execution_count":50,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-f100dbd5-848d-455f-8cab-d498ba2fa2ec\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>img</th>\n","      <th>tags</th>\n","      <th>ner</th>\n","      <th>testo</th>\n","      <th>img</th>\n","      <th>gender</th>\n","      <th>race</th>\n","      <th>emotion</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>img/42953.png</td>\n","      <td>[person, person]</td>\n","      <td>[character, color]</td>\n","      <td>its their character not their color that matters</td>\n","      <td>img/42953.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>img/23058.png</td>\n","      <td>[person, person, tie]</td>\n","      <td>[ex]</td>\n","      <td>don't be afraid to love again everyone is not ...</td>\n","      <td>img/23058.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>img/13894.png</td>\n","      <td>[cat, cat]</td>\n","      <td>[bows, pet]</td>\n","      <td>putting bows on your pet</td>\n","      <td>img/13894.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>img/37408.png</td>\n","      <td>[dog, dog]</td>\n","      <td>[squirrels, squirrels]</td>\n","      <td>i love everything and everybody! except for sq...</td>\n","      <td>img/37408.png</td>\n","      <td>Man</td>\n","      <td>white</td>\n","      <td>happy</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>img/82403.png</td>\n","      <td>[person, tie]</td>\n","      <td>[chocolate, chip, cookies, hitler]</td>\n","      <td>everybody loves chocolate chip cookies, even h...</td>\n","      <td>img/82403.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>img/16952.png</td>\n","      <td>[person, person, person, person, person]</td>\n","      <td>[go, sports, thing, points]</td>\n","      <td>go sports! do the thing! win the points!</td>\n","      <td>img/16952.png</td>\n","      <td>Man</td>\n","      <td>black</td>\n","      <td>neutral</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>img/76932.png</td>\n","      <td>[dog, dog]</td>\n","      <td>[]</td>\n","      <td>fine you're right. now can we fucking drop it?</td>\n","      <td>img/76932.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>img/70914.png</td>\n","      <td>[person]</td>\n","      <td>[tattoos, health, people, tattoos]</td>\n","      <td>tattoos are bad for your health i know 5 milli...</td>\n","      <td>img/70914.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>img/02973.png</td>\n","      <td>[dog]</td>\n","      <td>[chain]</td>\n","      <td>how long can i run? till the chain tightens</td>\n","      <td>img/02973.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>img/58306.png</td>\n","      <td>[person, person, person]</td>\n","      <td>[tax, returns, hillary, emails, bleach, bit, d...</td>\n","      <td>what is he hiding? we need to see his tax retu...</td>\n","      <td>img/58306.png</td>\n","      <td>Woman</td>\n","      <td>white</td>\n","      <td>angry</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>img/79351.png</td>\n","      <td>[person]</td>\n","      <td>[jew, mad]</td>\n","      <td>jew mad? get fuhrerious!</td>\n","      <td>img/79351.png</td>\n","      <td>Man</td>\n","      <td>white</td>\n","      <td>angry</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>img/34096.png</td>\n","      <td>[remote]</td>\n","      <td>[polish, chemicals, eye]</td>\n","      <td>removes polish with chemicals, nobody bats an eye</td>\n","      <td>img/34096.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>img/25489.png</td>\n","      <td>[person]</td>\n","      <td>[brother, day, blast, day]</td>\n","      <td>brother... a day without a blast is a day wasted</td>\n","      <td>img/25489.png</td>\n","      <td>Man</td>\n","      <td>white</td>\n","      <td>fear</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>img/19324.png</td>\n","      <td>[person, couch, person, person, person, book, ...</td>\n","      <td>[dog, dog, shelter, family]</td>\n","      <td>when you're the only dog left at the dog shelt...</td>\n","      <td>img/19324.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>img/79346.png</td>\n","      <td>[person, person]</td>\n","      <td>[hillary, casey, anthony, kids]</td>\n","      <td>voting for hillary is like letting casey antho...</td>\n","      <td>img/79346.png</td>\n","      <td>Woman</td>\n","      <td>white</td>\n","      <td>happy</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>img/13426.png</td>\n","      <td>[person, person, person, person, person]</td>\n","      <td>[]</td>\n","      <td>they will soon be free</td>\n","      <td>img/13426.png</td>\n","      <td>Woman</td>\n","      <td>white</td>\n","      <td>happy</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>img/97628.png</td>\n","      <td>[couch, person, person]</td>\n","      <td>[son, daughter, man, daddy]</td>\n","      <td>is it ok for your son or daughter to call anot...</td>\n","      <td>img/97628.png</td>\n","      <td>Man</td>\n","      <td>black</td>\n","      <td>surprise</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>img/14238.png</td>\n","      <td>[person]</td>\n","      <td>[religions, religions, peace]</td>\n","      <td>most religions are religions of peace</td>\n","      <td>img/14238.png</td>\n","      <td>Man</td>\n","      <td>middle eastern</td>\n","      <td>sad</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>img/28936.png</td>\n","      <td>[person, couch]</td>\n","      <td>[opening, role, lady]</td>\n","      <td>when you find out there's an opening for the r...</td>\n","      <td>img/28936.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>img/59784.png</td>\n","      <td>[person]</td>\n","      <td>[people, truth, problem]</td>\n","      <td>if people reject me because i stand on the tru...</td>\n","      <td>img/59784.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>img/02146.png</td>\n","      <td>[bird, bird, bird, bird]</td>\n","      <td>[bottom, heart]</td>\n","      <td>from the bottom of my heart i would like to si...</td>\n","      <td>img/02146.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>img/70146.png</td>\n","      <td>[person, dog]</td>\n","      <td>[lets, memorial, americans, alien, miles, feet...</td>\n","      <td>lets erect a memorial to all the americans mur...</td>\n","      <td>img/70146.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>img/28061.png</td>\n","      <td>[person, person]</td>\n","      <td>[]</td>\n","      <td>just because they do this dosen't mean they'll...</td>\n","      <td>img/28061.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>img/97180.png</td>\n","      <td>[person, person, person]</td>\n","      <td>[race]</td>\n","      <td>if there is no race it can't be homicide</td>\n","      <td>img/97180.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>img/49752.png</td>\n","      <td>[person, couch, cup, tie]</td>\n","      <td>[friends, politics, religion]</td>\n","      <td>here's to all my friends who never post about ...</td>\n","      <td>img/49752.png</td>\n","      <td>Man</td>\n","      <td>white</td>\n","      <td>happy</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>img/25610.png</td>\n","      <td>[person]</td>\n","      <td>[day, beach]</td>\n","      <td>enjoying a day at the beach</td>\n","      <td>img/25610.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>img/15872.png</td>\n","      <td>[person]</td>\n","      <td>[day, suicide, bombinb, wife]</td>\n","      <td>when you come home from a long day of suicide ...</td>\n","      <td>img/15872.png</td>\n","      <td>Woman</td>\n","      <td>white</td>\n","      <td>fear</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>img/72640.png</td>\n","      <td>[person]</td>\n","      <td>[muslims, votes, justin, trudeau, skill, justi...</td>\n","      <td>is bribing muslims for liberal votes justin tr...</td>\n","      <td>img/72640.png</td>\n","      <td>Man</td>\n","      <td>latino hispanic</td>\n","      <td>sad</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>img/64318.png</td>\n","      <td>[person, cake]</td>\n","      <td>[candle]</td>\n","      <td>when they take too long to blow out the candle</td>\n","      <td>img/64318.png</td>\n","      <td>Woman</td>\n","      <td>asian</td>\n","      <td>neutral</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>img/17956.png</td>\n","      <td>[person, tie]</td>\n","      <td>[question, man, nation, eyes, mitch]</td>\n","      <td>one simple question why are we letting one man...</td>\n","      <td>img/17956.png</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f100dbd5-848d-455f-8cab-d498ba2fa2ec')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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None\n","9   img/58306.png  ...     angry\n","10  img/79351.png  ...     angry\n","11  img/34096.png  ...      None\n","12  img/25489.png  ...      fear\n","13  img/19324.png  ...      None\n","14  img/79346.png  ...     happy\n","15  img/13426.png  ...     happy\n","16  img/97628.png  ...  surprise\n","17  img/14238.png  ...       sad\n","18  img/28936.png  ...      None\n","19  img/59784.png  ...      None\n","20  img/02146.png  ...      None\n","21  img/70146.png  ...      None\n","22  img/28061.png  ...      None\n","23  img/97180.png  ...      None\n","24  img/49752.png  ...     happy\n","25  img/25610.png  ...      None\n","26  img/15872.png  ...      fear\n","27  img/72640.png  ...       sad\n","28  img/64318.png  ...   neutral\n","29  img/17956.png  ...      None\n","\n","[30 rows x 8 columns]"]},"metadata":{},"execution_count":50}]},{"cell_type":"code","source":[""],"metadata":{"id":"okOfhdono4az"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Detecting Passive-Aggressive in text (PROVA)"],"metadata":{"id":"vNwR6t_E9luJ"}},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import itertools\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.linear_model import PassiveAggressiveClassifier\n","from sklearn.metrics import accuracy_score, confusion_matrix"],"metadata":{"id":"hJgWCOi-9k0a","executionInfo":{"status":"ok","timestamp":1642505668883,"user_tz":-60,"elapsed":654,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["metadata_pd = pd.concat([train, val])\n","metadata_pd = pd.concat([metadata_pd, test])"],"metadata":{"id":"z8avVVvOBo7E"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["labels=metadata_pd.label\n","x_train,x_test,y_train,y_test=train_test_split(metadata_pd['text'], labels, test_size=0.2, random_state=7)"],"metadata":{"id":"kUxc-cJO-5Hs","executionInfo":{"status":"ok","timestamp":1642506560343,"user_tz":-60,"elapsed":230,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":47,"outputs":[]},{"cell_type":"code","source":["#DataFlair - Initialize a TfidfVectorizer\n","tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)\n","#DataFlair - Fit and transform train set, transform test set\n","tfidf_train=tfidf_vectorizer.fit_transform(x_train) \n","tfidf_test=tfidf_vectorizer.transform(x_test)"],"metadata":{"id":"qeeBV9IK90ze","executionInfo":{"status":"ok","timestamp":1642506562536,"user_tz":-60,"elapsed":252,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":48,"outputs":[]},{"cell_type":"code","source":["#DataFlair - Initialize a PassiveAggressiveClassifier\n","pac=PassiveAggressiveClassifier(max_iter=50)\n","pac.fit(tfidf_train,y_train)\n","#DataFlair - Predict on the test set and calculate accuracy\n","y_pred=pac.predict(tfidf_test)\n","score=accuracy_score(y_test,y_pred)\n","print(f'Accuracy: {round(score*100,2)}%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ppjmLrSo-Ah8","executionInfo":{"status":"ok","timestamp":1642506574009,"user_tz":-60,"elapsed":248,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"7f3b8787-0852-417d-d2f4-3c57ac474731"},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 59.5%\n"]}]},{"cell_type":"code","source":["#DataFlair - Build confusion matrix\n","confusion_matrix(y_test,y_pred, labels=[0,1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_FZDNHog-biU","executionInfo":{"status":"ok","timestamp":1642506576874,"user_tz":-60,"elapsed":348,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"1a4035c8-172d-4f10-99dc-0aa298625f26"},"execution_count":52,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[829, 395],\n","       [415, 361]])"]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":[""],"metadata":{"id":"HmXGIvmU_cVH"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":[""],"metadata":{"id":"yoL-Hceih0jz"}},{"cell_type":"markdown","source":["# Image Captioning (OSCAR?)"],"metadata":{"id":"sshIvyxWh139"}},{"cell_type":"code","source":["!git clone https://github.com/microsoft/Oscar.git"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KCTMQbKVh66z","executionInfo":{"status":"ok","timestamp":1642582266009,"user_tz":-60,"elapsed":1232,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"e6a7c77d-cdcf-4789-ebb3-898b73f49201"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'Oscar'...\n","remote: Enumerating objects: 131, done.\u001b[K\n","remote: Counting objects: 100% (73/73), done.\u001b[K\n","remote: Compressing objects: 100% (39/39), done.\u001b[K\n","remote: Total 131 (delta 44), reused 34 (delta 34), pack-reused 58\u001b[K\n","Receiving objects: 100% (131/131), 726.16 KiB | 8.07 MiB/s, done.\n","Resolving deltas: 100% (57/57), done.\n"]}]},{"cell_type":"code","source":["%cd Oscar"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MpWNVV60iGii","executionInfo":{"status":"ok","timestamp":1642582280719,"user_tz":-60,"elapsed":375,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"b12932a0-8b06-439e-cdf3-fb2bcd96ffe3"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/Oscar\n"]}]},{"cell_type":"code","source":["!python setup.py build develop"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FJ7pHIfAiIOy","executionInfo":{"status":"ok","timestamp":1642582382734,"user_tz":-60,"elapsed":1101,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"0a2a13c5-6daa-4ff4-deca-e8c6d43fc852"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["running install\n","running bdist_egg\n","running egg_info\n","creating oscar.egg-info\n","writing oscar.egg-info/PKG-INFO\n","writing dependency_links to oscar.egg-info/dependency_links.txt\n","writing top-level names to oscar.egg-info/top_level.txt\n","writing manifest file 'oscar.egg-info/SOURCES.txt'\n","adding license file 'LICENSE'\n","writing manifest file 'oscar.egg-info/SOURCES.txt'\n","installing library code to build/bdist.linux-x86_64/egg\n","running install_lib\n","running build_py\n","creating build\n","creating build/lib\n","creating build/lib/oscar\n","copying oscar/run_oscarplus_pretrain.py -> build/lib/oscar\n","copying oscar/run_nlvr.py -> build/lib/oscar\n","copying oscar/run_captioning.py -> build/lib/oscar\n","copying oscar/run_vqa.py -> build/lib/oscar\n","copying oscar/__init__.py -> build/lib/oscar\n","copying oscar/run_retrieval.py -> build/lib/oscar\n","copying oscar/run_gqa.py -> build/lib/oscar\n","creating build/lib/oscar/utils\n","copying oscar/utils/cbs.py -> build/lib/oscar/utils\n","copying oscar/utils/tsv_file.py -> build/lib/oscar/utils\n","copying oscar/utils/caption_evaluate.py -> build/lib/oscar/utils\n","copying oscar/utils/logger.py -> build/lib/oscar/utils\n","copying oscar/utils/__init__.py -> build/lib/oscar/utils\n","copying oscar/utils/misc.py -> build/lib/oscar/utils\n","copying oscar/utils/metric_logger.py -> build/lib/oscar/utils\n","copying oscar/utils/tsv_file_ops.py -> build/lib/oscar/utils\n","copying oscar/utils/task_utils.py -> build/lib/oscar/utils\n","creating build/lib/oscar/modeling\n","copying oscar/modeling/modeling_bert.py -> build/lib/oscar/modeling\n","copying oscar/modeling/__init__.py -> build/lib/oscar/modeling\n","copying oscar/modeling/modeling_utils.py -> build/lib/oscar/modeling\n","creating build/lib/oscar/datasets\n","copying oscar/datasets/__init__.py -> build/lib/oscar/datasets\n","copying oscar/datasets/build.py -> build/lib/oscar/datasets\n","copying oscar/datasets/oscar_tsv.py -> build/lib/oscar/datasets\n","creating build/bdist.linux-x86_64\n","creating build/bdist.linux-x86_64/egg\n","creating build/bdist.linux-x86_64/egg/oscar\n","creating build/bdist.linux-x86_64/egg/oscar/utils\n","copying build/lib/oscar/utils/cbs.py -> build/bdist.linux-x86_64/egg/oscar/utils\n","copying build/lib/oscar/utils/tsv_file.py -> build/bdist.linux-x86_64/egg/oscar/utils\n","copying build/lib/oscar/utils/caption_evaluate.py -> build/bdist.linux-x86_64/egg/oscar/utils\n","copying build/lib/oscar/utils/logger.py -> build/bdist.linux-x86_64/egg/oscar/utils\n","copying build/lib/oscar/utils/__init__.py -> build/bdist.linux-x86_64/egg/oscar/utils\n","copying build/lib/oscar/utils/misc.py -> build/bdist.linux-x86_64/egg/oscar/utils\n","copying build/lib/oscar/utils/metric_logger.py -> build/bdist.linux-x86_64/egg/oscar/utils\n","copying build/lib/oscar/utils/tsv_file_ops.py -> build/bdist.linux-x86_64/egg/oscar/utils\n","copying build/lib/oscar/utils/task_utils.py -> build/bdist.linux-x86_64/egg/oscar/utils\n","copying build/lib/oscar/run_oscarplus_pretrain.py -> build/bdist.linux-x86_64/egg/oscar\n","copying build/lib/oscar/run_nlvr.py -> build/bdist.linux-x86_64/egg/oscar\n","copying build/lib/oscar/run_captioning.py -> build/bdist.linux-x86_64/egg/oscar\n","copying build/lib/oscar/run_vqa.py -> build/bdist.linux-x86_64/egg/oscar\n","copying build/lib/oscar/__init__.py -> build/bdist.linux-x86_64/egg/oscar\n","copying build/lib/oscar/run_retrieval.py -> build/bdist.linux-x86_64/egg/oscar\n","creating build/bdist.linux-x86_64/egg/oscar/modeling\n","copying build/lib/oscar/modeling/modeling_bert.py -> build/bdist.linux-x86_64/egg/oscar/modeling\n","copying build/lib/oscar/modeling/__init__.py -> build/bdist.linux-x86_64/egg/oscar/modeling\n","copying build/lib/oscar/modeling/modeling_utils.py -> build/bdist.linux-x86_64/egg/oscar/modeling\n","copying build/lib/oscar/run_gqa.py -> build/bdist.linux-x86_64/egg/oscar\n","creating build/bdist.linux-x86_64/egg/oscar/datasets\n","copying build/lib/oscar/datasets/__init__.py -> build/bdist.linux-x86_64/egg/oscar/datasets\n","copying build/lib/oscar/datasets/build.py -> build/bdist.linux-x86_64/egg/oscar/datasets\n","copying build/lib/oscar/datasets/oscar_tsv.py -> build/bdist.linux-x86_64/egg/oscar/datasets\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/utils/cbs.py to cbs.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/utils/tsv_file.py to tsv_file.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/utils/caption_evaluate.py to caption_evaluate.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/utils/logger.py to logger.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/utils/__init__.py to __init__.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/utils/misc.py to misc.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/utils/metric_logger.py to metric_logger.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/utils/tsv_file_ops.py to tsv_file_ops.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/utils/task_utils.py to task_utils.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/run_oscarplus_pretrain.py to run_oscarplus_pretrain.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/run_nlvr.py to run_nlvr.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/run_captioning.py to run_captioning.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/run_vqa.py to run_vqa.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/__init__.py to __init__.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/run_retrieval.py to run_retrieval.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/modeling/modeling_bert.py to modeling_bert.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/modeling/__init__.py to __init__.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/modeling/modeling_utils.py to modeling_utils.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/run_gqa.py to run_gqa.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/datasets/__init__.py to __init__.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/datasets/build.py to build.cpython-37.pyc\n","byte-compiling build/bdist.linux-x86_64/egg/oscar/datasets/oscar_tsv.py to oscar_tsv.cpython-37.pyc\n","creating build/bdist.linux-x86_64/egg/EGG-INFO\n","copying oscar.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n","copying oscar.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n","copying oscar.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n","copying oscar.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n","zip_safe flag not set; analyzing archive contents...\n","creating dist\n","creating 'dist/oscar-0.1.0-py3.7.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n","removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n","Processing oscar-0.1.0-py3.7.egg\n","Copying oscar-0.1.0-py3.7.egg to /usr/local/lib/python3.7/dist-packages\n","Adding oscar 0.1.0 to easy-install.pth file\n","\n","Installed /usr/local/lib/python3.7/dist-packages/oscar-0.1.0-py3.7.egg\n","Processing 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Import del dataset"],"metadata":{"id":"A5-fgPXsyewt"}},{"cell_type":"code","execution_count":14,"metadata":{"id":"AvIBo58qMPsQ","executionInfo":{"status":"ok","timestamp":1643210934254,"user_tz":-60,"elapsed":263,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"outputs":[],"source":["#Path file\n","percorso = '/content/drive/MyDrive/hateful_memes.zip'"]},{"cell_type":"code","source":["#Unzip the file\n","import zipfile\n","with zipfile.ZipFile(percorso, 'r') as zip_ref:\n","    zip_ref.extractall('.')"],"metadata":{"id":"rAvOAfxfMn9Y","executionInfo":{"status":"ok","timestamp":1643204280921,"user_tz":-60,"elapsed":176571,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["#import json files\n","import sys\n","import os\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","\n","train = pd.read_json('/content/hateful_memes/train.jsonl',lines=True)\n","val = pd.read_json('/content/hateful_memes/dev_seen.jsonl',lines=True)\n","test = pd.read_json('/content/hateful_memes/test_seen.jsonl',lines=True)"],"metadata":{"id":"KCKDKIa3NWCp","executionInfo":{"status":"ok","timestamp":1643210935982,"user_tz":-60,"elapsed":617,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["#Visualize dataset\n","path_base = '/content/hateful_memes'\n","plt.figure(figsize=(10,6))\n","img = plt.imread(path_base + '/' +train['img'][2])\n","plt.imshow(img)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":395},"id":"jnX8XUTBODIs","executionInfo":{"status":"ok","timestamp":1643210938063,"user_tz":-60,"elapsed":1081,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"6ef70e55-8837-4a72-e7c2-7ca7ef58fa1d"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fa51eebb250>"]},"metadata":{},"execution_count":16},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["#Funzione per visualizzare le immagini dato il path\n","def VisualizeImage(path):\n","  path_base = '/content/hateful_memes'\n","  plt.figure(figsize=(10,6))\n","  img = plt.imread(path_base + '/' +path)\n","  plt.imshow(img)"],"metadata":{"id":"BsBttolBvk0A","executionInfo":{"status":"ok","timestamp":1643210942380,"user_tz":-60,"elapsed":244,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":["# Name Entity Recognition"],"metadata":{"id":"8alL7g6bd_6Y"}},{"cell_type":"code","source":["!pip install spacy==3.0\n","!python -m spacy download en_core_web_trf"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"67Cm0KVSrytv","executionInfo":{"status":"ok","timestamp":1643204357021,"user_tz":-60,"elapsed":47525,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"05101e0f-cd6c-4ef0-f9d0-102a9cfe6f98"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: spacy==3.0 in /usr/local/lib/python3.7/dist-packages (3.0.0)\n","Requirement already satisfied: thinc<8.1.0,>=8.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (8.0.13)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (2.11.3)\n","Requirement already satisfied: catalogue<2.1.0,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (2.0.6)\n","Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (4.62.3)\n","Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (3.0.6)\n","Requirement already satisfied: pydantic<1.8.0,>=1.7.1 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (1.7.4)\n","Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (2.23.0)\n","Requirement already satisfied: typer<0.4.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (0.3.2)\n","Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (3.10.0.2)\n","Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (3.0.8)\n","Requirement already satisfied: srsly<3.0.0,>=2.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.0) (2.4.2)\n","Requirement already satisfied: 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spacy.load('en_core_web_trf')\n"]}]},{"cell_type":"code","source":["import spacy\n","print(spacy.__version__)\n","nlp = spacy.load(\"en_core_web_trf\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PAs5U4nbD_wt","executionInfo":{"status":"ok","timestamp":1643210894927,"user_tz":-60,"elapsed":8567,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"e7df6570-9010-42b0-b33a-eac61126c557"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["3.0.0\n"]}]},{"cell_type":"code","source":["def NameEntityRec(text):\n","  \n","  doc = nlp(text)\n","  words = []\n","\n","  #Niente verbi, wikidata non li riesce a gestire\n","  #Se si trova una soluzione scommentare  \n","\n","  for token in doc:\n","    #if token.pos_ == 'NOUN' or token.pos_ == 'PROPN' or token.pos_ == 'VERB':\n","    if token.pos_ == 'NOUN' or token.pos_ == 'PROPN' or token.pos_== 'ADJ':\n","      #if token.pos_ == 'VERB':\n","      #  words.append(token.lemma_)\n","      #else:\n","      #  words.append(token.text)\n","      words.append(token.text)\n","  \n","  if not words:\n","    return words.append('None')\n","      \n","  return words  "],"metadata":{"id":"qtaNc1mXFoNO","executionInfo":{"status":"ok","timestamp":1643211566503,"user_tz":-60,"elapsed":242,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":38,"outputs":[]},{"cell_type":"code","source":["#Prova per vedere cosa stampa\n","#text = train['text'][4]\n","#text = 'jew mad? get fuhrerious!'\n","#doc = nlp(text)\n","#print(text)\n","\n","#words = []\n","\n","#for token in doc:\n","#  print(token, token.pos_)\n","#  if token.pos_ == 'VERB':\n","#    print(token.lemma_)\n","#  if token.pos_ == 'NOUN' or token.pos_ == 'PROPN':\n","#    words.append(token.text)"],"metadata":{"id":"i9h5TmLymUjn"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Object Detection"],"metadata":{"id":"X3ByzgAFxJxp"}},{"cell_type":"code","source":["#Installo dipendenze per Object Detection\n","!pip install tensorflow==2.4.0\n","!pip install keras==2.4.3 numpy==1.19.3 pillow==7.0.0 scipy==1.4.1 h5py==2.10.0 matplotlib==3.3.2 opencv-python keras-resnet==0.2.0\n","!pip install imageai --upgrade"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"f9Le9rlyymLc","executionInfo":{"status":"ok","timestamp":1643204507803,"user_tz":-60,"elapsed":106133,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"bb0e8017-78a2-4812-b53d-9e8c3df28705"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting tensorflow==2.4.0\n","  Downloading tensorflow-2.4.0-cp37-cp37m-manylinux2010_x86_64.whl (394.7 MB)\n","\u001b[K     |████████████████████████████████| 394.7 MB 17 kB/s \n","\u001b[?25hRequirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (0.37.1)\n","Collecting wrapt~=1.12.1\n","  Downloading wrapt-1.12.1.tar.gz (27 kB)\n","Collecting flatbuffers~=1.12.0\n","  Downloading flatbuffers-1.12-py2.py3-none-any.whl (15 kB)\n","Requirement already satisfied: numpy~=1.19.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (1.19.5)\n","Requirement already satisfied: opt-einsum~=3.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (3.3.0)\n","Requirement already satisfied: absl-py~=0.10 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.0) (0.12.0)\n","Collecting tensorflow-estimator<2.5.0,>=2.4.0rc0\n","  Downloading tensorflow_estimator-2.4.0-py2.py3-none-any.whl (462 kB)\n","\u001b[K     |████████████████████████████████| 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dog,   pizza,   donot,   cake,   chair,   couch,   potted plant,   bed,\n","    dining table,   toilet,   tv,   laptop,   mouse,   remote,   keyboard,   cell phone,   microwave,\n","    oven,   toaster,   sink,   refrigerator,   book,   clock,   vase,   scissors,   teddy bear,   hair dryer,\n","    toothbrush.\n","\n","To detect only some of the objects above, you will need to call the CustomObjects function and set the name of the\n","object(s) yiu want to detect to through. The rest are False by default. In below example, we detected only chose detect only person and dog.\n","\"\"\""],"metadata":{"id":"EsRIZqflJUKZ","colab":{"base_uri":"https://localhost:8080/","height":122},"executionInfo":{"status":"ok","timestamp":1643204507803,"user_tz":-60,"elapsed":11,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"0d9ede84-f9c6-4997-8bce-dfe1f6586848"},"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'\\nThere are 80 possible objects that you can detect with the\\nObjectDetection class, and they are as seen below.\\n\\n    person,   bicycle,   car,   motorcycle,   airplane,\\n    bus,   train,   truck,   boat,   traffic light,   fire hydrant,   stop_sign,\\n    parking meter,   bench,   bird,   cat,   dog,   horse,   sheep,   cow,   elephant,   bear,   zebra,\\n    giraffe,   backpack,   umbrella,   handbag,   tie,   suitcase,   frisbee,   skis,   snowboard,\\n    sports ball,   kite,   baseball bat,   baseball glove,   skateboard,   surfboard,   tennis racket,\\n    bottle,   wine glass,   cup,   fork,   knife,   spoon,   bowl,   banana,   apple,   sandwich,   orange,\\n    broccoli,   carrot,   hot dog,   pizza,   donot,   cake,   chair,   couch,   potted plant,   bed,\\n    dining table,   toilet,   tv,   laptop,   mouse,   remote,   keyboard,   cell phone,   microwave,\\n    oven,   toaster,   sink,   refrigerator,   book,   clock,   vase,   scissors,   teddy bear,   hair dryer,\\n    toothbrush.\\n\\nTo detect only some of the objects above, you will need to call the CustomObjects function and set the name of the\\nobject(s) yiu want to detect to through. The rest are False by default. In below example, we detected only chose detect only person and dog.\\n'"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","source":["#Definisco qui il modello di Object Detection\n","from imageai.Detection import ObjectDetection\n","import os\n","\n","#path_drive_COCO = \"/content/drive/MyDrive/resnet50_coco_best_v2.1.0.h5\"\n","#path_drive_YOLO_tiny = \"/content/drive/MyDrive/yolo-tiny.h5\"\n","path_drive_YOLO = \"/content/drive/MyDrive/yolo.h5\"\n","\n","#execution_path = os.getcwd()\n","\n","detector = ObjectDetection()\n","detector.setModelTypeAsYOLOv3() #Risulta essere veloce e mediamente accurato\n","#detector.setModelTypeAsRetinaNet()\n","#detector.setModelTypeAsTinyYOLOv3()\n","detector.setModelPath(path_drive_YOLO)\n","detector.loadModel()\n"],"metadata":{"id":"Z9saV3-0xJfD","executionInfo":{"status":"ok","timestamp":1643210957189,"user_tz":-60,"elapsed":3590,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["#Prova per vedere i risultati di probabilità\n","#path_img = path_base + '/' +train['img'][50]\n","#print(path_img)\n","#tags = []\n","#detections = detector.detectObjectsFromImage(input_image=path_img, output_image_path=\"/content/output/imgnew1.jpg\")\n","\n","#for eachObject in detections:\n","#  print(eachObject)\n","#    print(eachObject[\"name\"] , \" : \" , eachObject[\"percentage_probability\"] )\n","#    tags.append(eachObject[\"name\"])"],"metadata":{"id":"xpBF1C0jzs4g","executionInfo":{"status":"aborted","timestamp":1643204508645,"user_tz":-60,"elapsed":4,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Definisco la funzione di Object Recognition\n","def ObjectRecognizer(path_img):\n","  \n","  detections = detector.detectObjectsFromImage(input_image=path_img, output_image_path=\"/content/output/imgnew1.jpg\")\n","  tags = []\n","\n","  for eachObject in detections:\n","    tags.append(eachObject[\"name\"])\n","\n","  return tags #Ritorno i tags con gli elementi trovati"],"metadata":{"id":"rkc8yMfMB_F1","executionInfo":{"status":"ok","timestamp":1643210960278,"user_tz":-60,"elapsed":385,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":["## Object Detection BERT"],"metadata":{"id":"demP4Q8W_ox7"}},{"cell_type":"code","source":["#ObjectDetection con BERT pipeline\n","!pip install timm transformers"],"metadata":{"id":"F60v3cbq29d1","executionInfo":{"status":"aborted","timestamp":1643204508646,"user_tz":-60,"elapsed":5,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import pipeline\n","obj_det = pipeline(task='object-detection',model='facebook/detr-resnet-50')\n","#obj_det=pipeline(task='object-detection')"],"metadata":{"id":"WRo3z-3uvRYU","executionInfo":{"status":"aborted","timestamp":1643204508646,"user_tz":-60,"elapsed":5,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["lista_immagini = np.array(train['img'].head(5))"],"metadata":{"id":"JqqaGBjBw07x","executionInfo":{"status":"aborted","timestamp":1643204508647,"user_tz":-60,"elapsed":6,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["lista_immagini = [path_base+'/'+elem for elem in lista_immagini]"],"metadata":{"id":"Lmkm35glxCZ1","executionInfo":{"status":"aborted","timestamp":1643204508647,"user_tz":-60,"elapsed":6,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["lista_immagini"],"metadata":{"id":"zO5wyQbTxb0J","executionInfo":{"status":"aborted","timestamp":1643204508647,"user_tz":-60,"elapsed":6,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dict_obj = obj_det(lista_immagini)"],"metadata":{"id":"xsF_cQBpvkct","executionInfo":{"status":"aborted","timestamp":1643204508648,"user_tz":-60,"elapsed":7,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dict_obj[1]"],"metadata":{"id":"Jw6rqqin0tQe","executionInfo":{"status":"aborted","timestamp":1643204508648,"user_tz":-60,"elapsed":6,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Face Analyzer"],"metadata":{"id":"swSaaoA3YgJT"}},{"cell_type":"code","source":["!pip install deepface\n","from deepface import DeepFace"],"metadata":{"id":"WpeV5pxtYfgn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1643204596103,"user_tz":-60,"elapsed":12796,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"095b97ce-f293-401e-b875-9ae88b11ee28"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting deepface\n","  Downloading deepface-0.0.72-py3-none-any.whl (62 kB)\n","\u001b[?25l\r\u001b[K     |█████▎                          | 10 kB 21.9 MB/s eta 0:00:01\r\u001b[K     |██████████▌                     | 20 kB 25.5 MB/s eta 0:00:01\r\u001b[K     |███████████████▊                | 30 kB 28.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 40 kB 23.5 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▏     | 51 kB 25.4 MB/s eta 0:00:01\r\u001b[K     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\u001b[?25l\u001b[?25hdone\n","  Created wheel for gdown: filename=gdown-4.2.0-py3-none-any.whl size=14273 sha256=c6e5eeb19c2fdee93401748ebdd870c91313082831c2e73613eab0c247ec6e80\n","  Stored in directory: /root/.cache/pip/wheels/8c/17/ff/58721d1fabdb87c21a0529948cf39e2be9af90ddbe4ad65944\n","Successfully built gdown\n","Installing collected packages: gdown, retina-face, mtcnn, deepface\n","  Attempting uninstall: gdown\n","    Found existing installation: gdown 3.6.4\n","    Uninstalling gdown-3.6.4:\n","      Successfully uninstalled gdown-3.6.4\n","Successfully installed deepface-0.0.72 gdown-4.2.0 mtcnn-0.1.1 retina-face-0.0.9\n","Directory  /root /.deepface created\n","Directory  /root /.deepface/weights created\n"]}]},{"cell_type":"code","source":["#Questa funzione rileva gender, race ed emotion in un meme\n","def RilevazioneVolto(path):\n","  \n","  path_deepface = '/content/hateful_memes/' + path\n","\n","  lista_rilevazione = []\n","\n","  try:\n","    obj = DeepFace.analyze(img_path = path_deepface, actions = ['gender', 'race', 'emotion'], prog_bar=False)\n","  except:\n","    obj = {\n","        \"gender\" : None,\n","        \"dominant_race\"   : None,\n","        \"dominant_emotion\": None\n","    }\n","  \n","  lista_rilevazione=[obj['gender'],obj['dominant_race'],obj['dominant_emotion']]\n","\n","  return lista_rilevazione"],"metadata":{"id":"z94a5Hg5ccyr","executionInfo":{"status":"ok","timestamp":1643210969147,"user_tz":-60,"elapsed":240,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":["# Generazione Dataframe"],"metadata":{"id":"m6gS301YB1T1"}},{"cell_type":"code","source":["path_of_images = path_base+\"/img\"\n","rows = []\n","n_elem_train = 20\n","\n","#Versione tagliata del train per motivi di tempo\n","train_cut = train.head(n_elem_train)\n","print('Tempo stimato: ' + str(6*n_elem_train) +'s')\n","\n","for i in range(len(train_cut)):\n","  print(\"Elementi rimanenti: \", (len(train_cut)-i), end='\\r')\n","  #Effettuo Object Detection\n","  tags = ObjectRecognizer(path_base+'/'+train_cut['img'][i])\n","\n","  #Effettuo Face Recognition se c'è una persona\n","  if \"person\" in tags:\n","    face_tags = RilevazioneVolto(train_cut['img'][i])\n","  else:\n","    face_tags = ['None', 'None', 'None']\n","\n","  #Cerco i tags nella frase\n","  ner = NameEntityRec(train_cut['text'][i])\n","\n","  #Creo la matrice\n","  rows.append([train_cut['img'][i], tags, face_tags,ner,train_cut['text'][i]])\n","\n","#Genero dalla matrice il DataFrame\n","informazioni_ricevute = pd.DataFrame(rows,columns=['img', 'obj_tags', 'face_tags','ner', 'testo'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PI3AQlgEB1H8","executionInfo":{"status":"ok","timestamp":1643213106849,"user_tz":-60,"elapsed":121355,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"a3fd1e77-20b5-4eb7-a878-d1def81d3846"},"execution_count":66,"outputs":[{"output_type":"stream","name":"stdout","text":["Tempo stimato: 120s\n","Elementi rimanenti:  20\r"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/autocast_mode.py:141: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n","  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n"]},{"output_type":"stream","name":"stdout","text":[""]}]},{"cell_type":"code","source":["informazioni_ricevute"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":677},"id":"fmQ6UVJKE1MZ","executionInfo":{"status":"ok","timestamp":1643205155308,"user_tz":-60,"elapsed":252,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"441105e3-ef59-47df-8385-f330838f7b87"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-d1d1ddaf-f4ce-46a3-b862-87f3194365e1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>img</th>\n","      <th>obj_tags</th>\n","      <th>face_tags</th>\n","      <th>ner</th>\n","      <th>testo</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>img/42953.png</td>\n","      <td>[person, person]</td>\n","      <td>[None, None, None]</td>\n","      <td>[character, color]</td>\n","      <td>its their character not their color that matters</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>img/23058.png</td>\n","      <td>[person, person, tie]</td>\n","      <td>[None, None, None]</td>\n","      <td>[afraid, ex]</td>\n","      <td>don't be afraid to love again everyone is not ...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>img/13894.png</td>\n","      <td>[cat, cat]</td>\n","      <td>[None, None, None]</td>\n","      <td>[bows, pet]</td>\n","      <td>putting bows on your pet</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>img/37408.png</td>\n","      <td>[dog, dog]</td>\n","      <td>[None, None, None]</td>\n","      <td>[squirrels, squirrels]</td>\n","      <td>i love everything and everybody! except for sq...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>img/82403.png</td>\n","      <td>[person, tie]</td>\n","      <td>[None, None, None]</td>\n","      <td>[chocolate, chip, cookies, hitler]</td>\n","      <td>everybody loves chocolate chip cookies, even h...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>img/16952.png</td>\n","      <td>[person, person, person, person, person]</td>\n","      <td>[Man, black, neutral]</td>\n","      <td>[sports, thing, points]</td>\n","      <td>go sports! do the thing! win the points!</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>img/76932.png</td>\n","      <td>[dog, dog]</td>\n","      <td>[None, None, None]</td>\n","      <td>[right]</td>\n","      <td>fine you're right. now can we fucking drop it?</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>img/70914.png</td>\n","      <td>[person]</td>\n","      <td>[None, None, None]</td>\n","      <td>[tattoos, bad, health, people, tattoos]</td>\n","      <td>tattoos are bad for your health i know 5 milli...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>img/02973.png</td>\n","      <td>[dog]</td>\n","      <td>[None, None, None]</td>\n","      <td>[chain]</td>\n","      <td>how long can i run? till the chain tightens</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>img/58306.png</td>\n","      <td>[person, person, person]</td>\n","      <td>[Woman, white, angry]</td>\n","      <td>[tax, returns, hillary, emails, bleach, bit, h...</td>\n","      <td>what is he hiding? we need to see his tax retu...</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>img/79351.png</td>\n","      <td>[person]</td>\n","      <td>[Man, white, angry]</td>\n","      <td>[jew, mad, fuhrerious]</td>\n","      <td>jew mad? get fuhrerious!</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>img/34096.png</td>\n","      <td>[remote]</td>\n","      <td>[None, None, None]</td>\n","      <td>[polish, chemicals, eye]</td>\n","      <td>removes polish with chemicals, nobody bats an eye</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>img/25489.png</td>\n","      <td>[person]</td>\n","      <td>[Man, white, fear]</td>\n","      <td>[brother, day, blast, day]</td>\n","      <td>brother... a day without a blast is a day wasted</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>img/19324.png</td>\n","      <td>[person, couch, person, person, person, book, ...</td>\n","      <td>[None, None, None]</td>\n","      <td>[only, dog, dog, shelter, black, family]</td>\n","      <td>when you're the only dog left at the dog shelt...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>img/79346.png</td>\n","      <td>[person, person]</td>\n","      <td>[Woman, white, happy]</td>\n","      <td>[hillary, casey, anthony, kids, guilty]</td>\n","      <td>voting for hillary is like letting casey antho...</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>img/13426.png</td>\n","      <td>[person, person, person, person, person]</td>\n","      <td>[Woman, white, happy]</td>\n","      <td>[free]</td>\n","      <td>they will soon be free</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>img/97628.png</td>\n","      <td>[couch, person, person]</td>\n","      <td>[Man, black, surprise]</td>\n","      <td>[ok, son, daughter, man, daddy]</td>\n","      <td>is it ok for your son or daughter to call anot...</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>img/14238.png</td>\n","      <td>[person]</td>\n","      <td>[Man, middle eastern, sad]</td>\n","      <td>[most, religions, religions, peace]</td>\n","      <td>most religions are religions of peace</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>img/28936.png</td>\n","      <td>[person, couch]</td>\n","      <td>[None, None, None]</td>\n","      <td>[opening, role, first, lady]</td>\n","      <td>when you find out there's an opening for the r...</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>img/59784.png</td>\n","      <td>[person]</td>\n","      <td>[None, None, None]</td>\n","      <td>[people, truth, problem]</td>\n","      <td>if people reject me because i stand on the tru...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1d1ddaf-f4ce-46a3-b862-87f3194365e1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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sq...\n","4   img/82403.png  ...  everybody loves chocolate chip cookies, even h...\n","5   img/16952.png  ...           go sports! do the thing! win the points!\n","6   img/76932.png  ...     fine you're right. now can we fucking drop it?\n","7   img/70914.png  ...  tattoos are bad for your health i know 5 milli...\n","8   img/02973.png  ...        how long can i run? till the chain tightens\n","9   img/58306.png  ...  what is he hiding? we need to see his tax retu...\n","10  img/79351.png  ...                           jew mad? get fuhrerious!\n","11  img/34096.png  ...  removes polish with chemicals, nobody bats an eye\n","12  img/25489.png  ...   brother... a day without a blast is a day wasted\n","13  img/19324.png  ...  when you're the only dog left at the dog shelt...\n","14  img/79346.png  ...  voting for hillary is like letting casey antho...\n","15  img/13426.png  ...                             they will soon be free\n","16  img/97628.png  ...  is it ok for your son or daughter to call anot...\n","17  img/14238.png  ...              most religions are religions of peace\n","18  img/28936.png  ...  when you find out there's an opening for the r...\n","19  img/59784.png  ...  if people reject me because i stand on the tru...\n","\n","[20 rows x 5 columns]"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["#Visualizzo il meme\n","VisualizeImage('img/23058.png')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":378},"id":"glPt50Wcv5N2","executionInfo":{"status":"ok","timestamp":1643106554772,"user_tz":-60,"elapsed":989,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"6bfd3342-558e-47b6-8f61-eca357696081"},"execution_count":74,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Wikidata"],"metadata":{"id":"pFQuZxvx3jeg"}},{"cell_type":"code","source":["#Importiamo le dipendenze\n","import requests\n","import json"],"metadata":{"id":"Hrom9Xrx6aGA","executionInfo":{"status":"ok","timestamp":1643210718246,"user_tz":-60,"elapsed":244,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["#Definiamo l'indirizzo base\n","API_WIKI = \"https://wikidata.org/w/api.php\"\n","n_risultati = 1\n","\n","def SearchOnWiki(query):\n","\n","  if 'None' in query:\n","    return None\n","\n","  params ={\n","      'action': 'wbsearchentities',\n","      'format': 'json',\n","      'language': 'en',\n","      'limit' : n_risultati,\n","      'search': query\n","  }\n","\n","  r = requests.get(API_WIKI, params=params)\n","\n","  if not r.json():\n","    return None\n","  if not r.json()['search']:\n","    return None\n","  if not 'description' in r.json()['search'][0].keys():\n","    return None\n","  else:\n","    return r.json()"],"metadata":{"id":"is_v7T9i5lAS","executionInfo":{"status":"ok","timestamp":1643211094310,"user_tz":-60,"elapsed":249,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":24,"outputs":[]},{"cell_type":"code","source":["#Rimuove i duplicati dalla lista\n","def remove_duplicates(lista):\n","  return list(dict.fromkeys(lista))"],"metadata":{"id":"hHrys6Fa-fBc","executionInfo":{"status":"ok","timestamp":1643210824832,"user_tz":-60,"elapsed":287,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["#Dato un vettore di tag ritorna un DF con label e descrizione\n","#Viene presa solo la prima descrizione dal WikiData \n","#Nel caso si vogliano ritornare più descrizioni si deve modificare n_risultati\n","def InformationRetrival(vettore_informazioni):\n","  #Rimuovo i duplicati\n","  if not vettore_informazioni:\n","    none_list = ['None', 'None', 'None'] \n","    informazioni_da_wiki_pd = pd.DataFrame([none_list],columns=['label', 'description', 'source'])\n","    return informazioni_da_wiki_pd\n","  else:\n","    vettore_informazioni = remove_duplicates(vettore_informazioni)\n","  #Nuova lista di conoscenza\n","  informazioni_da_wiki = []\n","\n","  #Devo gestire il caso in cui il vettore di informazioni\n","  #abbia più elementi all'interno\n","  if len(vettore_informazioni) > 1:\n","    for elem in vettore_informazioni:\n","      json_file = SearchOnWiki(elem)\n","      if not json_file:\n","        informazioni_da_wiki.append(['None', 'None', elem])\n","      else:\n","        informazioni_da_wiki.append([json_file['search'][0]['label'], json_file['search'][0]['description'], elem])\n","  #Se il vettore ha un solo elemento\n","  else:\n","    json_file = SearchOnWiki(vettore_informazioni)\n","    if not json_file:\n","      informazioni_da_wiki.append(['None', 'None', vettore_informazioni[0]])\n","    else:\n","      informazioni_da_wiki.append([json_file['search'][0]['label'], json_file['search'][0]['description'], vettore_informazioni[0]])\n","\n","  #Genero un pandas dataframe\n","  informazioni_da_wiki_pd = pd.DataFrame(informazioni_da_wiki,columns=['label', 'description', 'source'])\n","\n","  return informazioni_da_wiki_pd\n"],"metadata":{"id":"R2idHjWgKNX7","executionInfo":{"status":"ok","timestamp":1643212108496,"user_tz":-60,"elapsed":250,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":50,"outputs":[]},{"cell_type":"markdown","source":["# Estrazione Conoscenza"],"metadata":{"id":"zN73WeadNetg"}},{"cell_type":"code","source":["#tag_base  --> sono i tag estratti con NameEntityRec() dal testo dei meme\n","#tag_lvl_1 --> sono i tag estratti dalla descrizione dei tag_base\n","#tag_lvl_2 --> sono i tag estratti dalla descrizione dei tag_lvl_1\n","\n","def EstrazioneConoscenza(ner_list): \n","  #Crea un DF in cui abbiamo tags di conoscenza\n","\n","  matrix = []\n","  lvl_1_know=[]\n","  lvl_2_know=[]\n","  #Mi ritorna il primo livello di conoscenza\n","  base_know = InformationRetrival(ner_list)\n","\n","  #Secondo livello di conoscenza\n","  for i in range(len(base_know)):\n","    lvl_1_know = InformationRetrival(NameEntityRec(base_know['description'][i]))\n","    for j in range(len(lvl_1_know)):\n","      #Genero i tags finali con NER (mi fermo al secondo livello di conoscenza)\n","      lvl_2 = NameEntityRec(lvl_1_know['description'][j])\n","      lvl_2_know.append(lvl_2)\n","    #Inserisco gli elmenti nella matrice\n","    matrix.append([base_know['source'][i], list(lvl_1_know['source']), lvl_2_know])\n","    lvl_2_know=[]\n","\n","  return pd.DataFrame(matrix, columns=['tag_base', 'tag_lvl_1', 'tag_lvl_2'])"],"metadata":{"id":"b3zlGJZNGpn2","executionInfo":{"status":"ok","timestamp":1643212239610,"user_tz":-60,"elapsed":313,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":53,"outputs":[]},{"cell_type":"code","source":["#Dal DF informazioni_ricevute estraggo la conoscenza\n","grafo_pd = pd.DataFrame(columns=['tag_base', 'tag_lvl_1', 'tag_lvl_2'])\n","n_elem = 0\n","for i in range(len(informazioni_ricevute)):\n","  pd_temp = EstrazioneConoscenza(informazioni_ricevute['ner'][i])\n","  for j in range(len(pd_temp)):\n","    grafo_pd.loc[n_elem] = [pd_temp['tag_base'][j], pd_temp['tag_lvl_1'][j], pd_temp['tag_lvl_2'][j]]\n","    n_elem += 1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MDYggsoyoWUO","executionInfo":{"status":"ok","timestamp":1643212917562,"user_tz":-60,"elapsed":23498,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"44084a3d-672a-4634-f593-ff8996e75eae"},"execution_count":64,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/autocast_mode.py:141: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n","  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n"]}]},{"cell_type":"code","source":["grafo_pd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"-Ww7OwstFWol","executionInfo":{"status":"ok","timestamp":1643212922526,"user_tz":-60,"elapsed":287,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"94b4ca21-1584-4b0a-e039-5ff6866b61b8"},"execution_count":65,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-fcaab321-e65d-4f21-a420-1f292fceae87\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>tag_base</th>\n","      <th>tag_lvl_1</th>\n","      <th>tag_lvl_2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>character</td>\n","      <td>[fictional, human, non, -, character, narrativ...</td>\n","      <td>[[narrative, form, medium, people, events, pla...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>color</td>\n","      <td>[visual, perception, light, wavelengths]</td>\n","      <td>[[art, form, works, visual, nature], [organiza...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>afraid</td>\n","      <td>[single, Mötley, Crüe]</td>\n","      <td>[[conceptual, representation, musical, publica...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ex</td>\n","      <td>[termination, kind, organism, group, species, ...</td>\n","      <td>[[termination], [kind, variety], [contiguous, ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>bows</td>\n","      <td>[None]</td>\n","      <td>[[None]]</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>pet</td>\n","      <td>[medicine, imaging, technique]</td>\n","      <td>[[field, study, disease], [set, techniques, im...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>squirrels</td>\n","      <td>[mammal, family, common, rodent]</td>\n","      <td>[[class, tetrapods], [group, people, consangui...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>chocolate</td>\n","      <td>[dessert, seed, Theobroma, cacao]</td>\n","      <td>[[course, meal, sweet], [embryonic, plant, pro...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>chip</td>\n","      <td>[electronic, circuit, lithography, set, circui...</td>\n","      <td>[[alternative, dance, band], [interconnection,...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>cookies</td>\n","      <td>[small, piece, data, website, user, computer, ...</td>\n","      <td>[[American, botanist], [musical, work, art], [...</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>hitler</td>\n","      <td>[Austrian, politician, German, chancellor, Füh...</td>\n","      <td>[[Austrian, flag, carrier, airline], [person, ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fcaab321-e65d-4f21-a420-1f292fceae87')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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[[termination], [kind, variety], [contiguous, ...\n","4        bows  ...                                           [[None]]\n","5         pet  ...  [[field, study, disease], [set, techniques, im...\n","6   squirrels  ...  [[class, tetrapods], [group, people, consangui...\n","7   chocolate  ...  [[course, meal, sweet], [embryonic, plant, pro...\n","8        chip  ...  [[alternative, dance, band], [interconnection,...\n","9     cookies  ...  [[American, botanist], [musical, work, art], [...\n","10     hitler  ...  [[Austrian, flag, carrier, airline], [person, ...\n","\n","[11 rows x 3 columns]"]},"metadata":{},"execution_count":65}]},{"cell_type":"markdown","source":["# GraphRole\n","Automatic feature extraction and node role assignment for transfer learning on graphs"],"metadata":{"id":"ovX0X44XgnIe"}},{"cell_type":"code","source":["!pip install graphrole"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":729},"id":"o4bGh6q4g2kl","executionInfo":{"status":"ok","timestamp":1643202745927,"user_tz":-60,"elapsed":20218,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"88c80fae-61b1-4745-d54f-2760cbf4cee4"},"execution_count":145,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting graphrole\n","  Downloading graphrole-1.0.2-py3-none-any.whl (65 kB)\n","\u001b[?25l\r\u001b[K     |█████                           | 10 kB 17.3 MB/s eta 0:00:01\r\u001b[K     |██████████                      | 20 kB 21.8 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 30 kB 24.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 40 kB 25.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 51 kB 27.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 61 kB 28.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 65 kB 3.4 MB/s \n","\u001b[?25hRequirement already satisfied: numpy==1.19.5 in /usr/local/lib/python3.7/dist-packages (from graphrole) (1.19.5)\n","Collecting scikit-learn==0.24.2\n","  Downloading scikit_learn-0.24.2-cp37-cp37m-manylinux2010_x86_64.whl (22.3 MB)\n","\u001b[K     |████████████████████████████████| 22.3 MB 1.0 MB/s \n","\u001b[?25hRequirement already satisfied: pandas==1.1.5 in /usr/local/lib/python3.7/dist-packages (from graphrole) (1.1.5)\n","Collecting scipy==1.5.4\n","  Downloading scipy-1.5.4-cp37-cp37m-manylinux1_x86_64.whl (25.9 MB)\n","\u001b[K     |████████████████████████████████| 25.9 MB 1.6 MB/s \n","\u001b[?25hCollecting networkx==2.5.1\n","  Downloading networkx-2.5.1-py3-none-any.whl (1.6 MB)\n","\u001b[K     |████████████████████████████████| 1.6 MB 42.5 MB/s \n","\u001b[?25hRequirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx==2.5.1->graphrole) (4.4.2)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas==1.1.5->graphrole) (2018.9)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.1.5->graphrole) (2.8.2)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.24.2->graphrole) (1.1.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.24.2->graphrole) (3.0.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas==1.1.5->graphrole) (1.15.0)\n","Installing collected packages: scipy, scikit-learn, networkx, graphrole\n","  Attempting uninstall: scipy\n","    Found existing installation: scipy 1.4.1\n","    Uninstalling scipy-1.4.1:\n","      Successfully uninstalled scipy-1.4.1\n","  Attempting uninstall: scikit-learn\n","    Found existing installation: scikit-learn 1.0.2\n","    Uninstalling scikit-learn-1.0.2:\n","      Successfully uninstalled scikit-learn-1.0.2\n","  Attempting uninstall: networkx\n","    Found existing installation: networkx 2.6.3\n","    Uninstalling networkx-2.6.3:\n","      Successfully uninstalled networkx-2.6.3\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n","Successfully installed graphrole-1.0.2 networkx-2.5.1 scikit-learn-0.24.2 scipy-1.5.4\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["scipy"]}}},"metadata":{}}]},{"cell_type":"code","source":["from graphrole import RecursiveFeatureExtractor, RoleExtractor\n","import networkx as nx"],"metadata":{"id":"D5eyvioghLWn","executionInfo":{"status":"ok","timestamp":1643202819129,"user_tz":-60,"elapsed":1091,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["G = nx.karate_club_graph()"],"metadata":{"id":"THt0U0WMhTeq","executionInfo":{"status":"ok","timestamp":1643202825459,"user_tz":-60,"elapsed":262,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["print(G.edges)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AzCA6v7riBAs","executionInfo":{"status":"ok","timestamp":1643203027639,"user_tz":-60,"elapsed":245,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"334c1457-5f0b-4fe3-c39c-f8e9b64f74c6"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["[(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 10), (0, 11), (0, 12), (0, 13), (0, 17), (0, 19), (0, 21), (0, 31), (1, 2), (1, 3), (1, 7), (1, 13), (1, 17), (1, 19), (1, 21), (1, 30), (2, 3), (2, 7), (2, 8), (2, 9), (2, 13), (2, 27), (2, 28), (2, 32), (3, 7), (3, 12), (3, 13), (4, 6), (4, 10), (5, 6), (5, 10), (5, 16), (6, 16), (8, 30), (8, 32), (8, 33), (9, 33), (13, 33), (14, 32), (14, 33), (15, 32), (15, 33), (18, 32), (18, 33), (19, 33), (20, 32), (20, 33), (22, 32), (22, 33), (23, 25), (23, 27), (23, 29), (23, 32), (23, 33), (24, 25), (24, 27), (24, 31), (25, 31), (26, 29), (26, 33), (27, 33), (28, 31), (28, 33), (29, 32), (29, 33), (30, 32), (30, 33), (31, 32), (31, 33), (32, 33)]\n"]}]},{"cell_type":"code","source":["# plot graph\n","#plt.figure()\n","\n","\n","# catch matplotlib deprecation warning\n","\n","nx.draw(G, with_labels=True)\n","    \n","#plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":319},"id":"9dYY4JWChVKU","executionInfo":{"status":"ok","timestamp":1643203163343,"user_tz":-60,"elapsed":783,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"01a163be-273d-41d6-e23e-48cb0edb758e"},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","source":["feature_extractor = RecursiveFeatureExtractor(G)\n","feature = feature_extractor.extract_features()"],"metadata":{"id":"orVAs9_mi52y","executionInfo":{"status":"ok","timestamp":1643203363309,"user_tz":-60,"elapsed":633,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["feature"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"j2eWZMJ3i69L","executionInfo":{"status":"ok","timestamp":1643203368364,"user_tz":-60,"elapsed":248,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"9abac23c-6b51-43f1-e33a-7be4acf95b47"},"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-50d9dd42-2d09-477f-ba87-0021e0eeba15\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>external_edges(mean)(mean)</th>\n","      <th>degree(mean)</th>\n","      <th>degree(sum)</th>\n","      <th>external_edges(mean)</th>\n","      <th>degree</th>\n","      <th>external_edges</th>\n","      <th>internal_edges</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>19.637500</td>\n","      <td>4.312500</td>\n","      <td>69.0</td>\n","      <td>24.937500</td>\n","      <td>16</td>\n","      <td>17</td>\n","      <td>34</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>22.422685</td>\n","      <td>5.777778</td>\n","      <td>52.0</td>\n","      <td>27.666667</td>\n","      <td>9</td>\n","      <td>19</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>25.537083</td>\n","      <td>6.600000</td>\n","      <td>66.0</td>\n","      <td>27.100000</td>\n","      <td>10</td>\n","      <td>34</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>23.717361</td>\n","      <td>7.666667</td>\n","      <td>46.0</td>\n","      <td>25.666667</td>\n","      <td>6</td>\n","      <td>20</td>\n","      <td>16</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>17.979167</td>\n","      <td>7.666667</td>\n","      <td>23.0</td>\n","      <td>16.000000</td>\n","      <td>3</td>\n","      <td>16</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>17.234375</td>\n","      <td>6.250000</td>\n","      <td>25.0</td>\n","      <td>13.000000</td>\n","      <td>4</td>\n","      <td>15</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>17.234375</td>\n","      <td>6.250000</td>\n","      <td>25.0</td>\n","      <td>13.000000</td>\n","      <td>4</td>\n","      <td>15</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>26.342708</td>\n","      <td>10.250000</td>\n","      <td>41.0</td>\n","      <td>22.500000</td>\n","      <td>4</td>\n","      <td>25</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>27.214363</td>\n","      <td>11.800000</td>\n","      <td>59.0</td>\n","      <td>25.000000</td>\n","      <td>5</td>\n","      <td>44</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>28.108824</td>\n","      <td>13.500000</td>\n","      <td>27.0</td>\n","      <td>26.000000</td>\n","      <td>2</td>\n","      <td>25</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>17.979167</td>\n","      <td>7.666667</td>\n","      <td>23.0</td>\n","      <td>16.000000</td>\n","      <td>3</td>\n","      <td>16</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>24.937500</td>\n","      <td>16.000000</td>\n","      <td>16.0</td>\n","      <td>17.000000</td>\n","      <td>1</td>\n","      <td>15</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>25.302083</td>\n","      <td>11.000000</td>\n","      <td>22.0</td>\n","      <td>18.500000</td>\n","      <td>2</td>\n","      <td>18</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>26.897696</td>\n","      <td>11.600000</td>\n","      <td>58.0</td>\n","      <td>21.600000</td>\n","      <td>5</td>\n","      <td>41</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>29.017157</td>\n","      <td>14.500000</td>\n","      <td>29.0</td>\n","      <td>20.500000</td>\n","      <td>2</td>\n","      <td>25</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>29.017157</td>\n","      <td>14.500000</td>\n","      <td>29.0</td>\n","      <td>20.500000</td>\n","      <td>2</td>\n","      <td>25</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>13.000000</td>\n","      <td>4.000000</td>\n","      <td>8.0</td>\n","      <td>15.000000</td>\n","      <td>2</td>\n","      <td>4</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>26.302083</td>\n","      <td>12.500000</td>\n","      <td>25.0</td>\n","      <td>18.000000</td>\n","      <td>2</td>\n","      <td>21</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>29.017157</td>\n","      <td>14.500000</td>\n","      <td>29.0</td>\n","      <td>20.500000</td>\n","      <td>2</td>\n","      <td>25</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>27.240605</td>\n","      <td>14.000000</td>\n","      <td>42.0</td>\n","      <td>18.000000</td>\n","      <td>3</td>\n","      <td>37</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>29.017157</td>\n","      <td>14.500000</td>\n","      <td>29.0</td>\n","      <td>20.500000</td>\n","      <td>2</td>\n","      <td>25</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>26.302083</td>\n","      <td>12.500000</td>\n","      <td>25.0</td>\n","      <td>18.000000</td>\n","      <td>2</td>\n","      <td>21</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>29.017157</td>\n","      <td>14.500000</td>\n","      <td>29.0</td>\n","      <td>20.500000</td>\n","      <td>2</td>\n","      <td>25</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>25.340196</td>\n","      <td>8.000000</td>\n","      <td>40.0</td>\n","      <td>20.600000</td>\n","      <td>5</td>\n","      <td>27</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>21.527778</td>\n","      <td>4.333333</td>\n","      <td>13.0</td>\n","      <td>26.666667</td>\n","      <td>3</td>\n","      <td>8</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>21.477778</td>\n","      <td>4.666667</td>\n","      <td>14.0</td>\n","      <td>25.666667</td>\n","      <td>3</td>\n","      <td>9</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>25.183824</td>\n","      <td>10.500000</td>\n","      <td>21.0</td>\n","      <td>21.000000</td>\n","      <td>2</td>\n","      <td>17</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>25.871078</td>\n","      <td>8.750000</td>\n","      <td>35.0</td>\n","      <td>21.750000</td>\n","      <td>4</td>\n","      <td>29</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>24.461438</td>\n","      <td>11.000000</td>\n","      <td>33.0</td>\n","      <td>31.333333</td>\n","      <td>3</td>\n","      <td>28</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>24.908578</td>\n","      <td>9.000000</td>\n","      <td>36.0</td>\n","      <td>21.250000</td>\n","      <td>4</td>\n","      <td>24</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>27.675245</td>\n","      <td>10.750000</td>\n","      <td>43.0</td>\n","      <td>26.000000</td>\n","      <td>4</td>\n","      <td>33</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>27.773080</td>\n","      <td>9.000000</td>\n","      <td>54.0</td>\n","      <td>17.166667</td>\n","      <td>6</td>\n","      <td>42</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>22.394526</td>\n","      <td>5.083333</td>\n","      <td>61.0</td>\n","      <td>28.916667</td>\n","      <td>12</td>\n","      <td>23</td>\n","      <td>25</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>22.418627</td>\n","      <td>3.823529</td>\n","      <td>65.0</td>\n","      <td>29.117647</td>\n","      <td>17</td>\n","      <td>18</td>\n","      <td>32</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50d9dd42-2d09-477f-ba87-0021e0eeba15')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 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       29.017157     14.500000  ...              25               3\n","16                   13.000000      4.000000  ...               4               3\n","17                   26.302083     12.500000  ...              21               3\n","18                   29.017157     14.500000  ...              25               3\n","19                   27.240605     14.000000  ...              37               4\n","20                   29.017157     14.500000  ...              25               3\n","21                   26.302083     12.500000  ...              21               3\n","22                   29.017157     14.500000  ...              25               3\n","23                   25.340196      8.000000  ...              27               9\n","24                   21.527778      4.333333  ...               8               4\n","25                   21.477778      4.666667  ...               9               4\n","26                   25.183824     10.500000  ...              17               3\n","27                   25.871078      8.750000  ...              29               5\n","28                   24.461438     11.000000  ...              28               4\n","29                   24.908578      9.000000  ...              24               8\n","30                   27.675245     10.750000  ...              33               7\n","31                   27.773080      9.000000  ...              42               9\n","32                   22.394526      5.083333  ...              23              25\n","33                   22.418627      3.823529  ...              18              32\n","\n","[34 rows x 7 columns]"]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","source":["# Textual Feature Extraction"],"metadata":{"id":"NLs9L8Vualky"}},{"cell_type":"code","source":["!pip install transformers"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sqrvkbp2auPu","executionInfo":{"status":"ok","timestamp":1643100494512,"user_tz":-60,"elapsed":11791,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"23c62666-1203-42b9-ea39-21b39aec2d5f"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting transformers\n","  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n","\u001b[K     |████████████████████████████████| 3.4 MB 5.1 MB/s \n","\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.10.0)\n","Collecting sacremoses\n","  Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n","\u001b[K    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/usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n","Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n","  Attempting uninstall: pyyaml\n","    Found existing installation: PyYAML 3.13\n","    Uninstalling PyYAML-3.13:\n","      Successfully uninstalled PyYAML-3.13\n","Successfully installed huggingface-hub-0.4.0 pyyaml-6.0 sacremoses-0.0.47 tokenizers-0.10.3 transformers-4.15.0\n"]}]},{"cell_type":"code","source":["from transformers import pipeline"],"metadata":{"id":"gc_cG9LGa8dn","executionInfo":{"status":"ok","timestamp":1643100743343,"user_tz":-60,"elapsed":238,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["feature_extractor = pipeline('feature-extraction')\n","text_feature = feature_extractor('Mila')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SG9Kvdz-bCyL","executionInfo":{"status":"ok","timestamp":1643100943273,"user_tz":-60,"elapsed":3485,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"dc6dc7f8-cc78-45cd-9c00-6fcf2fc8262a"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to distilbert-base-cased (https://huggingface.co/distilbert-base-cased)\n","Some weights of the model checkpoint at distilbert-base-cased were not used when initializing DistilBertModel: ['vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_projector.weight']\n","- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]}]},{"cell_type":"code","source":["print(len(text_feature[0][0]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7EH00OvlcBSt","executionInfo":{"status":"ok","timestamp":1643100945908,"user_tz":-60,"elapsed":241,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"370d9724-5331-4ce6-f658-1b11aef03f29"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["768\n"]}]},{"cell_type":"markdown","source":["# Graph BERT"],"metadata":{"id":"_dDEixWSdDUU"}},{"cell_type":"code","source":["!git clone 'https://github.com/jwzhanggy/Graph-Bert.git'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rHS4uT3DdIxp","executionInfo":{"status":"ok","timestamp":1642849468696,"user_tz":-60,"elapsed":2089,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"7a305723-cb65-45f4-e05c-7ba817bf087a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'Graph-Bert'...\n","remote: Enumerating objects: 442, done.\u001b[K\n","remote: Counting objects: 100% (36/36), done.\u001b[K\n","remote: Compressing objects: 100% (36/36), done.\u001b[K\n","remote: Total 442 (delta 22), reused 1 (delta 0), pack-reused 406\u001b[K\n","Receiving objects: 100% (442/442), 2.23 MiB | 3.75 MiB/s, done.\n","Resolving deltas: 100% (223/223), done.\n"]}]},{"cell_type":"code","source":["!pip install transformers"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0-dJibendm1u","executionInfo":{"status":"ok","timestamp":1642849548649,"user_tz":-60,"elapsed":8966,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"94dd80f3-25ac-43a0-ed4b-a18b2d49a950"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting transformers\n","  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n","\u001b[K     |████████████████████████████████| 3.4 MB 4.3 MB/s \n","\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n","Collecting huggingface-hub<1.0,>=0.1.0\n","  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installation: PyYAML 3.13\n","    Uninstalling PyYAML-3.13:\n","      Successfully uninstalled PyYAML-3.13\n","Successfully installed huggingface-hub-0.4.0 pyyaml-6.0 sacremoses-0.0.47 tokenizers-0.10.3 transformers-4.15.0\n"]}]},{"cell_type":"code","source":["#Modify transformers.modeling_bert --> transformers.model.bert.modeling_bert\n","#Modify path\n","!python3 Graph-Bert/script_3_fine_tuning.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UOhkGTjndbZe","executionInfo":{"status":"ok","timestamp":1642850840953,"user_tz":-60,"elapsed":32203,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"06fba240-3178-479d-f277-c8186b728bac"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["************ Start ************\n","GrapBert, dataset: cora, residual: graph_raw, k: 7, hidden dimension: 32, hidden layer: 2, attention head: 2\n","Loading cora dataset...\n","Load WL Dictionary\n","Load Hop Distance Dictionary\n","Load Subgraph Batches\n","Epoch: 0001 loss_train: 1.9494 acc_train: 0.1071 loss_val: 1.7517 acc_val: 0.3500 loss_test: 1.6992 acc_test: 0.4060 time: 0.3118s\n","Epoch: 0011 loss_train: 0.0421 acc_train: 1.0000 loss_val: 0.7268 acc_val: 0.7800 loss_test: 0.6547 acc_test: 0.8020 time: 0.1398s\n","Epoch: 0021 loss_train: 0.0018 acc_train: 1.0000 loss_val: 0.7781 acc_val: 0.7833 loss_test: 0.7317 acc_test: 0.8090 time: 0.1410s\n","Epoch: 0031 loss_train: 0.0009 acc_train: 1.0000 loss_val: 0.8054 acc_val: 0.7867 loss_test: 0.7415 acc_test: 0.8140 time: 0.1288s\n","Epoch: 0041 loss_train: 0.0018 acc_train: 1.0000 loss_val: 0.7387 acc_val: 0.7967 loss_test: 0.6734 acc_test: 0.8190 time: 0.1293s\n","Epoch: 0051 loss_train: 0.0036 acc_train: 1.0000 loss_val: 0.7049 acc_val: 0.7733 loss_test: 0.6320 acc_test: 0.8200 time: 0.1397s\n","Epoch: 0061 loss_train: 0.0035 acc_train: 1.0000 loss_val: 0.7290 acc_val: 0.7733 loss_test: 0.6371 acc_test: 0.8170 time: 0.1446s\n","Epoch: 0071 loss_train: 0.0031 acc_train: 1.0000 loss_val: 0.7423 acc_val: 0.7700 loss_test: 0.6473 acc_test: 0.8140 time: 0.1491s\n","Epoch: 0081 loss_train: 0.0031 acc_train: 1.0000 loss_val: 0.7236 acc_val: 0.7833 loss_test: 0.6330 acc_test: 0.8240 time: 0.1548s\n","Epoch: 0091 loss_train: 0.0028 acc_train: 1.0000 loss_val: 0.7363 acc_val: 0.7767 loss_test: 0.6376 acc_test: 0.8200 time: 0.1361s\n","Epoch: 0101 loss_train: 0.0027 acc_train: 1.0000 loss_val: 0.7447 acc_val: 0.7700 loss_test: 0.6496 acc_test: 0.8210 time: 0.1349s\n","Epoch: 0111 loss_train: 0.0025 acc_train: 1.0000 loss_val: 0.7481 acc_val: 0.7633 loss_test: 0.6559 acc_test: 0.8170 time: 0.1440s\n","Epoch: 0121 loss_train: 0.0023 acc_train: 1.0000 loss_val: 0.7398 acc_val: 0.7833 loss_test: 0.6451 acc_test: 0.8190 time: 0.1424s\n","Epoch: 0131 loss_train: 0.0021 acc_train: 1.0000 loss_val: 0.7692 acc_val: 0.7767 loss_test: 0.6595 acc_test: 0.8180 time: 0.1391s\n","Epoch: 0141 loss_train: 0.0018 acc_train: 1.0000 loss_val: 0.7713 acc_val: 0.7800 loss_test: 0.6738 acc_test: 0.8190 time: 0.1431s\n","Optimization Finished!\n","Total time elapsed: 21.3858s, best testing performance  0.830000, minimun loss  0.609260\n","************ Finish ************\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"2NxCxHtleexg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[""],"metadata":{"id":"axtsmlEBasDg"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Detecting Passive-Aggressive in text (PROVA)"],"metadata":{"id":"vNwR6t_E9luJ"}},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import itertools\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.linear_model import PassiveAggressiveClassifier\n","from sklearn.metrics import accuracy_score, confusion_matrix"],"metadata":{"id":"hJgWCOi-9k0a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["metadata_pd = pd.concat([train, val])\n","metadata_pd = pd.concat([metadata_pd, test])"],"metadata":{"id":"z8avVVvOBo7E"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["labels=metadata_pd.label\n","x_train,x_test,y_train,y_test=train_test_split(metadata_pd['text'], labels, test_size=0.2, random_state=7)"],"metadata":{"id":"kUxc-cJO-5Hs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#DataFlair - Initialize a TfidfVectorizer\n","tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)\n","#DataFlair - Fit and transform train set, transform test set\n","tfidf_train=tfidf_vectorizer.fit_transform(x_train) \n","tfidf_test=tfidf_vectorizer.transform(x_test)"],"metadata":{"id":"qeeBV9IK90ze"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#DataFlair - Initialize a PassiveAggressiveClassifier\n","pac=PassiveAggressiveClassifier(max_iter=50)\n","pac.fit(tfidf_train,y_train)\n","#DataFlair - Predict on the test set and calculate accuracy\n","y_pred=pac.predict(tfidf_test)\n","score=accuracy_score(y_test,y_pred)\n","print(f'Accuracy: {round(score*100,2)}%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ppjmLrSo-Ah8","executionInfo":{"status":"ok","timestamp":1642506574009,"user_tz":-60,"elapsed":248,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"7f3b8787-0852-417d-d2f4-3c57ac474731"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 59.5%\n"]}]},{"cell_type":"code","source":["#DataFlair - Build confusion matrix\n","confusion_matrix(y_test,y_pred, labels=[0,1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_FZDNHog-biU","executionInfo":{"status":"ok","timestamp":1642506576874,"user_tz":-60,"elapsed":348,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"1a4035c8-172d-4f10-99dc-0aa298625f26"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[829, 395],\n","       [415, 361]])"]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":[""],"metadata":{"id":"HmXGIvmU_cVH"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":[""],"metadata":{"id":"yoL-Hceih0jz"}},{"cell_type":"markdown","source":["# Image Captioning (OSCAR?)"],"metadata":{"id":"sshIvyxWh139"}},{"cell_type":"code","source":["#Scarico e installo il tool AZCOPY per scaricare da Azure\n","!wget https://aka.ms/downloadazcopy-v10-linux\n","#Estraggo il file .tar\n","import tarfile\n","my_tar = tarfile.open('/content/downloadazcopy-v10-linux')\n","my_tar.extractall('.') # specify which folder to extract to\n","my_tar.close()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8UsTVBEQElIx","executionInfo":{"status":"ok","timestamp":1643034461095,"user_tz":-60,"elapsed":875,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"e70349ee-d461-4678-dcb6-590d0eec3848"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-01-24 14:27:40--  https://aka.ms/downloadazcopy-v10-linux\n","Resolving aka.ms (aka.ms)... 104.119.90.120\n","Connecting to aka.ms (aka.ms)|104.119.90.120|:443... connected.\n","HTTP request sent, awaiting response... 301 Moved Permanently\n","Location: https://azcopyvnext.azureedge.net/release20211027/azcopy_linux_amd64_10.13.0.tar.gz [following]\n","--2022-01-24 14:27:40--  https://azcopyvnext.azureedge.net/release20211027/azcopy_linux_amd64_10.13.0.tar.gz\n","Resolving azcopyvnext.azureedge.net (azcopyvnext.azureedge.net)... 23.204.145.64, 23.204.145.58, 2600:1406:6c00::17cc:93c0, ...\n","Connecting to azcopyvnext.azureedge.net (azcopyvnext.azureedge.net)|23.204.145.64|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 11876012 (11M) [application/gzip]\n","Saving to: ‘downloadazcopy-v10-linux’\n","\n","downloadazcopy-v10- 100%[===================>]  11.33M  --.-KB/s    in 0.1s    \n","\n","2022-01-24 14:27:40 (91.2 MB/s) - ‘downloadazcopy-v10-linux’ saved [11876012/11876012]\n","\n"]}]},{"cell_type":"code","source":["!/content/azcopy copy 'https://biglmdiag.blob.core.windows.net/vinvl/model_ckpts/image_captioning' '/content/ImageCapModel' --recursive"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wRR4XSNlEBIw","executionInfo":{"status":"ok","timestamp":1643034904323,"user_tz":-60,"elapsed":419638,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"41db2787-f89f-4515-f195-86f66f2609fb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["INFO: Scanning...\n","INFO: Any empty folders will not be processed, because source and/or destination doesn't have full folder support\n","\n","Job 63868e71-5312-494a-7b55-bd84d3cfdaf2 has started\n","Log file is located at: /root/.azcopy/63868e71-5312-494a-7b55-bd84d3cfdaf2.log\n","\n","100.0 %, 134 Done, 0 Failed, 1 Pending, 0 Skipped, 135 Total,                              \n","\n","\n","Job 63868e71-5312-494a-7b55-bd84d3cfdaf2 summary\n","Elapsed Time (Minutes): 6.9311\n","Number of File Transfers: 135\n","Number of Folder Property Transfers: 0\n","Total Number of Transfers: 135\n","Number of Transfers Completed: 135\n","Number of Transfers Failed: 0\n","Number of Transfers Skipped: 0\n","TotalBytesTransferred: 20438029396\n","Final Job Status: Completed\n","\n"]}]},{"cell_type":"code","source":["!pip install torch==1.2.0 torchvision==0.4.0"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jdXe2WQfRzAt","executionInfo":{"status":"ok","timestamp":1643035064645,"user_tz":-60,"elapsed":149877,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"317c6234-3a51-43de-b2a8-ab405f162257"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting torch==1.2.0\n","  Downloading torch-1.2.0-cp37-cp37m-manylinux1_x86_64.whl (748.9 MB)\n","\u001b[K     |████████████████████████████████| 748.9 MB 533 bytes/s \n","\u001b[?25hCollecting torchvision==0.4.0\n","\u001b[33m  WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))': /packages/51/83/2d77d040e34bd8f70dcb4770f7eb7d0aa71e07738abf6831be863ade00db/torchvision-0.4.0-cp37-cp37m-manylinux1_x86_64.whl\u001b[0m\n","  Downloading torchvision-0.4.0-cp37-cp37m-manylinux1_x86_64.whl (8.8 MB)\n","\u001b[K     |████████████████████████████████| 8.8 MB 2.7 MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.2.0) (1.19.5)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.4.0) (7.1.2)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from torchvision==0.4.0) (1.15.0)\n","Installing collected packages: torch, torchvision\n","  Attempting uninstall: torch\n","    Found existing installation: torch 1.10.0+cu111\n","    Uninstalling torch-1.10.0+cu111:\n","      Successfully uninstalled torch-1.10.0+cu111\n","  Attempting uninstall: torchvision\n","    Found existing installation: torchvision 0.11.1+cu111\n","    Uninstalling torchvision-0.11.1+cu111:\n","      Successfully uninstalled torchvision-0.11.1+cu111\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","torchtext 0.11.0 requires torch==1.10.0, but you have torch 1.2.0 which is incompatible.\n","torchaudio 0.10.0+cu111 requires torch==1.10.0, but you have torch 1.2.0 which is incompatible.\u001b[0m\n","Successfully installed torch-1.2.0 torchvision-0.4.0\n"]}]},{"cell_type":"code","source":["#Installo Oscar e le dipendenze\n","!git clone https://github.com/microsoft/Oscar.git\n","%cd /content/Oscar\n","!git clone https://github.com/LuoweiZhou/coco-caption.git coco_caption\n","!git clone https://github.com/huggingface/transformers.git\n","#!rm -r coco_caption/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KCTMQbKVh66z","executionInfo":{"status":"ok","timestamp":1643035087661,"user_tz":-60,"elapsed":18674,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"3461508a-6bf8-4bbe-a36c-4772bb924f04"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'Oscar'...\n","remote: Enumerating objects: 131, done.\u001b[K\n","remote: Counting objects: 100% (73/73), done.\u001b[K\n","remote: Compressing objects: 100% (39/39), done.\u001b[K\n","remote: Total 131 (delta 44), reused 34 (delta 34), pack-reused 58\u001b[K\n","Receiving objects: 100% (131/131), 726.16 KiB | 2.85 MiB/s, done.\n","Resolving deltas: 100% (57/57), done.\n","/content/Oscar\n","Cloning into 'coco_caption'...\n","remote: Enumerating objects: 790, done.\u001b[K\n","remote: Total 790 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.."],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SldLfOdjPcDF","executionInfo":{"status":"ok","timestamp":1643035162279,"user_tz":-60,"elapsed":270,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"b98e4d3d-449e-4569-da75-73f64a8e2f2c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n"]}]},{"cell_type":"code","source":["%cd /content/Oscar/coco_caption\n","!./get_stanford_models.sh\n","%cd ..\n","!python setup.py build develop"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MpWNVV60iGii","executionInfo":{"status":"ok","timestamp":1643035250417,"user_tz":-60,"elapsed":76878,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"c59a5a3f-0e1a-41a6-ec5a-a4a0266b5371"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/Oscar/coco_caption\n","Downloading...\n","--2022-01-24 14:39:33--  http://nlp.stanford.edu/software/stanford-corenlp-full-2015-12-09.zip\n","Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n","Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://nlp.stanford.edu/software/stanford-corenlp-full-2015-12-09.zip [following]\n","--2022-01-24 14:39:33--  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pycocoevalcap/spice/lib/stanford-corenlp-full-2015-12-09/SemgrexDemo.java  \n","  inflating: pycocoevalcap/spice/lib/stanford-corenlp-full-2015-12-09/LICENSE.txt  \n","/content/Oscar\n","running build\n","running build_py\n","creating build\n","creating build/lib\n","creating build/lib/oscar\n","copying oscar/run_retrieval.py -> build/lib/oscar\n","copying oscar/__init__.py -> build/lib/oscar\n","copying oscar/run_captioning.py -> build/lib/oscar\n","copying oscar/run_gqa.py -> build/lib/oscar\n","copying oscar/run_oscarplus_pretrain.py -> build/lib/oscar\n","copying oscar/run_nlvr.py -> build/lib/oscar\n","copying oscar/run_vqa.py -> build/lib/oscar\n","creating build/lib/oscar/datasets\n","copying oscar/datasets/oscar_tsv.py -> build/lib/oscar/datasets\n","copying oscar/datasets/__init__.py -> build/lib/oscar/datasets\n","copying oscar/datasets/build.py -> build/lib/oscar/datasets\n","creating build/lib/oscar/utils\n","copying oscar/utils/logger.py -> build/lib/oscar/utils\n","copying oscar/utils/__init__.py -> build/lib/oscar/utils\n","copying oscar/utils/caption_evaluate.py -> build/lib/oscar/utils\n","copying oscar/utils/cbs.py -> build/lib/oscar/utils\n","copying oscar/utils/task_utils.py -> build/lib/oscar/utils\n","copying oscar/utils/misc.py -> build/lib/oscar/utils\n","copying oscar/utils/tsv_file_ops.py -> build/lib/oscar/utils\n","copying oscar/utils/tsv_file.py -> build/lib/oscar/utils\n","copying oscar/utils/metric_logger.py -> build/lib/oscar/utils\n","creating build/lib/oscar/modeling\n","copying oscar/modeling/__init__.py -> build/lib/oscar/modeling\n","copying oscar/modeling/modeling_bert.py -> build/lib/oscar/modeling\n","copying oscar/modeling/modeling_utils.py -> build/lib/oscar/modeling\n","running develop\n","running egg_info\n","creating oscar.egg-info\n","writing oscar.egg-info/PKG-INFO\n","writing dependency_links to oscar.egg-info/dependency_links.txt\n","writing top-level names to oscar.egg-info/top_level.txt\n","writing manifest file 'oscar.egg-info/SOURCES.txt'\n","adding license file 'LICENSE'\n","writing manifest file 'oscar.egg-info/SOURCES.txt'\n","running build_ext\n","Creating /usr/local/lib/python3.7/dist-packages/oscar.egg-link (link to .)\n","Adding oscar 0.1.0 to easy-install.pth file\n","\n","Installed /content/Oscar\n","Processing dependencies for oscar==0.1.0\n","Finished processing dependencies for oscar==0.1.0\n"]}]},{"cell_type":"code","source":["# install requirements\n","!pip install -r requirements.txt"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FJ7pHIfAiIOy","executionInfo":{"status":"ok","timestamp":1643035317277,"user_tz":-60,"elapsed":8010,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"f5a025eb-b5df-4010-a1a9-6b25dfac4fd9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 1)) (4.62.3)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (3.13)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (3.2.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (2.23.0)\n","Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 5)) (0.18.3)\n","Collecting anytree\n","  Downloading anytree-2.8.0-py2.py3-none-any.whl (41 kB)\n","\u001b[K     |████████████████████████████████| 41 kB 479 kB/s \n","\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (2019.12.20)\n","Collecting boto3\n","  Downloading boto3-1.20.41-py3-none-any.whl (131 kB)\n","\u001b[K     |████████████████████████████████| 131 kB 10.7 MB/s \n","\u001b[?25hRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->-r requirements.txt (line 3)) (0.11.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->-r requirements.txt (line 3)) (3.0.6)\n","Requirement already satisfied: kiwisolver>=1.0.1 in 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   Successfully uninstalled urllib3-1.24.3\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n","Successfully installed anytree-2.8.0 boto3-1.20.41 botocore-1.23.41 jmespath-0.10.0 s3transfer-0.5.0 urllib3-1.25.11\n"]}]},{"cell_type":"code","source":["cd .."],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LjLuDrfRjLBm","executionInfo":{"status":"ok","timestamp":1643035620973,"user_tz":-60,"elapsed":245,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"79748b18-3fc0-4db9-cf62-c49c0879fc58"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n"]}]},{"cell_type":"code","source":["#download coco dataset\n","#non ho abbastanza spazio\n","!/content/azcopy copy 'https://biglmdiag.blob.core.windows.net/oscar/datasets/coco_caption.zip' 'content/Oscar'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5CrMs7MwjCxB","executionInfo":{"status":"ok","timestamp":1643036037353,"user_tz":-60,"elapsed":391925,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"4370575f-f024-4cdb-e2f5-372514903e88"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["INFO: Scanning...\n","INFO: Any empty folders will not be processed, because source and/or destination doesn't have full folder support\n","\n","Job a84711b3-4f5a-1640-5f8b-74b92a0c2b58 has started\n","Log file is located at: /root/.azcopy/a84711b3-4f5a-1640-5f8b-74b92a0c2b58.log\n","\n","100.0 %, 0 Done, 0 Failed, 1 Pending, 0 Skipped, 1 Total,                              \n","\n","\n","Job a84711b3-4f5a-1640-5f8b-74b92a0c2b58 summary\n","Elapsed Time (Minutes): 6.5001\n","Number of File Transfers: 1\n","Number of Folder Property Transfers: 0\n","Total Number of Transfers: 1\n","Number of Transfers Completed: 1\n","Number of Transfers Failed: 0\n","Number of Transfers Skipped: 0\n","TotalBytesTransferred: 23943475936\n","Final Job Status: Completed\n","\n"]}]},{"cell_type":"code","source":["!pip install transformers"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8qSeZl1ko3cF","executionInfo":{"status":"ok","timestamp":1643037545066,"user_tz":-60,"elapsed":7625,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"b7a98597-e9e0-42e5-de1d-b2a6ad17a8c0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting transformers\n","  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n","\u001b[K     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satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n","Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers\n","  Attempting uninstall: pyyaml\n","    Found existing installation: PyYAML 3.13\n","    Uninstalling PyYAML-3.13:\n","      Successfully uninstalled PyYAML-3.13\n","Successfully installed huggingface-hub-0.4.0 pyyaml-6.0 tokenizers-0.10.3 transformers-4.15.0\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["transformers"]}}},"metadata":{}}]},{"cell_type":"code","source":["!python /content/Oscar/oscar/run_captioning.py --data_dir='/content/content/Oscar' --do_test --do_eval --test_yaml test.yaml --per_gpu_eval_batch_size 64 --num_beams 5 --max_gen_length 20 --eval_model_dir '/content/ImageCapModel/image_captioning/pretrained_large'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sf4Lv3sN-kLp","executionInfo":{"status":"ok","timestamp":1643037648634,"user_tz":-60,"elapsed":2137,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"66dc9c6c-b6e6-46e7-c604-f24b63a94e1a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n","  File \"/content/Oscar/oscar/run_captioning.py\", line 22, in <module>\n","    from oscar.utils.cbs import ConstraintFilter, ConstraintBoxesReader\n","  File \"/content/Oscar/oscar/utils/cbs.py\", line 13, in <module>\n","    from oscar.modeling.modeling_utils import BeamHypotheses\n","  File \"/content/Oscar/oscar/modeling/modeling_utils.py\", line 10, in <module>\n","    from transformers.pytorch_transformers.modeling_bert import (BertConfig,\n","ModuleNotFoundError: No module named 'transformers.pytorch_transformers'\n"]}]},{"cell_type":"code","source":[""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O2i_mea4p91J","executionInfo":{"status":"error","timestamp":1643037668925,"user_tz":-60,"elapsed":685,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"91ef16a8-0618-40c7-b529-976b537a5907"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ModuleNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","\u001b[0;32m<ipython-input-4-cc06cb3207d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpytorch_transformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodeling_bert\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBertConfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'transformers.pytorch_transformers'","","\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"],"errorDetails":{"actions":[{"action":"open_url","actionText":"Open Examples","url":"/notebooks/snippets/importing_libraries.ipynb"}]}}]},{"cell_type":"markdown","source":["# VisualBERT (Feature extractor?)\n","[Feature extractor example](https://colab.research.google.com/drive/1bLGxKdldwqnMVA5x4neY7-l_8fKGWQYI?usp=sharing#scrollTo=mEB4OP33IOCl)"],"metadata":{"id":"f8MSiR3qX_ub"}},{"cell_type":"code","source":["from PIL import Image"],"metadata":{"id":"wkXXGRojYJn0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["image = Image.open('/content/hateful_memes/img/01269.png')"],"metadata":{"id":"xFvx0kRBZWxa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers.feature_extraction_utils import FeatureExtractionMixin"],"metadata":{"id":"YzZIyCVVbT3N"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.\n","from transformers import BertTokenizer, VisualBertForVisualReasoning\n","import torch\n","\n","tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n","model = VisualBertForVisualReasoning.from_pretrained('uclanlp/visualbert-nlvr2')\n","\n","text = \"Who is in photo?\"\n","inputs = tokenizer(text, return_tensors='pt')\n","\n","feature_extractor = FeatureExtractionMixin.from_pretrained('uclanlp/visualbert-nlvr2')\n","encoding = feature_extractor(images=image, return_tensors=\"pt\")\n","encoding.keys()\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","referenced_widgets":["4a0e06dc1a4844b98a450ce55aadfeb9","3e9452ce7bf9463fa6bdf1b385f06c0e","3cd30ce647f9416e9ee949498aa3be6f","d96ead0d8d2d4dcdb9e6d1776f58a939","e70bdd9a727d42628264e4c985babb87","1427d070bdd64ce4bce370a9acced522","27d2f76e1f554b78b863270cee0f4b6b","617ba3707f3b452bb71caa5e67b8185c","159e087d8b664018b569b3c11a59aa1d","1eb54efdb38f4afcbae2a1327841c982","226aa0ab75f343dbbcb436b9cd38f046","be1f8c34512b4b309730a7c9f6ecbfda","cbe3134906314325ad803915f0bd0fa9","5ac2e460df194516a1d23f6b83d2de59","e21f196df9c04eed9fc5d2fbddc3f15d","7b4c06fd4516480fafc120e7dc228934","76ba41cb9cfb4d529cdc5598201aafea","5fbd0519544a461d8fa5b41bc93baa8f","a147d2f2ba554a1b9e6e7430ba00ba4a","b558ffbec0904d629c2277d721f251b6","d77e53123c2f4e8da6d9e173b4d748a6","57863e7e733340f59f055c3f3e374c38","6ee067876dec427ca5183ac41dcd5761","7951c6f1438e4ec5b6ee97f499cd092f","891a71efa22b4decb19f8a2f60b8c0d8","4eeccb8dad9f4c3cacb31a93dc675d82","de84337af88f48ce94794a87f11eab61","129e40d552b34872bc4b6939cb679886","85474c74ae6e46dc8164e56de57b2330","c701b9e8c7cd4924ab203b2eb2bfa57c","f4860156f0394394b44a36518d6e1587","ab862abd8e614a5ab71cc48df21a1706","00d552efcaed498ab6284994736313f7","f9324c819a8744c7a7b0a49ec79e45e9","46fd808ccc9742b694b7f5fe0703ecb7","0641059713cc45d89ac2cfbf9a7f85c4","e5632f8b76b8484fbf26674b94afafb9","d0e8bc523d77419c8c84efb2011f6626","4e4ec83598f9409e929e90353c9815a2","a6d014d6eea24eca9b2ad7790774990c","b005007669144c3a92c1f1b16627781b","39cdbeadb60949e680dedf39103d1d4c","9c3e69a45ed44615951e680eea8eb95e","0dda52072db74fedb18f8f0543486ce6","cb20820032014515bf57ad7069072bbe","bb7c1bd5b9ef402dae68ad98a208c2e5","4e8cf5e3328f4640b318ed4a17a14b67","17bfccae4bac45afa7d360695c85c113","0373d91476284a4795e55e5e25c71c89","882afad4846342b69109d794936d2765","529b3b4856f344ebbccee1d329f9f8e2","32095e76bc8e42fe887f42e7910584c8","145c4be7d0d941aa9cbd47f8124b6b3b","aa5e8ecabb9148d4975d53114db1269d","d2d36ed55738490fa616ca548dc9369d","287bd3f2c86e44c4ac739b8c71cd8921","0a2ffade128d40ad996a38203dbb13c1","cfa872a415624bada7214342ac609adc","a0d04af0dadf4bfcb51a666225e5f116","d0cbbe7ad2144a7893505664fafc5c9e","5defc14f0f644c638bad500f26fd6c51","08a01ce331674bf489039a86386fcaf7","8b6b35fddf6d4db2992a0810395b024e","48bb5098f186434f8fae3e08f3da21cf","f185baa3829b46988f1a3c00a099207c","4ce0859a8a2f4657a7806f0b844cf898"]},"id":"ct3DFb7TYCI_","executionInfo":{"status":"error","timestamp":1642930561978,"user_tz":-60,"elapsed":17196,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"12d17bc7-cf02-4594-d048-a07dbd1ddfb5"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4a0e06dc1a4844b98a450ce55aadfeb9","version_minor":0,"version_major":2},"text/plain":["Downloading: 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last)","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/feature_extraction_utils.py\u001b[0m in \u001b[0;36mget_feature_extractor_dict\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m    379\u001b[0m                 \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 380\u001b[0;31m                 \u001b[0muser_agent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muser_agent\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    381\u001b[0m             )\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/file_utils.py\u001b[0m in \u001b[0;36mcached_path\u001b[0;34m(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, use_auth_token, local_files_only)\u001b[0m\n\u001b[1;32m   1807\u001b[0m             \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1808\u001b[0;31m             \u001b[0mlocal_files_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_files_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1809\u001b[0m         )\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/file_utils.py\u001b[0m in \u001b[0;36mget_from_cache\u001b[0;34m(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)\u001b[0m\n\u001b[1;32m   2003\u001b[0m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_redirects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0metag_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2004\u001b[0;31m             \u001b[0m_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2005\u001b[0m             \u001b[0metag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"X-Linked-Etag\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ETag\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/file_utils.py\u001b[0m in \u001b[0;36m_raise_for_status\u001b[0;34m(request)\u001b[0m\n\u001b[1;32m   1926\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0merror_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"EntryNotFound\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mEntryNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"404 Client Error: Entry Not Found for url: {request.url}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1928\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0merror_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"RevisionNotFound\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mEntryNotFoundError\u001b[0m: 404 Client Error: Entry Not Found for url: https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/preprocessor_config.json","\nDuring handling of the above exception, another exception occurred:\n","\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)","\u001b[0;32m<ipython-input-27-42b69d020141>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mfeature_extractor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFeatureExtractionMixin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'uclanlp/visualbert-nlvr2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature_extractor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/feature_extraction_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m    302\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0munused_kwargs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"foo\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m         ```\"\"\"\n\u001b[0;32m--> 304\u001b[0;31m         \u001b[0mfeature_extractor_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_feature_extractor_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_extractor_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/feature_extraction_utils.py\u001b[0m in \u001b[0;36mget_feature_extractor_dict\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m    399\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m             raise EnvironmentError(\n\u001b[0;32m--> 401\u001b[0;31m                 \u001b[0;34mf\"{pretrained_model_name_or_path} does not appear to have a file named {FEATURE_EXTRACTOR_NAME}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    402\u001b[0m             )\n\u001b[1;32m    403\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mOSError\u001b[0m: uclanlp/visualbert-nlvr2 does not appear to have a file named preprocessor_config.json."]}]},{"cell_type":"code","source":["\n","visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)\n","visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)\n","\n","inputs.update({\n","\"visual_embeds\": visual_embeds,\n","\"visual_token_type_ids\": visual_token_type_ids,\n","\"visual_attention_mask\": visual_attention_mask\n","})\n","\n","\n","outputs = model(**inputs)\n","loss = outputs.loss\n","scores = outputs.logits"],"metadata":{"id":"8U6ZJejNSiw7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# ViT"],"metadata":{"id":"VZjm5GVBK7yz"}},{"cell_type":"code","source":["!pip install -q git+https://github.com/huggingface/transformers.git"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iIWQTrTzK-Px","executionInfo":{"status":"ok","timestamp":1642928579714,"user_tz":-60,"elapsed":26261,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"849155bb-3846-4541-a2ff-8a9afe692756"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n","\u001b[K     |████████████████████████████████| 67 kB 3.2 MB/s \n","\u001b[K     |████████████████████████████████| 596 kB 10.9 MB/s \n","\u001b[K     |████████████████████████████████| 895 kB 58.2 MB/s \n","\u001b[K     |████████████████████████████████| 6.8 MB 43.3 MB/s \n","\u001b[?25h  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n"]}]},{"cell_type":"code","source":["from transformers import ViTForImageClassification\n","import torch\n","\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","\n","model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')\n","model.eval()\n","model.to(device)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","referenced_widgets":["2dc6043b35cd4447bf643e8dce9e5fff","899ea5b8ef4d47af83feb812688d7d4a","6d37afde751f4d14a9df97ff55078395","fdc79e680b564bdc90964753827052cb","6b6dbb5368a44385bffd18e54481b8f5","9f2222af110b4b7aa78e54978657d586","c3d6daf22f704fb6bd288d90c699321a","98d2cfcd89a64824b22f8c14987511d2","989a346a30fc4d0e85e675676f380b91","3c40723ac2074b3baab934e6e63d979d","df40415c27c6491e8ac2e1b3c032c08c","fe941bf93252470da5dc97bc9251d2b3","25ea9a12e4f44fcdbd0fb1bfd84cacaf","06a5cbc79e7240f28632e31455ca4773","cc9f70262602477b857ae46e146cb6b1","d9a7ff11e2254dfea31a59dafdc5d994","ef98ad13b5fb44de9bb60c68c4263075","8d2874d0c7c4439aaa73855443eb0d8b","a46300f59de54c4b9772f37b20ce3de1","5e545725e0e541d1956987fde66edc46","b0238f2e718e4f07abef1da9ec775e53","53eb0f222a92469c991b3a0c9ab943fa"]},"id":"3lNRzXT4LHTd","executionInfo":{"status":"ok","timestamp":1642928627997,"user_tz":-60,"elapsed":26517,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"db20a7ca-0dca-412e-c90a-6adcec5fc15e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2dc6043b35cd4447bf643e8dce9e5fff","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/68.0k [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fe941bf93252470da5dc97bc9251d2b3","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/330M [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["ViTForImageClassification(\n","  (vit): ViTModel(\n","    (embeddings): ViTEmbeddings(\n","      (patch_embeddings): PatchEmbeddings(\n","        (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n","      )\n","      (dropout): Dropout(p=0.0, inplace=False)\n","    )\n","    (encoder): ViTEncoder(\n","      (layer): ModuleList(\n","        (0): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (1): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (2): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (3): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (4): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (5): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (6): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (7): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (8): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (9): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (10): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","        (11): ViTLayer(\n","          (attention): ViTAttention(\n","            (attention): ViTSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","      )\n","    )\n","    (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","  )\n","  (classifier): Linear(in_features=768, out_features=1000, bias=True)\n",")"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["from transformers import ViTFeatureExtractor\n","\n","feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')\n","encoding = feature_extractor(images=image, return_tensors=\"pt\")\n","encoding.keys()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fPm5Tm4ALP_c","executionInfo":{"status":"ok","timestamp":1642928844364,"user_tz":-60,"elapsed":288,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"f957024f-cf33-4700-c5da-1eaea818a38f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['pixel_values'])"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["encoding['pixel_values'].shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pBWdAIi8LRGz","executionInfo":{"status":"ok","timestamp":1642928846538,"user_tz":-60,"elapsed":2,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"8dd38272-1085-463e-fe4f-c61935fe034b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1, 3, 224, 224])"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["pixel_values = encoding['pixel_values'].to(device)\n","\n","outputs = model(pixel_values)\n","logits = outputs.logits\n","logits.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fb5hkthiLiHm","executionInfo":{"status":"ok","timestamp":1642928849333,"user_tz":-60,"elapsed":243,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"6cd66f38-a692-4aec-fe0d-67beac5b088d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1, 1000])"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["prediction = logits.argmax(-1)\n","print(\"Predicted class:\", model.config.id2label[prediction.item()])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1yV8P-DBLrap","executionInfo":{"status":"ok","timestamp":1642928850597,"user_tz":-60,"elapsed":342,"user":{"displayName":"Umbylyno","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgO0iV7sHLgSeqk96CWRSyKP4e5uYYu8n8T_7BNjw=s64","userId":"02492417231911150447"}},"outputId":"42a7cb40-2b16-499d-baac-6b6b3aa354cc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Predicted class: comic book\n"]}]}]}
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