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Fine-tune Idefics2 on your own use-case\n","\n","

\n"," \"Idefics-Obelics\n","

\n","\n","This tutorial showcases how one can fine-tune Idefics2 on their own use-case.\n","\n","Idefics2 is an open multimodal model that accepts arbitrary sequences of image and text inputs and produces text outputs. The model can answer questions about images, describe visual content, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. It improves upon Idefics1, significantly enhancing capabilities around OCR, document understanding and visual reasoning.\n","\n","Read more about Idefics2 this [blogpost](https://huggingface.co/blog/idefics2) and the [model card](https://huggingface.co/HuggingFaceM4/idefics2-8b)."],"metadata":{"id":"UiDl78dSKKHK"}},{"cell_type":"markdown","source":["# Setup\n","\n","We first setup the environment with the primary necessary libraries and login into Hugging Face."],"metadata":{"id":"LEn_UMRFKMua"}},{"cell_type":"code","source":["!pip install -q git+https://github.com/huggingface/transformers.git\n","!pip install -q accelerate datasets peft bitsandbytes\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RsPQHbF3KM97","executionInfo":{"status":"ok","timestamp":1715533611368,"user_tz":-120,"elapsed":105228,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}},"outputId":"68bdfeac-87d8-4a74-d0c0-5e8238b75c72"},"execution_count":1,"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 metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.6/302.6 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.1/199.1 kB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m22.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}]},{"cell_type":"code","source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["03d462afc44d4e0aa1bce8467b70de43","53b7d4b493114063b41977820628a2e2","77bdad6f014b484f98bdd6e0547c1dfe","3e17e3e11c084a47affa36807e6d4295","25329a3c73cd48d9972bae2df3b59423","629c658e285f4b6a97e6db149e745508","23de99060d0047c38e3ef1597923f3b9","3ba29e1a318a4cf6b3a065f7ee4bdeb1","0cbec2a1a4c04798a8ff87cba5a8c628","3c9980f7c9d7425cbedcc34370766fb3","9a7ed016aa1b4af7887a8479ac890928","4261664530b047bf88adc2b5521eb9d6","c46def5b24054fb796991618642202e3","b83eece01c204e1db67f191ac4ebda20","c6b95e0a0ab5470babfd171bd0b63dc2","f523ba2d68844c83b98e6f994fac21a2","d1b856b16ea84158a662bc2a9bad897d","a9d9632cfbff4c3f9b86a59fd85129ab","88550470171d4c1fb0b2f6b255057079","754a44a709db439bb309f3cbfd773b12","5dd63f1a539e4a9d8659265b80c8a9ed","e3315558182943aaac4fa3d119c62715","f7dcb89241d54038a1d1894ec2ccd7ab","913bd895dc294b57b848b9a762ad9150","b299fac787a44659b3c6053eb30ce8bb","1a68570cfb884089b59a8b9ef0cae2d2","98dd1ac1d6da42e8a2c72d53d8025086","c441089422e74db495decab01014ce22","6f8c13a65a8348ce83147e456f9c196a","af1e6ffa86dc4a2e925d14b763311cd7","c89e84fad3614b1c9f2d84ca5fcaf83e","e1c907b33f9f485091a5aa29784f2736","ec21e69ba81e4375ba1dc36ba043cd9f","c8375e2ec80a4f0c877d5b0383358934","0453cf39148e40ab95be370687169c42","45917c19555a4bc0b16cab0dcf89815f","da863a35e1e54aa7a92dbff431d51dfd","dadf6af249b64d6fb0594bf8f5c75faf","1025bc80a5da4d98a1cce8a7b3782756","1cf5b4fbb2d14d6eba48bd08de1b97fc","2d3289d84e414d12ba037a60c44e4133","b74aba2f61ea4e36af9c56689d8ba510","eb7e15add7f34ab887d0d20dff0b1d26","51d38992c2ac404981dfa6c5cd8d6e46","b07b045e4eb64a74b487c4d897c6ba33","0d199bf4ad004b2ab38039034212e80d","85b2aed18fdf4168a14b97cb65104c6a"]},"id":"xHGtfnyBKXEb","executionInfo":{"status":"ok","timestamp":1715533611737,"user_tz":-120,"elapsed":377,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}},"outputId":"aca3d43b-828d-4c72-96b4-80dc7c3b40b3"},"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='
-Wide ho... \n","2 High-waist skort. False welt pockets on the fr... \n","3 High-waist skirt with an elastic waistband. A-... \n","4 Loose-fitting high-waist trousers with a strai... \n","... ... \n","23243 SLIM FIT - ROUND NECK - SHORT - LONG SLEEVES\n","
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Unnamed: 0PARTNUMBERCOLORSUBSECCIONFAMILYBASE_COLORCOLOR_HEXPHOTO_PLAINPHOTO_MODELDESCRIPTIONNAME
0113352110800-I2023800Trf['shoes', 'sandals']['black']['#19191b']/2023/I/1/1/p/3352/110/800/2/w/400/3352110800_.../2023/I/1/1/p/3352/110/800/2/w/400/3352110800_...Fabric high-heel sandals. Embellished floral f...HIGH-HEEL SANDALS WITH FLORAL DETAILS ...
13904548287817-I2023817Basic['jewelry', 'earrings', 'ear cuffs']['grey']['#d7d4d2']/2023/I/0/1/p/4548/287/817/2/w/400/4548287817_.../2023/I/0/1/p/4548/287/817/17/w/400/4548287817...-Pack of metal ear cuff earrings.<br/>-Wide ho...PACK OF EAR CUFFS ...
24508338909401-I2023401Trf['skorts']['blue']['#141827']/2023/I/0/1/p/8338/909/401/2/w/400/8338909401_.../2023/I/0/1/p/8338/909/401/2/w/400/8338909401_...High-waist skort. False welt pockets on the fr...TEXTURED ASYMMETRIC SKORT ...
34602142043712-V2023712Woman['skirts', 'knit', 'knitwear']['neutral']['#e9e5d8']/2023/V/0/1/p/2142/043/712/2/w/400/2142043712_.../2023/V/0/1/p/2142/043/712/2/w/400/2142043712_...High-waist skirt with an elastic waistband. A-...KNIT SKATER MINI SKIRT ...
46002216483631-I2023631Woman['trousers']['pink']['#ce0066']/2023/V/0/1/p/2216/483/631/2/w/400/2216483631_.../2023/V/0/1/p/2216/483/631/2/w/400/2216483631_...Loose-fitting high-waist trousers with a strai...LONG FLOWING TROUSERS ...
....................................
2324315891603641844641-I2023641Trf['t-shirts']['red']['#902121']/2023/I/0/1/p/3641/844/641/2/w/400/3641844641_.../2023/I/0/1/p/3641/844/641/2/w/400/3641844641_...SLIM FIT - ROUND NECK - SHORT - LONG SLEEVES<b...CROPPED POLYAMIDE T-SHIRT ...
2324415892103641844737-I2023737Trf['t-shirts']['grey']['#6f534a']/2023/I/0/1/p/3641/844/737/2/w/400/3641844737_.../2023/I/0/1/p/3641/844/737/17/w/400/3641844737...SLIM FIT - ROUND NECK - SHORT - LONG SLEEVES<b...CROPPED POLYAMIDE T-SHIRT ...
2324515892603641844805-I2023805Trf['t-shirts']['grey']['#e6e5e3']/2023/I/0/1/p/3641/844/805/2/w/400/3641844805_.../2023/I/0/1/p/3641/844/805/17/w/400/3641844805...SLIM FIT - ROUND NECK - SHORT - LONG SLEEVES<b...CROPPED POLYAMIDE T-SHIRT ...
2324615892803641844800-I2023800Trf['t-shirts']['black']['#0a0a0a']/2023/I/0/1/p/3641/844/800/2/w/400/3641844800_.../2023/I/0/1/p/7901/983/030/2/w/400/7901983030_...SLIM FIT - ROUND NECK - SHORT - LONG SLEEVES<b...CROPPED POLYAMIDE T-SHIRT ...
2324715894301758945922-I2023922Lencería['cardigans', 'lingerie']['grey']['#64686b']/2023/I/0/1/p/1758/945/922/2/w/400/1758945922_.../2023/I/0/1/p/1758/945/922/17/w/400/1758945922...Cardigan made of 100% spun wool. Round neck an...100% WOOL CARDIGAN ...
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\", \". \", descog)\n"," desc = re.sub(r\"

\", \". \", desc)\n"," desc = re.sub(r\"
\", \" \", desc)\n"," #desc = re.sub(r\"[\\d+.\\d]\\s?cm\\.\", \" \", desc)\n"," desc = re.sub(r\"[^.]+[0-9.]+\\s?cm\\.\\s?/\\s?[0-9.]+″\", \"\", desc) #uds cm\n"," desc = re.sub(r\"\\s?[0-9]+\\s?ML\\s?\\([0-9]+\\.[0-9]\\s?FL\\.\\s?OZ\\)\", \"\", desc) #uds volumen\n"," desc = re.sub(r\"[^.]+[0-9.]+\\s?x\\s?[0-9.]+\\s?cm\\s?/\\s?[0-9.]+\\s?x\\s?[0-9.]+″\", \"\", desc) #uds area\n"," desc = re.sub(r\"[^.]+[0-9]+%[^.]+\\.\", \"\", desc)\n"," desc = re.sub(r\"[^.]+[0-9.]+\\s?cm\\s?/\\s?[0-9.]+″[^.]+\", \"\", desc)\n"," desc = re.sub(r\"\\s-\\s\", \". \", desc)\n"," desc = re.sub(r\" ZARA WOMAN\", \". ZARA WOMAN\", desc)\n"," desc = re.sub(r\"\\w+®\", \"\", desc)\n","\n","\n"," desc = re.sub(r\"(\\.(\\s*\\.+\\s*)+)+\", \". \", desc)\n"," desc = re.sub(r\"\\.\\s-\", \". \", desc)\n"," desc = re.sub(r\",\\s\", \". \", desc)\n"," desc = re.sub(r\"\\.\\Z\", \"\", desc)\n"," if len(desc) == 0:\n"," continue\n"," while desc[0] == \"-\":\n"," desc = desc[1:]\n"," if (\":\" in desc) and 1==2:\n"," #print(\"======================================================\\n\")\n"," print(descog)\n"," print(\"\\n\")\n"," print(desc)\n"," print(\"\\n\")\n"," print(desc.split(\". \"))\n"," #print(\"\\n\")\n"," print(\"======================================================\\n\")\n"," if True:\n"," descs = []\n"," lista = desc.split(\". \")\n"," for i in lista:\n"," if i.strip() == \"\" or re.search(r\"\\d\", i) or i.strip()[0]==\"*\" or i.strip()[0]==\"-\":\n"," continue\n"," else:\n"," descs.append(i.strip().lower())\n"," df.at[j, 'DESCRIPTION'] = \". \".join(descs)\n","for j, row in df.iterrows():\n","\n"," df.at[j, 'NAME'] = f\" This image contains {row['NAME']}. Describe it\""],"metadata":{"id":"3nRxDC3wKgxs","executionInfo":{"status":"ok","timestamp":1715533849191,"user_tz":-120,"elapsed":10231,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["import zipfile\n","from PIL import Image\n","import os\n","\n","\n","\n","# Ruta al archivo ZIP en Google Drive\n","zip_file_path = 'drive/MyDrive/DESCRIBE - UPV/datos.zip'\n","# Directorio de extracción\n","extract_folder = '/content/extracted_images/'\n","\n","# Crea el directorio de extracción si no existe\n","if not os.path.exists(extract_folder):\n"," os.makedirs(extract_folder)\n","\n","# Extrae el archivo ZIP\n","with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n"," zip_ref.extractall(extract_folder)"],"metadata":{"id":"yJ5rbE8xz8U8","executionInfo":{"status":"ok","timestamp":1715533885012,"user_tz":-120,"elapsed":35833,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["import os\n","import cv2\n","from PIL import Image\n","import numpy as np\n","# Suponiendo que tienes un DataFrame df con la columna 'PARTNUMBER'\n","for i, row in df.iterrows():\n"," img_path = f'extracted_images/datos/{row[\"PARTNUMBER\"]}.png'\n"," if os.path.exists(img_path):\n"," img = Image.open(img_path)\n"," img=img.resize((100,100))\n"," img=img.convert(\"RGB\")\n","\n"," # Redimensionar la imagen al tamaño deseado\n","\n","\n","\n","\n"," # Agregar la imagen al conjunto de entrenamiento\n"," df.at[i, 'PHOTO_PLAIN'] =img\n","\n"],"metadata":{"id":"w-08E-WcKh0Q","executionInfo":{"status":"ok","timestamp":1715534092209,"user_tz":-120,"elapsed":207209,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["df = df.drop(['Unnamed: 0', 'PARTNUMBER','COLOR','COLOR_HEX','PHOTO_MODEL','FAMILY','BASE_COLOR','SUBSECCION'], axis=1)\n","df.head()\n","from PIL import Image\n","import torch\n","from torch.utils.data import Dataset\n","\n","class CustomDataset(Dataset):\n"," def __init__(self, dataframe):\n"," self.dataframe = dataframe\n","\n"," def __len__(self):\n"," return len(self.dataframe)\n","\n"," def __getitem__(self, idx):\n"," item = self.dataframe.iloc[idx] # Obtener el elemento del conjunto de datos\n"," image= item['PHOTO_PLAIN'] # Supongamos que 'PHOTO_PLAIN' contiene las rutas de las imágenes\n"," name = item['NAME']\n"," description = item['DESCRIPTION']\n"," return {'PHOTO_PLAIN': image, 'NAME': name, 'DESCRIPTION': description}\n","from sklearn.model_selection import train_test_split\n","\n","train_dataset, eval_dataset = train_test_split(df, test_size=0.2, random_state=42)\n","train_dataset = CustomDataset(train_dataset)\n","eval_dataset = CustomDataset(eval_dataset)"],"metadata":{"id":"FjM_28sehKA0","executionInfo":{"status":"ok","timestamp":1715534093116,"user_tz":-120,"elapsed":915,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"6hcrWPNtrf_b","executionInfo":{"status":"ok","timestamp":1715534093117,"user_tz":-120,"elapsed":6,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}},"outputId":"666145aa-09fa-4fdd-aa13-7232e5c97a7d"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" PHOTO_PLAIN \\\n","0 This image contains HIGH-HEEL SANDALS ... \n","1 This image contains PACK OF EAR CUFFS ... \n","2 This image contains TEXTURED ASYMMETRI... \n","3 This image contains KNIT SKATER MINI S... \n","4 This image contains LONG FLOWING TROUS... "],"text/html":["\n","
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PHOTO_PLAINDESCRIPTIONNAME
0<PIL.Image.Image image mode=RGB size=100x100 a...fabric high-heel sandals. embellished floral f...<Image> This image contains HIGH-HEEL SANDALS ...
1<PIL.Image.Image image mode=RGB size=100x100 a...pack of metal ear cuff earrings. wide hoop ear...<Image> This image contains PACK OF EAR CUFFS ...
2<PIL.Image.Image image mode=RGB size=100x100 a...high-waist skort. false welt pockets on the fr...<Image> This image contains TEXTURED ASYMMETRI...
3<PIL.Image.Image image mode=RGB size=100x100 a...high-waist skirt with an elastic waistband. a-...<Image> This image contains KNIT SKATER MINI S...
4<PIL.Image.Image image mode=RGB size=100x100 a...loose-fitting high-waist trousers with a strai...<Image> This image contains LONG FLOWING TROUS...
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df","summary":"{\n \"name\": \"df\",\n \"rows\": 23248,\n \"fields\": [\n {\n \"column\": \"PHOTO_PLAIN\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"DESCRIPTION\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 14403,\n \"samples\": [\n \"oversize. v-neck. long. short sleeves. faded-effect cotton t-shirt featuring a v-neck and short sleeves\",\n \"short sleeve t-shirt featuring a round neck. side vents with drawstrings. a dart at the back and an asymmetric hem\",\n \"short open waistcoat with a belt in the same material. zip pockets with flaps. matching lining\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"NAME\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10790,\n \"samples\": [\n \" This image contains OVERSIZE KNIT CROP TOP . Describe it\",\n \" This image contains ZW COLLECTION MANTECO WOOL BLEND COAT WITH TOGGLES . Describe it\",\n \" This image contains CROSSBODY PURSE . Describe it\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","source":["# Training loop\n","\n","We first define the data collator which takes list of samples and return input tensors fed to the model. There are 4 tensors types we are interested:\n","- `input_ids`: these are the input indices fed to the language model\n","- `attention_mask`: the attention mask for the `input_ids` in the language model\n","- `pixel_values`: the (pre-processed) pixel values that encode the image(s). Idefics2 treats images in their native resolution (up to 980) and their native aspect ratio\n","- `pixel_attention_mask`: when multiple image(s) are packed into the same sample (or in the batch), attention masks for the images are necessary because of these images can have different sizes and aspect ratio. This masking ensures that the vision encoder properly forwards the images.\n"],"metadata":{"id":"AIO-qNQQRm99"}},{"cell_type":"code","source":["import random\n","\n","class MyDataCollator:\n"," def __init__(self, processor):\n"," self.processor = processor\n"," self.image_token_id = processor.tokenizer.additional_special_tokens_ids[\n"," processor.tokenizer.additional_special_tokens.index(\"\")\n"," ]\n","\n"," def __call__(self, examples):\n"," texts = []\n"," images = []\n"," for example in examples:\n"," image = example[\"PHOTO_PLAIN\"]\n"," question = example[\"NAME\"]\n"," answer = example[\"DESCRIPTION\"]\n"," messages = [\n"," {\n"," \"role\": \"user\",\n"," \"content\": [\n"," {\"type\": \"text\", \"text\": \"Answer briefly.\"},\n"," {\"type\": \"image\"},\n"," {\"type\": \"text\", \"text\": question}\n"," ]\n"," },\n"," {\n"," \"role\": \"assistant\",\n"," \"content\": [\n"," {\"type\": \"text\", \"text\": answer}\n"," ]\n"," }\n"," ]\n"," text = processor.apply_chat_template(messages, add_generation_prompt=False)\n"," texts.append(text.strip())\n"," images.append([image])\n","\n"," batch = processor(text=texts, images=images, return_tensors=\"pt\", padding=True)\n","\n"," labels = batch[\"input_ids\"].clone()\n"," labels[labels == processor.tokenizer.pad_token_id] = self.image_token_id\n"," batch[\"labels\"] = labels\n","\n"," return batch\n","\n","data_collator = MyDataCollator(processor)\n"],"metadata":{"id":"06CMDrH7Kkdy","executionInfo":{"status":"ok","timestamp":1715534093117,"user_tz":-120,"elapsed":4,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":["We will use HuggingFace Trainer."],"metadata":{"id":"aIPmnaYsKmnv"}},{"cell_type":"code","source":["from transformers import TrainingArguments, Trainer\n","\n","\n","training_args = TrainingArguments(\n"," num_train_epochs=2,\n"," per_device_train_batch_size=2,\n"," per_device_eval_batch_size=8,\n"," gradient_accumulation_steps=8,\n"," warmup_steps=50,\n"," learning_rate=1e-4,\n"," weight_decay=0.01,\n"," logging_steps=25,\n"," output_dir=r\"drive/MyDrive/DESCRIBE - UPV\",\n"," save_strategy=\"steps\",\n"," save_steps=250,\n"," save_total_limit=1,\n"," eval_strategy=\"epoch\",\n"," fp16=True,\n"," hub_model_id=\"idefics2-8b-docvqa-finetuned-tutorial\",\n"," remove_unused_columns=False,\n"," report_to=\"none\",\n","\n","\n",")\n","\n","trainer = Trainer(\n"," model=model,\n"," args=training_args,\n"," data_collator=data_collator,\n"," train_dataset=train_dataset,\n"," eval_dataset=eval_dataset, # You can also evaluate (loss) on the eval set, note that it will incur some additional GPU memory\n",")\n"],"metadata":{"id":"EmlDaOxFKnpb","executionInfo":{"status":"ok","timestamp":1715539228300,"user_tz":-120,"elapsed":207,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":["# Training and pushing to the hub\n","\n","We have all the core building blocks now, so we fine-tune the model!\n","\n","The training can take a few minutes depending on the hardware you use."],"metadata":{"id":"90eTgvpAKpHC"}},{"cell_type":"code","source":["trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":485},"id":"PQy2oZ0BKqeF","executionInfo":{"status":"ok","timestamp":1715548987738,"user_tz":-120,"elapsed":9706007,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}},"outputId":"29247368-e26f-43e7-81a8-aece4ef77b98"},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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EpochTraining LossValidation Loss
00.2851000.339750
10.2142000.293700

"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:399: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:399: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:399: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:399: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:399: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:399: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:399: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:399: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:399: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n"," warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=2324, training_loss=0.2725233615162861, metrics={'train_runtime': 9705.9903, 'train_samples_per_second': 3.832, 'train_steps_per_second': 0.239, 'total_flos': 2.584157381900498e+17, 'train_loss': 0.2725233615162861, 'epoch': 1.9993547693300355})"]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","source":["We push to the fine-tuned checkpoint to the hub!"],"metadata":{"id":"y1FwigHsKru6"}},{"cell_type":"code","source":["trainer.push_to_hub()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RgSYgAHCKt5o","outputId":"da1f3ed4-6401-4184-f7bc-c6e7619d28be"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:399: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n"," warnings.warn(\n"]}]},{"cell_type":"code","source":["\n"],"metadata":{"id":"uNS6u9zhyHxu","executionInfo":{"status":"aborted","timestamp":1715538702865,"user_tz":-120,"elapsed":7,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Evaluation\n","\n","Let's evaluate the model. First, we can have a look at a qualitative generation from the model."],"metadata":{"id":"VJ30xncwKvNA"}},{"cell_type":"code","source":["example = eval_dataset\n","example\n"],"metadata":{"id":"qrC2OT9nKwep","executionInfo":{"status":"aborted","timestamp":1715538702865,"user_tz":-120,"elapsed":7,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["example[5]['PHOTO_PLAIN']"],"metadata":{"id":"EaeZdYXcKxgM","executionInfo":{"status":"aborted","timestamp":1715538702865,"user_tz":-120,"elapsed":6,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","for i in range(100):\n"," model.eval()\n"," image = example[i][\"PHOTO_PLAIN\"]\n"," query = example[i][\"NAME\"]\n","\n"," messages = [\n"," {\n"," \"role\": \"user\",\n"," \"content\": [\n"," {\"type\": \"text\", \"text\": \"Answer briefly.\"},\n"," {\"type\": \"image\"},\n"," {\"type\": \"text\", \"text\": query}\n"," ]\n"," }\n"," ]\n"," text = processor.apply_chat_template(messages, add_generation_prompt=True)\n"," inputs = processor(text=[text.strip()], images=[image], return_tensors=\"pt\", padding=True)\n"," generated_ids = model.generate(**inputs, max_new_tokens=64)\n"," generated_texts = processor.batch_decode(generated_ids[:, inputs[\"input_ids\"].size(1):], skip_special_tokens=True)\n"," print(generated_texts)\n"," print(example[i][\"DESCRIPTION\"])"],"metadata":{"id":"LA2wmpbqKyiw","executionInfo":{"status":"aborted","timestamp":1715538702865,"user_tz":-120,"elapsed":6,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["During the training, we tracked the loss on the evaluation split. It is interesting to measure the performance using the \"true metric\" used for DocVQA.\n","\n","The metric at hand is the *Average Normalized Levenshtein Similarity* (ANLS). The Average Normalized Levenshtein Similarity (ANLS) proposed by [Biten+ ICCV'19](https://arxiv.org/abs/1905.13648) smoothly captures the OCR mistakes applying a slight penalization in case of correct intended responses, but badly recognized. It also makes use of a threshold of value 0.5 that dictates whether the output of the metric will be the ANLS if its value is equal or bigger than 0.5 or 0 otherwise. The key point of this threshold is to determine if the answer has been correctly selected but not properly recognized, or on the contrary, the output is a wrong text selected from the options and given as an answer.\n","\n","We first define a few utilities to compute the ANLS."],"metadata":{"id":"9dx6jP6GK1Qh"}},{"cell_type":"code","source":["!pip install Levenshtein"],"metadata":{"id":"M51V73iTK3lS","executionInfo":{"status":"aborted","timestamp":1715538702865,"user_tz":-120,"elapsed":6,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import Levenshtein\n","\n","def normalized_levenshtein(s1, s2):\n"," len_s1, len_s2 = len(s1), len(s2)\n"," distance = Levenshtein.distance(s1, s2)\n"," return distance / max(len_s1, len_s2)\n","\n","def similarity_score(a_ij, o_q_i, tau=0.5):\n"," nl = normalized_levenshtein(a_ij, o_q_i)\n"," return 1 - nl if nl < tau else 0\n","\n","def average_normalized_levenshtein_similarity(ground_truth, predicted_answers):\n"," assert len(ground_truth) == len(predicted_answers), \"Length of ground_truth and predicted_answers must match.\"\n","\n"," N = len(ground_truth)\n"," total_score = 0\n","\n"," for i in range(N):\n"," a_i = ground_truth[i]\n"," o_q_i = predicted_answers[i]\n"," if o_q_i == \"\":\n"," print(\"Warning: Skipped an empty prediction.\")\n"," max_score = 0\n"," else:\n"," max_score = max(similarity_score(a_ij, o_q_i) for a_ij in a_i)\n","\n"," total_score += max_score\n","\n"," return total_score / N\n"],"metadata":{"id":"aHhkjnZGK5H-","executionInfo":{"status":"aborted","timestamp":1715538702865,"user_tz":-120,"elapsed":7,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Some gpu mem cleaning before inferencing eval. necessary because we are in memory constrained env\n","torch.cuda.empty_cache()\n"],"metadata":{"id":"W7yGE9lmK6h4","executionInfo":{"status":"aborted","timestamp":1715538702866,"user_tz":-120,"elapsed":7,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from tqdm import tqdm\n","\n","EVAL_BATCH_SIZE = 1\n","\n","answers_unique = []\n","generated_texts_unique = []\n","\n","for i in tqdm(range(0, len(eval_dataset), EVAL_BATCH_SIZE)):\n"," examples = eval_dataset[i: i + EVAL_BATCH_SIZE]\n"," answers_unique.extend(examples[\"DESCRIPTION\"])\n"," images = [[im] for im in examples[\"PHOTO_PLAIN\"]]\n"," texts = []\n"," for q in examples[\"NAME\"]:\n"," messages = [\n"," {\n"," \"role\": \"user\",\n"," \"content\": [\n"," {\"type\": \"text\", \"text\": \"Answer briefly.\"},\n"," {\"type\": \"image\"},\n"," {\"type\": \"text\", \"text\": q}\n"," ]\n"," }\n"," ]\n"," text = processor.apply_chat_template(messages, add_generation_prompt=True)\n"," texts.append(text.strip())\n"," inputs = processor(text=texts, images=images, return_tensors=\"pt\", padding=True)\n"," generated_ids = model.generate(**inputs, max_new_tokens=64)\n"," generated_texts = processor.batch_decode(generated_ids[:, inputs[\"input_ids\"].size(1):], skip_special_tokens=True)\n"," generated_texts_unique.extend(generated_texts)\n"],"metadata":{"id":"8Cem8ywHK7nf","executionInfo":{"status":"aborted","timestamp":1715538702866,"user_tz":-120,"elapsed":7,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["generated_texts_unique = [g.strip().strip(\".\") for g in generated_texts_unique]\n","anls = average_normalized_levenshtein_similarity(\n"," ground_truth=answers_unique, predicted_answers=generated_texts_unique,\n",")\n","print(anls)\n"],"metadata":{"id":"5zHANoWQK84e","executionInfo":{"status":"aborted","timestamp":1715538702866,"user_tz":-120,"elapsed":6,"user":{"displayName":"Qilu Diana Wu","userId":"15924277351051054428"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["We obtain an ANLS score of ~65. This is relatively low compared to well-trained models on DocVQA, although keep in mind that we are training and evaluating on a relatively small subset of the data as an exercise. Note that we have not tuned the hyper-parameters.\n","\n","You should now have all the tools you need to fine-tuned Idefics-2 on your own dataset!"],"metadata":{"id":"a0Mn33y1K-H1"}}]} \ No newline at end of file