{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":39911,"sourceType":"datasetVersion","datasetId":31296},{"sourceId":7947580,"sourceType":"datasetVersion","datasetId":4665333},{"sourceId":20831,"sourceType":"modelInstanceVersion","isSourceIdPinned":true,"modelInstanceId":17249},{"sourceId":20832,"sourceType":"modelInstanceVersion","isSourceIdPinned":true,"modelInstanceId":17250},{"sourceType":"modelInstanceVersion","sourceId":21354,"isSourceIdPinned":true}],"dockerImageVersionId":30664,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"

Fɪɴᴇ Tᴜɴɪɴɢ Rᴏʙᴇʀᴛᴀ Fᴏʀ Lᴀɴɢᴜᴀɢᴇ Eɴᴄᴏᴅɪɴɢ

","metadata":{}},{"cell_type":"markdown","source":"# Imports\n","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nfrom PIL import Image\nfrom collections import defaultdict\nimport torch \nimport torch.nn as nn\nfrom torch.utils.data import Dataset\nimport json\nimport os\nimport shutil\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom pathlib import Path\nfrom copy import deepcopy\nimport time\nfrom tokenizers import ByteLevelBPETokenizer\nfrom transformers import RobertaConfig\nfrom transformers import RobertaForMaskedLM\nfrom transformers import RobertaTokenizerFast\nfrom transformers import DataCollatorForLanguageModeling\nfrom transformers import Seq2SeqTrainer\nfrom transformers import Seq2SeqTrainingArguments\nfrom transformers import Trainer, TrainingArguments","metadata":{"execution":{"iopub.status.busy":"2024-03-29T05:40:06.428258Z","iopub.execute_input":"2024-03-29T05:40:06.428633Z","iopub.status.idle":"2024-03-29T05:40:23.270455Z","shell.execute_reply.started":"2024-03-29T05:40:06.428595Z","shell.execute_reply":"2024-03-29T05:40:23.269665Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"2024-03-29 05:40:15.215817: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-03-29 05:40:15.215929: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-03-29 05:40:15.316117: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Setting up device","metadata":{}},{"cell_type":"code","source":"device = torch.device(\"cuda\" if torch.cuda.is_available() else 'cpu')","metadata":{"execution":{"iopub.status.busy":"2024-03-29T05:40:23.272167Z","iopub.execute_input":"2024-03-29T05:40:23.272845Z","iopub.status.idle":"2024-03-29T05:40:23.299245Z","shell.execute_reply.started":"2024-03-29T05:40:23.272811Z","shell.execute_reply":"2024-03-29T05:40:23.298186Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"# creating json file for flickr10k","metadata":{}},{"cell_type":"markdown","source":"The files have already been created using the following code \n```python\ndf = pd.read_csv('/kaggle/input/flickr-image-dataset/flickr30k_images/results.csv', sep='|')\nimage_list = sorted(list(set(df['image_name'])))\nprint(f\"length of the dataset: {len(image_list)}\")\n\n\ndataset_dict = defaultdict(list)\ndataset_dict = defaultdict(list)\nfor idx, image_id in enumerate(image_list[:10000]):\n filter_df = df[df['image_name']==image_id]\n captions = list()\n for capt in filter_df[' comment']:\n if type(capt)==str:\n capt = f' {capt.strip()[:-2]} '\n captions.append(capt)\n else:\n continue\n dataset_dict[image_id] = deepcopy(captions)\n\njson_string = json.dumps(dataset_dict, indent=4) \nworking_dir = \"/kaggle/working/\"\nfilename = \"flickr10k.json\"\n\n\nfile_path = os.path.join(working_dir, filename)\nwith open(file_path, \"w\") as f:\n f.write(json_string)\n```","metadata":{}},{"cell_type":"markdown","source":"# Deleting directories if they exist","metadata":{}},{"cell_type":"code","source":"def deldir(dir_name):\n dir_path = f'/kaggle/working/{dir_name}'\n if os.path.exists(dir_path):\n shutil.rmtree(dir_path)\n print(f\"Directory '{dir_name}' deleted successfully.\")\n else:\n print(f\"Directory '{dir_name}' not found.\")\n\ndeldir(\"text_files\")\ndeldir('wandb')\ndeldir('RobertaMLM')\ndeldir('Byte_tokenizer')\n ","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:08.270096Z","iopub.execute_input":"2024-03-28T15:27:08.270447Z","iopub.status.idle":"2024-03-28T15:27:08.343814Z","shell.execute_reply.started":"2024-03-28T15:27:08.270416Z","shell.execute_reply":"2024-03-28T15:27:08.342819Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Directory 'text_files' not found.\nDirectory 'wandb' not found.\nDirectory 'RobertaMLM' not found.\nDirectory 'Byte_tokenizer' not found.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Importing json file and using it create BPE","metadata":{}},{"cell_type":"markdown","source":"## Importing json file and coverting to dataframe","metadata":{}},{"cell_type":"code","source":"with open('/kaggle/input/flickr-10k/flickr10k.json','r')as f:\n flickr10k = json.loads(f.read())\ndef findTotalSentence(dataset = flickr10k):\n l = 0\n for elem in dataset.values():l+=len(elem)\n return l\nprint('total number of sentences being: ',findTotalSentence())\n\nflickr10k_df = pd.DataFrame([])\n\ntemp_img = list()\ntemp_captions = list()\nfor image,captlist in flickr10k.items():\n for capt in captlist:\n temp_captions.append(capt.replace(' ','').replace(' ','').strip())\n temp_img.append(f'/kaggle/input/flickr-image-dataset/flickr30k_images/flickr30k_images/{image}')\nflickr10k_df['image'] = deepcopy(temp_img)\nflickr10k_df['captions'] = deepcopy(temp_captions)\nprint('the dataframe formed being: ')\nflickr10k_df[:5]\n\n ","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:08.345779Z","iopub.execute_input":"2024-03-28T15:27:08.346053Z","iopub.status.idle":"2024-03-28T15:27:08.653198Z","shell.execute_reply.started":"2024-03-28T15:27:08.346030Z","shell.execute_reply":"2024-03-28T15:27:08.652240Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"total number of sentences being: 50000\nthe dataframe formed being: \n","output_type":"stream"},{"execution_count":4,"output_type":"execute_result","data":{"text/plain":" image \\\n0 /kaggle/input/flickr-image-dataset/flickr30k_i... \n1 /kaggle/input/flickr-image-dataset/flickr30k_i... \n2 /kaggle/input/flickr-image-dataset/flickr30k_i... \n3 /kaggle/input/flickr-image-dataset/flickr30k_i... \n4 /kaggle/input/flickr-image-dataset/flickr30k_i... \n\n captions \n0 Two young guys with shaggy hair look at their ... \n1 Two young , White males are outside near many ... \n2 Two men in green shirts are standing in a yard \n3 A man in a blue shirt standing in a garden \n4 Two friends enjoy time spent together ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
imagecaptions
0/kaggle/input/flickr-image-dataset/flickr30k_i...Two young guys with shaggy hair look at their ...
1/kaggle/input/flickr-image-dataset/flickr30k_i...Two young , White males are outside near many ...
2/kaggle/input/flickr-image-dataset/flickr30k_i...Two men in green shirts are standing in a yard
3/kaggle/input/flickr-image-dataset/flickr30k_i...A man in a blue shirt standing in a garden
4/kaggle/input/flickr-image-dataset/flickr30k_i...Two friends enjoy time spent together
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## making textfiles out of the captions","metadata":{}},{"cell_type":"code","source":"idx = 0\nos.mkdir('text_files')\nfilter_df = flickr10k_df['captions']\nfilter_df = filter_df.replace('\\n',' ')\nfor caption in filter_df.to_list():\n with open(f'/kaggle/working/text_files/{idx}.txt','wb') as f:\n f.write(caption.encode('utf-8'))\n idx+=1\n \n","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:08.654210Z","iopub.execute_input":"2024-03-28T15:27:08.654470Z","iopub.status.idle":"2024-03-28T15:27:10.673682Z","shell.execute_reply.started":"2024-03-28T15:27:08.654448Z","shell.execute_reply":"2024-03-28T15:27:10.672890Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"## Creating BPE tokens","metadata":{}},{"cell_type":"code","source":"deldir('Byte_tokenizer')\nos.mkdir('Byte_tokenizer')\npaths = [str(x) for x in Path(\".\").glob(\"text_files/*.txt\")]\ntokenizer = ByteLevelBPETokenizer(lowercase=True)\ntokenizer.train(files=paths, vocab_size=20000, min_frequency=2, show_progress=True, special_tokens=[\"\",\"\",\"\",\"\",\"\"])\ntokenizer.save_model('Byte_tokenizer')","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:10.674713Z","iopub.execute_input":"2024-03-28T15:27:10.674970Z","iopub.status.idle":"2024-03-28T15:27:12.878588Z","shell.execute_reply.started":"2024-03-28T15:27:10.674949Z","shell.execute_reply":"2024-03-28T15:27:12.877700Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Directory 'Byte_tokenizer' not found.\n\n\n\n","output_type":"stream"},{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"['Byte_tokenizer/vocab.json', 'Byte_tokenizer/merges.txt']"},"metadata":{}}]},{"cell_type":"code","source":"with open('/kaggle/working/Byte_tokenizer/vocab.json','r') as f:\n temp = json.loads(f.read())\n\"vocab_size\", len(temp)","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:12.879590Z","iopub.execute_input":"2024-03-28T15:27:12.879843Z","iopub.status.idle":"2024-03-28T15:27:12.893192Z","shell.execute_reply.started":"2024-03-28T15:27:12.879822Z","shell.execute_reply":"2024-03-28T15:27:12.892351Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"('vocab_size', 12329)"},"metadata":{}}]},{"cell_type":"markdown","source":"# Fine-tuning Language Encoder using Masked Language modeling","metadata":{}},{"cell_type":"markdown","source":"## Preparing MLM Dataset and configuring Roberta for it","metadata":{}},{"cell_type":"code","source":"config = RobertaConfig(\n vocab_size=20000,\n max_position_embeddings=514,\n num_attention_heads=12,\n num_hidden_layers=6,\n type_vocab_size=1,\n)\n\nmodel = RobertaForMaskedLM(config=config)\nmodel.to(device)\nprint('Num parameters: ',model.num_parameters())\ntokenizer = RobertaTokenizerFast.from_pretrained('Byte_tokenizer', max_len = 512)\n\nclass MLMDataset(Dataset):\n def __init__(self, df, tokenizer):\n self.examples = []\n for example in df.values:\n x=tokenizer(example, max_length = 512, truncation=True, padding=True)\n self.examples += [x.input_ids]\n def __len__(self):\n return len(self.examples)\n def __getitem__(self, i):\n return torch.tensor(self.examples[i])\ntrain_set = MLMDataset(flickr10k_df['captions'][:38000],tokenizer)\nval_set = MLMDataset(flickr10k_df['captions'][38000:],tokenizer)","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2024-03-28T15:27:12.894311Z","iopub.execute_input":"2024-03-28T15:27:12.894597Z","iopub.status.idle":"2024-03-28T15:27:20.145078Z","shell.execute_reply.started":"2024-03-28T15:27:12.894566Z","shell.execute_reply":"2024-03-28T15:27:20.144049Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"Num parameters: 58896416\n","output_type":"stream"}]},{"cell_type":"code","source":"l = 0\nfor i in range(len(train_set)):\n l = max(l,len(train_set[i]))\nprint(l)","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:20.146425Z","iopub.execute_input":"2024-03-28T15:27:20.146736Z","iopub.status.idle":"2024-03-28T15:27:20.670312Z","shell.execute_reply.started":"2024-03-28T15:27:20.146711Z","shell.execute_reply":"2024-03-28T15:27:20.669402Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"85\n","output_type":"stream"}]},{"cell_type":"code","source":"len(train_set[1])","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:20.673962Z","iopub.execute_input":"2024-03-28T15:27:20.674225Z","iopub.status.idle":"2024-03-28T15:27:20.680125Z","shell.execute_reply.started":"2024-03-28T15:27:20.674203Z","shell.execute_reply":"2024-03-28T15:27:20.679197Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"16"},"metadata":{}}]},{"cell_type":"markdown","source":"## Using Datacollator for random masking of tokens during MLM","metadata":{}},{"cell_type":"code","source":"from transformers import DataCollatorForLanguageModeling\n\n# Define the Data Collator\ndata_collator = DataCollatorForLanguageModeling(\n tokenizer=tokenizer, mlm=True, mlm_probability=0.15\n)","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:20.681262Z","iopub.execute_input":"2024-03-28T15:27:20.681600Z","iopub.status.idle":"2024-03-28T15:27:20.689626Z","shell.execute_reply.started":"2024-03-28T15:27:20.681570Z","shell.execute_reply":"2024-03-28T15:27:20.688871Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"data_collator","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:20.690921Z","iopub.execute_input":"2024-03-28T15:27:20.691228Z","iopub.status.idle":"2024-03-28T15:27:20.701942Z","shell.execute_reply.started":"2024-03-28T15:27:20.691200Z","shell.execute_reply":"2024-03-28T15:27:20.701068Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"DataCollatorForLanguageModeling(tokenizer=RobertaTokenizerFast(name_or_path='Byte_tokenizer', vocab_size=12329, model_max_length=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '', 'sep_token': '', 'pad_token': '', 'cls_token': '', 'mask_token': ''}, clean_up_tokenization_spaces=True), added_tokens_decoder={\n\t0: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n\t1: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n\t3: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n\t4: AddedToken(\"\", rstrip=False, lstrip=True, single_word=False, normalized=False, special=True),\n\t12329: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n}, mlm=True, mlm_probability=0.15, pad_to_multiple_of=None, tf_experimental_compile=False, return_tensors='pt')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Training Roberta MLM Model","metadata":{}},{"cell_type":"code","source":"training_args = TrainingArguments(\n output_dir= 'RobertaMLM',\n overwrite_output_dir=True,\n evaluation_strategy = 'epoch',\n num_train_epochs=15,\n learning_rate=2e-5,\n weight_decay=5e-5,\n per_device_train_batch_size=8,\n per_device_eval_batch_size=32,\n save_steps=8192,\n save_total_limit=1,\n)\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n data_collator=data_collator,\n train_dataset=train_set,\n eval_dataset=val_set,\n)","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:20.703020Z","iopub.execute_input":"2024-03-28T15:27:20.703788Z","iopub.status.idle":"2024-03-28T15:27:21.892205Z","shell.execute_reply.started":"2024-03-28T15:27:20.703764Z","shell.execute_reply":"2024-03-28T15:27:21.891417Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"trainer.train()","metadata":{"execution":{"iopub.status.busy":"2024-03-28T15:27:21.893198Z","iopub.execute_input":"2024-03-28T15:27:21.893461Z","iopub.status.idle":"2024-03-28T16:11:35.079886Z","shell.execute_reply.started":"2024-03-28T15:27:21.893438Z","shell.execute_reply":"2024-03-28T16:11:35.078808Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit:","output_type":"stream"},{"output_type":"stream","name":"stdin","text":" ········································\n"},{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","text/html":"wandb version 0.16.5 is available! To upgrade, please run:\n $ pip install wandb --upgrade"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","text/html":"Tracking run with wandb version 0.16.3"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","text/html":"Run data is saved locally in /kaggle/working/wandb/run-20240328_152729-cdp1rdwp"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","text/html":"Syncing run crisp-pyramid-19 to Weights & Biases (docs)
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","text/html":" View project at https://wandb.ai/421150/huggingface"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","text/html":" View run at https://wandb.ai/421150/huggingface/runs/cdp1rdwp"},"metadata":{}},{"name":"stderr","text":"We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","text/html":"\n
\n \n \n [71250/71250 43:33, Epoch 15/15]\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
EpochTraining LossValidation Loss
14.8933004.851582
24.1339004.126752
33.8349003.752703
43.5470003.506014
53.4170003.330817
63.2119003.257908
73.1840003.161423
83.0728003.122272
92.9521003.041211
102.8772003.006900
112.9115002.986267
122.8719002.937514
132.8665002.941933
142.7969002.854058
152.7534002.890958

"},"metadata":{}},{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"TrainOutput(global_step=71250, training_loss=3.3569942896792764, metrics={'train_runtime': 2652.8506, 'train_samples_per_second': 214.863, 'train_steps_per_second': 26.858, 'total_flos': 3573062225806848.0, 'train_loss': 3.3569942896792764, 'epoch': 15.0})"},"metadata":{}}]},{"cell_type":"markdown","source":"## Checking the preplexity \n$\\text{Perplexity = } (\\frac{1}{P(s)})^{\\frac{1}{n}} \\text{ where n is the length of the sentence and P(s) is probability of the sentence found by the product of the probability of each masked model}$","metadata":{}},{"cell_type":"code","source":"import math\nf\"Perplexity: {math.exp(trainer.evaluate()['eval_loss']):.2f}\"","metadata":{"execution":{"iopub.status.busy":"2024-03-28T16:11:35.081432Z","iopub.execute_input":"2024-03-28T16:11:35.081877Z","iopub.status.idle":"2024-03-28T16:11:44.408912Z","shell.execute_reply.started":"2024-03-28T16:11:35.081843Z","shell.execute_reply":"2024-03-28T16:11:44.407899Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"","text/html":"\n

\n \n \n [375/375 00:09]\n
\n "},"metadata":{}},{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"'Perplexity: 18.36'"},"metadata":{}}]},{"cell_type":"markdown","source":"## Saving the model","metadata":{}},{"cell_type":"code","source":"tokenizer.save_pretrained('Byte_tokenizer')\ntrainer.save_model('RobertaMLM')","metadata":{"execution":{"iopub.status.busy":"2024-03-28T16:11:44.410147Z","iopub.execute_input":"2024-03-28T16:11:44.410512Z","iopub.status.idle":"2024-03-28T16:11:44.929674Z","shell.execute_reply.started":"2024-03-28T16:11:44.410480Z","shell.execute_reply":"2024-03-28T16:11:44.928634Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"## Evaluating Model","metadata":{}},{"cell_type":"code","source":"from transformers import pipeline\nmasked_testing = pipeline(\n 'fill-mask',\n model = r'RobertaMLM',\n tokenizer = 'Byte_tokenizer'\n)\n\nmasked_testing('A child in a pink dress is up a set of stairs in an entry way')","metadata":{"execution":{"iopub.status.busy":"2024-03-28T16:24:19.011100Z","iopub.execute_input":"2024-03-28T16:24:19.011486Z","iopub.status.idle":"2024-03-28T16:24:19.376102Z","shell.execute_reply.started":"2024-03-28T16:24:19.011455Z","shell.execute_reply":"2024-03-28T16:24:19.375110Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"[{'score': 0.13790640234947205,\n 'token': 435,\n 'token_str': ' holding',\n 'sequence': 'A child in a pink dress is holding up a set of stairs in an entry way'},\n {'score': 0.05985509604215622,\n 'token': 836,\n 'token_str': ' climbing',\n 'sequence': 'A child in a pink dress is climbing up a set of stairs in an entry way'},\n {'score': 0.058108482509851456,\n 'token': 452,\n 'token_str': ' walking',\n 'sequence': 'A child in a pink dress is walking up a set of stairs in an entry way'},\n {'score': 0.05070219188928604,\n 'token': 565,\n 'token_str': ' jumping',\n 'sequence': 'A child in a pink dress is jumping up a set of stairs in an entry way'},\n {'score': 0.026804568246006966,\n 'token': 505,\n 'token_str': ' looking',\n 'sequence': 'A child in a pink dress is looking up a set of stairs in an entry way'}]"},"metadata":{}}]},{"cell_type":"markdown","source":"

Fɪɴᴇ-ᴛᴜɴɪɴɢ VɪT Fᴏʀ Iᴍᴀɢᴇ Eɴᴄᴏᴅɪɴɢ

","metadata":{}},{"cell_type":"markdown","source":"Here we take the image and convert it into image patches that we pass to the Roberta model and apply cross attention. Thus we get attention matrix. The dimentions of the vectors (or matrices) are shown below considering batch size of 20:\n```python\n# without considering multiple heads\nvit_output = vit(image_patches)\nk,v = reshape(vit_output) # 20 x 512 x 768 each\nq = RobertaMLM(text) # 20 x 512 x 768\nattention = q.matmul(k.T) # 20 x 512 x 512\nattended_values = attention.matmul(v) # 20 x 512 x 768\nfinal_values = nn.Linear(768,vocabulary) # 20 x 512 x 12329 as vocab size is 12329\n```\n\n\n","metadata":{}},{"cell_type":"markdown","source":"# Data Processing","metadata":{}},{"cell_type":"code","source":"from IPython.display import display\nwith open('/kaggle/input/flickr-10k/flickr10k.json','r')as f:\n flickr10k = json.loads(f.read())\nprint('total number of images being: ',len(flickr10k))\n\n# train test splitting\nflickr10k_train = dict()\nflickr10k_val = dict()\nflick = list(flickr10k.items())\ntrain_indices = np.random.choice(np.arange(0, 10000), size=8000, replace=False)\nfor train_idx in train_indices:\n flickr10k_train[flick[train_idx][0]] = deepcopy(flick[train_idx][1])\nfor img in flickr10k.keys():\n if img not in flickr10k_train:\n flickr10k_val[img] = deepcopy(flickr10k[img])\n \nprint(f'length of trainset being: {len(flickr10k_train)} length of validation set being: {len(flickr10k_val)}') \n\n#converting to dataframes\ndef convertToDf(dataset):\n df = pd.DataFrame([])\n temp_img = list()\n temp_captions = list()\n for image,captlist in dataset.items():\n for capt in captlist:\n temp_captions.append(capt.replace(' ','').replace(' ','').strip())\n temp_img.append(f'/kaggle/input/flickr-image-dataset/flickr30k_images/flickr30k_images/{image}')\n df['image'] = deepcopy(temp_img)\n df['captions'] = deepcopy(temp_captions)\n return df\nflickr10k_train_df = convertToDf(flickr10k_train)\nflickr10k_val_df = convertToDf(flickr10k_val)\n\nprint('\\nVal Set:')\ndisplay(flickr10k_val_df.head())\nprint('Train Set:')\ndisplay(flickr10k_train_df.head())\n","metadata":{"execution":{"iopub.status.busy":"2024-03-29T07:10:10.751851Z","iopub.execute_input":"2024-03-29T07:10:10.752224Z","iopub.status.idle":"2024-03-29T07:10:11.056011Z","shell.execute_reply.started":"2024-03-29T07:10:10.752194Z","shell.execute_reply":"2024-03-29T07:10:11.054964Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"total number of images being: 10000\nlength of trainset being: 8000 length of validation set being: 2000\n\nVal Set:\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" image \\\n0 /kaggle/input/flickr-image-dataset/flickr30k_i... \n1 /kaggle/input/flickr-image-dataset/flickr30k_i... \n2 /kaggle/input/flickr-image-dataset/flickr30k_i... \n3 /kaggle/input/flickr-image-dataset/flickr30k_i... \n4 /kaggle/input/flickr-image-dataset/flickr30k_i... \n\n captions \n0 Several men in hard hats are operating a giant... \n1 Workers look down from up above on a piece of ... \n2 Two men working on a machine wearing hard hats \n3 Four men on top of a tall structure \n4 Three men on a large rig ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
imagecaptions
0/kaggle/input/flickr-image-dataset/flickr30k_i...Several men in hard hats are operating a giant...
1/kaggle/input/flickr-image-dataset/flickr30k_i...Workers look down from up above on a piece of ...
2/kaggle/input/flickr-image-dataset/flickr30k_i...Two men working on a machine wearing hard hats
3/kaggle/input/flickr-image-dataset/flickr30k_i...Four men on top of a tall structure
4/kaggle/input/flickr-image-dataset/flickr30k_i...Three men on a large rig
\n
"},"metadata":{}},{"name":"stdout","text":"Train Set:\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" image \\\n0 /kaggle/input/flickr-image-dataset/flickr30k_i... \n1 /kaggle/input/flickr-image-dataset/flickr30k_i... \n2 /kaggle/input/flickr-image-dataset/flickr30k_i... \n3 /kaggle/input/flickr-image-dataset/flickr30k_i... \n4 /kaggle/input/flickr-image-dataset/flickr30k_i... \n\n captions \n0 There are two boats full of people in a river ... \n1 A group of people in two boats with 1 guy in G... \n2 The people are riding a gondola down the calm ... \n3 Two boats are being maneuvered down a quiet river \n4 two boats float down on the riv ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
imagecaptions
0/kaggle/input/flickr-image-dataset/flickr30k_i...There are two boats full of people in a river ...
1/kaggle/input/flickr-image-dataset/flickr30k_i...A group of people in two boats with 1 guy in G...
2/kaggle/input/flickr-image-dataset/flickr30k_i...The people are riding a gondola down the calm ...
3/kaggle/input/flickr-image-dataset/flickr30k_i...Two boats are being maneuvered down a quiet river
4/kaggle/input/flickr-image-dataset/flickr30k_i...two boats float down on the riv
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Vision Encoder and tying the Decoder","metadata":{}},{"cell_type":"code","source":"from transformers import ViTImageProcessor, RobertaTokenizerFast, VisionEncoderDecoderModel, VisionEncoderDecoderConfig\nimage_processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')\ntokenizer = RobertaTokenizerFast.from_pretrained('/kaggle/input/byte_tokenizer/pytorch/bytetokenizer_flickr10k/1/Byte_tokenizer')\n\n# main model\n\nmodel = VisionEncoderDecoderModel.from_encoder_decoder_pretrained('google/vit-base-patch16-224-in21k',\n '/kaggle/input/robertamlm/pytorch/mlmtrainedbert/1/RobertaMLM',\n tie_encoder_decoder=True,\n )\nmodel.to(device)\nmodel.config.decoder_start_token_id = tokenizer.cls_token_id\nmodel.config.eos_token_id = tokenizer.eos_token_id\nmodel.config.pad_token_id = tokenizer.pad_token_id\nmodel.vocab_size = model.config.decoder.vocab_size\nmodel.config.max_length = 100\nmodel.config.early_stopping = True\nmodel.config.no_repeat_ngram_size = 3\nmodel.config.length_penalty = 2.0\nmodel.config.num_beams = 4\n# model.config.vocab_size","metadata":{"execution":{"iopub.status.busy":"2024-03-29T07:10:12.390201Z","iopub.execute_input":"2024-03-29T07:10:12.391032Z","iopub.status.idle":"2024-03-29T07:10:13.428939Z","shell.execute_reply.started":"2024-03-29T07:10:12.391002Z","shell.execute_reply":"2024-03-29T07:10:13.427944Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stderr","text":"Some weights of RobertaForCausalLM were not initialized from the model checkpoint at /kaggle/input/robertamlm/pytorch/mlmtrainedbert/1/RobertaMLM and are newly initialized: ['roberta.encoder.layer.0.crossattention.output.LayerNorm.bias', 'roberta.encoder.layer.0.crossattention.output.LayerNorm.weight', 'roberta.encoder.layer.0.crossattention.output.dense.bias', 'roberta.encoder.layer.0.crossattention.output.dense.weight', 'roberta.encoder.layer.0.crossattention.self.key.bias', 'roberta.encoder.layer.0.crossattention.self.key.weight', 'roberta.encoder.layer.0.crossattention.self.query.bias', 'roberta.encoder.layer.0.crossattention.self.query.weight', 'roberta.encoder.layer.0.crossattention.self.value.bias', 'roberta.encoder.layer.0.crossattention.self.value.weight', 'roberta.encoder.layer.1.crossattention.output.LayerNorm.bias', 'roberta.encoder.layer.1.crossattention.output.LayerNorm.weight', 'roberta.encoder.layer.1.crossattention.output.dense.bias', 'roberta.encoder.layer.1.crossattention.output.dense.weight', 'roberta.encoder.layer.1.crossattention.self.key.bias', 'roberta.encoder.layer.1.crossattention.self.key.weight', 'roberta.encoder.layer.1.crossattention.self.query.bias', 'roberta.encoder.layer.1.crossattention.self.query.weight', 'roberta.encoder.layer.1.crossattention.self.value.bias', 'roberta.encoder.layer.1.crossattention.self.value.weight', 'roberta.encoder.layer.2.crossattention.output.LayerNorm.bias', 'roberta.encoder.layer.2.crossattention.output.LayerNorm.weight', 'roberta.encoder.layer.2.crossattention.output.dense.bias', 'roberta.encoder.layer.2.crossattention.output.dense.weight', 'roberta.encoder.layer.2.crossattention.self.key.bias', 'roberta.encoder.layer.2.crossattention.self.key.weight', 'roberta.encoder.layer.2.crossattention.self.query.bias', 'roberta.encoder.layer.2.crossattention.self.query.weight', 'roberta.encoder.layer.2.crossattention.self.value.bias', 'roberta.encoder.layer.2.crossattention.self.value.weight', 'roberta.encoder.layer.3.crossattention.output.LayerNorm.bias', 'roberta.encoder.layer.3.crossattention.output.LayerNorm.weight', 'roberta.encoder.layer.3.crossattention.output.dense.bias', 'roberta.encoder.layer.3.crossattention.output.dense.weight', 'roberta.encoder.layer.3.crossattention.self.key.bias', 'roberta.encoder.layer.3.crossattention.self.key.weight', 'roberta.encoder.layer.3.crossattention.self.query.bias', 'roberta.encoder.layer.3.crossattention.self.query.weight', 'roberta.encoder.layer.3.crossattention.self.value.bias', 'roberta.encoder.layer.3.crossattention.self.value.weight', 'roberta.encoder.layer.4.crossattention.output.LayerNorm.bias', 'roberta.encoder.layer.4.crossattention.output.LayerNorm.weight', 'roberta.encoder.layer.4.crossattention.output.dense.bias', 'roberta.encoder.layer.4.crossattention.output.dense.weight', 'roberta.encoder.layer.4.crossattention.self.key.bias', 'roberta.encoder.layer.4.crossattention.self.key.weight', 'roberta.encoder.layer.4.crossattention.self.query.bias', 'roberta.encoder.layer.4.crossattention.self.query.weight', 'roberta.encoder.layer.4.crossattention.self.value.bias', 'roberta.encoder.layer.4.crossattention.self.value.weight', 'roberta.encoder.layer.5.crossattention.output.LayerNorm.bias', 'roberta.encoder.layer.5.crossattention.output.LayerNorm.weight', 'roberta.encoder.layer.5.crossattention.output.dense.bias', 'roberta.encoder.layer.5.crossattention.output.dense.weight', 'roberta.encoder.layer.5.crossattention.self.key.bias', 'roberta.encoder.layer.5.crossattention.self.key.weight', 'roberta.encoder.layer.5.crossattention.self.query.bias', 'roberta.encoder.layer.5.crossattention.self.query.weight', 'roberta.encoder.layer.5.crossattention.self.value.bias', 'roberta.encoder.layer.5.crossattention.self.value.weight']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Dataset","metadata":{}},{"cell_type":"code","source":"max_len = max(np.max(flickr10k_train_df['captions'].apply(lambda x : len(x.split()))), np.max(flickr10k_val_df['captions'].apply(lambda x : len(x.split()))))\ndef findMaxSenLength(captions):\n l = 0\n for capt in captions:\n l = max(l,len(capt.split()))\n return l\nmax_len = max(findMaxSenLength(flickr10k_train_df['captions']),findMaxSenLength(flickr10k_val_df['captions']))\nclass CaptioningDataset(Dataset):\n def __init__(self,df,tokenizer = tokenizer, processor = image_processor, max_length = max_len):\n self.df = df\n self.tokenizer = tokenizer\n self.processor = processor\n self.max_length = max_length\n def __len__(self):\n return len(self.df)\n def __getitem__(self,idx):\n image, caption = Image.open(self.df['image'][idx]).convert('RGB'), self.df['captions'][idx]\n pixel_values = self.processor(images=image, return_tensors=\"pt\").pixel_values\n tokenized_output = self.tokenizer(caption, truncation = True, padding=\"max_length\", max_length=self.max_length)\n lab = tokenized_output.input_ids\n labels = [label if label!=self.tokenizer.pad_token_id else -100 for label in lab]\n attention_mask = tokenized_output.attention_mask\n return {\n 'pixel_values':pixel_values.squeeze(),\n 'labels':torch.tensor(labels),\n 'attention_mask': torch.tensor(attention_mask)\n }\n \ntrain_set = CaptioningDataset(flickr10k_train_df)\nval_set = CaptioningDataset(flickr10k_val_df)\n \n \n \n","metadata":{"execution":{"iopub.status.busy":"2024-03-29T07:10:14.844821Z","iopub.execute_input":"2024-03-29T07:10:14.845678Z","iopub.status.idle":"2024-03-29T07:10:14.992661Z","shell.execute_reply.started":"2024-03-29T07:10:14.845645Z","shell.execute_reply":"2024-03-29T07:10:14.991638Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"markdown","source":"# ROUGE Metric\n* ROUGE-N measures the number of matching n-grams between the model-generated text and a human-produced reference.\n* ROUGE-N precision can be computed as the ratio of the number of n-grams in Candidate that appear also in Reference , over the number of n-grams in Candidate.\n* ROUGE-N recall can be computed as the ratio of the number of n-grams in Candidate that appear also in Reference , over the number of n-grams in Reference.\n* $ROUGE-N \\\\ F1-score = 2 \\times \\frac{(precision \\times recall)}{(precision + recall)} $","metadata":{}},{"cell_type":"code","source":"! pip install rouge_score\nimport datasets\n\ndef compute_metrics(pred):\n rouge = datasets.load_metric('rouge')\n labels_ids = pred.label_ids\n pred_ids = pred.predictions\n pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n labels_ids[labels_ids==-100] = tokenizer.pad_token_id\n label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n\n return {\n \"rouge2_precision\": round(rouge_output.precision, 4),\n \"rouge2_recall\": round(rouge_output.recall, 4),\n \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\n }","metadata":{"execution":{"iopub.status.busy":"2024-03-29T07:10:16.884820Z","iopub.execute_input":"2024-03-29T07:10:16.885155Z","iopub.status.idle":"2024-03-29T07:10:29.186065Z","shell.execute_reply.started":"2024-03-29T07:10:16.885129Z","shell.execute_reply":"2024-03-29T07:10:29.184917Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stderr","text":"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n","output_type":"stream"},{"name":"stdout","text":"Requirement already satisfied: rouge_score in /opt/conda/lib/python3.10/site-packages (0.1.2)\nRequirement already satisfied: absl-py in /opt/conda/lib/python3.10/site-packages (from rouge_score) (1.4.0)\nRequirement already satisfied: nltk in /opt/conda/lib/python3.10/site-packages (from rouge_score) (3.2.4)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from rouge_score) (1.26.4)\nRequirement already satisfied: six>=1.14.0 in /opt/conda/lib/python3.10/site-packages (from rouge_score) (1.16.0)\n","output_type":"stream"}]},{"cell_type":"code","source":"from transformers import default_data_collator\n\ncaptioning_model = 'VIT_Captioning'\ntraining_args = Seq2SeqTrainingArguments(\n output_dir=captioning_model,\n per_device_train_batch_size=20,\n per_device_eval_batch_size=20,\n predict_with_generate=True,\n fp16=True,\n evaluation_strategy=\"epoch\",\n do_train=True,\n do_eval=True,\n logging_steps=1024, \n save_steps=2048, \n warmup_steps=1024, \n num_train_epochs = 3, \n overwrite_output_dir=True,\n save_total_limit=1,\n)\n\n\ntrainer = Seq2SeqTrainer(\n tokenizer=image_processor,\n model=model,\n args=training_args,\n compute_metrics=compute_metrics,\n train_dataset=train_set,\n eval_dataset=val_set,\n data_collator=default_data_collator,\n)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-29T07:10:29.188613Z","iopub.execute_input":"2024-03-29T07:10:29.189004Z","iopub.status.idle":"2024-03-29T07:10:29.216416Z","shell.execute_reply.started":"2024-03-29T07:10:29.188964Z","shell.execute_reply":"2024-03-29T07:10:29.215324Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"trainer.train()","metadata":{"execution":{"iopub.status.busy":"2024-03-29T07:10:29.428025Z","iopub.execute_input":"2024-03-29T07:10:29.428828Z","iopub.status.idle":"2024-03-29T09:00:20.321523Z","shell.execute_reply.started":"2024-03-29T07:10:29.428800Z","shell.execute_reply":"2024-03-29T09:00:20.320646Z"},"trusted":true},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":"","text/html":"\n
\n \n \n [6000/6000 1:49:48, Epoch 3/3]\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
EpochTraining LossValidation LossRouge2 PrecisionRouge2 RecallRouge2 Fmeasure
13.8419003.1441350.0984000.1016000.095400
22.9137002.9607030.1191000.1174000.113300
32.6206002.9103230.1147000.1245000.114100

"},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/transformers/generation/utils.py:1339: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n warnings.warn(\nSome non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\nNon-default generation parameters: {'max_length': 100, 'early_stopping': True, 'num_beams': 4, 'length_penalty': 2.0, 'no_repeat_ngram_size': 3}\nRemoved shared tensor {'decoder.lm_head.decoder.weight', 'decoder.lm_head.decoder.bias'} while saving. This should be OK, but check by verifying that you don't receive any warning while reloading\nSome non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\nNon-default generation parameters: {'max_length': 100, 'early_stopping': True, 'num_beams': 4, 'length_penalty': 2.0, 'no_repeat_ngram_size': 3}\n","output_type":"stream"},{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"TrainOutput(global_step=6000, training_loss=3.0057042236328124, metrics={'train_runtime': 6589.8632, 'train_samples_per_second': 18.21, 'train_steps_per_second': 0.91, 'total_flos': 1.557569949401088e+19, 'train_loss': 3.0057042236328124, 'epoch': 3.0})"},"metadata":{}}]},{"cell_type":"code","source":"trainer.save_model('ViT_Roberta_Image_Captioning')\n","metadata":{"execution":{"iopub.status.busy":"2024-03-29T09:00:20.323529Z","iopub.execute_input":"2024-03-29T09:00:20.323892Z","iopub.status.idle":"2024-03-29T09:00:21.155937Z","shell.execute_reply.started":"2024-03-29T09:00:20.323860Z","shell.execute_reply":"2024-03-29T09:00:21.154884Z"},"trusted":true},"execution_count":38,"outputs":[{"name":"stderr","text":"Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\nNon-default generation parameters: {'max_length': 100, 'early_stopping': True, 'num_beams': 4, 'length_penalty': 2.0, 'no_repeat_ngram_size': 3}\n","output_type":"stream"}]},{"cell_type":"code","source":"t = VisionEncoderDecoderModel.from_pretrained('/kaggle/working/ViT_Roberta_Image_Captioning')\ntemp = flickr10k_val_df.sample(1).image.iloc[0]\nplt.imshow(Image.open(temp).convert(\"RGB\"))\ncap = tokenizer.decode(t.generate(image_processor(Image.open(temp).convert(\"RGB\"), return_tensors=\"pt\").pixel_values)[0])\nprint(f'caption is: {cap.replace(\"\",\"\").replace(\"\",\"\")}')","metadata":{"execution":{"iopub.status.busy":"2024-03-29T09:11:48.534922Z","iopub.execute_input":"2024-03-29T09:11:48.535487Z","iopub.status.idle":"2024-03-29T09:11:52.825368Z","shell.execute_reply.started":"2024-03-29T09:11:48.535456Z","shell.execute_reply":"2024-03-29T09:11:52.824324Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stderr","text":"The following encoder weights were not tied to the decoder ['vision_encoder_decoder/embeddings', 'vision_encoder_decoder/pooler', 'vision_encoder_decoder/encoder', 'vision_encoder_decoder/layernorm']\nThe following encoder weights were not tied to the decoder ['vision_encoder_decoder/embeddings', 'vision_encoder_decoder/pooler', 'vision_encoder_decoder/encoder', 'vision_encoder_decoder/layernorm']\nThe following encoder weights were not tied to the decoder ['vision_encoder_decoder/embeddings', 'vision_encoder_decoder/pooler', 'vision_encoder_decoder/encoder', 'vision_encoder_decoder/layernorm']\n","output_type":"stream"},{"name":"stdout","text":"caption is: A man in a red shirt and blue jeans is working on a rock\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"

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"},"metadata":{}}]}]}