Every picture tells a story: Controllable neural story generation from images

How to:

1. Make sure all requirements are installed, or install it via: pip install -r requirements.txt
2. Get your copy of bookcorpusopen_chunked dataset via copying to your/own/path, either from:
   • /home/bryan/datasets/bookcorpusopen/bookcorpusopen_chunked.arrow
   • /home/holy/datasets/bookcorpusopen/bookcorpusopen_chunked.arrow
3. Run git clone https://github.com/andreamad8/PPCM.git and cd PPCM
4. Download the PPCM models, run ./download_data.sh
5. Move the PPCM models to our repo, run:
   1. cd ..
   2. mkdir ppcm_models/dialoGPT
   3. mv PPCM/models/dialoGPT/* ppcm_models/dialoGPT/
6. In the run_adapter_train.sh change the dataset_path to your/own/path
7. Run bash run_adapter_train.sh to train the adapter with the designated book genres
8. Post training, predict and get the outputs using, e.g.:

   import os
   from transformers import GPT2Tokenizer, TrainingArguments
   from utils.helper import load_model_recursive
   from ppcm_models.pytorch_pretrained_bert.modeling_adapter import GPT2LMHeadModel, GPT2Config
   
   model_args.model_path = f'ppcm_models/dialoGPT/small/'
   config = GPT2Config.from_json_file(os.path.join(model_args.model_path, 'config.json'))
   tokenizer = GPT2Tokenizer.from_pretrained(model_args.model_path)
   
   path = './save/model_run_name/pytorch_model.bin'
   model = load_model_recursive(GPT2LMHeadModel(config), path, model_args, verbose=True)
   
   inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
   outputs = model(inputs['input_ids'], task_id=[0])
   # or
   outputs = model.transformer(inputs['input_ids'], task_id=0)

9. Or generate using, e.g.:

   import os
   import numpy as np
   import torch
   import torch.nn.functional as F
   from transformers import GPT2Tokenizer, TrainingArguments
   from utils.helper import load_model_recursive
   from ppcm_models.pytorch_pretrained_bert.modeling_adapter import GPT2LMHeadModel, GPT2Config
   
   model_args.model_path = f'ppcm_models/dialoGPT/small/'
   config = GPT2Config.from_json_file(os.path.join(model_args.model_path, 'config.json'))
   tokenizer = GPT2Tokenizer.from_pretrained(model_args.model_path)
   
   model_run_names = ['GPT2small_adapterid0_genreAction_matched3_sampleNone_maxseqlen512_bs8_lr5e-05_10.0epoch_wd0.0_ws0',
                      'GPT2small_adapterid0_genreAction_matched3_sampleNone_maxseqlen512_bs8_lr5e-05_2.0epoch_wd0.0_ws0',
                      'GPT2small_adapterid0_genreMystery_matched3_sampleNone_maxseqlen512_bs8_lr0.0005_5.0epoch_wd0.0_ws0',
                      'GPT2small_adapterid0_genreRomance_matched3_sampleNone_maxseqlen512_bs8_lr5e-05_2.0epoch_wd0.0_ws0']
   
   for i, model_run_name in enumerate(model_run_names):
   
       path = './save/'+model_run_name+'/pytorch_model.bin'
       model = load_model_recursive(GPT2LMHeadModel(config), path, model_args, verbose=True)
   
       length = 100
       text = "Hello, my dog is cute"
       generated = tokenizer.encode(text)
       context = torch.tensor([generated])
   
       generation = model.generate(inputs=context,
                                  num_beams=3, 
                                  length_penalty=3, 
                                  early_stopping=1, 
                                  num_beam_groups=3, 
                                  do_sample=False, 
                                  num_return_sequences=2, 
                                  bos_token_id=50256,
                                  eos_token_id=50256,
                                  pad_token_id=50256,
                                  output_scores=True,
                                  output_attentions=True,
                                  output_hidden_states=True,
                                  return_dict_in_generate=True,
                                  repetition_penalty=1.1,
                                  min_length = 0,
                                  max_length = length,
                                  no_repeat_ngram_size=2,
                                  encoder_no_repeat_ngram_size=False,
                                  bad_words_ids=[[100]], # tokenizer.decode(100)
                                  diversity_penalty=0.2,
                                  forced_bos_token_id=50256,
                                  forced_eos_token_id=50256,
                                  remove_invalid_values=True,
                                  exponential_decay_length_penalty=[1.0, 1.2])
   
       print(tokenizer.decode(generation[0][0]), '\n')