model/run_question_asking_model.py

from model.model.question_asking_model import get_question_model
from model.model.caption_model import get_caption_model
from model.model.response_model import get_response_model

import torch
from torch.utils.data import Dataset, DataLoader
from PIL import Image

import argparse
import random
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import logging
from model.utils import logging_handler, image_saver, assert_checks

random.seed(123)

parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--include_what', action='store_true')
parser.add_argument('--target_idx', type=int, default=0)
parser.add_argument('--max_num_questions', type=int, default=25)
parser.add_argument('--num_images', type=int, default=10)
parser.add_argument('--beam', type=int, default=1)
parser.add_argument('--num_samples', type=int, default=100)
parser.add_argument('--threshold', type=float, default=0.9)

parser.add_argument('--caption_strategy', type=str, default='simple', choices=['simple', 'granular', 'gtruth'])
parser.add_argument('--sample_strategy', type=str, default='random', choices=['random', 'attribute', 'clip'])
parser.add_argument('--attribute_n', type=int, default=1)               # Number of attributes to split
parser.add_argument('--response_type_simul', type=str, default='VQA1', choices=['simple', 'QA', 'VQA1', 'VQA2', 'VQA3', 'VQA4'])
parser.add_argument('--response_type_gtruth', type=str, default='VQA2', choices=['simple', 'QA', 'VQA1', 'VQA2', 'VQA3', 'VQA4'])
parser.add_argument('--question_strategy', type=str, default='gpt3', choices=['rule', 'gpt3'])
parser.add_argument('--multiplier_mode', type=str, default='soft', choices=['soft', 'hard', 'none'])

parser.add_argument('--gpt3_save_name', type=str, default='questions_gpt4')
parser.add_argument('--save_name', type=str, default=None)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
args.question_strategy='gpt3'
args.include_what=True
args.response_type_simul='VQA1'
args.response_type_gtruth='VQA3'
args.multiplier_mode='soft'
args.caption_strategy='gtruth'
assert_checks(args)
if args.save_name is None: logger = logging_handler(args.verbose, args.save_name)
args.load_response_model = True

print("1. Loading question model ...")
question_model = get_question_model(args)
args.question_generator = question_model.question_generator
print("2. Loading response model simul ...")
response_model_simul = get_response_model(args, args.response_type_simul)
response_model_simul.to(args.device)
print("3. Loading response model gtruth ...")
response_model_gtruth = get_response_model(args, args.response_type_gtruth)
response_model_gtruth.to(args.device)
print("4. Loading caption model ...")
caption_model = get_caption_model(args, question_model)



def return_modules():
    return question_model, response_model_simul, response_model_gtruth, caption_model 



args.question_strategy='rule'
args.include_what=False
args.response_type_simul='VQA1'
args.response_type_gtruth='VQA3'
args.multiplier_mode='none'
args.caption_strategy='gtruth'

print("1. Loading question model ...")
question_model_yn = get_question_model(args)
args.question_generator_yn = question_model_yn.question_generator
print("2. Loading response model simul ...")
response_model_simul_yn = get_response_model(args, args.response_type_simul)
response_model_simul_yn.to(args.device)
print("3. Loading response model gtruth ...")
response_model_gtruth_yn = get_response_model(args, args.response_type_gtruth)
response_model_gtruth_yn.to(args.device)
print("4. Loading caption model ...")
caption_model_yn = get_caption_model(args, question_model_yn)


def return_modules_yn():
    return question_model_yn, response_model_simul_yn, response_model_gtruth_yn, caption_model_yn 



# args.question_strategy='gpt3'
# args.include_what=True
# args.response_type_simul='VQA1'
# args.response_type_gtruth='VQA3'
# args.multiplier_mode='none'
# args.sample_strategy='attribute'
# args.attribute_n=1
# args.caption_strategy='gtruth'
# assert_checks(args)
# if args.save_name is None: logger = logging_handler(args.verbose, args.save_name)
# args.load_response_model = True

# print("1. Loading question model ...")
# question_model = get_question_model(args)
# args.question_generator = question_model.question_generator
# print("2. Loading response model simul ...")
# response_model_simul = get_response_model(args, args.response_type_simul)
# response_model_simul.to(args.device)
# print("3. Loading response model gtruth ...")
# response_model_gtruth = get_response_model(args, args.response_type_gtruth)
# response_model_gtruth.to(args.device)
# print("4. Loading caption model ...")
# caption_model = get_caption_model(args, question_model)

# # dataloader = DataLoader(dataset=ReferenceGameData(split='test', 
# #                                                   num_images=args.num_images, 
# #                                                   num_samples=args.num_samples,
# #                                                   sample_strategy=args.sample_strategy,
# #                                                   attribute_n=args.attribute_n))

# def return_modules():
#     return question_model, response_model_simul, response_model_gtruth, caption_model 
# # game_lens, game_preds = [], []
# for t, batch in enumerate(tqdm(dataloader)):
#     image_files = [image[0] for image in batch['images'][:args.num_images]]
#     image_files = [str(i).split('/')[1] for i in image_files]
#     with open("mscoco_images_attribute_n=1.txt", 'a') as f:
#         for i in image_files:
#             f.write(str(i)+"\n")
    # images = [np.asarray(Image.open(f"./../../../data/ms-coco/images/{i}")) for i in image_files]
    # images = [np.dstack([i]*3) if len(i.shape)==2 else i for i in images]
#     p_y_x = (torch.ones(args.num_images)/args.num_images).to(question_model.device)

#     if args.save_name is not None: 
#         logger = logging_handler(args.verbose, args.save_name, t)
#         image_saver(images, args.save_name, t)

#     captions = caption_model.get_captions(image_files)
#     questions, target_questions = question_model.get_questions(image_files, captions, args.target_idx)
    
#     question_model.reset_question_bank()
#     logger.info(questions)
#     for idx, c in enumerate(captions): logger.info(f"Image_{idx}: {c}")
    
#     num_questions_original = len(questions)
#     for j in range(min(args.max_num_questions, num_questions_original)):
#         # Select best question
#         question = question_model.select_best_question(p_y_x, questions, images, captions, response_model_simul)
#         logger.info(f"Question: {question}")

#         # Ask the question and get the model's response
#         response = response_model_gtruth.get_response(question, images[args.target_idx], captions[args.target_idx], target_questions, is_a=1-args.include_what)
#         logger.info(f"Response: {response}")

#         # Update the probabilities
#         p_r_qy = response_model_simul.get_p_r_qy(response, question, images, captions)
#         logger.info(f"P(r|q,y):\n{np.around(p_r_qy.cpu().detach().numpy(), 3)}")
#         p_y_xqr = p_y_x*p_r_qy
#         p_y_xqr = p_y_xqr/torch.sum(p_y_xqr)if torch.sum(p_y_xqr) != 0 else torch.zeros_like(p_y_xqr)        
#         p_y_x = p_y_xqr
#         logger.info(f"Updated distribution:\n{np.around(p_y_x.cpu().detach().numpy(), 3)}\n")

#         # Don't repeat the same question again in the future
#         questions.remove(question)

#         # Terminate if probability exceeds threshold or if out of questions to ask
#         top_prob = torch.max(p_y_x).item()
#         if top_prob >= args.threshold or j==min(args.max_num_questions, num_questions_original)-1:
#             game_preds.append(torch.argmax(p_y_x).item())
#             game_lens.append(j+1)
#             logger.info(f"pred:{game_preds[-1]} game_len:{game_lens[-1]}")
#             break

# logger = logging_handler(args.verbose, args.save_name, "final_results")
# logger.info(f"Game lenths:\n{game_lens}")
# logger.info(sum(game_lens)/len(game_lens))
# logger.info(f"Predictions:\n{game_preds}")
# logger.info(f"Accuracy:\n{sum([i==args.target_idx for i in game_preds])/len(game_preds)}")