Hugging Face's logo

Spaces:
sedrickkeh
/
vqa-guessing-game
Runtime error

App Files Community
vqa-guessing-game
File size: 8,398 Bytes 
016285f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
 
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_gpt3')
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.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)



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.sample_strategy='attribute'
args.attribute_n=1
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)}")