-
Notifications
You must be signed in to change notification settings - Fork 35
/
main.py
executable file
·156 lines (137 loc) · 6.83 KB
/
main.py
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
"""
This code is modified based on Jin-Hwa Kim's repository (Bilinear Attention Networks - https://github.com/jnhwkim/ban-vqa)
"""
import os
import argparse
import torch
from torch.utils.data import DataLoader, ConcatDataset
import dataset_RAD
import base_model
from train import train
import utils
try:
import _pickle as pickle
except:
import pickle
def parse_args():
parser = argparse.ArgumentParser()
# MODIFIABLE MEVF HYPER-PARAMETERS--------------------------------------------------------------------------------
# Model loading/saving
parser.add_argument('--input', type=str, default=None,
help='input file directory for continue training from stop one')
parser.add_argument('--output', type=str, default='saved_models/san_mevf',
help='save file directory')
# Utilities
parser.add_argument('--seed', type=int, default=1204,
help='random seed')
parser.add_argument('--epochs', type=int, default=20,
help='the number of epoches')
parser.add_argument('--lr', default=0.005, type=float, metavar='lr',
help='initial learning rate')
# Gradient accumulation
parser.add_argument('--batch_size', type=int, default=32,
help='batch size')
parser.add_argument('--update_freq', default='1', metavar='N',
help='update parameters every n batches in an epoch')
# Choices of attention models
parser.add_argument('--model', type=str, default='SAN', choices=['BAN', 'SAN'],
help='the model we use')
# Choices of RNN models
parser.add_argument('--rnn', type=str, default='LSTM', choices=['LSTM', 'GRU'],
help='the RNN we use')
# BAN - Bilinear Attention Networks
parser.add_argument('--gamma', type=int, default=2,
help='glimpse in Bilinear Attention Networks')
parser.add_argument('--use_counter', action='store_true', default=False,
help='use counter module')
# SAN - Stacked Attention Networks
parser.add_argument('--num_stacks', default=2, type=int,
help='num of stacks in Stack Attention Networks')
# Utilities - support testing, gpu training or sampling
parser.add_argument('--print_interval', default=20, type=int, metavar='N',
help='print per certain number of steps')
parser.add_argument('--gpu', type=int, default=0,
help='specify index of GPU using for training, to use CPU: -1')
parser.add_argument('--clip_norm', default=.25, type=float, metavar='NORM',
help='clip threshold of gradients')
# Question embedding
parser.add_argument('--question_len', default=12, type=int, metavar='N',
help='maximum length of input question')
parser.add_argument('--tfidf', type=bool, default=True,
help='tfidf word embedding?')
parser.add_argument('--op', type=str, default='c',
help='concatenated 600-D word embedding')
# Joint representation C dimension
parser.add_argument('--num_hid', type=int, default=1024,
help='dim of joint semantic features')
# Activation function + dropout for classification module
parser.add_argument('--activation', type=str, default='relu', choices=['relu'],
help='the activation to use for final classifier')
parser.add_argument('--dropout', default=0.5, type=float, metavar='dropout',
help='dropout of rate of final classifier')
# Train with RAD
parser.add_argument('--use_RAD', action='store_true', default=False,
help='Using TDIUC dataset to train')
parser.add_argument('--RAD_dir', type=str,
help='RAD dir')
# Optimization hyper-parameters
parser.add_argument('--eps_cnn', default=1e-5, type=float, metavar='eps_cnn',
help='eps - batch norm for cnn')
parser.add_argument('--momentum_cnn', default=0.05, type=float, metavar='momentum_cnn',
help='momentum - batch norm for cnn')
# input visual feature dimension
parser.add_argument('--feat_dim', default=64, type=int,
help='visual feature dim')
# Auto-encoder component hyper-parameters
parser.add_argument('--autoencoder', action='store_true', default=False,
help='End to end model?')
parser.add_argument('--ae_model_path', type=str, default='pretrained_ae.pth',
help='the maml_model_path we use')
parser.add_argument('--ae_alpha', default=0.001, type=float, metavar='ae_alpha',
help='ae_alpha')
# MAML component hyper-parameters
parser.add_argument('--maml', action='store_true', default=False,
help='End to end model?')
parser.add_argument('--maml_model_path', type=str, default='pretrained_maml.weights',
help='the maml_model_path we use')
# Return args
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
# create output directory and log file
utils.create_dir(args.output)
logger = utils.Logger(os.path.join(args.output, 'log.txt'))
logger.write(args.__repr__())
# Set GPU device
device = torch.device("cuda:" + str(args.gpu) if args.gpu >= 0 else "cpu")
args.device = device
# Fixed ramdom seed
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
# Load dictionary and RAD training dataset
if args.use_RAD:
dictionary = dataset_RAD.Dictionary.load_from_file(os.path.join(args.RAD_dir, 'dictionary.pkl'))
train_dset = dataset_RAD.VQAFeatureDataset('train', args, dictionary, question_len=args.question_len)
batch_size = args.batch_size
# Create VQA model
constructor = 'build_%s' % args.model
model = getattr(base_model, constructor)(train_dset, args)
optim = None
epoch = 0
# load snapshot
if args.input is not None:
print('loading %s' % args.input)
model_data = torch.load(args.input)
model.load_state_dict(model_data.get('model_state', model_data))
model.to(device)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()))
optim.load_state_dict(model_data.get('optimizer_state', model_data))
epoch = model_data['epoch'] + 1
# create training dataloader
train_loader = DataLoader(train_dset, batch_size, shuffle=True, num_workers=0, collate_fn=utils.trim_collate, pin_memory=True)
eval_loader = None
# training phase
train(args, model, train_loader, eval_loader, args.epochs, args.output, optim, epoch)