-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathgenerate_fakes.py
158 lines (140 loc) · 6.96 KB
/
generate_fakes.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
from models.model import VATr
import argparse
from train import rSeed
import os
import torch
from data.dataset import TextDatasetval, TextDataset, FidDataset
from pathlib import Path
def load_checkpoint(model, checkpoint):
old_model = model.state_dict()
if len(checkpoint.keys()) == 241: # default
counter = 0
for k, v in checkpoint.items():
if k in old_model:
old_model[k] = v
counter += 1
elif 'netG.' + k in old_model:
old_model['netG.' + k] = v
counter += 1
ckeys = [k for k in checkpoint.keys() if 'Feat_Encoder' in k]
okeys = [k for k in old_model.keys() if 'Feat_Encoder' in k]
for ck, ok in zip(ckeys, okeys):
old_model[ok] = checkpoint[ck]
counter += 1
assert counter == 241
checkpoint_dict = old_model
else:
assert len(old_model) == len(checkpoint)
checkpoint_dict = {k2: v1 for (k1, v1), (k2, v2) in zip(checkpoint.items(), old_model.items()) if
v1.shape == v2.shape}
assert len(old_model) == len(checkpoint_dict)
model.load_state_dict(checkpoint_dict, strict=False)
return model
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-c", "--checkpoint", type=str, required=True)
parser.add_argument("-o", "--output", type=str, default='saved_images')
parser.add_argument("--feat_model_path", type=str, default='files/resnet_18_pretrained.pth')
parser.add_argument("--label_encoder", default='default', type=str)
parser.add_argument("--save_model_path", default='saved_models', type=str)
parser.add_argument("--dataset", default='IAM', type=str)
parser.add_argument("--english_words_path", default='files/english_words.txt', type=str)
parser.add_argument("--wandb", action='store_true')
parser.add_argument("--no_writer_loss", action='store_true')
parser.add_argument("--writer_loss_weight", type=float, default=1.0)
parser.add_argument("--no_ocr_loss", action='store_true')
parser.add_argument("--img_height", default=32, type=int)
parser.add_argument("--resolution", default=16, type=int)
parser.add_argument("--batch_size", default=8, type=int)
parser.add_argument("--num_examples", default=15, type=int)
parser.add_argument("--tn_hidden_dim", default=512, type=int)
parser.add_argument("--tn_dropout", default=0.1, type=float)
parser.add_argument("--tn_nheads", default=8, type=int)
parser.add_argument("--tn_dim_feedforward", default=512, type=int)
parser.add_argument("--tn_enc_layers", default=3, type=int)
parser.add_argument("--tn_dec_layers", default=3, type=int)
parser.add_argument("--alphabet",
default='Only thewigsofrcvdampbkuq.A-210xT5\'MDL,RYHJ"ISPWENj&BC93VGFKz();#:!7U64Q8?+*ZX/%',
type=str)
parser.add_argument("--special_alphabet", default='ΑαΒβΓγΔδΕεΖζΗηΘθΙιΚκΛλΜμΝνΞξΟοΠπΡρΣσςΤτΥυΦφΧχΨψΩω', type=str)
parser.add_argument("--vocab_size", type=int)
parser.add_argument("--g_lr", default=0.00005, type=float)
parser.add_argument("--d_lr", default=0.000025, type=float)
parser.add_argument("--w_lr", default=0.00005, type=float)
parser.add_argument("--ocr_lr", default=0.00005, type=float)
parser.add_argument("--epochs", default=100_000, type=int)
parser.add_argument("--num_critic_gocr_train", default=1, type=int)
parser.add_argument("--num_critic_docr_train", default=1, type=int)
parser.add_argument("--num_critic_gwl_train", default=1, type=int)
parser.add_argument("--num_critic_dwl_train", default=1, type=int)
parser.add_argument("--num_fid_freq", default=100, type=int)
parser.add_argument("--num_workers", default=0, type=int)
parser.add_argument("--seed", default=742, type=int)
parser.add_argument("--is_seq", default=True, type=bool)
parser.add_argument("--num_words", default=3, type=int)
parser.add_argument("--is_cycle", default=False, type=bool)
parser.add_argument("--is_kld", default=False, type=bool)
parser.add_argument("--add_noise", action='store_true')
parser.add_argument("--all_chars", default=False, type=bool)
parser.add_argument("--save_model", default=5, type=int)
parser.add_argument("--save_model_history", default=500, type=int)
parser.add_argument("--tag", default='debug', type=str)
parser.add_argument("--device", default='cuda', type=str)
parser.add_argument("--query_input", default='unifont', type=str)
parser.add_argument("--query_linear", default=True, type=bool)
args = parser.parse_args()
rSeed(args.seed)
if args.dataset == 'IAM':
args.dataset_path = 'files/IAM-32.pickle'
args.num_writers = 339
elif args.dataset == 'CVL':
args.dataset_path = 'files/CVL-32.pickle'
args.num_writers = 283
else:
raise ValueError
args.vocab_size = len(args.alphabet)
if not args.is_seq: args.num_words = args.num_examples
def filter_nums(loader):
for data in loader:
numeric_labels = [l.decode('utf-8').isnumeric() for l in data['label']]
if not any(numeric_labels): continue
numeric_labels = torch.tensor(numeric_labels).to(args.device)
data = {
'img': data['img'][numeric_labels],
'label': [l for l, b in zip(data['label'], numeric_labels) if b == True],
'swids': data['swids'][numeric_labels],
'simg': data['simg'][numeric_labels],
'wcl': data['wcl'][numeric_labels],
}
yield data
dataset_train = FidDataset(base_path=args.dataset_path, num_examples=args.num_examples, collator_resolution=args.resolution, mode='train')
train_loader = torch.utils.data.DataLoader(
dataset_train,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True, drop_last=False,
collate_fn=dataset_train.collate_fn
)
dataset_test = FidDataset(base_path=args.dataset_path, num_examples=args.num_examples, collator_resolution=args.resolution, mode='test')
test_loader = torch.utils.data.DataLoader(
dataset_test,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
pin_memory=True, drop_last=False,
collate_fn=dataset_test.collate_fn
)
model = VATr(args)
args.output = Path(args.output) / Path(args.checkpoint).stem
print(f'Loading checkpoint {args.checkpoint}')
checkpoint = torch.load(args.checkpoint)
epoch = 'unknown'
if 'epoch' in checkpoint: epoch = checkpoint['epoch']
if 'model' in checkpoint: checkpoint = checkpoint['model']
load_checkpoint(model, checkpoint)
model.eval()
with torch.no_grad():
model.save_images_for_fid_calculation(args.output, train_loader, 'train')
model.save_images_for_fid_calculation(args.output, test_loader, 'test')
print('Done')