forked from yizhiwang96/deepvecfont-v2
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
220 lines (182 loc) · 10.8 KB
/
train.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
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
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
import os
import random
import numpy as np
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam, AdamW
from torchvision.utils import save_image
import wandb
from dataloader import get_loader
from models import util_funcs
from models.model_main import ModelMain
from options import get_parser_main_model
from data_utils.svg_utils import render
from time import time
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def train_main_model(opts):
setup_seed(opts.seed)
dir_exp = os.path.join(f"{opts.exp_path}", "experiments", opts.name_exp)
dir_sample = os.path.join(dir_exp, "samples")
dir_ckpt = os.path.join(dir_exp, "checkpoints")
dir_log = os.path.join(dir_exp, "logs")
logfile_train = open(os.path.join(dir_log, "train_loss_log.txt"), 'w')
logfile_val = open(os.path.join(dir_log, "val_loss_log.txt"), 'w')
train_loader = get_loader(opts.data_root, opts.img_size, opts.language, opts.char_num, opts.max_seq_len, opts.dim_seq, opts.batch_size, opts.mode)
val_loader = get_loader(opts.data_root, opts.img_size, opts.language, opts.char_num, opts.max_seq_len, opts.dim_seq, opts.batch_size_val, 'val')
run = wandb.init(project=opts.wandb_project_name, config=opts) # initialize wandb project
model_main = ModelMain(opts)
model_main.cuda()
parameters_all = [{"params": model_main.img_encoder.parameters()}, {"params": model_main.img_decoder.parameters()},
{"params": model_main.modality_fusion.parameters()}, {"params": model_main.transformer_main.parameters()},
{"params": model_main.transformer_seqdec.parameters()}]
optimizer = AdamW(parameters_all, lr=opts.lr, betas=(opts.beta1, opts.beta2), eps=opts.eps, weight_decay=opts.weight_decay)
if torch.cuda.is_available() and opts.multi_gpu:
model_main = torch.nn.DataParallel(model_main)
# For Continue Training
if opts.continue_training:
checkpoint = torch.load(opts.continue_ckpt)
model_main.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['opt'])
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.997)
for epoch in range(opts.init_epoch, opts.n_epochs):
t0 = time()
for idx, data in enumerate(train_loader):
for key in data: data[key] = data[key].cuda()
ret_dict, loss_dict = model_main(data)
loss = opts.loss_w_l1 * loss_dict['img']['l1'] + opts.loss_w_pt_c * loss_dict['img']['vggpt'] + opts.kl_beta * loss_dict['kl'] \
+ loss_dict['svg']['total'] + loss_dict['svg_para']['total']
# perform optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
batches_done = epoch * len(train_loader) + idx + 1
message = (
f"Time: {'{} seconds'.format(time() - t0)}, "
f"Epoch: {epoch}/{opts.n_epochs}, Batch: {idx}/{len(train_loader)}, "
f"Loss: {loss.item():.6f}, "
f"img_l1_loss: {opts.loss_w_l1 * loss_dict['img']['l1'].item():.6f}, "
f"img_pt_c_loss: {opts.loss_w_pt_c * loss_dict['img']['vggpt']:.6f}, "
f"svg_total_loss: {loss_dict['svg']['total'].item():.6f}, "
f"svg_cmd_loss: {opts.loss_w_cmd * loss_dict['svg']['cmd'].item():.6f}, "
f"svg_args_loss: {opts.loss_w_args * loss_dict['svg']['args'].item():.6f}, "
f"svg_smooth_loss: {opts.loss_w_smt * loss_dict['svg']['smt'].item():.6f}, "
f"svg_aux_loss: {opts.loss_w_aux * loss_dict['svg']['aux'].item():.6f}, "
f"lr: {optimizer.param_groups[0]['lr']:.6f}, "
f"Step: {batches_done}"
)
if batches_done % opts.freq_log == 0:
logfile_train.write(message + '\n')
print(message)
if opts.wandb:
# print("Running With Wandb")
# Define the items for image and SVG losses
loss_img_items = ['l1', 'vggpt']
loss_svg_items = ['total', 'cmd', 'args', 'aux', 'smt']
# Log image loss items
for item in loss_img_items:
wandb.log({f'Loss/img_{item}': loss_dict['img'][item].item()}, step=batches_done)
# Log SVG loss items
for item in loss_svg_items:
wandb.log({f'Loss/svg_{item}': loss_dict['svg'][item].item()}, step=batches_done)
wandb.log({f'Loss/svg_para_{item}': loss_dict['svg_para'][item].item()}, step=batches_done)
# Log KL loss
wandb.log({'Loss/img_kl_loss': opts.kl_beta * loss_dict['kl'].item()}, step=batches_done)
wandb.log({
'Images/trg_img': wandb.Image(ret_dict['img']['trg'][0], caption="Target"),
'Images/img_output': wandb.Image(ret_dict['img']['out'][0], caption="Output")
}, step=batches_done)
if opts.freq_sample > 0 and batches_done % opts.freq_sample == 0:
img_sample = torch.cat((ret_dict['img']['trg'].data, ret_dict['img']['out'].data), -2)
save_file = os.path.join(dir_sample, f"train_epoch_{epoch}_batch_{batches_done}.png")
save_image(img_sample, save_file, nrow=8, normalize=True)
if opts.freq_val > 0 and batches_done % opts.freq_val == 0:
with torch.no_grad():
model_main.eval()
loss_val = {'img':{'l1':0.0, 'vggpt':0.0}, 'svg':{'total':0.0, 'cmd':0.0, 'args':0.0, 'aux':0.0},
'svg_para':{'total':0.0, 'cmd':0.0, 'args':0.0, 'aux':0.0}}
for val_idx, val_data in enumerate(val_loader):
for key in val_data: val_data[key] = val_data[key].cuda()
ret_dict_val, loss_dict_val = model_main(val_data, mode='val')
for loss_cat in ['img', 'svg']:
for key, _ in loss_val[loss_cat].items():
loss_val[loss_cat][key] += loss_dict_val[loss_cat][key]
for loss_cat in ['img', 'svg']:
for key, _ in loss_val[loss_cat].items():
loss_val[loss_cat][key] /= len(val_loader)
if opts.wandb:
for loss_cat in ['img', 'svg']:
# Iterate over keys and values in the loss dictionary
for key, value in loss_val[loss_cat].items():
# Log loss value to WandB
wandb.log({f'VAL/loss_{loss_cat}_{key}': value})
wandb.log({
'VAL_Images/val_trg_img': wandb.Image(ret_dict_val['img']['trg'][0], caption="Val Target"),
'VAL_Images/val_img_output': wandb.Image(ret_dict_val['img']['out'][0], caption="Val Output")
})
val_msg = (
f"Epoch: {epoch}/{opts.n_epochs}, Batch: {idx}/{len(train_loader)}, "
f"Val loss img l1: {loss_val['img']['l1']: .6f}, "
f"Val loss img pt: {loss_val['img']['vggpt']: .6f}, "
f"Val loss total: {loss_val['svg']['total']: .6f}, "
f"Val loss cmd: {loss_val['svg']['cmd']: .6f}, "
f"Val loss args: {loss_val['svg']['args']: .6f}, "
)
logfile_val.write(val_msg + "\n")
print(val_msg)
scheduler.step()
if epoch % opts.freq_ckpt == 0 and epoch >= opts.threshold_ckpt:
if opts.multi_gpu:
print(f"Saved {dir_ckpt}/{epoch}_{batches_done}.ckpt")
torch.save({'model':model_main.module.state_dict(), 'opt':optimizer.state_dict(), 'n_epoch':epoch, 'n_iter':batches_done}, f'{dir_ckpt}/{epoch}_{batches_done}.ckpt')
else:
print(f"Saved {dir_ckpt}/{epoch}_{batches_done}.ckpt")
torch.save({'model':model_main.state_dict(), 'opt':optimizer.state_dict(), 'n_epoch':epoch, 'n_iter':batches_done}, f'{dir_ckpt}/{epoch}_{batches_done}.ckpt')
if opts.wandb:
artifact = wandb.Artifact('model_main_checkpoints', type='model')
artifact.add_file(f'{dir_ckpt}/{epoch}_{batches_done}.ckpt')
run.log_artifact(artifact)
logfile_train.close()
logfile_val.close()
def backup_code(name_exp, exp_path):
os.makedirs(os.path.join(exp_path,'experiments', name_exp, 'code'), exist_ok=True)
shutil.copy('models/transformers.py', os.path.join(exp_path,'experiments', name_exp, 'code', 'transformers.py') )
shutil.copy('models/model_main.py', os.path.join(exp_path,'experiments', name_exp, 'code', 'model_main.py'))
shutil.copy('models/image_encoder.py', os.path.join(exp_path,'experiments', name_exp, 'code', 'image_encoder.py'))
shutil.copy('models/image_decoder.py', os.path.join(exp_path,'experiments', name_exp, 'code', 'image_decoder.py'))
shutil.copy('./train.py', os.path.join(exp_path,'experiments', name_exp, 'code', 'train.py'))
shutil.copy('./options.py', os.path.join(exp_path,'experiments', name_exp, 'code', 'options.py'))
def train(opts):
if opts.model_name == 'main_model':
train_main_model(opts)
elif opts.model_name == 'others':
train_others(opts)
else:
raise NotImplementedError
def main():
opts = get_parser_main_model().parse_args()
opts.name_exp = opts.name_exp + '_' + opts.model_name
os.makedirs(f"{opts.exp_path}/experiments", exist_ok=True)
debug = True
# Create directories
experiment_dir = os.path.join(f"{opts.exp_path}","experiments", opts.name_exp)
backup_code(opts.name_exp, opts.exp_path)
os.makedirs(experiment_dir, exist_ok=debug) # False to prevent multiple train run by mistake
os.makedirs(os.path.join(experiment_dir, "samples"), exist_ok=True)
os.makedirs(os.path.join(experiment_dir, "checkpoints"), exist_ok=True)
os.makedirs(os.path.join(experiment_dir, "results"), exist_ok=True)
os.makedirs(os.path.join(experiment_dir, "logs"), exist_ok=True)
print(f"Training on experiment {opts.name_exp}...")
# Dump options
with open(os.path.join(experiment_dir, "opts.txt"), "w") as f:
for key, value in vars(opts).items():
f.write(str(key) + ": " + str(value) + "\n")
train(opts)
if __name__ == "__main__":
main()