/
train_lfw.py
224 lines (196 loc) · 10.8 KB
/
train_lfw.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
221
222
223
224
from __future__ import print_function, division
import argparse
from data.data_loader import CreateDataLoader
from models.models import create_model
import os
import time
import torch
from random import shuffle
import numpy as np
from util.visualizer import Visualizer
parser = argparse.ArgumentParser()
parser.add_argument('--model_name', type=str, default='attr_sketch_face', help='name of the model to be used: stackgan, attr2img')
parser.add_argument('--gpu_ids', type=str , default= '0' , help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
parser.add_argument('--name', type=str, default='lfw_attr2sketch2face', help='name of the experiment. It decides where to store samples and models')
parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
parser.add_argument('--batchSize', type=int, default=128, help='input batch size')
parser.add_argument('--loadSize', type=int, default=64, help='scale images to this size')
parser.add_argument('--fineSize', type=int, default=64, help='then crop to this size')
parser.add_argument('--attrB_dim', type=int, default=17, help='# of input attribute channels')
parser.add_argument('--attrA_dim', type=int, default=6, help='# of input attribute channels')
parser.add_argument('--sketch_nc', type=int, default=1, help='# of output image channels')
parser.add_argument('--image_nc', type=int, default=3, help='# of output image channels')
parser.add_argument('--nt', type=int, default=256, help='# of dim for text features ')
parser.add_argument('--nz', type=int, default=1024, help='# of dim for Z')
parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
parser.add_argument('--ndf', type=int, default=64, help='# of descriminator filters in first conv layer')
parser.add_argument('--norm', type=str, default='batch', help='instance normalization or batch normalization')
parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
parser.add_argument('--niter', type=int, default=1, help='# of iter at starting learning rate')
parser.add_argument('--niter_decay', type=int, default=1,help='# of iter to linearly decay learning rate to zero')
parser.add_argument('--lambda_A', type=float, default=100.0, help='weight for L1 loss')
parser.add_argument('--display_winsize', type=int, default=64, help='display window size')
parser.add_argument('--display_single_pane_ncols', type=int, default=0,
help='if positive, display all images in a single visdom web panel with certain number of images per row.')
parser.add_argument('--display_id', type=int, default=100, help='window id of the web display')
parser.add_argument('--display_port', type=int, default=8099, help='visdom port of the web display')
parser.add_argument('--display_freq', type=int, default=100,help='frequency of showing training results on screen')
parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
parser.add_argument('--save_latest_freq', type=int, default=3100, help='frequency of saving the latest results')
parser.add_argument('--save_epoch_freq', type=int, default=2,help='frequency of saving checkpoints at the end of epochs')
parser.add_argument('--no_html', action='store_true',help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
parser.add_argument('--isTrain', type = bool,default=True,help='training flag')
parser.add_argument('--manualSeed', type=int, default =None, help='manual seed')
parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
parser.add_argument('--which_epoch', type=str, default='latest',
help='which epoch to load? set to latest to use latest cached model')
parser.add_argument('--pool_size', type=int, default=62, help='the size of image buffer that stores previously generated images')
parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN')
opt = parser.parse_args()
print(opt)
face_dataset = CreateDataLoader(opt, csv_fileA='./dataset/lfw/fine_grained_attribute_trainA.txt',root_dirA='./dataset/lfw/trainA/',
csv_fileB='./dataset/lfw/fine_grained_attribute_trainB.txt', root_dirB='./dataset/lfw/trainB/')
dataset_size = len(face_dataset)
print('#training images = %d' % dataset_size)
if opt.manualSeed is None:
opt.manualSeed = np.random.randint(1, 10000)
np.random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
torch.cuda.manual_seed_all(opt.manualSeed)
model = create_model(opt)
visualizer = Visualizer(opt)
train_num = dataset_size
#### stage-1 ###########
print('---------- Stage1 training -------------')
stage = 1
total_steps = 0
for epoch in range(1, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
# batch_indices = np.random.choice(train_num, train_num, replace=False)
batch_idx = range(np.int(train_num/opt.batchSize))
shuffle(batch_idx)
epoch_iter = 0
for i in batch_idx:
# print(i)
idx = np.arange(i*opt.batchSize,(i+1)*opt.batchSize)
iter_start_time = time.time()
total_steps += 1*opt.batchSize
# epoch_iter = total_steps - dataset_size * (epoch - 1)
epoch_iter += opt.batchSize
model.set_input(face_dataset[idx])
model.optimize_stage1_parameters()
# if total_steps % opt.display_freq == 0:
visualizer.display_current_results(model.get_current_visuals(stage), epoch, stage)
# if total_steps % opt.print_freq == 0:
errors = model.get_current_errors(stage)
# t = (time.time() - iter_start_time) / opt.batchSize
t = time.time() - iter_start_time
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(epoch, float(epoch_iter) / dataset_size, opt, errors, stage)
# print(total_steps)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest', stage)
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save('latest', stage)
model.save(epoch, stage)
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
if epoch > opt.niter:
model.update_learning_rate()
stage1_epoch = opt.niter+opt.niter_decay
# # ########## stage-2 ###########
print('---------- Stage2 training -------------')
stage = 2
model.old_lr = opt.lr
total_steps = 0
for epoch in range(1, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
# batch_indices = np.random.choice(train_num, train_num, replace=False)
batch_idx = range(np.int(train_num/opt.batchSize))
shuffle(batch_idx)
epoch_iter = 0
for i in batch_idx:
# print(i)
idx = np.arange(i*opt.batchSize,(i+1)*opt.batchSize)
iter_start_time = time.time()
total_steps += 1*opt.batchSize
# epoch_iter = total_steps - dataset_size * (epoch - 1)
epoch_iter += opt.batchSize
model.set_input(face_dataset[idx])
model.optimize_stage2_parameters()
# if total_steps % opt.display_freq == 0:
visualizer.display_current_results(model.get_current_visuals(stage), epoch, stage)
# if total_steps % opt.print_freq == 0:
errors = model.get_current_errors(stage)
# t = (time.time() - iter_start_time) / opt.batchSize
t = time.time() - iter_start_time
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(stage1_epoch+epoch, float(epoch_iter) / dataset_size, opt, errors, stage)
# print(total_steps)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest',stage)
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save('latest',stage)
model.save(stage1_epoch+epoch, stage)
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
if epoch > opt.niter:
model.update_learning_rate()
stage2_epoch = (opt.niter+opt.niter_decay)*2
print('---------- Stage3 training -------------')
stage = 3
model.old_lr = opt.lr
total_steps = 0
for epoch in range(1, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
# batch_indices = np.random.choice(train_num, train_num, replace=False)
batch_idx = range(np.int(train_num/opt.batchSize))
shuffle(batch_idx)
epoch_iter = 0
for i in batch_idx:
# print(i)
idx = np.arange(i*opt.batchSize,(i+1)*opt.batchSize)
iter_start_time = time.time()
total_steps += 1*opt.batchSize
# epoch_iter = total_steps - dataset_size * (epoch - 1)
epoch_iter += opt.batchSize
model.set_input(face_dataset[idx])
model.optimize_stage3_parameters()
model.optimize_stage2_parameters()
model.optimize_stage1_parameters()
# if total_steps % opt.display_freq == 0:
visualizer.display_current_results(model.get_current_visuals(stage), epoch, stage)
# if total_steps % opt.print_freq == 0:
errors = model.get_current_errors(stage)
# t = (time.time() - iter_start_time) / opt.batchSize
t = time.time() - iter_start_time
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(stage2_epoch+epoch, float(epoch_iter) / dataset_size, opt, errors, stage)
# print(total_steps)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest',stage)
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save('latest',stage)
model.save(stage2_epoch+epoch, stage)
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
if epoch > opt.niter:
model.update_learning_rate()