/
Embedding.py
238 lines (168 loc) · 8.41 KB
/
Embedding.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
225
226
227
228
229
230
231
232
233
234
235
236
237
238
import torch
from torch import nn
from models.Net import Net
import numpy as np
import os
from functools import partial
from utils.bicubic import BicubicDownSample
from datasets.image_dataset import ImagesDataset
from losses.embedding_loss import EmbeddingLossBuilder
from torch.utils.data import DataLoader
from tqdm import tqdm
import PIL
import torchvision
from utils.data_utils import convert_npy_code
toPIL = torchvision.transforms.ToPILImage()
class Embedding(nn.Module):
def __init__(self, opts):
super(Embedding, self).__init__()
self.opts = opts
self.net = Net(self.opts)
self.load_downsampling()
self.setup_embedding_loss_builder()
def load_downsampling(self):
factor = self.opts.size // 256
self.downsample = BicubicDownSample(factor=factor)
def setup_W_optimizer(self):
opt_dict = {
'sgd': torch.optim.SGD,
'adam': torch.optim.Adam,
'sgdm': partial(torch.optim.SGD, momentum=0.9),
'adamax': torch.optim.Adamax
}
latent = []
if (self.opts.tile_latent):
tmp = self.net.latent_avg.clone().detach().cuda()
tmp.requires_grad = True
for i in range(self.net.layer_num):
latent.append(tmp)
optimizer_W = opt_dict[self.opts.opt_name]([tmp], lr=self.opts.learning_rate)
else:
for i in range(self.net.layer_num):
tmp = self.net.latent_avg.clone().detach().cuda()
tmp.requires_grad = True
latent.append(tmp)
optimizer_W = opt_dict[self.opts.opt_name](latent, lr=self.opts.learning_rate)
return optimizer_W, latent
def setup_FS_optimizer(self, latent_W, F_init):
latent_F = F_init.clone().detach().requires_grad_(True)
latent_S = []
opt_dict = {
'sgd': torch.optim.SGD,
'adam': torch.optim.Adam,
'sgdm': partial(torch.optim.SGD, momentum=0.9),
'adamax': torch.optim.Adamax
}
for i in range(self.net.layer_num):
tmp = latent_W[0, i].clone()
if i < self.net.S_index:
tmp.requires_grad = False
else:
tmp.requires_grad = True
latent_S.append(tmp)
optimizer_FS = opt_dict[self.opts.opt_name](latent_S[self.net.S_index:] + [latent_F], lr=self.opts.learning_rate)
return optimizer_FS, latent_F, latent_S
def setup_dataloader(self, image_path=None):
self.dataset = ImagesDataset(opts=self.opts,image_path=image_path)
self.dataloader = DataLoader(self.dataset, batch_size=1, shuffle=False)
print("Number of images: {}".format(len(self.dataset)))
def setup_embedding_loss_builder(self):
self.loss_builder = EmbeddingLossBuilder(self.opts)
def invert_images_in_W(self, image_path=None):
self.setup_dataloader(image_path=image_path)
device = self.opts.device
ibar = tqdm(self.dataloader, desc='Images')
for ref_im_H, ref_im_L, ref_name in ibar:
optimizer_W, latent = self.setup_W_optimizer()
pbar = tqdm(range(self.opts.W_steps), desc='Embedding', leave=False)
for step in pbar:
optimizer_W.zero_grad()
latent_in = torch.stack(latent).unsqueeze(0)
gen_im, _ = self.net.generator([latent_in], input_is_latent=True, return_latents=False)
im_dict = {
'ref_im_H': ref_im_H.to(device),
'ref_im_L': ref_im_L.to(device),
'gen_im_H': gen_im,
'gen_im_L': self.downsample(gen_im)
}
loss, loss_dic = self.cal_loss(im_dict, latent_in)
loss.backward()
optimizer_W.step()
if self.opts.verbose:
pbar.set_description('Embedding: Loss: {:.3f}, L2 loss: {:.3f}, Perceptual loss: {:.3f}, P-norm loss: {:.3f}'
.format(loss, loss_dic['l2'], loss_dic['percep'], loss_dic['p-norm']))
if self.opts.save_intermediate and step % self.opts.save_interval== 0:
self.save_W_intermediate_results(ref_name, gen_im, latent_in, step)
self.save_W_results(ref_name, gen_im, latent_in)
def invert_images_in_FS(self, image_path=None):
self.setup_dataloader(image_path=image_path)
output_dir = self.opts.output_dir
device = self.opts.device
ibar = tqdm(self.dataloader, desc='Images')
for ref_im_H, ref_im_L, ref_name in ibar:
latent_W_path = os.path.join(output_dir, 'W+', f'{ref_name[0]}.npy')
latent_W = torch.from_numpy(convert_npy_code(np.load(latent_W_path))).to(device)
F_init, _ = self.net.generator([latent_W], input_is_latent=True, return_latents=False, start_layer=0, end_layer=3)
optimizer_FS, latent_F, latent_S = self.setup_FS_optimizer(latent_W, F_init)
pbar = tqdm(range(self.opts.FS_steps), desc='Embedding', leave=False)
for step in pbar:
optimizer_FS.zero_grad()
latent_in = torch.stack(latent_S).unsqueeze(0)
gen_im, _ = self.net.generator([latent_in], input_is_latent=True, return_latents=False,
start_layer=4, end_layer=8, layer_in=latent_F)
im_dict = {
'ref_im_H': ref_im_H.to(device),
'ref_im_L': ref_im_L.to(device),
'gen_im_H': gen_im,
'gen_im_L': self.downsample(gen_im)
}
loss, loss_dic = self.cal_loss(im_dict, latent_in)
loss.backward()
optimizer_FS.step()
if self.opts.verbose:
pbar.set_description(
'Embedding: Loss: {:.3f}, L2 loss: {:.3f}, Perceptual loss: {:.3f}, P-norm loss: {:.3f}, L_F loss: {:.3f}'
.format(loss, loss_dic['l2'], loss_dic['percep'], loss_dic['p-norm'], loss_dic['l_F']))
self.save_FS_results(ref_name, gen_im, latent_in, latent_F)
def cal_loss(self, im_dict, latent_in, latent_F=None, F_init=None):
loss, loss_dic = self.loss_builder(**im_dict)
p_norm_loss = self.net.cal_p_norm_loss(latent_in)
loss_dic['p-norm'] = p_norm_loss
loss += p_norm_loss
if latent_F is not None and F_init is not None:
l_F = self.net.cal_l_F(latent_F, F_init)
loss_dic['l_F'] = l_F
loss += l_F
return loss, loss_dic
def save_W_results(self, ref_name, gen_im, latent_in):
save_im = toPIL(((gen_im[0] + 1) / 2).detach().cpu().clamp(0, 1))
save_latent = latent_in.detach().cpu().numpy()
output_dir = os.path.join(self.opts.output_dir, 'W+')
os.makedirs(output_dir, exist_ok=True)
latent_path = os.path.join(output_dir, f'{ref_name[0]}.npy')
image_path = os.path.join(output_dir, f'{ref_name[0]}.png')
save_im.save(image_path)
np.save(latent_path, save_latent)
def save_W_intermediate_results(self, ref_name, gen_im, latent_in, step):
save_im = toPIL(((gen_im[0] + 1) / 2).detach().cpu().clamp(0, 1))
save_latent = latent_in.detach().cpu().numpy()
intermediate_folder = os.path.join(self.opts.output_dir, 'W+', ref_name[0])
os.makedirs(intermediate_folder, exist_ok=True)
latent_path = os.path.join(intermediate_folder, f'{ref_name[0]}_{step:04}.npy')
image_path = os.path.join(intermediate_folder, f'{ref_name[0]}_{step:04}.png')
save_im.save(image_path)
np.save(latent_path, save_latent)
def save_FS_results(self, ref_name, gen_im, latent_in, latent_F):
save_im = toPIL(((gen_im[0] + 1) / 2).detach().cpu().clamp(0, 1))
output_dir = os.path.join(self.opts.output_dir, 'FS')
os.makedirs(output_dir, exist_ok=True)
latent_path = os.path.join(output_dir, f'{ref_name[0]}.npz')
image_path = os.path.join(output_dir, f'{ref_name[0]}.png')
save_im.save(image_path)
np.savez(latent_path, latent_in=latent_in.detach().cpu().numpy(),
latent_F=latent_F.detach().cpu().numpy())
def set_seed(self):
if self.opt.seed:
torch.manual_seed(self.opt.seed)
torch.cuda.manual_seed(self.opt.seed)
torch.backends.cudnn.deterministic = True