-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathtrain_cd.py
253 lines (188 loc) · 8.49 KB
/
train_cd.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
239
240
241
242
243
244
245
246
247
import argparse
import json
import os
import sys
import PIL
import numpy as np
import torchvision
import torchvision.datasets.folder
import wandb
from PIL import Image
from PIL import ImageFile
from torch.backends import cudnn
from torchvision import transforms as T
from surrogate import *
from utils import *
PIL.Image.MAX_IMAGE_PIXELS = 933120000
ImageFile.LOAD_TRUNCATED_IMAGES = True
def plot_grid(w):
import matplotlib.pyplot as plt
grid_img = torchvision.utils.make_grid(w)
plt.imshow(grid_img.permute(1,2,0).cpu())
plt.show()
parser = argparse.ArgumentParser(description='Train')
parser.add_argument('--project', type=str, default="APR")
parser.add_argument('--entity', type=str, default="hashmatshadab")
parser.add_argument('--wandb_mode', type=str, default="disabled")
parser.add_argument('--start_epoch', type=int, default=0)
parser.add_argument('--end_epoch', type=int, default=35)
parser.add_argument('--save_epoch', type=int, default=1)
parser.add_argument('--resume', type=lambda x: (str(x).lower() == 'true'), default=False)
parser.add_argument('--chk_pth', type=str, default='....')
parser.add_argument('--batch_size', type=int, default=64, help='number of all reference images')
parser.add_argument('--mode', type=str, default='rotate', choices=["rotate","jigsaw"])
parser.add_argument('--data_dir', type=str, default='./data/ILSVRC2012_img_val')
parser.add_argument('--save_dir', type=str, default='./single_trained_models')
parser.add_argument('--fgsm_step', type=int, default=2)
parser.add_argument('--adv_train', type=lambda x: (str(x).lower() == 'true'), default=False)
parser.add_argument('--lr', type=float, default=0.001)
def create_json(args):
"""
To create json file to save the arguments (args)
"""
with open(f"{args.save_dir}/config_train_single.json", "w") as write_file:
json.dump(args.__dict__, write_file, indent=4)
def initialize_model(decoder_num):
"""
Initialize the auto-encoder model with given number of decoders
:param decoder_num: Number of decoders (20 for prototypical and 1 for other modes)
"""
model = autoencoder(input_nc=3, output_nc=3, n_blocks=3, decoder_num=decoder_num)
model = nn.Sequential(
Normalize(),
model,
)
model.to(device)
return model
def train_unsup(model, data_loader,optimizer,args):
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
for epoch in range(args.start_epoch, args.end_epoch):
avg_batch_loss_epoch = 0
running_loss = 0
for idx, (img, _) in enumerate(data_loader):
img =img.to(device)
img_input = img
img_tar = img.clone()
for img_ind in range(img_input.shape[0]):
if args.mode == 'rotate':
img_input[img_ind:img_ind + 1] = rot(img_input[img_ind:img_ind + 1])
elif args.mode == 'jigsaw':
img_input[img_ind] = shuffle(img_input[img_ind], 1)
else:
sys.exit("Enter the correct mode")
outputs, _, _ = model(img_input)
loss = nn.MSELoss()(outputs[0], img_tar)
optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_batch_loss_epoch += loss.item()
if idx % 10 == 9:
print('Epoch: {0} \t Batch: {1} \t loss: {2:.5f}'.format(epoch, idx, running_loss / 10))
wandb.log({"Running Loss": running_loss / 10})
running_loss = 0
running_loss += abs(loss.item())
length = len(data_loader) // 2
if (idx + 1) % length == 0:
rand_ind = torch.randint(0, img_input.shape[0], size=(1,))
outputs = outputs[0]
wandb.log(
{'Image': [wandb.Image(img_input[rand_ind.item()].permute(1, 2, 0).cpu().numpy(), caption="Input"),
wandb.Image(img_tar[rand_ind.item()].permute(1, 2, 0).cpu().numpy(), caption="Target"),
wandb.Image(outputs[rand_ind.item()].permute(1, 2, 0).detach().cpu().numpy(),
caption="Output")],
})
print(f"Epoch {epoch}: Average Loss on Batch::: {avg_batch_loss_epoch / (len(data_loader))}")
wandb.log({"Loss": avg_batch_loss_epoch / (len(data_loader))})
os.makedirs(args.save_dir + f'/models', exist_ok=True)
if (epoch + 1) % args.save_epoch == 0:
model.eval()
torch.save(model.state_dict(), args.save_dir + f'/models/{args.mode}_{epoch}.pth')
model.train()
def train_unsup_adv(model, data_loader,optimizer,args):
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
fgsm_step = args.fgsm_step/255
attack = FGSM(model, eps=fgsm_step)
for epoch in range(args.start_epoch, args.end_epoch):
avg_batch_loss_epoch = 0
running_loss = 0
for idx, (img, _) in enumerate(data_loader):
img =img.to(device)
img_input = img
img_tar = img.clone()
for img_ind in range(img_input.shape[0]):
if args.mode == 'rotate':
img_input[img_ind:img_ind + 1] = rot(img_input[img_ind:img_ind + 1])
elif args.mode == 'jigsaw':
img_input[img_ind] = shuffle(img_input[img_ind], 1)
else:
sys.exit("Enter the correct mode")
adv_images = attack(img_input, img_tar)
clean_output, clean_enc, _ = model(img_input)
adv_output, adv_enc, _ = model(adv_images)
loss_clean = nn.MSELoss()(clean_output[0], img_tar)
loss_adv = nn.MSELoss()(adv_output[0], img_tar)
sim_loss = nn.MSELoss()(adv_enc, clean_enc)
loss = loss_clean + loss_adv + sim_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_batch_loss_epoch += loss.item()
if idx % 10 == 9:
print('Epoch: {0} \t Batch: {1} \t sim_loss: {2:.5f}\t running_loss: {3:.5f}'.format(epoch, idx, sim_loss.item(), running_loss / 10))
wandb.log({"Running Loss": running_loss / 10})
running_loss = 0
running_loss += abs(loss.item())
length = len(data_loader) // 2
if (idx + 1) % length == 0:
rand_ind = torch.randint(0, img_input.shape[0], size=(1,))
wandb.log(
{'Image': [
wandb.Image(img_input[rand_ind.item()].permute(1, 2, 0).cpu().numpy(), caption="Input-Clean"),
wandb.Image(adv_images[rand_ind.item()].permute(1, 2, 0).cpu().numpy(), caption="Input-Adv"),
],
})
print(f"Epoch {epoch}: Average Loss on Batch::: {avg_batch_loss_epoch / (len(data_loader))}")
wandb.log({"Loss": avg_batch_loss_epoch / (len(data_loader))})
os.makedirs(args.save_dir + f'/models', exist_ok=True)
if (epoch + 1) % args.save_epoch == 0:
model.eval()
torch.save(model.state_dict(), args.save_dir + f'/models/{args.mode}_{epoch}.pth')
model.train()
if __name__ =="__main__":
args = parser.parse_args()
wandb.init(project=args.project, entity=args.entity, mode=args.wandb_mode, name=args.save_dir.split("/")[-1])
cudnn.benchmark = False
cudnn.deterministic = True
SEED = 0
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
np.random.seed(SEED)
print(args)
config = wandb.config
config.update(args)
transform = T.Compose([
T.Resize((256,256)),
T.CenterCrop(224),
T.ToTensor()
])
os.makedirs(args.save_dir + '/models', exist_ok=True)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
dataset = torchvision.datasets.folder.ImageFolder(root=args.data_dir, transform=transform)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True)
model = initialize_model(1)
if args.resume:
state = torch.load(f"{args.chk_pth}")
model.load_state_dict(state)
model.to(device)
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
if args.adv_train:
train_unsup_adv(model, data_loader, optimizer, args)
else:
train_unsup(model, data_loader, optimizer, args)