-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmain_loss.py
173 lines (138 loc) · 7.14 KB
/
main_loss.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
from __future__ import print_function
import os
import sys
import argparse
import time
import random
import numpy as np
import torch
import torch.nn as nn
from torchvision import transforms
import torch.optim as optim
from model import SiameseNetwork, ContrastiveLoss
from dataset import CubDataset
from torch.utils.data import DataLoader
from evaluation import get_feature_and_label, evaluation
import datetime
parser = argparse.ArgumentParser()
parser.add_argument('--root_dir', type=str, \
default='/data/Guoxian_Dai/CUB_200_2011/CUB_200_2011/images')
parser.add_argument('--image_txt', type=str, \
default='/data/Guoxian_Dai/CUB_200_2011/CUB_200_2011/images.txt')
parser.add_argument('--train_test_split_txt', type=str, \
default='/data/Guoxian_Dai/CUB_200_2011/CUB_200_2011/train_test_split.txt')
parser.add_argument('--label_txt', type=str, \
default='/data/Guoxian_Dai/CUB_200_2011/CUB_200_2011/image_class_labels.txt')
parser.add_argument('--pretrained_model_path', default='./weights/inception_v3.ckpt', type=str)
parser.add_argument('--pretrained', default=False, type=bool)
parser.add_argument('--aux_logits', default=False, type=bool)
parser.add_argument('--pair_type', default='vector', type=str)
parser.add_argument('--mode', default='train', type=str)
parser.add_argument('--with_regularizer', help='whether to use regularizer for parameters', action='store_true')
parser.add_argument('--optimizer', default='rmsprop', type=str)
parser.add_argument('--loss_type', default='contrastive_loss', type=str)
parser.add_argument('--learning_rate_decay_type', default='fixed', type=str)
parser.add_argument('--train_batch_size', default=64, type=int)
parser.add_argument('--test_batch_size', default=32, type=int)
parser.add_argument('--num_workers', default=4, type=int)
parser.add_argument('--num_epochs', default=10000, type=int)
parser.add_argument('--learning_rate', default=1e-2, type=float)
parser.add_argument('--momentum', default=1e-2, type=float)
parser.add_argument('--learning_rate2', default=1e-4, type=float)
parser.add_argument('--dropout_keep_prob', default=0.5, type=float)
parser.add_argument('--weight_decay', default=5e-4, type=float)
parser.add_argument('--restore_ckpt', default=0, type=int) # 1 for True
parser.add_argument('--evaluation', default=0, type=int) # 1 for True
parser.add_argument('--weightFile', default='./models/my-model', type=str)
parser.add_argument('--ckpt_dir', default='./checkpoint', type=str)
parser.add_argument('--class_num', default=5, type=int)
parser.add_argument('--targetNum', default=1000, type=int)
parser.add_argument('--margin', default=1.0, type=float)
parser.add_argument('--gamma', default=0.98, type=float, help="weight decay factor")
parser.add_argument('--focal_decay_factor', default=1.0, type=float)
parser.add_argument('--display_step', default=5, type=int, help='step interval for displaying loss')
parser.add_argument('--eval_step', default=5, type=int, help='step interval for evaluate loss')
# image information
parser.add_argument('--width', default=512, type=int)
parser.add_argument('--height', default=512, type=int)
parser.add_argument('--embedding_size', default=128, type=int)
parser.add_argument('--num_epochs_per_decay', default=2, type=int)
parser.add_argument('--ngpu', default=2, type=int)
args = parser.parse_args()
# Inception_v3 input transformation.
"""
Before transform
x ~ [0, 1]
After transform:
(x - 0.485) / 0.229
Expected:
x ~ [-1, 1]
To do
"""
def train(args):
# basic arguments.
ngpu = args.ngpu
margin = args.margin
num_epochs = args.num_epochs
train_batch_size = args.train_batch_size
test_batch_size = args.test_batch_size
gamma = args.gamma # for learning rate decay
root_dir = args.root_dir
image_txt = args.image_txt
train_test_split_txt = args.train_test_split_txt
label_txt = args.label_txt
ckpt_dir = args.ckpt_dir
eval_step = args.eval_step
pretrained = args.pretrained
aux_logits = args.aux_logits
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
kargs = {'ngpu': ngpu, 'pretrained': pretrained, 'aux_logits':aux_logits}
# network and loss
siamese_network = SiameseNetwork(**kargs)
gpu_number = torch.cuda.device_count()
if device.type == 'cuda' and gpu_number > 1:
siamese_network = nn.DataParallel(siamese_network, list(range(torch.cuda.device_count())))
siamese_network.to(device)
contrastive_loss = ContrastiveLoss(margin=margin)
# params = siamese_network.parameters()
# optimizer = optim.Adam(params, lr=0.0005)
# optimizer = optim.SGD(params, lr=0.01, momentum=0.9)
# using different lr
optimizer = optim.SGD([
{'params': siamese_network.module.inception_v3.parameters() if gpu_number > 1 else siamese_network.inception_v3.parameters()},
{'params': siamese_network.module.main.parameters() if gpu_number > 1 else siamese_network.main.parameters(), 'lr': 1e-2}
], lr=0.00001, momentum=0.9)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma, last_epoch=-1)
transform = transforms.Compose([transforms.Resize((299, 299)),
transforms.CenterCrop(299),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]
)
cub_dataset = CubDataset(root_dir, image_txt, train_test_split_txt, label_txt, transform=transform, is_train=True, offset=1)
dataloader = DataLoader(dataset=cub_dataset, batch_size=train_batch_size, shuffle=True, num_workers=4)
cub_dataset_eval = CubDataset(root_dir, image_txt, train_test_split_txt, label_txt, transform=transform, is_train=False, offset=1)
dataloader_eval = DataLoader(dataset=cub_dataset_eval, batch_size=test_batch_size, shuffle=False, num_workers=4)
for epoch in range(num_epochs):
if epoch == 0:
feature_set, label_set = get_feature_and_label(siamese_network, dataloader_eval, device)
evaluation(feature_set, label_set)
siamese_network.train()
for i, data in enumerate(dataloader, 0):
img_1, img_2, sim_label = data['img_1'].to(device), data['img_2'].to(device), data['sim_label'].type(torch.FloatTensor).to(device)
optimizer.zero_grad()
output_1, output_2 = siamese_network(img_1, img_2)
loss = contrastive_loss(output_1, output_2, sim_label)
loss.backward()
optimizer.step()
if i % 20 == 0 and i > 0:
print("{}, Epoch [{:3d}/{:3d}], Iter [{:3d}/{:3d}], Current loss: {}".format(
datetime.datetime.now(), epoch, num_epochs, i, len(dataloader), loss.item()))
if epoch % eval_step == 0:
print("Start evalution")
feature_set, label_set = get_feature_and_label(siamese_network, dataloader_eval, device)
evaluation(feature_set, label_set)
torch.save(siamese_network.module.state_dict(), os.path.join(ckpt_dir, 'model_' + str(epoch) +'_.pth'))
if __name__ == '__main__':
print("Hello world")
print(args)
train(args)