-
Notifications
You must be signed in to change notification settings - Fork 24
/
Copy pathparameters.py
195 lines (189 loc) · 8.59 KB
/
parameters.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
import os
from torchvision import transforms
import random
SEED = 4666
args_pool = {'MNIST':
{'n_epoch': 20,
'name': 'MNIST',
'transform_train': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]),
'transform': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]),
'loader_tr_args':{'batch_size': 128, 'num_workers': 4},
'loader_te_args':{'batch_size': 1000, 'num_workers': 4},
'num_class':10,
'optimizer':'Adam',
'pretrained': False,
'optimizer_args':{'lr': 0.001}},
'MNIST_pretrain':
{'n_epoch': 20,
'name': 'MNIST',
'transform_train': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]),
'transform': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]),
'loader_tr_args':{'batch_size': 128, 'num_workers': 4},
'loader_te_args':{'batch_size': 1000, 'num_workers': 4},
'num_class':10,
'optimizer':'Adam',
'pretrained': True,
'optimizer_args':{'lr': 0.001}},
'FashionMNIST':
{'n_epoch': 20,
'name': 'FashionMNIST',
'transform_train': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.286,), (0.3529,))]),
'transform': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.286,), (0.3529,))]),
'loader_tr_args':{'batch_size': 128, 'num_workers': 4},
'loader_te_args':{'batch_size': 1000, 'num_workers': 4},
'optimizer':'Adam',
'num_class':10,
'pretrained': False,
'optimizer_args':{'lr': 0.001}},
'EMNIST':
{'n_epoch': 20,
'name': 'EMNIST',
'transform_train': transforms.Compose([transforms.ToTensor()]),
'transform': transforms.Compose([transforms.ToTensor()]),
'loader_tr_args':{'batch_size': 128, 'num_workers': 4},
'loader_te_args':{'batch_size': 1000, 'num_workers': 4},
'optimizer':'Adam',
'num_class':62,
'pretrained': False,
'optimizer_args':{'lr': 0.001}},
'SVHN':
{'n_epoch': 20,
'name': 'SVHN',
'transform_train': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.4377, 0.4438, 0.4728), (0.1980, 0.2010, 0.1970))]),
'transform': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.4377, 0.4438, 0.4728), (0.1980, 0.2010, 0.1970))]),
'loader_tr_args':{'batch_size': 128, 'num_workers': 4},
'loader_te_args':{'batch_size': 1000, 'num_workers': 4},
'optimizer':'Adam',
'num_class':10,
'pretrained': False,
'optimizer_args':{'lr': 0.001}},
'CIFAR10':
{'n_epoch': 30,
'name': 'CIFAR10',
'transform_train': transforms.Compose([transforms.RandomCrop(size=32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616))]),
'transform': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616))]),
'loader_tr_args':{'batch_size': 128, 'num_workers': 4},
'loader_te_args':{'batch_size': 1000, 'num_workers': 4},
'num_class':10,
'pretrained': False,
'optimizer':'Adam',
'optimizer_args':{'lr': 0.001}},
'CIFAR10_imb':
{'n_epoch': 30,
'name': 'CIFAR10_imb',
'transform_train': transforms.Compose([transforms.RandomCrop(size=32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616))]),
'transform': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616))]),
'loader_tr_args':{'batch_size': 128, 'num_workers': 4},
'loader_te_args':{'batch_size': 1000, 'num_workers': 4},
'num_class':10,
'optimizer':'Adam',
'pretrained': False,
'optimizer_args':{'lr': 0.001}},
'CIFAR100':
{'n_epoch': 40,
'name': 'CIFAR100',
'transform_train': transforms.Compose([transforms.RandomCrop(size=32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4865, 0.4409), (0.2673, 0.2564, 0.2762))]),
'transform': transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5071, 0.4865, 0.4409), (0.2673, 0.2564, 0.2762))]),
'loader_tr_args':{'batch_size': 128, 'num_workers': 4},
'loader_te_args':{'batch_size': 1000, 'num_workers': 4},
'num_class':100,
'optimizer':'Adam',
'pretrained': False,
'optimizer_args':{'lr': 0.001}},
'TinyImageNet':
{'n_epoch': 40,
'name': 'TinyImageNet',
'transform_train': transforms.Compose([transforms.RandomRotation(20), transforms.RandomHorizontalFlip(0.5), transforms.ToTensor(), transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262])]),
'transform': transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262])]),
'loader_tr_args':{'batch_size': 128, 'num_workers': 4},
'loader_te_args':{'batch_size': 1000, 'num_workers': 4},
'num_class':200,
'optimizer':'Adam',
'pretrained': False,
'optimizer_args':{'lr': 0.001}},
'openml':
{'n_epoch': 5,
'name': 'openml',
'transform_train':transforms.Compose([transforms.ToTensor()]),
'transform':transforms.Compose([transforms.ToTensor()]),
'loader_tr_args':{'batch_size': 128, 'num_workers': 2},
'loader_te_args':{'batch_size': 1000, 'num_workers': 2},
'num_class':26,
'optimizer':'Adam',
'pretrained': False,
'optimizer_args':{'lr': 0.01}},
'PneumoniaMNIST':
{'n_epoch': 10,
'name': 'PneumoniaMNIST',
'transform_train':transforms.Compose([transforms.Resize(255),
transforms.CenterCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.RandomGrayscale(),
transforms.RandomAffine(translate=(0.05,0.05), degrees=0),
transforms.ToTensor()]),
'transform':transforms.Compose([transforms.Resize(255),
transforms.CenterCrop(224),
transforms.ToTensor()]),
'loader_tr_args':{'batch_size': 32, 'num_workers': 2},
'loader_te_args':{'batch_size': 128, 'num_workers': 2},
'num_class':2,
'optimizer':'SGD',
'pretrained': False,
'optimizer_args':{'lr': 0.001}},
'waterbirds':
{'n_epoch': 30,
'name': 'waterbirds',
'transform_train':transforms.Compose([transforms.RandomHorizontalFlip(),
transforms.ToTensor()]),
#transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
'transform':transforms.Compose([transforms.ToTensor()]),
#transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
'loader_tr_args':{'batch_size': 32, 'num_workers': 2},
'loader_te_args':{'batch_size': 128, 'num_workers': 2},
'optimizer':'SGD',
'num_class':2,
'pretrained': False,
'optimizer_args':{'lr': 0.0001, 'weight_decay': 1e-5, 'momentum': 0.9}},
'waterbirds_pretrain':
{'n_epoch': 30,
'name': 'waterbirds_pretrain',
'transform_train':transforms.Compose([transforms.RandomHorizontalFlip(),
transforms.ToTensor()]),
#transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
'transform':transforms.Compose([transforms.ToTensor()]),
#transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
'loader_tr_args':{'batch_size': 32, 'num_workers': 2},
'loader_te_args':{'batch_size': 128, 'num_workers': 2},
'optimizer':'SGD',
'pretrained': True,
'num_class':2,
'optimizer_args':{'lr': 0.0005, 'weight_decay': 1e-5, 'momentum': 0.9}},
'BreakHis':
{'n_epoch': 10,
'name': 'BreakHis',
'transform_train': transforms.Compose([transforms.RandomRotation(90),
transforms.RandomHorizontalFlip(0.8),
transforms.RandomResizedCrop(224),
transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]),
'transform': transforms.Compose([transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]),
'loader_tr_args':{'batch_size': 32, 'num_workers': 2},
'loader_te_args':{'batch_size': 128, 'num_workers': 2},
'optimizer':'SGD',
'num_class':2,
'pretrained': False,
'optimizer_args':{'lr': 0.001}}
}