-
Notifications
You must be signed in to change notification settings - Fork 273
/
train.py
197 lines (167 loc) · 9.03 KB
/
train.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
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim
import torch.optim.lr_scheduler as lr_scheduler
import time
import os
import glob
import configs
import backbone
from data.datamgr import SimpleDataManager, SetDataManager
from methods.baselinetrain import BaselineTrain
from methods.baselinefinetune import BaselineFinetune
from methods.protonet import ProtoNet
from methods.matchingnet import MatchingNet
from methods.relationnet import RelationNet
from methods.maml import MAML
from io_utils import model_dict, parse_args, get_resume_file
def train(base_loader, val_loader, model, optimization, start_epoch, stop_epoch, params):
if optimization == 'Adam':
optimizer = torch.optim.Adam(model.parameters())
else:
raise ValueError('Unknown optimization, please define by yourself')
max_acc = 0
for epoch in range(start_epoch,stop_epoch):
model.train()
model.train_loop(epoch, base_loader, optimizer ) #model are called by reference, no need to return
model.eval()
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
acc = model.test_loop( val_loader)
if acc > max_acc : #for baseline and baseline++, we don't use validation in default and we let acc = -1, but we allow options to validate with DB index
print("best model! save...")
max_acc = acc
outfile = os.path.join(params.checkpoint_dir, 'best_model.tar')
torch.save({'epoch':epoch, 'state':model.state_dict()}, outfile)
if (epoch % params.save_freq==0) or (epoch==stop_epoch-1):
outfile = os.path.join(params.checkpoint_dir, '{:d}.tar'.format(epoch))
torch.save({'epoch':epoch, 'state':model.state_dict()}, outfile)
return model
if __name__=='__main__':
np.random.seed(10)
params = parse_args('train')
if params.dataset == 'cross':
base_file = configs.data_dir['miniImagenet'] + 'all.json'
val_file = configs.data_dir['CUB'] + 'val.json'
elif params.dataset == 'cross_char':
base_file = configs.data_dir['omniglot'] + 'noLatin.json'
val_file = configs.data_dir['emnist'] + 'val.json'
else:
base_file = configs.data_dir[params.dataset] + 'base.json'
val_file = configs.data_dir[params.dataset] + 'val.json'
if 'Conv' in params.model:
if params.dataset in ['omniglot', 'cross_char']:
image_size = 28
else:
image_size = 84
else:
image_size = 224
if params.dataset in ['omniglot', 'cross_char']:
assert params.model == 'Conv4' and not params.train_aug ,'omniglot only support Conv4 without augmentation'
params.model = 'Conv4S'
optimization = 'Adam'
if params.stop_epoch == -1:
if params.method in ['baseline', 'baseline++'] :
if params.dataset in ['omniglot', 'cross_char']:
params.stop_epoch = 5
elif params.dataset in ['CUB']:
params.stop_epoch = 200 # This is different as stated in the open-review paper. However, using 400 epoch in baseline actually lead to over-fitting
elif params.dataset in ['miniImagenet', 'cross']:
params.stop_epoch = 400
else:
params.stop_epoch = 400 #default
else: #meta-learning methods
if params.n_shot == 1:
params.stop_epoch = 600
elif params.n_shot == 5:
params.stop_epoch = 400
else:
params.stop_epoch = 600 #default
if params.method in ['baseline', 'baseline++'] :
base_datamgr = SimpleDataManager(image_size, batch_size = 16)
base_loader = base_datamgr.get_data_loader( base_file , aug = params.train_aug )
val_datamgr = SimpleDataManager(image_size, batch_size = 64)
val_loader = val_datamgr.get_data_loader( val_file, aug = False)
if params.dataset == 'omniglot':
assert params.num_classes >= 4112, 'class number need to be larger than max label id in base class'
if params.dataset == 'cross_char':
assert params.num_classes >= 1597, 'class number need to be larger than max label id in base class'
if params.method == 'baseline':
model = BaselineTrain( model_dict[params.model], params.num_classes)
elif params.method == 'baseline++':
model = BaselineTrain( model_dict[params.model], params.num_classes, loss_type = 'dist')
elif params.method in ['protonet','matchingnet','relationnet', 'relationnet_softmax', 'maml', 'maml_approx']:
n_query = max(1, int(16* params.test_n_way/params.train_n_way)) #if test_n_way is smaller than train_n_way, reduce n_query to keep batch size small
train_few_shot_params = dict(n_way = params.train_n_way, n_support = params.n_shot)
base_datamgr = SetDataManager(image_size, n_query = n_query, **train_few_shot_params)
base_loader = base_datamgr.get_data_loader( base_file , aug = params.train_aug )
test_few_shot_params = dict(n_way = params.test_n_way, n_support = params.n_shot)
val_datamgr = SetDataManager(image_size, n_query = n_query, **test_few_shot_params)
val_loader = val_datamgr.get_data_loader( val_file, aug = False)
#a batch for SetDataManager: a [n_way, n_support + n_query, dim, w, h] tensor
if params.method == 'protonet':
model = ProtoNet( model_dict[params.model], **train_few_shot_params )
elif params.method == 'matchingnet':
model = MatchingNet( model_dict[params.model], **train_few_shot_params )
elif params.method in ['relationnet', 'relationnet_softmax']:
if params.model == 'Conv4':
feature_model = backbone.Conv4NP
elif params.model == 'Conv6':
feature_model = backbone.Conv6NP
elif params.model == 'Conv4S':
feature_model = backbone.Conv4SNP
else:
feature_model = lambda: model_dict[params.model]( flatten = False )
loss_type = 'mse' if params.method == 'relationnet' else 'softmax'
model = RelationNet( feature_model, loss_type = loss_type , **train_few_shot_params )
elif params.method in ['maml' , 'maml_approx']:
backbone.ConvBlock.maml = True
backbone.SimpleBlock.maml = True
backbone.BottleneckBlock.maml = True
backbone.ResNet.maml = True
model = MAML( model_dict[params.model], approx = (params.method == 'maml_approx') , **train_few_shot_params )
if params.dataset in ['omniglot', 'cross_char']: #maml use different parameter in omniglot
model.n_task = 32
model.task_update_num = 1
model.train_lr = 0.1
else:
raise ValueError('Unknown method')
model = model.cuda()
params.checkpoint_dir = '%s/checkpoints/%s/%s_%s' %(configs.save_dir, params.dataset, params.model, params.method)
if params.train_aug:
params.checkpoint_dir += '_aug'
if not params.method in ['baseline', 'baseline++']:
params.checkpoint_dir += '_%dway_%dshot' %( params.train_n_way, params.n_shot)
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
start_epoch = params.start_epoch
stop_epoch = params.stop_epoch
if params.method == 'maml' or params.method == 'maml_approx' :
stop_epoch = params.stop_epoch * model.n_task #maml use multiple tasks in one update
if params.resume:
resume_file = get_resume_file(params.checkpoint_dir)
if resume_file is not None:
tmp = torch.load(resume_file)
start_epoch = tmp['epoch']+1
model.load_state_dict(tmp['state'])
elif params.warmup: #We also support warmup from pretrained baseline feature, but we never used in our paper
baseline_checkpoint_dir = '%s/checkpoints/%s/%s_%s' %(configs.save_dir, params.dataset, params.model, 'baseline')
if params.train_aug:
baseline_checkpoint_dir += '_aug'
warmup_resume_file = get_resume_file(baseline_checkpoint_dir)
tmp = torch.load(warmup_resume_file)
if tmp is not None:
state = tmp['state']
state_keys = list(state.keys())
for i, key in enumerate(state_keys):
if "feature." in key:
newkey = key.replace("feature.","") # an architecture model has attribute 'feature', load architecture feature to backbone by casting name from 'feature.trunk.xx' to 'trunk.xx'
state[newkey] = state.pop(key)
else:
state.pop(key)
model.feature.load_state_dict(state)
else:
raise ValueError('No warm_up file')
model = train(base_loader, val_loader, model, optimization, start_epoch, stop_epoch, params)