-
Notifications
You must be signed in to change notification settings - Fork 0
/
single_experiment.py
200 lines (176 loc) · 9.75 KB
/
single_experiment.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
### execute this function to train and test the vae-model
from vaemodel import Model
import numpy as np
import pickle
import torch
import os
import argparse
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
parser = argparse.ArgumentParser()
parser.add_argument('--dataset')
parser.add_argument('--num_shots',type=int)
parser.add_argument('--generalized', type = str2bool)
args = parser.parse_args()
########################################
# the basic hyperparameters
########################################
hyperparameters = {
'num_shots': 0,
'device': 'cuda',
'model_specifics': {'cross_reconstruction': True,
'name': 'CADA',
'distance': 'wasserstein',
'warmup': {'beta': {'factor': 0.25,
'end_epoch': 93,
'start_epoch': 0},
'cross_reconstruction': {'factor': 2.37,
'end_epoch': 75,
'start_epoch': 21},
'distance': {'factor': 8.13,
'end_epoch': 22,
'start_epoch': 6}}},
# 'model_specifics': {'cross_reconstruction': True,
# 'name': 'CADA',
# 'distance': 'wasserstein',
# 'warmup': {'beta': {'factor': 0.25,
# 'end_epoch': 93,
# 'start_epoch': 0},
# 'cross_reconstruction': {'factor': 20,
# 'end_epoch': 75,
# 'start_epoch': 21},
# 'distance': {'factor': 8.13,
# 'end_epoch': 22,
# 'start_epoch': 6}}},
'lr_gen_model': 0.00015,
'generalized': True,
'batch_size': 50,
'xyu_samples_per_class': {'SUN': (200, 0, 400, 0),
'APY': (200, 0, 400, 0),
'CUB': (200, 0, 400, 0),
'AWA2': (200, 0, 400, 0),
'FLO': (200, 0, 400, 0),
'AWA1': (200, 0, 400, 0)},
'epochs': 100,
'loss': 'l1',
'auxiliary_data_source' : 'attributes',
'lr_cls': 0.001,
'dataset': 'CUB',
'hidden_size_rule': {'resnet_features': (1560, 1660),
'attributes': (1450, 665),
'sentences': (1450, 665) },
'latent_size': 64
}
# The training epochs for the final classifier, for early stopping,
# as determined on the validation spit
cls_train_steps = [
# {'dataset': 'SUN', 'num_shots': 0, 'generalized': True, 'cls_train_steps': 21},
{'dataset': 'SUN', 'num_shots': 0, 'generalized': True, 'cls_train_steps': 31},
{'dataset': 'SUN', 'num_shots': 0, 'generalized': False, 'cls_train_steps': 30},
{'dataset': 'SUN', 'num_shots': 1, 'generalized': True, 'cls_train_steps': 22},
{'dataset': 'SUN', 'num_shots': 1, 'generalized': False, 'cls_train_steps': 96},
{'dataset': 'SUN', 'num_shots': 5, 'generalized': True, 'cls_train_steps': 29},
{'dataset': 'SUN', 'num_shots': 5, 'generalized': False, 'cls_train_steps': 78},
{'dataset': 'SUN', 'num_shots': 2, 'generalized': True, 'cls_train_steps': 29},
{'dataset': 'SUN', 'num_shots': 2, 'generalized': False, 'cls_train_steps': 61},
{'dataset': 'SUN', 'num_shots': 10, 'generalized': True, 'cls_train_steps': 79},
{'dataset': 'SUN', 'num_shots': 10, 'generalized': False, 'cls_train_steps': 94},
{'dataset': 'AWA1', 'num_shots': 0, 'generalized': True, 'cls_train_steps': 63},
# {'dataset': 'AWA1', 'num_shots': 0, 'generalized': True, 'cls_train_steps': 33},
{'dataset': 'AWA1', 'num_shots': 0, 'generalized': False, 'cls_train_steps': 25},
{'dataset': 'AWA1', 'num_shots': 1, 'generalized': True, 'cls_train_steps': 40},
{'dataset': 'AWA1', 'num_shots': 1, 'generalized': False, 'cls_train_steps': 81},
{'dataset': 'AWA1', 'num_shots': 5, 'generalized': True, 'cls_train_steps': 89},
{'dataset': 'AWA1', 'num_shots': 5, 'generalized': False, 'cls_train_steps': 62},
{'dataset': 'AWA1', 'num_shots': 2, 'generalized': True, 'cls_train_steps': 56},
{'dataset': 'AWA1', 'num_shots': 2, 'generalized': False, 'cls_train_steps': 59},
{'dataset': 'AWA1', 'num_shots': 10, 'generalized': True, 'cls_train_steps': 100},
{'dataset': 'AWA1', 'num_shots': 10, 'generalized': False, 'cls_train_steps': 50},
{'dataset': 'CUB', 'num_shots': 0, 'generalized': True, 'cls_train_steps': 23},
{'dataset': 'CUB', 'num_shots': 0, 'generalized': False, 'cls_train_steps': 22},
{'dataset': 'CUB', 'num_shots': 1, 'generalized': True, 'cls_train_steps': 34},
{'dataset': 'CUB', 'num_shots': 1, 'generalized': False, 'cls_train_steps': 46},
{'dataset': 'CUB', 'num_shots': 5, 'generalized': True, 'cls_train_steps': 64},
{'dataset': 'CUB', 'num_shots': 5, 'generalized': False, 'cls_train_steps': 73},
{'dataset': 'CUB', 'num_shots': 2, 'generalized': True, 'cls_train_steps': 39},
{'dataset': 'CUB', 'num_shots': 2, 'generalized': False, 'cls_train_steps': 31},
{'dataset': 'CUB', 'num_shots': 10, 'generalized': True, 'cls_train_steps': 85},
{'dataset': 'CUB', 'num_shots': 10, 'generalized': False, 'cls_train_steps': 67},
{'dataset': 'AWA2', 'num_shots': 0, 'generalized': True, 'cls_train_steps': 29},
{'dataset': 'AWA2', 'num_shots': 0, 'generalized': False, 'cls_train_steps': 39},
{'dataset': 'AWA2', 'num_shots': 1, 'generalized': True, 'cls_train_steps': 44},
{'dataset': 'AWA2', 'num_shots': 1, 'generalized': False, 'cls_train_steps': 96},
{'dataset': 'AWA2', 'num_shots': 5, 'generalized': True, 'cls_train_steps': 99},
{'dataset': 'AWA2', 'num_shots': 5, 'generalized': False, 'cls_train_steps': 100},
{'dataset': 'AWA2', 'num_shots': 2, 'generalized': True, 'cls_train_steps': 69},
{'dataset': 'AWA2', 'num_shots': 2, 'generalized': False, 'cls_train_steps': 79},
{'dataset': 'AWA2', 'num_shots': 10, 'generalized': True, 'cls_train_steps': 86},
{'dataset': 'AWA2', 'num_shots': 10, 'generalized': False, 'cls_train_steps': 78}
]
##################################
# change some hyperparameters here
##################################
hyperparameters['dataset'] = args.dataset
hyperparameters['num_shots']= args.num_shots
hyperparameters['generalized']= args.generalized
hyperparameters['cls_train_steps'] = [x['cls_train_steps'] for x in cls_train_steps
if all([hyperparameters['dataset']==x['dataset'],
hyperparameters['num_shots']==x['num_shots'],
hyperparameters['generalized']==x['generalized'] ])][0]
print('***')
print(hyperparameters['cls_train_steps'] )
if hyperparameters['generalized']:
if hyperparameters['num_shots']==0:
hyperparameters['samples_per_class'] = {'CUB': (200, 0, 400, 0), 'SUN': (200, 0, 400, 0),
'APY': (200, 0, 400, 0), 'AWA1': (200, 0, 400, 0),
'AWA2': (200, 0, 400, 0), 'FLO': (200, 0, 400, 0)}
else:
hyperparameters['samples_per_class'] = {'CUB': (200, 0, 200, 200), 'SUN': (200, 0, 200, 200),
'APY': (200, 0, 200, 200), 'AWA1': (200, 0, 200, 200),
'AWA2': (200, 0, 200, 200), 'FLO': (200, 0, 200, 200)}
else:
if hyperparameters['num_shots']==0:
hyperparameters['samples_per_class'] = {'CUB': (0, 0, 200, 0), 'SUN': (0, 0, 200, 0),
'APY': (0, 0, 200, 0), 'AWA1': (0, 0, 200, 0),
'AWA2': (0, 0, 200, 0), 'FLO': (0, 0, 200, 0)}
else:
hyperparameters['samples_per_class'] = {'CUB': (0, 0, 200, 200), 'SUN': (0, 0, 200, 200),
'APY': (0, 0, 200, 200), 'AWA1': (0, 0, 200, 200),
'AWA2': (0, 0, 200, 200), 'FLO': (0, 0, 200, 200)}
model = Model(hyperparameters)
model.to(hyperparameters['device'])
"""
########################################
### load model where u left
########################################
saved_state = torch.load('./saved_models/CADA_trained.pth.tar')
model.load_state_dict(saved_state['state_dict'])
for d in model.all_data_sources_without_duplicates:
model.encoder[d].load_state_dict(saved_state['encoder'][d])
model.decoder[d].load_state_dict(saved_state['decoder'][d])
########################################
"""
losses = model.train_vae()
np.save("losses.npy", losses)
u,s,h,history = model.train_classifier()
if hyperparameters['generalized']==True:
acc = [hi[2] for hi in history]
elif hyperparameters['generalized']==False:
acc = [hi[1] for hi in history]
print(acc[-1])
state = {
'state_dict': model.state_dict() ,
'hyperparameters':hyperparameters,
'encoder':{},
'decoder':{}
}
for d in model.all_data_sources:
state['encoder'][d] = model.encoder[d].state_dict()
state['decoder'][d] = model.decoder[d].state_dict()
torch.save(state, 'CADA_trained.pth.tar')
print('>> saved')