/
run_sluice_net.py
236 lines (176 loc) · 10.3 KB
/
run_sluice_net.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
"""
Main script
"""
import argparse
import os
import random
import sys
import numpy as np
import dynet
from constants import TASK_NAMES, LANGUAGES, EMBEDS, BALANCED, IMBALANCED, SGD, ADAM
from sluice_net import SluiceNetwork, load
import utils
def check_activation_function(arg):
"""Checks allowed argument for --ac option."""
try:
functions = [dynet.rectify, dynet.tanh]
functions = {function.__name__: function for function in functions}
functions['None'] = None
return functions[str(arg)]
except:
raise argparse.ArgumentTypeError(
'String {} does not match required format'.format(arg, ))
def main(args):
train_score = {task: 0 for task in args.task_names}
dev_score = {task: 0 for task in args.task_names}
avg_train_score = 0
avg_dev_score = 0
if args.load:
assert os.path.exists(args.model_dir),\
('Error: Trying to load the model but %s does not exist.' %
args.model_dir)
print('Loading model from directory %s...' % args.model_dir)
model_file = None
params_file = None
#Load models from different directory based on the type (STSL, MTSL, STML, MTML)
if(len(args.task_names) ==1):
if(len(args.languages) == 1):
model_file = os.path.join(args.model_dir, 'STSL/{}_{}.model'.format(args.languages[0],args.task_names[0]))
params_file = os.path.join(args.model_dir, 'STSL/{}_{}.pkl'.format(args.languages[0],args.task_names[0]))
else:
model_file = os.path.join(args.model_dir, 'STML/{}.model'.format(args.task_names[0]))
params_file = os.path.join(args.model_dir, 'STML/{}.pkl'.format(args.task_names[0]))
else:
if(len(args.languages) ==1):
model_file = os.path.join(args.model_dir, 'MTSL/{}.model'.format(args.languages[0]))
params_file = os.path.join(args.model_dir, 'MTSL/{}.pkl'.format(args.languages[0]))
else:
model_file = os.path.join(args.model_dir, 'MTML/MTML.model')
params_file = os.path.join(args.model_dir, 'MTML/MTML.pkl')
model, train_score, dev_score, avg_train_score, avg_dev_score = load(params_file, model_file, args)
if(args.continue_train):#Continue to train the loaded model
train_score, dev_score, avg_train_score, avg_dev_score= model.fit(args.languages, args.test_languages, args.epochs, args.patience, args.opt, args.threshold,
train_dir=args.train_dir, dev_dir=args.dev_dir)#added args.threshold
else:
model = SluiceNetwork(args.h_dim,
args.h_layers,
args.model_dir,
args.log_dir,
embeds=args.embeds,
activation=args.activation,
lower=args.lower,
noise_sigma=args.sigma,
task_names=args.task_names,
languages = args.languages,
cross_stitch=args.cross_stitch,
num_subspaces=args.num_subspaces,
constraint_weight=args.constraint_weight,
constrain_matrices=args.constrain_matrices,
cross_stitch_init_scheme=
args.cross_stitch_init_scheme,
layer_stitch_init_scheme=
args.layer_stitch_init_scheme)
train_score, dev_score, avg_train_score, avg_dev_score = model.fit(args.languages, args.test_languages, args.epochs, args.patience, args.opt, args.threshold, train_dir=args.train_dir, dev_dir=args.dev_dir)
print('='*50)
print('Start testing', ','.join(args.test_languages))
for test_lang in args.test_languages:
test_X, test_Y, _ = utils.get_data(
[test_lang], model.task_names, model.word2id,
model.task2label2id, data_dir=args.test_dir, train=False)
test_score = model.evaluate(test_X, test_Y, test_lang, args.threshold)
print('='*50)
print('\tStart logging {}'.format(test_lang))
utils.log_score(args.log_dir, args.languages, [test_lang], args.task_names, args.embeds, args.h_dim, args.cross_stitch_init_scheme,
args.constraint_weight, args.sigma, args.opt, train_score, dev_score, test_score)
print('\tFinished logging{}'.format(test_lang))
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Run the Sluice Network',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# DyNet parameters
parser.add_argument('--dynet-autobatch', type=int, #automatically batch some operations to speed up computations
help='use auto-batching (1) (should be first argument)')
parser.add_argument('--dynet-gpus', type=int,
help='Specify how many GPUs you want to use, if DyNet is compiled with CUDA')
parser.add_argument('--dynet-devices', nargs='+', choices=['CPU', 'GPU:0', 'GPU:1', 'GPU:2', 'GPU:3'],
help='Specify which GPUs do use')
parser.add_argument('--dynet-seed', type=int, help='random seed for DyNet')
parser.add_argument('--dynet-mem', type=int, help='memory for DyNet')
# languages, tasks, and paths
parser.add_argument('--languages', nargs='+', choices=LANGUAGES,
help='the language datasets to be trained on ')
parser.add_argument('--test-languages', nargs='+', choices=LANGUAGES,
help='the language datasets to be tested on')
parser.add_argument('--train-dir', required=True,
help='the directory containing the training data')
parser.add_argument('--dev-dir', required=True,
help='the directory containing the development data')
parser.add_argument('--test-dir', required=True,
help='the directory containing the test data')
parser.add_argument('--load', action='store_true',
help='load the pre-trained model')
parser.add_argument('--load-action', default='test',
choices=['train', 'test'],
help='action after loading the model')
parser.add_argument('--task-names', nargs='+', default=TASK_NAMES,
choices=TASK_NAMES,
help='the names of the tasks (main task is first)')
parser.add_argument('--model-dir', required=True,
help='directory where to save model and param files')
parser.add_argument('--log-dir', required=True,
help='the directory where the results should be logged')
parser.add_argument('--w-in-dim', type=int, default=64,
help='default word embeddings dimension [default: 64]')
#parser.add_argument('--c-in-dim', type=int, default=100,
# help='input dim for char embeddings [default:100]')
parser.add_argument('--h-dim', type=int, default=100,
help='hidden dimension [default: 100]')
parser.add_argument('--h-layers', type=int, default=1,
help='number of stacked LSTMs [default: 1=no stacking]')
parser.add_argument('--lower', action='store_true',
help='lowercase words (not used)')
parser.add_argument('--embeds', nargs='?',help='word embeddings file',
choices=EMBEDS, default=None)
parser.add_argument('--sigma', help='noise sigma', default=0.2, type=float)
parser.add_argument('--activation', default='tanh',
help='activation function [rectify, tanh, ...]',
type=check_activation_function)
parser.add_argument('--opt', '--optimizer', default=SGD,
choices=[SGD, ADAM],
help='trainer [sgd, adam] default: sgd')
# training hyperparameters
parser.add_argument('--epochs', type=int, default=30,
help='training epochs [default: 30]')
parser.add_argument('--patience', default=1, type=int,
help='patience for early stopping')
parser.add_argument('--cross-stitch', action='store_true',
help='use cross-stitch units between LSTM layers')
parser.add_argument('--num-subspaces', default=1, type=int, choices=[1, 2],
help='the number of subspaces for cross-stitching; '
'only 1 (no subspace) or 2 allowed currently')
parser.add_argument('--constraint-weight', type=float, default=0.,
help='weighting factor for orthogonality constraint on '
'cross-stitch subspaces; 0 = no constraint')
parser.add_argument('--constrain-matrices', type=int, nargs='+',
default=[1, 2],
help='the indices of the LSTM matrices that should be '
'constrained; indices correspond to: Wix,Wih,Wic,'
'bi,Wox,Woh,Woc,bo,Wcx,Wch,bc. Best indices so '
'far: [1, 2] http://dynet.readthedocs.io/en/latest/python_ref.html#dynet.LSTMBuilder.get_parameter_expressions)')
parser.add_argument('--cross-stitch-init-scheme', type=str,
default=BALANCED, choices=[IMBALANCED, BALANCED],
help='which initialisation scheme to use for the '
'alpha matrix - currently available: imbalanced '
'and balanced (which sets all to '
'1/(num_tasks*num_subspaces)). Only available '
'with subspaces.')
parser.add_argument('--layer-stitch-init-scheme', type=str,
default=BALANCED,
choices=[BALANCED, IMBALANCED],
help='initialisation scheme for layer-stitch units; '
'default: imbalanced (.9) for last layer weights;'
'other choice: balanced (1. / num_layers).')
parser.add_argument('--threshold', type=float,default=0.5,
help='threshold for classfication')
args = parser.parse_args()
main(args)