-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathcl_si.py
169 lines (151 loc) · 8.04 KB
/
cl_si.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
"""
Training script of KWS models using SI as the CL method.
Reference: Continual Learning Through Synaptic Intelligence
https://arxiv.org/abs/1703.04200
@author huangyz0918
@date 05/09/2021
"""
import time
import neptune
import argparse
import numpy as np
import torch
from model import STFT_TCResnet, MFCC_TCResnet, STFT_MLP, MFCC_RNN, parameter_number
from model import Trainer, Evaluator, get_dataloader_keyword
from model.util import get_params
def on_task_update(model, big_omega, small_omega,
cached_checkpoint, device, elambda=0):
"""
Update the regularization after each task learning.
"""
if big_omega is None:
big_omega = torch.zeros_like(get_params(model)).to(device)
# online EWC lambda.
big_omega += small_omega / ((get_params(model).data - cached_checkpoint) ** 2 + elambda)
# store parameters checkpoint and reset small_omega
return big_omega, 0, get_params(model).data.clone().to(device)
if __name__ == "__main__":
def options(config):
parser = argparse.ArgumentParser(description="Input optional guidance for training")
parser.add_argument("--epoch", default=1, type=int, help="The number of training epoch")
parser.add_argument("--lr", default=0.01, type=float, help="Learning rate")
parser.add_argument("--elambda", default=0.0005, type=float, help="online EWC lambda")
parser.add_argument("--c", default=10, type=float, help="SI surrogate loss coefficient")
parser.add_argument("--batch", default=128, type=int, help="Training batch size")
parser.add_argument("--step", default=30, type=int, help="Training step size")
parser.add_argument("--gpu", default=4, type=int, help="Number of GPU device")
parser.add_argument("--log", default=False, action='store_true',
help="record the experiment into web neptune.ai")
parser.add_argument("--ek", default=False, action='store_true', help="evaluate the CL by keywords")
parser.add_argument("--dpath", default="./dataset", type=str, help="The path of dataset")
parser.add_argument("--tqdm", default=False, action='store_true', help="enable terminal tqdm output.")
parser.add_argument("--model", default="stft", type=str, help="[stft, mfcc]")
parser.add_argument("--cha", default=config["tc-resnet8"], type=list,
help="The channel of model layers (in list)")
parser.add_argument("--scale", default=1, type=float, help="The scale of model channel")
parser.add_argument("--freq", default=30, type=int, help="Model saving frequency (in step)")
parser.add_argument("--save", default="stft", type=str, help="The save name")
args = parser.parse_args()
return args
class_list_0 = ["yes", "no", "nine",
"three", "bed", "up",
"down", "wow", "happy",
"four", "stop", "go",
"dog", "cat", "five"]
class_list_1 = ["tree", "one", "eight"]
class_list_2 = ["left", "right", "bird"]
class_list_3 = ["seven", "six", "two"]
class_list_4 = ["marvin", "on", "sheila"]
class_list_5 = ["off", "house", "zero"]
config = {
"tc-resnet8": [16, 24, 32, 48],
"tc-resnet14": [16, 24, 24, 32, 32, 48, 48]}
parameters = options(config)
# initialize and setup Neptune
if parameters.log:
neptune.init('huangyz0918/kws')
neptune.create_experiment(name='kws_model', tags=['pytorch', 'KWS', 'GSC', 'TC-ResNet', 'SI'],
params=vars(parameters))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# build a multi-head setting for learning process.
total_class_list = []
learning_tasks = [class_list_0, class_list_1, class_list_2, class_list_3, class_list_4, class_list_5]
for x in learning_tasks:
total_class_list += x
total_class_num = len([i for j, i in enumerate(total_class_list) if i not in total_class_list[:j]])
class_list = []
for task in learning_tasks:
class_list += task
class_encoding = {category: index for index, category in enumerate(class_list)}
# load the model.
if parameters.model == "stft":
model = STFT_TCResnet(
filter_length=256, hop_length=129, bins=129,
channels=parameters.cha, channel_scale=parameters.scale, num_classes=total_class_num)
elif parameters.model == "mfcc":
model = MFCC_TCResnet(bins=40, channels=parameters.cha, channel_scale=parameters.scale,
num_classes=total_class_num)
elif parameters.model == "stft-mlp":
model = STFT_MLP(filter_length=256, hop_length=129, bins=129, num_classes=total_class_num)
elif parameters.model == "rnn":
model = MFCC_RNN(n_mfcc=12, sampling_rate=16000,
num_classes=total_class_num) # sample length for the dataset is 16000.
else:
model = None
# continuous learning by SI.
big_omega = None
small_omega = 0
la_list = []
acc_list = []
bwt_list = []
learned_class_list = []
trainer = Trainer(parameters, model)
start_time = time.time()
# store a chaced mdoel checkpoint.
cached_checkpoint = get_params(trainer.model).data.clone().to(trainer.device)
for task_id, task_class in enumerate(learning_tasks):
optimizer = torch.optim.SGD(model.parameters(), lr=parameters.lr, momentum=0.9)
print(">>> Learned Class: ", learned_class_list, " To Learn: ", task_class)
learned_class_list += task_class
train_loader, test_loader = get_dataloader_keyword(parameters.dpath, task_class, class_encoding,
parameters.batch)
# starting training.
if parameters.log:
small_omega = trainer.si_train(optimizer, train_loader, test_loader, big_omega, small_omega,
cached_checkpoint, coefficient=parameters.c, tag=task_id)
else:
small_omega = trainer.si_train(optimizer, train_loader, test_loader, big_omega, small_omega,
cached_checkpoint, coefficient=parameters.c)
# update the SI parameters.
big_omega, small_omega, cached_checkpoint = on_task_update(trainer.model, big_omega, small_omega,
cached_checkpoint, trainer.device,
parameters.elambda)
# start evaluating the CL on previous tasks.
total_learned_acc = 0
if parameters.ek:
evaluate_list = class_list
else:
evaluate_list = learning_tasks
for val_id, task in enumerate(evaluate_list):
print(f">>> Testing on task {val_id}, Keywords: {task}")
_, val_loader = get_dataloader_keyword(parameters.dpath, task, class_encoding, parameters.batch)
if parameters.log:
log_data = Evaluator(trainer.model, tag=f't{task_id}v{val_id}').evaluate(val_loader)
else:
log_data = Evaluator(trainer.model).evaluate(val_loader)
if parameters.log:
neptune.log_metric(f'TASK-{task_id}-acc', log_data["test_accuracy"])
if val_id <= task_id:
total_learned_acc += log_data["test_accuracy"]
if val_id == task_id:
la_list.append(log_data["test_accuracy"])
acc_list.append(total_learned_acc / (task_id + 1))
print(f'ACC on TASK {task_id}: {total_learned_acc / (task_id + 1)}')
if task_id > 0:
bwt_list.append(np.mean([acc_list[i + 1] - acc_list[i] for i in range(len(acc_list) - 1)]))
duration = time.time() - start_time
print(f'Total time {duration}, Avg: {duration / len(learning_tasks)}s')
print(f'ACC: {np.mean(acc_list)}, std: {np.std(acc_list)}')
print(f'LA: {np.mean(la_list)}, std: {np.std(la_list)}')
print(f'BWT: {np.mean(bwt_list)}, std: {np.std(bwt_list)}')
print(f'Parameter: {parameter_number(trainer.model) / 1024} K')