-
Notifications
You must be signed in to change notification settings - Fork 1
/
run.py
185 lines (162 loc) · 7.34 KB
/
run.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
import argparse
import torch
from tqdm import tqdm
import numpy as np
from torch.utils.data import DataLoader
from transformers import RobertaConfig, RobertaTokenizer, BertConfig, BertTokenizer
from transformers.optimization import AdamW, get_linear_schedule_with_warmup
from utils import set_seed, collate_fn, pad0s, CDataLoader,CDataLoaderDev
from datasets import load_metric
from model import RobertaForSequenceClassification
from evaluation import evaluate_ood
import warnings
from data import load , load_ood
import time
task_to_labels = {
'sst2': 2,
'imdb': 2,
'20ng': 20,
'trec': 6,
}
task_to_metric = {
'sst2': 'sst2',
'imdb': 'sst2',
'20ng': 'mnli',
'trec': 'mnli',
}
def train(args, model, train_dataset, dev_dataset, test_dataset, benchmarks):
train_dataloader = CDataLoader(train_dataset, batch_size=args.batch_size, collate_fn=collate_fn, shuffle=True, drop_last=True)
dev_dataloader = CDataLoader(dev_dataset, batch_size=args.batch_size, collate_fn=collate_fn,shuffle=False, drop_last=False)
total_steps = int(len(train_dataloader) * args.num_train_epochs)
warmup_steps = int(total_steps * args.warmup_ratio)
no_decay = ["LayerNorm.weight", "bias"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps)
ood_minfpr=100#
ood_maxaoc=0
results_dic={}
score_dic={'maha':[], 'cosine':[], 'softmax':[], 'energy':[]}
def detect_ood():
model.prepare_ood(dev_dataloader)
for tag, ood_features in benchmarks:
print('..Evaluating ood for DataSet ', tag)
results = evaluate_ood(args, model, test_dataset, ood_features, tag=tag)
print("detect_ood: ", results)
num_steps = 0
results_list=[]
ood_results_dic={}
for epoch in range(int(args.num_train_epochs)):
model.zero_grad()
for step, batch in enumerate(tqdm(train_dataloader)):
model.train()
for key, value in batch.items():
if not ('SRL' in key.split('_')):
batch[key]=value.to(0)
else:
batch[key]=value
outputs = model(**batch)
loss, cos_loss = outputs[0], outputs[1]
loss.backward()
num_steps += 1
optimizer.step()
scheduler.step()
model.zero_grad()
results = evaluate(args, model, dev_dataset, tag="dev")
results = evaluate(args, model, test_dataset, tag="test")
detect_ood()
def evaluate(args, model, eval_dataset, tag="train"):
metric_name = task_to_metric[args.task_name]
metric = load_metric("glue", metric_name)
def compute_metrics(preds, labels):
preds = np.argmax(preds, axis=1)
result = metric.compute(predictions=preds, references=labels)
if len(result) > 1:
result["score"] = np.mean(list(result.values())).item()
return result
dataloader = CDataLoader(eval_dataset, batch_size=args.batch_size, collate_fn=collate_fn,shuffle=False, drop_last=False)
label_list, logit_list = [], []
for step, batch in enumerate(tqdm(dataloader)):
model.eval()
labels = batch["labels"].detach().cpu().numpy()
for key, value in batch.items():
if not ('SRL' in key.split('_')):
batch[key]=value.to(0)
else:
batch[key]=value
outputs = model(**batch)
logits = outputs[2].detach().cpu().numpy()
label_list.append(labels)
logit_list.append(logits)
preds = np.concatenate(logit_list, axis=0)
labels = np.concatenate(label_list, axis=0)
results = compute_metrics(preds, labels)
print("evaluate::",results)
results = {"{}_{}".format(tag, key): value for key, value in results.items()}
return results
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name_or_path", default="roberta-large", type=str)
parser.add_argument("--max_seq_length", default=256, type=int)
parser.add_argument("--task_name", default="sst2", type=str)
parser.add_argument("--batch_size", default=12, type=int)
parser.add_argument("--learning_rate", default=1e-5, type=float)
parser.add_argument("--adam_epsilon", default=1e-6, type=float)
parser.add_argument("--warmup_ratio", default=0.06, type=float)###
parser.add_argument("--weight_decay", default=0.01, type=float)
parser.add_argument("--num_train_epochs", default=10.0, type=float)
parser.add_argument("--seed", type=int, default=900)
parser.add_argument("--project_name", type=str, default="ood")
parser.add_argument("--beta", type=float, default=1.0)
parser.add_argument("--alpha", type=float, default=3.0)
parser.add_argument("--theta", type=float, default=1.0)
parser.add_argument("--loss", type=str, default="margin")
parser.add_argument("--cuda", type=str, default="0")
parser.add_argument("--masking_probability_train", type=float, default=0.30)
parser.add_argument("--masking_probability", type=float, default=0.30)
args = parser.parse_args()
args.n_gpu = torch.cuda.device_count()
args.device = device
set_seed(args)
num_labels = task_to_labels[args.task_name]
if args.model_name_or_path.startswith('roberta'):
model_class = 'roberta'
config = RobertaConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels)
config.gradient_checkpointing = True
config.alpha = args.alpha
config.beta = args.beta
config.theta = args.theta
config.loss = args.loss
config.cuda = args.cuda
config.masking_probability = args.masking_probability
config.masking_probability_train = args.masking_probability_train
config.seed = args.seed
tokenizer = RobertaTokenizer.from_pretrained(args.model_name_or_path)
model = RobertaForSequenceClassification.from_pretrained(
args.model_name_or_path, config=config,
)
model.to(0)
datasets = ['rte', 'sst2', 'mnli', '20ng', 'trec', 'imdb', 'wmt16', 'multi30k']
benchmarks = ()
for dataset in datasets:
if dataset == args.task_name:
print("Loading ", dataset, " as ID")
train_dataset, dev_dataset, test_dataset = load(dataset, tokenizer, max_seq_length=args.max_seq_length, is_id=True)
else:
if args.task_name=='sst2' and dataset=='imdb':
pass
elif args.task_name=='imdb' and dataset=='sst2':
pass
else:
print("Loading ", dataset, " as OOD")
ood_dataset = load_ood(dataset, tokenizer, max_seq_length=args.max_seq_length)
benchmarks = (('ood_' + dataset, ood_dataset),) + benchmarks
train(args, model, train_dataset, dev_dataset, test_dataset, benchmarks)
if __name__ == "__main__":
main()