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eval.py
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eval.py
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import os
import argparse
import logging
import torch
import evaluate
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from transformers import (BertConfig, BertTokenizer, default_data_collator,
DataCollatorWithPadding)
from torch.utils.data import DataLoader
from sklearn.metrics import classification_report
from utils.dataloader import load_data
from utils.models import BertForSequenceClassification, RobertaForSequenceClassification
MODEL_CLASS_MAPPING = {
'bert-base-chinese': BertForSequenceClassification,
'hfl/chinese-roberta-wwm-ext-large': RobertaForSequenceClassification
}
logger = get_logger(__name__)
def parse_args():
parser = argparse.ArgumentParser(
'Finetune a transformers model on a text classification task.')
parser.add_argument(
'--max_length',
type=int,
default=512,
help=
('The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,'
' sequences shorter will be padded if `--pad_to_max_length` is passed.'
))
parser.add_argument(
'--pad_to_max_length',
action='store_true',
help=
'If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.'
)
parser.add_argument(
'--model_name_or_path',
type=str,
help=
'Path to pretrained model or model identifier from huggingface.co/models.',
required=True)
parser.add_argument(
'--per_device_test_batch_size',
type=int,
default=16,
help='Batch size (per device) for the training dataloader.')
parser.add_argument('--seed',
type=int,
default=42,
help='A seed for reproducible training.')
parser.add_argument('--cuda', type=str, default='0', help='cuda device')
args = parser.parse_args()
return args
def main():
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda
accelerator = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
accelerator.wait_for_everyone()
datasets = load_data(['test'])
label_list = (datasets['test']).unique('label')
label_list.sort()
num_labels = len(label_list)
config = BertConfig.from_pretrained(args.model_name_or_path,
num_labels=num_labels,
finetuning_task='text classification')
tokenizer = BertTokenizer.from_pretrained(
args.model_name_or_path,
use_fast=False,
never_split=['[seeker]', '[supporter]'])
tokenizer.vocab['[seeker]'] = tokenizer.vocab.pop('[unused1]')
tokenizer.vocab['[supporter]'] = tokenizer.vocab.pop('[unused2]')
MODEL_CLASS = MODEL_CLASS_MAPPING[args.model_name_or_path]
model = MODEL_CLASS(config, args.model_name_or_path)
model_name_or_path: str = args.model_name_or_path
model_name_or_path = model_name_or_path.replace('/', '-')
PATH = f'out/{model_name_or_path}/{args.seed}/pytorch_model.bin'
model.load_state_dict(torch.load(PATH))
padding = 'max_length' if args.pad_to_max_length else False
def preprocess_function(examples):
# tokenize the texts
context = examples['context']
response = examples['response']
result = tokenizer(text=context,
text_pair=response,
padding=padding,
max_length=args.max_length,
truncation=True,
add_special_tokens=True,
return_token_type_ids=True)
result['labels'] = examples['label']
return result
with accelerator.main_process_first():
process_datasets = datasets.map(
preprocess_function,
batched=True,
remove_columns=['context', 'response', 'label'],
desc='running tokenizer on dataset')
test_dataset = process_datasets['test']
if args.pad_to_max_length:
data_collator = default_data_collator
else:
data_collator = DataCollatorWithPadding(
tokenizer,
pad_to_multiple_of=(8 if Accelerator.mixed_precision == 'fp16' else
None))
test_dataloader = DataLoader(test_dataset,
shuffle=True,
collate_fn=data_collator,
batch_size=args.per_device_test_batch_size)
# Prepare everything with our `accelerator`.
model, test_dataloader = accelerator.prepare(model, test_dataloader)
acc_metric = evaluate.load('accuracy')
test_preds = []
test_trues = []
model.eval()
test_loss = 0
for batch in test_dataloader:
with torch.no_grad():
outputs = model(**batch)
loss = outputs.loss
test_loss += loss.detach().float()
predictions = outputs.logits.argmax(dim=-1)
predictions, references = accelerator.gather(
(predictions, batch["labels"]))
acc_metric.add_batch(predictions=predictions, references=references)
test_preds.extend(list(predictions.detach().cpu().numpy()))
test_trues.extend(list(references.detach().cpu().numpy()))
eval_acc = acc_metric.compute()
logger.info(f'accuracy: {eval_acc}')
logger.info(f'validation loss: {test_loss}')
report = classification_report(test_trues, test_preds, digits=5)
logger.info(f'report: \n{report}')
if __name__ == '__main__':
main()