def create_and_check_mobilebert_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = MobileBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_mobilebert_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels):
config.num_labels = self.num_labels
model = MobileBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
loss, logits = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["logits"].size()),
[self.batch_size, self.num_labels])
self.check_loss_output(result)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Export bert onnx model',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--input_dir',
type=str,
help='input_dir of bert model, must contain config.json')
parser.add_argument('--task_name',
type=str,
choices=["mrpc", "mnli"],
help='tasks names of bert model')
parser.add_argument('--max_len',
type=int,
default=128,
help='Maximum length of the sentence pairs')
parser.add_argument('--do_lower_case',
type=bool,
default=True,
help='whether lower the tokenizer')
parser.add_argument('--output_model',
type=str,
default='bert.onnx',
help='path to exported model file')
args = parser.parse_args()
model = MobileBertForSequenceClassification.from_pretrained(args.input_dir)
export_onnx_model(args, model, args.output_model)
