predict_g2p_bert.py报错

[anaivebird]

predict.sh:
python scripts/predict_g2p_bert.py
--config saved_models/CPP_BERT_M_DescWS-Sec-cLin-B_POSw01/config.py
--checkpoint saved_models/CPP_BERT_M_DescWS-Sec-cLin-B_POSw01/best_accuracy.pth
--sent_path cpp_dataset/test2.sent
--output_path output_pred.txt

(chatchat) root@a100:/home/data/tts/wetts/shtel/g2pW# sh predict.sh
Some weights of G2PW were not initialized from the model checkpoint at /home/nas/modelhub/llm/bert-base-chinese and are newly initialized: ['char_descriptor.weight', 'classifier.bias', 'pos_classifier.bias', 'second_order_descriptor.weight', 'classifier.weight', 'pos_classifier.weight', 'descriptor_bias.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Traceback (most recent call last):
File "/home/data/tts/wetts/shtel/g2pW/scripts/predict_g2p_bert.py", line 86, in
main(config, opt.checkpoint, opt.sent_path, output_path=opt.output_path)
File "/home/data/tts/wetts/shtel/g2pW/scripts/predict_g2p_bert.py", line 58, in main
preds, confidences = predict(model, dataloader, device, labels)
File "/home/data/tts/wetts/shtel/g2pW/./g2pw/api.py", line 29, in predict
probs = onnx_session.run(
File "/opt/conda/envs/chatchat/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1695, in getattr
raise AttributeError(f"'{type(self).name}' object has no attribute '{name}'")
AttributeError: 'G2PW' object has no attribute 'run'

看起来predict函数的第一个参数应该是一个onnx-session，但是predict_g2p_bert.py里传入predict第一个参数的是model = G2PW.from_pretrained

[GitYCC]

Can you try to revise the function to:

def predict(model, dataloader, device, labels, turnoff_tqdm=False):
    model.eval()
    all_preds = []
    all_confidences = []
    with torch.no_grad():
        generator = dataloader if turnoff_tqdm else tqdm(dataloader, desc='predict')
        for data in generator:
            input_ids, token_type_ids, attention_mask, phoneme_mask, char_ids, position_ids = \
                [data[name].to(device) for name in ('input_ids', 'token_type_ids', 'attention_mask', 'phoneme_mask', 'char_ids', 'position_ids')]
            probs = model(
                input_ids=input_ids,
                token_type_ids=token_type_ids,
                attention_mask=attention_mask,
                phoneme_mask=phoneme_mask,
                char_ids=char_ids,
                position_ids=position_ids
            )
            max_probs, preds = map(lambda x: x.cpu().tolist(), probs.max(dim=-1))
            all_preds += [labels[pred] for pred in preds]
            all_confidences += max_probs
    return all_preds, all_confidences