ModelScope问题之模型encoder配置报错如何解决

import os
from modelscope.metainfo import Trainers
from modelscope.msdatasets import MsDataset
from modelscope.pipelines import pipeline
from modelscope.trainers import build_trainer
from modelscope.utils.config import Config
from modelscope.utils.hub import read_config
  
train_dataset = MsDataset.load("./qa.csv", split='train').remap_columns({'text': 'text'})
print(train_dataset)
eval_dataset = train_dataset
cfg: Config = read_config("damo/nlp_structbert_faq-question-answering_chinese-base")
cfg.train.train_iters_per_epoch = 30
cfg.evaluation.val_iters_per_epoch = 2
cfg.train.seed = 1234
cfg.train.optimizer.lr = 2e-5
cfg.train.hooks = [{
    'type': 'CheckpointHook',
    'by_epoch': False,
    'interval': 50
}, {
    'type': 'EvaluationHook',
    'by_epoch': False,
    'interval': 50
}, {
    'type': 'TextLoggerHook',
    'by_epoch': False,
    'rounding_digits': 5,
    'interval': 10
}]
cfg_file = os.path.join("./model/temp", 'config.json')
cfg.dump(cfg_file)
trainer = build_trainer(
    Trainers.faq_question_answering_trainer,
    default_args=dict(
        model="damo/nlp_structbert_faq-question-answering_chinese-base",
        work_dir="./model/temp",
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        cfg_file=cfg_file))
trainer.train()
evaluate_result = trainer.evaluate()
print(evaluate_result)

Dataset({
    features: ['text', 'label', 'answer'],
    num_rows: 26
})
2023-06-26 20:32:21,659 - modelscope - INFO - initialize model from ./model/damo/nlp_structbert_faq-question-answering_chinese-base
2023-06-26 20:32:22,949 - modelscope - INFO - faq task build protonet network
2023-06-26 20:32:28,135 - modelscope - INFO - All model checkpoint weights were used when initializing SbertForFaqQuestionAnswering.
2023-06-26 20:32:28,136 - modelscope - INFO - All the weights of SbertForFaqQuestionAnswering were initialized from the model checkpoint If your task is similar to the task the model of the checkpoint was trained on, you can already use SbertForFaqQuestionAnswering for predictions without further training.
2023-06-26 20:32:28,137 - modelscope - WARNING - No train key and type key found in preprocessor domain of configuration.json file.
2023-06-26 20:32:28,138 - modelscope - WARNING - Cannot find available config to build preprocessor at mode train, current config: {'max_seq_length': 50, 'model_dir': './model/damo/nlp_structbert_faq-question-answering_chinese-base'}. trying to build by task and model information.
2023-06-26 20:32:28,172 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-06-26 20:32:28,172 - modelscope - WARNING - Cannot find available config to build preprocessor at mode eval, current config: {'max_seq_length': 50, 'model_dir': './model/damo/nlp_structbert_faq-question-answering_chinese-base'}. trying to build by task and model information.
2023-06-26 20:32:28,185 - modelscope - WARNING - ('CUSTOM_DATASETS', 'faq-question-answering', 'structbert') not found in ast index file
2023-06-26 20:32:28,186 - modelscope - WARNING - ('CUSTOM_DATASETS', 'faq-question-answering', 'structbert') not found in ast index file
2023-06-26 20:32:28,187 - modelscope - INFO - cuda is not available, using cpu instead.
2023-06-26 20:32:28,187 - modelscope - INFO - ==========================Training Config Start==========================
2023-06-26 20:32:28,187 - modelscope - INFO - {
    "framework": "pytorch",
    "task": "faq-question-answering",
    "pipeline": {
        "type": "faq-question-answering"
    },
    "model": {
        "type": "structbert",
        "pooling": "avg",
        "metric": "relation"
    },
    "preprocessor": {
        "max_seq_length": 50,
        "model_dir": "./model/damo/nlp_structbert_faq-question-answering_chinese-base"
    },
    "train": {
        "seed": 1234,
        "hooks": [
            {
                "type": "IterTimerHook"
            }
        ],
        "train_iters_per_epoch": 30,
        "max_epochs": 1,
        "sampler": {
            "n_way": 5,
            "k_shot": 5,
            "r_query": 5,
            "min_labels": 2
        },
        "optimizer": {
            "type": "Adam",
            "lr": 2e-05,
            "options": {
                "grad_clip": {
                    "max_norm": 5.0
                }
            }
        },
        "lr_scheduler": {
            "type": "LinearLR",
            "options": {
                "by_epoch": false
            }
        },
        "dataloader": {
            "workers_per_gpu": 1
        },
        "checkpoint": {
            "period": {
                "by_epoch": false,
                "interval": 50
            }
        },
        "logging": {
            "by_epoch": false,
            "rounding_digits": 5,
            "interval": 10
        },
        "work_dir": "./model/temp"
    },
    "evaluation": {
        "metrics": "seq-cls-metric",
        "val_iters_per_epoch": 2,
        "dataloader": {
            "workers_per_gpu": 1
        },
        "period": {
            "by_epoch": false,
            "interval": 50
        }
    }
}
2023-06-26 20:32:28,188 - modelscope - INFO - ===========================Training Config End===========================
2023-06-26 20:32:28,190 - modelscope - INFO - num. of bad sample ids:5/26
2023-06-26 20:32:28,192 - modelscope - INFO - train: label size:3.0, data size:18,                 domain_size:1
2023-06-26 20:32:28,193 - modelscope - WARNING - ('OPTIMIZER', 'default', 'Adam') not found in ast index file
2023-06-26 20:32:28,194 - modelscope - WARNING - ('LR_SCHEDULER', 'default', 'LinearLR') not found in ast index file
2023-06-26 20:32:28,194 - modelscope - INFO - Stage: before_run:
    (ABOVE_NORMAL) OptimizerHook                      
    (LOW         ) LrSchedulerHook                    
    (LOW         ) CheckpointHook                     
    (VERY_LOW    ) TextLoggerHook                     
 -------------------- 
Stage: before_train_epoch:
    (LOW         ) LrSchedulerHook                    
 -------------------- 
Stage: before_train_iter:
    (ABOVE_NORMAL) OptimizerHook                      
 -------------------- 
Stage: after_train_iter:
    (ABOVE_NORMAL) OptimizerHook                      
    (NORMAL      ) EvaluationHook                     
    (LOW         ) LrSchedulerHook                    
    (LOW         ) CheckpointHook                     
    (VERY_LOW    ) TextLoggerHook                     
 -------------------- 
Stage: after_train_epoch:
    (NORMAL      ) EvaluationHook                     
    (LOW         ) LrSchedulerHook                    
    (LOW         ) CheckpointHook                     
    (VERY_LOW    ) TextLoggerHook                     
 -------------------- 
Stage: after_val_epoch:
    (VERY_LOW    ) TextLoggerHook                     
 -------------------- 
Stage: after_run:
    (LOW         ) CheckpointHook                     
 -------------------- 
2023-06-26 20:32:28,197 - modelscope - INFO - Checkpoints will be saved to ./model/temp
2023-06-26 20:32:28,197 - modelscope - INFO - Text logs will be saved to ./model/temp
** build_dataset error log: 'structbert is not in the custom_datasets registry group faq-question-answering. Please make sure the correct version of ModelScope library is used.'
** build_dataset error log: 'structbert is not in the custom_datasets registry group faq-question-answering. Please make sure the correct version of ModelScope library is used.'

!pip install onnx torch

import torch
import torchvision.models as models
import onnx
from onnx import helper, TensorProto
# 加载预训练模型
model = models.resnet50(pretrained=True)
model.eval()
# 将模型设置为评估模式
model.eval()
# 定义输入张量的大小和类型
x = torch.randn(1, 3, 224, 224) # 假设输入张量的形状为 (batch_size, channels, height, width)
x = x.to('cuda') # 将输入张量移动到 GPU 上
x = x.detach().cpu() # 将输入张量从 GPU 上移除并转换为 CPU 张量
# 导出模型为 ONNX 格式
onnx_filename = "space_t_cn.onnx" # 定义输出文件名
input_name = "input" # 定义输入节点名称
output_name = "output" # 定义输出节点名称
dynamic_axes = {'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}} # 定义输入和输出张量的维度信息
torch.onnx.export(model, x, onnx_filename, input_names=[input_name], output_names=[output_name], dynamic_axes=dynamic_axes)
print("Model exported to", onnx_filename) # 打印导出结果