java: 无法访问com.google.common.base.Predicate 找不到com.google.common.base.Predicate的类文件
时间: 2023-08-31 10:06:12 浏览: 176
这个错误通常是因为缺少Guava库导致的。Guava是Google为Java开发者提供的一个开源库,其中包含许多实用程序和工具类。
请确保您的项目中包含Guava库,或者在您的构建工具中添加以下依赖项:
Maven:
```xml
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>29.0-jre</version>
</dependency>
```
Gradle:
```groovy
implementation 'com.google.guava:guava:29.0-jre'
```
如果您已经添加了Guava库但仍然遇到这个错误,请检查您的依赖项是否正确,并且Guava库是否在您的构建路径中。
相关问题
".apis(RequestHandlerSelectors.basePackage("your.package.name")) "报错 需要的类型: com.google.common.base.Predicate <RequestHandler> 提供的类型: java.util.function.Predicate <RequestHandler>
抱歉,我之前提供的代码中的错误。在新版本的Springfox中,`RequestHandlerSelectors.basePackage()`方法返回的类型已经改变,需要使用`RequestHandlerSelectors.basePackage(String basePackage)`方法来指定要扫描的包路径。
下面是修正后的示例代码:
```java
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import springfox.documentation.builders.ApiInfoBuilder;
import springfox.documentation.builders.PathSelectors;
import springfox.documentation.builders.RequestHandlerSelectors;
import springfox.documentation.service.ApiInfo;
import springfox.documentation.spi.DocumentationType;
import springfox.documentation.spring.web.plugins.Docket;
import springfox.documentation.swagger2.annotations.EnableSwagger2;
@Configuration
@EnableSwagger2
public class SwaggerConfig {
@Bean
public Docket api() {
return new Docket(DocumentationType.SWAGGER_2)
.apiInfo(apiInfo())
.select()
.apis(RequestHandlerSelectors.basePackage("your.package.name"))
.paths(PathSelectors.any())
.build();
}
private ApiInfo apiInfo() {
return new ApiInfoBuilder()
.title("Your API Title")
.description("Your API Description")
.version("1.0.0")
.build();
}
}
```
请将`your.package.name`替换为实际的包路径,然后尝试重新编译运行代码,应该不会再出现上述报错。
如果还有其他问题,请随时提问!
https://github.com/weizhepei/CasRel中run.py解读
`run.py` 是 `CasRel` 项目的入口文件,用于训练和测试模型。以下是 `run.py` 的主要代码解读和功能说明:
### 导入依赖包和模块
首先,`run.py` 导入了所需的依赖包和模块,包括 `torch`、`numpy`、`argparse`、`logging` 等。
```python
import argparse
import logging
import os
import random
import time
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from casrel import CasRel
from dataset import RE_Dataset
from utils import init_logger, load_tokenizer, set_seed, collate_fn
```
### 解析命令行参数
接下来,`run.py` 解析了命令行参数,包括训练数据路径、模型保存路径、预训练模型路径、学习率等参数。
```python
def set_args():
parser = argparse.ArgumentParser()
parser.add_argument("--train_data", default=None, type=str, required=True,
help="The input training data file (a text file).")
parser.add_argument("--dev_data", default=None, type=str, required=True,
help="The input development data file (a text file).")
parser.add_argument("--test_data", default=None, type=str, required=True,
help="The input testing data file (a text file).")
parser.add_argument("--model_path", default=None, type=str, required=True,
help="Path to save, load model")
parser.add_argument("--pretrain_path", default=None, type=str,
help="Path to pre-trained model")
parser.add_argument("--vocab_path", default=None, type=str, required=True,
help="Path to vocabulary")
parser.add_argument("--batch_size", default=32, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--gradient_accumulation_steps", default=1, type=int,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument("--learning_rate", default=5e-5, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs", default=3, type=int,
help="Total number of training epochs to perform.")
parser.add_argument("--max_seq_length", default=256, type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--warmup_proportion", default=0.1, type=float,
help="Linear warmup over warmup_steps.")
parser.add_argument("--weight_decay", default=0.01, type=float,
help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--logging_steps", type=int, default=500,
help="Log every X updates steps.")
parser.add_argument("--save_steps", type=int, default=500,
help="Save checkpoint every X updates steps.")
parser.add_argument("--seed", type=int, default=42,
help="random seed for initialization")
parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu",
help="selected device (default: cuda if available)")
args = parser.parse_args()
return args
```
### 加载数据和模型
接下来,`run.py` 加载了训练、验证和测试数据,以及 `CasRel` 模型。
```python
def main():
args = set_args()
init_logger()
set_seed(args)
tokenizer = load_tokenizer(args.vocab_path)
train_dataset = RE_Dataset(args.train_data, tokenizer, args.max_seq_length)
dev_dataset = RE_Dataset(args.dev_data, tokenizer, args.max_seq_length)
test_dataset = RE_Dataset(args.test_data, tokenizer, args.max_seq_length)
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.batch_size,
collate_fn=collate_fn)
dev_sampler = SequentialSampler(dev_dataset)
dev_dataloader = DataLoader(dev_dataset, sampler=dev_sampler, batch_size=args.batch_size,
collate_fn=collate_fn)
test_sampler = SequentialSampler(test_dataset)
test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=args.batch_size,
collate_fn=collate_fn)
model = CasRel(args)
if args.pretrain_path:
model.load_state_dict(torch.load(args.pretrain_path, map_location="cpu"))
logging.info(f"load pre-trained model from {args.pretrain_path}")
model.to(args.device)
```
### 训练模型
接下来,`run.py` 开始训练模型,包括前向传播、反向传播、梯度更新等步骤。
```python
optimizer = torch.optim.Adam([{'params': model.bert.parameters(), 'lr': args.learning_rate},
{'params': model.subject_fc.parameters(), 'lr': args.learning_rate},
{'params': model.object_fc.parameters(), 'lr': args.learning_rate},
{'params': model.predicate_fc.parameters(), 'lr': args.learning_rate},
{'params': model.linear.parameters(), 'lr': args.learning_rate}],
lr=args.learning_rate, eps=args.adam_epsilon, weight_decay=args.weight_decay)
total_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
warmup_steps = int(total_steps * args.warmup_proportion)
scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lambda epoch: 1 / (1 + 0.05 * (epoch - 1))
)
global_step = 0
best_f1 = 0
for epoch in range(args.num_train_epochs):
for step, batch in enumerate(train_dataloader):
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
"subj_pos": batch[3],
"obj_pos": batch[4],
"subj_type": batch[5],
"obj_type": batch[6],
"subj_label": batch[7],
"obj_label": batch[8],
"predicate_label": batch[9],
}
outputs = model(**inputs)
loss = outputs[0]
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
global_step += 1
if global_step % args.logging_steps == 0:
logging.info(f"Epoch:[{epoch + 1}]/[{args.num_train_epochs}] Step:[{global_step}] "
f"Train loss:{loss.item():.6f}")
if global_step % args.save_steps == 0:
f1 = evaluate(model, dev_dataloader, args)
if f1 > best_f1:
best_f1 = f1
torch.save(model.state_dict(), os.path.join(args.model_path, "best_model.bin"))
logging.info(f"Save model at step [{global_step}] with best f1 {best_f1:.4f}")
```
### 测试模型
最后,`run.py` 对模型进行测试,输出模型在测试集上的预测结果。
```python
model.load_state_dict(torch.load(os.path.join(args.model_path, "best_model.bin"), map_location="cpu"))
logging.info(f"load best model from {os.path.join(args.model_path, 'best_model.bin')}")
f1, precision, recall = evaluate(model, test_dataloader, args)
logging.info(f"Test f1:{f1:.4f} precision:{precision:.4f} recall:{recall:.4f}")
```
以上就是 `run.py` 的主要代码解读和功能说明。
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