paddleocr训练HWDB完整代码

以下是使用PaddleOCR训练HWDB的完整代码：

import os
import paddle
import paddlehub as hub
from paddleocr import PaddleOCR, draw_ocr

# 定义数据集路径
data_dir = "/path/to/HWDB"

# 定义模型保存路径
model_dir = "/path/to/save/model"

# 加载PaddleOCR模型
ocr = PaddleOCR(lang='ch')

# 加载预训练的检测模型
det = hub.Module(name='chinese_ocr_db_crnn_server')

# 定义训练器
trainer = hub.Trainer()

# 定义数据集
train_dataset = hub.datasets.OCRDataset(data_dir=data_dir, mode='train')
val_dataset = hub.datasets.OCRDataset(data_dir=data_dir, mode='val')

# 定义数据增强方法
transforms = hub.transforms.Compose(
    [hub.transforms.Resize(target_size=(32, 280)),
     hub.transforms.Normalize(mean=[0.5], std=[0.5])])

# 定义训练集和验证集的loader
train_loader = paddle.io.DataLoader(
    train_dataset,
    batch_size=64,
    shuffle=True,
    num_workers=0,
    collate_fn=hub.datasets.collate_fn)
val_loader = paddle.io.DataLoader(
    val_dataset,
    batch_size=64,
    shuffle=True,
    num_workers=0,
    collate_fn=hub.datasets.collate_fn)

# 定义模型
model = hub.Module(name='chinese_ocr_db_crnn_server')

# 定义优化器
optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())

# 定义损失函数
loss_fn = paddle.nn.CTCLoss(blank=0, reduction='mean')

# 定义评价指标
metric = hub.metrics.Accuracy()

# 训练模型
trainer.train(model=model,
              optimizer=optimizer,
              loss_fn=loss_fn,
              train_dataset=train_dataset,
              eval_dataset=val_dataset,
              epochs=100,
              batch_size=64,
              save_dir=model_dir,
              save_freq=1,
              verbose=True,
              transforms=transforms,
              train_loader=train_loader,
              val_loader=val_loader,
              metric=metric)

需要注意的是，训练HWDB需要准备好HWDB的数据集，并将其存放在`data_dir`指定的路径下。同时，训练过程中需要使用到PaddleOCR和PaddleHub的相关模型和工具，需要提前安装好相应的依赖。