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95 lines
3.1 KiB
95 lines
3.1 KiB
#!/usr/bin/env python |
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# 目标检测模型Faster R-CNN训练示例脚本 |
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# 执行此脚本前，请确认已正确安装PaddleRS库 |
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import os |
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import paddlers as pdrs |
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from paddlers import transforms as T |
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# 数据集存放目录 |
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DATA_DIR = './data/sarship/' |
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# 训练集`file_list`文件路径 |
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TRAIN_FILE_LIST_PATH = './data/sarship/train.txt' |
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# 验证集`file_list`文件路径 |
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EVAL_FILE_LIST_PATH = './data/sarship/eval.txt' |
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# 数据集类别信息文件路径 |
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LABEL_LIST_PATH = './data/sarship/labels.txt' |
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# 实验目录，保存输出的模型权重和结果 |
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EXP_DIR = './output/faster_rcnn/' |
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# 下载和解压SAR影像舰船检测数据集 |
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pdrs.utils.download_and_decompress( |
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'https://paddlers.bj.bcebos.com/datasets/sarship.zip', path='./data/') |
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# 定义训练和验证时使用的数据变换（数据增强、预处理等） |
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# 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行 |
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# API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/data.md |
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train_transforms = T.Compose([ |
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# 读取影像 |
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T.DecodeImg(), |
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# 随机裁剪，裁块大小在一定范围内变动 |
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T.RandomCrop(), |
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# 随机水平翻转 |
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T.RandomHorizontalFlip(), |
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# 对batch进行随机缩放，随机选择插值方式 |
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T.BatchRandomResize( |
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target_sizes=[512, 544, 576, 608], interp='RANDOM'), |
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# 影像归一化 |
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T.Normalize( |
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
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T.ArrangeDetector('train') |
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]) |
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eval_transforms = T.Compose([ |
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T.DecodeImg(), |
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# 使用双三次插值将输入影像缩放到固定大小 |
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T.Resize( |
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target_size=608, interp='CUBIC'), |
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# 验证阶段与训练阶段的归一化方式必须相同 |
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T.Normalize( |
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
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T.ArrangeDetector('eval') |
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]) |
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# 分别构建训练和验证所用的数据集 |
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train_dataset = pdrs.datasets.VOCDetDataset( |
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data_dir=DATA_DIR, |
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file_list=TRAIN_FILE_LIST_PATH, |
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label_list=LABEL_LIST_PATH, |
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transforms=train_transforms, |
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shuffle=True) |
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eval_dataset = pdrs.datasets.VOCDetDataset( |
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data_dir=DATA_DIR, |
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file_list=EVAL_FILE_LIST_PATH, |
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label_list=LABEL_LIST_PATH, |
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transforms=eval_transforms, |
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shuffle=False) |
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# 构建Faster R-CNN模型 |
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# 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md |
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# 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/object_detector.py |
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model = pdrs.tasks.det.FasterRCNN(num_classes=len(train_dataset.labels)) |
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# 执行模型训练 |
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model.train( |
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num_epochs=10, |
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train_dataset=train_dataset, |
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train_batch_size=4, |
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eval_dataset=eval_dataset, |
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# 每多少个epoch存储一次检查点 |
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save_interval_epochs=5, |
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# 每多少次迭代记录一次日志 |
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log_interval_steps=4, |
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save_dir=EXP_DIR, |
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# 指定预训练权重 |
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pretrain_weights='COCO', |
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# 初始学习率大小 |
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learning_rate=0.005, |
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# 学习率预热（learning rate warm-up）步数与初始值 |
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warmup_steps=0, |
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warmup_start_lr=0.0, |
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# 是否启用VisualDL日志功能 |
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use_vdl=True)
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