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99 lines
3.4 KiB
99 lines
3.4 KiB
3 years ago
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#!/usr/bin/env python
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# 目标检测模型YOLOv3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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# 下载文件存放目录
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DOWNLOAD_DIR = './data/sarship/'
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# 数据集存放目录
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DATA_DIR = './data/sarship/sar_ship_1/'
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# 训练集`file_list`文件路径
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TRAIN_FILE_LIST_PATH = './data/sarship/sar_ship_1/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/sar_ship_1/valid.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/sar_ship_1/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/yolov3/'
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# 下载和解压SAR影像舰船检测数据集
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# 若目录已存在则不重复下载
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sarship_dataset = 'https://paddleseg.bj.bcebos.com/dataset/sar_ship_1.tar.gz'
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if not os.path.exists(DATA_DIR):
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pdrs.utils.download_and_decompress(sarship_dataset, path=DOWNLOAD_DIR)
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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/PaddleCV-SIG/PaddleRS/blob/develop/docs/apis/transforms.md
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train_transforms = T.Compose([
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# 对输入影像施加随机色彩扰动
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T.RandomDistort(),
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# 在影像边界进行随机padding
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T.RandomExpand(),
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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=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608],
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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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])
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eval_transforms = T.Compose([
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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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])
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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.VOCDetection(
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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.VOCDetection(
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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YOLOv3模型，使用DarkNet53作为backbone
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# 目前已支持的模型请参考：https://github.com/PaddleCV-SIG/PaddleRS/blob/develop/docs/apis/model_zoo.md
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# 模型输入参数请参考：https://github.com/PaddleCV-SIG/PaddleRS/blob/develop/paddlers/tasks/object_detector.py
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model = pdrs.tasks.YOLOv3(
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num_classes=len(train_dataset.labels), backbone='DarkNet53')
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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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# 初始学习率大小
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learning_rate=0.0001,
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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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