You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
95 lines
3.1 KiB
95 lines
3.1 KiB
#!/usr/bin/env python |
|
|
|
# 目标检测模型PP-YOLOv2训练示例脚本 |
|
# 执行此脚本前，请确认已正确安装PaddleRS库 |
|
|
|
import os |
|
|
|
import paddlers as pdrs |
|
from paddlers import transforms as T |
|
|
|
# 数据集存放目录 |
|
DATA_DIR = './data/sarship/' |
|
# 训练集`file_list`文件路径 |
|
TRAIN_FILE_LIST_PATH = './data/sarship/train.txt' |
|
# 验证集`file_list`文件路径 |
|
EVAL_FILE_LIST_PATH = './data/sarship/eval.txt' |
|
# 数据集类别信息文件路径 |
|
LABEL_LIST_PATH = './data/sarship/labels.txt' |
|
# 实验目录，保存输出的模型权重和结果 |
|
EXP_DIR = './output/ppyolo2/' |
|
|
|
# 下载和解压SAR影像舰船检测数据集 |
|
pdrs.utils.download_and_decompress( |
|
'https://paddlers.bj.bcebos.com/datasets/sarship.zip', path='./data/') |
|
|
|
# 定义训练和验证时使用的数据变换（数据增强、预处理等） |
|
# 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 |
|
# API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/data.md |
|
train_transforms = T.Compose([ |
|
# 读取影像 |
|
T.DecodeImg(), |
|
# 随机裁剪，裁块大小在一定范围内变动 |
|
T.RandomCrop(), |
|
# 随机水平翻转 |
|
T.RandomHorizontalFlip(), |
|
# 对batch进行随机缩放，随机选择插值方式 |
|
T.BatchRandomResize( |
|
target_sizes=[512, 544, 576, 608], interp='RANDOM'), |
|
# 影像归一化 |
|
T.Normalize( |
|
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
|
T.ArrangeDetector('train') |
|
]) |
|
|
|
eval_transforms = T.Compose([ |
|
T.DecodeImg(), |
|
# 使用双三次插值将输入影像缩放到固定大小 |
|
T.Resize( |
|
target_size=608, interp='CUBIC'), |
|
# 验证阶段与训练阶段的归一化方式必须相同 |
|
T.Normalize( |
|
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
|
T.ArrangeDetector('eval') |
|
]) |
|
|
|
# 分别构建训练和验证所用的数据集 |
|
train_dataset = pdrs.datasets.VOCDetDataset( |
|
data_dir=DATA_DIR, |
|
file_list=TRAIN_FILE_LIST_PATH, |
|
label_list=LABEL_LIST_PATH, |
|
transforms=train_transforms, |
|
shuffle=True) |
|
|
|
eval_dataset = pdrs.datasets.VOCDetDataset( |
|
data_dir=DATA_DIR, |
|
file_list=EVAL_FILE_LIST_PATH, |
|
label_list=LABEL_LIST_PATH, |
|
transforms=eval_transforms, |
|
shuffle=False) |
|
|
|
# 构建PP-YOLOv2模型 |
|
# 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md |
|
# 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/object_detector.py |
|
model = pdrs.tasks.det.PPYOLOv2(num_classes=len(train_dataset.labels)) |
|
|
|
# 执行模型训练 |
|
model.train( |
|
num_epochs=10, |
|
train_dataset=train_dataset, |
|
train_batch_size=4, |
|
eval_dataset=eval_dataset, |
|
# 每多少个epoch存储一次检查点 |
|
save_interval_epochs=5, |
|
# 每多少次迭代记录一次日志 |
|
log_interval_steps=4, |
|
save_dir=EXP_DIR, |
|
# 指定预训练权重 |
|
pretrain_weights='COCO', |
|
# 初始学习率大小 |
|
learning_rate=0.0001, |
|
# 学习率预热（learning rate warm-up）步数与初始值 |
|
warmup_steps=0, |
|
warmup_start_lr=0.0, |
|
# 是否启用VisualDL日志功能 |
|
use_vdl=True)
|
|
|