PaddleX/tutorials/train/instance_segmentation/mask_rcnn_r18_fpn.py

# 环境变量配置，用于控制是否使用GPU
# 说明文档：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html#gpu
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from paddlex.det import transforms
import paddlex as pdx

# 下载和解压小度熊分拣数据集
xiaoduxiong_dataset = 'https://bj.bcebos.com/paddlex/datasets/xiaoduxiong_ins_det.tar.gz'
pdx.utils.download_and_decompress(xiaoduxiong_dataset, path='./')

# 定义训练和验证时的transforms
# API说明 https://paddlex.readthedocs.io/zh_CN/develop/apis/transforms/det_transforms.html
train_transforms = transforms.Compose([
    transforms.RandomHorizontalFlip(), 
    transforms.Normalize(),
    transforms.ResizeByShort(short_size=800, max_size=1333), 
    transforms.Padding(coarsest_stride=32)
])

eval_transforms = transforms.Compose([
    transforms.Normalize(), 
    transforms.ResizeByShort(short_size=800, max_size=1333), 
    transforms.Padding(coarsest_stride=32)
])

# 定义训练和验证所用的数据集
# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/datasets.html#paddlex-datasets-cocodetection
train_dataset = pdx.datasets.CocoDetection(
    data_dir='xiaoduxiong_ins_det/JPEGImages',
    ann_file='xiaoduxiong_ins_det/train.json',
    transforms=train_transforms,
    shuffle=True)
eval_dataset = pdx.datasets.CocoDetection(
    data_dir='xiaoduxiong_ins_det/JPEGImages',
    ann_file='xiaoduxiong_ins_det/val.json',
    transforms=eval_transforms)

# 初始化模型，并进行训练
# 可使用VisualDL查看训练指标
# VisualDL启动方式: visualdl --logdir output/mask_rcnn_r50_fpn/vdl_log --port 8001
# 浏览器打开 https://0.0.0.0:8001即可
# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
# num_classes 需要设置为包含背景类的类别数，即: 目标类别数量 + 1
num_classes = len(train_dataset.labels) + 1

# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/models/instance_segmentation.html#maskrcnn
model = pdx.det.MaskRCNN(num_classes=num_classes, backbone='ResNet18')

# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/models/instance_segmentation.html#train
# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
model.train(
    num_epochs=12,
    train_dataset=train_dataset,
    train_batch_size=1,
    eval_dataset=eval_dataset,
    learning_rate=0.00125,
    warmup_steps=10,
    lr_decay_epochs=[8, 11],
    save_dir='output/mask_rcnn_r18_fpn',
    use_vdl=True)