PaddleX/dygraph/tutorials/slim/quantize/instance_segmentation/mask_rcnn_train.py

import paddlex as pdx
from paddlex import transforms as T

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

# 定义训练和验证时的transforms
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
train_transforms = T.Compose([
    T.RandomResizeByShort(
        short_sizes=[640, 672, 704, 736, 768, 800],
        max_size=1333,
        interp='CUBIC'), T.RandomHorizontalFlip(), T.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

eval_transforms = T.Compose([
    T.ResizeByShort(
        short_size=800, max_size=1333, interp='CUBIC'), T.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 定义训练和验证所用的数据集
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/paddlex/cv/datasets/coco.py#L26
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，参考https://github.com/PaddlePaddle/PaddleX/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
num_classes = len(train_dataset.labels)
model = pdx.det.MaskRCNN(
    num_classes=num_classes, backbone='ResNet50', with_fpn=True)

# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155
# 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,
    pretrain_weights='COCO',
    learning_rate=0.00125,
    lr_decay_epochs=[8, 11],
    warmup_steps=10,
    warmup_start_lr=0.0,
    save_dir='output/mask_rcnn_r50_fpn',
    use_vdl=True)