PaddleX/tutorials/slim/prune/semantic_segmentation/unet_train.py

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

import paddlex as pdx
from paddlex.seg import transforms

optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
pdx.utils.download_and_decompress(optic_dataset, path='./')

train_transforms = transforms.Compose([
    transforms.RandomHorizontalFlip(), transforms.ResizeRangeScaling(),
    transforms.RandomPaddingCrop(crop_size=512), transforms.Normalize()
])

eval_transforms = transforms.Compose([
    transforms.ResizeByLong(long_size=512), transforms.Padding(target_size=512),
    transforms.Normalize()
])

train_dataset = pdx.datasets.SegDataset(
    data_dir='optic_disc_seg',
    file_list='optic_disc_seg/train_list.txt',
    label_list='optic_disc_seg/labels.txt',
    transforms=train_transforms,
    shuffle=True)
eval_dataset = pdx.datasets.SegDataset(
    data_dir='optic_disc_seg',
    file_list='optic_disc_seg/val_list.txt',
    label_list='optic_disc_seg/labels.txt',
    transforms=eval_transforms)

num_classes = len(train_dataset.labels)

model = pdx.seg.UNet(num_classes=num_classes)

model.train(
    num_epochs=20,
    train_dataset=train_dataset,
    train_batch_size=4,
    eval_dataset=eval_dataset,
    learning_rate=0.01,
    save_dir='output/unet',
    use_vdl=True)