paddlers/tutorials/train/classification/resnet50_vd_rs.py

import paddlers as pdrs
from paddlers import transforms as T

# 下载aistudio的数据到当前文件夹并解压、整理
# https://aistudio.baidu.com/aistudio/datasetdetail/63189

# 定义训练和验证时的transforms
train_transforms = T.Compose([
    T.Resize(target_size=512),
    T.RandomHorizontalFlip(),
    T.Normalize(
        mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])

eval_transforms = T.Compose([
    T.Resize(target_size=512),
    T.Normalize(
        mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])

# 定义训练和验证所用的数据集
train_dataset = pdrs.datasets.ClasDataset(
    data_dir='tutorials/train/classification/DataSet',
    file_list='tutorials/train/classification/DataSet/train_list.txt',
    label_list='tutorials/train/classification/DataSet/label_list.txt',
    transforms=train_transforms,
    num_workers=0,
    shuffle=True)

eval_dataset = pdrs.datasets.ClasDataset(
    data_dir='tutorials/train/classification/DataSet',
    file_list='tutorials/train/classification/DataSet/test_list.txt',
    label_list='tutorials/train/classification/DataSet/label_list.txt',
    transforms=eval_transforms,
    num_workers=0,
    shuffle=False)

# 初始化模型
num_classes = len(train_dataset.labels)
model = pdrs.tasks.ResNet50_vd(num_classes=num_classes)

# 进行训练
model.train(
    num_epochs=10,
    train_dataset=train_dataset,
    train_batch_size=4,
    eval_dataset=eval_dataset,
    learning_rate=0.1,
    save_dir='output/resnet_vd')