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

from paddlex import transforms as T
import paddlex as pdx

# 定义训练和验证时的transforms
# API说明https://paddlex.readthedocs.io/zh_CN/develop/apis/transforms/cls_transforms.html
# train_transforms = T.Compose(
#     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])

train_transforms = T.Compose(
    [T.Resize(target_size=224), T.Normalize()]
)

eval_transforms = T.Compose([
    T.Resize(target_size=224), T.Normalize()
])

# 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-imagenet
train_dataset = pdx.datasets.ImageNet(
    data_dir='/home/xiongweixp/data/handwriting/train',
    file_list='/home/xiongweixp/data/handwriting/train/train_list.txt',
    label_list='/home/xiongweixp/data/handwriting/train/labels.txt',
    transforms=train_transforms,
    shuffle=True)
eval_dataset = pdx.datasets.ImageNet(
    data_dir='/home/xiongweixp/data/handwriting/train',
    file_list='/home/xiongweixp/data/handwriting/train/val_list.txt',
    label_list='/home/xiongweixp/data/handwriting/train/labels.txt',
    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://paddlex.readthedocs.io/zh_CN/develop/train/visualdl.html
# model = pdx.cls.MobileNetV2(num_classes=len(train_dataset.labels))
model = pdx.cls.MobileNetV3_small(num_classes=len(train_dataset.labels))

# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/models/classification.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=10,
    train_dataset=train_dataset,
    train_batch_size=64,
    eval_dataset=eval_dataset,
    lr_decay_epochs=[4, 6, 8],
    learning_rate=0.025,
    save_dir='output/mobilenetv3_small', 
    use_vdl=True
    #resume_checkpoint='output/resnet/epoch_9'
    )