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tutorials/train/image_classification/mobilenetv3_large.py

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-# 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.cls import transforms
-import paddlex as pdx
-
-# 下载和解压蔬菜分类数据集
-veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
-pdx.utils.download_and_decompress(veg_dataset, path='./')
-
-# 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 = transforms.Compose([
-    transforms.RandomCrop(crop_size=224), transforms.RandomHorizontalFlip(),
-    transforms.Normalize()
-])
-eval_transforms = transforms.Compose([
-    transforms.ResizeByShort(short_size=256),
-    transforms.CenterCrop(crop_size=224), transforms.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='vegetables_cls',
-    file_list='vegetables_cls/train_list.txt',
-    label_list='vegetables_cls/labels.txt',
-    transforms=train_transforms,
-    shuffle=True)
-eval_dataset = pdx.datasets.ImageNet(
-    data_dir='vegetables_cls',
-    file_list='vegetables_cls/val_list.txt',
-    label_list='vegetables_cls/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
-# VisualDL启动方式: visualdl --logdir output/mobilenetv2/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
-model = pdx.cls.MobileNetV3_large(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=32,
-    eval_dataset=eval_dataset,
-    lr_decay_epochs=[4, 6, 8],
-    learning_rate=0.025,
-    save_dir='output/mobilenetv3_large',
-    use_vdl=True)

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tutorials/train/image_classification/resnet50_vd.py

@@ -1,52 +0,0 @@
-# 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.cls import transforms
-import paddlex as pdx
-
-# 下载和解压蔬菜分类数据集
-veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
-pdx.utils.download_and_decompress(veg_dataset, path='./')
-
-# 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 = transforms.Compose([
-    transforms.RandomCrop(crop_size=224), transforms.RandomHorizontalFlip(),
-    transforms.Normalize()
-])
-eval_transforms = transforms.Compose([
-    transforms.ResizeByShort(short_size=256),
-    transforms.CenterCrop(crop_size=224), transforms.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='vegetables_cls',
-    file_list='vegetables_cls/train_list.txt',
-    label_list='vegetables_cls/labels.txt',
-    transforms=train_transforms,
-    shuffle=True)
-eval_dataset = pdx.datasets.ImageNet(
-    data_dir='vegetables_cls',
-    file_list='vegetables_cls/val_list.txt',
-    label_list='vegetables_cls/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.ResNet50_vd(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=32,
-    eval_dataset=eval_dataset,
-    lr_decay_epochs=[4, 6, 8],
-    learning_rate=0.025,
-    save_dir='output/resnet50_vd_ssld',
-    use_vdl=True)