remove new tutorials

new_tutorials/train/README.md

-# 使用教程——训练模型
-
-本目录下整理了使用PaddleX训练模型的示例代码，代码中均提供了示例数据的自动下载，并均使用单张GPU卡进行训练。
-
-|代码 | 模型任务 | 数据 |
-|------|--------|---------|
-|classification/mobilenetv2.py | 图像分类MobileNetV2 | 蔬菜分类 |
-|classification/resnet50.py | 图像分类ResNet50 | 蔬菜分类 |
-|detection/faster_rcnn_r50_fpn.py | 目标检测FasterRCNN | 昆虫检测 |
-|detection/mask_rcnn_f50_fpn.py | 实例分割MaskRCNN | 垃圾分拣 |
-|segmentation/deeplabv3p.py | 语义分割DeepLabV3| 视盘分割 |
-|segmentation/unet.py | 语义分割UNet | 视盘分割 |
-|segmentation/hrnet.py | 语义分割HRNet | 视盘分割 |
-|segmentation/fast_scnn.py | 语义分割FastSCNN | 视盘分割 |
-
-
-## 开始训练
-在安装PaddleX后，使用如下命令开始训练
-```
-python classification/mobilenetv2.py
-```

new_tutorials/train/classification/mobilenetv2.py

-import os
-# 选择使用0号卡
-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/latest/apis/transforms/cls_transforms.html#composedclstransforms
-train_transforms = transforms.ComposedClsTransforms(mode='train', crop_size=[224, 224])
-eval_transforms = transforms.ComposedClsTransforms(mode='eval', crop_size=[224, 224])
-
-# 定义训练和验证所用的数据集
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/classification.html#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
-
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/classification.html#resnet50
-model = pdx.cls.MobileNetV2(num_classes=len(train_dataset.labels))
-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/mobilenetv2',
-    use_vdl=True)

new_tutorials/train/classification/resnet50.py

-import os
-# 选择使用0号卡
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-import paddle.fluid as fluid
-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/latest/apis/transforms/cls_transforms.html#composedclstransforms
-train_transforms = transforms.ComposedClsTransforms(mode='train', crop_size=[224, 224])
-eval_transforms = transforms.ComposedClsTransforms(mode='eval', crop_size=[224, 224])
-
-# 定义训练和验证所用的数据集
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/classification.html#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)
-
-# PaddleX支持自定义构建优化器
-step_each_epoch = train_dataset.num_samples // 32
-learning_rate = fluid.layers.cosine_decay(
-    learning_rate=0.025, step_each_epoch=step_each_epoch, epochs=10)
-optimizer = fluid.optimizer.Momentum(
-    learning_rate=learning_rate,
-    momentum=0.9,
-    regularization=fluid.regularizer.L2Decay(4e-5))
-
-# 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/resnet50/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
-
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/classification.html#resnet50
-model = pdx.cls.ResNet50(num_classes=len(train_dataset.labels))
-model.train(
-    num_epochs=10,
-    train_dataset=train_dataset,
-    train_batch_size=32,
-    eval_dataset=eval_dataset,
-    optimizer=optimizer,
-    save_dir='output/resnet50',
-    use_vdl=True)

new_tutorials/train/detection/faster_rcnn_r50_fpn.py

-import os
-# 选择使用0号卡
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-from paddlex.det import transforms
-import paddlex as pdx
-
-# 下载和解压昆虫检测数据集
-insect_dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
-pdx.utils.download_and_decompress(insect_dataset, path='./')
-
-# 定义训练和验证时的transforms
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#composedrcnntransforms
-train_transforms = transforms.ComposedRCNNTransforms(mode='train', min_max_size=[800, 1333])
-eval_transforms = transforms.ComposedRCNNTransforms(mode='eval', min_max_size=[800, 1333])
-
-# 定义训练和验证所用的数据集
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/detection.html#vocdetection
-train_dataset = pdx.datasets.VOCDetection(
-    data_dir='insect_det',
-    file_list='insect_det/train_list.txt',
-    label_list='insect_det/labels.txt',
-    transforms=train_transforms,
-    shuffle=True)
-eval_dataset = pdx.datasets.VOCDetection(
-    data_dir='insect_det',
-    file_list='insect_det/val_list.txt',
-    label_list='insect_det/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/faster_rcnn_r50_fpn/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
-# num_classes 需要设置为包含背景类的类别数，即: 目标类别数量 + 1
-
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/detection.html#fasterrcnn
-num_classes = len(train_dataset.labels) + 1
-model = pdx.det.FasterRCNN(num_classes=num_classes)
-model.train(
-    num_epochs=12,
-    train_dataset=train_dataset,
-    train_batch_size=2,
-    eval_dataset=eval_dataset,
-    learning_rate=0.0025,
-    lr_decay_epochs=[8, 11],
-    save_dir='output/faster_rcnn_r50_fpn',
-    use_vdl=True)

new_tutorials/train/detection/mask_rcnn_r50_fpn.py

-import os
-# 选择使用0号卡
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-from paddlex.det import transforms
-import paddlex as pdx
-
-# 下载和解压小度熊分拣数据集
-xiaoduxiong_dataset = 'https://bj.bcebos.com/paddlex/datasets/xiaoduxiong_ins_det.tar.gz'
-pdx.utils.download_and_decompress(xiaoduxiong_dataset, path='./')
-
-# 定义训练和验证时的transforms
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#composedrcnntransforms
-train_transforms = transforms.ComposedRCNNTransforms(mode='train', min_max_size=[800, 1333])
-eval_transforms = transforms.ComposedRCNNTransforms(mode='eval', min_max_size=[800, 1333])
-
-# 定义训练和验证所用的数据集
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/detection.html#cocodetection
-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
-# VisualDL启动方式: visualdl --logdir output/mask_rcnn_r50_fpn/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
-# num_classes 需要设置为包含背景类的类别数，即: 目标类别数量 + 1
-
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/instance_segmentation.html#maskrcnn
-num_classes = len(train_dataset.labels) + 1
-model = pdx.det.MaskRCNN(num_classes=num_classes)
-model.train(
-    num_epochs=12,
-    train_dataset=train_dataset,
-    train_batch_size=1,
-    eval_dataset=eval_dataset,
-    learning_rate=0.00125,
-    warmup_steps=10,
-    lr_decay_epochs=[8, 11],
-    save_dir='output/mask_rcnn_r50_fpn',
-    use_vdl=True)

new_tutorials/train/detection/yolov3_darknet53.py

-import os
-# 选择使用0号卡
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-from paddlex.det import transforms
-import paddlex as pdx
-
-# 下载和解压昆虫检测数据集
-insect_dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
-pdx.utils.download_and_decompress(insect_dataset, path='./')
-
-# 定义训练和验证时的transforms
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#composedyolotransforms
-train_transforms = transforms.ComposedYOLOv3Transforms(
-    mode='train', shape=[608, 608])
-eval_transforms = transforms.ComposedYOLOv3Transforms(
-    mode='eval', shape=[608, 608])
-
-# 定义训练和验证所用的数据集
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/detection.html#vocdetection
-train_dataset = pdx.datasets.VOCDetection(
-    data_dir='insect_det',
-    file_list='insect_det/train_list.txt',
-    label_list='insect_det/labels.txt',
-    transforms=train_transforms,
-    shuffle=True)
-eval_dataset = pdx.datasets.VOCDetection(
-    data_dir='insect_det',
-    file_list='insect_det/val_list.txt',
-    label_list='insect_det/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/yolov3_darknet/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
-
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/detection.html#yolov3
-num_classes = len(train_dataset.labels)
-model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
-model.train(
-    num_epochs=270,
-    train_dataset=train_dataset,
-    train_batch_size=8,
-    eval_dataset=eval_dataset,
-    learning_rate=0.000125,
-    lr_decay_epochs=[210, 240],
-    save_dir='output/yolov3_darknet53',
-    use_vdl=True)

new_tutorials/train/segmentation/deeplabv3p.py

-import os
-# 选择使用0号卡
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-import paddlex as pdx
-from paddlex.seg import transforms
-
-# 下载和解压视盘分割数据集
-optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
-pdx.utils.download_and_decompress(optic_dataset, path='./')
-
-# 定义训练和验证时的transforms
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/seg_transforms.html#composedsegtransforms
-train_transforms = transforms.ComposedSegTransforms(mode='train', train_crop_size=[769, 769])
-eval_transforms = transforms.ComposedSegTransforms(mode='eval')
-
-train_transforms.add_augmenters([
-    transforms.RandomRotate()
-])
-
-# 定义训练和验证所用的数据集
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/semantic_segmentation.html#segdataset
-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)
-
-# 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/deeplab/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
-
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#deeplabv3p
-num_classes = len(train_dataset.labels)
-model = pdx.seg.DeepLabv3p(num_classes=num_classes)
-model.train(
-    num_epochs=40,
-    train_dataset=train_dataset,
-    train_batch_size=4,
-    eval_dataset=eval_dataset,
-    learning_rate=0.01,
-    save_dir='output/deeplab',
-    use_vdl=True)

new_tutorials/train/segmentation/hrnet.py

-import os
-# 选择使用0号卡
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-import paddlex as pdx
-from paddlex.seg import transforms
-
-# 下载和解压视盘分割数据集
-optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
-pdx.utils.download_and_decompress(optic_dataset, path='./')
-
-# 定义训练和验证时的transforms
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/seg_transforms.html#composedsegtransforms
-train_transforms = transforms.ComposedSegTransforms(mode='train', train_crop_size=[769, 769])
-eval_transforms = transforms.ComposedSegTransforms(mode='eval')
-
-# 定义训练和验证所用的数据集
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/semantic_segmentation.html#segdataset
-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)
-
-# 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/unet/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
-
-# https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#hrnet
-num_classes = len(train_dataset.labels)
-model = pdx.seg.HRNet(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/hrnet',
-    use_vdl=True)

new_tutorials/train/segmentation/unet.py

-import os
-# 选择使用0号卡
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-import paddlex as pdx
-from paddlex.seg import transforms
-
-# 下载和解压视盘分割数据集
-optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
-pdx.utils.download_and_decompress(optic_dataset, path='./')
-
-# 定义训练和验证时的transforms
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/seg_transforms.html#composedsegtransforms
-train_transforms = transforms.ComposedSegTransforms(mode='train', train_crop_size=[769, 769])
-eval_transforms = transforms.ComposedSegTransforms(mode='eval')
-
-# 定义训练和验证所用的数据集
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/semantic_segmentation.html#segdataset
-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)
-
-# 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/unet/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
-
-# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#unet
-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)