[Feature] Add training tutorials for segmentation tasks (#34)

tutorials/train/semantic_segmentation/data/.gitignore

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+*.zip
+*.tar.gz
+rsseg/
+optic/

tutorials/train/semantic_segmentation/deeplabv3p.py

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+#!/usr/bin/env python
+
+# 图像分割模型DeepLab V3+训练示例脚本
+# 执行此脚本前，请确认已正确安装PaddleRS库
+
+import paddlers as pdrs
+from paddlers import transforms as T
+
+# 下载文件存放目录
+DOWNLOAD_DIR = './data/rsseg/'
+# 数据集存放目录
+DATA_DIR = './data/rsseg/remote_sensing_seg/'
+# 训练集`file_list`文件路径
+TRAIN_FILE_LIST_PATH = './data/rsseg/remote_sensing_seg/train.txt'
+# 验证集`file_list`文件路径
+EVAL_FILE_LIST_PATH = './data/rsseg/remote_sensing_seg/val.txt'
+# 数据集类别信息文件路径
+LABEL_LIST_PATH = './data/rsseg/remote_sensing_seg/labels.txt'
+# 实验目录，保存输出的模型权重和结果
+EXP_DIR = './output/deeplabv3p/'
+
+# 影像波段数量
+NUM_BANDS = 10
+
+# 下载和解压多光谱地块分类数据集
+seg_dataset = 'https://paddleseg.bj.bcebos.com/dataset/remote_sensing_seg.zip'
+pdrs.utils.download_and_decompress(seg_dataset, path=DOWNLOAD_DIR)
+
+# 定义训练和验证时使用的数据变换（数据增强、预处理等）
+# 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
+# API说明：https://github.com/PaddleCV-SIG/PaddleRS/blob/develop/docs/apis/transforms.md
+train_transforms = T.Compose([
+    # 将影像缩放到512x512大小
+    T.Resize(target_size=512),
+    # 以50%的概率实施随机水平翻转
+    T.RandomHorizontalFlip(prob=0.5),
+    # 将数据归一化到[-1,1]
+    T.Normalize(
+        mean=[0.5] * NUM_BANDS, std=[0.5] * NUM_BANDS),
+])
+
+eval_transforms = T.Compose([
+    T.Resize(target_size=512),
+    # 验证阶段与训练阶段的数据归一化方式必须相同
+    T.Normalize(
+        mean=[0.5] * NUM_BANDS, std=[0.5] * NUM_BANDS),
+])
+
+# 分别构建训练和验证所用的数据集
+train_dataset = pdrs.datasets.SegDataset(
+    data_dir=DATA_DIR,
+    file_list=TRAIN_FILE_LIST_PATH,
+    label_list=LABEL_LIST_PATH,
+    transforms=train_transforms,
+    num_workers=0,
+    shuffle=True)
+
+eval_dataset = pdrs.datasets.SegDataset(
+    data_dir=DATA_DIR,
+    file_list=EVAL_FILE_LIST_PATH,
+    label_list=LABEL_LIST_PATH,
+    transforms=eval_transforms,
+    num_workers=0,
+    shuffle=False)
+
+# 构建DeepLab V3+模型，使用ResNet-50作为backbone
+# 目前已支持的模型请参考：https://github.com/PaddleCV-SIG/PaddleRS/blob/develop/docs/apis/model_zoo.md
+# 模型输入参数请参考：https://github.com/PaddleCV-SIG/PaddleRS/blob/develop/paddlers/tasks/segmenter.py
+model = pdrs.tasks.DeepLabV3P(
+    input_channel=NUM_BANDS,
+    num_classes=len(train_dataset.labels),
+    backbone='ResNet50_vd')
+
+# 执行模型训练
+model.train(
+    num_epochs=10,
+    train_dataset=train_dataset,
+    train_batch_size=4,
+    eval_dataset=eval_dataset,
+    save_interval_epochs=5,
+    # 每多少次迭代记录一次日志
+    log_interval_steps=50,
+    save_dir=EXP_DIR,
+    # 初始学习率大小
+    learning_rate=0.01,
+    # 是否使用early stopping策略，当精度不再改善时提前终止训练
+    early_stop=False,
+    # 是否启用VisualDL日志功能
+    use_vdl=True,
+    # 指定从某个检查点继续训练
+    resume_checkpoint=None)

tutorials/train/semantic_segmentation/deeplabv3p_resnet50_multi_channel.py

@@ -1,54 +0,0 @@
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-import paddlers as pdrs
-from paddlers import transforms as T
-
-# 下载和解压多光谱地块分类数据集
-dataset = 'https://paddleseg.bj.bcebos.com/dataset/remote_sensing_seg.zip'
-pdrs.utils.download_and_decompress(dataset, path='./data')
-
-# 定义训练和验证时的transforms
-channel = 10
-train_transforms = T.Compose([
-    T.Resize(target_size=512),
-    T.RandomHorizontalFlip(),
-    T.Normalize(
-        mean=[0.5] * channel, std=[0.5] * channel),
-])
-
-eval_transforms = T.Compose([
-    T.Resize(target_size=512),
-    T.Normalize(
-        mean=[0.5] * channel, std=[0.5] * channel),
-])
-
-# 定义训练和验证所用的数据集
-train_dataset = pdrs.datasets.SegDataset(
-    data_dir='./data/remote_sensing_seg',
-    file_list='./data/remote_sensing_seg/train.txt',
-    label_list='./data/remote_sensing_seg/labels.txt',
-    transforms=train_transforms,
-    num_workers=0,
-    shuffle=True)
-
-eval_dataset = pdrs.datasets.SegDataset(
-    data_dir='./data/remote_sensing_seg',
-    file_list='./data/remote_sensing_seg/val.txt',
-    label_list='./data/remote_sensing_seg/labels.txt',
-    transforms=eval_transforms,
-    num_workers=0,
-    shuffle=False)
-
-# 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标
-num_classes = len(train_dataset.labels)
-model = pdrs.tasks.DeepLabV3P(input_channel=channel, num_classes=num_classes, backbone='ResNet50_vd')
-
-model.train(
-    num_epochs=10,
-    train_dataset=train_dataset,
-    train_batch_size=4,
-    eval_dataset=eval_dataset,
-    learning_rate=0.01,
-    save_dir='output/deeplabv3p_r50vd')

tutorials/train/semantic_segmentation/farseg_test.py

@@ -1,58 +0,0 @@
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-import paddlers as pdrs
-from paddlers import transforms as T
-
-# 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'
-pdrs.utils.download_and_decompress(optic_dataset, path='./')
-
-# 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/paddlers/blob/develop/docs/apis/transforms/transforms.md
-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
-# API说明：https://github.com/PaddlePaddle/paddlers/blob/develop/docs/apis/datasets.md
-train_dataset = pdrs.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,
-    num_workers=0,
-    shuffle=True)
-
-eval_dataset = pdrs.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_workers=0,
-    shuffle=False)
-
-# 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/paddlers/blob/develop/docs/visualdl.md
-num_classes = len(train_dataset.labels)
-model = pdrs.tasks.FarSeg(num_classes=num_classes)
-
-# API说明：https://github.com/PaddlePaddle/paddlers/blob/develop/docs/apis/models/semantic_segmentation.md
-# 各参数介绍与调整说明：https://github.com/PaddlePaddle/paddlers/blob/develop/docs/parameters.md
-model.train(
-    num_epochs=10,
-    train_dataset=train_dataset,
-    train_batch_size=4,
-    eval_dataset=eval_dataset,
-    learning_rate=0.01,
-    pretrain_weights=None,
-    save_dir='output/farseg')

tutorials/train/semantic_segmentation/unet.py

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+#!/usr/bin/env python
+
+# 图像分割模型UNet训练示例脚本
+# 执行此脚本前，请确认已正确安装PaddleRS库
+
+import paddlers as pdrs
+from paddlers import transforms as T
+
+# 下载文件存放目录
+DOWNLOAD_DIR = './data/rsseg/'
+# 数据集存放目录
+DATA_DIR = './data/rsseg/remote_sensing_seg/'
+# 训练集`file_list`文件路径
+TRAIN_FILE_LIST_PATH = './data/rsseg/remote_sensing_seg/train.txt'
+# 验证集`file_list`文件路径
+EVAL_FILE_LIST_PATH = './data/rsseg/remote_sensing_seg/val.txt'
+# 数据集类别信息文件路径
+LABEL_LIST_PATH = './data/rsseg/remote_sensing_seg/labels.txt'
+# 实验目录，保存输出的模型权重和结果
+EXP_DIR = './output/unet/'
+
+# 影像波段数量
+NUM_BANDS = 10
+
+# 下载和解压多光谱地块分类数据集
+seg_dataset = 'https://paddleseg.bj.bcebos.com/dataset/remote_sensing_seg.zip'
+pdrs.utils.download_and_decompress(seg_dataset, path=DOWNLOAD_DIR)
+
+# 定义训练和验证时使用的数据变换（数据增强、预处理等）
+# 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
+# API说明：https://github.com/PaddleCV-SIG/PaddleRS/blob/develop/docs/apis/transforms.md
+train_transforms = T.Compose([
+    # 将影像缩放到512x512大小
+    T.Resize(target_size=512),
+    # 以50%的概率实施随机水平翻转
+    T.RandomHorizontalFlip(prob=0.5),
+    # 将数据归一化到[-1,1]
+    T.Normalize(
+        mean=[0.5] * NUM_BANDS, std=[0.5] * NUM_BANDS),
+])
+
+eval_transforms = T.Compose([
+    T.Resize(target_size=512),
+    # 验证阶段与训练阶段的数据归一化方式必须相同
+    T.Normalize(
+        mean=[0.5] * NUM_BANDS, std=[0.5] * NUM_BANDS),
+])
+
+# 分别构建训练和验证所用的数据集
+train_dataset = pdrs.datasets.SegDataset(
+    data_dir=DATA_DIR,
+    file_list=TRAIN_FILE_LIST_PATH,
+    label_list=LABEL_LIST_PATH,
+    transforms=train_transforms,
+    num_workers=0,
+    shuffle=True)
+
+eval_dataset = pdrs.datasets.SegDataset(
+    data_dir=DATA_DIR,
+    file_list=EVAL_FILE_LIST_PATH,
+    label_list=LABEL_LIST_PATH,
+    transforms=eval_transforms,
+    num_workers=0,
+    shuffle=False)
+
+# 构建UNet模型
+# 目前已支持的模型请参考：https://github.com/PaddleCV-SIG/PaddleRS/blob/develop/docs/apis/model_zoo.md
+# 模型输入参数请参考：https://github.com/PaddleCV-SIG/PaddleRS/blob/develop/paddlers/tasks/segmenter.py
+model = pdrs.tasks.UNet(
+    input_channel=NUM_BANDS, num_classes=len(train_dataset.labels))
+
+# 执行模型训练
+model.train(
+    num_epochs=10,
+    train_dataset=train_dataset,
+    train_batch_size=4,
+    eval_dataset=eval_dataset,
+    save_interval_epochs=5,
+    # 每多少次迭代记录一次日志
+    log_interval_steps=50,
+    save_dir=EXP_DIR,
+    # 初始学习率大小
+    learning_rate=0.01,
+    # 是否使用early stopping策略，当精度不再改善时提前终止训练
+    early_stop=False,
+    # 是否启用VisualDL日志功能
+    use_vdl=True,
+    # 指定从某个检查点继续训练
+    resume_checkpoint=None)

tutorials/train/semantic_segmentation/unet_multi_channel.py

@@ -1,55 +0,0 @@
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-import paddlers as pdrs
-from paddlers import transforms as T
-
-# 下载和解压多光谱地块分类数据集
-dataset = 'https://paddleseg.bj.bcebos.com/dataset/remote_sensing_seg.zip'
-pdrs.utils.download_and_decompress(dataset, path='./data')
-
-# 定义训练和验证时的transforms
-channel = 10
-train_transforms = T.Compose([
-    T.Resize(target_size=512),
-    T.RandomHorizontalFlip(),
-    T.Normalize(
-        mean=[0.5] * channel, std=[0.5] * channel),
-])
-
-eval_transforms = T.Compose([
-    T.Resize(target_size=512),
-    T.Normalize(
-        mean=[0.5] * channel, std=[0.5] * channel),
-])
-
-# 定义训练和验证所用的数据集
-train_dataset = pdrs.datasets.SegDataset(
-    data_dir='./data/remote_sensing_seg',
-    file_list='./data/remote_sensing_seg/train.txt',
-    label_list='./data/remote_sensing_seg/labels.txt',
-    transforms=train_transforms,
-    num_workers=0,
-    shuffle=True)
-
-eval_dataset = pdrs.datasets.SegDataset(
-    data_dir='./data/remote_sensing_seg',
-    file_list='./data/remote_sensing_seg/val.txt',
-    label_list='./data/remote_sensing_seg/labels.txt',
-    transforms=eval_transforms,
-    num_workers=0,
-    shuffle=False)
-
-# 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标
-num_classes = len(train_dataset.labels)
-model = pdrs.tasks.UNet(input_channel=channel, 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)