diff --git a/contrib/RemoteSensing/README.md b/contrib/RemoteSensing/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3b84b5a274a882f892be4efb4a44f72b50ae8c2c
--- /dev/null
+++ b/contrib/RemoteSensing/README.md
@@ -0,0 +1,244 @@
+# 遥感分割（RemoteSensing）
+遥感影像分割是图像分割领域中的重要应用场景，广泛应用于土地测绘、环境监测、城市建设等领域。遥感影像分割的目标多种多样，有诸如积雪、农作物、道路、建筑、水源等地物目标，也有例如云层的空中目标。
+
+PaddleSeg提供了针对遥感专题的语义分割库RemoteSensing，涵盖图像预处理、数据增强、模型训练、预测流程，帮助大家利用深度学习技术解决遥感影像分割问题。
+
+针对遥感数据多通道、分布范围大、分布不均的特点，我们支持多通道训练预测，内置一系列多通道预处理和数据增强的策略，可结合实际业务场景进行定制组合，提升模型泛化能力和鲁棒性。
+
+**Note:** 所有命令需要在`PaddleSeg/contrib/RemoteSensing/`目录下执行。
+
+## 前置依赖
+- Paddle 1.7.1+
+由于图像分割模型计算开销大，推荐在GPU版本的PaddlePaddle下使用。
+PaddlePaddle的安装, 请按照[官网指引](https://paddlepaddle.org.cn/install/quick)安装合适自己的版本。
+
+- Python 3.5+
+
+- 其他依赖安装
+通过以下命令安装python包依赖，请确保至少执行过一次以下命令：
+```
+cd RemoteSensing
+pip install -r requirements.txt
+```
+
+## 目录结构说明
+ ```
+RemoteSensing               # 根目录
+ |-- dataset                # 数据集
+ |-- docs                   # 文档
+ |-- models                 # 模型类定义模块
+ |-- nets                   # 组网模块
+ |-- readers                # 数据读取模块
+ |-- tools                  # 工具集
+ |-- transforms             # 数据增强模块
+ |-- utils                  # 公用模块
+ |-- train_demo.py          # 训练demo脚本
+ |-- predict_demo.py        # 预测demo脚本
+ |-- README.md              # 使用手册
+
+ ```
+## 数据协议
+数据集包含原图、标注图及相应的文件列表文件。
+
+参考数据文件结构如下：
+```
+./dataset/  # 数据集根目录
+|--images  # 原图目录
+|  |--xxx1.npy
+|  |--...
+|  └--...
+|
+|--annotations  # 标注图目录
+|  |--xxx1.png
+|  |--...
+|  └--...
+|
+|--train_list.txt  # 训练文件列表文件
+|
+|--val_list.txt  # 验证文件列表文件
+|
+└--labels.txt  # 标签列表
+
+```
+其中，相应的文件名可根据需要自行定义。
+
+由于遥感领域图像格式多种多样，不同传感器产生的数据格式可能不同。本分割库目前采用npy格式作为遥感数据的格式，采用png无损压缩格式作为标注图片格式。
+
+标注图像为单通道图像，像素值即为对应的类别,像素标注类别需要从0开始递增，
+例如0，1，2，3表示有4种类别，标注类别最多为256类。其中可以指定特定的像素值用于表示该值的像素不参与训练和评估（默认为255）。
+
+`train_list.txt`和`val_list.txt`文本以空格为分割符分为两列，第一列为图像文件相对于dataset的相对路径，第二列为标注图像文件相对于dataset的相对路径。如下所示：
+```
+images/xxx1.npy annotations/xxx1.png
+images/xxx2.npy annotations/xxx2.png
+...
+```
+
+具体要求和如何生成文件列表可参考[文件列表规范](../../docs/data_prepare.md#文件列表)。
+
+`labels.txt`: 每一行为一个单独的类别，相应的行号即为类别对应的id（行号从0开始)，如下所示：
+```
+labelA
+labelB
+...
+```
+
+
+
+## 快速上手
+
+本章节在一个小数据集上展示了如何通过RemoteSensing进行训练预测。
+
+### 1. 准备数据集
+为了快速体验，我们准备了一个小型demo数据集，已位于`RemoteSensing/dataset/demo/`目录下.
+
+对于您自己的数据集，您需要按照上述的数据协议进行格式转换，可分别使用numpy和pil库保存遥感数据和标注图片。其中numpy api示例如下：
+```python
+import numpy as np
+
+# 保存遥感数据
+# img类型：numpy.ndarray
+np.save(save_path, img)
+```
+
+### 2. 训练代码开发
+通过如下`train_demo.py`代码进行训练。
+
+> 导入RemoteSensing api
+```python
+import transforms.transforms as T
+from readers.reader import Reader
+from models import UNet
+```
+
+> 定义训练和验证时的数据处理和增强流程, 在`train_transforms`中加入了`RandomVerticalFlip`,`RandomHorizontalFlip`等数据增强方式。
+```python
+train_transforms = T.Compose([
+    T.RandomVerticalFlip(0.5),
+    T.RandomHorizontalFlip(0.5),
+    T.ResizeStepScaling(0.5, 2.0, 0.25),
+    T.RandomPaddingCrop(256),
+    T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
+])
+
+eval_transforms = T.Compose([
+    T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
+])
+```
+
+> 定义数据读取器
+```python
+import os
+import os.path as osp
+
+train_list = osp.join(data_dir, 'train.txt')
+val_list = osp.join(data_dir, 'val.txt')
+label_list = osp.join(data_dir, 'labels.txt')
+
+train_reader = Reader(
+    data_dir=data_dir,
+    file_list=train_list,
+    label_list=label_list,
+    transforms=train_transforms,
+    num_workers=8,
+    buffer_size=16,
+    shuffle=True,
+    parallel_method='thread')
+
+eval_reader = Reader(
+    data_dir=data_dir,
+    file_list=val_list,
+    label_list=label_list,
+    transforms=eval_transforms,
+    num_workers=8,
+    buffer_size=16,
+    shuffle=False,
+    parallel_method='thread')
+```
+> 模型构建
+```python
+model = UNet(
+    num_classes=2, input_channel=channel, use_bce_loss=True, use_dice_loss=True)
+```
+> 模型训练，并开启边训边评估
+```python
+model.train(
+    num_epochs=num_epochs,
+    train_reader=train_reader,
+    train_batch_size=train_batch_size,
+    eval_reader=eval_reader,
+    save_interval_epochs=5,
+    log_interval_steps=10,
+    save_dir=save_dir,
+    pretrain_weights=None,
+    optimizer=None,
+    learning_rate=lr,
+)
+```
+
+
+### 3. 模型训练
+> 设置GPU卡号
+```shell script
+export CUDA_VISIBLE_DEVICES=0
+```
+> 在RemoteSensing目录下运行`train_demo.py`即可开始训练。
+```shell script
+python train_demo.py --data_dir dataset/demo/ --save_dir saved_model/unet/ --channel 3 --num_epochs 20
+```
+### 4. 模型预测代码开发
+通过如下`predict_demo.py`代码进行预测。
+
+> 导入RemoteSensing api
+```python
+from models import load_model
+```
+> 加载训练过程中最好的模型，设置预测结果保存路径。
+```python
+import os
+import os.path as osp
+model = load_model(osp.join(save_dir, 'best_model'))
+pred_dir = osp.join(save_dir, 'pred')
+if not osp.exists(pred_dir):
+    os.mkdir(pred_dir)
+```
+
+> 使用模型对验证集进行测试，并保存预测结果。
+```python
+import numpy as np
+from PIL import Image as Image
+val_list = osp.join(data_dir, 'val.txt')
+color_map = [0, 0, 0, 255, 255, 255]
+with open(val_list) as f:
+    lines = f.readlines()
+    for line in lines:
+        img_path = line.split(' ')[0]
+        print('Predicting {}'.format(img_path))
+        img_path_ = osp.join(data_dir, img_path)
+
+        pred = model.predict(img_path_)
+
+        # 以伪彩色png图片保存预测结果
+        pred_name = osp.basename(img_path).rstrip('npy') + 'png'
+        pred_path = osp.join(pred_dir, pred_name)
+        pred_mask = Image.fromarray(pred.astype(np.uint8), mode='P')
+        pred_mask.putpalette(color_map)
+        pred_mask.save(pred_path)
+```
+
+### 5. 模型预测
+> 设置GPU卡号
+```shell script
+export CUDA_VISIBLE_DEVICES=0
+```
+> 在RemoteSensing目录下运行`predict_demo.py`即可开始训练。
+```shell script
+python predict_demo.py --data_dir dataset/demo/ --load_model_dir saved_model/unet/
+```
+
+
+## Api说明
+
+您可以使用`RemoteSensing`目录下提供的api构建自己的分割代码。
+
+- [数据处理-transforms](docs/transforms.md)
diff --git a/contrib/RemoteSensing/__init__.py b/contrib/RemoteSensing/__init__.py
index 236eb52217024227624afb662a4a421b7ee16c1c..ea9751219a9eda9e50a80e9dff2a8b3d7cba0066 100644
--- a/contrib/RemoteSensing/__init__.py
+++ b/contrib/RemoteSensing/__init__.py
@@ -12,12 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from . import utils
-from . import nets
-from . import models
-from . import transforms
-from . import readers
-from .utils.utils import get_environ_info
+import utils
+import nets
+import models
+import transforms
+import readers
+from utils.utils import get_environ_info
 
 env_info = get_environ_info()
 
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new file mode 100644
index 0000000000000000000000000000000000000000..69548aabb6c89d4535c6567b7e1160c3ba2874ca
--- /dev/null
+++ b/contrib/RemoteSensing/dataset/demo/labels.txt
@@ -0,0 +1,2 @@
+__background__
+cloud
\ No newline at end of file
diff --git a/contrib/RemoteSensing/dataset/demo/train.txt b/contrib/RemoteSensing/dataset/demo/train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..babb17608b22ecda5c38db00e11e6c4579722784
--- /dev/null
+++ b/contrib/RemoteSensing/dataset/demo/train.txt
@@ -0,0 +1,7 @@
+images/1001.npy annotations/1001.png
+images/1002.npy annotations/1002.png
+images/1005.npy annotations/1005.png
+images/0.npy annotations/0.png
+images/1003.npy annotations/1003.png
+images/1000.npy annotations/1000.png
+images/1004.npy annotations/1004.png
diff --git a/contrib/RemoteSensing/dataset/demo/val.txt b/contrib/RemoteSensing/dataset/demo/val.txt
new file mode 100644
index 0000000000000000000000000000000000000000..073dbf76d4309dfeea0b242e6eace3bc6024ba61
--- /dev/null
+++ b/contrib/RemoteSensing/dataset/demo/val.txt
@@ -0,0 +1,3 @@
+images/100.npy annotations/100.png
+images/1.npy annotations/1.png
+images/10.npy annotations/10.png
diff --git a/contrib/RemoteSensing/docs/transforms.md b/contrib/RemoteSensing/docs/transforms.md
new file mode 100644
index 0000000000000000000000000000000000000000..a35e6cd1bdcf03dc84687a6bb7a4e13c274dc572
--- /dev/null
+++ b/contrib/RemoteSensing/docs/transforms.md
@@ -0,0 +1,145 @@
+# transforms.transforms
+
+对用于分割任务的数据进行操作。可以利用[Compose](#compose)类将图像预处理/增强操作进行组合。
+
+
+## Compose类
+```python
+transforms.transforms.Compose(transforms)
+```
+根据数据预处理/数据增强列表对输入数据进行操作。
+### 参数
+* **transforms** (list): 数据预处理/数据增强列表。
+
+
+## RandomHorizontalFlip类
+```python
+transforms.transforms.RandomHorizontalFlip(prob=0.5)
+```
+以一定的概率对图像进行水平翻转,模型训练时的数据增强操作。
+### 参数
+* **prob** (float): 随机水平翻转的概率。默认值为0.5。
+
+
+## RandomVerticalFlip类
+```python
+transforms.transforms.RandomVerticalFlip(prob=0.1)
+```
+以一定的概率对图像进行垂直翻转,模型训练时的数据增强操作。
+### 参数
+* **prob**  (float): 随机垂直翻转的概率。默认值为0.1。
+
+
+## Resize类
+```python
+transforms.transforms.Resize(target_size, interp='LINEAR')
+```
+调整图像大小（resize）。
+
+- 当目标大小（target_size）类型为int时，根据插值方式，
+      将图像resize为[target_size, target_size]。
+- 当目标大小（target_size）类型为list或tuple时，根据插值方式，
+  将图像resize为target_size, target_size的输入应为[w, h]或（w, h）。
+### 参数
+* **target_size** (int|list|tuple): 目标大小
+* **interp** (str): resize的插值方式，与opencv的插值方式对应，
+可选的值为['NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4']，默认为"LINEAR"。
+
+
+## ResizeByLong类
+```python
+transforms.transforms.ResizeByLong(long_size)
+```
+对图像长边resize到固定值，短边按比例进行缩放。
+### 参数
+* **long_size** (int): resize后图像的长边大小。
+
+
+## ResizeRangeScaling类
+```python
+transforms.transforms.ResizeRangeScaling(min_value=400, max_value=600)
+```
+对图像长边随机resize到指定范围内，短边按比例进行缩放,模型训练时的数据增强操作。
+### 参数
+* **min_value** (int): 图像长边resize后的最小值。默认值400。
+* **max_value** (int): 图像长边resize后的最大值。默认值600。
+
+
+## ResizeStepScaling类
+```python
+transforms.transforms.ResizeStepScaling(min_scale_factor=0.75, max_scale_factor=1.25, scale_step_size=0.25)
+```
+对图像按照某一个比例resize，这个比例以scale_step_size为步长，在[min_scale_factor, max_scale_factor]随机变动,模型训练时的数据增强操作。
+### 参数
+* **min_scale_factor**（float), resize最小尺度。默认值0.75。
+* **max_scale_factor** (float), resize最大尺度。默认值1.25。
+* **scale_step_size** (float), resize尺度范围间隔。默认值0.25。
+
+
+## Clip类
+```python
+transforms.transforms.Clip(min_val=[0, 0, 0], max_val=[255.0, 255.0, 255.0])
+```
+对图像上超出一定范围的数据进行裁剪。
+
+### 参数
+* **min_var** (list): 裁剪的下限，小于min_val的数值均设为min_val. 默认值[0, 0, 0].
+* **max_var** (list): 裁剪的上限，大于max_val的数值均设为max_val. 默认值[255.0, 255.0, 255.0]
+
+
+## Normalize类
+```python
+transforms.transforms.Normalize(min_val=[0, 0, 0], max_val=[255.0, 255.0, 255.0], mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+```
+对图像进行标准化。
+
+1.图像像素归一化到区间 [0.0, 1.0]。
+2.对图像进行减均值除以标准差操作。
+### 参数
+* **min_val** (list): 图像数据集的最小值。默认值[0, 0, 0].
+* **max_val** (list): 图像数据集的最大值。默认值[255.0, 255.0, 255.0]
+* **mean** (list): 图像数据集的均值。默认值[0.5, 0.5, 0.5]。
+* **std** (list): 图像数据集的标准差。默认值[0.5, 0.5, 0.5]。
+
+
+## Padding类
+```python
+transforms.transforms.Padding(target_size, im_padding_value=127.5, label_padding_value=255)
+```
+对图像或标注图像进行padding，padding方向为右和下。根据提供的值对图像或标注图像进行padding操作。
+### 参数
+* **target_size** (int|list|tuple): padding后图像的大小。
+* **im_padding_value** (list): 图像padding的值。默认为127.5
+* **label_padding_value** (int): 标注图像padding的值。默认值为255（仅在训练时需要设定该参数）。
+
+
+## RandomPaddingCrop类
+```python
+transforms.transforms.RandomPaddingCrop(crop_size=512, im_padding_value=127.5, label_padding_value=255)
+```
+对图像和标注图进行随机裁剪，当所需要的裁剪尺寸大于原图时，则进行padding操作，模型训练时的数据增强操作。
+### 参数
+* **crop_size**（int|list|tuple): 裁剪图像大小。默认为512。
+* **im_padding_value** (list): 图像padding的值。默认为127.5。
+* **label_padding_value** (int): 标注图像padding的值。默认值为255。
+
+
+## RandomBlur类
+```python
+transforms.transforms.RandomBlur(prob=0.1)
+```
+以一定的概率对图像进行高斯模糊，模型训练时的数据增强操作。
+### 参数
+* **prob** (float): 图像模糊概率。默认为0.1。
+
+
+## RandomScaleAspect类
+```python
+transforms.transforms.RandomScaleAspect(min_scale=0.5, aspect_ratio=0.33)
+```
+裁剪并resize回原始尺寸的图像和标注图像,模型训练时的数据增强操作。
+
+按照一定的面积比和宽高比对图像进行裁剪，并reszie回原始图像的图像，当存在标注图时，同步进行。
+### 参数
+* **min_scale**  (float)：裁取图像占原始图像的面积比，取值[0，1]，为0时则返回原图。默认为0.5。
+* **aspect_ratio** (float): 裁取图像的宽高比范围，非负值，为0时返回原图。默认为0.33。
diff --git a/contrib/RemoteSensing/main.py b/contrib/RemoteSensing/main.py
deleted file mode 100644
index 346a595a931928478a19c4c69a6a9901a1587b73..0000000000000000000000000000000000000000
--- a/contrib/RemoteSensing/main.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import sys
-import os
-import os.path as osp
-import cv2
-import numpy as np
-from PIL import Image as Image
-
-#================================setting========================
-os.environ['CUDA_VISIBLE_DEVICES'] = "1"
-
-batch_size = 4
-channel = 10
-epochs = 1
-
-save_dir = 'saved_model/snow2019_unet_all_channel_vertical'
-data_dir = "../../../dataset/snow2019/all_channel_data/"
-#=============================================================
-
-sys.path.append(osp.join(os.getcwd(), '..'))
-import RemoteSensing.transforms.transforms as T
-from RemoteSensing.readers.reader import Reader
-from RemoteSensing.models import UNet, load_model
-
-if not os.path.exists(save_dir):
-    os.makedirs(save_dir)
-
-train_list = osp.join(data_dir, 'train.txt')
-val_list = osp.join(data_dir, 'val.txt')
-label_list = osp.join(data_dir, 'labels.txt')
-
-os.system('cp ./{} {}'.format(__file__, osp.join(save_dir, __file__)))
-
-train_transforms = T.Compose([
-    T.RandomVerticalFlip(0.5),
-    T.RandomHorizontalFlip(0.5),
-    T.ResizeStepScaling(0.5, 2.0, 0.25),
-    T.RandomPaddingCrop(769),
-    T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
-])
-
-eval_transforms = T.Compose([
-    T.Padding([1049, 1049]),
-    T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
-])
-
-test_transforms = T.Compose([
-    T.Padding([1049, 1049]),
-    T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
-])
-
-train_reader = Reader(
-    data_dir=data_dir,
-    file_list=train_list,
-    label_list=label_list,
-    transforms=train_transforms,
-    num_workers=8,
-    buffer_size=16,
-    shuffle=True,
-    parallel_method='thread')
-
-eval_reader = Reader(
-    data_dir=data_dir,
-    file_list=val_list,
-    label_list=label_list,
-    transforms=eval_transforms,
-    num_workers=8,
-    buffer_size=16,
-    shuffle=False,
-    parallel_method='thread')
-
-model = UNet(
-    num_classes=2, input_channel=channel, use_bce_loss=True, use_dice_loss=True)
-
-model.train(
-    num_epochs=epochs,
-    train_reader=train_reader,
-    train_batch_size=batch_size,
-    eval_reader=eval_reader,
-    save_interval_epochs=5,
-    log_interval_steps=10,
-    save_dir=save_dir,
-    pretrain_weights=None,
-    optimizer=None,
-    learning_rate=0.01,
-)
-
-# predict
-model = load_model(osp.join(save_dir, 'best_model'))
-pred_dir = osp.join(save_dir, 'pred')
-if not osp.exists(pred_dir):
-    os.mkdir(pred_dir)
-
-color_map = [0, 0, 0, 255, 255, 255]
-
-with open(val_list) as f:
-    lines = f.readlines()
-    for line in lines:
-        img_path = line.split(' ')[0]
-        print('Predicting {}'.format(img_path))
-        img_path_ = osp.join(data_dir, img_path)
-
-        pred = model.predict(img_path_)
-
-        pred_name = osp.basename(img_path).rstrip('npy') + 'png'
-        pred_path = osp.join(pred_dir, pred_name)
-        pred_mask = Image.fromarray(pred.astype(np.uint8), mode='P')
-        pred_mask.putpalette(color_map)
-        pred_mask.save(pred_path)
diff --git a/contrib/RemoteSensing/models/base.py b/contrib/RemoteSensing/models/base.py
index de1dca4c1ca79adf9ee7a9528dc000ff9d8f75a3..6c3fada7bf9eeb18b132d11c24b403785c03f743 100644
--- a/contrib/RemoteSensing/models/base.py
+++ b/contrib/RemoteSensing/models/base.py
@@ -21,13 +21,13 @@ import math
 import yaml
 import copy
 import json
-import functools
-import RemoteSensing.utils.logging as logging
-import RemoteSensing
+import utils.logging as logging
 from collections import OrderedDict
 from os import path as osp
-from paddle.fluid.framework import Program
-from ..utils.pretrain_weights import get_pretrain_weights
+from utils.pretrain_weights import get_pretrain_weights
+import transforms.transforms as T
+import utils
+import __init__
 
 
 def dict2str(dict_input):
@@ -46,7 +46,7 @@ class BaseAPI:
         # 现有的CV模型都有这个属性，而这个属且也需要在eval时用到
         self.num_classes = None
         self.labels = None
-        if RemoteSensing.env_info['place'] == 'cpu':
+        if __init__.env_info['place'] == 'cpu':
             self.places = fluid.cpu_places()
         else:
             self.places = fluid.cuda_places()
@@ -73,8 +73,8 @@ class BaseAPI:
         else:
             raise Exception("Please support correct batch_size, \
                             which can be divided by available cards({}) in {}".
-                            format(RemoteSensing.env_info['num'],
-                                   RemoteSensing.env_info['place']))
+                            format(__init__.env_info['num'],
+                                   __init__.env_info['place']))
 
     def build_program(self):
         # 构建训练网络
@@ -93,12 +93,9 @@ class BaseAPI:
     def arrange_transforms(self, transforms, mode='train'):
         # 给transforms添加arrange操作
         if transforms.transforms[-1].__class__.__name__.startswith('Arrange'):
-            transforms.transforms[
-                -1] = RemoteSensing.transforms.transforms.ArrangeSegmenter(
-                    mode=mode)
+            transforms.transforms[-1] = T.ArrangeSegmenter(mode=mode)
         else:
-            transforms.transforms.append(
-                RemoteSensing.transforms.transforms.ArrangeSegmenter(mode=mode))
+            transforms.transforms.append(T.ArrangeSegmenter(mode=mode))
 
     def build_train_data_loader(self, reader, batch_size):
         # 初始化data_loader
@@ -134,8 +131,8 @@ class BaseAPI:
         if pretrain_weights is not None:
             logging.info(
                 "Load pretrain weights from {}.".format(pretrain_weights))
-            RemoteSensing.utils.utils.load_pretrain_weights(
-                self.exe, self.train_prog, pretrain_weights, fuse_bn)
+            utils.utils.load_pretrain_weights(self.exe, self.train_prog,
+                                              pretrain_weights, fuse_bn)
         # 进行裁剪
         if sensitivities_file is not None:
             from .slim.prune_config import get_sensitivities
@@ -211,46 +208,6 @@ class BaseAPI:
         open(osp.join(save_dir, '.success'), 'w').close()
         logging.info("Model saved in {}.".format(save_dir))
 
-    def export_inference_model(self, save_dir):
-        test_input_names = [var.name for var in list(self.test_inputs.values())]
-        test_outputs = list(self.test_outputs.values())
-        if self.__class__.__name__ == 'MaskRCNN':
-            from RemoteSensing.utils.save import save_mask_inference_model
-            save_mask_inference_model(
-                dirname=save_dir,
-                executor=self.exe,
-                params_filename='__params__',
-                feeded_var_names=test_input_names,
-                target_vars=test_outputs,
-                main_program=self.test_prog)
-        else:
-            fluid.io.save_inference_model(
-                dirname=save_dir,
-                executor=self.exe,
-                params_filename='__params__',
-                feeded_var_names=test_input_names,
-                target_vars=test_outputs,
-                main_program=self.test_prog)
-        model_info = self.get_model_info()
-        model_info['status'] = 'Infer'
-
-        # 保存模型输出的变量描述
-        model_info['_ModelInputsOutputs'] = dict()
-        model_info['_ModelInputsOutputs']['test_inputs'] = [
-            [k, v.name] for k, v in self.test_inputs.items()
-        ]
-        model_info['_ModelInputsOutputs']['test_outputs'] = [
-            [k, v.name] for k, v in self.test_outputs.items()
-        ]
-
-        with open(
-                osp.join(save_dir, 'model.yml'), encoding='utf-8',
-                mode='w') as f:
-            yaml.dump(model_info, f)
-        # 模型保存成功的标志
-        open(osp.join(save_dir, '.success'), 'w').close()
-        logging.info("Model for inference deploy saved in {}.".format(save_dir))
-
     def train_loop(self,
                    num_epochs,
                    train_reader,
@@ -287,8 +244,7 @@ class BaseAPI:
         if self.parallel_train_prog is None:
             build_strategy = fluid.compiler.BuildStrategy()
             build_strategy.fuse_all_optimizer_ops = False
-            if RemoteSensing.env_info['place'] != 'cpu' and len(
-                    self.places) > 1:
+            if __init__.env_info['place'] != 'cpu' and len(self.places) > 1:
                 build_strategy.sync_batch_norm = self.sync_bn
             exec_strategy = fluid.ExecutionStrategy()
             exec_strategy.num_iteration_per_drop_scope = 1
diff --git a/contrib/RemoteSensing/models/load_model.py b/contrib/RemoteSensing/models/load_model.py
index c572b77d890796c00791ba16e504b6696e889cc1..fb55c13125c7ad194196082be00fb5df7c037dd8 100644
--- a/contrib/RemoteSensing/models/load_model.py
+++ b/contrib/RemoteSensing/models/load_model.py
@@ -19,8 +19,8 @@ import copy
 from collections import OrderedDict
 import paddle.fluid as fluid
 from paddle.fluid.framework import Parameter
-from ..utils import logging
-import RemoteSensing
+from utils import logging
+import models
 
 
 def load_model(model_dir):
@@ -30,12 +30,11 @@ def load_model(model_dir):
         info = yaml.load(f.read(), Loader=yaml.Loader)
     status = info['status']
 
-    if not hasattr(RemoteSensing.models, info['Model']):
-        raise Exception(
-            "There's no attribute {} in RemoteSensing.models".format(
-                info['Model']))
+    if not hasattr(models, info['Model']):
+        raise Exception("There's no attribute {} in models".format(
+            info['Model']))
 
-    model = getattr(RemoteSensing.models, info['Model'])(**info['_init_params'])
+    model = getattr(models, info['Model'])(**info['_init_params'])
     if status == "Normal" or \
             status == "Prune":
         startup_prog = fluid.Program()
@@ -82,7 +81,7 @@ def load_model(model_dir):
 
 
 def build_transforms(transforms_info):
-    from ..transforms import transforms as T
+    from transforms import transforms as T
     transforms = list()
     for op_info in transforms_info:
         op_name = list(op_info.keys())[0]
diff --git a/contrib/RemoteSensing/models/unet.py b/contrib/RemoteSensing/models/unet.py
index 571cca3a296dbf54a566c798346daa1d8dd398af..ff57b2b7f3fc704a4dff227917fc9c35e0f6670f 100644
--- a/contrib/RemoteSensing/models/unet.py
+++ b/contrib/RemoteSensing/models/unet.py
@@ -18,11 +18,11 @@ import numpy as np
 import math
 import cv2
 import paddle.fluid as fluid
-import RemoteSensing
-import RemoteSensing.utils.logging as logging
+import utils.logging as logging
 from collections import OrderedDict
 from .base import BaseAPI
-from ..utils.metrics import ConfusionMatrix
+from utils.metrics import ConfusionMatrix
+import nets
 
 
 class UNet(BaseAPI):
@@ -90,7 +90,7 @@ class UNet(BaseAPI):
         self.trainable = True
 
     def build_net(self, mode='train'):
-        model = RemoteSensing.nets.UNet(
+        model = nets.UNet(
             self.num_classes,
             mode=mode,
             upsample_mode=self.upsample_mode,
@@ -152,9 +152,9 @@ class UNet(BaseAPI):
 
         Args:
             num_epochs (int): 训练迭代轮数。
-            train_reader (RemoteSensing.readers): 训练数据读取器。
+            train_reader (readers): 训练数据读取器。
             train_batch_size (int): 训练数据batch大小。同时作为验证数据batch大小。默认2。
-            eval_reader (RemoteSensing.readers): 评估数据读取器。
+            eval_reader (readers): 评估数据读取器。
             save_interval_epochs (int): 模型保存间隔（单位：迭代轮数）。默认为1。
             log_interval_steps (int): 训练日志输出间隔（单位：迭代次数）。默认为2。
             save_dir (str): 模型保存路径。默认'output'。
@@ -216,7 +216,7 @@ class UNet(BaseAPI):
         """评估。
 
         Args:
-            eval_reader (RemoteSensing.readers): 评估数据读取器。
+            eval_reader (readers): 评估数据读取器。
             batch_size (int): 评估时的batch大小。默认1。
             verbose (bool): 是否打印日志。默认True。
             epoch_id (int): 当前评估模型所在的训练轮数。
@@ -241,6 +241,8 @@ class UNet(BaseAPI):
 
         for step, data in enumerate(data_generator()):
             images = np.array([d[0] for d in data])
+            images = images.astype(np.float32)
+
             labels = np.array([d[1] for d in data])
             num_samples = images.shape[0]
             if num_samples < batch_size:
@@ -283,7 +285,7 @@ class UNet(BaseAPI):
         """预测。
         Args:
             img_file(str): 预测图像路径。
-            transforms(RemoteSensing.transforms): 数据预处理操作。
+            transforms(transforms): 数据预处理操作。
 
         Returns:
             np.ndarray: 预测结果灰度图。
@@ -297,6 +299,7 @@ class UNet(BaseAPI):
             self.arrange_transforms(
                 transforms=self.test_transforms, mode='test')
             im, im_info = self.test_transforms(im_file)
+        im = im.astype(np.float32)
         im = np.expand_dims(im, axis=0)
         result = self.exe.run(
             self.test_prog,
diff --git a/contrib/RemoteSensing/predict_demo.py b/contrib/RemoteSensing/predict_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..444887b05e9178ecb93394e5833650f4cf18d99d
--- /dev/null
+++ b/contrib/RemoteSensing/predict_demo.py
@@ -0,0 +1,53 @@
+import os
+import os.path as osp
+import numpy as np
+from PIL import Image as Image
+import argparse
+from models import load_model
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='RemoteSensing predict')
+    parser.add_argument(
+        '--data_dir',
+        dest='data_dir',
+        help='dataset directory',
+        default=None,
+        type=str)
+    parser.add_argument(
+        '--load_model_dir',
+        dest='load_model_dir',
+        help='model load directory',
+        default=None,
+        type=str)
+    return parser.parse_args()
+
+
+args = parse_args()
+
+data_dir = args.data_dir
+load_model_dir = args.load_model_dir
+
+# predict
+model = load_model(osp.join(load_model_dir, 'best_model'))
+pred_dir = osp.join(load_model_dir, 'pred')
+if not osp.exists(pred_dir):
+    os.mkdir(pred_dir)
+
+val_list = osp.join(data_dir, 'val.txt')
+color_map = [0, 0, 0, 255, 255, 255]
+with open(val_list) as f:
+    lines = f.readlines()
+    for line in lines:
+        img_path = line.split(' ')[0]
+        print('Predicting {}'.format(img_path))
+        img_path_ = osp.join(data_dir, img_path)
+
+        pred = model.predict(img_path_)
+
+        # 以伪彩色png图片保存预测结果
+        pred_name = osp.basename(img_path).rstrip('npy') + 'png'
+        pred_path = osp.join(pred_dir, pred_name)
+        pred_mask = Image.fromarray(pred.astype(np.uint8), mode='P')
+        pred_mask.putpalette(color_map)
+        pred_mask.save(pred_path)
diff --git a/contrib/RemoteSensing/readers/base.py b/contrib/RemoteSensing/readers/base.py
index e0517613b6bc9614a36d471aa81a35f095c7a881..1427bd60ad4637a3f13c8a08f59291f15fe5ac82 100644
--- a/contrib/RemoteSensing/readers/base.py
+++ b/contrib/RemoteSensing/readers/base.py
@@ -22,7 +22,7 @@ import copy
 import random
 import platform
 import chardet
-from ..utils import logging
+from utils import logging
 
 
 class EndSignal():
diff --git a/contrib/RemoteSensing/readers/reader.py b/contrib/RemoteSensing/readers/reader.py
index d4d32a238828925e116a73931d2e6fc178ba926f..343d25b15034e1905a1e55ae926fbdfa62916cf1 100644
--- a/contrib/RemoteSensing/readers/reader.py
+++ b/contrib/RemoteSensing/readers/reader.py
@@ -14,7 +14,7 @@
 from __future__ import absolute_import
 import os.path as osp
 import random
-from ..utils import logging
+from utils import logging
 from .base import BaseReader
 from .base import get_encoding
 from collections import OrderedDict
diff --git a/contrib/RemoteSensing/requirements.txt b/contrib/RemoteSensing/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..23b5cfdf626667c7c042e6a18eaee9154e95766b
--- /dev/null
+++ b/contrib/RemoteSensing/requirements.txt
@@ -0,0 +1,9 @@
+pre-commit
+yapf == 0.26.0
+flake8
+pyyaml >= 5.1
+Pillow
+numpy
+six
+opencv-python
+tqdm
\ No newline at end of file
diff --git a/contrib/RemoteSensing/tools/create_dataset_list.py b/contrib/RemoteSensing/tools/create_dataset_list.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fec77d55455a242f5cf20f435c9cbf3b04e50de
--- /dev/null
+++ b/contrib/RemoteSensing/tools/create_dataset_list.py
@@ -0,0 +1,143 @@
+# coding: utf8
+# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import glob
+import os.path
+import argparse
+import warnings
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='PaddleSeg generate file list on your customized dataset.')
+    parser.add_argument('dataset_root', help='dataset root directory', type=str)
+    parser.add_argument(
+        '--separator',
+        dest='separator',
+        help='file list separator',
+        default=" ",
+        type=str)
+    parser.add_argument(
+        '--folder',
+        help='the folder names of images and labels',
+        type=str,
+        nargs=2,
+        default=['images', 'annotations'])
+    parser.add_argument(
+        '--second_folder',
+        help=
+        'the second-level folder names of train set, validation set, test set',
+        type=str,
+        nargs='*',
+        default=['train', 'val', 'test'])
+    parser.add_argument(
+        '--format',
+        help='data format of images and labels, default npy, png.',
+        type=str,
+        nargs=2,
+        default=['npy', 'png'])
+    parser.add_argument(
+        '--label_class',
+        help='label class names',
+        type=str,
+        nargs='*',
+        default=['__background__', '__foreground__'])
+    parser.add_argument(
+        '--postfix',
+        help='postfix of images or labels',
+        type=str,
+        nargs=2,
+        default=['', ''])
+
+    return parser.parse_args()
+
+
+def get_files(image_or_label, dataset_split, args):
+    dataset_root = args.dataset_root
+    postfix = args.postfix
+    format = args.format
+    folder = args.folder
+
+    pattern = '*%s.%s' % (postfix[image_or_label], format[image_or_label])
+
+    search_files = os.path.join(dataset_root, folder[image_or_label],
+                                dataset_split, pattern)
+    search_files2 = os.path.join(dataset_root, folder[image_or_label],
+                                 dataset_split, "*", pattern)  # 包含子目录
+    search_files3 = os.path.join(dataset_root, folder[image_or_label],
+                                 dataset_split, "*", "*", pattern)  # 包含三级目录
+
+    filenames = glob.glob(search_files)
+    filenames2 = glob.glob(search_files2)
+    filenames3 = glob.glob(search_files3)
+
+    filenames = filenames + filenames2 + filenames3
+
+    return sorted(filenames)
+
+
+def generate_list(args):
+    dataset_root = args.dataset_root
+    separator = args.separator
+
+    file_list = os.path.join(dataset_root, 'labels.txt')
+    with open(file_list, "w") as f:
+        for label_class in args.label_class:
+            f.write(label_class + '\n')
+
+    for dataset_split in args.second_folder:
+        print("Creating {}.txt...".format(dataset_split))
+        image_files = get_files(0, dataset_split, args)
+        label_files = get_files(1, dataset_split, args)
+        if not image_files:
+            img_dir = os.path.join(dataset_root, args.folder[0], dataset_split)
+            warnings.warn("No images in {} !!!".format(img_dir))
+        num_images = len(image_files)
+
+        if not label_files:
+            label_dir = os.path.join(dataset_root, args.folder[1],
+                                     dataset_split)
+            warnings.warn("No labels in {} !!!".format(label_dir))
+        num_label = len(label_files)
+
+        if num_images != num_label and num_label > 0:
+            raise Exception(
+                "Number of images = {}    number of labels = {} \n"
+                "Either number of images is equal to number of labels, "
+                "or number of labels is equal to 0.\n"
+                "Please check your dataset!".format(num_images, num_label))
+
+        file_list = os.path.join(dataset_root, dataset_split + '.txt')
+        with open(file_list, "w") as f:
+            for item in range(num_images):
+                left = image_files[item].replace(dataset_root, '')
+                if left[0] == os.path.sep:
+                    left = left.lstrip(os.path.sep)
+
+                try:
+                    right = label_files[item].replace(dataset_root, '')
+                    if right[0] == os.path.sep:
+                        right = right.lstrip(os.path.sep)
+                    line = left + separator + right + '\n'
+                except:
+                    line = left + '\n'
+
+                f.write(line)
+                print(line)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    generate_list(args)
diff --git a/contrib/RemoteSensing/train_demo.py b/contrib/RemoteSensing/train_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b70c1343f5a326b1aefb69966e6f23b64381ac7
--- /dev/null
+++ b/contrib/RemoteSensing/train_demo.py
@@ -0,0 +1,106 @@
+import os.path as osp
+import argparse
+import transforms.transforms as T
+from readers.reader import Reader
+from models import UNet
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='RemoteSensing training')
+    parser.add_argument(
+        '--data_dir',
+        dest='data_dir',
+        help='dataset directory',
+        default=None,
+        type=str)
+    parser.add_argument(
+        '--save_dir',
+        dest='save_dir',
+        help='model save directory',
+        default=None,
+        type=str)
+    parser.add_argument(
+        '--channel',
+        dest='channel',
+        help='number of data channel',
+        default=3,
+        type=int)
+    parser.add_argument(
+        '--num_epochs',
+        dest='num_epochs',
+        help='number of traing epochs',
+        default=100,
+        type=int)
+    parser.add_argument(
+        '--train_batch_size',
+        dest='train_batch_size',
+        help='training batch size',
+        default=4,
+        type=int)
+    parser.add_argument(
+        '--lr', dest='lr', help='learning rate', default=0.01, type=float)
+    return parser.parse_args()
+
+
+args = parse_args()
+
+data_dir = args.data_dir
+save_dir = args.save_dir
+channel = args.channel
+num_epochs = args.num_epochs
+train_batch_size = args.train_batch_size
+lr = args.lr
+
+# 定义训练和验证时的transforms
+train_transforms = T.Compose([
+    T.RandomVerticalFlip(0.5),
+    T.RandomHorizontalFlip(0.5),
+    T.ResizeStepScaling(0.5, 2.0, 0.25),
+    T.RandomPaddingCrop(256),
+    T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
+])
+
+eval_transforms = T.Compose([
+    T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
+])
+
+train_list = osp.join(data_dir, 'train.txt')
+val_list = osp.join(data_dir, 'val.txt')
+label_list = osp.join(data_dir, 'labels.txt')
+
+# 定义数据读取器
+train_reader = Reader(
+    data_dir=data_dir,
+    file_list=train_list,
+    label_list=label_list,
+    transforms=train_transforms,
+    num_workers=8,
+    buffer_size=16,
+    shuffle=True,
+    parallel_method='thread')
+
+eval_reader = Reader(
+    data_dir=data_dir,
+    file_list=val_list,
+    label_list=label_list,
+    transforms=eval_transforms,
+    num_workers=8,
+    buffer_size=16,
+    shuffle=False,
+    parallel_method='thread')
+
+model = UNet(
+    num_classes=2, input_channel=channel, use_bce_loss=True, use_dice_loss=True)
+
+model.train(
+    num_epochs=num_epochs,
+    train_reader=train_reader,
+    train_batch_size=train_batch_size,
+    eval_reader=eval_reader,
+    save_interval_epochs=5,
+    log_interval_steps=10,
+    save_dir=save_dir,
+    pretrain_weights=None,
+    optimizer=None,
+    learning_rate=lr,
+)
diff --git a/contrib/RemoteSensing/transforms/ops.py b/contrib/RemoteSensing/transforms/ops.py
index e81c414380765bd3c7fe1de364e049273aefcc9a..e04e695410e5f1e089de838526889c02cadd7da1 100644
--- a/contrib/RemoteSensing/transforms/ops.py
+++ b/contrib/RemoteSensing/transforms/ops.py
@@ -18,8 +18,12 @@ import numpy as np
 from PIL import Image, ImageEnhance
 
 
-def normalize(im, mean, std):
-    im = im.astype(np.float32, copy=False) / 255.0
+def normalize(im, min_value, max_value, mean, std):
+    # Rescaling (min-max normalization)
+    range_value = [max_value[i] - min_value[i] for i in range(len(max_value))]
+    im = (im.astype(np.float32, copy=False) - min_value) / range_value
+
+    # Standardization (Z-score Normalization)
     im -= mean
     im /= std
     return im
diff --git a/contrib/RemoteSensing/transforms/transforms.py b/contrib/RemoteSensing/transforms/transforms.py
index 81ba9a507bfc2eb06ec553f88853ae83b4846d37..abac1746e09e8e95d4149e8243d6ea4258f347ef 100644
--- a/contrib/RemoteSensing/transforms/transforms.py
+++ b/contrib/RemoteSensing/transforms/transforms.py
@@ -170,7 +170,7 @@ class Resize:
     def __init__(self, target_size, interp='LINEAR'):
         self.interp = interp
         assert interp in self.interp_dict, "interp should be one of {}".format(
-            interp_dict.keys())
+            self.interp_dict.keys())
         if isinstance(target_size, list) or isinstance(target_size, tuple):
             if len(target_size) != 2:
                 raise ValueError(
@@ -271,17 +271,6 @@ class ResizeByLong:
                     -shape_before_resize (tuple): 保存resize之前图像的形状(h, w）。
         """
         if im_info is None:
-            im = np.pad(
-                im,
-                pad_width=((0, pad_height), (0, pad_width), (0, 0)),
-                mode='constant',
-                constant_values=(self.im_padding_value, self.im_padding_value))
-            label = np.pad(
-                label,
-                pad_width=((0, pad_height), (0, pad_width)),
-                mode='constant',
-                constant_values=(self.label_padding_value,
-                                 self.label_padding_value))
             im_info = OrderedDict()
 
         im_info['shape_before_resize'] = im.shape[:2]
@@ -420,20 +409,58 @@ class ResizeStepScaling:
             return (im, im_info, label)
 
 
+class Clip:
+    """
+    对图像上超出一定范围的数据进行裁剪。
+
+    Args:
+        min_val (list): 裁剪的下限，小于min_val的数值均设为min_val. 默认值[0, 0, 0].
+        max_val (list): 裁剪的上限，大于max_val的数值均设为max_val. 默认值[255.0, 255.0, 255.0]
+    """
+
+    def __init__(self, min_val=[0, 0, 0], max_val=[255.0, 255.0, 255.0]):
+        self.min_val = min_val
+        self.max_val = max_val
+
+    def __call__(self, im, im_info=None, label=None):
+        if isinstance(self.min_val, list) and isinstance(self.max_val, list):
+            for k in range(im.shape[2]):
+                np.clip(
+                    im[:, :, k],
+                    self.min_val[k],
+                    self.max_val[k],
+                    out=im[:, :, k])
+        else:
+            raise TypeError('min_val and max_val must be list')
+
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
 class Normalize:
     """对图像进行标准化。
-    1.尺度缩放到 [0,1]。
+    1.图像像素归一化到区间 [0.0, 1.0]。
     2.对图像进行减均值除以标准差操作。
 
     Args:
-        mean (list): 图像数据集的均值。默认值[0.5, 0.5, 0.5]。
-        std (list): 图像数据集的标准差。默认值[0.5, 0.5, 0.5]。
+        min_val (list): 图像数据集的最小值。默认值[0, 0, 0].
+        max_val (list): 图像数据集的最大值。默认值[255.0, 255.0, 255.0]
+        mean (list): 图像数据集的均值。默认值[0.5, 0.5, 0.5].
+        std (list): 图像数据集的标准差。默认值[0.5, 0.5, 0.5].
 
     Raises:
         ValueError: mean或std不是list对象。std包含0。
     """
 
-    def __init__(self, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
+    def __init__(self,
+                 min_val=[0, 0, 0],
+                 max_val=[255.0, 255.0, 255.0],
+                 mean=[0.5, 0.5, 0.5],
+                 std=[0.5, 0.5, 0.5]):
+        self.min_val = min_val
+        self.max_val = max_val
         self.mean = mean
         self.std = std
         if not (isinstance(self.mean, list) and isinstance(self.std, list)):
@@ -457,7 +484,8 @@ class Normalize:
 
         mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
         std = np.array(self.std)[np.newaxis, np.newaxis, :]
-        im = normalize(im, mean, std)
+
+        im = normalize(im, self.min_val, self.max_val, mean, std)
 
         if label is None:
             return (im, im_info)
@@ -471,7 +499,7 @@ class Padding:
 
     Args:
         target_size (int/list/tuple): padding后图像的大小。
-        im_padding_value (list): 图像padding的值。默认为[127.5, 127.5, 127.5]。
+        im_padding_value (list): 图像padding的值。默认为127.5。
         label_padding_value (int): 标注图像padding的值。默认值为255。
 
     Raises:
@@ -554,7 +582,7 @@ class RandomPaddingCrop:
 
     Args:
         crop_size（int or list or tuple): 裁剪图像大小。默认为512。
-        im_padding_value (list): 图像padding的值。默认为[127.5, 127.5, 127.5]。
+        im_padding_value (list): 图像padding的值。默认为127.5
         label_padding_value (int): 标注图像padding的值。默认值为255。
 
     Raises:
@@ -684,75 +712,6 @@ class RandomBlur:
             return (im, im_info, label)
 
 
-class RandomRotation:
-    """对图像进行随机旋转。
-    在不超过最大旋转角度的情况下，图像进行随机旋转，当存在标注图像时，同步进行，
-    并对旋转后的图像和标注图像进行相应的padding。
-
-    Args:
-        max_rotation (float): 最大旋转角度。默认为15度。
-        im_padding_value (list): 图像padding的值。默认为[127.5, 127.5, 127.5]。
-        label_padding_value (int): 标注图像padding的值。默认为255。
-
-    """
-
-    def __init__(self,
-                 max_rotation=15,
-                 im_padding_value=[127.5, 127.5, 127.5],
-                 label_padding_value=255):
-        self.max_rotation = max_rotation
-        self.im_padding_value = im_padding_value
-        self.label_padding_value = label_padding_value
-
-    def __call__(self, im, im_info=None, label=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (dict): 存储与图像相关的信息。
-            label (np.ndarray): 标注图像np.ndarray数据。
-
-        Returns:
-            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
-                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
-                存储与图像相关信息的字典和标注图像np.ndarray数据。
-        """
-        if self.max_rotation > 0:
-            (h, w) = im.shape[:2]
-            do_rotation = np.random.uniform(-self.max_rotation,
-                                            self.max_rotation)
-            pc = (w // 2, h // 2)
-            r = cv2.getRotationMatrix2D(pc, do_rotation, 1.0)
-            cos = np.abs(r[0, 0])
-            sin = np.abs(r[0, 1])
-
-            nw = int((h * sin) + (w * cos))
-            nh = int((h * cos) + (w * sin))
-
-            (cx, cy) = pc
-            r[0, 2] += (nw / 2) - cx
-            r[1, 2] += (nh / 2) - cy
-            dsize = (nw, nh)
-            im = cv2.warpAffine(
-                im,
-                r,
-                dsize=dsize,
-                flags=cv2.INTER_LINEAR,
-                borderMode=cv2.BORDER_CONSTANT,
-                borderValue=self.im_padding_value)
-            label = cv2.warpAffine(
-                label,
-                r,
-                dsize=dsize,
-                flags=cv2.INTER_NEAREST,
-                borderMode=cv2.BORDER_CONSTANT,
-                borderValue=self.label_padding_value)
-
-        if label is None:
-            return (im, im_info)
-        else:
-            return (im, im_info, label)
-
-
 class RandomScaleAspect:
     """裁剪并resize回原始尺寸的图像和标注图像。
     按照一定的面积比和宽高比对图像进行裁剪，并reszie回原始图像的图像，当存在标注图时，同步进行。
@@ -813,116 +772,6 @@ class RandomScaleAspect:
             return (im, im_info, label)
 
 
-class RandomDistort:
-    """对图像进行随机失真。
-
-    1. 确定随机失真操作[变换明亮度、变换对比度、变换饱和度、变换色彩]的执行顺序。
-    2. 以一定的概率执行每个随机扰动操作。
-
-    Args:
-        brightness_range (float): 明亮度因子的范围。默认为0.5。
-        brightness_prob (float): 随机调整明亮度的概率。默认为0.5。
-        contrast_range (float): 对比度因子的范围。默认为0.5。
-        contrast_prob (float): 随机调整对比度的概率。默认为0.5。
-        saturation_range (float): 饱和度因子的范围。默认为0.5。
-        saturation_prob (float): 随机调整饱和度的概率。默认为0.5。
-        hue_range (int): 色调因子的范围。默认为18。
-        hue_prob (float): 随机调整色调的概率。默认为0.5。
-        is_order (bool): 是否按照固定顺序
-                        [变换明亮度、变换对比度、变换饱和度、变换色彩]
-                        执行像素内容变换操作。默认为False。
-    """
-
-    def __init__(self,
-                 brightness_range=0.5,
-                 brightness_prob=0.5,
-                 contrast_range=0.5,
-                 contrast_prob=0.5,
-                 saturation_range=0.5,
-                 saturation_prob=0.5,
-                 hue_range=18,
-                 hue_prob=0.5,
-                 is_order=False):
-        self.brightness_range = brightness_range
-        self.brightness_prob = brightness_prob
-        self.contrast_range = contrast_range
-        self.contrast_prob = contrast_prob
-        self.saturation_range = saturation_range
-        self.saturation_prob = saturation_prob
-        self.hue_range = hue_range
-        self.hue_prob = hue_prob
-        self.is_order = is_order
-
-    def __call__(self, im, im_info=None, label_info=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (dict): 存储与图像相关的信息。
-            label (np.ndarray): 标注图像np.ndarray数据。
-
-        Returns:
-            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
-                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
-                存储与图像相关信息的字典和标注图像np.ndarray数据。
-        """
-        brightness_lower = 1 - self.brightness_range
-        brightness_upper = 1 + self.brightness_range
-        contrast_lower = 1 - self.contrast_range
-        contrast_upper = 1 + self.contrast_range
-        saturation_lower = 1 - self.saturation_range
-        saturation_upper = 1 + self.saturation_range
-        hue_lower = -self.hue_range
-        hue_upper = self.hue_range
-        ops = [brightness, contrast, saturation, hue]
-        if self.is_order:
-            prob = np.random.uniform(0, 1)
-            if prob < 0.5:
-                ops = [
-                    brightness,
-                    saturation,
-                    hue,
-                    contrast,
-                ]
-        else:
-            random.shuffle(ops)
-        params_dict = {
-            'brightness': {
-                'brightness_lower': brightness_lower,
-                'brightness_upper': brightness_upper
-            },
-            'contrast': {
-                'contrast_lower': contrast_lower,
-                'contrast_upper': contrast_upper
-            },
-            'saturation': {
-                'saturation_lower': saturation_lower,
-                'saturation_upper': saturation_upper
-            },
-            'hue': {
-                'hue_lower': hue_lower,
-                'hue_upper': hue_upper
-            }
-        }
-        prob_dict = {
-            'brightness': self.brightness_prob,
-            'contrast': self.contrast_prob,
-            'saturation': self.saturation_prob,
-            'hue': self.hue_prob
-        }
-        im = Image.fromarray(im)
-        for id in range(4):
-            params = params_dict[ops[id].__name__]
-            prob = prob_dict[ops[id].__name__]
-            params['im'] = im
-            if np.random.uniform(0, 1) < prob:
-                im = ops[id](**params)
-        im = np.asarray(im)
-        if label is None:
-            return (im, im_info)
-        else:
-            return (im, im_info, label)
-
-
 class ArrangeSegmenter:
     """获取训练/验证/预测所需的信息。
 
diff --git a/contrib/RemoteSensing/utils/logging.py b/contrib/RemoteSensing/utils/logging.py
index 46f8e38b99f4832f4ac5596230eb32e8f45878b8..6d14b1a5df23827c2ddea2a0959801fab6e70552 100644
--- a/contrib/RemoteSensing/utils/logging.py
+++ b/contrib/RemoteSensing/utils/logging.py
@@ -15,7 +15,7 @@
 import time
 import os
 import sys
-import RemoteSensing
+import __init__
 
 levels = {0: 'ERROR', 1: 'WARNING', 2: 'INFO', 3: 'DEBUG'}
 
@@ -24,7 +24,7 @@ def log(level=2, message=""):
     current_time = time.time()
     time_array = time.localtime(current_time)
     current_time = time.strftime("%Y-%m-%d %H:%M:%S", time_array)
-    if RemoteSensing.log_level >= level:
+    if __init__.log_level >= level:
         print("{} [{}]\t{}".format(current_time, levels[level],
                                    message).encode("utf-8").decode("latin1"))
         sys.stdout.flush()
diff --git a/contrib/RemoteSensing/utils/pretrain_weights.py b/contrib/RemoteSensing/utils/pretrain_weights.py
index df104fc0a49f090162d8a03cccecf5088c05d668..e23686406897bc84e705640640bd7ee17d9d95ec 100644
--- a/contrib/RemoteSensing/utils/pretrain_weights.py
+++ b/contrib/RemoteSensing/utils/pretrain_weights.py
@@ -1,5 +1,3 @@
-import RemoteSensing
-import os
 import os.path as osp