Merge branch 'develop' into recalib_as_op

README.md

@@ -64,30 +64,35 @@ PaddleRS具有以下五大特色：
           <li>ResNet50-vd</li>
           <li>MobileNetV3</li>
           <li>HRNet</li>
+          <li>...</li>
         </ul>
         <b>语义分割</b><br>
         <ul>
-          <li>UNet</li>
           <li>FarSeg</li>
+          <li>UNet</li>
           <li>DeepLab V3+</li>
+          <li>...</li>
         </ul>
         <b>目标检测</b><br>
         <ul>
           <li>PP-YOLO</li>
           <li>Faster R-CNN</li>
           <li>YOLOv3</li>
+          <li>...</li>
         </ul>
         <b>图像复原</b><br>
         <ul>
           <li>DRNet</li>
           <li>LESRCNN</li>
           <li>ESRGAN</li>
+          <li>...</li>
         </ul>
         <b>变化检测</b><br>
         <ul>
           <li>DSIFN</li>
           <li>STANet</li>
           <li>ChangeStar</li>
+          <li>...</li>
         </ul>
       </td>
       <td>
@@ -114,6 +119,7 @@ PaddleRS具有以下五大特色：
           <li>ReduceDim</li>  
           <li>SelectBand</li>  
           <li>RandomSwap</li>
+          <li>...</li>
         </ul>  
       </td>
       <td>
@@ -122,12 +128,15 @@ PaddleRS具有以下五大特色：
           <li>coco to mask</li>
           <li>mask to shpfile</li>
           <li>mask to geojson</li>
+          <li>...</li>
         </ul>
         <b>数据预处理</b><br>
         <ul>
           <li>影像切片</li>
           <li>影像配准</li>
           <li>波段选择</li>
+          <li>辐射校正</li>
+          <li>...</li>
         </ul>
       </td>
       <td>
@@ -135,7 +144,7 @@ PaddleRS具有以下五大特色：
         <ul>
           <li>待更</li>
         </ul>
-        <b>遥感语义分割</b><br>
+        <b>遥感图像分割</b><br>
         <ul>
           <li>待更</li>
         </ul>
@@ -147,7 +156,7 @@ PaddleRS具有以下五大特色：
         <ul>
           <li>待更</li>
         </ul>
-        <b>遥感影像超分</b><br>
+        <b>遥感图像复原</b><br>
         <ul>
           <li>待更</li>
         </ul>
@@ -191,8 +200,9 @@ PaddleRS目录树中关键部分如下：
   * [智能标注工具EISeg](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/EISeg)
   * [遥感影像处理工具集](./docs/data/tools.md)
 * 组件介绍
-  * [数据预处理/数据增强](./docs/intro/transforms.md)
+  * [数据集预处理脚本](./docs/intro/data_prep.md)
   * [模型库](./docs/intro/model_zoo.md)
+  * [数据变换算子](./docs/intro/transforms.md)
 * 模型训练
   * [模型训练API说明](./docs/apis/train.md)
 * 模型部署

docs/apis/data.md

@@ -86,6 +86,22 @@
 
 ### 图像复原数据集`ResDataset`
 
+`ResDataset`定义在：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/datasets/res_dataset.py
+
+其初始化参数列表如下：
+
+|参数名称|类型|参数说明|默认值|
+|-------|----|--------|-----|
+|`data_dir`|`str`|数据集存放目录。||
+|`file_list`|`str`|file list路径。file list是一个文本文件，其中每一行包含一个样本的路径信息。`ResDataset`对file list的具体要求请参见下文。||
+|`transforms`|`paddlers.transforms.Compose`|对输入数据应用的数据变换算子。||
+|`num_workers`|`int` \| `str`|加载数据时使用的辅助进程数。若设置为`'auto'`，则按照如下规则确定使用进程数：当CPU核心数大于16时，使用8个数据读取辅助进程；否则，使用CPU核心数一半数量的辅助进程。|`'auto'`|
+|`shuffle`|`bool`|是否随机打乱数据集中的样本。|`False`|
+|`sr_factor`|`int` \| `None`|对于超分辨率重建任务，指定为超分辨率倍数；对于其它任务，指定为`None`。|`None`|
+
+`ResDataset`对file list的要求如下：
+
+- file list中的每一行应该包含2个以空格分隔的项，依次表示输入影像（例如超分辨率重建任务中的低分辨率影像）相对`data_dir`的路径以及目标影像（例如超分辨率重建任务中的高分辨率影像）相对`data_dir`的路径。
 
 ### 图像分割数据集`SegDataset`
 

docs/apis/infer.md

@@ -89,7 +89,28 @@ def predict(self, img_file, transforms=None):
 
 #### `BaseRestorer.predict()`
 
+接口形式：
+
+```python
+def predict(self, img_file, transforms=None):
+```
+
+输入参数：
+
+|参数名称|类型|参数说明|默认值|
+|-------|----|--------|-----|
+|`img_file`|`list[str\|np.ndarray]` \| `str` \| `np.ndarray`|输入影像数据（NumPy数组形式）或输入影像路径。若需要一次性预测一组影像，以列表包含这些影像的数据或路径（每幅影像对应列表中的一个元素）。||
+|`transforms`|`paddlers.transforms.Compose` \| `None`|对输入数据应用的数据变换算子。若为`None`，则使用训练器在验证阶段使用的数据变换算子。|`None`|
+
+返回格式：
+
+若`img_file`是一个字符串或NumPy数组，则返回对象为包含下列键值对的字典：
 
+```
+{"res_map": 模型输出的复原或重建影像（以[h, w, c]格式排布）}
+```
+
+若`img_file`是一个列表，则返回对象为与`img_file`等长的列表，其中的每一项为一个字典（键值对如上所示），顺序对应`img_file`中的每个元素。
 
 #### `BaseSegmenter.predict()`
 
@@ -190,7 +211,7 @@ def predict(self,
 
 |参数名称|类型|参数说明|默认值|
 |-------|----|--------|-----|
-|`img_file`|`list[str\|tuple\|np.ndarray]` \| `str` \| `tuple` \| `np.ndarray`|对于场景分类、目标检测和图像分割任务来说，该参数可为单一图像路径，或是解码后的、排列格式为[h, w, c]且具有float32类型的图像数据（表示为NumPy数组形式），或者是一组图像路径或np.ndarray对象构成的列表；对于变化检测任务来说，该参数可以为图像路径二元组（分别表示前后两个时相影像路径），或是解码后的两幅图像组成的二元组，或者是上述两种二元组之一构成的列表。||
+|`img_file`|`list[str\|tuple\|np.ndarray]` \| `str` \| `tuple` \| `np.ndarray`|对于场景分类、目标检测、图像复原和图像分割任务来说，该参数可为单一图像路径，或是解码后的、排列格式为[h, w, c]且具有float32类型的图像数据（表示为NumPy数组形式），或者是一组图像路径或np.ndarray对象构成的列表；对于变化检测任务来说，该参数可以为图像路径二元组（分别表示前后两个时相影像路径），或是解码后的两幅图像组成的二元组，或者是上述两种二元组之一构成的列表。||
 |`topk`|`int`|场景分类模型预测时使用，表示选取模型输出概率大小排名前`topk`的类别作为最终结果。|`1`|
 |`transforms`|`paddlers.transforms.Compose`\|`None`|对输入数据应用的数据变换算子。若为`None`，则使用从`model.yml`中读取的算子。|`None`|
 |`warmup_iters`|`int`|预热轮数，用于评估模型推理以及前后处理速度。若大于1，将预先重复执行`warmup_iters`次推理，而后才开始正式的预测及其速度评估。|`0`|

docs/apis/train.md

@@ -1,6 +1,6 @@
 # PaddleRS训练API说明
 
-**训练器**封装了模型训练、验证、量化以及动态图推理等逻辑，定义在`paddlers/tasks/`目录下的文件中。为了方便用户使用，PaddleRS为所有支持的模型均提供了继承自父类[`BaseModel`](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/base.py)的训练器，并对外提供数个API。变化检测、场景分类、图像分割以及目标检测任务对应的训练器类型分别为`BaseChangeDetector`、`BaseClassifier`、`BaseDetector`和`BaseSegmenter`。本文档介绍训练器的初始化函数以及`train()`、`evaluate()` API。
+**训练器**封装了模型训练、验证、量化以及动态图推理等逻辑，定义在`paddlers/tasks/`目录下的文件中。为了方便用户使用，PaddleRS为所有支持的模型均提供了继承自父类[`BaseModel`](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/base.py)的训练器，并对外提供数个API。变化检测、场景分类、目标检测、图像复原以及图像分割任务对应的训练器类型分别为`BaseChangeDetector`、`BaseClassifier`、`BaseDetector`、`BaseRestorer`和`BaseSegmenter`。本文档介绍训练器的初始化函数以及`train()`、`evaluate()` API。
 
 ## 初始化训练器
 
@@ -10,27 +10,33 @@
 
 - 一般支持设置`num_classes`、`use_mixed_loss`以及`in_channels`参数，分别表示模型输出类别数、是否使用预置的混合损失以及输入通道数。部分子类如`DSIFN`暂不支持对`in_channels`参数的设置。
 - `use_mixed_loss`参将在未来被弃用，因此不建议使用。
+- 可通过`losses`参数指定模型训练时使用的损失函数。`losses`需为一个字典，其中`'types'`键和`'coef'`键对应的值为两个等长的列表，分别表示损失函数对象（一个可调用对象）和损失函数的权重。例如：`losses={'types': [LossType1(), LossType2()], 'coef': [1.0, 0.5]}`在训练过程中将等价于计算如下损失函数：`1.0*LossType1()(logits, labels)+0.5*LossType2()(logits, labels)`，其中`logits`和`labels`分别是模型输出和真值标签。
 - 不同的子类支持与模型相关的输入参数，详情请参考[模型定义](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/rs_models/cd)和[训练器定义](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/change_detector.py)。
 
 ### 初始化`BaseClassifier`子类对象
 
 - 一般支持设置`num_classes`和`use_mixed_loss`参数，分别表示模型输出类别数以及是否使用预置的混合损失。
 - `use_mixed_loss`参将在未来被弃用，因此不建议使用。
+- 可通过`losses`参数指定模型训练时使用的损失函数，传入实参需为`paddlers.models.clas_losses.CombinedLoss`类型对象。
 - 不同的子类支持与模型相关的输入参数，详情请参考[模型定义](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/rs_models/clas)和[训练器定义](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/classifier.py)。
 
 ### 初始化`BaseDetector`子类对象
 
 - 一般支持设置`num_classes`和`backbone`参数，分别表示模型输出类别数以及所用的骨干网络类型。相比其它任务，目标检测任务的训练器支持设置的初始化参数较多，囊括网络结构、损失函数、后处理策略等方面。
+- 与分割、分类、变化检测等任务不同，检测任务不支持通过`losses`参数指定损失函数。不过对于部分训练器如`PPYOLO`，可通过`use_iou_loss`等参数定制损失函数。
 - 不同的子类支持与模型相关的输入参数，详情请参考[模型定义](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/rs_models/det)和[训练器定义](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/object_detector.py)。
 
 ### 初始化`BaseRestorer`子类对象
 
-
+- 一般支持设置`sr_factor`参数，表示超分辨率倍数；对于不支持超分辨率重建任务的模型，`sr_factor`设置为`None`。
+- 可通过`losses`参数指定模型训练时使用的损失函数，传入实参需为可调用对象或字典。手动指定的`losses`与子类的`default_loss()`方法返回值必须具有相同的格式。
+- 不同的子类支持与模型相关的输入参数，详情请参考[模型定义](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/rs_models/res)和[训练器定义](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/restorer.py)。
 
 ### 初始化`BaseSegmenter`子类对象
 
 - 一般支持设置`in_channels`、`num_classes`以及`use_mixed_loss`参数，分别表示输入通道数、输出类别数以及是否使用预置的混合损失。部分模型如`FarSeg`暂不支持对`in_channels`参数的设置。
 - `use_mixed_loss`参将在未来被弃用，因此不建议使用。
+- 可通过`losses`参数指定模型训练时使用的损失函数。`losses`需为一个字典，其中`'types'`键和`'coef'`键对应的值为两个等长的列表，分别表示损失函数对象（一个可调用对象）和损失函数的权重。例如：`losses={'types': [LossType1(), LossType2()], 'coef': [1.0, 0.5]}`在训练过程中将等价于计算如下损失函数：`1.0*LossType1()(logits, labels)+0.5*LossType2()(logits, labels)`，其中`logits`和`labels`分别是模型输出和真值标签。
 - 不同的子类支持与模型相关的输入参数，详情请参考[模型定义](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/rs_models/seg)和[训练器定义](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmentor.py)。
 
 ## `train()`
@@ -176,6 +182,46 @@ def train(self,
 
 ### `BaseRestorer.train()`
 
+接口形式：
+
+```python
+def train(self,
+          num_epochs,
+          train_dataset,
+          train_batch_size=2,
+          eval_dataset=None,
+          optimizer=None,
+          save_interval_epochs=1,
+          log_interval_steps=2,
+          save_dir='output',
+          pretrain_weights='CITYSCAPES',
+          learning_rate=0.01,
+          lr_decay_power=0.9,
+          early_stop=False,
+          early_stop_patience=5,
+          use_vdl=True,
+          resume_checkpoint=None):
+```
+
+其中各参数的含义如下：
+
+|参数名称|类型|参数说明|默认值|
+|-------|----|--------|-----|
+|`num_epochs`|`int`|训练的epoch数目。||
+|`train_dataset`|`paddlers.datasets.ResDataset`|训练数据集。||
+|`train_batch_size`|`int`|训练时使用的batch size。|`2`|
+|`eval_dataset`|`paddlers.datasets.ResDataset` \| `None`|验证数据集。|`None`|
+|`optimizer`|`paddle.optimizer.Optimizer` \| `None`|训练时使用的优化器。若为`None`，则使用默认定义的优化器。|`None`|
+|`save_interval_epochs`|`int`|训练时存储模型的间隔epoch数。|`1`|
+|`log_interval_steps`|`int`|训练时打印日志的间隔step数（即迭代数）。|`2`|
+|`save_dir`|`str`|存储模型的路径。|`'output'`|
+|`pretrain_weights`|`str` \| `None`|预训练权重的名称/路径。若为`None`，则不适用预训练权重。|`'CITYSCAPES'`|
+|`learning_rate`|`float`|训练时使用的学习率大小，适用于默认优化器。|`0.01`|
+|`lr_decay_power`|`float`|学习率衰减系数，适用于默认优化器。|`0.9`|
+|`early_stop`|`bool`|训练过程是否启用早停策略。|`False`|
+|`early_stop_patience`|`int`|启用早停策略时的`patience`参数（参见[`EarlyStop`](https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/utils/utils.py)）。|`5`|
+|`use_vdl`|`bool`|是否启用VisualDL日志。|`True`|
+|`resume_checkpoint`|`str` \| `None`|检查点路径。PaddleRS支持从检查点（包含先前训练过程中存储的模型权重和优化器权重）继续训练，但需注意`resume_checkpoint`与`pretrain_weights`不得同时设置为`None`以外的值。|`None`|
 
 ### `BaseSegmenter.train()`
 
@@ -280,7 +326,7 @@ def evaluate(self, eval_dataset, batch_size=1, return_details=False):
 
 ```
 {"top1": top1准确率,
- "top5": `top5准确率}
+ "top5": top5准确率}
 ```
 
 ### `BaseDetector.evaluate()`
@@ -320,6 +366,26 @@ def evaluate(self,
 
 ### `BaseRestorer.evaluate()`
 
+接口形式：
+
+```python
+def evaluate(self, eval_dataset, batch_size=1, return_details=False):
+```
+
+输入参数如下：
+
+|参数名称|类型|参数说明|默认值|
+|-------|----|--------|-----|
+|`eval_dataset`|`paddlers.datasets.ResDataset`|评估数据集。||
+|`batch_size`|`int`|评估时使用的batch size（多卡训练时，为所有设备合计batch size）。|`1`|
+|`return_details`|`bool`|*当前版本请勿手动设置此参数。*|`False`|
+
+输出为一个`collections.OrderedDict`对象，包含如下键值对：
+
+```
+{"psnr": PSNR指标,
+ "ssim": SSIM指标}
+```
 
 ### `BaseSegmenter.evaluate()`
 

docs/data/tools.md

@@ -8,8 +8,9 @@ PaddleRS在`tools`目录中提供了丰富的遥感影像处理工具，包括
 - `match.py`：用于实现两幅影像的配准。
 - `split.py`：用于对大幅面影像数据进行切片。
 - `coco_tools/`：COCO工具合集，用于统计处理COCO格式标注文件。
+- `prepare_dataset/`：数据集预处理脚本合集。
 
-## 使用示例
+## 使用说明
 
 首先请确保您已将PaddleRS下载到本地。进入`tools`目录：
 
@@ -101,3 +102,24 @@ python split.py --image_path {输入影像路径} [--mask_path {真值标签路
 - `json_Merge.py`:       将多个json文件合并为一个。
 
 详细使用方法请参见[coco_tools使用说明](coco_tools.md)。
+
+### prepare_dataset
+
+`prepare_dataset`目录中包含一系列数据预处理脚本，主要用于预处理已下载到本地的遥感开源数据集，使其符合PaddleRS训练、验证、测试的标准。
+
+在执行脚本前，您可以通过`--help`选项获取帮助信息。例如：
+
+```shell
+python prepare_dataset/prepare_levircd.py --help
+```
+
+以下列出了脚本中常见的命令行选项：
+
+- `--in_dataset_dir`：下载到本地的原始数据集所在路径。示例：`--in_dataset_dir downloads/LEVIR-CD`。
+- `--out_dataset_dir`：处理后的数据集存放路径。示例：`--out_dataset_dir data/levircd`。
+- `--crop_size`：对于支持影像裁块的数据集，指定切分的影像块大小。示例：`--crop_size 256`。
+- `--crop_stride`：对于支持影像裁块的数据集，指定切分时滑窗移动的步长。示例：`--crop_stride 256`。
+- `--seed`：随机种子。可用于固定随机数生成器产生的伪随机数序列，从而得到固定的数据集划分结果。示例：`--seed 1919810`
+- `--ratios`：对于支持子集随机划分的数据集，指定需要划分的各个子集的样本比例。示例：`--ratios 0.7 0.2 0.1`。
+
+您可以在[此文档](https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/data_prep.md)中查看PaddleRS提供哪些数据集的预处理脚本。

docs/dev/dev_guide.md

@@ -70,6 +70,15 @@ Args:
 
 4. 在全局变量`__all__`中添加新增训练器的类名。
 
+需要注意的是，对于图像复原任务，模型的前向、反向逻辑均实现在训练器定义中。对于GAN等需要用到多个网络的模型，训练器的编写请参照如下规范：
+- 重写`build_net()`方法，使用`GANAdapter`维护所有网络。`GANAdapter`对象在构造时接受两个列表作为输入：第一个列表中包含所有的生成器，其中第一个元素为主生成器；第二个列表中包含所有的判别器。
+- 重写`default_loss()`方法，构建损失函数。若训练过程中需要用到多个损失函数，推荐以字典的形式组织。
+- 重写`default_optimizer()`方法，构建一个或多个优化器。当`build_net()`返回值的类型为`GANAdapter`时，`parameters`参数为一个字典。其中，`parameters['params_g']`是一个列表，顺序包含各个生成器的state dict；`parameters['params_d']`是一个列表，顺序包含各个判别器的state dict。若构建多个优化器，在返回时应使用`OptimizerAdapter`包装。
+- 重写`run_gan()`方法，该方法接受`net`、`inputs`、`mode`、和`gan_mode`四个参数，用于执行训练过程中的某一个子任务，例如生成器的前向计算、判别器的前向计算等等。
+- 重写`train_step()`方法，在其中编写模型训练过程中一次迭代的具体逻辑。通常的做法是反复调用`run_gan()`，每次调用时都根据需要构造不同的`inputs`、并使其工作在不同的`gan_mode`，并从每次返回的`outputs`字典中抽取有用的字段（如各项损失），汇总至最终结果。
+
+GAN训练器的具体例子可以参考`ESRGAN`。
+
 ## 2 新增数据预处理/数据增强函数或算子
 
 ### 2.1 新增数据预处理/数据增强函数

docs/intro/data_prep.md

@@ -0,0 +1,9 @@
+# 数据集预处理脚本
+
+## PaddleRS已支持的数据集预处理脚本列表
+
+| 任务 | 数据集名称 | 数据集地址 | 预处理脚本 |
+|-----|-----------|----------|----------|
+| 变化检测 | LEVIR-CD | https://justchenhao.github.io/LEVIR/ | [prepare_levircd.py](https://github.com/PaddlePaddle/PaddleRS/blob/develop/tools/prepare_dataset/prepare_levircd.py) |
+| 变化检测 | Season-varying | https://paperswithcode.com/dataset/cdd-dataset-season-varying | [prepare_svcd.py](https://github.com/PaddlePaddle/PaddleRS/blob/develop/tools/prepare_dataset/prepare_svcd.py) |
+| 目标检测 | RSOD | https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset- | [prepare_rsod](https://github.com/PaddlePaddle/PaddleRS/blob/develop/tools/prepare_dataset/prepare_rsod.py) |

docs/intro/transforms.md

@@ -1,4 +1,4 @@
-# 数据预处理/数据增强
+# 数据变换算子
 
 ## PaddleRS已支持的数据变换算子列表
 

examples/README.md

@@ -8,9 +8,31 @@ PaddleRS提供从科学研究到产业应用的丰富示例，希望帮助遥感
 
 ## 2 社区贡献案例
 
-[AI Studio](https://aistudio.baidu.com/aistudio/index)是基于百度深度学习平台飞桨的人工智能学习与实训社区，提供在线编程环境、免费GPU算力、海量开源算法和开放数据，帮助开发者快速创建和部署模型。您可以在AI Studio上探索PaddleRS的更多玩法：
+### 2.1 基于PaddleRS的遥感解译平台
 
-[AI Studio上的PaddleRS相关项目](https://aistudio.baidu.com/aistudio/projectoverview/public?kw=PaddleRS)
+#### 小桨神瞳
+
+<p>
+<img src="https://user-images.githubusercontent.com/21275753/188320924-99c2915e-7371-4dc6-a50e-92fe11fc05a6.gif", width="400", hspace="50"> <img src="https://user-images.githubusercontent.com/21275753/188320957-f82348ee-c4cf-4799-b006-8389cb5e9380.gif", width="400">
+</p>
+
+- 作者：白菜
+- 代码仓库：https://github.com/CrazyBoyM/webRS
+- 演示视频：https://www.bilibili.com/video/BV1W14y1s7fs?vd_source=0de109a09b98176090b8aa3295a45bb6
+
+#### 遥感图像智能解译平台
+
+<p>
+<img src="https://user-images.githubusercontent.com/21275753/187441111-e992e0ff-93d1-4fb3-90b2-79ff698db8d8.gif", width="400", hspace="50"> <img src="https://user-images.githubusercontent.com/21275753/187441219-08668c78-8426-4e19-ad7d-d1a22e1def49.gif", width="400">
+</p>
+
+- 作者：HHU-河马海牛队
+- 代码仓库：https://github.com/terayco/Intelligent-RS-System
+- 演示视频：https://www.bilibili.com/video/BV1eY4y1u7Eq/?vd_source=75a73fc15a4e8b25195728ee93a5b322
+
+### 2.2 AI Studio上的PaddleRS相关项目
+
+[AI Studio](https://aistudio.baidu.com/aistudio/index)是基于百度深度学习平台飞桨的人工智能学习与实训社区，提供在线编程环境、免费GPU算力、海量开源算法和开放数据，帮助开发者快速创建和部署模型。您可以[在AI Studio上探索PaddleRS的更多玩法](https://aistudio.baidu.com/aistudio/projectoverview/public?kw=PaddleRS)。
 
 本文档收集了部分由开源爱好者贡献的精品项目：
 

paddlers/rs_models/cd/bit.py

@@ -56,7 +56,7 @@ class BIT(nn.Layer):
             Default: 2.
         enc_with_pos (bool, optional): Whether to add leanred positional embedding to the input feature sequence of the 
             encoder. Default: True.
-        enc_depth (int, optional): Number of attention blocks used in the encoder. Default: 1
+        enc_depth (int, optional): Number of attention blocks used in the encoder. Default: 1.
         enc_head_dim (int, optional): Embedding dimension of each encoder head. Default: 64.
         dec_depth (int, optional): Number of attention blocks used in the decoder. Default: 8.
         dec_head_dim (int, optional): Embedding dimension of each decoder head. Default: 8.

paddlers/rs_models/seg/farseg.py

@@ -11,11 +11,10 @@
 # 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.
-"""
-This code is based on https://github.com/Z-Zheng/FarSeg
-Ths copyright of Z-Zheng/FarSeg is as follows:
-Apache License [see LICENSE for details]
-"""
+
+# This code is based on https://github.com/Z-Zheng/FarSeg
+# The copyright of Z-Zheng/FarSeg is as follows:
+# Apache License (see https://github.com/Z-Zheng/FarSeg/blob/master/LICENSE for details).
 
 import math
 
@@ -164,7 +163,7 @@ class SceneRelation(nn.Layer):
         return refined_feats
 
 
-class AssymetricDecoder(nn.Layer):
+class AsymmetricDecoder(nn.Layer):
     def __init__(self,
                  in_channels,
                  out_channels,
@@ -172,7 +171,7 @@ class AssymetricDecoder(nn.Layer):
                  out_feat_output_stride=4,
                  norm_fn=nn.BatchNorm2D,
                  num_groups_gn=None):
-        super(AssymetricDecoder, self).__init__()
+        super(AsymmetricDecoder, self).__init__()
         if norm_fn == nn.BatchNorm2D:
             norm_fn_args = dict(num_features=out_channels)
         elif norm_fn == nn.GroupNorm:
@@ -215,9 +214,12 @@ class AssymetricDecoder(nn.Layer):
 
 
 class ResNet50Encoder(nn.Layer):
-    def __init__(self, pretrained=True):
+    def __init__(self, in_ch=3, pretrained=True):
         super(ResNet50Encoder, self).__init__()
         self.resnet = resnet50(pretrained=pretrained)
+        if in_ch != 3:
+            self.resnet.conv1 = nn.Conv2D(
+                in_ch, 64, kernel_size=7, stride=2, padding=3, bias_attr=False)
 
     def forward(self, inputs):
         x = inputs
@@ -234,25 +236,35 @@ class ResNet50Encoder(nn.Layer):
 
 class FarSeg(nn.Layer):
     """
-        The FarSeg implementation based on PaddlePaddle.
+    The FarSeg implementation based on PaddlePaddle.
+
+    The original article refers to
+    Zheng, Zhuo, et al. "Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution 
+        Remote Sensing Imagery"
+    (https://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Foreground-Aware_Relation_Network_for_Geospatial_Object_Segmentation_in_High_Spatial_CVPR_2020_paper.pdf)
 
-        The original article refers to
-        Zheng, Zhuo, et al. "Foreground-Aware Relation Network for Geospatial Object 
-            Segmentation in High Spatial Resolution Remote Sensing Imagery"
-        (https://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Foreground-Aware_Relation_Network_for_Geospatial_Object_Segmentation_in_High_Spatial_CVPR_2020_paper.pdf)
+    Args:
+        in_channels (int, optional): Number of bands of the input images. Default: 3.
+        num_classes (int, optional): Number of target classes. Default: 16.
+        fpn_ch_list (list[int]|tuple[int], optional): Channel list of the FPN. Default: (256, 512, 1024, 2048).
+        mid_ch (int, optional): Output channels of the FPN. Default: 256.
+        out_ch (int, optional): Output channels of the decoder. Default: 128.
+        sr_ch_list (list[int]|tuple[int], optional): Channel list of the foreground-scene relation module. Default: (256, 256, 256, 256).
+        pretrained_encoder (bool, optional): Whether to use a pretrained encoder. Default: True.
     """
 
     def __init__(self,
+                 in_channels=3,
                  num_classes=16,
                  fpn_ch_list=(256, 512, 1024, 2048),
                  mid_ch=256,
                  out_ch=128,
                  sr_ch_list=(256, 256, 256, 256),
-                 encoder_pretrained=True):
+                 pretrained_encoder=True):
         super(FarSeg, self).__init__()
-        self.en = ResNet50Encoder(encoder_pretrained)
+        self.en = ResNet50Encoder(in_channels, pretrained_encoder)
         self.fpn = FPN(in_channels_list=fpn_ch_list, out_channels=mid_ch)
-        self.decoder = AssymetricDecoder(
+        self.decoder = AsymmetricDecoder(
             in_channels=mid_ch, out_channels=out_ch)
         self.cls_pred_conv = nn.Conv2D(out_ch, num_classes, 1)
         self.upsample4x_op = nn.UpsamplingBilinear2D(scale_factor=4)
@@ -273,5 +285,4 @@ class FarSeg(nn.Layer):
         final_feat = self.decoder(refined_fpn_feat_list)
         cls_pred = self.cls_pred_conv(final_feat)
         cls_pred = self.upsample4x_op(cls_pred)
-        cls_pred = F.softmax(cls_pred, axis=1)
         return [cls_pred]

paddlers/tasks/change_detector.py

@@ -31,7 +31,7 @@ import paddlers.utils.logging as logging
 from paddlers.models import seg_losses
 from paddlers.transforms import Resize, decode_image
 from paddlers.utils import get_single_card_bs
-from paddlers.utils.checkpoint import seg_pretrain_weights_dict
+from paddlers.utils.checkpoint import cd_pretrain_weights_dict
 from .base import BaseModel
 from .utils import seg_metrics as metrics
 from .utils.infer_nets import InferCDNet
@@ -275,7 +275,7 @@ class BaseChangeDetector(BaseModel):
                 exit=True)
         if pretrain_weights is not None and resume_checkpoint is not None:
             logging.error(
-                "pretrain_weights and resume_checkpoint cannot be set simultaneously.",
+                "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.",
                 exit=True)
         self.labels = train_dataset.labels
         if self.losses is None:
@@ -289,23 +289,30 @@ class BaseChangeDetector(BaseModel):
         else:
             self.optimizer = optimizer
 
-        if pretrain_weights is not None and not osp.exists(pretrain_weights):
-            if pretrain_weights not in seg_pretrain_weights_dict[
-                    self.model_name]:
-                logging.warning(
-                    "Path of pretrain_weights('{}') does not exist!".format(
-                        pretrain_weights))
-                logging.warning("Pretrain_weights is forcibly set to '{}'. "
-                                "If don't want to use pretrain weights, "
-                                "set pretrain_weights to be None.".format(
-                                    seg_pretrain_weights_dict[self.model_name][
-                                        0]))
-                pretrain_weights = seg_pretrain_weights_dict[self.model_name][0]
-        elif pretrain_weights is not None and osp.exists(pretrain_weights):
-            if osp.splitext(pretrain_weights)[-1] != '.pdparams':
-                logging.error(
-                    "Invalid pretrain weights. Please specify a '.pdparams' file.",
-                    exit=True)
+        if pretrain_weights is not None:
+            if not osp.exists(pretrain_weights):
+                if self.model_name not in cd_pretrain_weights_dict:
+                    logging.warning(
+                        "Path of pretrained weights ('{}') does not exist!".
+                        format(pretrain_weights))
+                    pretrain_weights = None
+                elif pretrain_weights not in cd_pretrain_weights_dict[
+                        self.model_name]:
+                    logging.warning(
+                        "Path of pretrained weights ('{}') does not exist!".
+                        format(pretrain_weights))
+                    pretrain_weights = cd_pretrain_weights_dict[
+                        self.model_name][0]
+                    logging.warning(
+                        "`pretrain_weights` is forcibly set to '{}'. "
+                        "If you don't want to use pretrained weights, "
+                        "please set `pretrain_weights` to None.".format(
+                            pretrain_weights))
+            else:
+                if osp.splitext(pretrain_weights)[-1] != '.pdparams':
+                    logging.error(
+                        "Invalid pretrained weights. Please specify a .pdparams file.",
+                        exit=True)
         pretrained_dir = osp.join(save_dir, 'pretrain')
         is_backbone_weights = pretrain_weights == 'IMAGENET'
         self.net_initialize(
@@ -409,18 +416,18 @@ class BaseChangeDetector(BaseModel):
                 key-value pairs:
                 For binary change detection (number of classes == 2), the key-value 
                     pairs are like:
-                    {"iou": `intersection over union for the change class`,
-                    "f1": `F1 score for the change class`,
-                    "oacc": `overall accuracy`,
-                    "kappa": ` kappa coefficient`}.
+                    {"iou": intersection over union for the change class,
+                    "f1": F1 score for the change class,
+                    "oacc": overall accuracy,
+                    "kappa": kappa coefficient}.
                 For multi-class change detection (number of classes > 2), the key-value 
                     pairs are like:
-                    {"miou": `mean intersection over union`,
-                    "category_iou": `category-wise mean intersection over union`,
-                    "oacc": `overall accuracy`,
-                    "category_acc": `category-wise accuracy`,
-                    "kappa": ` kappa coefficient`,
-                    "category_F1-score": `F1 score`}.
+                    {"miou": mean intersection over union,
+                    "category_iou": category-wise mean intersection over union,
+                    "oacc": overall accuracy,
+                    "category_acc": category-wise accuracy,
+                    "kappa": kappa coefficient,
+                    "category_F1-score": F1 score}.
         """
 
         self._check_transforms(eval_dataset.transforms, 'eval')

paddlers/tasks/classifier.py

@@ -246,7 +246,7 @@ class BaseClassifier(BaseModel):
                 exit=True)
         if pretrain_weights is not None and resume_checkpoint is not None:
             logging.error(
-                "pretrain_weights and resume_checkpoint cannot be set simultaneously.",
+                "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.",
                 exit=True)
         self.labels = train_dataset.labels
         if self.losses is None:
@@ -262,25 +262,32 @@ class BaseClassifier(BaseModel):
         else:
             self.optimizer = optimizer
 
-        if pretrain_weights is not None and not osp.exists(pretrain_weights):
-            if pretrain_weights not in cls_pretrain_weights_dict[
-                    self.model_name]:
-                logging.warning(
-                    "Path of pretrain_weights('{}') does not exist!".format(
-                        pretrain_weights))
-                logging.warning("Pretrain_weights is forcibly set to '{}'. "
-                                "If don't want to use pretrain weights, "
-                                "set pretrain_weights to be None.".format(
-                                    cls_pretrain_weights_dict[self.model_name][
-                                        0]))
-                pretrain_weights = cls_pretrain_weights_dict[self.model_name][0]
-        elif pretrain_weights is not None and osp.exists(pretrain_weights):
-            if osp.splitext(pretrain_weights)[-1] != '.pdparams':
-                logging.error(
-                    "Invalid pretrain weights. Please specify a '.pdparams' file.",
-                    exit=True)
+        if pretrain_weights is not None:
+            if not osp.exists(pretrain_weights):
+                if self.model_name not in cls_pretrain_weights_dict:
+                    logging.warning(
+                        "Path of `pretrain_weights` ('{}') does not exist!".
+                        format(pretrain_weights))
+                    pretrain_weights = None
+                elif pretrain_weights not in cls_pretrain_weights_dict[
+                        self.model_name]:
+                    logging.warning(
+                        "Path of `pretrain_weights` ('{}') does not exist!".
+                        format(pretrain_weights))
+                    pretrain_weights = cls_pretrain_weights_dict[
+                        self.model_name][0]
+                    logging.warning(
+                        "`pretrain_weights` is forcibly set to '{}'. "
+                        "If you don't want to use pretrained weights, "
+                        "set `pretrain_weights` to None.".format(
+                            pretrain_weights))
+            else:
+                if osp.splitext(pretrain_weights)[-1] != '.pdparams':
+                    logging.error(
+                        "Invalid pretrained weights. Please specify a .pdparams file.",
+                        exit=True)
         pretrained_dir = osp.join(save_dir, 'pretrain')
-        is_backbone_weights = False  # pretrain_weights == 'IMAGENET'  # TODO: this is backbone
+        is_backbone_weights = False
         self.net_initialize(
             pretrain_weights=pretrain_weights,
             save_dir=pretrained_dir,
@@ -380,8 +387,8 @@ class BaseClassifier(BaseModel):
         Returns:
             If `return_details` is False, return collections.OrderedDict with 
                 key-value pairs:
-                {"top1": `acc of top1`,
-                 "top5": `acc of top5`}.
+                {"top1": acc of top1,
+                 "top5": acc of top5}.
         """
 
         self._check_transforms(eval_dataset.transforms, 'eval')

paddlers/tasks/object_detector.py

@@ -274,7 +274,7 @@ class BaseDetector(BaseModel):
                 exit=True)
         if pretrain_weights is not None and resume_checkpoint is not None:
             logging.error(
-                "pretrain_weights and resume_checkpoint cannot be set simultaneously.",
+                "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.",
                 exit=True)
         if train_dataset.__class__.__name__ == 'VOCDetDataset':
             train_dataset.data_fields = {
@@ -323,23 +323,29 @@ class BaseDetector(BaseModel):
             self.optimizer = optimizer
 
         # Initiate weights
-        if pretrain_weights is not None and not osp.exists(pretrain_weights):
-            if pretrain_weights not in det_pretrain_weights_dict['_'.join(
-                [self.model_name, self.backbone_name])]:
-                logging.warning(
-                    "Path of pretrain_weights('{}') does not exist!".format(
-                        pretrain_weights))
-                pretrain_weights = det_pretrain_weights_dict['_'.join(
-                    [self.model_name, self.backbone_name])][0]
-                logging.warning("Pretrain_weights is forcibly set to '{}'. "
-                                "If you don't want to use pretrain weights, "
-                                "set pretrain_weights to be None.".format(
-                                    pretrain_weights))
-        elif pretrain_weights is not None and osp.exists(pretrain_weights):
-            if osp.splitext(pretrain_weights)[-1] != '.pdparams':
-                logging.error(
-                    "Invalid pretrain weights. Please specify a '.pdparams' file.",
-                    exit=True)
+        if pretrain_weights is not None:
+            if not osp.exists(pretrain_weights):
+                key = '_'.join([self.model_name, self.backbone_name])
+                if key not in det_pretrain_weights_dict:
+                    logging.warning(
+                        "Path of pretrained weights ('{}') does not exist!".
+                        format(pretrain_weights))
+                    pretrain_weights = None
+                elif pretrain_weights not in det_pretrain_weights_dict[key]:
+                    logging.warning(
+                        "Path of pretrained weights ('{}') does not exist!".
+                        format(pretrain_weights))
+                    pretrain_weights = det_pretrain_weights_dict[key][0]
+                    logging.warning(
+                        "`pretrain_weights` is forcibly set to '{}'. "
+                        "If you don't want to use pretrained weights, "
+                        "please set `pretrain_weights` to None.".format(
+                            pretrain_weights))
+            else:
+                if osp.splitext(pretrain_weights)[-1] != '.pdparams':
+                    logging.error(
+                        "Invalid pretrained weights. Please specify a .pdparams file.",
+                        exit=True)
         pretrained_dir = osp.join(save_dir, 'pretrain')
         self.net_initialize(
             pretrain_weights=pretrain_weights,
@@ -477,7 +483,7 @@ class BaseDetector(BaseModel):
 
         Returns:
             If `return_details` is False, return collections.OrderedDict with key-value pairs: 
-                {"bbox_mmap":`mean average precision (0.50, 11point)`}.
+                {"bbox_mmap": mean average precision (0.50, 11point)}.
         """
 
         if metric is None:

paddlers/tasks/restorer.py

@@ -31,6 +31,7 @@ from paddlers.models import res_losses
 from paddlers.transforms import Resize, decode_image
 from paddlers.transforms.functions import calc_hr_shape
 from paddlers.utils import get_single_card_bs
+from paddlers.utils.checkpoint import res_pretrain_weights_dict
 from .base import BaseModel
 from .utils.res_adapters import GANAdapter, OptimizerAdapter
 from .utils.infer_nets import InferResNet
@@ -234,7 +235,7 @@ class BaseRestorer(BaseModel):
                 exit=True)
         if pretrain_weights is not None and resume_checkpoint is not None:
             logging.error(
-                "pretrain_weights and resume_checkpoint cannot be set simultaneously.",
+                "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.",
                 exit=True)
 
         if self.losses is None:
@@ -256,14 +257,30 @@ class BaseRestorer(BaseModel):
         else:
             self.optimizer = optimizer
 
-        if pretrain_weights is not None and not osp.exists(pretrain_weights):
-            logging.warning("Path of pretrain_weights('{}') does not exist!".
-                            format(pretrain_weights))
-        elif pretrain_weights is not None and osp.exists(pretrain_weights):
-            if osp.splitext(pretrain_weights)[-1] != '.pdparams':
-                logging.error(
-                    "Invalid pretrain weights. Please specify a '.pdparams' file.",
-                    exit=True)
+        if pretrain_weights is not None:
+            if not osp.exists(pretrain_weights):
+                if self.model_name not in res_pretrain_weights_dict:
+                    logging.warning(
+                        "Path of pretrained weights ('{}') does not exist!".
+                        format(pretrain_weights))
+                    pretrain_weights = None
+                elif pretrain_weights not in res_pretrain_weights_dict[
+                        self.model_name]:
+                    logging.warning(
+                        "Path of pretrained weights ('{}') does not exist!".
+                        format(pretrain_weights))
+                    pretrain_weights = res_pretrain_weights_dict[
+                        self.model_name][0]
+                    logging.warning(
+                        "`pretrain_weights` is forcibly set to '{}'. "
+                        "If you don't want to use pretrained weights, "
+                        "please set `pretrain_weights` to None.".format(
+                            pretrain_weights))
+            else:
+                if osp.splitext(pretrain_weights)[-1] != '.pdparams':
+                    logging.error(
+                        "Invalid pretrained weights. Please specify a .pdparams file.",
+                        exit=True)
         pretrained_dir = osp.join(save_dir, 'pretrain')
         is_backbone_weights = pretrain_weights == 'IMAGENET'
         self.net_initialize(
@@ -365,8 +382,8 @@ class BaseRestorer(BaseModel):
         Returns:
             If `return_details` is False, return collections.OrderedDict with 
                 key-value pairs:
-                {"psnr": `peak signal-to-noise ratio`,
-                 "ssim": `structural similarity`}.
+                {"psnr": peak signal-to-noise ratio,
+                 "ssim": structural similarity}.
 
         """
 

paddlers/tasks/segmenter.py

@@ -267,7 +267,7 @@ class BaseSegmenter(BaseModel):
                 exit=True)
         if pretrain_weights is not None and resume_checkpoint is not None:
             logging.error(
-                "pretrain_weights and resume_checkpoint cannot be set simultaneously.",
+                "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.",
                 exit=True)
         self.labels = train_dataset.labels
         if self.losses is None:
@@ -281,23 +281,30 @@ class BaseSegmenter(BaseModel):
         else:
             self.optimizer = optimizer
 
-        if pretrain_weights is not None and not osp.exists(pretrain_weights):
-            if pretrain_weights not in seg_pretrain_weights_dict[
-                    self.model_name]:
-                logging.warning(
-                    "Path of pretrain_weights('{}') does not exist!".format(
-                        pretrain_weights))
-                logging.warning("Pretrain_weights is forcibly set to '{}'. "
-                                "If don't want to use pretrain weights, "
-                                "set pretrain_weights to be None.".format(
-                                    seg_pretrain_weights_dict[self.model_name][
-                                        0]))
-                pretrain_weights = seg_pretrain_weights_dict[self.model_name][0]
-        elif pretrain_weights is not None and osp.exists(pretrain_weights):
-            if osp.splitext(pretrain_weights)[-1] != '.pdparams':
-                logging.error(
-                    "Invalid pretrain weights. Please specify a '.pdparams' file.",
-                    exit=True)
+        if pretrain_weights is not None:
+            if not osp.exists(pretrain_weights):
+                if self.model_name not in seg_pretrain_weights_dict:
+                    logging.warning(
+                        "Path of pretrained weights ('{}') does not exist!".
+                        format(pretrain_weights))
+                    pretrain_weights = None
+                elif pretrain_weights not in seg_pretrain_weights_dict[
+                        self.model_name]:
+                    logging.warning(
+                        "Path of pretrained weights ('{}') does not exist!".
+                        format(pretrain_weights))
+                    pretrain_weights = seg_pretrain_weights_dict[
+                        self.model_name][0]
+                    logging.warning(
+                        "`pretrain_weights` is forcibly set to '{}'. "
+                        "If you don't want to use pretrained weights, "
+                        "please set `pretrain_weights` to None.".format(
+                            pretrain_weights))
+            else:
+                if osp.splitext(pretrain_weights)[-1] != '.pdparams':
+                    logging.error(
+                        "Invalid pretrained weights. Please specify a .pdparams file.",
+                        exit=True)
         pretrained_dir = osp.join(save_dir, 'pretrain')
         is_backbone_weights = pretrain_weights == 'IMAGENET'
         self.net_initialize(
@@ -398,12 +405,12 @@ class BaseSegmenter(BaseModel):
 
         Returns:
             collections.OrderedDict with key-value pairs:
-                {"miou": `mean intersection over union`,
-                 "category_iou": `category-wise mean intersection over union`,
-                 "oacc": `overall accuracy`,
-                 "category_acc": `category-wise accuracy`,
-                 "kappa": ` kappa coefficient`,
-                 "category_F1-score": `F1 score`}.
+                {"miou": mean intersection over union,
+                 "category_iou": category-wise mean intersection over union,
+                 "oacc": overall accuracy,
+                 "category_acc": category-wise accuracy,
+                 "kappa": kappa coefficient,
+                 "category_F1-score": F1 score}.
 
         """
 
@@ -909,6 +916,7 @@ class BiSeNetV2(BaseSegmenter):
 
 class FarSeg(BaseSegmenter):
     def __init__(self,
+                 in_channels=3,
                  num_classes=2,
                  use_mixed_loss=False,
                  losses=None,
@@ -918,4 +926,5 @@ class FarSeg(BaseSegmenter):
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
             losses=losses,
+            in_channels=in_channels,
             **params)

paddlers/utils/checkpoint.py

@@ -21,20 +21,14 @@ import paddle
 from . import logging
 from .download import download_and_decompress
 
+cd_pretrain_weights_dict = {}
+
 cls_pretrain_weights_dict = {
     'ResNet50_vd': ['IMAGENET'],
     'MobileNetV3_small_x1_0': ['IMAGENET'],
     'HRNet_W18_C': ['IMAGENET'],
 }
 
-seg_pretrain_weights_dict = {
-    'UNet': ['CITYSCAPES'],
-    'DeepLabV3P': ['CITYSCAPES', 'PascalVOC', 'IMAGENET'],
-    'FastSCNN': ['CITYSCAPES'],
-    'HRNet': ['CITYSCAPES', 'PascalVOC'],
-    'BiSeNetV2': ['CITYSCAPES']
-}
-
 det_pretrain_weights_dict = {
     'PicoDet_ESNet_s': ['COCO', 'IMAGENET'],
     'PicoDet_ESNet_m': ['COCO', 'IMAGENET'],
@@ -74,6 +68,16 @@ det_pretrain_weights_dict = {
     'MaskRCNN_ResNet101_vd_fpn': ['COCO', 'IMAGENET']
 }
 
+res_pretrain_weights_dict = {}
+
+seg_pretrain_weights_dict = {
+    'UNet': ['CITYSCAPES'],
+    'DeepLabV3P': ['CITYSCAPES', 'PascalVOC', 'IMAGENET'],
+    'FastSCNN': ['CITYSCAPES'],
+    'HRNet': ['CITYSCAPES', 'PascalVOC'],
+    'BiSeNetV2': ['CITYSCAPES']
+}
+
 cityscapes_weights = {
     'UNet_CITYSCAPES':
     'https://bj.bcebos.com/paddleseg/dygraph/cityscapes/unet_cityscapes_1024x512_160k/model.pdparams',

test_tipc/README.md

@@ -44,6 +44,7 @@
 | 目标检测 | PP-YOLOv2 | 支持 | - | - | - |
 | 目标检测 | YOLOv3 | 支持 | - | - | - |
 | 图像分割 | DeepLab V3+ | 支持 | - | - | - |
+| 图像分割 | FarSeg | 支持 | - | - | - |
 | 图像分割 | UNet | 支持 | - | - | - |
 
 ## 3 测试工具简介

test_tipc/configs/seg/farseg/farseg_rsseg.yaml

@@ -0,0 +1,11 @@
+# Configurations of FarSeg with RSSeg dataset
+
+_base_: ../_base_/rsseg.yaml
+
+save_dir: ./test_tipc/output/seg/farseg/
+
+model: !Node
+    type: FarSeg
+    args:
+        in_channels: 10
+        num_classes: 5

test_tipc/configs/seg/farseg/train_infer_python.txt

@@ -0,0 +1,53 @@
+===========================train_params===========================
+model_name:seg:farseg
+python:python
+gpu_list:0|0,1
+use_gpu:null|null
+--precision:null
+--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=20
+--save_dir:adaptive
+--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4
+--model_path:null
+--config:lite_train_lite_infer=./test_tipc/configs/seg/farseg/farseg_rsseg.yaml|lite_train_whole_infer=./test_tipc/configs/seg/farseg/farseg_rsseg.yaml|whole_train_whole_infer=./test_tipc/configs/seg/farseg/farseg_rsseg.yaml
+train_model_name:best_model
+null:null
+##
+trainer:norm
+norm_train:test_tipc/run_task.py train seg
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:null
+null:null
+##
+===========================export_params===========================
+--save_dir:adaptive
+--model_dir:adaptive
+--fixed_input_shape:[-1,10,512,512]
+norm_export:deploy/export/export_model.py
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+===========================infer_params===========================
+infer_model:null
+infer_export:null
+infer_quant:False
+inference:test_tipc/infer.py
+--device:cpu|gpu
+--enable_mkldnn:True
+--cpu_threads:6
+--batch_size:1
+--use_trt:False
+--precision:fp32
+--model_dir:null
+--config:null
+--save_log_path:null
+--benchmark:True
+--model_name:farseg
+null:null

test_tipc/docs/test_train_inference_python.md

@@ -31,6 +31,7 @@ Linux GPU/CPU 基础训练推理测试的主程序为`test_train_inference_pytho
 |  目标检测  | PP-YOLOv2 | 正常训练 | 正常训练 | mAP=59.37% |
 |  目标检测  | YOLOv3 | 正常训练 | 正常训练 | mAP=47.33% |
 |  图像分割  | DeepLab V3+ | 正常训练 | 正常训练 | mIoU=56.05% |
+|  图像分割  | FarSeg | 正常训练 | 正常训练 | mIoU=49.58% |
 |  图像分割  | UNet | 正常训练 | 正常训练 | mIoU=55.50% |
 
 *注：参考预测精度为whole_train_whole_infer模式下单卡训练汇报的精度数据。*
@@ -61,6 +62,7 @@ Linux GPU/CPU 基础训练推理测试的主程序为`test_train_inference_pytho
 |  目标检测  | PP-YOLOv2 | 支持 | 支持 | 1 |
 |  目标检测  | YOLOv3 | 支持 | 支持 | 1 |
 |  图像分割  | DeepLab V3+ | 支持 | 支持 | 1 |
+|  图像分割  | FarSeg | 支持 | 支持 | 1 |
 |  图像分割  | UNet | 支持 | 支持 | 1 |
 
 ## 2 测试流程

tests/deploy/test_predictor.py

@@ -105,7 +105,7 @@ class TestPredictor(CommonTest):
                     dict_[key], expected_dict[key], rtol=1.e-4, atol=1.e-6)
 
 
-# @TestPredictor.add_tests
+@TestPredictor.add_tests
 class TestCDPredictor(TestPredictor):
     MODULE = pdrs.tasks.change_detector
     TRAINER_NAME_TO_EXPORT_OPTS = {
@@ -177,7 +177,7 @@ class TestCDPredictor(TestPredictor):
         self.assertEqual(len(out_multi_array_t), num_inputs)
 
 
-# @TestPredictor.add_tests
+@TestPredictor.add_tests
 class TestClasPredictor(TestPredictor):
     MODULE = pdrs.tasks.classifier
     TRAINER_NAME_TO_EXPORT_OPTS = {
@@ -242,7 +242,7 @@ class TestClasPredictor(TestPredictor):
         self.check_dict_equal(out_multi_array_p, out_multi_array_t)
 
 
-# @TestPredictor.add_tests
+@TestPredictor.add_tests
 class TestDetPredictor(TestPredictor):
     MODULE = pdrs.tasks.object_detector
     TRAINER_NAME_TO_EXPORT_OPTS = {
@@ -355,7 +355,7 @@ class TestResPredictor(TestPredictor):
         self.assertEqual(len(out_multi_array_t), num_inputs)
 
 
-# @TestPredictor.add_tests
+@TestPredictor.add_tests
 class TestSegPredictor(TestPredictor):
     MODULE = pdrs.tasks.segmenter
     TRAINER_NAME_TO_EXPORT_OPTS = {

tests/rs_models/test_cd_models.py

@@ -21,7 +21,7 @@ __all__ = [
     'TestBITModel', 'TestCDNetModel', 'TestChangeStarModel', 'TestDSAMNetModel',
     'TestDSIFNModel', 'TestFCEarlyFusionModel', 'TestFCSiamConcModel',
     'TestFCSiamDiffModel', 'TestSNUNetModel', 'TestSTANetModel',
-    'TestChangeFormerModel'
+    'TestChangeFormerModel', 'TestFCCDNModel'
 ]
 
 
@@ -32,8 +32,11 @@ class TestCDModel(TestModel):
         self.assertIsInstance(output, list)
         self.check_output_equal(len(output), len(target))
         for o, t in zip(output, target):
-            o = o.numpy()
-            self.check_output_equal(o.shape, t.shape)
+            if isinstance(o, list):
+                self.check_output(o, t)
+            else:
+                o = o.numpy()
+                self.check_output_equal(o.shape, t.shape)
 
     def set_inputs(self):
         if self.EF_MODE == 'Concat':
@@ -225,3 +228,27 @@ class TestChangeFormerModel(TestCDModel):
             dict(**base_spec, decoder_softmax=True),
             dict(**base_spec, embed_dim=56)
         ]   # yapf: disable
+
+
+class TestFCCDNModel(TestCDModel):
+    MODEL_CLASS = paddlers.rs_models.cd.FCCDN
+
+    def set_specs(self):
+        self.specs = [
+            dict(in_channels=3, num_classes=2),
+            dict(in_channels=8, num_classes=2),
+            dict(in_channels=3, num_classes=8),
+            dict(in_channels=3, num_classes=2, _phase='eval', _stop_grad=True)
+        ]   # yapf: disable
+
+    def set_targets(self):
+        b = self.DEFAULT_BATCH_SIZE
+        h = self.DEFAULT_HW[0] // 2
+        w = self.DEFAULT_HW[1] // 2
+        tar_c2 = [
+            self.get_zeros_array(2), [self.get_zeros_array(1, b, h, w)] * 2
+        ]
+        self.targets = [
+            tar_c2, tar_c2, [self.get_zeros_array(8), tar_c2[1]],
+            [self.get_zeros_array(2)]
+        ]

tests/rs_models/test_seg_models.py

@@ -25,8 +25,11 @@ class TestSegModel(TestModel):
         self.assertIsInstance(output, list)
         self.check_output_equal(len(output), len(target))
         for o, t in zip(output, target):
-            o = o.numpy()
-            self.check_output_equal(o.shape, t.shape)
+            if isinstance(o, list):
+                self.check_output(o, t)
+            else:
+                o = o.numpy()
+                self.check_output_equal(o.shape, t.shape)
 
     def set_inputs(self):
         def _gen_data(specs):
@@ -50,7 +53,8 @@ class TestFarSegModel(TestSegModel):
 
     def set_specs(self):
         self.specs = [
-            dict(), dict(num_classes=20), dict(encoder_pretrained=False)
+            dict(), dict(num_classes=20), dict(pretrained_encoder=False),
+            dict(in_channels=10)
         ]
 
     def set_targets(self):

tools/prepare_dataset/common.py

@@ -107,7 +107,7 @@ def crop_patches(crop_size,
     if max_workers < 0:
         raise ValueError("`max_workers` must be a non-negative integer!")
 
-    if subset is None:
+    if subsets is None:
         subsets = ('', )
 
     if max_workers == 0:
@@ -280,4 +280,4 @@ def random_split(samples,
         # Append remainder to the last split
         splits[-1].append(splits[ed_idx:])
 
-    return splits
+    return splits

tutorials/train/README.md

@@ -27,6 +27,7 @@
 |object_detection/ppyolov2.py | 目标检测 | PP-YOLOv2 |
 |object_detection/yolov3.py | 目标检测 | YOLOv3 |
 |semantic_segmentation/deeplabv3p.py | 图像分割 | DeepLab V3+ |
+|semantic_segmentation/farseg.py | 图像分割 | FarSeg |
 |semantic_segmentation/unet.py | 图像分割 | UNet |
 
 ## 环境准备

tutorials/train/semantic_segmentation/deeplabv3p.py

@@ -71,7 +71,7 @@ eval_dataset = pdrs.datasets.SegDataset(
 # 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md
 # 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py
 model = pdrs.tasks.seg.DeepLabV3P(
-    input_channel=NUM_BANDS,
+    in_channels=NUM_BANDS,
     num_classes=len(train_dataset.labels),
     backbone='ResNet50_vd')
 

tutorials/train/semantic_segmentation/farseg.py

@@ -0,0 +1,94 @@
+#!/usr/bin/env python
+
+# 图像分割模型FarSeg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
+
+# 数据集存放目录
+DATA_DIR = './data/rsseg/'
+# 训练集`file_list`文件路径
+TRAIN_FILE_LIST_PATH = './data/rsseg/train.txt'
+# 验证集`file_list`文件路径
+EVAL_FILE_LIST_PATH = './data/rsseg/val.txt'
+# 数据集类别信息文件路径
+LABEL_LIST_PATH = './data/rsseg/labels.txt'
+# 实验目录，保存输出的模型权重和结果
+EXP_DIR = './output/farseg/'
+
+# 下载和解压多光谱地块分类数据集
+pdrs.utils.download_and_decompress(
+    'https://paddlers.bj.bcebos.com/datasets/rsseg.zip', path='./data/')
+
+# 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/PaddlePaddle/PaddleRS/blob/develop/docs/apis/data.md
+train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
+    # 选择前三个波段
+    T.SelectBand([1, 2, 3]),
+    # 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, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeSegmenter('train')
+])
+
+eval_transforms = T.Compose([
+    T.DecodeImg(),
+    # 验证阶段与训练阶段应当选择相同的波段
+    T.SelectBand([1, 2, 3]),
+    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, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeSegmenter('eval')
+])
+
+# 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FarSeg模型
+# 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md
+# 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py
+model = pdrs.tasks.seg.FarSeg(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=4,
+    save_dir=EXP_DIR,
+    pretrain_weights=None,
+    # 初始学习率大小
+    learning_rate=0.001,
+    # 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.py

@@ -71,7 +71,7 @@ eval_dataset = pdrs.datasets.SegDataset(
 # 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md
 # 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py
 model = pdrs.tasks.seg.UNet(
-    input_channel=NUM_BANDS, num_classes=len(train_dataset.labels))
+    in_channels=NUM_BANDS, num_classes=len(train_dataset.labels))
 
 # 执行模型训练
 model.train(