Upadte docs

docs/apis/backbones/backbones.md

@@ -1,3 +1,4 @@
+English | [简体中文](backbones_cn.md)
 # paddleseg.models.backbone
 
 The models subpackage contains backbones extracting features for sementic segmentation models.

docs/apis/backbones/backbones_cn.md

@@ -1,3 +1,4 @@
+简体中文 | [English](backbones.md)
 # paddleseg.models.backbone
 
 该models子包中包含了为语义分割模型提取特征的骨干网络。

docs/apis/core/core.md

@@ -1,3 +1,4 @@
+English | [简体中文](core_cn.md)
 # paddleseg.core
 
 The interface for training, evaluation and prediction.

docs/apis/core/core_cn.md

@@ -1,3 +1,4 @@
+简体中文 | [English](core.md)
 # paddleseg.core
 
 启动模型训练，评估与预测的接口

docs/apis/cvlibs/cvlibs.md

@@ -1,20 +1,22 @@
+English | [简体中文](cvlibs_cn.md)
 # paddleseg.cvlibs
 
-- [Manager](#Manager)
-- [Parameters Initialization](#parameters-initialization)
+- [ComponentManager](#ComponentManager)
+- [constant_init](#constant_init)
 - [Config](#Config)
 
 
-## [Manager](../../paddleseg/cvlibs/manager.py)
-> CLASS paddleseg.cvlibs.manager.ComponentManager(name)
-
-    Implement a manager class to add the new component properly.
-    The component can be added as either class or function type.
+## [ComponentManager](../../../paddleseg/cvlibs/manager.py)
+```python
+class paddleseg.cvlibs.manager.ComponentManager(name)
+```
+> Implement a Manager class to correctly add new components to the model.
+> Components are added as classes or functions.
 
-> > Args
-> > > - **name** (str): The name of component.
+### Args
+* **name** (str): The name of component.
 
-> > Examples 1
+### Examples 1
 
 ```python
 from paddleseg.cvlibs.manager import ComponentManager
@@ -33,7 +35,7 @@ print(model_manager.components_dict)
 # {'AlexNet': <class '__main__.AlexNet'>, 'ResNet': <class '__main__.ResNet'>}
 ```
 
-> > Examples 2:
+### Examples 2:
 
 ```python
 # Or an easier way, using it as a Python decorator, while just add it above the class declaration.
@@ -51,28 +53,28 @@ print(model_manager.components_dict)
 # {'AlexNet': <class '__main__.AlexNet'>, 'ResNet': <class '__main__.ResNet'>}
 ```
 
-> > add_component(components)
-
-    Add component(s) into the corresponding manager.
-
-> > > Args
-> > > > - **components** (function|class|list|tuple): Support four types of components.
-> > Returns
-> > > > - **components** (function|class|list|tuple): Same with input components.
+```python
+add_component(components)
+```
+> Add component(s) into the corresponding manager.
 
-## [Parameters Initialization](../../paddleseg/cvlibs/param_init.py)
-initialization method
+### Args
+* **components** (function|class|list|tuple): Support four types of components.
+### Returns
+* **components** (function|class|list|tuple): Same with input components.
 
-### Constant Initialization
 
-> constant_init(param, **kwargs):
+## [constant_init](../../../paddleseg/cvlibs/param_init.py)
 
-    Initialize the `param` with constants.
+```python
+constant_init(param, **kwargs):
+```
+> Initialize the `param` with constants.
 
-> > Args
-> > > - **param** (Tensor): Tensor that needs to be initialized.
+### Args
+* **param** (Tensor): Tensor that needs to be initialized.
 
-> > Examples
+### Examples
 
 ```python
 from paddleseg.cvlibs import param_init
@@ -84,15 +86,17 @@ print(linear.weight.numpy())
 # result is [[2. 2. 2. 2.], [2. 2. 2. 2.]]
 ```
 
-### Normal Initialization
-> normal_init(param, **kwargs)
+### [normal_init](../../../paddleseg/cvlibs/param_init.py)
+```python
+normal_init(param, **kwargs)
+```
 
     Initialize the `param` with a Normal distribution.
 
-> > Args
-> > > - **param** (Tensor): Tensor that needs to be initialized.
+### Args
+* **param** (Tensor): Tensor that needs to be initialized.
 
-> > Examples
+### Examples
 
 ```python
 from paddleseg.cvlibs import param_init
@@ -102,28 +106,30 @@ linear = nn.Linear(2, 4)
 param_init.normal_init(linear.weight, loc=0.0, scale=1.0)
 ```
 
-### Kaiming Normal Initialization
-> kaiming_normal_init(param, **kwargs):
+### [kaiming_normal_init](../../../paddleseg/cvlibs/param_init.py)
+```python
+kaiming_normal_init(param, **kwargs):
+```
 
-    Initialize the input tensor with Kaiming Normal initialization.
+> Initialize the input tensor with Kaiming Normal initialization.
 
-    This function implements the `param` initialization from the paper
+> This function implements the `param` initialization from the paper
     `Delving Deep into Rectifiers: Surpassing Human-Level Performance on
     ImageNet Classification <https://arxiv.org/abs/1502.01852>`
     by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a
     robust initialization method that particularly considers the rectifier
-    nonlinearities. In case of Uniform distribution, the range is [-x, x], where
-    .. math::
-        x = \sqrt{\\frac{6.0}{fan\_in}}
-    In case of Normal distribution, the mean is 0 and the standard deviation
+    nonlinearities. 
+> In case of Uniform distribution, the range is [-x, x], where
+    <a href="https://www.codecogs.com/eqnedit.php?latex=x&space;=&space;\sqrt{\frac{6.0}{fan_in}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?x&space;=&space;\sqrt{\frac{6.0}{fan_in}}" title="x = \sqrt{\frac{6.0}{fan_in}}" /></a>
+    
+> In case of Normal distribution, the mean is 0 and the standard deviation
     is
-    .. math::
-        \sqrt{\\frac{2.0}{fan\_in}}
+    <a href="https://www.codecogs.com/eqnedit.php?latex=\sqrt{\frac{2.0}{fan_in}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\sqrt{\frac{2.0}{fan_in}}" title="\sqrt{\frac{2.0}{fan_in}}" /></a>
 
-> > Args
-> > > - **param** (Tensor): Tensor that needs to be initialized.
+### Args
+* **param** (Tensor): Tensor that needs to be initialized.
 
-> > Examples
+### Examples
 
 ```python
 from paddleseg.cvlibs import param_init
@@ -134,34 +140,36 @@ linear = nn.Linear(2, 4)
 param_init.kaiming_normal_init(linear.weight)
 ```
 
-## [Config](../../paddleseg/cvlibs/config.py)
-> CLASS Config(path, learning_rate, batch_size, iters)
-
-    Training configuration parsing. The only yaml/yml file is supported.
-
-    The following hyper-parameters are available in the config file:
-        batch_size: The number of samples per gpu.
-        iters: The total training steps.
-        train_dataset: A training data config including type/data_root/transforms/mode.
-            For data type, please refer to paddleseg.datasets.
-            For specific transforms, please refer to paddleseg.transforms.transforms.
-        val_dataset: A validation data config including type/data_root/transforms/mode.
-        optimizer: A optimizer config, but currently PaddleSeg only supports sgd with momentum in config file.
-            In addition, weight_decay could be set as a regularization.
-        learning_rate: A learning rate config. If decay is configured, learning _rate value is the starting learning rate,
-             where only poly decay is supported using the config file. In addition, decay power and end_lr are tuned experimentally.
-        loss: A loss config. Multi-loss config is available. The loss type order is consistent with the seg model outputs,
-            where the coef term indicates the weight of corresponding loss. Note that the number of coef must be the same as the number of
-            model outputs, and there could be only one loss type if using the same loss type among the outputs, otherwise the number of
-            loss type must be consistent with coef.
-        model: A model config including type/backbone and model-dependent arguments.
-            For model type, please refer to paddleseg.models.
-            For backbone, please refer to paddleseg.models.backbones.
-
-> > Args
-> > > - **path** (str) : The path of config file, supports yaml format only.
-
-> > Examples
+## [Config](../../../paddleseg/cvlibs/config.py)
+```python
+class Config(
+    path, 
+    learning_rate, 
+    batch_size, 
+    iters
+)
+```
+* Training configuration parsing. The only yaml/yml file is supported.
+
+* The following hyper-parameters are available in the config file:
+> **batch_size**: The number of samples per gpu.
+> **iters**: The total training steps.
+> **train_dataset**: A training data config including type/data_root/transforms/mode.For data type, please refer to paddleseg.datasets.For specific transforms, please refer to paddleseg.transforms.transforms.
+
+> **val_dataset**: A validation data config including type/data_root/transforms/mode.
+
+> **optimizer**: A optimizer config, but currently PaddleSeg only supports sgd with momentum in config file.In addition, weight_decay could be set as a regularization.
+
+> **learning_rate**: A learning rate config. If decay is configured, learning _rate value is the starting learning rate,where only poly decay is supported using the config file. In addition, decay power and end_lr are tuned experimentally.
+
+> **loss**: A loss config. Multi-loss config is available. The loss type order is consistent with the seg model outputs,where the coef term indicates the weight of corresponding loss. Note that the number of coef must be the same as the number of model outputs, and there could be only one loss type if using the same loss type among the outputs, otherwise the number of loss type must be consistent with coef.
+
+> **model**: A model config including type/backbone and model-dependent arguments.For model type, please refer to paddleseg.models.For backbone, please refer to paddleseg.models.backbones.
+
+### Args
+* **path** (str) : The path of config file, supports yaml format only.
+
+### Examples
 
 ```python
 from paddleseg.cvlibs.config import Config