[Docs] translate 2_data_pipeline.md and 3_new_module.md into Chinese …

…and fix some typos. (#168)

* [Docs] translate 2_data_pipeline.md into Chinese

* [Docs] translate 3_new_module.md into Chinese

* [Docs] Fix typos from py to python

docs/en/tutorials/1_new_dataset.md

@@ -28,7 +28,7 @@ To write a new dataset, you need to implement:
 
 Assume the name of your `DataSource` is `NewDataSource`, you can create a file, named `new_data_source.py` under `mmselfsup/datasets/data_sources` and implement `NewDataSource` in it.
 
-```py
+```python
 import mmcv
 import numpy as np
 
@@ -49,7 +49,7 @@ class NewDataSource(BaseDataSource):
 
 Then, add `NewDataSource` in `mmselfsup/dataset/data_sources/__init__.py`.
 
-```py
+```python
 from .base import BaseDataSource
 ...
 from .new_data_source import NewDataSource
@@ -63,7 +63,7 @@ __all__ = [
 
 Assume the name of your `Dataset` is `NewDataset`, you can create a file, named `new_dataset.py` under `mmselfsup/datasets` and implement `NewDataset` in it.
 
-```py
+```python
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 from mmcv.utils import build_from_cfg
@@ -89,7 +89,7 @@ class NewDataset(BaseDataset):
 
 Then, add `NewDataset` in `mmselfsup/dataset/__init__.py`.
 
-```py
+```python
 from .base import BaseDataset
 ...
 from .new_dataset import NewDataset
@@ -103,7 +103,7 @@ __all__ = [
 
 To use `NewDataset`, you can modify the config as the following:
 
-```py
+```python
 train=dict(
         type='NewDataset',
         data_source=dict(

docs/en/tutorials/2_data_pipeline.md

@@ -10,7 +10,7 @@
 
 Here is a config example of `Pipeline` for `SimCLR` training:
 
-```py
+```python
 train_pipeline = [
     dict(type='RandomResizedCrop', size=224),
     dict(type='RandomHorizontalFlip'),
@@ -36,7 +36,7 @@ Every augmentation in the `Pipeline` receives an image as input and outputs an a
 
 1.Write a new transformation function in [transforms.py](../../mmselfsup/datasets/pipelines/transforms.py) and overwrite the `__call__` function, which takes a `Pillow` image as input:
 
-```py
+```python
 @PIPELINES.register_module()
 class MyTransform(object):
 
@@ -47,7 +47,7 @@ class MyTransform(object):
 
 2.Use it in config files. We reuse the config file shown above and add `MyTransform` to it.
 
-```py
+```python
 train_pipeline = [
     dict(type='RandomResizedCrop', size=224),
     dict(type='RandomHorizontalFlip'),

docs/en/tutorials/3_new_module.md

@@ -19,7 +19,7 @@ Assuming we are going to create a customized backbone `CustomizedBackbone`
 
 1.Create a new file `mmselfsup/models/backbones/customized_backbone.py` and implement `CustomizedBackbone` in it.
 
-```py
+```python
 import torch.nn as nn
 from ..builder import BACKBONES
 
@@ -45,7 +45,7 @@ class CustomizedBackbone(nn.Module):
 
 2.Import the customized backbone in `mmselfsup/models/backbones/__init__.py`.
 
-```py
+```python
 from .customized_backbone import CustomizedBackbone
 
 __all__ = [
@@ -55,7 +55,7 @@ __all__ = [
 
 3.Use it in your config file.
 
-```py
+```python
 model = dict(
     ...
     backbone=dict(
@@ -71,7 +71,7 @@ we include all projection heads in `mmselfsup/models/necks`. Assuming we are goi
 
 1.Create a new file `mmselfsup/models/necks/customized_proj_head.py` and implement `CustomizedProjHead` in it.
 
-```py
+```python
 import torch.nn as nn
 from mmcv.runner import BaseModule
 
@@ -92,7 +92,7 @@ You need to implement the forward function, which takes the feature from the bac
 
 2.Import the `CustomizedProjHead` in `mmselfsup/models/necks/__init__`.
 
-```py
+```python
 from .customized_proj_head import CustomizedProjHead
 
 __all__ = [
@@ -104,7 +104,7 @@ __all__ = [
 
 3.Use it in your config file.
 
-```py
+```python
 model = dict(
     ...,
     neck=dict(
@@ -119,7 +119,7 @@ To add a new loss function, we mainly implement the `forward` function in the lo
 
 1.Create a new file `mmselfsup/models/heads/customized_head.py` and implement your customized `CustomizedHead` in it.
 
-```py
+```python
 import torch
 import torch.nn as nn
 from mmcv.runner import BaseModule
@@ -142,15 +142,15 @@ class CustomizedHead(BaseModule):
 
 2.Import the module in `mmselfsup/models/heads/__init__.py`
 
-```py
+```python
 from .customized_head import CustomizedHead
 
 __all__ = [..., CustomizedHead, ...]
 ```
 
 3.Use it in your config file.
 
-```py
+```python
 model = dict(
     ...,
     head=dict(type='CustomizedHead')
@@ -163,7 +163,7 @@ After creating each component, mentioned above, we need to create a `CustomizedA
 
 1.Create a new file `mmselfsup/models/algorithms/customized_algorithm.py` and implement `CustomizedAlgorithm` in it.
 
-```py
+```python
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
@@ -187,15 +187,15 @@ class CustomizedAlgorithm(BaseModel):
 
 2.Import the module in `mmselfsup/models/algorithms/__init__.py`
 
-```py
+```python
 from .customized_algorithm import CustomizedAlgorithm
 
 __all__ = [..., CustomizedAlgorithm, ...]
 ```
 
 3.Use it in your config file.
 
-```py
+```python
 model = dict(
     type='CustomizedAlgorightm',
     backbone=...,

docs/en/tutorials/4_schedule.md

@@ -20,15 +20,15 @@ We already support to use all the optimizers implemented by PyTorch, and to use
 
 For example, if you want to use SGD, the modification could be as the following.
 
-```py
+```python
 optimizer = dict(type='SGD', lr=0.0003, weight_decay=0.0001)
 ```
 
 To modify the learning rate of the model, just modify the `lr` in the config of optimizer. You can also directly set other arguments according to the [API doc](https://pytorch.org/docs/stable/optim.html?highlight=optim#module-torch.optim) of PyTorch.
 
 For example, if you want to use `Adam` with the setting like `torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)` in PyTorch, the config should looks like:
 
-```py
+```python
 optimizer = dict(type='Adam', lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)
 ```
 
@@ -42,7 +42,7 @@ Learning rate decay is widely used to improve performance. And to use learning r
 
 For example, we use CosineAnnealing policy to train SimCLR, and the config is:
 
-```py
+```python
 lr_config = dict(
     policy='CosineAnnealing',
     ...)
@@ -67,7 +67,7 @@ Here are some examples:
 
 1.linear & warmup by iter
 
-```py
+```python
 lr_config = dict(
     policy='CosineAnnealing',
     by_epoch=False,
@@ -80,7 +80,7 @@ lr_config = dict(
 
 2.exp & warmup by epoch
 
-```py
+```python
 lr_config = dict(
     policy='CosineAnnealing',
     min_lr=0,
@@ -98,7 +98,7 @@ Momentum scheduler is usually used with LR scheduler, for example, the following
 
 Here is an example:
 
-```py
+```python
 lr_config = dict(
     policy='cyclic',
     target_ratio=(10, 1e-4),
@@ -119,7 +119,7 @@ Some models may have some parameter-specific settings for optimization, for exam
 
 For example, if we do not want to apply weight decay to the parameters of BatchNorm or GroupNorm, and the bias in each layer, we can use following config file:
 
-```py
+```python
 optimizer = dict(
     type=...,
     lr=...,
@@ -140,7 +140,7 @@ Currently we support `grad_clip` option in `optimizer_config`, and you can refer
 
 Here is an example:
 
-```py
+```python
 optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
 # norm_type: type of the used p-norm, here norm_type is 2.
 ```
@@ -153,14 +153,14 @@ When there is not enough computation resource, the batch size can only be set to
 
 Here is an example:
 
-```py
+```python
 data = dict(imgs_per_gpu=64)
 optimizer_config = dict(type="DistOptimizerHook", update_interval=4)
 ```
 
 Indicates that during training, back-propagation is performed every 4 iters. And the above is equivalent to:
 
-```py
+```python
 data = dict(imgs_per_gpu=256)
 optimizer_config = dict(type="OptimizerHook")
 ```
@@ -171,7 +171,7 @@ In academic research and industrial practice, it is likely that you need some op
 
 Implement your `CustomizedOptim` in `mmselfsup/core/optimizer/optimizers.py`
 
-```py
+```python
 import torch
 from torch.optim import *  # noqa: F401,F403
 from torch.optim.optimizer import Optimizer, required
@@ -193,7 +193,7 @@ class CustomizedOptim(Optimizer):
 
 Import it in `mmselfsup/core/optimizer/__init__.py`
 
-```py
+```python
 from .optimizers import CustomizedOptim
 from .builder import build_optimizer
 
@@ -202,7 +202,7 @@ __all__ = ['CustomizedOptim', 'build_optimizer', ...]
 
 Use it in your config file
 
-```py
+```python
 optimizer = dict(
     type='CustomizedOptim',
     ...

docs/en/tutorials/5_runtime.md

@@ -21,13 +21,13 @@ Workflow is a list of (phase, duration) to specify the running order and duratio
 
 For example, we use epoch-based runner by default, and the "duration" means how many epochs the phase to be executed in a cycle. Usually, we only want to execute training phase, just use the following config.
 
-```py
+```python
 workflow = [('train', 1)]
 ```
 
 Sometimes we may want to check some metrics (e.g. loss, accuracy) about the model on the validate set. In such case, we can set the workflow as
 
-```py
+```python
 [('train', 1), ('val', 1)]
 ```
 

docs/en/tutorials/6_benchmarks.md

@@ -11,7 +11,7 @@ In MMSelfSup, we provide many benchmarks, thus the models can be evaluated on di
   - [Segmentation](#segmentation)
 
 First, you are supposed to extract your backbone weights by `tools/model_converters/extract_backbone_weights.py`
-```
+```shell
 python ./tools/misc/extract_backbone_weights.py {CHECKPOINT} {MODEL_FILE}
 ```
 
@@ -115,11 +115,10 @@ Remarks:
 - `CONFIG`: Use config files under `configs/benchmarks/mmdetection/` or write your own config files
 - `PRETRAIN`: the pretrained model file.
 
-
 Or if you want to do detection task with [detectron2](https://github.com/facebookresearch/detectron2), we also provides some config files.
 Please refer [INSTALL.md](https://github.com/facebookresearch/detectron2/blob/main/INSTALL.md) for installation and follow the [directory structure](https://github.com/facebookresearch/detectron2/tree/main/datasets) to prepare your datasets required by detectron2.
 
-```
+```shell
 conda activate detectron2 # use detectron2 environment here, otherwise use open-mmlab environment
 cd benchmarks/detection
 python convert-pretrain-to-detectron2.py ${WEIGHT_FILE} ${OUTPUT_FILE} # must use .pkl as the output extension.

docs/zh_cn/tutorials/1_new_dataset.md

@@ -28,7 +28,7 @@
 
 假设你基于父类`DataSource` 创建的子类名为 `NewDataSource`， 你可以在`mmselfsup/datasets/data_sources` 目录下创建一个文件，文件名为 `new_data_source.py` ，并在这个文件中实现 `NewDataSource` 创建。
 
-```py
+```python
 import mmcv
 import numpy as np
 
@@ -49,7 +49,7 @@ class NewDataSource(BaseDataSource):
 
 然后， 在 `mmselfsup/dataset/data_sources/__init__.py`中添加`NewDataSource`。
 
-```py
+```python
 from .base import BaseDataSource
 ...
 from .new_data_source import NewDataSource
@@ -63,7 +63,7 @@ __all__ = [
 
 假设你基于父类 `Dataset` 创建的子类名为 `NewDataset`，你可以在`mmselfsup/datasets`目录下创建一个文件，文件名为`new_dataset.py` ，并在这个文件中实现 `NewDataset` 创建。
 
-```py
+```python
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 from mmcv.utils import build_from_cfg
@@ -89,7 +89,7 @@ class NewDataset(BaseDataset):
 
 然后，在 `mmselfsup/dataset/__init__.py`中添加 `NewDataset`。
 
-```py
+```python
 from .base import BaseDataset
 ...
 from .new_dataset import NewDataset
@@ -103,7 +103,7 @@ __all__ = [
 
 为了使用 `NewDataset`，你可以修改配置如下：
 
-```py
+```python
 train=dict(
         type='NewDataset',
         data_source=dict(