[MMSIG] [Doc] Update data_preprocessor.md (#2055)

[Doc] Update data_preprocessor.md

docs/en/advanced_guides/data_preprocessor.md

@@ -1,5 +1,45 @@
-# Data pre-processor \[Coming Soon!\]
+# Data pre-processor
 
-We're improving this documentation. Don't hesitate to join us!
+## The position of the data preprocessor in the training pipeline.
 
-[Make a pull request](https://github.com/open-mmlab/mmagic/compare) or [discuss with us](https://github.com/open-mmlab/mmagic/discussions/1429)!
+During the model training process, image data undergoes data augmentation using the transforms provided by mmcv. The augmented data is then loaded into a dataloader. Subsequently, a preprocessor is used to move the data from the CPU to CUDA (GPU), perform padding, and normalize the data.
+
+Below is an example of the `train_pipeline` in the complete configuration file using `configs/_base_/datasets/unpaired_imgs_256x256.py`. The train_pipeline typically defines a sequence of transformations applied to training images using the mmcv library. This pipeline is designed to prevent redundancy in the transformation functions across different downstream algorithm libraries.
+
+```python
+...
+train_pipeline = [
+    dict(color_type='color', key='img_A', type='LoadImageFromFile'),
+    dict(color_type='color', key='img_B', type='LoadImageFromFile'),
+    dict(auto_remap=True, mapping=dict(img=['img_A', 'img_B',]),
+        share_random_params=True,
+        transforms=[dict(interpolation='bicubic', scale=(286, 286,), type='Resize'),
+                    dict(crop_size=(256, 256,), keys=['img',], random_crop=True, type='Crop'),],
+        type='TransformBroadcaster'),
+    dict(direction='horizontal', keys=['img_A', ], type='Flip'),
+    dict(direction='horizontal', keys=['img_B', ], type='Flip'),
+    dict(mapping=dict(img_mask='img_B', img_photo='img_A'),
+        remapping=dict(img_mask='img_mask', img_photo='img_photo'),
+        type='KeyMapper'),
+    dict(data_keys=['img_photo', 'img_mask',],
+        keys=['img_photo', 'img_mask',], type='PackInputs'),
+]
+...
+```
+
+In the `train_step` function in the `mmagic/models/editors/cyclegan/cyclegan.py` script, the data preprocessing steps involve moving, concatenating, and normalizing the transformed data before feeding it into the neural network. Below is an example of the relevant code logic:
+
+```python
+...
+message_hub = MessageHub.get_current_instance()
+curr_iter = message_hub.get_info('iter')
+data = self.data_preprocessor(data, True)
+disc_optimizer_wrapper = optim_wrapper['discriminators']
+
+inputs_dict = data['inputs']
+outputs, log_vars = dict(), dict()
+...
+```
+
+In mmagic, the code implementation for the data processor is located at `mmagic/models/data_preprocessors/data_preprocessor.py`. The data processing workflow is as follows:
+![image](https://github.com/jinxianwei/CloudImg/assets/81373517/f52a92ab-f86d-486d-86ac-a2f388a83ced)

docs/zh_cn/advanced_guides/data_preprocessor.md

@@ -1 +1,45 @@
-# 数据预处理器（待更新）
+# 数据预处理器
+
+## 数据preprocessor在训练流程中的位置
+
+在模型训练过程中，图片数据先通过mmcv中的transform进行数据增强，并加载为dataloader，而后通过preprocessor将数据从cpu搬运到cuda上，并进行padding和归一化
+
+mmcv中的transform来自各下游算法库中transform的迁移，防止各下游算法库中transform的冗余，以`configs/_base_/datasets/unpaired_imgs_256x256.py`为例，其完整config中的`train_pipeline`如下所示
+
+```python
+...
+train_pipeline = [
+    dict(color_type='color', key='img_A', type='LoadImageFromFile'),
+    dict(color_type='color', key='img_B', type='LoadImageFromFile'),
+    dict(auto_remap=True, mapping=dict(img=['img_A', 'img_B',]),
+        share_random_params=True,
+        transforms=[dict(interpolation='bicubic', scale=(286, 286,), type='Resize'),
+                    dict(crop_size=(256, 256,), keys=['img',], random_crop=True, type='Crop'),],
+        type='TransformBroadcaster'),
+    dict(direction='horizontal', keys=['img_A', ], type='Flip'),
+    dict(direction='horizontal', keys=['img_B', ], type='Flip'),
+    dict(mapping=dict(img_mask='img_B', img_photo='img_A'),
+        remapping=dict(img_mask='img_mask', img_photo='img_photo'),
+        type='KeyMapper'),
+    dict(data_keys=['img_photo', 'img_mask',],
+        keys=['img_photo', 'img_mask',], type='PackInputs'),
+]
+...
+```
+
+data_preprocessor会对transform后的数据进行数据搬移，拼接和归一化，而后输入到网络中，以`mmagic/models/editors/cyclegan/cyclegan.py`中的`train_step`函数为例，代码中的引用逻辑如下
+
+```python
+...
+message_hub = MessageHub.get_current_instance()
+curr_iter = message_hub.get_info('iter')
+data = self.data_preprocessor(data, True)
+disc_optimizer_wrapper = optim_wrapper['discriminators']
+
+inputs_dict = data['inputs']
+outputs, log_vars = dict(), dict()
+...
+```
+
+在mmagic中的data_processor，其代码实现路径为`mmagic/models/data_preprocessors/data_preprocessor.py`，其数据处理流程如下图
+![image](https://github.com/jinxianwei/CloudImg/assets/81373517/f52a92ab-f86d-486d-86ac-a2f388a83ced)