[Doc] Refine MMSegmentation documentation (#2668)

open-mmlab · Mar 3, 2023 · aaa08dc · aaa08dc
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diff --git a/docs/en/advanced_guides/add_dataset.md → docs/en/advanced_guides/add_datasets.md b/docs/en/advanced_guides/add_dataset.md → docs/en/advanced_guides/add_datasets.md
@@ -1,4 +1,4 @@
-# Add New Datasets
+# \[WIP\] Add New Datasets
 
 ## Customize datasets by reorganizing data
 

diff --git a/docs/en/advanced_guides/add_metrics.md b/docs/en/advanced_guides/add_metrics.md
@@ -0,0 +1 @@
+# Add New Metrics
diff --git a/docs/en/advanced_guides/add_transform.md b/docs/en/advanced_guides/add_transform.md
diff --git a/docs/en/advanced_guides/add_transforms.md b/docs/en/advanced_guides/add_transforms.md
@@ -0,0 +1,52 @@
+# Adding New Data Transforms
+
+## Customization data transformation
+
+The customized data transformation must inherited from `BaseTransform` and implement `transform` function.
+Here we use a simple flipping transformation as example:
+
+```python
+import random
+import mmcv
+from mmcv.transforms import BaseTransform, TRANSFORMS
+
+@TRANSFORMS.register_module()
+class MyFlip(BaseTransform):
+ def __init__(self, direction: str):
+ super().__init__()
+ self.direction = direction
+
+ def transform(self, results: dict) -> dict:
+ img = results['img']
+ results['img'] = mmcv.imflip(img, direction=self.direction)
+ return results
+```
+
+Moreover, import the new class.
+
+```python
+from .my_pipeline import MyFlip
+```
+
+Thus, we can instantiate a `MyFlip` object and use it to process the data dict.
+
+```python
+import numpy as np
+
+transform = MyFlip(direction='horizontal')
+data_dict = {'img': np.random.rand(224, 224, 3)}
+data_dict = transform(data_dict)
+processed_img = data_dict['img']
+```
+
+Or, we can use `MyFlip` transformation in data pipeline in our config file.
+
+```python
+pipeline = [
+ ...
+ dict(type='MyFlip', direction='horizontal'),
+ ...
+]
+```
+
+Note that if you want to use `MyFlip` in config, you must ensure the file containing `MyFlip` is imported during runtime.
diff --git a/docs/en/advanced_guides/evaluation.md b/docs/en/advanced_guides/evaluation.md
@@ -81,7 +81,7 @@ The arguments of the constructor:
 - `process` method processes one batch of data and data_samples.
 - `compute_metrics` method computes the metrics from processed results.
 
-#### IoUMetric.process
+### IoUMetric.process
 
 Parameters:
 
@@ -92,7 +92,7 @@ Returns:
 
 This method doesn't have returns since the processed results would be stored in `self.results`, which will be used to compute the metrics when all batches have been processed.
 
-#### IoUMetric.compute_metrics
+### IoUMetric.compute_metrics
 
 Parameters:
 

diff --git a/docs/en/advanced_guides/index.rst b/docs/en/advanced_guides/index.rst
@@ -19,7 +19,7 @@ Component Customization
 .. toctree::
  :maxdepth: 1
 
- add_modules.md
+ add_models.md
  add_datasets.md
  add_transforms.md
  add_metrics.md

diff --git a/docs/en/advanced_guides/training_tricks.md b/docs/en/advanced_guides/training_tricks.md
@@ -1,4 +1,4 @@
-# Training Tricks
+# \[WIP\] Training Tricks
 
 MMSegmentation support following training tricks out of box.
 

diff --git a/docs/en/advanced_guides/transforms.md b/docs/en/advanced_guides/transforms.md
@@ -6,7 +6,9 @@ The structure of this guide is as follows:
 
 - [Data Transforms](#data-transforms)
  - [Design of Data pipelines](#design-of-data-pipelines)
- - [Customization data transformation](#customization-data-transformation)
+ - [Data loading](#data-loading)
+ - [Pre-processing](#pre-processing)
+ - [Formatting](#formatting)
 
 ## Design of Data pipelines
 
@@ -125,48 +127,3 @@ The position of random contrast is in second or second to last(mode 0 or 1 below
 
 - add: `inputs`, `data_sample`
 - remove: keys specified by `meta_keys` (merged into the metainfo of data_sample), all other keys
-
-## Customization data transformation
-
-The customized data transformation must inherited from `BaseTransform` and implement `transform` function.
-Here we use a simple flipping transformation as example:
-
-```python
-import random
-import mmcv
-from mmcv.transforms import BaseTransform, TRANSFORMS
-
-@TRANSFORMS.register_module()
-class MyFlip(BaseTransform):
- def __init__(self, direction: str):
- super().__init__()
- self.direction = direction
-
- def transform(self, results: dict) -> dict:
- img = results['img']
- results['img'] = mmcv.imflip(img, direction=self.direction)
- return results
-```
-
-Thus, we can instantiate a `MyFlip` object and use it to process the data dict.
-
-```python
-import numpy as np
-
-transform = MyFlip(direction='horizontal')
-data_dict = {'img': np.random.rand(224, 224, 3)}
-data_dict = transform(data_dict)
-processed_img = data_dict['img']
-```
-
-Or, we can use `MyFlip` transformation in data pipeline in our config file.
-
-```python
-pipeline = [
- ...
- dict(type='MyFlip', direction='horizontal'),
- ...
-]
-```
-
-Note that if you want to use `MyFlip` in config, you must ensure the file containing `MyFlip` is imported during runtime.
diff --git a/docs/en/api.rst b/docs/en/api.rst
@@ -11,11 +11,6 @@ datasets
 .. automodule:: mmseg.datasets
  :members:
 
-samplers
-^^^^^^^^^^
-.. automodule:: mmseg.datasets.samplers
- :members:
-
 transforms
 ^^^^^^^^^^^^
 .. automodule:: mmseg.datasets.transforms
@@ -35,7 +30,7 @@ optimizers
  :members:
 
 mmseg.evaluation
---------------
+-----------------
 
 metrics
 ^^^^^^^^^^

diff --git a/docs/en/modelzoo_statistics.md b/docs/en/modelzoo_statistics.md
@@ -0,0 +1,102 @@
+# Model Zoo Statistics
+
+- Number of papers: 47
+
+ - ALGORITHM: 36
+ - BACKBONE: 11
+
+- Number of checkpoints: 612
+
+ - \[ALGORITHM\] [ANN](https:/open-mmlab/mmsegmentation/blob/master/configs/ann) (16 ckpts)
+
+ - \[ALGORITHM\] [APCNet](https:/open-mmlab/mmsegmentation/blob/master/configs/apcnet) (12 ckpts)
+
+ - \[BACKBONE\] [BEiT](https:/open-mmlab/mmsegmentation/blob/master/configs/beit) (2 ckpts)
+
+ - \[ALGORITHM\] [BiSeNetV1](https:/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1) (11 ckpts)
+
+ - \[ALGORITHM\] [BiSeNetV2](https:/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2) (4 ckpts)
+
+ - \[ALGORITHM\] [CCNet](https:/open-mmlab/mmsegmentation/blob/master/configs/ccnet) (16 ckpts)
+
+ - \[ALGORITHM\] [CGNet](https:/open-mmlab/mmsegmentation/blob/master/configs/cgnet) (2 ckpts)
+
+ - \[BACKBONE\] [ConvNeXt](https:/open-mmlab/mmsegmentation/blob/master/configs/convnext) (6 ckpts)
+
+ - \[ALGORITHM\] [DANet](https:/open-mmlab/mmsegmentation/blob/master/configs/danet) (16 ckpts)
+
+ - \[ALGORITHM\] [DeepLabV3](https:/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3) (41 ckpts)
+
+ - \[ALGORITHM\] [DeepLabV3+](https:/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus) (42 ckpts)
+
+ - \[ALGORITHM\] [DMNet](https:/open-mmlab/mmsegmentation/blob/master/configs/dmnet) (12 ckpts)
+
+ - \[ALGORITHM\] [DNLNet](https:/open-mmlab/mmsegmentation/blob/master/configs/dnlnet) (12 ckpts)
+
+ - \[ALGORITHM\] [DPT](https:/open-mmlab/mmsegmentation/blob/master/configs/dpt) (1 ckpts)
+
+ - \[ALGORITHM\] [EMANet](https:/open-mmlab/mmsegmentation/blob/master/configs/emanet) (4 ckpts)
+
+ - \[ALGORITHM\] [EncNet](https:/open-mmlab/mmsegmentation/blob/master/configs/encnet) (12 ckpts)
+
+ - \[ALGORITHM\] [ERFNet](https:/open-mmlab/mmsegmentation/blob/master/configs/erfnet) (1 ckpts)
+
+ - \[ALGORITHM\] [FastFCN](https:/open-mmlab/mmsegmentation/blob/master/configs/fastfcn) (12 ckpts)
+
+ - \[ALGORITHM\] [Fast-SCNN](https:/open-mmlab/mmsegmentation/blob/master/configs/fastscnn) (1 ckpts)
+
+ - \[ALGORITHM\] [FCN](https:/open-mmlab/mmsegmentation/blob/master/configs/fcn) (41 ckpts)
+
+ - \[ALGORITHM\] [GCNet](https:/open-mmlab/mmsegmentation/blob/master/configs/gcnet) (16 ckpts)
+
+ - \[BACKBONE\] [HRNet](https:/open-mmlab/mmsegmentation/blob/master/configs/hrnet) (37 ckpts)
+
+ - \[ALGORITHM\] [ICNet](https:/open-mmlab/mmsegmentation/blob/master/configs/icnet) (12 ckpts)
+
+ - \[ALGORITHM\] [ISANet](https:/open-mmlab/mmsegmentation/blob/master/configs/isanet) (16 ckpts)
+
+ - \[ALGORITHM\] [K-Net](https:/open-mmlab/mmsegmentation/blob/master/configs/knet) (7 ckpts)
+
+ - \[BACKBONE\] [MAE](https:/open-mmlab/mmsegmentation/blob/master/configs/mae) (1 ckpts)
+
+ - \[ALGORITHM\] [Mask2Former](https:/open-mmlab/mmsegmentation/blob/master/configs/mask2former) (13 ckpts)
+
+ - \[ALGORITHM\] [MaskFormer](https:/open-mmlab/mmsegmentation/blob/master/configs/maskformer) (4 ckpts)
+
+ - \[BACKBONE\] [MobileNetV2](https:/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2) (8 ckpts)
+
+ - \[BACKBONE\] [MobileNetV3](https:/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v3) (4 ckpts)
+
+ - \[ALGORITHM\] [NonLocal Net](https:/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net) (16 ckpts)
+
+ - \[ALGORITHM\] [OCRNet](https:/open-mmlab/mmsegmentation/blob/master/configs/ocrnet) (24 ckpts)
+
+ - \[ALGORITHM\] [PointRend](https:/open-mmlab/mmsegmentation/blob/master/configs/point_rend) (4 ckpts)
+
+ - \[BACKBONE\] [PoolFormer](https:/open-mmlab/mmsegmentation/blob/master/configs/poolformer) (5 ckpts)
+
+ - \[ALGORITHM\] [PSANet](https:/open-mmlab/mmsegmentation/blob/master/configs/psanet) (16 ckpts)
+
+ - \[ALGORITHM\] [PSPNet](https:/open-mmlab/mmsegmentation/blob/master/configs/pspnet) (54 ckpts)
+
+ - \[BACKBONE\] [ResNeSt](https:/open-mmlab/mmsegmentation/blob/master/configs/resnest) (8 ckpts)
+
+ - \[ALGORITHM\] [SegFormer](https:/open-mmlab/mmsegmentation/blob/master/configs/segformer) (13 ckpts)
+
+ - \[ALGORITHM\] [Segmenter](https:/open-mmlab/mmsegmentation/blob/master/configs/segmenter) (5 ckpts)
+
+ - \[ALGORITHM\] [Semantic FPN](https:/open-mmlab/mmsegmentation/blob/master/configs/sem_fpn) (4 ckpts)
+
+ - \[ALGORITHM\] [SETR](https:/open-mmlab/mmsegmentation/blob/master/configs/setr) (7 ckpts)
+
+ - \[ALGORITHM\] [STDC](https:/open-mmlab/mmsegmentation/blob/master/configs/stdc) (4 ckpts)
+
+ - \[BACKBONE\] [Swin Transformer](https:/open-mmlab/mmsegmentation/blob/master/configs/swin) (6 ckpts)
+
+ - \[BACKBONE\] [Twins](https:/open-mmlab/mmsegmentation/blob/master/configs/twins) (12 ckpts)
+
+ - \[ALGORITHM\] [UNet](https:/open-mmlab/mmsegmentation/blob/master/configs/unet) (25 ckpts)
+
+ - \[ALGORITHM\] [UPerNet](https:/open-mmlab/mmsegmentation/blob/master/configs/upernet) (16 ckpts)
+
+ - \[BACKBONE\] [Vision Transformer](https:/open-mmlab/mmsegmentation/blob/master/configs/vit) (11 ckpts)
diff --git a/docs/en/notes/faq.md b/docs/en/notes/faq.md
@@ -1,4 +1,4 @@
-# Frequently Asked Questions (FAQ)
+# \[WIP\] Frequently Asked Questions (FAQ)
 
 We list some common troubles faced by many users and their corresponding solutions here. Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them. If the contents here do not cover your issue, please create an issue using the [provided templates](https:/open-mmlab/mmsegmentation/blob/master/.github/ISSUE_TEMPLATE/error-report.md/) and make sure you fill in all required information in the template.
 

diff --git a/docs/en/user_guides/deployment.md b/docs/en/user_guides/deployment.md
@@ -1,4 +1,4 @@
-# Deployment
+# \[WIP\] Deployment
 
 > ## [Try the new MMDeploy to deploy your model](https://mmdeploy.readthedocs.io/)
 

diff --git a/docs/en/user_guides/useful_tools.md b/docs/en/user_guides/useful_tools.md
@@ -1,4 +1,4 @@
-# Useful Tools
+# \[WIP\] Useful Tools
 
 Apart from training/testing scripts, We provide lots of useful tools under the
 `tools/` directory.

diff --git a/docs/zh_cn/advanced_guides/add_datasets.md b/docs/zh_cn/advanced_guides/add_datasets.md
@@ -1,4 +1,4 @@
-# 自定义数据集（待更新）
+# 新增自定义数据集（待更新）
 
 ## 通过重新组织数据来定制数据集
 

diff --git a/docs/zh_cn/advanced_guides/add_metric.md b/docs/zh_cn/advanced_guides/add_metric.md
diff --git a/docs/zh_cn/advanced_guides/add_metrics.md b/docs/zh_cn/advanced_guides/add_metrics.md
@@ -0,0 +1 @@
+# 新增评测指标 (待更新)
diff --git a/docs/zh_cn/advanced_guides/add_models.md b/docs/zh_cn/advanced_guides/add_models.md
@@ -0,0 +1,3 @@
+# 新增模块（待更新）
+
+中文版文档支持中，请先阅读[英文版本](../../en/advanced_guides/add_models.md)