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[Fix]: Fix empty link bug (#14)

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2
docs/tutorials/2_data_pipeline.md
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@ -6,7 +6,7 @@
## Overview of `Pipeline`
`DataSource` and `Pipeline` are two important components in `Dataset`. We have introduced `DataSource` in [add_new_dataset](./new_dataset.md). And the `Pipeline` is responsible for applying a series of data augmentations to images, such as random flip.
`DataSource` and `Pipeline` are two important components in `Dataset`. We have introduced `DataSource` in [add_new_dataset](./1_new_dataset.md). And the `Pipeline` is responsible for applying a series of data augmentations to images, such as random flip.
Here is a config example of `Pipeline` for `SimCLR` training:

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docs/tutorials/5_runtime.md
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@ -79,7 +79,7 @@ Some common hooks are not registered through `custom_hooks`, they are
| `EvalHook` | LOW (70) |
| `LoggerHook(s)` | VERY_LOW (90) |
`OptimizerHook`, `MomentumUpdaterHook` and `LrUpdaterHook` have been introduced in [sehedule strategy](./schedule.md). `IterTimerHook` is used to record elapsed time and does not support modification.
`OptimizerHook`, `MomentumUpdaterHook` and `LrUpdaterHook` have been introduced in [sehedule strategy](./4_schedule.md). `IterTimerHook` is used to record elapsed time and does not support modification.
Here we reveal how to customize `CheckpointHook`, `LoggerHooks`, and `EvalHook`.

7
docs/tutorials/6_benchmarks.md
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@ -94,13 +94,13 @@ Remarks:
## Detection
Here we prefer to use MMDetection to do the detection task. First, make sure you have installed `MIM`, which is also a project of OpenMMLab. Please refer to [MIM](https://github.com/open-mmlab/mim/blob/main/docs/installation.md) for installation. Or simply from pypi
Here we prefer to use MMDetection to do the detection task. First, make sure you have installed [MIM](https://github.com/open-mmlab/mim), which is also a project of OpenMMLab.
```shell
pip install openmim
```
It is very easy to install the package.
Besides, please refer to MMDet for [installation](https://github.com/open-mmlab/mmdetection/blob/master/docs/get_started.md) and [data preparation](https://github.com/open-mmlab/mmdetection/blob/master/docs/1_exist_data_model.md)
Besides, please refer to MMDet for [installation](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md) and [data preparation](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/1_exist_data_model.md)
After installation, you can run MMDet with simple command
```shell
@ -127,7 +127,8 @@ bash run.sh ${DET_CFG} ${OUTPUT_FILE}
```
## Segmentation
For semantic segmentation task, we are using MMSegmentation. First, make sure you have installed `MIM`, which is also a project of OpenMMLab. Please refer to [MIM](https://github.com/open-mmlab/mim/blob/main/docs/installation.md) for installation. Or simply from pypi
For semantic segmentation task, we are using MMSegmentation. First, make sure you have installed [MIM](https://github.com/open-mmlab/mim), which is also a project of OpenMMLab.
```shell
pip install openmim
```

10
docs_zh-CN/tutorials/1_new_dataset.md
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@ -1,8 +1,16 @@
# Tutorial 1: Adding New Dataset
In this tutorial, we introduce the basic steps to create your customized dataset:
- [Tutorial 1: Adding New Dataset](#tutorial-1-adding-new-dataset)
- [An example of customized dataset](#an-example-of-customized-dataset)
- [Creating the `DataSource`](#creating-the-datasource)
- [Creating the `Dataset`](#creating-the-dataset)
- [Modify config file](#modify-config-file)
If your algorithm does not need any customized dataset, you can use these off-the-shelf datasets under [datasets](../../mmselfsup/datasets). But to use these existing datasets, you have to convert your dataset to existing dataset format.
## An example of customized dataset
### An example of customized dataset
Assuming the format of your dataset's annotation file is:

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docs_zh-CN/tutorials/2_data_pipeline.md
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@ -1,8 +1,12 @@
# Tutorial 2: Customize Data Pipelines
- [Tutorial 2: Customize Data Pipelines](#tutorial-2-customize-data-pipelines)
- [Overview of `Pipeline`](#overview-of-pipeline)
- [Creating new augmentations in `Pipeline`](#creating-new-augmentations-in-pipeline)
## Overview of `Pipeline`
`DataSource` and `Pipeline` are two important components in `Dataset`. We have introduced `DataSource` in [add_new_dataset](./new_dataset.md). And the `Pipeline` is responsible for applying a series of data augmentations to images, such as random flip.
`DataSource` and `Pipeline` are two important components in `Dataset`. We have introduced `DataSource` in [add_new_dataset](./1_new_dataset.md). And the `Pipeline` is responsible for applying a series of data augmentations to images, such as random flip.
Here is a config example of `Pipeline` for `SimCLR` training:

6
docs_zh-CN/tutorials/3_new_module.md
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@ -1,5 +1,11 @@
# Tutorial 3: Adding New Modules
- [Tutorial 3: Adding New Modules](#tutorial-3-adding-new-modules)
- [Add new backbone](#add-new-backbone)
- [Add new necks](#add-new-necks)
- [Add new loss](#add-new-loss)
- [Combine all](#combine-all)
In self-supervised learning domain, each model can be divided into following four parts:
- backbone: used to extract image's feature

14
docs_zh-CN/tutorials/4_schedule.md
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@ -1,5 +1,17 @@
# Tutorial 4: Customize Schedule
- [Tutorial 4: Customize Schedule](#tutorial-4-customize-schedule)
- [Customize optimizer supported by Pytorch](#customize-optimizer-supported-by-pytorch)
- [Customize learning rate schedules](#customize-learning-rate-schedules)
- [Learning rate decay](#learning-rate-decay)
- [Warmup strategy](#warmup-strategy)
- [Customize momentum schedules](#customize-momentum-schedules)
- [Parameter-wise configuration](#parameter-wise-configuration)
- [Gradient clipping and gradient accumulation](#gradient-clipping-and-gradient-accumulation)
- [Gradient clipping](#gradient-clipping)
- [Gradient accumulation](#gradient-accumulation)
- [Customize self-implemented optimizer](#customize-self-implemented-optimizer)
In this tutorial, we will introduce some methods about how to construct optimizers, customize learning rate, momentum schedules, parameter-wise configuration, gradient clipping, gradient accumulation, and customize self-implemented methods for the project.
## Customize optimizer supported by Pytorch
@ -143,7 +155,7 @@ Here is an example:
```py
data = dict(imgs_per_gpu=64)
optimizer_config = dict(type="GradientCumulativeOptimizerHook", cumulative_iters=4)
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:

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docs_zh-CN/tutorials/5_runtime.md
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@ -1,5 +1,18 @@
# Tutorial 5: Customize Runtime Settings
- [Tutorial 5: Customize Runtime Settings](#tutorial-5-customize-runtime-settings)
- [Customize Workflow](#customize-workflow)
- [Hooks](#hooks)
- [default training hooks](#default-training-hooks)
- [CheckpointHook](#checkpointhook)
- [LoggerHooks](#loggerhooks)
- [EvalHook](#evalhook)
- [Use other implemented hooks](#use-other-implemented-hooks)
- [Customize self-implemented hooks](#customize-self-implemented-hooks)
- [1. Implement a new hook](#1-implement-a-new-hook)
- [2. Import the new hook](#2-import-the-new-hook)
- [3. Modify the config](#3-modify-the-config)
In this tutorial, we will introduce some methods about how to customize workflow and hooks when running your own settings for the project.
## Customize Workflow
@ -38,17 +51,17 @@ The custom hooks are registered through custom_hooks. Generally, they are hooks
Priority list
| Level | Value |
|:--:|:--:|
| HIGHEST | 0 |
| VERY_HIGH | 10 |
| HIGH | 30 |
| ABOVE_NORMAL | 40 |
| NORMAL(default) | 50 |
| BELOW_NORMAL | 60 |
| LOW | 70 |
| VERY_LOW | 90 |
| LOWEST | 100 |
| Level | Value |
| :-------------: | :---: |
| HIGHEST | 0 |
| VERY_HIGH | 10 |
| HIGH | 30 |
| ABOVE_NORMAL | 40 |
| NORMAL(default) | 50 |
| BELOW_NORMAL | 60 |
| LOW | 70 |
| VERY_LOW | 90 |
| LOWEST | 100 |
The priority determines the execution order of the hooks. Before training, the log will print out the execution order of the hooks at each stage to facilitate debugging.
@ -56,17 +69,17 @@ The priority determines the execution order of the hooks. Before training, the l
Some common hooks are not registered through `custom_hooks`, they are
| Hooks | Priority |
|:--:|:--:|
| `LrUpdaterHook` | VERY_HIGH (10) |
| `MomentumUpdaterHook` | HIGH (30) |
| `OptimizerHook` | ABOVE_NORMAL (40) |
| `CheckpointHook` | NORMAL (50) |
| `IterTimerHook` | LOW (70) |
| `EvalHook` | LOW (70) |
| `LoggerHook(s)` | VERY_LOW (90) |
| Hooks | Priority |
| :-------------------: | :---------------: |
| `LrUpdaterHook` | VERY_HIGH (10) |
| `MomentumUpdaterHook` | HIGH (30) |
| `OptimizerHook` | ABOVE_NORMAL (40) |
| `CheckpointHook` | NORMAL (50) |
| `IterTimerHook` | LOW (70) |
| `EvalHook` | LOW (70) |
| `LoggerHook(s)` | VERY_LOW (90) |
`OptimizerHook`, `MomentumUpdaterHook` and `LrUpdaterHook` have been introduced in [sehedule strategy](./schedule.md). `IterTimerHook` is used to record elapsed time and does not support modification.
`OptimizerHook`, `MomentumUpdaterHook` and `LrUpdaterHook` have been introduced in [sehedule strategy](./4_schedule.md). `IterTimerHook` is used to record elapsed time and does not support modification.
Here we reveal how to customize `CheckpointHook`, `LoggerHooks`, and `EvalHook`.

7
docs_zh-CN/tutorials/6_benchmarks.md
View File

@ -94,13 +94,13 @@ Remarks:
## Detection
Here we prefer to use MMDetection to do the detection task. First, make sure you have installed `MIM`, which is also a project of OpenMMLab. Please refer to [MIM](https://github.com/open-mmlab/mim/blob/main/docs/installation.md) for installation. Or simply from pypi
Here we prefer to use MMDetection to do the detection task. First, make sure you have installed [MIM](https://github.com/open-mmlab/mim), which is also a project of OpenMMLab.
```shell
pip install openmim
```
It is very easy to install the package.
Besides, please refer to MMDet for [installation](https://github.com/open-mmlab/mmdetection/blob/master/docs/get_started.md) and [data preparation](https://github.com/open-mmlab/mmdetection/blob/master/docs/1_exist_data_model.md)
Besides, please refer to MMDet for [installation](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md) and [data preparation](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/1_exist_data_model.md)
After installation, you can run MMDet with simple command
```shell
@ -127,7 +127,8 @@ bash run.sh ${DET_CFG} ${OUTPUT_FILE}
```
## Segmentation
For semantic segmentation task, we are using MMSegmentation. First, make sure you have installed `MIM`, which is also a project of OpenMMLab. Please refer to [MIM](https://github.com/open-mmlab/mim/blob/main/docs/installation.md) for installation. Or simply from pypi
For semantic segmentation task, we are using MMSegmentation. First, make sure you have installed [MIM](https://github.com/open-mmlab/mim), which is also a project of OpenMMLab.
```shell
pip install openmim
```