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[Docs] Add training and test docs. (#994) 

* [Docs] Add train and test docs.

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# Training and Test
## Training
### Training with your PC
You can use `tools/train.py` to train a model on a single machine with a CPU and optionally a GPU.
Here is the full usage of the script:
```shell
python tools/train.py ${CONFIG_FILE} [ARGS]
```
````{note}
By default, MMClassification prefers GPU to CPU. If you want to train a model on CPU, please empty `CUDA_VISIBLE_DEVICES` or set it to -1 to make GPU invisible to the program.
```bash
CUDA_VISIBLE_DEVICES=-1 python tools/train.py ${CONFIG_FILE} [ARGS]
```
````
| ARGS | Description |
| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `CONFIG_FILE` | The path to the config file. |
| `--work-dir WORK_DIR` | The target folder to save logs and checkpoints. Defaults to a folder with the same name of the config file under `./work_dirs`. |
| `--resume [RESUME]` | Resume training. If specify a path, resume from it, while if not specify, try to auto resume from the latest checkpoint. |
| `--amp` | Enable automatic-mixed-precision training. |
| `--no-validate` | **Not suggested**. Disable checkpoint evaluation during training. |
| `--auto-scale-lr` | Auto scale the learning rate according to the actual batch size and the original batch size. |
| `--cfg-options CFG_OPTIONS` | Override some settings in the used config, the key-value pair in xxx=yyy format will be merged into the config file. If the value to be overwritten is a list, it should be of the form of either `key="[a,b]"` or `key=a,b`. The argument also allows nested list/tuple values, e.g. `key="[(a,b),(c,d)]"`. Note that the quotation marks are necessary and that no white space is allowed. |
| `--launcher {none,pytorch,slurm,mpi}` | Options for job launcher. |
### Training with multiple GPUs
We provide a shell script to start a multi-GPUs task with `torch.distributed.launch`.
```shell
bash ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [PY_ARGS]
```
| ARGS | Description |
| ------------- | ---------------------------------------------------------------------------------- |
| `CONFIG_FILE` | The path to the config file. |
| `GPU_NUM` | The number of GPUs to be used. |
| `[PY_ARGS]` | The other optional arguments of `tools/train.py`, see [here](#train-with-your-pc). |
You can also specify extra arguments of the launcher by environment variables. For example, change the
communication port of the launcher to 29666 by the below command:
```shell
PORT=29666 bash ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [PY_ARGS]
```
If you want to startup multiple training jobs and use different GPUs, you can launch them by specifying
different ports and visible devices.
```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash ./tools/dist_train.sh ${CONFIG_FILE1} 4 [PY_ARGS]
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 bash ./tools/dist_train.sh ${CONFIG_FILE2} 4 [PY_ARGS]
```
### Training with multiple machines
#### Multiple machines in the same network
If you launch a training job with multiple machines connected with ethernet, you can run the following commands:
On the first machine:
```shell
NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR bash tools/dist_train.sh $CONFIG $GPUS
```
On the second machine:
```shell
NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR bash tools/dist_train.sh $CONFIG $GPUS
```
Comparing with multi-GPUs in a single machine, you need to specify some extra environment variables:
| ENV_VARS | Description |
| ------------- | ---------------------------------------------------------------------------- |
| `NNODES` | The total number of machines. |
| `NODE_RANK` | The index of the local machine. |
| `PORT` | The communication port, it should be the same in all machines. |
| `MASTER_ADDR` | The IP address of the master machine, it should be the same in all machines. |
Usually it is slow if you do not have high speed networking like InfiniBand.
#### Multiple machines managed with slurm
If you run MMClassification on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `tools/slurm_train.sh`.
```shell
[ENV_VARS] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} [PY_ARGS]
```
Here are the arguments description of the script.
| ARGS | Description |
| ------------- | ---------------------------------------------------------------------------------- |
| `PARTITION` | The partition to use in your cluster. |
| `JOB_NAME` | The name of your job, you can name it as you like. |
| `CONFIG_FILE` | The path to the config file. |
| `WORK_DIR` | The target folder to save logs and checkpoints. |
| `[PY_ARGS]` | The other optional arguments of `tools/train.py`, see [here](#train-with-your-pc). |
Here are the environment variables can be used to configure the slurm job.
| ENV_VARS | Description |
| --------------- | ---------------------------------------------------------------------------------------------------------- |
| `GPUS` | The number of GPUs to be used. Defaults to 8. |
| `GPUS_PER_NODE` | The number of GPUs to be allocated per node.. |
| `CPUS_PER_TASK` | The number of CPUs to be allocated per task (Usually one GPU corresponds to one task). Defaults to 5. |
| `SRUN_ARGS` | The other arguments of `srun`. Available options can be found [here](https://slurm.schedmd.com/srun.html). |
## Test
### Test with your PC
You can use `tools/test.py` to test a model on a single machine with a CPU and optionally a GPU.
Here is the full usage of the script:
```shell
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [ARGS]
```
````{note}
By default, MMClassification prefers GPU to CPU. If you want to test a model on CPU, please empty `CUDA_VISIBLE_DEVICES` or set it to -1 to make GPU invisible to the program.
```bash
CUDA_VISIBLE_DEVICES=-1 python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [ARGS]
```
````
| ARGS | Description |
| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `CONFIG_FILE` | The path to the config file. |
| `CHECKPOINT_FILE` | The path to the checkpoint file (It can be a http link, and you can find checkpoints [here](https://mmclassification.readthedocs.io/en/1.x/modelzoo_statistics.html)). |
| `--work-dir WORK_DIR` | The directory to save the file containing evaluation metrics. |
| `--out OUT` | The path to save the file containing evaluation metrics. |
| `--dump DUMP` | The path to dump all outputs of the model for offline evaluation. |
| `--cfg-options CFG_OPTIONS` | Override some settings in the used config, the key-value pair in xxx=yyy format will be merged into the config file. If the value to be overwritten is a list, it should be of the form of either `key="[a,b]"` or `key=a,b`. The argument also allows nested list/tuple values, e.g. `key="[(a,b),(c,d)]"`. Note that the quotation marks are necessary and that no white space is allowed. |
| `--show-dir SHOW_DIR` | The directory to save the result visualization images. |
| `--show` | Visualize the prediction result in a window. |
| `--interval INTERVAL` | The interval of samples to visualize. |
| `--wait-time WAIT_TIME` | The display time of every window (in seconds). Defaults to 1. |
| `--launcher {none,pytorch,slurm,mpi}` | Options for job launcher. |
### Test with multiple GPUs
We provide a shell script to start a multi-GPUs task with `torch.distributed.launch`.
```shell
bash ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]
```
| ARGS | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `CONFIG_FILE` | The path to the config file. |
| `CHECKPOINT_FILE` | The path to the checkpoint file (It can be a http link, and you can find checkpoints [here](https://mmclassification.readthedocs.io/en/1.x/modelzoo_statistics.html)). |
| `GPU_NUM` | The number of GPUs to be used. |
| `[PY_ARGS]` | The other optional arguments of `tools/test.py`, see [here](#test-with-your-pc). |
You can also specify extra arguments of the launcher by environment variables. For example, change the
communication port of the launcher to 29666 by the below command:
```shell
PORT=29666 bash ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]
```
If you want to startup multiple test jobs and use different GPUs, you can launch them by specifying
different port and visible devices.
```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash ./tools/dist_test.sh ${CONFIG_FILE1} ${CHECKPOINT_FILE} 4 [PY_ARGS]
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 bash ./tools/dist_test.sh ${CONFIG_FILE2} ${CHECKPOINT_FILE} 4 [PY_ARGS]
```
### Test with multiple machines
#### Multiple machines in the same network
If you launch a test job with multiple machines connected with ethernet, you can run the following commands:
On the first machine:
```shell
NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR bash tools/dist_test.sh $CONFIG $CHECKPOINT_FILE $GPUS
```
On the second machine:
```shell
NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR bash tools/dist_test.sh $CONFIG $CHECKPOINT_FILE $GPUS
```
Comparing with multi-GPUs in a single machine, you need to specify some extra environment variables:
| ENV_VARS | Description |
| ------------- | ---------------------------------------------------------------------------- |
| `NNODES` | The total number of machines. |
| `NODE_RANK` | The index of the local machine. |
| `PORT` | The communication port, it should be the same in all machines. |
| `MASTER_ADDR` | The IP address of the master machine, it should be the same in all machines. |
Usually it is slow if you do not have high speed networking like InfiniBand.
#### Multiple machines managed with slurm
If you run MMClassification on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `tools/slurm_test.sh`.
```shell
[ENV_VARS] ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${CHECKPOINT_FILE} [PY_ARGS]
```
Here are the arguments description of the script.
| ARGS | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `PARTITION` | The partition to use in your cluster. |
| `JOB_NAME` | The name of your job, you can name it as you like. |
| `CONFIG_FILE` | The path to the config file. |
| `CHECKPOINT_FILE` | The path to the checkpoint file (It can be a http link, and you can find checkpoints [here](https://mmclassification.readthedocs.io/en/1.x/modelzoo_statistics.html)). |
| `[PY_ARGS]` | The other optional arguments of `tools/test.py`, see [here](#test-with-your-pc). |
Here are the environment variables can be used to configure the slurm job.
| ENV_VARS | Description |
| --------------- | ---------------------------------------------------------------------------------------------------------- |
| `GPUS` | The number of GPUs to be used. Defaults to 8. |
| `GPUS_PER_NODE` | The number of GPUs to be allocated per node. |
| `CPUS_PER_TASK` | The number of CPUs to be allocated per task (Usually one GPU corresponds to one task). Defaults to 5. |
| `SRUN_ARGS` | The other arguments of `srun`. Available options can be found [here](https://slurm.schedmd.com/srun.html). |

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# 训练与测试
## 训练
### 单机单卡训练
你可以使用 `tools/train.py` 在电脑上用 CPU 或是 GPU 进行模型的训练。
以下是训练脚本的完整用法：
```shell
python tools/train.py ${CONFIG_FILE} [ARGS]
```
````{note}
默认情况下，MMClassification 会自动调用你的 GPU 进行训练。如果你有 GPU 但仍想使用 CPU 进行训练，请设置环境变量 `CUDA_VISIBLE_DEVICES` 为空或者 -1 来对禁用 GPU。
```bash
CUDA_VISIBLE_DEVICES=-1 python tools/train.py ${CONFIG_FILE} [ARGS]
```
````
| 参数 | 描述 |
| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `CONFIG_FILE` | 配置文件的路径。 |
| `--work-dir WORK_DIR` | 用来保存训练日志和权重文件的文件夹，默认是 `./work_dirs` 目录下，与配置文件同名的文件夹。 |
| `--resume [RESUME]` | 恢复训练。如果指定了权重文件路径，则从指定的权重文件恢复；如果没有指定，则尝试从最新的权重文件进行恢复。 |
| `--amp` | 启用混合精度训练。 |
| `--no-validate` | **不建议** 在训练过程中不进行验证集上的精度验证。 |
| `--auto-scale-lr` | 自动根据实际的批次大小（batch size）和预设的批次大小对学习率进行缩放。 |
| `--cfg-options CFG_OPTIONS` | 重载配置文件中的一些设置。使用类似 `xxx=yyy` 的键值对形式指定，这些设置会被融合入从配置文件读取的配置。你可以使用 `key="[a,b]"` 或者 `key=a,b` 的格式来指定列表格式的值，且支持嵌套，例如 \`key="\[(a,b),(c,d)\]"，这里的引号是不可省略的。另外每个重载项内部不可出现空格。 |
| `--launcher {none,pytorch,slurm,mpi}` | 启动器，默认为 "none"。 |
### 单机多卡训练
我们提供了一个 shell 脚本，可以使用 `torch.distributed.launch` 启动多 GPU 任务。
```shell
bash ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [PY_ARGS]
```
| 参数 | 描述 |
| ------------- | ---------------------------------------------------------------- |
| `CONFIG_FILE` | 配置文件的路径。 |
| `GPU_NUM` | 使用的 GPU 数量。 |
| `[PY_ARGS]` | `tools/train.py` 支持的其他可选参数，参见[上文](#单机单卡训练)。 |
你还可以使用环境变量来指定启动器的额外参数，比如用如下命令将启动器的通讯端口变更为 29666：
```shell
PORT=29666 bash ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [PY_ARGS]
```
如果你希望使用不同的 GPU 进行多项训练任务，可以在启动时指定不同的通讯端口和不同的可用设备。
```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash ./tools/dist_train.sh ${CONFIG_FILE1} 4 [PY_ARGS]
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 bash ./tools/dist_train.sh ${CONFIG_FILE2} 4 [PY_ARGS]
```
### 多机训练
#### 同一网络下的多机
如果你希望使用同一局域网下连接的多台电脑进行一个训练任务，可以使用如下命令：
在第一台机器上：
```shell
NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR bash tools/dist_train.sh $CONFIG $GPUS
```
在第二台机器上：
```shell
NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR bash tools/dist_train.sh $CONFIG $GPUS
```
和单机多卡相比，你需要指定一些额外的环境变量：
| 环境变量 | 描述 |
| ------------- | ---------------------------------------------- |
| `NNODES` | 机器总数。 |
| `NODE_RANK` | 本机的序号 |
| `PORT` | 通讯端口，它在所有机器上都应当是一致的。 |
| `MASTER_ADDR` | 主机的 IP 地址，它在所有机器上都应当是一致的。 |
通常来说，如果这几台机器之间不是高速网络连接，训练速度会非常慢。
#### Slurm 管理下的多机集群
如果你在 [slurm](https://slurm.schedmd.com/) 集群上，可以使用 `tools/slurm_train.sh` 脚本启动任务。
```shell
[ENV_VARS] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} [PY_ARGS]
```
这里是该脚本的一些参数：
| 参数 | 描述 |
| ------------- | ---------------------------------------------------------------- |
| `PARTITION` | 使用的集群分区。 |
| `JOB_NAME` | 任务的名称，你可以随意起一个名字。 |
| `CONFIG_FILE` | 配置文件路径。 |
| `WORK_DIR` | 用以保存日志和权重文件的文件夹。 |
| `[PY_ARGS]` | `tools/train.py` 支持的其他可选参数，参见[上文](#单机单卡训练)。 |
这里是一些你可以用来配置 slurm 任务的环境变量：
| 环境变量 | 描述 |
| --------------- | ------------------------------------------------------------------------------------------ |
| `GPUS` | 使用的 GPU 总数，默认为 8。 |
| `GPUS_PER_NODE` | 每个节点分配的 GPU 数，你可以根据节点情况指定。默认为 8。 |
| `CPUS_PER_TASK` | 每个任务分配的 CPU 数（通常一个 GPU 对应一个任务）。默认为 5。 |
| `SRUN_ARGS` | `srun` 命令支持的其他参数。可用的选项参见[官方文档](https://slurm.schedmd.com/srun.html)。 |
## 测试
### 单机单卡测试
你可以使用 `tools/test.py` 在电脑上用 CPU 或是 GPU 进行模型的测试。
以下是测试脚本的完整用法：
```shell
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [ARGS]
```
````{note}
默认情况下，MMClassification 会自动调用你的 GPU 进行测试。如果你有 GPU 但仍想使用 CPU 进行测试，请设置环境变量 `CUDA_VISIBLE_DEVICES` 为空或者 -1 来对禁用 GPU。
```bash
CUDA_VISIBLE_DEVICES=-1 python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [ARGS]
```
````
| 参数 | 描述 |
| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `CONFIG_FILE` | 配置文件的路径。 |
| `CHECKPOINT_FILE` | 权重文件路径（支持 http 链接，你可以在[这里](https://mmclassification.readthedocs.io/en/1.x/modelzoo_statistics.html)寻找需要的权重文件）。 |
| `--work-dir WORK_DIR` | 用来保存测试指标结果的文件夹。 |
| `--out OUT` | 用来保存测试指标结果的文件。 |
| `--dump DUMP` | 用来保存所有模型输出的文件，这些数据可以用于离线测评。 |
| `--cfg-options CFG_OPTIONS` | 重载配置文件中的一些设置。使用类似 `xxx=yyy` 的键值对形式指定，这些设置会被融合入从配置文件读取的配置。你可以使用 `key="[a,b]"` 或者 `key=a,b` 的格式来指定列表格式的值，且支持嵌套，例如 \`key="\[(a,b),(c,d)\]"，这里的引号是不可省略的。另外每个重载项内部不可出现空格。 |
| `--show-dir SHOW_DIR` | 用于保存可视化预测结果图像的文件夹。 |
| `--show` | 在窗口中显示预测结果图像。 |
| `--interval INTERVAL` | 每隔多少样本进行一次预测结果可视化。 |
| `--wait-time WAIT_TIME` | 每个窗口的显示时间（单位为秒）。 |
| `--launcher {none,pytorch,slurm,mpi}` | 启动器，默认为 "none"。 |
### 单机多卡测试
我们提供了一个 shell 脚本，可以使用 `torch.distributed.launch` 启动多 GPU 任务。
```shell
bash ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]
```
| 参数 | 描述 |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| `CONFIG_FILE` | 配置文件的路径。 |
| `CHECKPOINT_FILE` | 权重文件路径（支持 http 链接，你可以在[这里](https://mmclassification.readthedocs.io/en/1.x/modelzoo_statistics.html)寻找需要的权重文件）。 |
| `GPU_NUM` | 使用的 GPU 数量。 |
| `[PY_ARGS]` | `tools/test.py` 支持的其他可选参数，参见[上文](#单机单卡测试)。 |
你还可以使用环境变量来指定启动器的额外参数，比如用如下命令将启动器的通讯端口变更为 29666：
```shell
PORT=29666 bash ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]
```
如果你希望使用不同的 GPU 进行多项测试任务，可以在启动时指定不同的通讯端口和不同的可用设备。
```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash ./tools/dist_test.sh ${CONFIG_FILE1} ${CHECKPOINT_FILE} 4 [PY_ARGS]
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 bash ./tools/dist_test.sh ${CONFIG_FILE2} ${CHECKPOINT_FILE} 4 [PY_ARGS]
```
### 多机测试
#### 同一网络下的多机
如果你希望使用同一局域网下连接的多台电脑进行一个测试任务，可以使用如下命令：
在第一台机器上：
```shell
NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR bash tools/dist_test.sh $CONFIG $CHECKPOINT_FILE $GPUS
```
在第二台机器上：
```shell
NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR bash tools/dist_test.sh $CONFIG $CHECKPOINT_FILE $GPUS
```
和单机多卡相比，你需要指定一些额外的环境变量：
| 环境变量 | 描述 |
| ------------- | ---------------------------------------------- |
| `NNODES` | 机器总数。 |
| `NODE_RANK` | 本机的序号 |
| `PORT` | 通讯端口，它在所有机器上都应当是一致的。 |
| `MASTER_ADDR` | 主机的 IP 地址，它在所有机器上都应当是一致的。 |
#### Slurm 管理下的多机集群
如果你在 [slurm](https://slurm.schedmd.com/) 集群上，可以使用 `tools/slurm_test.sh` 脚本启动任务。
```shell
[ENV_VARS] ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${CHECKPOINT_FILE} [PY_ARGS]
```
这里是该脚本的一些参数：
| 参数 | 描述 |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| `PARTITION` | 使用的集群分区。 |
| `JOB_NAME` | 任务的名称，你可以随意起一个名字。 |
| `CONFIG_FILE` | 配置文件路径。 |
| `CHECKPOINT_FILE` | 权重文件路径（支持 http 链接，你可以在[这里](https://mmclassification.readthedocs.io/en/1.x/modelzoo_statistics.html)寻找需要的权重文件）。 |
| `[PY_ARGS]` | `tools/test.py` 支持的其他可选参数，参见[上文](#单机单卡测试)。 |
这里是一些你可以用来配置 slurm 任务的环境变量：
| 环境变量 | 描述 |
| --------------- | ------------------------------------------------------------------------------------------ |
| `GPUS` | 使用的 GPU 总数，默认为 8。 |
| `GPUS_PER_NODE` | 每个节点分配的 GPU 数，你可以根据节点情况指定。默认为 8。 |
| `CPUS_PER_TASK` | 每个任务分配的 CPU 数（通常一个 GPU 对应一个任务）。默认为 5。 |
| `SRUN_ARGS` | `srun` 命令支持的其他参数。可用的选项参见[官方文档](https://slurm.schedmd.com/srun.html)。 |