# Getting Started

This page provides basic tutorials about the usage of mmdetection.
For installation instructions, please see [INSTALL.md](INSTALL.md).

## Inference with pretrained models

We provide testing scripts to evaluate a whole dataset (COCO, PASCAL VOC, etc.),
and also some high-level apis for easier integration to other projects.

### Test a dataset

- [x] single GPU testing
- [x] multiple GPU testing
- [x] visualize detection results

You can use the following commands to test a dataset.

```shell
# single-gpu testing
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]

# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
```

Optional arguments:
- `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.
- `EVAL_METRICS`: Items to be evaluated on the results. Allowed values are: `proposal_fast`, `proposal`, `bbox`, `segm`, `keypoints`.
- `--show`: If specified, detection results will be ploted on the images and shown in a new window. (Only applicable for single GPU testing.)

Examples:

Assume that you have already downloaded the checkpoints to `checkpoints/`.

1. Test Faster R-CNN and show the results.

```shell
python tools/test.py configs/faster_rcnn_r50_fpn_1x.py \
    checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth \
    --show
```

2. Test Mask R-CNN and evaluate the bbox and mask AP.

```shell
python tools/test.py configs/mask_rcnn_r50_fpn_1x.py \
    checkpoints/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth \
    --out results.pkl --eval bbox segm
```

3. Test Mask R-CNN with 8 GPUs, and evaluate the bbox and mask AP.

```shell
./tools/dist_test.sh configs/mask_rcnn_r50_fpn_1x.py \
    checkpoints/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth \
    8 --out results.pkl --eval bbox segm
```

### Webcam demo

We provide a webcam demo to illustrate the results.

```shell
python tools/webcam_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--camera-id ${CAMERA-ID}] [--score-thr ${CAMERA-ID}]
```

Examples:

```shell
python tools/webcam_demo.py configs/faster_rcnn_r50_fpn_1x.py \
    checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth
```

### High-level APIs for testing images

Here is an example of building the model and test given images.

```python
from mmdet.apis import init_detector, inference_detector, show_result
import mmcv

config_file = 'configs/faster_rcnn_r50_fpn_1x.py'
checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth'

# build the model from a config file and a checkpoint file
model = init_detector(config_file, checkpoint_file, device='cuda:0')

# test a single image and show the results
img = 'test.jpg'  # or img = mmcv.imread(img), which will only load it once
result = inference_detector(model, img)
show_result(img, result, model.CLASSES)

# test a list of images and write the results to image files
imgs = ['test1.jpg', 'test2.jpg']
for i, result in enumerate(inference_detector(model, imgs)):
    show_result(imgs[i], result, model.CLASSES, out_file='result_{}.jpg'.format(i))

# test a video and show the results
video = mmcv.VideoReader('video.mp4')
for frame in video:
    result = inference_detector(model, frame)
    show_result(frame, result, model.CLASSES, wait_time=1)
```


## Train a model

mmdetection implements distributed training and non-distributed training,
which uses `MMDistributedDataParallel` and `MMDataParallel` respectively.

All outputs (log files and checkpoints) will be saved to the working directory,
which is specified by `work_dir` in the config file.

**\*Important\***: The default learning rate in config files is for 8 GPUs and 2 img/gpu (batch size = 8*2 = 16).
According to the [Linear Scaling Rule](https://arxiv.org/abs/1706.02677), you need to set the learning rate proportional to the batch size if you use different GPUs or images per GPU, e.g., lr=0.01 for 4 GPUs * 2 img/gpu and lr=0.08 for 16 GPUs * 4 img/gpu.

### Train with a single GPU

```shell
python tools/train.py ${CONFIG_FILE}
```

If you want to specify the working directory in the command, you can add an argument `--work_dir ${YOUR_WORK_DIR}`.

### Train with multiple GPUs

```shell
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
```

Optional arguments are:

- `--validate` (**strongly recommended**): Perform evaluation at every k (default value is 1, which can be modified like [this](configs/mask_rcnn_r50_fpn_1x.py#L174)) epochs during the training.
- `--work_dir ${WORK_DIR}`: Override the working directory specified in the config file.
- `--resume_from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.

Difference between `resume_from` and `load_from`:
`resume_from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally.
`load_from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning.

### Train with multiple machines

If you run mmdetection on a cluster managed with [slurm](https://slurm.schedmd.com/), you can just use the script `slurm_train.sh`.

```shell
./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} [${GPUS}]
```

Here is an example of using 16 GPUs to train Mask R-CNN on the dev partition.

```shell
./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x.py /nfs/xxxx/mask_rcnn_r50_fpn_1x 16
```

You can check [slurm_train.sh](tools/slurm_train.sh) for full arguments and environment variables.

If you have just multiple machines connected with ethernet, you can refer to
pytorch [launch utility](https://pytorch.org/docs/stable/distributed_deprecated.html#launch-utility).
Usually it is slow if you do not have high speed networking like infiniband.


## How-to

### Use my own datasets

The simplest way is to convert your dataset to existing dataset formats (COCO or PASCAL VOC).

Here we show an example of adding a custom dataset of 5 classes, assuming it is also in COCO format.

In `mmdet/datasets/my_dataset.py`:

```python
from .coco import CocoDataset
from .registry import DATASETS


@DATASETS.register_module
class MyDataset(CocoDataset):

    CLASSES = ('a', 'b', 'c', 'd', 'e')
```

In `mmdet/datasets/__init__.py`:

```python
from .my_dataset import MyDataset
```

Then you can use `MyDataset` in config files, with the same API as CocoDataset.


It is also fine if you do not want to convert the annotation format to COCO or PASCAL format.
Actually, we define a simple annotation format and all existing datasets are
processed to be compatible with it, either online or offline.

The annotation of a dataset is a list of dict, each dict corresponds to an image.
There are 3 field `filename` (relative path), `width`, `height` for testing,
and an additional field `ann` for training. `ann` is also a dict containing at least 2 fields:
`bboxes` and `labels`, both of which are numpy arrays. Some datasets may provide
annotations like crowd/difficult/ignored bboxes, we use `bboxes_ignore` and `labels_ignore`
to cover them.

Here is an example.
```
[
    {
        'filename': 'a.jpg',
        'width': 1280,
        'height': 720,
        'ann': {
            'bboxes': <np.ndarray, float32> (n, 4),
            'labels': <np.ndarray, int64> (n, ),
            'bboxes_ignore': <np.ndarray, float32> (k, 4),
            'labels_ignore': <np.ndarray, int64> (k, ) (optional field)
        }
    },
    ...
]
```

There are two ways to work with custom datasets.

- online conversion

  You can write a new Dataset class inherited from `CustomDataset`, and overwrite two methods
  `load_annotations(self, ann_file)` and `get_ann_info(self, idx)`,
  like [CocoDataset](mmdet/datasets/coco.py) and [VOCDataset](mmdet/datasets/voc.py).

- offline conversion

  You can convert the annotation format to the expected format above and save it to
  a pickle or json file, like [pascal_voc.py](tools/convert_datasets/pascal_voc.py).
  Then you can simply use `CustomDataset`.

### Develop new components

We basically categorize model components into 4 types.

- backbone: usually an FCN network to extract feature maps, e.g., ResNet, MobileNet.
- neck: the component between backbones and heads, e.g., FPN, PAFPN.
- head: the component for specific tasks, e.g., bbox prediction and mask prediction.
- roi extractor: the part for extracting RoI features from feature maps, e.g., RoI Align.

Here we show how to develop new components with an example of MobileNet.

1. Create a new file `mmdet/models/backbones/mobilenet.py`.

```python
import torch.nn as nn

from ..registry import BACKBONES


@BACKBONES.register_module
class MobileNet(nn.Module):

    def __init__(self, arg1, arg2):
        pass

    def forward(x):  # should return a tuple
        pass
```

2. Import the module in `mmdet/models/backbones/__init__.py`.

```python
from .mobilenet import MobileNet
```

3. Use it in your config file.

```python
model = dict(
    ...
    backbone=dict(
        type='MobileNet',
        arg1=xxx,
        arg2=xxx),
    ...
```

For more information on how it works, you can refer to [TECHNICAL_DETAILS.md](TECHNICAL_DETAILS.md) (TODO).