GETTING_STARTED.md

# 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
```

### High-level APIs for testing images.

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

```python
import mmcv
from mmcv.runner import load_checkpoint
from mmdet.models import build_detector
from mmdet.apis import inference_detector, show_result

cfg = mmcv.Config.fromfile('configs/faster_rcnn_r50_fpn_1x.py')
cfg.model.pretrained = None

# construct the model and load checkpoint
model = build_detector(cfg.model, test_cfg=cfg.test_cfg)
_ = load_checkpoint(model, 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth')

# test a single image
img = mmcv.imread('test.jpg')
result = inference_detector(model, img, cfg)
show_result(img, result)

# test a list of images
imgs = ['test1.jpg', 'test2.jpg']
for i, result in enumerate(inference_detector(model, imgs, cfg, device='cuda:0')):
    print(i, imgs[i])
    show_result(imgs[i], result)
```


## 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.
If you use less or more than 8 GPUs, you need to set the learning rate proportional
to the GPU num, e.g., 0.01 for 4 GPUs and 0.04 for 16 GPUs.

### 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` (recommended): Perform evaluation at every k (default=1) 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.

### 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


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, float32> (n, ),
            'bboxes_ignore': <np.ndarray, float32> (k, 4),
            'labels_ignore': <np.ndarray, float32> (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 a 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
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).