We provide lots of useful tools under the `tools/` directory. In addition, you can also quickly run other open source libraries of OpenMMLab through MIM.
Take MMDetection as an example. If you want to use [print_config.py](https://github.com/open-mmlab/mmdetection/blob/3.x/tools/misc/print_config.py), you can directly use the following commands without copying the source code to the MMYOLO library.
If images and labels are in the same folder, you can specify `--data-root` to the folder, and then `--img-dir` and `--ann-file` to specify the relative path of the folder. The code will be automatically spliced.
If the image and label files are not in the same folder, you do not need to specify `--data-root`, but directly specify `--img-dir` and `--ann-file` of the absolute path.
`tools/analysis_tools/browse_dataset.py` helps the user to browse a detection dataset (both images and bounding box annotations) visually, or save the image to a designated directory.
1. Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` to visualize the picture. The picture will pop up directly and be saved to the directory `work_dirs/browse_ dataset` at the same time:
2. Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` to visualize the picture. The picture will pop up and display directly. Each picture lasts for `10` seconds. At the same time, it will be saved to the directory `work_dirs/browse_ dataset`:
3. Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` to visualize the picture. The picture will pop up and display directly. Each picture lasts for `10` seconds and the picture will not be saved:
4. Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` to visualize the picture. The picture will not pop up directly, but only saved to the directory `work_dirs/browse_ dataset`:
`tools/analysis_tools/dataset_analysis.py` help users get the renderings of the four functions, and save the pictures to the `dataset_analysis` folder under the current running directory.
1.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, By default,the data loading type is `train_dataset`, the area rule is `[0,32,96,1e5]`, generate a result graph containing all functions and save the graph to the current running directory `./dataset_analysis` folder:
2.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, change the data loading type from the default `train_dataset` to `val_dataset` through the `--val-dataset` setting:
3.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, change the display of all generated classes to specific classes. Take the display of `person` classes as an example:
4.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, redefine the area rule through `--area-rule` . Take `30 70 125` as an example, the area rule becomes `[0,30,70,125,1e5]`：
5.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, change the display of four function renderings to only display `Function 1` as an example:
6.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, modify the picture saving address to `work_dirs/dataset_analysis`:
`tools/analysis_tools/vis_scheduler` aims to help the user to check the hyper-parameter scheduler of the optimizer(without training), which support the "learning rate", "momentum", and "weight_decay".
```bash
python tools/analysis_tools/vis_scheduler.py \
${CONFIG_FILE} \
[-p, --parameter ${PARAMETER_NAME}] \
[-d, --dataset-size ${DATASET_SIZE}] \
[-n, --ngpus ${NUM_GPUs}] \
[-o, --out-dir ${OUT_DIR}] \
[--title ${TITLE}] \
[--style ${STYLE}] \
[--window-size ${WINDOW_SIZE}] \
[--cfg-options]
```
**Description of all arguments**：
-`config`: The path of a model config file.
- **`-p, --parameter`**: The param to visualize its change curve, choose from "lr", "momentum" or "wd". Default to use "lr".
- **`-d, --dataset-size`**: The size of the datasets. If set，`DATASETS.build` will be skipped and `${DATASET_SIZE}` will be used as the size. Default to use the function `DATASETS.build`.
- **`-n, --ngpus`**: The number of GPUs used in training, default to be 1.
- **`-o, --out-dir`**: The output path of the curve plot, default not to output.
-`--title`: Title of figure. If not set, default to be config file name.
-`--style`: Style of plt. If not set, default to be `whitegrid`.
-`--window-size`: The shape of the display window. If not specified, it will be set to `12*7`. If used, it must be in the format `'W*H'`.
-`--cfg-options`: Modifications to the configuration file, refer to [Learn about Configs](../user_guides/config.md).
```{note}
Loading annotations maybe consume much time, you can directly specify the size of the dataset with `-d, dataset-size` to save time.
```
You can use the following command to plot the step learning rate schedule used in the config `configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py`:
-`ballon2coco.py` converts the `balloon` dataset (this small dataset is for starters only) to COCO format.
For a detailed description of this script, please see the `Dataset Preparation` section in [From getting started to deployment with YOLOv5](./yolov5_tutorial.md).
1.`image_dir` is the root directory of the yolo-style dataset you need to pass to the script, which should contain `images`, `labels`, and `classes.txt`. `classes.txt` is the class declaration corresponding to the current dataset. One class a line. The structure of the root directory should be formatted as this example shows:
```bash
.
└── $ROOT_PATH
├── classes.txt
├── labels
│ ├── a.txt
│ ├── b.txt
│ └── ...
├── images
│ ├── a.jpg
│ ├── b.png
│ └── ...
└── ...
```
2. The script will automatically check if `train.txt`, `val.txt`, and `test.txt` have already existed under `image_dir`. If these files are located, the script will organize the dataset accordingly. Otherwise, the script will convert the dataset into one file. The image paths in these files must be **ABSOLUTE** paths.
3. By default, the script will create a folder called `annotations` in the `image_dir` directory which stores the converted JSON file. If `train.txt`, `val.txt`, and `test.txt` are not found, the output file is `result.json`. Otherwise, the corresponding JSON file will be generated, named as `train.json`, `val.json`, and `test.json`. The `annotations` folder may look similar to this:
The six scripts under the `tools/model_converters` directory can help users convert the keys in the official pre-trained model of YOLO to the format of MMYOLO, and use MMYOLO to fine-tune the model.
The training dataset of the COCO2017 dataset includes 118K images, and the validation set includes 5K images, which is a relatively large dataset. Loading JSON in debugging or quick verification scenarios will consume more resources and bring slower startup speed.
The `extract_subcoco.py` script provides the ability to extract a specified number/classes/area-size of images. The user can use the `--num-img`, `--classes`, `--area-size` parameter to get a COCO subset of the specified condition of images.
For example, extract images use scripts as follows:
```shell
python tools/misc/extract_subcoco.py \
${ROOT} \
${OUT_DIR} \
--num-img 20 \
--classes cat dog person \
--area-size small
```
It gone be extract 20 images, and only includes annotations which belongs to cat(or dog/person) and bbox area size is small, after filter by class and area size, the empty annotation images won't be chosen, guarantee the images be extracted definitely has annotation info.
