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Learn about Configs

We use python files as our config system. You can find all the provided configs under $MMTracking/configs.

We incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments. If you wish to inspect the config file, you may run python tools/print_config.py /PATH/TO/CONFIG to see the complete config.

Modify config through script arguments

When submitting jobs using "tools/train.py" or "tools/test.py", you may specify --cfg-options to in-place modify the config.

  • Update config keys of dict chains.

    The config options can be specified following the order of the dict keys in the original config. For example, --cfg-options model.detector.backbone.norm_eval=False changes the all BN modules in model backbones to train mode.

  • Update keys inside a list of configs.

    Some config dicts are composed as a list in your config. For example, the testing pipeline data.test.pipeline is normally a list e.g. [dict(type='LoadImageFromFile'), ...]. If you want to change 'LoadImageFromFile' to 'LoadImageFromWebcam' in the pipeline, you may specify --cfg-options data.test.pipeline.0.type=LoadImageFromWebcam.

  • Update values of list/tuples.

    If the value to be updated is a list or a tuple. For example, the config file normally sets workflow=[('train', 1)]. If you want to change this key, you may specify --cfg-options workflow="[(train,1),(val,1)]". Note that the quotation mark " is necessary to support list/tuple data types, and that NO white space is allowed inside the quotation marks in the specified value.

Config File Structure

There are 3 basic component types under config/_base_, dataset, model, default_runtime. Many methods could be easily constructed with one of each like DFF, FGFA, SELSA, SORT, DeepSORT. The configs that are composed by components from _base_ are called primitive.

For all configs under the same folder, it is recommended to have only one primitive config. All other configs should inherit from the primitive config. In this way, the maximum of inheritance level is 3.

For easy understanding, we recommend contributors to inherit from exiting methods. For example, if some modification is made base on Faster R-CNN, user may first inherit the basic Faster R-CNN structure by specifying _base_ = ../../_base_/models/faster_rcnn_r50_dc5.py, then modify the necessary fields in the config files.

If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder xxx_rcnn under configs,

Please refer to mmcv for detailed documentation.

Config Name Style

We follow the below style to name config files. Contributors are advised to follow the same style.

{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{schedule}_{dataset}

{xxx} is required field and [yyy] is optional.

  • {model}: model type like dff, tracktor, siamese_rpn, etc.
  • [model setting]: specific setting for some model, like faster_rcnn for dff,tracktor, etc.
  • {backbone}: backbone type like r50 (ResNet-50), x101 (ResNeXt-101).
  • {neck}: neck type like fpn, c5.
  • [norm_setting]: bn (Batch Normalization) is used unless specified, other norm layer type could be gn (Group Normalization), syncbn (Synchronized Batch Normalization). gn-head/gn-neck indicates GN is applied in head/neck only, while gn-all means GN is applied in the entire model, e.g. backbone, neck, head.
  • [misc]: miscellaneous setting/plugins of model, e.g. dconv, gcb, attention, albu, mstrain.
  • [gpu x batch_per_gpu]: GPUs and samples per GPU, 8x2 is used by default.
  • {schedule}: training schedule, options is 4e, 7e, 20e, etc. 20e denotes 20 epochs.
  • {dataset}: dataset like imagenetvid, mot17, lasot.

Detailed analysis of Config File

Please refer to the corresponding page for config file structure of different tasks.

Video Object Detection

Multi Object Tracking

Single Object Tracking

FAQ

Ignore some fields in the base configs

Sometimes, you may set _delete_=True to ignore some of fields in base configs. You may refer to mmcv for simple illustration.

Use intermediate variables in configs

Some intermediate variables are used in the configs files, like train_pipeline/test_pipeline in datasets. It's worth noting that when modifying intermediate variables in the children configs, user need to pass the intermediate variables into corresponding fields again. For example, we would like to use testing strategy of adaptive stride to test a SELSA. ref_img_sampler is intermediate variable we would like modify.

_base_ = ['./selsa_faster_rcnn_r50_dc5_1x_imagenetvid.py']

# dataset settings
ref_img_sampler = dict(
    _delete_=True,
    num_ref_imgs=14,
    frame_range=[-7, 7],
    method='test_with_adaptive_stride')
data = dict(
    val=dict(
        ref_img_sampler=ref_img_sampler),
    test=dict(
        ref_img_sampler=ref_img_sampler))

We first define the new ref_img_sampler and pass them into data.