Frequently Asked Questions¶

We list some common troubles faced by many users and their corresponding solutions here. Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them. If the contents here do not cover your issue, please create an issue using the provided templates and make sure you fill in all required information in the template.

Installation¶

Compatibility issue between MMEngine, MMCV and MMPretrain

Compatible MMPretrain and MMEngine, MMCV versions are shown as below. Please choose the correct version of MMEngine and MMCV to avoid installation issues.

MMPretrain version	MMEngine version	MMCV version
1.2.0 (main)	mmengine >= 0.8.3	mmcv >= 2.0.0
1.1.1	mmengine >= 0.8.3	mmcv >= 2.0.0
1.0.0	mmengine >= 0.8.0	mmcv >= 2.0.0
1.0.0rc8	mmengine >= 0.7.1	mmcv >= 2.0.0rc4
1.0.0rc7	mmengine >= 0.5.0	mmcv >= 2.0.0rc4

Note

Since the dev branch is under frequent development, the MMEngine and MMCV version dependency may be inaccurate. If you encounter problems when using the dev branch, please try to update MMEngine and MMCV to the latest version.

Using Albumentations

If you would like to use albumentations, we suggest using pip install -r requirements/albu.txt or pip install -U albumentations --no-binary qudida,albumentations.

If you simply use pip install albumentations>=0.3.2, it will install opencv-python-headless simultaneously (even though you have already installed opencv-python). Please refer to the official documentation for details.

General Questions¶

Do I need to reinstall mmpretrain after some code modifications?¶

If you follow the best practice and install mmpretrain from source, any local modifications made to the code will take effect without reinstallation.

How to develop with multiple MMPretrain versions?¶

Generally speaking, we recommend to use different virtual environments to manage MMPretrain in different working directories. However, you can also use the same environment to develop MMPretrain in different folders, like mmpretrain-0.21, mmpretrain-0.23. When you run the train or test shell script, it will adopt the mmpretrain package in the current folder. And when you run other Python script, you can also add PYTHONPATH=`pwd` at the beginning of your command to use the package in the current folder.

Conversely, to use the default MMPretrain installed in the environment rather than the one you are working with, you can remove the following line in those shell scripts:

PYTHONPATH="$(dirname $0)/..":$PYTHONPATH

What’s the relationship between the `load_from` and the `init_cfg`?¶

load_from: If resume=False, only imports model weights, which is mainly used to load trained models; If resume=True, load all of the model weights, optimizer state, and other training information, which is mainly used to resume interrupted training.
init_cfg: You can also specify init=dict(type="Pretrained", checkpoint=xxx) to load checkpoint, it means load the weights during model weights initialization. That is, it will be only done at the beginning of the training. It’s mainly used to fine-tune a pre-trained model, and you can set it in the backbone config and use prefix field to only load backbone weights, for example:

model = dict(
  backbone=dict(
      type='ResNet',
      depth=50,
      init_cfg=dict(type='Pretrained', checkpoints=xxx, prefix='backbone'),
  )
  ...
)

See the Fine-tune Models for more details about fine-tuning.

What’s the difference between `default_hooks` and `custom_hooks`?¶

Almost no difference. Usually, the default_hooks field is used to specify the hooks that will be used in almost all experiments, and the custom_hooks field is used in only some experiments.

Another difference is the default_hooks is a dict while the custom_hooks is a list, please don’t be confused.

During training, I got no training log, what’s the reason?¶

If your training dataset is small while the batch size is large, our default log interval may be too large to record your training log.

You can shrink the log interval and try again, like:

default_hooks = dict(
    ...
    logger=dict(type='LoggerHook', interval=10),
    ...
)

How to train with other datasets, like my own dataset or COCO?¶

We provide specific examples to show how to train with other datasets.