How to use the mmpretrain model in mmdetection?

[sjtu-cz]

for example, I want use simmim (https://github.com/open-mmlab/mmpretrain/tree/main/configs/simmim) to pretrain the swin_v2 (https://github.com/open-mmlab/mmpretrain/blob/main/mmpretrain/models/backbones/swin_transformer_v2.py), and use it in mmdetection, such as mmconfigs/cascade_rcnn Is it easy to use?

[Darren759]

Did you solved it? I have the same problem!

[sjtu-cz]

@ZwwWayne @Keiku @anurag1paul help please!

[Keiku]

@sjtu-cz ChatGPT says the following, so could you try it for reference? In my environment, I can't run large models, so I can't verify it, so I'll consider whether there is any way to do it.

Yes, it's feasible to use SimMIM to pretrain the SwinV2 model and then use it in MMDetection with configurations like cascade_rcnn. However, the process involves several steps to ensure compatibility across different components. Here's a general outline of how you can achieve this:

1. Pretrain SwinV2 with SimMIM (MMPretrain)

• Follow the official SimMIM configuration to pretrain the SwinV2 model using the mmpretrain framework.
• Modify the SimMIM config file to specify the use of the SwinV2 backbone by editing the configuration file under the model key, like so:

   model = dict(
       type='SimMIM',
       backbone=dict(
           type='SwinTransformerV2',
           # Add necessary backbone parameters based on your requirements
       ),
       # Other model parameters as needed...
   )

• Train the model using this modified config and save the pre-trained weights.

2. Save and Export Pretrained Weights

• After training, save the pretrained weights, ensuring that you properly export the model checkpoint. This will be used as the backbone weights in MMDetection.

3. Integrate Pretrained Weights into MMDetection

• In MMDetection, you can use the pretrained SwinV2 model by modifying the backbone field in your detection model’s configuration (such as cascade_rcnn). Update the pretrained path to point to your SimMIM pre-trained SwinV2 checkpoint.
• For example, in cascade_rcnn_swin_fpn.py, you would modify the backbone like so:

   model = dict(
       backbone=dict(
           type='SwinTransformerV2',
           init_cfg=dict(type='Pretrained', checkpoint='path/to/simmim_swinv2_pretrained.pth'),
           # Other SwinV2 specific configurations
       ),
       neck=dict(...),  # FPN or other neck architectures
       roi_head=dict(...),  # Standard Cascade RCNN head or any other head
   )

4. Ensure Compatibility (Input Size, Feature Maps)

• Ensure that the output feature maps of the SwinV2 backbone match what is expected by the Cascade R-CNN or any other detection head you plan to use.
• You may need to adjust the feature dimensions or change some parameters in the FPN or head to maintain compatibility.

5. Finetuning with Detection Task

• After integrating the pretrained SwinV2 backbone into MMDetection, fine-tune the entire model on your detection dataset using the modified detection config file.

6. Run the Training

• Use MMDetection's training script to run the training process. Ensure that your environment is correctly set up to use both mmpretrain and mmdetection configurations.

Potential Challenges:

• Configuration tuning: You may need to experiment with hyperparameters, especially if there are significant differences in pretraining and finetuning tasks.
• Weight loading issues: Ensure that the key names in the pretrained checkpoint are compatible with the MMDetection model structure.

This workflow should allow you to pretrain SwinV2 with SimMIM and transfer the backbone for detection tasks like Cascade R-CNN within MMDetection.