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How to finetune from pretrained models trained on coco data with different number of classes?

See original GitHub issue

Is there a config option to load pretrained coco models for finetuning? The last layers where the number of classes may be different, so those weights should not be loaded. If I just use faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth, Then I got

While copying the parameter named bbox_head.fc_cls.weight, whose dimensions in the model are torch.Size([21, 1024]) and whose dimensions in the checkpoint are torch.Size([81, 1024]).

Or, how should I modify the pretrained weight for my model?

Thank you!

Issue Analytics

State:
Created 4 years ago
Comments:11 (2 by maintainers)

Top GitHub Comments

8reactions

cowry5commented, Jun 1, 2019

Thank you so much @spytensor , I’ve solved the problem by this

import torch
pretrained_weights  = torch.load('faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth')

num_class = 21
pretrained_weights['state_dict']['bbox_head.fc_cls.weight'].resize_(num_class, 1024)
pretrained_weights['state_dict']['bbox_head.fc_cls.bias'].resize_(num_class)
pretrained_weights['state_dict']['bbox_head.fc_reg.weight'].resize_(num_class*4, 1024)
pretrained_weights['state_dict']['bbox_head.fc_reg.bias'].resize_(num_class*4)

torch.save(pretrained_weights, "faster_rcnn_r50_fpn_1x_%d.pth"%num_class)

3reactions

moshelivcommented, Aug 8, 2019

Thank you so much @spytensor , I’ve solved the problem by this

import torch
pretrained_weights  = torch.load('faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth')

num_class = 21
pretrained_weights['state_dict']['bbox_head.fc_cls.weight'].resize_(num_class, 1024)
pretrained_weights['state_dict']['bbox_head.fc_cls.bias'].resize_(num_class)
pretrained_weights['state_dict']['bbox_head.fc_reg.weight'].resize_(num_class*4, 1024)
pretrained_weights['state_dict']['bbox_head.fc_reg.bias'].resize_(num_class*4)

torch.save(pretrained_weights, "faster_rcnn_r50_fpn_1x_%d.pth"%num_class)

Can you please share your experience with fine tuning like this? What lr schedule did you use? How many epochs?

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