size mismatch when loading the trained checkpoint

For the adaptation from VOC to watercolor, VOC has 21 classes (background included) and the watercolor dataset has 7 classes, how do you deal with the inconsistency? It seems that the classifier obtained in the training stage cannot be applied for the test stage. Thanks.

*** RuntimeError: Error(s) in loading state_dict for resnet:
	size mismatch for RCNN_cls_score.weight: copying a param of torch.Size([7, 2304]) from checkpoint, where the shape is torch.Size([21, 2304]) in current model.
	size mismatch for RCNN_cls_score.bias: copying a param of torch.Size([7]) from checkpoint, where the shape is torch.Size([21]) in current model.
	size mismatch for RCNN_bbox_pred.weight: copying a param of torch.Size([28, 2304]) from checkpoint, where the shape is torch.Size([84, 2304]) in current model.
	size mismatch for RCNN_bbox_pred.bias: copying a param of torch.Size([28]) from checkpoint, where the shape is torch.Size([84]) in current model.

[123zhen123]

I have the same questions

[123zhen123]

对于从VOC到水彩的适应，VOC有21个类（包括背景），水彩数据集有7个类，您如何处理不一致的情况？看来在训练阶段获得的分类器无法应用于测试阶段。谢谢。

*** RuntimeError: Error(s) in loading state_dict for resnet:
	size mismatch for RCNN_cls_score.weight: copying a param of torch.Size([7, 2304]) from checkpoint, where the shape is torch.Size([21, 2304]) in current model.
	size mismatch for RCNN_cls_score.bias: copying a param of torch.Size([7]) from checkpoint, where the shape is torch.Size([21]) in current model.
	size mismatch for RCNN_bbox_pred.weight: copying a param of torch.Size([28, 2304]) from checkpoint, where the shape is torch.Size([84, 2304]) in current model.
	size mismatch for RCNN_bbox_pred.bias: copying a param of torch.Size([28]) from checkpoint, where the shape is torch.Size([84]) in current model.

Have you solved the problem？