ViTAE_NC_Win_RVSA_V3_WSZ7预训练权重加载

[Dawn-creat]

当我加载vitae-b-checkpoint-1599-transform-no-average.pth时，出现了如下错误，我使用的数据集时potsdam
Error(s) in loading state_dict for ViTAE_NC_Win_RVSA_V3_WSZ7:
size mismatch for pos_embed: copying a param with shape torch.Size([1, 197, 768]) from checkpoint, the shape in current model is torch.Size([1, 1024, 768]).

` def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.

    Args:
        pretrained (str, optional): Path to pre-trained weights.
            Defaults to None.
    """
    pretrained = pretrained or self.pretrained
    def _init_weights(m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    if isinstance(pretrained, str):
        self.apply(_init_weights)
        logger = get_root_logger()
        print(f"load from {pretrained}")
        checkpoint = _load_checkpoint(self.pretrained, logger=logger,map_location='cpu')
        if 'state_dict' in checkpoint:
            state_dict = checkpoint['state_dict']
        elif 'model' in checkpoint:
            state_dict = checkpoint['model']
        else:
            state_dict = checkpoint
        self.load_state_dict(state_dict, False)
    elif pretrained is None:
        self.apply(_init_weights)
    else:
        raise TypeError('pretrained must be a str or None')`

[DotWang]

@Dawn-creat 不影响，位置编码会跟随尺寸自动插值缩放