Errow while loading dictionaries from swinunetr

[aaekay]

I am getting the following error while checkpoint loading

RuntimeError: Error(s) in loading state_dict for Universal_model:
Unexpected key(s) in state_dict: "swinViT.patch_embed.proj.weight", "swinViT.patch_embed.proj.bias", "swinViT.layers1.0.blocks.0.norm1.weight", "swinViT.layers1.0.blocks.0.norm1.bias", "swinViT.layers1.0.blocks.0.attn.relative_position_bias_table", "swinViT.layers1.0.blocks.0.attn.relative_position_index", "swinViT.layers1.0.blocks.0.attn.qkv.weight", "swinViT.layers1.0.blocks.0.attn.qkv.bias", "swinViT.layers1.0.blocks.0.attn.proj.weight", "swinViT.layers1.0.blocks.0.attn.proj.bias", "swinViT.layers1.0.blocks.0.norm2.weight", "swinViT.layers1.0.blocks.0.norm2.bias", "swinViT.layers1.0.blocks.0.mlp.linear1.weight", "swinViT.layers1.0.blocks.0.mlp.linear1.bias", "swinViT.layers1.0.blocks.0.mlp.linear2.weight", "swinViT.layers1.0.blocks.0.mlp.linear2.bias", "swinViT.layers1.0.blocks.1.norm1.weight", "swinViT.layers1.0.blocks.1.norm1.bias", "swinViT.layers1.0.blocks.1.attn.relative_position_bias_table", "swinViT.layers1.0.blocks.1.attn.relative_position_index", "swinViT.layers1.0.blocks.1.attn.qkv.weight", "swinViT.layers1.0.blocks.1.attn.qkv.bias", "swinViT.layers1.0.blocks.1.attn.proj.weight", "swinViT.layers1.0.blocks.1.attn.proj.bias", "swinViT.layers1.0.blocks.1.norm2.weight", "swinViT.layers1.0.blocks.1.norm2.bias", "swinViT.layers1.0.blocks.1.mlp.linear1.weight", "swinViT.layers1.0.blocks.1.mlp.linear1.bias", "swinViT.layers1.0.blocks.1.mlp.linear2.weight", "swinViT.layers1.0.blocks.1.mlp.linear2.bias", "swinViT.layers1.0.downsample.reduction.weight", "swinViT.layers1.0.downsample.norm.weight", "swinViT.layers1.0.downsample.norm.bias", "swinViT.layers2.0.blocks.0.norm1.weight", "swinViT.layers2.0.blocks.0.norm1.bias", "swinViT.layers2.0.blocks.0.attn.relative_position_bias_table", "swinViT.layers2.0.blocks.0.attn.relative_position_index", "swinViT.layers2.0.blocks.0.attn.qkv.weight", "swinViT.layers2.0.blocks.0.attn.qkv.bias", "swinViT.layers2.0.blocks.0.attn.proj.weight", "swinViT.layers2.0.blocks.0.attn.proj.bias", "swinViT.layers2.0.blocks.0.norm2.weight", "swinViT.layers2.0.blocks.0.norm2.bias", "swinViT.layers2.0.blocks.0.mlp.linear1.weight", "swinViT.layers2.0.blocks.0.mlp.linear1.bias", "swinViT.layers2.0.blocks.0.mlp.linear2.weight", "swinViT.layers2.0.blocks.0.mlp.linear2.bias", "swinViT.layers2.0.blocks.1.norm1.weight", "swinViT.layers2.0.blocks.1.norm1.bias", "swinViT.layers2.0.blocks.1.attn.relative_position_bias_table", "swinViT.layers2.0.blocks.1.attn.relative_position_index", "swinViT.layers2.0.blocks.1.attn.qkv.weight", "swinViT.layers2.0.blocks.1.attn.qkv.bias", "swinViT.layers2.0.blocks.1.attn.proj.weight", "swinViT.layers2.0.blocks.1.attn.proj.bias", "swinViT.layers2.0.blocks.1.norm2.weight", "swinViT.layers2.0.blocks.1.norm2.bias", "swinViT.layers2.0.blocks.1.mlp.linear1.weight", "swinViT.layers2.0.blocks.1.mlp.linear1.bias", "swinViT.layers2.0.blocks.1.mlp.linear2.weight", "swinViT.layers2.0.blocks.1.mlp.linear2.bias", "swinViT.layers2.0.downsample.reduction.weight", "swinViT.layers2.0.downsample.norm.weight", "swinViT.layers2.0.downsample.norm.bias", "swinViT.layers3.0.blocks.0.norm1.weight", "swinViT.layers3.0.blocks.0.norm1.bias", "swinViT.layers3.0.blocks.0.attn.relative_position_bias_table", "swinViT.layers3.0.blocks.0.attn.relative_position_index", "swinViT.layers3.0.blocks.0.attn.qkv.weight", "swinViT.layers3.0.blocks.0.attn.qkv.bias", "swinViT.layers3.0.blocks.0.attn.proj.weight", "swinViT.layers3.0.blocks.0.attn.proj.bias", "swinViT.layers3.0.blocks.0.norm2.weight", "swinViT.layers3.0.blocks.0.norm2.bias", "swinViT.layers3.0.blocks.0.mlp.linear1.weight", "swinViT.layers3.0.blocks.0.mlp.linear1.bias", "swinViT.layers3.0.blocks.0.mlp.linear2.weight", "swinViT.layers3.0.blocks.0.mlp.linear2.bias", "swinViT.layers3.0.blocks.1.norm1.weight", "swinViT.layers3.0.blocks.1.norm1.bias", "swinViT.layers3.0.blocks.1.attn.relative_position_bias_table", "swinViT.layers3.0.blocks.1.attn.relative_position_index", "swinViT.layers3.0.blocks.1.attn.qkv.weight", "swinViT.layers3.0.blocks.1.attn.qkv.bias", "swinViT.layers3.0.blocks.1.attn.proj.weight", "swinViT.layers3.0.blocks.1.attn.proj.bias", "swinViT.layers3.0.blocks.1.norm2.weight", "swinViT.layers3.0.blocks.1.norm2.bias", "swinViT.layers3.0.blocks.1.mlp.linear1.weight", "swinViT.layers3.0.blocks.1.mlp.linear1.bias", "swinViT.layers3.0.blocks.1.mlp.linear2.weight", "swinViT.layers3.0.blocks.1.mlp.linear2.bias", "swinViT.layers3.0.downsample.reduction.weight", "swinViT.layers3.0.downsample.norm.weight", "swinViT.layers3.0.downsample.norm.bias", "swinViT.layers4.0.blocks.0.norm1.weight", "swinViT.layers4.0.blocks.0.norm1.bias", "swinViT.layers4.0.blocks.0.attn.relative_position_bias_table", "swinViT.layers4.0.blocks.0.attn.relative_position_index", "swinViT.layers4.0.blocks.0.attn.qkv.weight", "swinViT.layers4.0.blocks.0.attn.qkv.bias", "swinViT.layers4.0.blocks.0.attn.proj.weight", "swinViT.layers4.0.blocks.0.attn.proj.bias", "swinViT.layers4.0.blocks.0.norm2.weight", "swinViT.layers4.0.blocks.0.norm2.bias", "swinViT.layers4.0.blocks.0.mlp.linear1.weight", "swinViT.layers4.0.blocks.0.mlp.linear1.bias", "swinViT.layers4.0.blocks.0.mlp.linear2.weight", "swinViT.layers4.0.blocks.0.mlp.linear2.bias", "swinViT.layers4.0.blocks.1.norm1.weight", "swinViT.layers4.0.blocks.1.norm1.bias", "swinViT.layers4.0.blocks.1.attn.relative_position_bias_table", "swinViT.layers4.0.blocks.1.attn.relative_position_index", "swinViT.layers4.0.blocks.1.attn.qkv.weight", "swinViT.layers4.0.blocks.1.attn.qkv.bias", "swinViT.layers4.0.blocks.1.attn.proj.weight", "swinViT.layers4.0.blocks.1.attn.proj.bias", "swinViT.layers4.0.blocks.1.norm2.weight", "swinViT.layers4.0.blocks.1.norm2.bias", "swinViT.layers4.0.blocks.1.mlp.linear1.weight", "swinViT.layers4.0.blocks.1.mlp.linear1.bias", "swinViT.layers4.0.blocks.1.mlp.linear2.weight", "swinViT.layers4.0.blocks.1.mlp.linear2.bias", "swinViT.layers4.0.downsample.reduction.weight", "swinViT.layers4.0.downsample.norm.weight", "swinViT.layers4.0.downsample.norm.bias", "encoder1.layer.conv1.conv.weight", "encoder1.layer.conv2.conv.weight", "encoder1.layer.conv3.conv.weight", "encoder2.layer.conv1.conv.weight", "encoder2.layer.conv2.conv.weight", "encoder3.layer.conv1.conv.weight", "encoder3.layer.conv2.conv.weight", "encoder4.layer.conv1.conv.weight", "encoder4.layer.conv2.conv.weight", "encoder10.layer.conv1.conv.weight", "encoder10.layer.conv2.conv.weight", "decoder5.transp_conv.conv.weight", "decoder5.conv_block.conv1.conv.weight", "decoder5.conv_block.conv2.conv.weight", "decoder5.conv_block.conv3.conv.weight", "decoder4.transp_conv.conv.weight", "decoder4.conv_block.conv1.conv.weight", "decoder4.conv_block.conv2.conv.weight", "decoder4.conv_block.conv3.conv.weight", "decoder3.transp_conv.conv.weight", "decoder3.conv_block.conv1.conv.weight", "decoder3.conv_block.conv2.conv.weight", "decoder3.conv_block.conv3.conv.weight", "decoder2.transp_conv.conv.weight", "decoder2.conv_block.conv1.conv.weight", "decoder2.conv_block.conv2.conv.weight", "decoder2.conv_block.conv3.conv.weight", "decoder1.transp_conv.conv.weight", "decoder1.conv_block.conv1.conv.weight", "decoder1.conv_block.conv2.conv.weight", "decoder1.conv_block.conv3.conv.weight".

[chenluda]

You can try changing the lines 233-241 in test.py:

    #Load pre-trained weights
    store_dict = model.state_dict()
    checkpoint = torch.load(args.resume)
    load_dict = checkpoint['net']
    # args.epoch = checkpoint['epoch']

    for key, value in load_dict.items():
        name = '.'.join(key.split('.')[1:])
        store_dict[name] = value

to:

    # Load pre-trained weights
    store_dict = model.state_dict()
    store_dict_keys = [key for key, value in store_dict.items()]
    checkpoint = torch.load(args.resume)
    load_dict = checkpoint['net']
    load_dict_value = [value for key, value in load_dict.items()]
    # args.epoch = checkpoint['epoch']

    for i in range(len(store_dict)):
        store_dict[store_dict_keys[i]] = load_dict_value[i]

[aaekay]

it is working now