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给当前模型换了个开源的主干网络#xff0c;并且删除了某些层后#xff0c;但是发现预训练权重一直加载不上。strict为True时加载报错#xff0c;strict为False时又什么都加载不上#xff0c;然后不知道哪里出问题了。
解决
当strict为False时#xff0c;load_sta…问题
给当前模型换了个开源的主干网络并且删除了某些层后但是发现预训练权重一直加载不上。strict为True时加载报错strict为False时又什么都加载不上然后不知道哪里出问题了。
解决
当strict为False时load_state_dict函数会返回一个字典该字典含有以下两个键
missing_keys在当前模型中存在但在预训练权重中不存在的键。
unexpected_keys在当前模型不存在但在预训练权重中存在的键。resultself.backbone.load_state_dict(model_weight,strictFalse)print(Missing keys:, result.missing_keys)print(Unexpected keys:, result.unexpected_keys)得到输出:
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levels.2.blocks.17.norm2.0.bias, levels.2.blocks.17.mlp.fc1.weight, levels.2.blocks.17.mlp.fc1.bias, levels.2.blocks.17.mlp.fc2.weight, levels.2.norm.0.weight, levels.2.norm.0.bias, levels.2.downsample.conv.weight, levels.2.downsample.norm.1.weight, levels.2.downsample.norm.1.bias, levels.3.blocks.0.norm1.0.weight, levels.3.blocks.0.norm1.0.bias, levels.3.blocks.0.dcn.offset_mask.weight, levels.3.blocks.0.dcn.offset_mask.bias, levels.3.blocks.0.dcn.value_proj.weight, levels.3.blocks.0.dcn.value_proj.bias, levels.3.blocks.0.dcn.output_proj.weight, levels.3.blocks.0.norm2.0.weight, levels.3.blocks.0.norm2.0.bias, levels.3.blocks.0.mlp.fc1.weight, levels.3.blocks.0.mlp.fc1.bias, levels.3.blocks.0.mlp.fc2.weight, levels.3.blocks.1.norm1.0.weight, levels.3.blocks.1.norm1.0.bias, levels.3.blocks.1.dcn.offset_mask.weight, levels.3.blocks.1.dcn.offset_mask.bias, levels.3.blocks.1.dcn.value_proj.weight, levels.3.blocks.1.dcn.value_proj.bias, levels.3.blocks.1.dcn.output_proj.weight, levels.3.blocks.1.norm2.0.weight, levels.3.blocks.1.norm2.0.bias, levels.3.blocks.1.mlp.fc1.weight, levels.3.blocks.1.mlp.fc1.bias, levels.3.blocks.1.mlp.fc2.weight, levels.3.blocks.2.norm1.0.weight, levels.3.blocks.2.norm1.0.bias, levels.3.blocks.2.dcn.offset_mask.weight, levels.3.blocks.2.dcn.offset_mask.bias, levels.3.blocks.2.dcn.value_proj.weight, levels.3.blocks.2.dcn.value_proj.bias, levels.3.blocks.2.dcn.output_proj.weight, levels.3.blocks.2.norm2.0.weight, levels.3.blocks.2.norm2.0.bias, levels.3.blocks.2.mlp.fc1.weight, levels.3.blocks.2.mlp.fc1.bias, levels.3.blocks.2.mlp.fc2.weight, levels.3.blocks.3.norm1.0.weight, levels.3.blocks.3.norm1.0.bias, levels.3.blocks.3.dcn.offset_mask.weight, levels.3.blocks.3.dcn.offset_mask.bias, levels.3.blocks.3.dcn.value_proj.weight, levels.3.blocks.3.dcn.value_proj.bias, levels.3.blocks.3.dcn.output_proj.weight, levels.3.blocks.3.norm2.0.weight, levels.3.blocks.3.norm2.0.bias, levels.3.blocks.3.mlp.fc1.weight, levels.3.blocks.3.mlp.fc1.bias, levels.3.blocks.3.mlp.fc2.weight, levels.3.norm.0.weight, levels.3.norm.0.bias, conv_head.0.weight, conv_head.1.0.weight, conv_head.1.0.bias, conv_head.1.0.running_mean, conv_head.1.0.running_var, conv_head.1.0.num_batches_tracked, head.weight, head.bias]可以看到我的模型的名字每一层都比预训练的权重多了一个’model.这就导致了无法加载权重。 于是就把预训练的权重的键名加上‘model.’即可。 model_weight {model. key: value for key, value in model_weight.items()}然后重新调试可以看到输出
Missing keys: []
Unexpected keys: [model.head.weight, model.head.bias]可以看到Missing keys为空所以需要的权重全部加载了。
