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复现Rethinking the Learning Paradigm for Dynamic Facial Expression Recognition这篇论文时，加载已经训练好的文件进行推理，发现准确率很低。利用下面两行代码加载预训练的权重：

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提示如下：

_IncompatibleKeys(missing_keys=[‘features.0.0.weight’, ‘features.0.1.weight’, ‘features.0.1.bias’, ‘features.0.1.running_mean’, ‘features.0.1.running_var’, ‘features.1.0.conv1.0.weight’, ‘features.1.0.conv1.1.weight’, ‘features.1.0.conv1.1.bias’, ‘features.1.0.conv1.1.running_mean’, ‘features.1.0.conv1.1.running_var’, ‘features.1.0.conv2.0.weight’, ‘features.1.0.conv2.1.weight’, ‘features.1.0.conv2.1.bias’, ‘features.1.0.conv2.1.running_mean’, ‘features.1.0.conv2.1.running_var’, ‘features.1.1.conv1.0.weight’, ‘features.1.1.conv1.1.weight’, ‘features.1.1.conv1.1.bias’, ‘features.1.1.conv1.1.running_mean’, ‘features.1.1.conv1.1.running_var’, ‘features.1.1.conv2.0.weight’, ‘features.1.1.conv2.1.weight’, ‘features.1.1.conv2.1.bias’, ‘features.1.1.conv2.1.running_mean’, ‘features.1.1.conv2.1.running_var’, ‘features.2.0.conv1.0.weight’, ‘features.2.0.conv1.1.weight’, ‘features.2.0.conv1.1.bias’, ‘features.2.0.conv1.1.running_mean’, ‘features.2.0.conv1.1.running_var’, ‘features.2.0.conv2.0.weight’, ‘features.2.0.conv2.1.weight’, ‘features.2.0.conv2.1.bias’, ‘features.2.0.conv2.1.running_mean’, ‘features.2.0.conv2.1.running_var’, ‘features.2.0.downsample.0.weight’, ‘features.2.0.downsample.1.weight’, ‘features.2.0.downsample.1.bias’, ‘features.2.0.downsample.1.running_mean’, ‘features.2.0.downsample.1.running_var’, ‘features.2.1.conv1.0.weight’, ‘features.2.1.conv1.1.weight’, ‘features.2.1.conv1.1.bias’, ‘features.2.1.conv1.1.running_mean’, ‘features.2.1.conv1.1.running_var’, ‘features.2.1.conv2.0.weight’, ‘features.2.1.conv2.1.weight’, ‘features.2.1.conv2.1.bias’, ‘features.2.1.conv2.1.running_mean’, ‘features.2.1.conv2.1.running_var’, ‘features.3.0.conv1.0.weight’, ‘features.3.0.conv1.1.weight’, ‘features.3.0.conv1.1.bias’, ‘features.3.0.conv1.1.running_mean’, ‘features.3.0.conv1.1.running_var’, ‘features.3.0.conv2.0.weight’, ‘features.3.0.conv2.1.weight’, ‘features.3.0.conv2.1.bias’, ‘features.3.0.conv2.1.running_mean’, ‘features.3.0.conv2.1.running_var’, ‘features.3.0.downsample.0.weight’, ‘features.3.0.downsample.1.weight’, ‘features.3.0.downsample.1.bias’, ‘features.3.0.downsample.1.running_mean’, ‘features.3.0.downsample.1.running_var’, ‘features.3.1.conv1.0.weight’, ‘features.3.1.conv1.1.weight’, ‘features.3.1.conv1.1.bias’, ‘features.3.1.conv1.1.running_mean’, ‘features.3.1.conv1.1.running_var’, ‘features.3.1.conv2.0.weight’, ‘features.3.1.conv2.1.weight’, ‘features.3.1.conv2.1.bias’, ‘features.3.1.conv2.1.running_mean’, ‘features.3.1.conv2.1.running_var’, ‘features.4.0.conv1.0.weight’, ‘features.4.0.conv1.1.weight’, ‘features.4.0.conv1.1.bias’, ‘features.4.0.conv1.1.running_mean’, ‘features.4.0.conv1.1.running_var’, ‘features.4.0.conv2.0.weight’, ‘features.4.0.conv2.1.weight’, ‘features.4.0.conv2.1.bias’, ‘features.4.0.conv2.1.running_mean’, ‘features.4.0.conv2.1.running_var’, ‘features.4.0.downsample.0.weight’, ‘features.4.0.downsample.1.weight’, ‘features.4.0.downsample.1.bias’, ‘features.4.0.downsample.1.running_mean’, ‘features.4.0.downsample.1.running_var’, ‘features.4.1.conv1.0.weight’, ‘features.4.1.conv1.1.weight’, ‘features.4.1.conv1.1.bias’, ‘features.4.1.conv1.1.running_mean’, ‘features.4.1.conv1.1.running_var’, ‘features.4.1.conv2.0.weight’, ‘features.4.1.conv2.1.weight’, ‘features.4.1.conv2.1.bias’, ‘features.4.1.conv2.1.running_mean’, ‘features.4.1.conv2.1.running_var’, ‘lstm.weight_ih_l0’, ‘lstm.weight_hh_l0’, ‘lstm.bias_ih_l0’, ‘lstm.bias_hh_l0’, ‘lstm.weight_ih_l0_reverse’, ‘lstm.weight_hh_l0_reverse’, ‘lstm.bias_ih_l0_reverse’, ‘lstm.bias_hh_l0_reverse’, ‘lstm.weight_ih_l1’, ‘lstm.weight_hh_l1’, ‘lstm.bias_ih_l1’, ‘lstm.bias_hh_l1’, ‘lstm.weight_ih_l1_reverse’, ‘lstm.weight_hh_l1_reverse’, ‘lstm.bias_ih_l1_reverse’, ‘lstm.bias_hh_l1_reverse’, ‘to_qkv.weight’, ‘norm.weight’, ‘norm.bias’, ‘norm.mean_weight’, ‘norm.var_weight’, ‘pwconv.weight’, ‘pwconv.bias’, ‘fc.weight’, ‘fc.bias’], unexpected_keys=[‘epoch’, ‘state_dict’, ‘best_wa’, ‘best_ua’, ‘optimizer’, ‘args’])

查看预训练的权重字典的键值发现，这些键对于模型来说都是多余的

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dict_keys([‘epoch’, ‘state_dict’, ‘best_wa’, ‘best_ua’, ‘optimizer’, ‘args’])

进一步探究发现，模型中不兼容的键都在中，我们通过提取权重字典中与模型适配的键值，修改后的代码如下：

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<All keys matched successfully>

此时我们发现预训练的模型可以很好的进行推理了！！！

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