本文主要是介绍visionTransformer window平台下报错，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
错误：
KeyError: 'Transformer/encoderblock_0/MlpBlock_3/Dense_0kernel is not a file in the archive'
解决方法：
修改这个函数即可，主要原因是Linux系统与window系统路径分隔符不一样导致
def load_from(self, weights, n_block):ROOT = f"Transformer/encoderblock_{n_block}"with torch.no_grad():# query_weight = np2th(weights[pjoin(ROOT, ATTENTION_Q, "kernel")]).view(self.hidden_size, self.hidden_size).t()# key_weight = np2th(weights[pjoin(ROOT, ATTENTION_K, "kernel")]).view(self.hidden_size, self.hidden_size).t()# value_weight = np2th(weights[pjoin(ROOT, ATTENTION_V, "kernel")]).view(self.hidden_size, self.hidden_size).t()# out_weight = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "kernel")]).view(self.hidden_size, self.hidden_size).t()query_weight = np2th(weights[(ROOT + '/' + ATTENTION_Q + "/kernel")]).view(self.hidden_size,self.hidden_size).t()key_weight = np2th(weights[(ROOT + '/' + ATTENTION_K + "/kernel")]).view(self.hidden_size,self.hidden_size).t()value_weight = np2th(weights[(ROOT + '/' + ATTENTION_V + "/kernel")]).view(self.hidden_size,self.hidden_size).t()out_weight = np2th(weights[(ROOT + '/' + ATTENTION_OUT + "/kernel")]).view(self.hidden_size,self.hidden_size).t()# query_bias = np2th(weights[pjoin(ROOT, ATTENTION_Q, "bias")]).view(-1)# key_bias = np2th(weights[pjoin(ROOT, ATTENTION_K, "bias")]).view(-1)# value_bias = np2th(weights[pjoin(ROOT, ATTENTION_V, "bias")]).view(-1)# out_bias = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "bias")]).view(-1)query_bias = np2th(weights[(ROOT + '/' + ATTENTION_Q + "/bias")]).view(-1)key_bias = np2th(weights[(ROOT + '/' + ATTENTION_K + "/bias")]).view(-1)value_bias = np2th(weights[(ROOT + '/' + ATTENTION_V + "/bias")]).view(-1)out_bias = np2th(weights[(ROOT + '/' + ATTENTION_OUT + "/bias")]).view(-1)self.attn.query.weight.copy_(query_weight)self.attn.key.weight.copy_(key_weight)self.attn.value.weight.copy_(value_weight)self.attn.out.weight.copy_(out_weight)self.attn.query.bias.copy_(query_bias)self.attn.key.bias.copy_(key_bias)self.attn.value.bias.copy_(value_bias)self.attn.out.bias.copy_(out_bias)mlp_weight_0 = np2th(weights[(ROOT + '/' + FC_0 + "/kernel")]).t()mlp_weight_1 = np2th(weights[(ROOT + '/' + FC_1 + "/kernel")]).t()mlp_bias_0 = np2th(weights[(ROOT + '/' + FC_0 +"/bias")]).t()mlp_bias_1 = np2th(weights[(ROOT + '/' + FC_1 + "/bias")]).t()self.ffn.fc1.weight.copy_(mlp_weight_0)self.ffn.fc2.weight.copy_(mlp_weight_1)self.ffn.fc1.bias.copy_(mlp_bias_0)self.ffn.fc2.bias.copy_(mlp_bias_1)self.attention_norm.weight.copy_(np2th(weights[(ROOT + '/' + ATTENTION_NORM + "/scale")]))self.attention_norm.bias.copy_(np2th(weights[(ROOT + '/' + ATTENTION_NORM + "/bias")]))self.ffn_norm.weight.copy_(np2th(weights[(ROOT + '/' + MLP_NORM + "/scale")]))self.ffn_norm.bias.copy_(np2th(weights[(ROOT + '/' + MLP_NORM + "/bias")]))
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