visionTransformer window平台下报错-Toy模板网

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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")]))