参考提供的引用内容，未提及Transformer模型是否具有局部特性相关信息。不过从普遍的专业知识来讲，Transformer模型本身在结构设计上初始不具备天然的局部特性。 Transformer模型以自注意力机制为核心，该机制允许模型在处理序列时对序列中的所有元素进行全局的交互和关注，也就是在计算某个位置的表示时会考虑序列中的所有位置。这使得它能够捕捉长距离依赖关系，但在处理局部信息时，并没有像卷积神经网络（CNN）那样具有内在的局部连接和局部感知能力。 然而，后续有一些基于Transformer的改进模型开始引入局部特性。例如，Swin Transformer通过引入滑动窗口机制，在窗口内计算注意力，从而在一定程度上引入了局部性，使得模型能够更好地处理图像等数据中的局部结构。 ```python # 这里简单示意一个Transformer中自注意力机制的代码框架 import torch import torch.nn as nn class SelfAttention(nn.Module): def __init__(self, input_dim, heads): super(SelfAttention, self).__init__() self.input_dim = input_dim self.heads = heads self.head_dim = input_dim // heads assert ( self.head_dim * heads == input_dim ), "Input dimension needs to be divisible by heads" self.values = nn.Linear(self.head_dim, self.head_dim, bias=False) self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False) self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False) self.fc_out = nn.Linear(heads * self.head_dim, input_dim) def forward(self, values, keys, query, mask): N = query.shape[0] value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1] # Split the embedding into self.heads different pieces values = values.reshape(N, value_len, self.heads, self.head_dim) keys = keys.reshape(N, key_len, self.heads, self.head_dim) queries = query.reshape(N, query_len, self.heads, self.head_dim) values = self.values(values) keys = self.keys(keys) queries = self.queries(queries) # Scaled dot-product attention energy = torch.einsum("nqhd,nkhd->nhqk", [queries, keys]) if mask is not None: energy = energy.masked_fill(mask == 0, float("-1e20")) attention = torch.softmax(energy / (self.input_dim ** (1 / 2)), dim=3) out = torch.einsum("nhql,nlhd->nqhd", [attention, values]).reshape( N, query_len, self.heads * self.head_dim ) out = self.fc_out(out) return out ```