day任务是写transformer的block

看了一下参数，是写vit，但是函数的写法应该是借鉴的swintransformer，所以我是照着swin写的，去掉了swin的部分。注意一点在normlayer的写法上要加上eval()来声明，作业里传的是字符串，而不是类。ps：今天没GPU训的有点慢，开始写的时候就剩4小时截至了就不放结果了，以下是我实现的代码

# Block类实现Transformer encoder的一个层
class Block(nn.Layer):
    # 定义Transformer层
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer='nn.LayerNorm', epsilon=1e-5):
        super().__init__()
        # 此处添加代码
        self.dim = dim
        self.num_heads = num_heads
        self.mlp_ratio = mlp_ratio
        self.norm1 = eval(norm_layer)(dim)
        self.attn = Attention(self.dim, num_heads=self.num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
        self.norm2 = eval(norm_layer)(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x):
        # 此处添加代码
        shortcut = x
        x = self.norm1(x)
        attn = self.attn(x)
        x = shortcut + self.drop_path(attn)
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x