self.norm2 = nn.LayerNorm(dim) if sr_ratio==2: self.sr1 = nn.Conv2d(dim, dim, kernel_size=2, stride=2) self.norm1 = nn.LayerNorm(dim) self.sr2 = nn.Conv2d(dim, dim, kernel_size=1, stride=1) self.norm2 = nn.LayerNorm(dim) self.kv1 = nn.Linear(dim, dim, bias=qkv_...
norm1 = norm_layer(dim) self.class_token = class_token if tokens_type == 'transformer': self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) elif tokens_type == 'performer': self.attn = AttentionPerformer(...
self.sr2 = nn.Conv2d(dim, dim, kernel_size=1, stride=1) self.norm2 = nn.LayerNorm(dim) self.kv1 = nn.Linear(dim, dim, bias=qkv_bias) self.kv2 = nn.Linear(dim, dim, bias=qkv_bias) self.local_conv1 = nn.Conv2d(dim//2, dim//2, kernel_size=3, padding=1, stride=1, ...
self.norm2 = 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_ratio) def forward(self, x): x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self...
1、CrossNorm / SelfNorm 现有的传统归一化方法(如 Batch Normalization 和 Instance Normalization),其假设训练数据和测试数据来自同一分布,这在实际应用中往往不成立。这篇论文提出两种归一化方式交叉归一化(CrossNorm )和 自归一化(SelfNorm ),CrossNorm 和 SelfNorm 旨在解决深度学习模型在面对数据分布变化时泛化能...
LayerNorm(dim) self.sr2 = nn.Conv2d(dim, dim, kernel_size=4, stride=4) self.norm2 = nn.LayerNorm(dim) self.local_conv1 = nn.Conv2d(dim//2, dim//2, kernel_size=3, padding=1, stride=1, groups=dim//2) self.local_conv2 = nn.Conv2d(dim//2, dim//2, kernel_size=3, ...
if self.layer==1: # 避免進入 InstanceNorm (1,1) 出錯 if self.norm_layer == nn.InstanceNorm2d and down_x.shape[2] == 1 and down_x.shape[3] == 1: pass else: down_x = self.down_norm(down_x) down_x = F.leaky_relu(down_x, 0.2, inplace=True) ...
激活函数σ的Lipschitz常数趋近1(如使用LayerNorm后的Softmax) 输入X的流形结构被编码到权重空间W中 参数更新方程∇W = η(X⊗δ) 将导致动力系统出现奇异吸引子 哥德尔不完备性的认知实现: 在1e23参数尺度下,网络隐状态空间H的维度超过3×10^22,这与人脑神经活动空间(~1e86种可能状态)形成代数对应。此时:...
计算机视觉(computer vision)中的注意力机制(attention)的基本思想就是想让系统学会注意力——能够忽略无关信息而关注重点信息。
1、标准Layer Norm 在Transformer中LayerNorm是对(batch_size, seq_length, hidden_size)中的hidden_size维度进行normalize。具体来说,给定一个向量 x,则normalize的过程为: 其中, E(x)表示向量x的期望,Var(x)是向量x的方差, ϵ是为了防止分母为0的偏置项,γ和β是两个可学习参数。