q = rearrange(q, 'b(headc) h w -> b head c (hw)', head=self.num_heads) k = rearrange(k, 'b(headc) h w -> b head c (hw)', head=self.num_heads) v = rearrange(v, 'b(headc) h w -> b head c (hw)', head=self.num_heads) q = torch.nn.functional.normalize(q,...
【CVPR2022】On the Integration of Self-Attention and Convolution 论文地址:https://arxiv.org/pdf/2111.14556.pdf 代码地址:https://github.com/LeapLabTHU/ACmix 卷积和自
While convolutions are translation equivariant and not invariant, an approximative translation invariance can be achieved in neural networks by combining convolutions with spatial pooling operators. https://chriswolfvision.medium.com/what-is-translation-equivariance-and-why-do-we-use-convolutions-to-get-...
这里的n_heads是原始Multi-head的头数，n_kv_heads是Grouped-query分组后每组中key-value对的数量。
在图像上操作的模型时至关重要的归纳偏置(inductive biase). 但是卷积的局部性质(the local nature of the convolutional kernel)阻碍了其捕获全局的上下文信息(global context), 而这些信息对于图像识别是很必要的. 这是卷积的重要的弱点. (convolution operator is limited by its locality and lack of understanding...
self.num_heads=num_heads self.embed_dims=embed_dims self.token_dims=token_dims self.in_chans=in_chans self.downsample_ratios=downsample_ratios self.kernel_size=kernel_size self.outSize=img_size PCMStride=[]residual=downsample_ratios// 2for_inrange(3):PCMStride.append((residual>0)+1)residual...
【CVPR2022】On the Integration of Self-Attention and Convolution 论文地址:arxiv.org/pdf/2111.1455 代码地址:https://github.com/LeapLabTHU/ACmix 卷积和自注意力是表征学习的两种强大技术,它们通常被认为是两种截然不同的对等方法。在这个论文中,作者表明它们之间存在很强的潜在关系,并将二者高效结合在一起。
self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 #(sr_ratio+1)x (sr_ratio+1) 的 DW 卷积 self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio + 1, stride=sr_ratio, padding=sr_ratio // 2, groups=dim) self.sr_norm = nn.LayerNorm(...
Intuitively, the more attention a node attracts, the more influential and vital it is. In this paper, we measure the influence of nodes by adding its neighborhoods’ attention values towards the central node across the second layer’s heads (see Methods for detail). The attention accumulating ...
By using 128x128 image pixels resolution, training using 16 attention heads, the ConViT model has achieved an accuracy of 98.01%, sensitivity of 90.83%, specificity of 99.69%, positive predictive value (PPV) of 95.58%, negative predictive value (NPV) of 97.89% and F1-score of 94.55%. The...