Layer-Folding和DepthShrinker在块内移除非线性激活函数,并使用结构重参化技术将多个层合并为单个层。Layer-Folding和DepthShrinker只在一个或几个有限模型上进行了验证,而ReLU的硬性移除可能会对子网的准确性产生影响。 Transformer家族模型在各种视觉任务中表现出色;然而,其高推理成本和显著的内存占用限制了其广泛的应用。
Layer-Folding和DepthShrinker在块内移除非线性激活函数,并使用结构重参化技术将多个层合并为单个层。Layer-Folding和DepthShrinker只在一个或几个有限模型上进行了验证,而ReLU的硬性移除可能会对子网的准确性产生影响。 Transformer家族模型在各种视觉任务中表现出色;然而,其高推理成本和显著的内存占用限制了其广泛的应用。
super().__init__() self.gn = GroupBatchnorm2d(oup_channels, group_num=group_num) self.gate_threshold = gate_threshold self.sigmoid = nn.Sigmoid() def forward(self, x): gn_x = self.gn(x) w_gamma = self.gn.gamma / sum(self.gn.gamma) reweights = self.sigmoid(gn_x * w_gamma...
x = F.relu(x) return x 1. 2. 3. 4. 5. 6. 步骤五:编写测试代码进行验证 编写测试代码对gnconv模块进行验证: #创建输入数据和邻接矩阵x = torch.randn(10, 5) # 输入数据维度为10x5 adj = torch.randn(10, 10) # 邻接矩阵维度为10x10#创建gnconv模块并进行前向传播gnconv = GNConv(5, 3,...
gn(x) # 组归一化 x = self.act(x) # 激活函数 return x class DSC(object): def __init__(self, input_shape, kernel_size, extend_scope, morph): self.num_points = kernel_size # 卷积核的点数 self.width = input_shape[2] # 输入特征图的宽度 self.height = input_shape[3] # 输入...
, act=nn.ReLU(), n_levels=4, n_points=4): """初始化DeformableTransformerDecoderLayer,指定参数。""" super().__init__() self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) # 自注意力机制 self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points...
为了解决上述问题,本研究提出了一种改进的YOLOv8车辆测距预警系统,即融合空间和通道重建卷积(Spatial and Channel Reconstructive Convolution,简称SCConv)。SCConv是一种新型的卷积操作,它能够在保持空间信息的同时,有效地提取目标的通道特征。通过引入SCConv,我们可以改善YOLOv8在小目标检测和目标遮挡方面的性能,从而提高...
def __init__(self, inplanes, gn=False): super(hourglass, self).__init__() self.conv1 = nn.Sequential(convbn_3d(inplanes, inplanes * 2, kernel_size=3, stride=2, pad=1, gn=gn), nn.ReLU(inplace=True)) self.conv2 = convbn_3d(inplanes * 2, inplanes * 2, kernel_size=3...
(ReLU) or more recently Leaky ReLU58; The symbol (\(\circledast\)) acts as a convolution operation which usessharedweights to reduce expensive matrix computation59; Window (\(\boxplus _{n,m}\)) shows an average or a max pooling operation which computes average or max values over ...
ReLU, nn.ReLU6, nn.SiLU, Detect, Model]: m.inplace = True # pytorch 1.7.0 compatibility elif type(m) is Conv: m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility from_to_map = pruned_model.from_to_map pruned_model_state = pruned_model.state_dict() ...