k,v = self.conv_kv(pre_ln).chunk(2, dim=1)
时间: 2024-02-22 21:01:35 浏览: 21
这是一个神经网络模型的代码片段,其中self.conv_kv是一个卷积层序列,pre_ln是输入的特征向量,chunk是一个张量分块函数,用于将一个张量按照指定的维度进行分块。dim=1表示在通道维度上进行分块,将输入特征向量在通道维度上分成两份,即k和v。由于self.conv_kv包含两个卷积层,因此k和v的通道数都是输入通道数的一半。
相关问题
x = self.conv1(self.af(feature)) x = self.af(x) x = self.conv2(x) residual = self.conv_res(feature) return x + residual 定位到代码这里
根据你提供的代码,错误定位在以下代码段中:
```python
x = self.conv1(self.af(feature))
x = self.af(x)
x = self.conv2(x)
residual = self.conv_res(feature)
return x + residual
```
这个错误通常是由于就地修改视图引起的。在这个代码段中,`self.af` 方法可能会返回一个视图,并且在这个视图上进行就地修改,导致出现错误。
为了解决这个问题,你可以尝试使用非就地操作来替换就地修改操作。你可以使用类似 `torch.clone()` 或 `torch.tensor.clone()` 的方法创建一个新的张量,并对其进行修改。例如,你可以修改代码如下:
```python
x = self.conv1(self.af(feature).clone())
x = self.af(x.clone())
x = self.conv2(x.clone())
residual = self.conv_res(feature)
return x + residual
```
这样,就会创建 `self.af(feature)` 的副本,并在副本上进行操作,而不是直接修改原始视图。同样地,对于 `x` 张量的操作也要使用副本进行。这样可以避免出现就地修改错误。
class Partial_conv3(nn.Module): def __init__(self, dim, n_div, forward): super().__init__() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.global_pool = GlobalAvgPool2d() if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.partial_conv3(x1) x1 = self.global_pool(x1) x = torch.cat((x1, x2), 1) return x在这段代码中插入全局平均池化做通道增强的模块
你可以在forward_slicing和forward_split_cat的输出结果之前加入一个全局平均池化层进行通道增强,代码如下:
```
class Partial_conv3(nn.Module):
def __init__(self, dim, n_div, forward):
super().__init__()
self.dim_conv3 = dim // n_div
self.dim_untouched = dim - self.dim_conv3
self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False)
self.global_pool = GlobalAvgPool2d()
self.channel_enhance = nn.Sequential(
nn.Conv2d(dim, dim, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(dim),
nn.ReLU(inplace=True),
self.global_pool
)
if forward == 'slicing':
self.forward = self.forward_slicing
elif forward == 'split_cat':
self.forward = self.forward_split_cat
else:
raise NotImplementedError
def forward_slicing(self, x: Tensor) -> Tensor:
# only for inference
x = x.clone() # !!! Keep the original input intact for the residual connection later
x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :])
x = self.channel_enhance(x)
return x
def forward_split_cat(self, x: Tensor) -> Tensor:
x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1)
x1 = self.partial_conv3(x1)
x1 = self.channel_enhance(x1)
x = torch.cat((x1, x2), 1)
return x
```
这里使用了一个nn.Sequential模块,包含了一个1x1的卷积层、BatchNorm层、ReLU激活层和全局平均池化层,对输入的特征图进行通道增强,从而提高模型的性能。在forward_slicing和forward_split_cat的输出结果之前,将输入特征图通过这个通道增强模块之后再输出。