x = torch.cat((x_s, x_t), dim=0)
时间: 2024-06-04 19:06:29 浏览: 5
这行代码使用了 PyTorch 中的 `torch.cat()` 函数,它用于将多个张量(tensor)沿着指定的维度进行拼接(concatenate)。在这里,`x_s` 和 `x_t` 是两个张量(tensor),`dim=0` 表示沿着第0维(即行)进行拼接。因此,这行代码的作用是将 `x_s` 和 `x_t` 沿着行方向拼接成一个新的张量 `x`。
相关问题
matching_bs[i] = torch.cat(matching_bs[i], dim=0) RuntimeError: torch.cat(): expected a non-empty list of Tensors
这个错误提示是因为`matching_bs[i]`这个列表中有一个或多个元素为空,而`torch.cat()`函数需要至少一个非空张量作为输入。你可以在调用`torch.cat()`函数之前检查一下`matching_bs[i]`列表中的元素是否为空,如果为空则需要先将其填充为一个非空张量。
以下是一个可能的解决方案:
```python
# 先检查matching_bs[i]列表中是否有空元素
if any([x is None for x in matching_bs[i]]):
# 如果有空元素,则将其替换为一个空的1维张量
matching_bs[i] = [torch.tensor([]) if x is None else x for x in matching_bs[i]]
# 然后再调用torch.cat()函数
matching_bs[i] = torch.cat(matching_bs[i], dim=0)
```
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) 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) 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
可以将全局平均池化放在每个 forward 函数的最后,对每个 channel 进行平均池化操作,然后将结果与未被改变的 channel 拼接起来,得到增强后的输出。修改后的代码如下:
```
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_avg_pool = nn.AdaptiveAvgPool2d((1, 1)) # 添加全局平均池化层
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.dim_conv3, :, :] = self.global_avg_pool(x[:, :self.dim_conv3, :, :]) # 添加全局平均池化
x = torch.cat((x, x[:, self.dim_conv3:, :, :]), 1)
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_avg_pool(x1) # 添加全局平均池化
x = torch.cat((x1, x2), 1)
return x
```