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.global_pool(x1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) return x这段代码有什么错误
时间: 2024-02-14 22:15:20 浏览: 64
这段代码并没有明显的语法错误,但是存在一些潜在的问题和需要改进的地方:
1. 类名应该以大写字母开头,应该将 `Partial_conv3` 改为 `PartialConv3`。
2. `super().init()` 应该改为 `super().__init__()`。
3. `self.forward` 方法应该在 `init` 方法中定义,在 `init` 中加入 `self.forward = None`。
4. `forward_split_cat` 方法中,应该先使用 `self.partial_conv3` 对 `x1` 进行卷积操作,再将 `x1` 和 `x2` 进行拼接,即应该将 `x1 = self.partial_conv3(x1)` 放在 `torch.cat((x1, x2), 1)` 之前。
5. `dim` 应该改为 `in_channels`,以更加清晰地表示输入张量的通道数。
6. `forward` 方法的输入参数应该改为 `inputs`,以更加符合命名规范。
以下是修改后的代码:
```
class PartialConv3(nn.Module):
def __init__(self, in_channels, n_div, forward):
super().__init__()
self.dim_conv3 = in_channels // n_div
self.dim_untouched = in_channels - self.dim_conv3
self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False)
self.global_pool = GlobalAvgPool2d()
self.forward = None
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, inputs: Tensor) -> Tensor:
# only for inference
x = inputs.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, inputs: Tensor) -> Tensor:
x1, x2 = torch.split(inputs, [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
```
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