forward() 需要 1 个位置参数，但给出了 2 个

Question

我正在尝试使用EfficientNet-B0构建模型。 模型的详细信息显示在下面的代码中。 当我尝试学习时，我遇到了以下错误。

TypeError                                 Traceback (most recent call last)
'''
<ipython-input-17-fb3850894108> in forward(self, *x)
24         #x: bs*N x 3 x 128 x 128
25         print(x.shape)     #([384, 3, 224, 224])
---> 26         x = self.enc(x)
27         #x: bs*N x C x 4 x 4
28         shape = x.shape
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
548             result = self._slow_forward(*input, **kwargs)
549         else:
--> 550             result = self.forward(*input, **kwargs)
551         for hook in self._forward_hooks.values():
552             hook_result = hook(self, input, result)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
98     def forward(self, input):
99         for module in self:
--> 100             input = module(input)
101         return input
102 
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
548             result = self._slow_forward(*input, **kwargs)
549         else:
--> 550             result = self.forward(*input, **kwargs)
551         for hook in self._forward_hooks.values():
552             hook_result = hook(self, input, result)
TypeError: forward() takes 1 positional argument but 2 were given

我怀疑m.children((可能会产生影响。 如果有人知道此错误的原因，请告诉我。 谢谢。

class Model(nn.Module):
def __init__(self, arch='efficientnet-b0', n=6, pre=True):
super().__init__()
m = EfficientNet.from_pretrained('efficientnet-b0')
#print(*list(m.children())
nc = m._fc.in_features
print(nc)
self.enc = nn.Sequential(*list(m.children())[:-2])
#nc = list(m.children())[-2].in_features
self.head = nn.Sequential(*list(m.children())[-2:])
self.head._fc = nn.Linear(nc, n)
#self.enc = nn.Sequential(*list(m.children()))
#print('fc_infeatures : {}'.format(nc))
#self.head = nn.Sequential(Flatten(),nn.Linear(nc,512),
#                    relu(),nn.BatchNorm1d(512), nn.Dropout(0.5),nn.Linear(512,n))

def forward(self, *x):
print(x[0].shape)  #([32, 3, 224, 224])
shape = x[0].shape
n = len(x)           # n = 12
#torch.stack直後では32*12*3*224*224(bs x N x 3 x 224 x 224)
x = torch.stack(x,1).view(-1,shape[-3],shape[-2],shape[-1])
#x: bs*N x 3 x 128 x 128
print(x.shape)     #([384, 3, 224, 224])
x = self.enc(x)
#x: bs*N x C x 4 x 4
shape = x.shape
#concatenate the output for tiles into a single map
x = x.view(-1,n,shape[1],shape[2],shape[3]).permute(0,2,1,3,4).contiguous()
.view(-1,shape[1],shape[2]*n,shape[3])
#x: bs x C x N*4 x 4
x = self.head(x)
return x

Answer 1

现在，您可以使用include_top参数轻松获取没有最后层的网络：

m = EfficientNet.from_pretrained('efficientnet-b0', include_top=False)

从代码中很容易看出，它的作用不是为最后一层(AveragePool、Dropout、FC(调用前向方法。

其他替代方法可总结如下：

1. 定义一个新的MyEfficientNet，覆盖转发方法以避免调用最后一层

2. 使用标识层覆盖不需要的图层。例如：

   m._fc = nn。身份((

当使用高效网络作为特征提取器时，应首选第一种方法，而当需要更多自定义时，应使用其他方法。