I am trying to use inception model as extractor in different layers

So I implemented a class like follow:

```
class InceptExt(nn.Module):
def __init__(self, inception):
super(InceptExt, self).__init__()
self.Conv2d_1a_3x3 = inception.Conv2d_1a_3x3
self.Conv2d_2a_3x3 = inception.Conv2d_2a_3x3
self.Conv2d_2b_3x3 = inception.Conv2d_2b_3x3
self.Conv2d_3b_1x1 = inception.Conv2d_3b_1x1
self.Conv2d_4a_3x3 = inception.Conv2d_4a_3x3
self.Mixed_5b = inception.Mixed_5b
self.Mixed_5c = inception.Mixed_5c
self.Mixed_5d = inception.Mixed_5d
self.Mixed_6a = inception.Mixed_6a
self.Mixed_6b = inception.Mixed_6b
self.Mixed_6c = inception.Mixed_6c
self.Mixed_6d = inception.Mixed_6d
self.Mixed_6e = inception.Mixed_6e
self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2)
self.Mixed_7a = inception.Mixed_7a
self.Mixed_7b = inception.Mixed_7b
self.Mixed_7c = inception.Mixed_7c
def forward(self, x):
# N x 3 x 299 x 299
x = self.Conv2d_1a_3x3(x)
# N x 32 x 149 x 149
x = self.Conv2d_2a_3x3(x)
# N x 32 x 147 x 147
x = self.Conv2d_2b_3x3(x)
# N x 64 x 147 x 147
x = self.maxpool1(x)
# N x 64 x 73 x 73
x = self.Conv2d_3b_1x1(x)
# N x 80 x 73 x 73
x = self.Conv2d_4a_3x3(x)
# N x 192 x 71 x 71
x = self.maxpool2(x)
# N x 192 x 35 x 35
x = self.Mixed_5b(x)
# N x 256 x 35 x 35
x = self.Mixed_5c(x)
# N x 288 x 35 x 35
x = self.Mixed_5d(x)
# N x 288 x 35 x 35
x = self.Mixed_6a(x)
# N x 768 x 17 x 17
x = self.Mixed_6b(x)
# N x 768 x 17 x 17
x = self.Mixed_6c(x)
# N x 768 x 17 x 17
x = self.Mixed_6d(x)
# N x 768 x 17 x 17
x = self.Mixed_6e(x)
# N x 768 x 17 x 17
x = self.Mixed_7a(x)
# N x 1280 x 8 x 8
x = self.Mixed_7b(x)
# N x 2048 x 8 x 8
x = self.Mixed_7c(x)
# N x 2048 x 8 x 8
return x
inception = torchvision.models.inception_v3(pretrained=True)
my_inception = InceptExt(inception)
```

I am wondering if the above code takes the pretrained weights to the preserved layers or not

If it does not, could you suggest a way how to load the weights from the pretrained model to the modified one ?