how Pruning the last conv layer affects the first linear layer of the classifier

[Saharkakavand]

I trained the vgg and saved the model as pth file. then I load it for pruning some filters of it.
the last conv after pruning is not 512 anymore, some filters are gone.
how Pruning the last conv layer affects the first linear layer of the classifier which is (512 7 7, 4096).
how can I prune the input weights of classifier according to the last conv layer.

[Eric-mingjie]

how Pruning the last conv layer affects the first linear layer of the classifier which is (512 7 7, 4096).

It changed the input dimension of the first fc layer.

how can I prune the input weights of classifier according to the last conv layer.

The input weights of classifier does not change with the last conv layer.

[Saharkakavand]

how Pruning the last conv layer affects the first linear layer of the classifier which is (512 7 7, 4096).

It changed the input dimension of the first fc layer.

how can I prune the input weights of classifier according to the last conv layer.

The input weights of classifier does not change with the last conv layer.

nn.Conv2d(512, 512, 3, padding=1), is changed to nn.Conv2d(450, 412, 3, padding=1)
how this one will change: nn.Linear(in_features=512 * 7 * 7, out_features=4096, bias=True),

class VGGOWN(nn.Module):

def __init__(self):
    super(VGGOWN, self).__init__()
    self.features = nn.Sequential(
        nn.Conv2d(3, 64, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(64, 64, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, stride=2, ceil_mode=True),  # 1/2

        # conv2
        nn.Conv2d(64, 128, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(128, 128, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, stride=2, ceil_mode=True),  # 1/4

        # conv3
        nn.Conv2d(128, 256, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(256, 256, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(256, 256, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, stride=2, ceil_mode=True), # 1/8

        # conv4
        nn.Conv2d(256, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, stride=2, ceil_mode=True),  # 1/16

        # conv5
        nn.Conv2d(512, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/32
    )
    self.avgpool = nn.AdaptiveAvgPool2d((7, 7))

    self.classifier = nn.Sequential(

        nn.Linear(in_features=512 * 7 * 7, out_features=4096, bias=True),
        nn.ReLU(inplace=True),
        nn.Dropout(p=0.5),
        nn.Linear(in_features=4096, out_features=4096, bias=True),
        nn.ReLU(inplace=True),
        nn.Dropout(p=0.5),
        nn.Linear(in_features=4096, out_features=1000, bias=True)
    )

def forward(self, *input):
    x = self.features(input)
    #print(x.size)
    x = self.avgpool(x)
    #print(x.size)
    x = x.view(x.size(0), -1)
    #print(x.size)
    y = self.classifier(x)
    return y

thanks

[Eric-mingjie]

Should be nn.Linear(in_features=450 * 7 * 7, out_features=4096, bias=True) now if i understand the definition of nn.conv2d correctly.

[Saharkakavand]

when I load the pretrained state-dict it has already (25088, 4096) weights in linear layer how can I know which one should be prun?
I know which filters I pruned in prev layer I dont know how conv layer weights is mapped to next layer

[Eric-mingjie]

Please try to understand vggprune.py in detail. Then you will know.