You could use forward hooks to print the std deviation of the layer outputs:
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 3, 1, 1)
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(6, 1, 3, 1, 1)
        self.pool2 = nn.MaxPool2d(2)
        self.fc = nn.Linear(6*6, 10)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool1(x)
        x = F.relu(self.conv2(x))
        x = self.pool2(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


model = MyModel()
model.conv1.register_forward_hook(lambda m, x, out: print(out.std()))
model.conv2.register_forward_hook(lambda m, x, out: print(out.std()))
model.fc.register_forward_hook(lambda m, x, out: print(out.std()))
x = torch.randn(1, 3, 24, 24)
output = model(x)