Is there a way to load specific layers of convolutional layers (e.g. only 1-3), when I saved the model in following way:
torch.save({'state_dict': net.state_dict(),'optimizer': optimizer.state_dict()},PATH)

instead of
torch.save(net.state_dict(), PATH)

My network looks like:
        self.conv.add_module('conv1_s1',nn.Conv2d(3, 32, kernel_size=9, stride=1, padding=0))
        self.conv.add_module('relu1_s1',nn.ReLU(inplace=True))
        self.conv.add_module('pool1_s1',nn.MaxPool2d(kernel_size=3, stride=1))                           

        self.conv.add_module('conv2_s1',nn.Conv2d(32, 48, kernel_size=3, padding=2, groups=2))
        self.conv.add_module('relu2_s1',nn.ReLU(inplace=True))
        self.conv.add_module('pool2_s1',nn.MaxPool2d(kernel_size=3, stride=2))

        self.conv.add_module('conv3_s1',nn.Conv2d(48, 96, kernel_size=3, padding=2, groups=2))
        ..
        self.conv.add_module('conv4_s1',nn.Conv2d(96, 96, kernel_size=3, padding=2, groups=2))
        ..
        self.conv.add_module('conv5_s1',nn.Conv2d(96, 48, kernel_size=3, padding=2, groups=2))



I tried to load the layer partially from saved model like:
        checkpointL = torch.load(PATH)
        originalNetwork.load_state_dict(checkpointL['conv.conv1_s1.weight'])
        originalNetwork.load_state_dict(checkpointL['conv.conv1_s1.bias'])

and got error: KeyError: 'conv.conv1_s1.weight'
Do I have to save the model using torch.save(net.state_dict(), PATH) in order to be able to load only specific layers of my convolutional layers.
Thanks for help!