Modifying pre-trained gluon model zoo

Gluon
2

I think the problem in your code is that it is not recursively replacing the layers and as such you get a mismatch in the tensor shapes. The following code should work:

def replace_conv2D(net):
    for key, layer in net._children.items():
        if isinstance(layer, gluon.nn.Conv2D):
            new_conv = gluon.nn.Conv2D(
                channels=layer._channels // 2,
                kernel_size=layer._kwargs['kernel'],
                strides=layer._kwargs['stride'],
                padding=layer._kwargs['pad'],
                in_channels=layer._in_channels // 2)
            with net.name_scope():
                net.register_child(new_conv, key)
            new_conv.initialize(mx.init.Xavier())
        else:
            replace_conv2D(layer)
net = gluon.model_zoo.vision.get_model("resnet18_v1", pretrained=True)
replace_conv2D(net)