Copying the weights from one model to another

vision
5

@ptrblck, Here is the code

class src_model1(nn.Module):
    def __init__(self):
        super(src_model1, self).__init__()
        resnet = models.resnet18(pretrained=True)
        layers = list(resnet.children())
        self.features = nn.Sequential(*layers[:4])

    def forward(self, x):
        return 

class dest_model1(nn.Module):
        def __init__(self):
            super(dest_model1, self).__init__()
            resnet = models.resnet18(pretrained=True)
            layers = list(resnet.children())
            self.features = nn.Sequential(*layers[:4])

        def forward(self, x):
            return self.features(x)

src_model = src_model1()
src_model = torch.load('src_model.pth')

dest_model = dest_model1()

dest_model.features[0].weight.data=src_model.features[0].weight.data.to(device).type(torch.cuda.FloatTensor)
dest_model.features[1].weight.data=src_model.features[1].weight.data.to(device).type(torch.cuda.FloatTensor)
dest_model.features[1].bias.data=src_model.features[1].bias.data.to(device).type(torch.cuda.FloatTensor)

activation = OrderedDict()
def get_activation(name):
    def hook(model, input, output):
        activation[name] = output.detach()
    return hook

for name, layer in src_model.named_modules():
    layer.register_forward_hook(get_activation(name))

output = src_model.forward(samples) # only one sample is given as input
 
activation1 = OrderedDict()
def get_activation1(name):
    def hook1(model, input, output):
        activation1[name] = output.detach()
    return hook1

for name, layer in dest_model.named_modules():
    layer.register_forward_hook(get_activation1(name))

output1 = dest_model.forward(samples) # only one sample is given as input which is same input given to src_model

mae = nn.L1Loss()

for key, key1 in zip(activation, activation1):
    print(key, key1)
    print(torch.equal(activation[key], activation1[key1]), mae(activation[key], activation1[key1]))
    print(activation[key].shape, activation1[key1].shape)

This is the flow, I used,

thanks