activation = {}
    ofmap = {}
    def get_ofmap(name):
        def hook(model, input, output):
            ofmap[name] = output.detach()
        return hook
    def get_activation(name):
        def hook(model, input, output):
            activation[name] = input[0].detach()
        return hook

    for idx, (data, target) in enumerate(tqdm.tqdm(ds, total=n_sample)):
        n_passed += len(data)
        data =  Variable(torch.FloatTensor(data)).cuda()
        indx_target = torch.LongTensor(target)
        h1 = model.module.model._modules["features"]._modules.get("0_linear_quant").register_forward_hook(get_activation('0_linear_quant'))
        h2 = model.module.model._modules["features"]._modules.get("0").register_forward_hook(get_ofmap('fea0'))

        output = model(data)
        bs = output.size(0)

        torch.save(activation['0_linear_quant'], './LQ0_IFMAP.pt')
        torch.save(ofmap['fea0'], './CONV0_OFMAP.pt')

Thanks for your help!
But I tried that solution before but the output of h1 and h2 is different.
Because 0_LinearQuant layer’s input feature map is output of features.0 layer, h1 and h2 should be same.