8cc023c568f2cdcdf5c34c5fa1b76090073bca73,gluon/gluoncv2/models/resattnet.py,,_test,#,524

Before Change

        assert (model != resattnet236 or weight_count == 103778984)
        assert (model != resattnet452 or weight_count == 182285224)

        x = Variable(torch.randn(1, 3, 224, 224))
        y = net(x)
        assert (tuple(y.size()) == (1, 1000))

After Change

        net = model(pretrained=pretrained)

        ctx = mx.cpu()
        if not pretrained:
            net.initialize(ctx=ctx)

        // net.hybridize()
        net_params = net.collect_params()
        weight_count = 0
        for param in net_params.values():
            if (param.shape is None) or (not param._differentiable):
                continue
            weight_count += np.prod(param.shape)
        print("m={}, {}".format(model.__name__, weight_count))
        assert (model != resattnet56 or weight_count == 31810728)
        assert (model != resattnet92 or weight_count == 52466344)
        assert (model != resattnet128 or weight_count == 65294504)
        assert (model != resattnet164 or weight_count == 78122664)
        assert (model != resattnet200 or weight_count == 90950824)
        assert (model != resattnet236 or weight_count == 103778984)
        assert (model != resattnet452 or weight_count == 182285224)

        x = mx.nd.zeros((1, 3, 224, 224), ctx=ctx)
        y = net(x)
        assert (y.shape == (1, 1000))

Instances