eea901c51359e07debed2f5ec84298eb5319d281,pytorch/pytorchcv/models/shufflenet.py,,_test,#,472

Before Change

        // net.train()
        net.eval()
        net_params = filter(lambda p: p.requires_grad, net.parameters())
        weight_count = 0
        for param in net_params:
            weight_count += np.prod(param.size())
        assert (model != shufflenet_g1_w1 or weight_count == 1531936)
        assert (model != shufflenet_g2_w1 or weight_count == 1733848)
        assert (model != shufflenet_g3_w1 or weight_count == 1865728)
        assert (model != shufflenet_g4_w1 or weight_count == 1968344)

After Change

        // net.train()
        net.eval()
        weight_count = _calc_width(net)
        print("m={}, {}".format(model.__name__, weight_count))
        assert (model != shufflenet_g1_w1 or weight_count == 1531936)
        assert (model != shufflenet_g2_w1 or weight_count == 1733848)
        assert (model != shufflenet_g3_w1 or weight_count == 1865728)
        assert (model != shufflenet_g4_w1 or weight_count == 1968344)

Instances