git clone https://github.com/gmalivenko/gluon2pytorch
cd gluon2pytorch
pip install -e . 

pip install gluon2pytorch

class ReLUTest(mx.gluon.nn.HybridSequential):
    def __init__(self):
        super(ReLUTest, self).__init__()
        from mxnet.gluon import nn
        with self.name_scope():
            self.conv1 = nn.Conv2D(3, 32)
            self.relu = nn.Activation('relu')

    def hybrid_forward(self, F, x):
        x = F.relu(self.relu(self.conv1(x)))
        return x


if __name__ == '__main__':
    net = ReLUTest()
    
    # Make sure it's hybrid and initialized
    net.hybridize()
    net.collect_params().initialize()

    pytorch_model = gluon2pytorch(net, [(1, 3, 224, 224)], dst_dir=None, pytorch_module_name='ReLUTest')

    import torch
    input_np = np.random.uniform(-1, 1, (1, 3, 224, 224))

    gluon_output = net(mx.nd.array(input_np))
    pytorch_model.eval()
    pytorch_output = pytorch_model(torch.FloatTensor(input_np))
    check_error(gluon_output, pytorch_output)