from dcn_v2 import DCN
    input = torch.randn(2, 64, 128, 128).cuda()
    # wrap all things (offset and mask) in DCN
    dcn = DCN(64, 64, kernel_size=(3,3), stride=1, padding=1, deformable_groups=2).cuda()
    output = dcn(input)
    print(output.shape)

    from dcn_v2 import DCNPooling
    input = torch.randn(2, 32, 64, 64).cuda()
    batch_inds = torch.randint(2, (20, 1)).cuda().float()
    x = torch.randint(256, (20, 1)).cuda().float()
    y = torch.randint(256, (20, 1)).cuda().float()
    w = torch.randint(64, (20, 1)).cuda().float()
    h = torch.randint(64, (20, 1)).cuda().float()
    rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1)
    # mdformable pooling (V2)
    # wrap all things (offset and mask) in DCNPooling
    dpooling = DCNPooling(spatial_scale=1.0 / 4,
                         pooled_size=7,
                         output_dim=32,
                         no_trans=False,
                         group_size=1,
                         trans_std=0.1).cuda()
    dout = dpooling(input, rois)