1、安装DCNv2

（1）github上最新版的DCNv2源码在"https://github.com/CharlesShang/DCNv2"，但是该版本源码不支持PyTorch1.7，如果使其支持PyTorch1.7需要做以下修改：
将DCNv2/src/cuda/dcn_v2_cuda.cu中所有的THCState_getCurrentStream(state)替换成c10::cuda::getCurrentCUDAStream()。
（2）github上有支持PyTorch1.7的源码，“https://github.com/lbin/DCNv2/tree/pytorch_1.7”，可以直接使用。

2、 Deformable Convolutional Networks V2 with Pytorch 1.0，不支持PyTorch1.7

Build

    ./make.sh         # build
    python test.py    # run examples and gradient check

An Example

deformable conv

    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)

deformable roi pooling

    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)