竞赛资料源码-首届“全国人工智能大赛”（AI+4K HDR赛项）.zip

## Deformable Convolutional Networks V2 with Pytorch 1.0 ### Build ```bash ./make.sh # build python test.py # run examples and gradient check ``` ### An Example - deformable conv ```python 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 ```python 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) ``` ### Note Now the master branch is for pytorch 1.0 (new ATen API), you can switch back to pytorch 0.4 with, ```bash git checkout pytorch_0.4 ``` ### Known Issues: - [x] Gradient check w.r.t offset (solved) - [ ] Backward is not reentrant (minor) This is an adaption of the official [Deformable-ConvNets](https://github.com/msracver/Deformable-ConvNets/tree/master/DCNv2_op). <s>I have ran the gradient check for many times with DOUBLE type. Every tensor **except offset** passes. However, when I set the offset to 0.5, it passes. I'm still wondering what cause this problem. Is it because some non-differential points? </s> Update: all gradient check passes with double precision. Another issue is that it raises `RuntimeError: Backward is not reentrant`. However, the error is very small (`<1e-7` for float `<1e-15` for double), so it may not be a serious problem (?) Please post an issue or PR if you have any comments.