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Thanks for your contribution. However, this function requires creating two existing coords map A.C, B.C. |
chrischoy
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Apr 30, 2020
| assert cm == B.coords_man, "different coords_man" | ||
| assert A.tensor_stride == B.tensor_stride, "different tensor_stride" | ||
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| zeros_cat_with_A = torch.zeros([A.F.shape[0], B.F.shape[1]]).to(A.device) |
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dtype specification required.
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zeros(shape, dtype=A.device)
| feats_A = torch.cat([A.F, zeros_cat_with_A], dim=1) | ||
| feats_B = torch.cat([zeros_cat_with_B, B.F], dim=1) | ||
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| new_A = SparseTensor( |
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Unnecessary coordinate creation. This essentially force create the same existing coords hash map.
Try using get_union_map. https://github.com/StanfordVL/MinkowskiEngine/blob/ded4c0c42ae50482f44f909a37fef16af16c36ec/MinkowskiEngine/MinkowskiCoords.py#L314
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Hello,
I made a simple function to concatenate the tensors which have different sparsity.
This can be used in UNet based auto-encoder, where generated tensors and skipped tensors have different sparsity.
Thanks