@@ -31,9 +31,11 @@ from spconv.pytorch.hash import HashTable
| ```spconv.SparseConv3d``` | Downsample | ```nn.Conv3d``` | Use ```indice_key``` to save data for inverse |
| ```spconv.SubMConv3d``` | Convolution | N/A | Use ```indice_key``` to save data for reuse |
| ```spconv.SparseInverseConv3d``` | Upsample | N/A | Use pre-saved ```indice_key``` to upsample |
| ```spconv.SparseConvTranspose3d``` | Upsample (don't use this)| ```nn.ConvTranspose3d``` | VERY SLOW and CAN'T RECOVER ORIGIN POINT CLOUD |
| ```spconv.SparseConvTranspose3d``` | Upsample (for generative model)| ```nn.ConvTranspose3d``` | VERY SLOW and CAN'T RECOVER ORIGIN POINT CLOUD |
| ```spconv.SparseMaxPool3d``` | Downsample | ```nn.MaxPool3d``` | Use ```indice_key``` to save data for inverse |
| ```spconv.SparseSequential``` | Container | ```nn.Sequential``` | support layers above and ```nn.ReLU, nn.BatchNorm, ...```|
| ```spconv.SparseGlobalMaxPool``` | global pool | N/A | return dense tensor instead of SparseConvTensor|
| ```spconv.SparseGlobalAvgPool``` | global pool | N/A | return dense tensor instead of SparseConvTensor|
| Functional APIs | Usage |
...
...
@@ -143,6 +145,16 @@ class ExampleNet(nn.Module):
return self.net(x)
```
### How To Use SparseConvTranspose
```SparseConvTranspose``` (standard upsampling) should only be used in generative model. You need to use a classifier to check if a output coordicates is empty, then set batch indices (or xyz) of that sparse tensor to a negative number:
```Python
spt.indices[empty_mask, 0] = -1
```
In next sparse convolution, invalid coordinates will be removed until you perform next ```spt.indices[empty_mask, 0] = -1```.
