USAGE.md

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# Usage

## Short API description

```Python
import spconv.pytorch as spconv
from spconv.pytorch import functional as Fsp
from torch import nn
from spconv.pytorch.utils import PointToVoxel
from spconv.pytorch.hash import HashTable
```

| Layer APIs                         | Common Usage             |            Dense Version    |Note    |
|----------------------------------- |:------------------------:|----------------------------:|----------------------------:| 
| ```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.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, ...```|


| Functional APIs                    | Usage                    |
|----------------------------------- |:------------------------:|
| ```Fsp.sparse_add```               | Add sparse tensors with same shape and different indices    |

| Input APIs                         | Usage                    |
|----------------------------------- |:------------------------:|
| ```PointToVoxel```                 | point cloud to voxels    |

| Misc APIs                         | Usage                    |
|----------------------------------- |:------------------------:|
| ```HashTable```                   | hash table, one-slot     |

| Layer APIs                         | [torchsparse](https://github.com/mit-han-lab/torchsparse)             |    [MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine)             |   
|----------------------------------- |:------------------------:|:------------------------:|
| ```spconv.SparseConv3d```          | ```Conv3d(stride!=1, transpose=False)```               |```MinkowskiConvolution(stride!=1)```| 
| ```spconv.SubMConv3d```            | ```Conv3d(stride=1, transpose=False)```              | ```MinkowskiConvolution(stride=1)```| 
| ```spconv.SparseInverseConv3d```   | ```Conv3d(stride!=1, transpose=True)```                 |```MinkowskiConvolutionTranspose```| 
| ```spconv.SparseConvTranspose3d``` | N/A                |```MinkowskiConvolutionTranspose```| 
| ```spconv.SparseMaxPool3d```       | N/A               | ```MinkowskiMaxPooling```| 


## Concept

* Sparse Conv Tensor: like hybird [torch.sparse_coo_tensor](https://pytorch.org/docs/stable/sparse.html#sparse-coo-docs) but only have two difference: 1. SparseConvTensor only have one dense dim, 2. indice of SparseConvTensor is transposed. see torch doc for more details.

* Sparse Convolution: equivalent to perform dense convolution when you convert SparseConvTensor to dense. Sparse Convolution only run calculation on valid data.

* Submanifold Convolution (SubMConv): like Sparse Convolution but indices keeps same. imagine that you copy same spatial structure to output, then iterate them, get input coordinates by conv rule, finally apply convolution **ONLY** in these output coordinates.



## SparseConvTensor

* features: ```[N, num_channels]``` tensor.

* indices: ```[N, (batch_idx + x + y + z)]``` coordinate tensor with batch axis. note that the coordinates xyz order MUST match spatial shape and conv params such as kernel size

```Python
import spconv.pytorch as spconv
features = # your features with shape [N, num_channels]
indices = # your indices/coordinates with shape [N, ndim + 1], batch index must be put in indices[:, 0]
spatial_shape = # spatial shape of your sparse tensor, spatial_shape[i] is shape of indices[:, 1 + i].
batch_size = # batch size of your sparse tensor.
x = spconv.SparseConvTensor(features, indices, spatial_shape, batch_size)
x_dense_NCHW = x.dense() # convert sparse tensor to dense NCHW tensor.
```


### Sparse Convolution

```Python
import spconv.pytorch as spconv
from torch import nn
class ExampleNet(nn.Module):
    def __init__(self, shape):
        super().__init__()
        self.net = spconv.SparseSequential(
            spconv.SparseConv3d(32, 64, 3), # just like nn.Conv3d but don't support group
            nn.BatchNorm1d(64), # non-spatial layers can be used directly in SparseSequential.
            nn.ReLU(),
            spconv.SubMConv3d(64, 64, 3, indice_key="subm0"),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            # when use submanifold convolutions, their indices can be shared to save indices generation time.
            spconv.SubMConv3d(64, 64, 3, indice_key="subm0"),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            spconv.SparseConvTranspose3d(64, 64, 3, 2),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            spconv.ToDense(), # convert spconv tensor to dense and convert it to NCHW format.
            nn.Conv3d(64, 64, 3),
            nn.BatchNorm1d(64),
            nn.ReLU(),
        )
        self.shape = shape

    def forward(self, features, coors, batch_size):
        coors = coors.int() # unlike torch, this library only accept int coordinates.
        x = spconv.SparseConvTensor(features, coors, self.shape, batch_size)
        return self.net(x)# .dense()
```

### Inverse Convolution

Inverse sparse convolution means "inv" of sparse convolution. the output of inverse convolution contains same indices as input of sparse convolution.

**WARNING** ```SparseInverseConv``` isn't equivalent to ```SparseConvTranspose```. SparseConvTranspose is equivalent to ```ConvTranspose``` in pytorch, but SparseInverseConv isn't.

Inverse convolution usually used in semantic segmentation.

```Python
class ExampleNet(nn.Module):
    def __init__(self, shape):
        super().__init__()
        self.net = spconv.SparseSequential(
            spconv.SparseConv3d(32, 64, 3, 2, indice_key="cp0"),
            spconv.SparseInverseConv3d(64, 32, 3, indice_key="cp0"), # need provide kernel size to create weight
        )
        self.shape = shape

    def forward(self, features, coors, batch_size):
        coors = coors.int()
        x = spconv.SparseConvTensor(features, coors, self.shape, batch_size)
        return self.net(x)
```

### Sparse Add

In sematic segmentation network, we may use conv1x3, 3x1 and 3x3 in a block, but it's impossible to sum result from these layers because regular add requires same indices.

spconv >= 2.1.17 provide a operation to add sparse tensors with different indices (shape must same), but with limits:

```Python
from spconv.pytorch import functional as Fsp
res_1x3 = conv1x3(x)
res_3x1 = conv3x1(x)
# WRONG
# because we can't "inverse" this operation
wrong_usage_cant_inverse = Fsp.sparse_add(res_1x3, res_3x1)

# CORRECT
# res_3x3 already contains all indices of res_1x3 and res_3x1, 
# so output spatial structure isn't changed, we can "inverse" back.
res_3x3 = conv3x3(x)
correct = Fsp.sparse_add(res_1x3, res_3x1, res_3x3)
```

If you use a network without ```SparseInverseConv```, limits above aren't exists, the only drawback of ```sparse_add``` is that it run slower than simple aligned add.

### Fast Mixed Percision Training

see example/mnist_sparse. we support ```torch.cuda.amp```.

### Utility functions

* convert point cloud to voxel

voxel generator in spconv generate indices in **ZYX** order, the params format are **XYZ**.

generated indices don't include batch axis, you need to add it by yourself.

see examples/voxel_gen.py for examples.

```Python
from spconv.pytorch.utils import PointToVoxel, gather_features_by_pc_voxel_id
# this generator generate ZYX indices.
gen = PointToVoxel(
    vsize_xyz=[0.1, 0.1, 0.1], 
    coors_range_xyz=[-80, -80, -2, 80, 80, 6], 
    num_point_features=3, 
    max_num_voxels=5000, 
    max_num_points_per_voxel=5)
pc = np.random.uniform(-10, 10, size=[1000, 3])
pc_th = torch.from_numpy(pc)
voxels, coords, num_points_per_voxel = gen(pc_th, empty_mean=True)
```

If you want to get label for every point of your pc, you need to use another function to get pc_voxel_id and gather features from sematic segmentation result:
```Python
voxels, coords, num_points_per_voxel, pc_voxel_id = gen.generate_voxel_with_id(pc_th, empty_mean=True)
seg_features = YourSegNet(...)
# if voxel id is invalid (point out of range, or no space left in a voxel)
# features will be zero.
point_features = gather_features_by_pc_voxel_id(seg_features, pc_voxel_id)
```