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# Usage

## 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.

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)
```

### 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.

```Python
from spconv.pytorch.utils import PointToVoxel
# 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)
```