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Zero
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import torch
import torch.nn as nn
from .. import SparseTensor
from . import config
import spconv.pytorch as spconv
def sparse_conv3d_init(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, padding=None, bias=True, indice_key=None):
algo = None
if config.SPCONV_ALGO == 'native':
algo = spconv.ConvAlgo.Native
elif config.SPCONV_ALGO == 'implicit_gemm':
algo = spconv.ConvAlgo.MaskImplicitGemm
if stride == 1 and (padding is None):
self.conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, dilation=dilation, bias=bias, indice_key=indice_key, algo=algo)
else:
self.conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, padding=padding, bias=bias, indice_key=indice_key, algo=algo)
self.stride = tuple(stride) if isinstance(stride, (list, tuple)) else (stride, stride, stride)
self.padding = padding
def sparse_conv3d_forward(self, x: SparseTensor) -> SparseTensor:
spatial_changed = any(s != 1 for s in self.stride) or (self.padding is not None)
new_data = self.conv(x.data)
new_shape = [x.shape[0], self.conv.out_channels]
new_layout = None if spatial_changed else x.layout
if spatial_changed and (x.shape[0] != 1):
# spconv was non-1 stride will break the contiguous of the output tensor, sort by the coords
fwd = new_data.indices[:, 0].argsort()
bwd = torch.zeros_like(fwd).scatter_(0, fwd, torch.arange(fwd.shape[0], device=fwd.device))
sorted_feats = new_data.features[fwd]
sorted_coords = new_data.indices[fwd]
unsorted_data = new_data
new_data = spconv.SparseConvTensor(sorted_feats, sorted_coords, unsorted_data.spatial_shape, unsorted_data.batch_size) # type: ignore
out = SparseTensor(
new_data, shape=torch.Size(new_shape), layout=new_layout,
scale=tuple([s * stride for s, stride in zip(x._scale, self.stride)]),
spatial_cache=x._spatial_cache,
)
if spatial_changed and (x.shape[0] != 1):
out.register_spatial_cache(f'conv_{self.stride}_unsorted_data', unsorted_data)
out.register_spatial_cache(f'conv_{self.stride}_sort_bwd', bwd)
return out
def sparse_inverse_conv3d_init(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None):
self.conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, bias=bias, indice_key=indice_key)
self.stride = tuple(stride) if isinstance(stride, (list, tuple)) else (stride, stride, stride)
def sparse_inverse_conv3d_forward(self, x: SparseTensor) -> SparseTensor:
spatial_changed = any(s != 1 for s in self.stride)
if spatial_changed:
# recover the original spconv order
data = x.get_spatial_cache(f'conv_{self.stride}_unsorted_data')
bwd = x.get_spatial_cache(f'conv_{self.stride}_sort_bwd')
data = data.replace_feature(x.feats[bwd])
else:
data = x.data
new_data = self.conv(data)
new_shape = [x.shape[0], self.conv.out_channels]
new_layout = None if spatial_changed else x.layout
out = SparseTensor(
new_data, shape=torch.Size(new_shape), layout=new_layout,
scale=tuple([s // stride for s, stride in zip(x._scale, self.stride)]),
spatial_cache=x._spatial_cache,
)
return out
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