File size: 2,114 Bytes
db6a3b7 a6bbecf db6a3b7 a6bbecf db6a3b7 a6bbecf db6a3b7 a6bbecf db6a3b7 a6bbecf db6a3b7 a6bbecf db6a3b7 a6bbecf db6a3b7 a6bbecf db6a3b7 a6bbecf db6a3b7 a6bbecf db6a3b7 a6bbecf db6a3b7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import torch
import torch.nn as nn
from .. import SparseTensor
class SparseConv3d(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
dilation=1,
bias=True,
indice_key=None,
):
super(SparseConv3d, self).__init__()
if "torchsparse" not in globals():
import torchsparse
self.conv = torchsparse.nn.Conv3d(
in_channels, out_channels, kernel_size, stride, 0, dilation, bias
)
def forward(self, x: SparseTensor) -> SparseTensor:
out = self.conv(x.data)
new_shape = [x.shape[0], self.conv.out_channels]
out = SparseTensor(
out,
shape=torch.Size(new_shape),
layout=x.layout if all(s == 1 for s in self.conv.stride) else None,
)
out._spatial_cache = x._spatial_cache
out._scale = tuple(
[s * stride for s, stride in zip(x._scale, self.conv.stride)]
)
return out
class SparseInverseConv3d(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
dilation=1,
bias=True,
indice_key=None,
):
super(SparseInverseConv3d, self).__init__()
if "torchsparse" not in globals():
import torchsparse
self.conv = torchsparse.nn.Conv3d(
in_channels,
out_channels,
kernel_size,
stride,
0,
dilation,
bias,
transposed=True,
)
def forward(self, x: SparseTensor) -> SparseTensor:
out = self.conv(x.data)
new_shape = [x.shape[0], self.conv.out_channels]
out = SparseTensor(
out,
shape=torch.Size(new_shape),
layout=x.layout if all(s == 1 for s in self.conv.stride) else None,
)
out._spatial_cache = x._spatial_cache
out._scale = tuple(
[s // stride for s, stride in zip(x._scale, self.conv.stride)]
)
return out
|