Laurent Erignoux
144fb65246
git-subtree-dir: Stable3DGen git-subtree-mainline:846d6cfad8git-subtree-split:c29f668ece
63 lines
2.8 KiB
Python
Executable File
63 lines
2.8 KiB
Python
Executable File
# MIT License
|
|
|
|
# Copyright (c) Microsoft
|
|
|
|
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
|
# of this software and associated documentation files (the "Software"), to deal
|
|
# in the Software without restriction, including without limitation the rights
|
|
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
|
# copies of the Software, and to permit persons to whom the Software is
|
|
# furnished to do so, subject to the following conditions:
|
|
|
|
# The above copyright notice and this permission notice shall be included in all
|
|
# copies or substantial portions of the Software.
|
|
|
|
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
|
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
|
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
|
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
|
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
|
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|
# SOFTWARE.
|
|
|
|
# Copyright (c) [2025] [Microsoft]
|
|
# SPDX-License-Identifier: MIT
|
|
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
|
|
|
|
|
|
|