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# 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 | |