wehub-resource-sync
31 lines
1.4 KiB
Python
31 lines
1.4 KiB
Python
import torch
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import torch.nn as nn
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from .. import SparseTensor
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import torchsparse
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def sparse_conv3d_init(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, padding=None, bias=True, indice_key=None):
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self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias)
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def sparse_conv3d_forward(self, x: SparseTensor) -> SparseTensor:
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out = self.conv(x.data)
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new_shape = [x.shape[0], self.conv.out_channels]
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out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None)
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out._spatial_cache = x._spatial_cache
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out._scale = tuple([s * stride for s, stride in zip(x._scale, self.conv.stride)])
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return out
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def sparse_inverse_conv3d_init(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None):
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self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias, transposed=True)
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def sparse_inverse_conv3d_forward(self, x: SparseTensor) -> SparseTensor:
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out = self.conv(x.data)
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new_shape = [x.shape[0], self.conv.out_channels]
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out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None)
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out._spatial_cache = x._spatial_cache
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out._scale = tuple([s / stride for s, stride in zip(x._scale, self.conv.stride)])
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return out
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