class _UpsampleConv3dBase(_nn.ConvNormAct3d):
upsample_constructor = _nn.LayerPartial(_torch.nn.Upsample,
scale_factor=2,
align_corners=True,
mode='trilinear')
def __init__(self, *args, **kwargs):
super(_UpsampleConv3dBase, self).__init__(*args, **kwargs)
self.upsampling_layer = self.upsample_constructor()
self.out_channels = self.convolution_layer.out_channels
def main_forward(self, x):
x = self.upsampling_layer(x)
x = super(_UpsampleConv3dBase, self).main_forward(x)
return x
class InstanceNorm3d(_BINormCommon):
layer_constructor = _nn.LayerPartial(_torch.nn.InstanceNorm3d,
**_BINormCommon.default_kwargs)
class BatchNorm2d(_BINormCommon):
layer_constructor = _nn.LayerPartial(_torch.nn.BatchNorm2d,
**_BINormCommon.default_kwargs)
