(
self,
hidden_states: torch.FloatTensor,
image_only_indicator: torch.FloatTensor,
)
| 1852 | self.upsamplers = None |
| 1853 | |
| 1854 | def forward( |
| 1855 | self, |
| 1856 | hidden_states: torch.FloatTensor, |
| 1857 | image_only_indicator: torch.FloatTensor, |
| 1858 | ) -> torch.FloatTensor: |
| 1859 | for resnet in self.resnets: |
| 1860 | hidden_states = resnet( |
| 1861 | hidden_states, |
| 1862 | image_only_indicator=image_only_indicator, |
| 1863 | ) |
| 1864 | |
| 1865 | if self.upsamplers is not None: |
| 1866 | for upsampler in self.upsamplers: |
| 1867 | hidden_states = upsampler(hidden_states) |
| 1868 | |
| 1869 | return hidden_states |
| 1870 | |
| 1871 | |
| 1872 | class UNetMidBlockSpatioTemporal(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected