Use AutoUnit.device for SWA Model #922
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This pull request was exported from Phabricator. Differential Revision: D64206735 |
Summary: Add EMA to the recognizer: - Separate out learning rate scheduler updates and EMA model updates: in d2go, the EMA weights were updated every step, while the scheduler was updated every epoch. We separate them to implement the same functionality in Vizard and override `on_train_step_end` to update the EMA weights every step (irrespective of other parameters). - Update torchtnt auto_unit to use self.device for the EMA / SWA model, which may be set from environment in the superclass init. This enables model evaluation in GPU. Differential Revision: D64206735
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This pull request was exported from Phabricator. Differential Revision: D64206735 |
vbourgin
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Summary:
Update torchtnt auto_unit to use self.device for the EMA / SWA model, which may be set from environment in the superclass init. This enables model evaluation in GPU.
Differential Revision: D64206735