core.modules.scheduler#
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.
Classes#
Learning rate scheduler class for torch.optim learning rate schedulers |
Functions#
|
Returns a learning rate multiplier. |
Module Contents#
- core.modules.scheduler.warmup_lr_lambda(current_step: int, optim_config)#
Returns a learning rate multiplier. Till warmup_steps, learning rate linearly increases to initial_lr, and then gets multiplied by lr_gamma every time a milestone is crossed.
- class core.modules.scheduler.CosineLRLambda(warmup_epochs: int, warmup_factor: float, epochs: int, lr_min_factor: float)#
- warmup_epochs#
- lr_warmup_factor#
- max_epochs#
- lr_min_factor#
- __call__(current_step: int) float #
- class core.modules.scheduler.LRScheduler(optimizer, config)#
Learning rate scheduler class for torch.optim learning rate schedulers
Notes
If no learning rate scheduler is specified in the config the default scheduler is warmup_lr_lambda (fairchem.core.common.utils) not no scheduler, this is for backward-compatibility reasons. To run without a lr scheduler specify scheduler: “Null” in the optim section of the config.
- Parameters:
optimizer (obj) – torch optim object
config (dict) – Optim dict from the input config
- optimizer#
- config#
- step(metrics=None, epoch=None) None #
- filter_kwargs(config)#
- get_lr()#