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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. | |
| """Learning rate policy.""" | |
| import math | |
| def get_lr_at_epoch(cfg, cur_epoch): | |
| """ | |
| Retrieve the learning rate of the current epoch with the option to perform | |
| warm up in the beginning of the training stage. | |
| Args: | |
| cfg (CfgNode): configs. Details can be found in | |
| slowfast/config/defaults.py | |
| cur_epoch (float): the number of epoch of the current training stage. | |
| """ | |
| lr = get_lr_func(cfg.SOLVER.LR_POLICY)(cfg, cur_epoch) | |
| # Perform warm up. | |
| if cur_epoch < cfg.SOLVER.WARMUP_EPOCHS: | |
| lr_start = cfg.SOLVER.WARMUP_START_LR | |
| lr_end = get_lr_func(cfg.SOLVER.LR_POLICY)( | |
| cfg, cfg.SOLVER.WARMUP_EPOCHS | |
| ) | |
| alpha = (lr_end - lr_start) / cfg.SOLVER.WARMUP_EPOCHS | |
| lr = cur_epoch * alpha + lr_start | |
| return lr | |
| def lr_func_cosine(cfg, cur_epoch): | |
| """ | |
| Retrieve the learning rate to specified values at specified epoch with the | |
| cosine learning rate schedule. Details can be found in: | |
| Ilya Loshchilov, and Frank Hutter | |
| SGDR: Stochastic Gradient Descent With Warm Restarts. | |
| Args: | |
| cfg (CfgNode): configs. Details can be found in | |
| slowfast/config/defaults.py | |
| cur_epoch (float): the number of epoch of the current training stage. | |
| """ | |
| assert cfg.SOLVER.COSINE_END_LR < cfg.SOLVER.BASE_LR | |
| return ( | |
| cfg.SOLVER.COSINE_END_LR | |
| + (cfg.SOLVER.BASE_LR - cfg.SOLVER.COSINE_END_LR) | |
| * (math.cos(math.pi * cur_epoch / cfg.SOLVER.MAX_EPOCH) + 1.0) | |
| * 0.5 | |
| ) | |
| def lr_func_steps_with_relative_lrs(cfg, cur_epoch): | |
| """ | |
| Retrieve the learning rate to specified values at specified epoch with the | |
| steps with relative learning rate schedule. | |
| Args: | |
| cfg (CfgNode): configs. Details can be found in | |
| slowfast/config/defaults.py | |
| cur_epoch (float): the number of epoch of the current training stage. | |
| """ | |
| ind = get_step_index(cfg, cur_epoch) | |
| return cfg.SOLVER.LRS[ind] * cfg.SOLVER.BASE_LR | |
| def get_step_index(cfg, cur_epoch): | |
| """ | |
| Retrieves the lr step index for the given epoch. | |
| Args: | |
| cfg (CfgNode): configs. Details can be found in | |
| slowfast/config/defaults.py | |
| cur_epoch (float): the number of epoch of the current training stage. | |
| """ | |
| steps = cfg.SOLVER.STEPS + [cfg.SOLVER.MAX_EPOCH] | |
| for ind, step in enumerate(steps): # NoQA | |
| if cur_epoch < step: | |
| break | |
| return ind - 1 | |
| def get_lr_func(lr_policy): | |
| """ | |
| Given the configs, retrieve the specified lr policy function. | |
| Args: | |
| lr_policy (string): the learning rate policy to use for the job. | |
| """ | |
| policy = "lr_func_" + lr_policy | |
| if policy not in globals(): | |
| raise NotImplementedError("Unknown LR policy: {}".format(lr_policy)) | |
| else: | |
| return globals()[policy] | |