TimeSformer/timesformer/models/optimizer.py

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.

"""Optimizer."""

import torch

import timesformer.utils.lr_policy as lr_policy


def construct_optimizer(model, cfg):
    """
    Construct a stochastic gradient descent or ADAM optimizer with momentum.
    Details can be found in:
    Herbert Robbins, and Sutton Monro. "A stochastic approximation method."
    and
    Diederik P.Kingma, and Jimmy Ba.
    "Adam: A Method for Stochastic Optimization."

    Args:
        model (model): model to perform stochastic gradient descent
        optimization or ADAM optimization.
        cfg (config): configs of hyper-parameters of SGD or ADAM, includes base
        learning rate,  momentum, weight_decay, dampening, and etc.
    """
    # Batchnorm parameters.
    bn_params = []
    # Non-batchnorm parameters.
    non_bn_parameters = []
    for name, p in model.named_parameters():
        if "bn" in name:
            bn_params.append(p)
        else:
            non_bn_parameters.append(p)
    # Apply different weight decay to Batchnorm and non-batchnorm parameters.
    # In Caffe2 classification codebase the weight decay for batchnorm is 0.0.
    # Having a different weight decay on batchnorm might cause a performance
    # drop.
    optim_params = [
        {"params": bn_params, "weight_decay": cfg.BN.WEIGHT_DECAY},
        {"params": non_bn_parameters, "weight_decay": cfg.SOLVER.WEIGHT_DECAY},
    ]
    # Check all parameters will be passed into optimizer.
    assert len(list(model.parameters())) == len(non_bn_parameters) + len(
        bn_params
    ), "parameter size does not match: {} + {} != {}".format(
        len(non_bn_parameters), len(bn_params), len(list(model.parameters()))
    )

    if cfg.SOLVER.OPTIMIZING_METHOD == "sgd":
        return torch.optim.SGD(
            optim_params,
            lr=cfg.SOLVER.BASE_LR,
            momentum=cfg.SOLVER.MOMENTUM,
            weight_decay=cfg.SOLVER.WEIGHT_DECAY,
            dampening=cfg.SOLVER.DAMPENING,
            nesterov=cfg.SOLVER.NESTEROV,
        )
    elif cfg.SOLVER.OPTIMIZING_METHOD == "adam":
        return torch.optim.Adam(
            optim_params,
            lr=cfg.SOLVER.BASE_LR,
            betas=(0.9, 0.999),
            eps=1e-08,
            weight_decay=cfg.SOLVER.WEIGHT_DECAY,
        )
    elif cfg.SOLVER.OPTIMIZING_METHOD == "adamw":
        return torch.optim.AdamW(
            optim_params,
            lr=cfg.SOLVER.BASE_LR,
            betas=(0.9, 0.999),
            eps=1e-08,
            weight_decay=cfg.SOLVER.WEIGHT_DECAY,
        )
    else:
        raise NotImplementedError(
            "Does not support {} optimizer".format(cfg.SOLVER.OPTIMIZING_METHOD)
        )


def get_epoch_lr(cur_epoch, cfg):
    """
    Retrieves the lr for the given epoch (as specified by the lr policy).
    Args:
        cfg (config): configs of hyper-parameters of ADAM, includes base
        learning rate, betas, and weight decays.
        cur_epoch (float): the number of epoch of the current training stage.
    """
    return lr_policy.get_lr_at_epoch(cfg, cur_epoch)


def set_lr(optimizer, new_lr):
    """
    Sets the optimizer lr to the specified value.
    Args:
        optimizer (optim): the optimizer using to optimize the current network.
        new_lr (float): the new learning rate to set.
    """
    for param_group in optimizer.param_groups:
        param_group["lr"] = new_lr