<filename>banding_removal/fastmri/optimizer/__init__.py
"""
Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""


import torch
import torch.optim

def load(p, args):
    if args.method == "adam":
        # Default is 0.9, 0.999, so we kind of immitate that
        if args.beta2 is None:
            beta2 = 1-((1-args.momentum)/100)
        else:
            beta2 = args.beta2
        optimizer = torch.optim.Adam(p, lr=args.lr, betas=(args.momentum, beta2),
            weight_decay=args.decay, eps=args.adam_eps)
    elif args.method == "sgd":
        optimizer = torch.optim.SGD(p, lr=args.lr, momentum=args.momentum,
            weight_decay=args.decay)
    elif args.method == "rmsprop":
        optimizer = torch.optim.RMSprop(p, lr=args.lr, momentum=args.momentum,
            weight_decay=args.decay)
    elif args.method == "lbfgs":
        optimizer = torch.optim.LBFGS(p, lr=args.lr)
    else:
        raise Exception(f"Unrecognised optimizer method name: {args.method}")

    return optimizer

<filename>banding_removal/fastmri/optimizer/__init__.py
"""
Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""


import torch
import torch.optim

def load(p, args):
    if args.method == "adam":
        # Default is 0.9, 0.999, so we kind of immitate that
        if args.beta2 is None:
            beta2 = 1-((1-args.momentum)/100)
        else:
            beta2 = args.beta2
        optimizer = torch.optim.Adam(p, lr=args.lr, betas=(args.momentum, beta2),
            weight_decay=args.decay, eps=args.adam_eps)
    elif args.method == "sgd":
        optimizer = torch.optim.SGD(p, lr=args.lr, momentum=args.momentum,
            weight_decay=args.decay)
    elif args.method == "rmsprop":
        optimizer = torch.optim.RMSprop(p, lr=args.lr, momentum=args.momentum,
            weight_decay=args.decay)
    elif args.method == "lbfgs":
        optimizer = torch.optim.LBFGS(p, lr=args.lr)
    else:
        raise Exception(f"Unrecognised optimizer method name: {args.method}")

    return optimizer

Copyright (c) Facebook, Inc. and its affiliates.  This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. # Default is 0.9, 0.999, so we kind of immitate that