utils.py

import math
import shutil
import json
import random
import torch.backends.cudnn as cudnn
import torch
import numpy as np

from torch.optim.adam import Adam


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self, name="", fmt=":f"):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print("\t".join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = "{:" + str(num_digits) + "d}"
        return "[" + fmt + "/" + fmt.format(num_batches) + "]"

def save_config(opt, file_path):
    with open(file_path, "w") as f:
        json.dump(opt.__dict__, f, indent=2)

def save_checkpoint(state, is_best, filename="checkpoint.pth.tar", prefix=""):
    tries = 15
    error = None

    # deal with unstable I/O. Usually not necessary.
    while tries:
        try:
            torch.save(state, prefix + filename)
            if is_best:
                shutil.copyfile(prefix + filename, prefix + "model_best.pth.tar")
        except IOError as e:
            error = e
            tries -= 1
        else:
            break
        print("model save {} failed, remaining {} trials".format(filename, tries))
        if not tries:
            raise error

def set_seed(seed=1):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)

def init_seeds(seed=0, cuda_deterministic=False):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
    if cuda_deterministic:  # slower, more reproducible
        cudnn.deterministic = True
        cudnn.benchmark = False
    else:  # faster, less reproducible
        cudnn.deterministic = False
        cudnn.benchmark = True


class MetaAdam(Adam):
    def __init__(self, net, *args, **kwargs):
        super(MetaAdam, self).__init__(*args, **kwargs)
        self.net = net
    def set_parameter(self, current_module, name, parameters):
        if '.' in name:
            name_split = name.split('.')
            module_name = name_split[0]
            rest_name = '.'.join(name_split[1:])
            for children_name, children in current_module.named_children():
                if module_name == children_name:
                    self.set_parameter(children, rest_name, parameters)
                    break
        else:
            current_module._parameters[name] = parameters

    def meta_step(self, grads):
        group = self.param_groups[0]
        lr = group['lr']

        for (name, parameter), grad in zip(self.net.named_parameters(), grads):
            parameter.detach_()
            self.set_parameter(self.net, name, parameter.add(grad, alpha=-lr))