lib/utils.py

import os
import logging
from datetime import datetime
import numpy as np
from munch import Munch


def get_logger(logdir):
    logger = logging.getLogger("gan")
    ts = str(datetime.now()).split(".")[0].replace(" ", "_")
    ts = ts.replace(":", "_").replace("-", "_")
    file_path = os.path.join(logdir, "run_{}.log".format(ts))

    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(levelname)s - %(message)s',
        filename= file_path,
        filemode='w'
    )
    # define a new Handler to log to console as well
    console = logging.StreamHandler()
    # optional, set the logging level
    console.setLevel(logging.INFO)
    # set a format which is the same for console use
    formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
    # tell the handler to use this format
    console.setFormatter(formatter)
    # add the handler to the root logger
    logging.getLogger('').addHandler(console)
    return logger


import yaml, munch
def yaml2config(yml_path):
    with open(yml_path) as fp:
        json = yaml.load(fp, Loader=yaml.FullLoader)

    def to_munch(json):
        for key, val in json.items():
            if isinstance(val, dict):
                json[key] = to_munch(val)
        return munch.Munch(json)

    cfg = to_munch(json)
    return cfg


from torchvision.utils import make_grid
def draw_image(tensor, nrow=8, padding=2,
           normalize=False, range=None, scale_each=False, pad_value=0):
    from PIL import Image
    grid = make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value,
                     normalize=normalize, value_range=range, scale_each=scale_each)
    # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
    ndarr = grid.mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).cpu().numpy().astype(np.uint8)
    return ndarr


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

    def __init__(self):
        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 eval(self):
        return self.avg


class AverageMeterManager(object):
    def __init__(self, keys):
        self.meters = {}
        for key in keys:
            self.meters[key] = AverageMeter()

    def reset(self, key):
        self.meters[key].reset()

    def reset_all(self):
        for key in self.meters.keys():
            self.meters[key].reset()

    def update(self, key, val, n=1):
        self.meters[key].update(val, n)

    def eval(self, keys):
        if isinstance(keys, str):
            keys = [keys]
        res = {}
        for key in keys:
            res[key] = self.meters[key].eval()
        return res

    def eval_all(self):
        res = {}
        for key in self.meters.keys():
            res[key] = self.meters[key].eval()
        return res


def option_to_string(opt, row_blanks=20):
    def opt_to_str(opt, depth=0):
        res = ''
        for key, val in opt.items():
            if isinstance(val, Munch) or isinstance(val, dict):
                res += '-'*row_blanks + '\n' + key + '\n' + opt_to_str(val, depth + 2)
            else:
                res += '{}{}: {}\n'.format('|' + '-' * depth, key, val)
        return res

    res = '='*row_blanks + '\nRoot\n' + '-'*row_blanks + '\n' + opt_to_str(opt) + '='*row_blanks
    return res

def pad(img, img_lens, h=32, w=128, lenlb=0):
    curr_h, curr_w = img.shape
    img = img.cpu().numpy()
    if curr_w < w:
        img = np.pad(img, [(0, 0), (0, w - curr_w)], "constant", constant_values=255)
    else:
        img = img[:, :w]

    for col in range(img.shape[1]):
        if np.all(img[:, col : col + 16] == 0):
            img[:, col : col + 16] = 255

    return img