utils.py

import os
import json
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import torch

from PIL import Image


def denormalize(T, coords):
    return (0.5 * ((coords + 1.0) * T))


class AverageMeter(object):

    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 accuracy(output, target, topk=(1,)):
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].reshape(-1, 1).view(-1).float().sum(0)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


def prepare_dirs(config):
    for path in [config.ckpt_dir, config.logs_dir]:
        if not os.path.exists(path):
            os.makedirs(path)


def save_config(config):
    model_name = config.save_name
    filename = model_name + '_params.json'
    param_path = os.path.join(config.ckpt_dir, filename)

    print("[*] Model Checkpoint Dir: {}".format(config.ckpt_dir))
    print("[*] Param Path: {}".format(param_path))

    with open(param_path, 'w') as fp:
        json.dump(config.__dict__, fp, indent=4, sort_keys=True)