utils.py

import numpy as np


def poly_lr_scheduler(optimizer, init_lr, iter, lr_decay_iter=1,
                      max_iter=300, power=0.9):
    """Polynomial decay of learning rate
            :param init_lr is base learning rate
            :param iter is a current iteration
            :param lr_decay_iter how frequently decay occurs, default is 1
            :param max_iter is number of maximum iterations
            :param power is a polymomial power

    """
    # if iter % lr_decay_iter or iter > max_iter:
    # 	return optimizer

    lr = init_lr*(1 - iter/max_iter)**power
    optimizer.param_groups[0]['lr'] = lr
    return lr
    # return lr


def fast_hist(a, b, n):
    '''
    a and b are label and prediction respectively
    n is the number of classes
    '''
    k = (a >= 0) & (a < n)
    return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)


def per_class_iou(hist):
    epsilon = 1e-5
    return (np.diag(hist)) / (hist.sum(1) + hist.sum(0) - np.diag(hist) + epsilon)