utils.py

import numpy as np
import torch

lr_base = 0.0003

def adjust_learning_rate(optimizer, epoch):
    lr = lr_base * (0.8 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def batch_pix_accuracy(output, target):
    """Batch Pixel Accuracy
    Args:
        predict: input 4D tensor
        target: label 3D tensor
    """
    _, predict = torch.max(output, 1)

    predict = predict.cpu().numpy().astype('int64') + 1
    target = target.cpu().numpy().astype('int64') + 1

    pixel_labeled = np.sum(target > 0)
    pixel_correct = np.sum((predict == target)*(target > 0))
    assert pixel_correct <= pixel_labeled, \
        "Correct area should be smaller than Labeled"
    return pixel_correct, pixel_labeled


def batch_intersection_union(output, target, nclass):
    """Batch Intersection of Union
    Args:
        predict: input 4D tensor
        target: label 3D tensor
        nclass: number of categories (int)
    """
    _, predict = torch.max(output, 1)
    mini = 1
    maxi = nclass
    nbins = nclass
    predict = predict.cpu().numpy().astype('int64') + 1
    target = target.cpu().numpy().astype('int64') + 1

    predict = predict * (target > 0).astype(predict.dtype)
    intersection = predict * (predict == target)
    # areas of intersection and union
    area_inter, _ = np.histogram(intersection, bins=nbins, range=(mini, maxi))
    area_pred, _ = np.histogram(predict, bins=nbins, range=(mini, maxi))
    area_lab, _ = np.histogram(target, bins=nbins, range=(mini, maxi))
    area_union = area_pred + area_lab - area_inter
    assert (area_inter <= area_union).all(), \
        "Intersection area should be smaller than Union area"
    return area_inter, area_union