Enhanced-U-Net/utils/metrics.py

import torch

"""
The evaluation implementation refers to the following paper:
"Selective Feature Aggregation Network with Area-Boundary Constraints for Polyp Segmentation"
https://github.com/Yuqi-cuhk/Polyp-Seg
"""
def evaluate(pred, gt, th):
    if isinstance(pred, (list, tuple)):
        pred = pred[0]

    pred_binary = (pred >= th).float()
    pred_binary_inverse = (pred_binary == 0).float()

    gt_binary = (gt >= th).float()
    gt_binary_inverse = (gt_binary == 0).float()

    TP = pred_binary.mul(gt_binary).sum()
    FP = pred_binary.mul(gt_binary_inverse).sum()
    TN = pred_binary_inverse.mul(gt_binary_inverse).sum()
    FN = pred_binary_inverse.mul(gt_binary).sum()

    if TP.item() == 0:
        # print('TP=0 now!')
        # print('Epoch: {}'.format(epoch))
        # print('i_batch: {}'.format(i_batch))
        TP = torch.Tensor([1]).cuda()

    # recall
    Recall = TP / (TP + FN)

    # Specificity or true negative rate
    Specificity = TN / (TN + FP)

    # Precision or positive predictive value
    Precision = TP / (TP + FP)

    # F1 score = Dice
    F1 = 2 * Precision * Recall / (Precision + Recall)

    # F2 score
    F2 = 5 * Precision * Recall / (4 * Precision + Recall)

    # Overall accuracy
    ACC_overall = (TP + TN) / (TP + FP + FN + TN)

    # IoU for poly
    IoU_poly = TP / (TP + FP + FN)

    # IoU for background
    IoU_bg = TN / (TN + FP + FN)

    # mean IoU
    IoU_mean = (IoU_poly + IoU_bg) / 2.0

    #Dice
    Dice = (2 * TP)/(2*TP + FN + FP)
    
    return Recall, Specificity, Precision, F1, F2, ACC_overall, IoU_poly, IoU_bg, IoU_mean, Dice


class Metrics(object):
    def __init__(self, metrics_list):
        self.metrics = {}
        for metric in metrics_list:
            self.metrics[metric] = 0

    def update(self, **kwargs):
        for k, v in kwargs.items():
            assert (k in self.metrics.keys()), "The k {} is not in metrics".format(k)
            if isinstance(v, torch.Tensor):
                v = v.item()

            self.metrics[k] += v

    def mean(self, total):
        mean_metrics = {}
        for k, v in self.metrics.items():
            mean_metrics[k] = v / total
        return mean_metrics