loss.py

# -*- coding: utf-8 -*-
"""
Created on Sun May  9 21:06:19 2021

@author: angelou
"""

from keras import backend as K 

def dice_coef(y_true, y_pred):
    smooth = 1.0  #0.0
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

def jacard(y_true, y_pred):

    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum ( y_true_f * y_pred_f)
    union = K.sum ( y_true_f + y_pred_f - y_true_f * y_pred_f)

    return intersection/union

def dice_coef_loss(y_true,y_pred):
    return 1 - dice_coef(y_true,y_pred)

def iou_loss(y_true,y_pred):
    return 1 - jacard(y_true, y_pred)

def tversky(y_true, y_pred):
    y_true_pos = K.flatten(y_true)
    y_pred_pos = K.flatten(y_pred)
    true_pos = K.sum(y_true_pos * y_pred_pos)
    false_neg = K.sum(y_true_pos * (1-y_pred_pos))
    false_pos = K.sum((1-y_true_pos)*y_pred_pos)
    alpha = 0.75
    smooth = 1
    return (true_pos + smooth)/(true_pos + alpha*false_neg + (1-alpha)*false_pos + smooth)

def tversky_loss(y_true, y_pred):
    return 1 - tversky(y_true,y_pred)

def focal_tversky(y_true,y_pred):
    pt_1 = tversky(y_true, y_pred)
    gamma = 0.75
    return K.pow((1-pt_1), gamma)

def generalized_dice_coeff(y_true, y_pred):
    Ncl = y_pred.shape[-1]
    w = K.zeros(shape=(Ncl,))
    w = K.sum(y_true, axis=(0,1,2))
    w = 1/(w**2+0.000001)
    # Compute gen dice coef:
    numerator = y_true*y_pred
    numerator = w*K.sum(numerator,(0,1,2,3))
    numerator = K.sum(numerator)
    denominator = y_true+y_pred
    denominator = w*K.sum(denominator,(0,1,2,3))
    denominator = K.sum(denominator)
    gen_dice_coef = 2*numerator/denominator
    return gen_dice_coef

def generalized_dice_loss(y_true, y_pred):
    return 1 - generalized_dice_coeff(y_true, y_pred)