loss.py

import keras
import keras.backend as K
import tensorflow as tf

smooth = 1.

def dice_coef(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)
    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

def _CE(y_true, y_pred):
        y_pred = K.clip(y_pred, K.epsilon(), 1.0 - K.epsilon())

        CE_loss = - y_true[...] * K.log(y_pred)
        CE_loss = K.mean(K.sum(CE_loss, axis = -1))
        # dice_loss = tf.Print(CE_loss, [CE_loss])
        return CE_loss

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

class modelLoss():
    def __init__(self, lambda_, alpha, width, height, batchsize):
        self.lambda_ = lambda_
        self.width = width
        self.height = height
        self.batchsize = batchsize
        self.alpha = alpha

    def test(self, y_true, y_pred):
        # rename and split values
        # [batch, width, height, channel]
        img = y_true[:,:,:,0:3]
        seg = y_true[:,:,:,3:6]

        disp0        = K.expand_dims(y_pred[:,:,:,0],-1)
        disp1        = K.expand_dims(y_pred[:,:,:,1],-1)
        disp2        = K.expand_dims(y_pred[:,:,:,2],-1)
        disp3        = K.expand_dims(y_pred[:,:,:,3],-1)

        return None

    def applyLoss(self, y_true, y_pred):
        return _CE(y_true, y_pred)
    
    
        #return L_p