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metricsandloss.py
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metricsandloss.py
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import tensorflow.keras.backend as K
from tensorflow.keras.losses import binary_crossentropy
# Tversky loss
def tversky(y_true, y_pred, smooth=1, alpha=0.7):
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)
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_loss(y_true, y_pred, gamma=2):
tv = tversky(y_true, y_pred)
return K.pow((1 - tv), gamma)
# Dice coefficient - F1-score
def dice_coef(y_true, y_pred, smooth=1):
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)
return (2. * true_pos + smooth) /(2.*true_pos + false_neg + false_pos + smooth)
def dice_coef_loss(y_true, y_pred):
return 1 - dice_coef(y_true, y_pred)
def bce_dice_loss(y_true, y_pred):
bce_loss = binary_crossentropy(y_true, y_pred)
return bce_loss+dice_coef_loss(y_true,y_pred)
# Jaccard index - IoU
def jacard_coef(y_true, y_pred, smooth=1): # TP/(FN+FP+TP)
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) - intersection + smooth)
def jacard_coef_loss(y_true, y_pred):
return -jacard_coef(y_true, y_pred)
def iou_class(y_true,y_pred,n_classes):
EPS = 1e-12
class_wise = K.zeros(n_classes)
for cl in range(n_classes):
intersection = K.sum((gt == cl)*(pr == cl))
union = K.sum(K.maximum((gt == cl), (pr == cl)))
iou = float(intersection)/(union + EPS)
class_wise[cl] = iou
return class_wise
def bce_jaccard_loss(y_true, y_pred):
bce_loss = binary_crossentropy(y_true, y_pred)
return bce_loss+jaccard_coef_loss(y_true,y_pred)
# OTHER METRICS
def precision(y_true, y_pred, smooth=1): # TP/(TP+FP)
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_pos = K.sum((1 - y_true_pos) * y_pred_pos)
return (true_pos+smooth)/(true_pos+false_pos+smooth)
def recall(y_true, y_pred,smooth=1):
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))
return (true_pos+smooth)/(true_pos+false_neg+smooth)
# TO DO:
## focal loss