88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
import tensorflow as tf
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from tensorflow.keras import layers, models
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def LossDice(y_true, y_pred):
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numerateur =tf.reduce_sum(y_true*y_pred, axis=(1, 2))
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denominateur=tf.reduce_sum(y_true+y_pred, axis=(1, 2))
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dice=2*numerateur/(denominateur+1E-4)
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return 1-dice
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def LossJaccard(y_true, y_pred):
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intersection=tf.reduce_sum(y_true*y_pred, axis=(1, 2))
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union =tf.reduce_sum(y_true+y_pred, axis=(1, 2))
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jaccard=intersection/(union-intersection+1E-4)
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return 1-jaccard
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def model(nbr):
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entree=layers.Input(shape=(576, 560, 3), dtype='float32')
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result=layers.Conv2D(nbr, 3, activation='relu', padding='same')(entree)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(nbr, 3, activation='relu', padding='same')(result)
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result1=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result1)
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result=layers.Conv2D(2*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(2*nbr, 3, activation='relu', padding='same')(result)
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result2=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result2)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result3=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result3)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result4=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result4)
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result=layers.Conv2D(8*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.UpSampling2D()(result)
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result=tf.concat([result, result4], axis=3)
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result=layers.Conv2D(8*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.UpSampling2D()(result)
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result=tf.concat([result, result3], axis=3)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(2*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.UpSampling2D()(result)
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result=tf.concat([result, result2], axis=3)
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result=layers.Conv2D(2*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.UpSampling2D()(result)
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result=tf.concat([result, result1], axis=3)
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result=layers.Conv2D(nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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sortie=layers.Conv2D(1, 1, activation='sigmoid', padding='same')(result)
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model=models.Model(inputs=entree, outputs=sortie)
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return model
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