Initial commit

Tensorflow/concours_drive_2/model.py

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