Initial commit

Tensorflow/tutoriel27/model.py

@@ -0,0 +1,54 @@
+import tensorflow as tf
+from tensorflow.keras import layers, models
+import config
+
+def block_resnet(input, filters, kernel_size, reduce=False):
+    result=layers.Conv2D(filters, kernel_size, strides=1, padding='SAME')(input)
+    result=layers.BatchNormalization()(result)
+    result=layers.LeakyReLU(alpha=0.1)(result)
+
+    if reduce is True:
+        result=layers.Conv2D(filters, kernel_size, strides=2, padding='SAME')(result)
+    else:
+        result=layers.Conv2D(filters, kernel_size, strides=1, padding='SAME')(result)
+        
+    if input.shape[-1]==filters:
+        if reduce is True:
+            shortcut=layers.Conv2D(filters, 1, strides=2, padding='SAME')(input)
+        else:
+            shortcut=input
+    else:
+        if reduce is True:
+            shortcut=layers.Conv2D(filters, 1, strides=2, padding='SAME')(input)
+        else:
+            shortcut=layers.Conv2D(filters, 1, strides=1, padding='SAME')(input)
+    
+    result=layers.add([result, shortcut])
+    result=layers.LeakyReLU(alpha=0.1)(result)
+    result=layers.BatchNormalization()(result)
+    return result
+
+def model(nbr_classes, nbr_boxes, cellule_y, cellule_x):
+    entree=layers.Input(shape=(config.largeur, config.hauteur, 3), dtype='float32')
+
+    result=block_resnet(entree, 16, 3, False)
+    result=block_resnet(result, 16, 3, True)
+
+    result=block_resnet(result, 32, 3, False)
+    result=block_resnet(result, 32, 3, True)
+
+    result=block_resnet(result, 64, 3, False)
+    result=block_resnet(result, 64, 3, False)
+    result=block_resnet(result, 64, 3, True)
+
+    result=block_resnet(result, 128, 3, False)
+    result=block_resnet(result, 128, 3, False)
+    result=block_resnet(result, 128, 3, True)
+
+    result=layers.Conv2D(config.nbr_boxes*(5+config.nbr_classes), 1, padding='SAME')(result)
+    sortie=layers.Reshape((config.cellule_y, config.cellule_x, config.nbr_boxes, 5+config.nbr_classes))(result)
+
+    model=models.Model(inputs=entree, outputs=sortie)
+
+    return model
+