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