def darknet_yolo_body(inputs, num_anchors, num_classes):
"""Create YOLO_V3 model CNN body in Keras."""
if not hasattr(inputs, '_keras_history'):
inputs = tf.keras.layers.Input(tensor=inputs)
darknet = darknet_body(inputs, include_top=False)
x, y1 = make_last_layers(darknet.output, 512,
num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)),
tf.keras.layers.UpSampling2D(2))(x)
x = tf.keras.layers.Concatenate()([x, darknet.layers[152].output])
x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(128, (1, 1)),
tf.keras.layers.UpSampling2D(2))(x)
x = tf.keras.layers.Concatenate()([x, darknet.layers[92].output])
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))
y1 = tf.keras.layers.Lambda(lambda y: tf.reshape(y, [
-1, tf.shape(y)[1],
tf.shape(y)[2], num_anchors, num_classes + 5
]),
name='y1')(y1)
y2 = tf.keras.layers.Lambda(lambda y: tf.reshape(y, [
-1, tf.shape(y)[1],
tf.shape(y)[2], num_anchors, num_classes + 5
]),
name='y2')(y2)
y3 = tf.keras.layers.Lambda(lambda y: tf.reshape(y, [
-1, tf.shape(y)[1],
tf.shape(y)[2], num_anchors, num_classes + 5
]),
name='y3')(y3)
return AdvLossModel(inputs, [y1, y2, y3])
def make_last_layers(x, num_filters, out_filters):
'''6 Conv2D_BN_Leaky layers followed by a Conv2D_linear layer'''
x = compose(DarknetConv2D_BN_Leaky(num_filters, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D_BN_Leaky(num_filters, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D_BN_Leaky(num_filters, (1, 1)))(x)
y = compose(DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D(out_filters, (1, 1)))(x)
return x, y