Creates a Feature Pyramid Network (FPN).

This implemets the paper: Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature Pyramid Networks for Object Detection. (

input_specs A dict of input specifications. A dictionary consists of {level: TensorShape} from a backbone.
min_level An int of minimum level in FPN output feature maps.
max_level An int of maximum level in FPN output feature maps.
num_filters An int number of filters in FPN layers.
fusion_type A str of sum or concat. Whether performing sum or concat for feature fusion.
use_separable_conv A bool. If True use separable convolution for convolution in FPN layers.
activation A str name of the activation function.
use_sync_bn A bool. If True, use synchronized batch normalization.
norm_momentum A float of normalization momentum for the moving average.
norm_epsilon A float added to variance to avoid dividing by zero.
kernel_initializer A str name of kernel_initializer for convolutional layers.
kernel_regularizer A tf.keras.regularizers.Regularizer object for Conv2D. Default is None.
bias_regularizer A tf.keras.regularizers.Regularizer object for Conv2D.
**kwargs Additional keyword arguments to be passed.

output_specs A dict of {level: TensorShape} pairs for the model output.



Calls the model on new inputs and returns the outputs as tensors.

In this case call() just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

inputs Input tensor, or dict/list/tuple of input tensors.
training Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide here.

A tensor if there is a single output, or a list of tensors if there are more than one outputs.