tfm.vision.decoders.NASFPN

Creates a NAS-FPN model.

tfm.vision.decoders.NASFPN(
    input_specs: Mapping[str, tf.TensorShape],
    min_level: int = 3,
    max_level: int = 7,
    block_specs: List[BlockSpec] = build_block_specs(),
    num_filters: int = 256,
    num_repeats: int = 5,
    use_separable_conv: bool = False,
    activation: str = 'relu',
    use_sync_bn: bool = False,
    norm_momentum: float = 0.99,
    norm_epsilon: float = 0.001,
    kernel_initializer: str = 'VarianceScaling',
    kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    **kwargs
)

This implements the paper: Golnaz Ghiasi, Tsung-Yi Lin, Ruoming Pang, Quoc V. Le. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. (https://arxiv.org/abs/1904.07392)

Args
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.
block_specs	a list of BlockSpec objects that specifies the NAS-FPN network topology. By default, the previously discovered architecture is used.
num_filters	An int number of filters in FPN layers.
num_repeats	number of repeats for feature pyramid network.
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.

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

Methods

call

call(
    inputs, training=None, mask=None
)

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).

Note: This method should not be called directly. It is only meant to be overridden when subclassing tf.keras.Model. To call a model on an input, always use the __call__() method, i.e. model(inputs), which relies on the underlying call() method.

Args
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.

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