tfm.vision.backbones.RevNet

Creates a Reversible ResNet (RevNet) family model.

tfm.vision.backbones.RevNet(
    model_id: int,
    input_specs: tf_keras.layers.InputSpec = tf_keras.layers.InputSpec(shape=[None, None, None, 3]),
    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,
    **kwargs
)

Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse. The Reversible Residual Network: Backpropagation Without Storing Activations. (https://arxiv.org/pdf/1707.04585.pdf)

model_id An int of depth/id of ResNet backbone model.
input_specs A tf_keras.layers.InputSpec of the input tensor.
activation A str name of the activation function.
use_sync_bn 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 for kernel initializer of convolutional layers.
kernel_regularizer A tf_keras.regularizers.Regularizer object for Conv2D. Default to None.
**kwargs Additional keyword arguments to be passed.

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).

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.