tfm.vision.layers.DepthwiseSeparableConvBlock

Creates a depthwise separable convolution block with batch normalization.

tfm.vision.layers.DepthwiseSeparableConvBlock(
    filters: int,
    kernel_size: int = 3,
    strides: int = 1,
    regularize_depthwise=False,
    activation: Text = 'relu6',
    kernel_initializer: Text = 'VarianceScaling',
    kernel_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
    dilation_rate: int = 1,
    use_sync_bn: bool = False,
    norm_momentum: float = 0.99,
    norm_epsilon: float = 0.001,
    **kwargs
)

filters An int number of filters for the first two convolutions. Note that the third and final convolution will use 4 times as many filters.
kernel_size An int that specifies the height and width of the 2D convolution window.
strides An int of block stride. If greater than 1, this block will ultimately downsample the input.
regularize_depthwise A bool. If Ture, apply regularization on depthwise.
activation A str name of the activation function.
kernel_initializer A str of kernel_initializer for convolutional layers.
kernel_regularizer A tf_keras.regularizers.Regularizer object for Conv2D. Default to None.
dilation_rate An int or tuple/list of 2 int, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions.
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.
**kwargs Additional keyword arguments to be passed.

Methods

call

View source

call(
    inputs, training=None
)

This is where the layer's logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances, in __init__(), or in the build() method that is called automatically before call() executes for the first time.

Args
inputs Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules:

  • inputs must be explicitly passed. A layer cannot have zero arguments, and inputs cannot be provided via the default value of a keyword argument.
  • NumPy array or Python scalar values in inputs get cast as tensors.
  • TF-Keras mask metadata is only collected from inputs.
  • Layers are built (build(input_shape) method) using shape info from inputs only.
  • input_spec compatibility is only checked against inputs.
  • Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.
  • The SavedModel input specification is generated using inputs only.
  • Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.
*args Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved:
  • training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference.
  • mask: Boolean input mask. If the layer's call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a TF-Keras layer with masking support).

    • Returns
    A tensor or list/tuple of tensors.