tfm.vision.layers.InvertedBottleneckBlock

An inverted bottleneck block.

tfm.vision.layers.InvertedBottleneckBlock(
    in_filters,
    out_filters,
    expand_ratio,
    strides,
    kernel_size=3,
    se_ratio=None,
    stochastic_depth_drop_rate=None,
    kernel_initializer='VarianceScaling',
    kernel_regularizer=None,
    bias_regularizer=None,
    activation='relu',
    se_inner_activation='relu',
    se_gating_activation='sigmoid',
    se_round_down_protect=True,
    expand_se_in_filters=False,
    depthwise_activation=None,
    use_sync_bn=False,
    dilation_rate=1,
    divisible_by=1,
    regularize_depthwise=False,
    use_depthwise=True,
    use_residual=True,
    norm_momentum=0.99,
    norm_epsilon=0.001,
    output_intermediate_endpoints=False,
    **kwargs
)

in_filters An int number of filters of the input tensor.
out_filters An int number of filters of the output tensor.
expand_ratio An int of expand_ratio for an inverted bottleneck block.
strides An int block stride. If greater than 1, this block will ultimately downsample the input.
kernel_size An int kernel_size of the depthwise conv layer.
se_ratio A float or None. If not None, se ratio for the squeeze and excitation layer.
stochastic_depth_drop_rate A float or None. if not None, drop rate for the stochastic depth layer.
kernel_initializer A str of kernel_initializer for convolutional layers.
kernel_regularizer A tf_keras.regularizers.Regularizer object for Conv2D. Default to None.
bias_regularizer A tf_keras.regularizers.Regularizer object for Conv2d. Default to None.
activation A str name of the activation function.
se_inner_activation A str name of squeeze-excitation inner activation.
se_gating_activation A str name of squeeze-excitation gating activation.
se_round_down_protect A bool of whether round down more than 10% will be allowed in SE layer.
expand_se_in_filters A bool of whether or not to expand in_filter in squeeze and excitation layer.
depthwise_activation A str name of the activation function for depthwise only.
use_sync_bn A bool. If True, use synchronized batch normalization.
dilation_rate An int that specifies the dilation rate to use for.
divisible_by An int that ensures all inner dimensions are divisible by this number. dilated convolution: An int to specify the same value for all spatial dimensions.
regularize_depthwise A bool of whether or not apply regularization on depthwise.
use_depthwise A bool of whether to uses fused convolutions instead of depthwise.
use_residual A bool of whether to include residual connection between input and output.
norm_momentum A float of normalization momentum for the moving average.
norm_epsilon A float added to variance to avoid dividing by zero.
output_intermediate_endpoints A bool of whether or not output the intermediate endpoints.
**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.