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Layers package definition.
Classes
class BottleneckBlock: A standard bottleneck block.
class BottleneckBlock3D: Creates a 3D bottleneck block.
class BottleneckResidualInner: Creates a single inner block of a bottleneck.
class BoxSampler: Creates a BoxSampler to sample positive and negative boxes.
class CausalConvMixin: Mixin class to implement CausalConv for tf.keras.layers.Conv layers.
class Conv2D: Conv2D layer supporting CausalConv.
class Conv3D: Conv3D layer supporting CausalConv.
class DepthwiseConv2D: DepthwiseConv2D layer supporting CausalConv.
class DepthwiseSeparableConvBlock: Creates a depthwise separable convolution block with batch normalization.
class DetectionGenerator: Generates the final detected boxes with scores and classes.
class GlobalAveragePool3D: Creates a global average pooling layer with causal mode.
class InvertedBottleneckBlock: An inverted bottleneck block.
class MaskSampler: Samples and creates mask training targets.
class MultilevelDetectionGenerator: Generates detected boxes with scores and classes for one-stage detector.
class MultilevelROIAligner: Performs ROIAlign for the second stage processing.
class MultilevelROIGenerator: Proposes RoIs for the second stage processing.
class PositionalEncoding: Creates a network layer that adds a sinusoidal positional encoding.
class ROISampler: Samples ROIs and assigns targets to the sampled ROIs.
class ResidualBlock: A residual block.
class ResidualInner: Creates a single inner block of a residual.
class ReversibleLayer: Creates a reversible layer.
class Scale: Scales the input by a trainable scalar weight.
class SelfGating: Feature gating as used in S3D-G.
class SpatialAveragePool3D: Creates a global average pooling layer pooling across spatial dimentions.
class SqueezeExcitation: Creates a squeeze and excitation layer.
class StochasticDepth: Creates a stochastic depth layer.
class TemporalSoftmaxPool: Creates a network layer corresponding to temporal softmax pooling.