Rename AutoSequencedDefinition to AutoSequenced for naming concistency

Author: shadaj

Models/ImageClassification/ResNet.swift

@@ -25,15 +25,15 @@ import LayerInit
 // The structure of this implementation was inspired by the Flax ResNet example:
 // https://github.com/google/flax/blob/master/examples/imagenet/models.py
 
-public typealias AutoConvBN = AutoSequencedDefinition<AutoBatchNorm<(Int, Int, Int), Float>, AutoConv2D<Float>>
+public typealias AutoConvBN = AutoSequenced<AutoBatchNorm<(Int, Int, Int), Float>, AutoConv2D<Float>>
 public func autoConvBN(filterShape: (Int, Int), outputChannels: Int, strides: (Int, Int) = (1, 1), padding: Padding = .valid) -> AutoConvBN {
     return AutoBatchNorm<(Int, Int, Int), Float>(momentum: 0.9, epsilon: 1e-5)
         .then(AutoConv2D<Float>(filterShape: filterShape, outputChannels: outputChannels, strides: strides, padding: padding, useBias: false))
 }
 
 // TODO(shadaj): OH NO
-public typealias ConvPlusResidual = AutoSplitMerge<AutoSequencedMany<AutoConvBN>, AutoSequencedDefinition<AutoSequencedMany<AutoSequencedDefinition<AutoConvBN, AutoFunction<Tensor<Float>, Tensor<Float>, AutoConv2D<Float>.OutputShape, AutoConv2D<Float>.OutputShape>>>, AutoConvBN>, Tensor<Float>, AutoBatchNorm<(Int, Int, Int), Float>.InputShape>
-public typealias AutoResidualBlock = AutoSequencedDefinition<ConvPlusResidual, AutoFunction<Tensor<Float>, Tensor<Float>, (Int, Int, Int), (Int, Int, Int)>>
+public typealias ConvPlusResidual = AutoSplitMerge<AutoSequencedMany<AutoConvBN>, AutoSequenced<AutoSequencedMany<AutoSequenced<AutoConvBN, AutoFunction<Tensor<Float>, Tensor<Float>, (Int, Int, Int), (Int, Int, Int)>>>, AutoConvBN>, Tensor<Float>, (Int, Int, Int)>
+public typealias AutoResidualBlock = AutoSequenced<ConvPlusResidual, AutoFunction<Tensor<Float>, Tensor<Float>, (Int, Int, Int), (Int, Int, Int)>>
 public func autoResidualBlock(inputFilters: Int, filters: Int, strides: (Int, Int), useLaterStride: Bool, isBasic: Bool) -> AutoResidualBlock {
     let outFilters = filters * (isBasic ? 1 : 4)
     let needsProjection = (inputFilters != outFilters) || (strides.0 != 1)
@@ -79,7 +79,7 @@ public func autoResidualBlock(inputFilters: Int, filters: Int, strides: (Int, In
     return convPlusResidual.then(AutoFunction(fnShape: { $0 }, fn: { (prev: Tensor<Float>) in relu(prev) }))
 }
 
-public typealias AutoResNet = AutoSequencedDefinition<AutoSequencedDefinition<AutoSequencedDefinition<AutoSequencedDefinition<AutoSequencedDefinition<AutoConvBN, AutoFunction<Tensor<Float>, Tensor<Float>, AutoConv2D<Float>.OutputShape, AutoMaxPool2D<Float>.InputShape>>, AutoMaxPool2D<Float>>, AutoSequencedMany<AutoResidualBlock>>, AutoGlobalAvgPool2D<Float>>, AutoDense<Float>>
+public typealias AutoResNet = AutoSequenced<AutoSequenced<AutoSequenced<AutoSequenced<AutoSequenced<AutoConvBN, AutoFunction<Tensor<Float>, Tensor<Float>, AutoConv2D<Float>.OutputShape, AutoMaxPool2D<Float>.InputShape>>, AutoMaxPool2D<Float>>, AutoSequencedMany<AutoResidualBlock>>, AutoGlobalAvgPool2D<Float>>, AutoDense<Float>>
 public func autoResNet(
     classCount: Int, depth: ResNet.Depth, downsamplingInFirstStage: Bool = true,
     useLaterStride: Bool = true

Models/ImageClassification/VGG.swift

@@ -20,7 +20,7 @@ import LayerInit
 // Karen Simonyan, Andrew Zisserman
 // https://arxiv.org/abs/1409.1556
 
-public typealias AutoVGGBlock = AutoSequencedDefinition<AutoSequencedMany<AutoConv2D<Float>>, AutoMaxPool2D<Float>>
+public typealias AutoVGGBlock = AutoSequenced<AutoSequencedMany<AutoConv2D<Float>>, AutoMaxPool2D<Float>>
 func makeVGGBlock(featureCounts: (Int, Int, Int, Int), blockCount: Int) -> AutoVGGBlock {
     var blocks: [AutoConv2D<Float>] = [
         AutoConv2D<Float>(filterShape: (3, 3), outputChannels: featureCounts.1,
@@ -37,8 +37,8 @@ func makeVGGBlock(featureCounts: (Int, Int, Int, Int), blockCount: Int) -> AutoV
 }
 
 // TODO(shadaj): oh no
-public typealias AutoVGG16Backbone = AutoSequencedDefinition<AutoSequencedDefinition<AutoSequencedDefinition<AutoSequencedDefinition<AutoVGGBlock, AutoVGGBlock>, AutoVGGBlock>, AutoVGGBlock>, AutoVGGBlock>
-public typealias AutoVGG16 = AutoSequencedDefinition<AutoSequencedDefinition<AutoSequencedDefinition<AutoSequencedDefinition<AutoVGG16Backbone, AutoFlatten<Float>>, AutoDense<Float>>, AutoDense<Float>>, AutoDense<Float>>
+public typealias AutoVGG16Backbone = AutoSequenced<AutoSequenced<AutoSequenced<AutoSequenced<AutoVGGBlock, AutoVGGBlock>, AutoVGGBlock>, AutoVGGBlock>, AutoVGGBlock>
+public typealias AutoVGG16 = AutoSequenced<AutoSequenced<AutoSequenced<AutoSequenced<AutoVGG16Backbone, AutoFlatten<Float>>, AutoDense<Float>>, AutoDense<Float>>, AutoDense<Float>>
 
 public func makeVGG16(classCount: Int = 1000) -> AutoVGG16 {
     let layer1 = makeVGGBlock(featureCounts: (3, 64, 64, 64), blockCount: 2)

Models/LayerInit/AutoSequenced.swift

@@ -1,6 +1,6 @@
 import TensorFlow
 
-public struct AutoSequencedDefinition<Layer1: AutoLayer, Layer2: AutoLayer>: AutoLayer
+public struct AutoSequenced<Layer1: AutoLayer, Layer2: AutoLayer>: AutoLayer
 where
   Layer1.OutputShape == Layer2.InputShape,
   Layer1.InstanceType.Output == Layer2.InstanceType.Input,
@@ -23,8 +23,8 @@ where
 }
 
 extension AutoLayer {
-    public func then<T: AutoLayer>(_ other: T) -> AutoSequencedDefinition<Self, T> {
-        return AutoSequencedDefinition<Self, T>(first: self, second: other)
+    public func then<T: AutoLayer>(_ other: T) -> AutoSequenced<Self, T> {
+        return AutoSequenced<Self, T>(first: self, second: other)
     }
 }