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Revert "Add Avgpool3d and Maxpool3d Layers & Tests" #118

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Apr 29, 2019
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Revert "Add Avgpool3d and Maxpool3d Layers & Tests" #118

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119 changes: 10 additions & 109 deletions Sources/DeepLearning/Layer.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -828,73 +828,23 @@ public struct MaxPool2D<Scalar: TensorFlowFloatingPoint>: Layer {
/// - poolSize: Vertical and horizontal factors by which to downscale.
/// - strides: The strides.
/// - padding: The padding.
self.init(poolSize: (1, poolSize.0, poolSize.1, 1),
strides: (1, strides.0, strides.1, 1),
padding: padding)

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
return input.maxPooled(
kernelSize: poolSize, strides: strides, padding: padding)
}
}

/// A max pooling layer for spatial or spatio-temporal data.
@_fixed_layout
public struct MaxPool3D<Scalar: TensorFlowFloatingPoint>: Layer {
/// The size of the sliding reduction window for pooling.
@noDerivative let poolSize: (Int, Int, Int, Int, Int)
/// The strides of the sliding window for each dimension of a 5-D input.
/// Strides in non-spatial dimensions must be `1`.
@noDerivative let strides: (Int, Int, Int, Int, Int)
/// The padding algorithm for pooling.
@noDerivative let padding: Padding

/// Creates a max pooling layer.
public init(
poolSize: (Int, Int, Int, Int, Int),
strides: (Int, Int, Int, Int, Int),
padding: Padding
) {
self.poolSize = poolSize
self.strides = strides
public init(poolSize: (Int, Int), strides: (Int, Int), padding: Padding = .valid) {
self.poolSize = (1, poolSize.0, poolSize.1, 1)
self.strides = (1, strides.0, strides.1, 1)
self.padding = padding
}

/// Creates a max pooling layer.
///
/// - Parameters:
/// - poolSize: Vertical and horizontal factors by which to downscale.
/// - strides: The strides.
/// - padding: The padding.
self.init(poolSize: (1, poolSize.0, poolSize.1, poolSize.2, 1),
strides: (1, strides.0, strides.1, strides.2, 1),
padding: padding)

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
return input.maxPooled(kernelSize: poolSize, strides: strides, padding: padding)
return input.maxPooled(
kernelSize: poolSize, strides: strides, padding: padding)
}
}

public extension MaxPool3D {
/// Creates a max pooling layer with the specified pooling window size and stride. All
/// pooling sizes and strides are the same.
init(poolSize: Int, stride: Int, padding: Padding = .valid) {
self.init(poolsize: (poolSize, poolSize, poolSize),
strides: (stride, stride, stride),
padding: padding)
}
}

/// An average pooling layer for temporal data.
@_fixed_layout
public struct AvgPool1D<Scalar: TensorFlowFloatingPoint>: Layer {
Expand Down Expand Up @@ -944,7 +894,7 @@ public struct AvgPool2D<Scalar: TensorFlowFloatingPoint>: Layer {
/// The padding algorithm for pooling.
@noDerivative let padding: Padding

/// Creates an average pooling layer.
/// Creates a average pooling layer.
public init(
poolSize: (Int, Int, Int, Int),
strides: (Int, Int, Int, Int),
Expand All @@ -955,58 +905,18 @@ public struct AvgPool2D<Scalar: TensorFlowFloatingPoint>: Layer {
self.padding = padding
}

/// Creates an average pooling layer.
/// Creates a average pooling layer.
///
/// - Parameters:
/// - poolSize: Vertical and horizontal factors by which to downscale.
/// - strides: The strides.
/// - padding: The padding.
self.init(poolSize: (1, poolSize.0, poolSize.1, 1),
strides: (1, strides.0, strides.1, 1),
padding: padding)

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
return input.averagePooled(kernelSize: poolSize, strides: strides, padding: padding)
}
}

/// An average pooling layer for spatial or spatio-temporal data.
@_fixed_layout
public struct AvgPool3D<Scalar: TensorFlowFloatingPoint>: Layer {
/// The size of the sliding reduction window for pooling.
@noDerivative let poolSize: (Int, Int, Int, Int, Int)
/// The strides of the sliding window for each dimension of a 5-D input.
/// Strides in non-spatial dimensions must be `1`.
@noDerivative let strides: (Int, Int, Int, Int, Int)
/// The padding algorithm for pooling.
@noDerivative let padding: Padding

/// Creates an average pooling layer.
public init(
poolSize: (Int, Int, Int, Int, Int),
strides: (Int, Int, Int, Int, Int),
padding: Padding
) {
self.poolSize = poolSize
self.strides = strides
public init(poolSize: (Int, Int), strides: (Int, Int), padding: Padding = .valid) {
self.poolSize = (1, poolSize.0, poolSize.1, 1)
self.strides = (1, strides.0, strides.1, 1)
self.padding = padding
}

/// Creates an average pooling layer.
///
/// - Parameters:
/// - poolSize: Vertical and horizontal factors by which to downscale.
/// - strides: The strides.
/// - padding: The padding.
self.init(poolSize: (1, poolSize.0, poolSize.1, poolSize.2, 1),
strides: (1, strides.0, strides.1, strides.2, 1),
padding: padding)

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameter input: The input to the layer.
Expand All @@ -1017,15 +927,6 @@ public struct AvgPool3D<Scalar: TensorFlowFloatingPoint>: Layer {
}
}

public extension AvgPool3D {
/// Creates an average pooling layer with the specified pooling window size and stride. All
/// pooling sizes and strides are the same.
init(poolSize: Int, strides: Int, padding: Padding = .valid) {
self.init(poolSize: (poolSize, poolSize, poolSize),
strides: (strides, strides, strides),
padding: padding)
}
}

/// A global average pooling layer for temporal data.
@_fixed_layout
Expand Down
36 changes: 0 additions & 36 deletions Tests/DeepLearningTests/LayerTests.swift
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Expand Up @@ -34,22 +34,6 @@ final class LayerTests: XCTestCase {
XCTAssertEqual(round(output), expected)
}

func testMaxPool2D() {
let layer = MaxPool2D<Float>(poolSize: (2, 2), strides: (1, 1), padding:.valid)
let input = Tensor(shape: [1, 2, 2, 1], scalars: (0..<4).map(Float.init))
let output = layer.inferring(from: input)
let expected = Tensor<Float>([[[[3]]]])
XCTAssertEqual(round(output), expected)
}

func testMaxPool3D() {
let layer = MaxPool3D<Float>(poolSize: (2 ,2, 2), strides: (1, 1, 1), padding:.valid)
let input = Tensor(shape: [1, 2, 2, 2, 1], scalars: (0..<8).map(Float.init))
let output = layer.inferring(from: input)
let expected = Tensor<Float>([[[[[7]]]]])
XCTAssertEqual(round(output), expected)
}

func testAvgPool1D() {
let layer = AvgPool1D<Float>(poolSize: 3, stride: 1, padding: .valid)
let input = Tensor<Float>([[0, 1, 2, 3, 4], [10, 11, 12, 13, 14]]).expandingShape(at: 2)
Expand All @@ -58,22 +42,6 @@ final class LayerTests: XCTestCase {
XCTAssertEqual(round(output), expected)
}

func testAvgPool2D() {
let layer = AvgPool2D<Float>(poolSize: (2, 5), strides: (1, 1), padding:.valid)
let input = Tensor(shape: [1, 2, 5, 1], scalars: (0..<10).map(Float.init))
let output = layer.inferring(from: input)
let expected = Tensor<Float>([[[[4.5]]]])
XCTAssertEqual(output, expected)
}

func testAvgPool3D() {
let layer = AvgPool3D<Float>(poolSize: (2, 4, 5), stride: (1, 1, 1), padding: .valid)
let input = Tensor(shape: [1, 2, 4, 5, 1], scalars: (0..<20).map(Float.init))
let output = layer.inferring(from: input)
let expected = Tensor<Float>([[[[[9.5]]]]])
XCTAssertEqual(output, expected)
}

func testGlobalAvgPool1D() {
let layer = GlobalAvgPool1D<Float>()
let input = Tensor(shape: [2, 5, 1], scalars: (0..<10).map(Float.init))
Expand Down Expand Up @@ -182,11 +150,7 @@ final class LayerTests: XCTestCase {
static var allTests = [
("testConv1D", testConv1D),
("testMaxPool1D", testMaxPool1D),
("testMaxPool2D", testMaxPool2D),
("testMaxPool3D", testMaxPool3D),
("testAvgPool1D", testAvgPool1D),
("testAvgPool2D", testAvgPool2D),
("testAvgPool3D", testAvgPool3D),
("testGlobalAvgPool1D", testGlobalAvgPool1D),
("testGlobalAvgPool2D", testGlobalAvgPool2D),
("testGlobalAvgPool3D", testGlobalAvgPool3D),
Expand Down