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Updated models to use revised name for callable (SE-0253), fixed a few issues caused by swift-apis changes. #166

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dan-zheng merged 5 commits into from
Jun 8, 2019
Merged

Updated models to use revised name for callable (SE-0253), fixed a few issues caused by swift-apis changes. #166

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3284036
Updated Transformer and MNIST models to work with 2019-06-04 developm…
leoxzhao Jun 6, 2019
4d91a26
Updated other models.
leoxzhao Jun 6, 2019
09b9692
Updated ResNet to work with 2019-06-04 development snapshot.
leoxzhao Jun 6, 2019
4483d48
Updated Catch and CIFAR models.
leoxzhao Jun 6, 2019
20274f8
Added a method to satisfy TensorGroup protocol that can pass build.
leoxzhao Jun 7, 2019
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2 changes: 1 addition & 1 deletion Autoencoder/main.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -94,7 +94,7 @@ struct Autoencoder: Layer {
activation: tanh)

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let encoder = input.sequenced(through: encoder1, encoder2, encoder3, encoder4)
return encoder.sequenced(through: decoder1, decoder2, decoder3, decoder4)
}
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17 changes: 17 additions & 0 deletions CIFAR/Data.swift
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Expand Up @@ -32,6 +32,23 @@ func downloadCIFAR10IfNotPresent(to directory: String = ".") {
struct Example: TensorGroup {
var label: Tensor<Int32>
var data: Tensor<Float>

init(label: Tensor<Int32>, data: Tensor<Float>) {
self.label = label
self.data = data
}

public init<C: RandomAccessCollection>(
_handles: C
) where C.Element: _AnyTensorHandle {
precondition(_handles.count == 2)
let labelIndex = _handles.startIndex
let dataIndex = _handles.index(labelIndex, offsetBy: 1)
label = Tensor<Int32>(handle: TensorHandle<Int32>(handle: _handles[labelIndex]))
data = Tensor<Float>(handle: TensorHandle<Float>(handle: _handles[dataIndex]))
}

public var _tensorHandles: [_AnyTensorHandle] { [label.handle.handle, data.handle.handle] }
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@bgogul bgogul Jun 7, 2019

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@leoxzhao you should be able to use label.handle._tfeTensorHandle instead of label.handle.handle. You might even want to add an extension so that you can use label._tfeTensorHandle:

/// Something like this...
extension Tensor {
   public var _tfeTensorHandle { handle._tfeTensorHandle }
}

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@leoxzhao leoxzhao Jun 7, 2019

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Thanks for the hint. I guess you meant handle._cTensorHandle. I found that handle (TensorHandle) does not have _tfeTensorHandle, it has _cTensorHandle of which _AnyTensorHandle is a wrapper. So, I use _cTensorHandle to create one _AnyTensorHandle for our purpose.

}

// Each CIFAR data file is provided as a Python pickle of NumPy arrays
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4 changes: 2 additions & 2 deletions CIFAR/Models.swift
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Expand Up @@ -29,7 +29,7 @@ struct PyTorchModel: Layer {
var dense3 = Dense<Float>(inputSize: 84, outputSize: 10, activation: identity)

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let convolved = input.sequenced(through: conv1, pool1, conv2, pool2)
return convolved.sequenced(through: flatten, dense1, dense2, dense3)
}
Expand All @@ -54,7 +54,7 @@ struct KerasModel: Layer {
var dense2 = Dense<Float>(inputSize: 512, outputSize: 10, activation: identity)

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let conv1 = input.sequenced(through: conv1a, conv1b, pool1, dropout1)
let conv2 = conv1.sequenced(through: conv2a, conv2b, pool2, dropout2)
return conv2.sequenced(through: flatten, dense1, dropout3, dense2)
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6 changes: 3 additions & 3 deletions CIFAR/ResNet.swift
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Expand Up @@ -37,7 +37,7 @@ struct Conv2DBatchNorm: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return input.sequenced(through: conv, norm)
}
}
Expand Down Expand Up @@ -68,7 +68,7 @@ struct BasicBlock: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let blocksReduced = blocks.differentiableReduce(input) { last, layer in
relu(layer(last))
}
Expand Down Expand Up @@ -97,7 +97,7 @@ struct ResNet: Layer {
var classifier = Dense<Float>(inputSize: 64, outputSize: 10, activation: softmax)

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let tmp = relu(inputLayer(input))
let convolved = tmp.sequenced(through: basicBlock1, basicBlock2, basicBlock3)
return convolved.sequenced(through: averagePool, flatten, classifier)
Expand Down
6 changes: 3 additions & 3 deletions CIFAR/WideResNet.swift
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Expand Up @@ -41,7 +41,7 @@ struct BatchNormConv2DBlock: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let firstLayer = conv1(relu(norm1(input)))
return conv2(relu(norm2(firstLayer)))
}
Expand Down Expand Up @@ -87,7 +87,7 @@ struct WideResNetBasicBlock: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let blocksReduced = blocks.differentiableReduce(input) { last, layer in
relu(layer(last))
}
Expand Down Expand Up @@ -126,7 +126,7 @@ struct WideResNet: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let inputLayer = input.sequenced(through: l1, l2, l3, l4)
let finalNorm = relu(norm(inputLayer))
return finalNorm.sequenced(through: avgPool, flatten, classifier)
Expand Down
2 changes: 1 addition & 1 deletion Catch/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -23,5 +23,5 @@ installed. Make sure you've added the correct version of `swift` to your path.
To train the model, run:

```
swift -O catch.swift
swift -O main.swift
```
4 changes: 2 additions & 2 deletions Catch/main.swift
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Expand Up @@ -53,7 +53,7 @@ struct Model: Layer {
generator: &rng)

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return input.sequenced(through: layer1, layer2)
}
}
Expand Down Expand Up @@ -83,7 +83,7 @@ extension CatchAgent {
let (ŷ, backprop) = model.appliedForBackpropagation(to: x)
let maxIndex = ŷ.argmax().scalarized()

let 𝛁loss = -log(Tensor(ŷ.max())).broadcast(like: ŷ) * previousReward
let 𝛁loss = -log(Tensor<Float>(ŷ.max())).broadcasted(like: ŷ) * previousReward
let (𝛁model, _) = backprop(𝛁loss)
optimizer.update(&model.allDifferentiableVariables, along: 𝛁model)

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2 changes: 1 addition & 1 deletion Gym/CartPole/main.swift
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Expand Up @@ -48,7 +48,7 @@ struct Net: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return input.sequenced(through: l1, l2)
}
}
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2 changes: 1 addition & 1 deletion MNIST/main.swift
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Expand Up @@ -64,7 +64,7 @@ struct Classifier: Layer {
var layer1b = Dense<Float>(inputSize: 128, outputSize: 10, activation: softmax)

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let convolved = input.sequenced(through: conv1a, conv1b, pool1)
return convolved.sequenced(through: dropout1a, flatten, layer1a, dropout1b, layer1b)
}
Expand Down
6 changes: 3 additions & 3 deletions MiniGo/Models/GoModel.swift
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Expand Up @@ -61,7 +61,7 @@ struct ConvBN: Layer {
}

@differentiable
func call(_ input: Tensor<Float>) -> Tensor<Float> {
func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
return norm(conv(input))
}
}
Expand Down Expand Up @@ -90,7 +90,7 @@ struct ResidualIdentityBlock: Layer {
}

@differentiable
func call(_ input: Tensor<Float>) -> Tensor<Float> {
func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
var tmp = relu(layer1(input))
tmp = layer2(tmp)
return relu(tmp + input)
Expand Down Expand Up @@ -158,7 +158,7 @@ public struct GoModel: Layer {
}

@differentiable(wrt: (self, input), vjp: _vjpCall)
public func call(_ input: Tensor<Float>) -> GoModelOutput {
public func callAsFunction(_ input: Tensor<Float>) -> GoModelOutput {
let batchSize = input.shape[0]
var output = relu(initialConv(input))

Expand Down
17 changes: 17 additions & 0 deletions ResNet/Data.swift
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Expand Up @@ -35,6 +35,23 @@ func downloadCIFAR10IfNotPresent(to directory: String = ".") {
struct Example: TensorGroup {
var label: Tensor<Int32>
var data: Tensor<Float>

init(label: Tensor<Int32>, data: Tensor<Float>) {
self.label = label
self.data = data
}

public init<C: RandomAccessCollection>(
_handles: C
) where C.Element: _AnyTensorHandle {
precondition(_handles.count == 2)
let labelIndex = _handles.startIndex
let dataIndex = _handles.index(labelIndex, offsetBy: 1)
label = Tensor<Int32>(handle: TensorHandle<Int32>(handle: _handles[labelIndex]))
data = Tensor<Float>(handle: TensorHandle<Float>(handle: _handles[dataIndex]))
}

public var _tensorHandles: [_AnyTensorHandle] { [label.handle.handle, data.handle.handle] }
}

// Each CIFAR data file is provided as a Python pickle of NumPy arrays
Expand Down
22 changes: 11 additions & 11 deletions ResNet/ResNet50.swift
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Expand Up @@ -37,7 +37,7 @@ struct ConvBN: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return input.sequenced(through: conv, norm)
}
}
Expand Down Expand Up @@ -65,7 +65,7 @@ struct ResidualBasicBlock: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return layer2(relu(layer1(input)))
}
}
Expand Down Expand Up @@ -94,7 +94,7 @@ struct ResidualBasicBlockShortcut: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return layer2(relu(layer1(input))) + shortcut(input)
}
}
Expand Down Expand Up @@ -127,7 +127,7 @@ struct ResidualConvBlock: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let tmp = relu(layer2(relu(layer1(input))))
return relu(layer3(tmp) + shortcut(input))
}
Expand All @@ -150,7 +150,7 @@ struct ResidualIdentityBlock: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let tmp = relu(layer2(relu(layer1(input))))
return relu(layer3(tmp) + input)
}
Expand All @@ -175,7 +175,7 @@ struct ResidualIdentityBlockStack: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return input.sequenced(through: block1, block2, block3, block4, block5)
}
}
Expand Down Expand Up @@ -218,7 +218,7 @@ struct ResNet18: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let inputLayer = maxPool(relu(l1(input)))
let level2 = inputLayer.sequenced(through: l2a, l2b)
let level3 = level2.sequenced(through: l3a, l3b)
Expand Down Expand Up @@ -274,7 +274,7 @@ struct ResNet34: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let inputLayer = maxPool(relu(l1(input)))
let level2 = inputLayer.sequenced(through: l2a, l2b, l2c)
let level3 = level2.sequenced(through: l3a, l3b, l3c, l3d)
Expand Down Expand Up @@ -326,7 +326,7 @@ struct ResNet50: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let inputLayer = maxPool(relu(l1(input)))
let level2 = inputLayer.sequenced(through: l2a, l2b, l2c)
let level3 = level2.sequenced(through: l3a, l3b, l3c, l3d)
Expand Down Expand Up @@ -383,7 +383,7 @@ struct ResNet101: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let inputLayer = maxPool(relu(l1(input)))
let level2 = inputLayer.sequenced(through: l2a, l2b, l2c)
let level3 = level2.sequenced(through: l3a, l3b, l3c, l3d)
Expand Down Expand Up @@ -441,7 +441,7 @@ struct ResNet152: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let inputLayer = maxPool(relu(l1(input)))
let level2 = inputLayer.sequenced(through: l2a, l2b, l2c)
let level3 = level2.sequenced(through: l3a, l3b, l3c, l3d)
Expand Down
12 changes: 6 additions & 6 deletions ResNet/ResNetV2.swift
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Expand Up @@ -38,7 +38,7 @@ struct Conv2DBatchNorm: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return input.sequenced(through: conv, norm)
}
}
Expand All @@ -60,7 +60,7 @@ struct BatchNormConv2D: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return conv(relu(norm(input)))
}
}
Expand Down Expand Up @@ -88,7 +88,7 @@ struct PreActivatedResidualBasicBlock: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return input.sequenced(through: layer1, layer2)
}
}
Expand Down Expand Up @@ -117,7 +117,7 @@ struct PreActivatedResidualBasicBlockShortcut: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
return input.sequenced(through: layer1, layer2) + shortcut(input)
}
}
Expand Down Expand Up @@ -162,7 +162,7 @@ struct PreActivatedResNet18: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let inputLayer = input.sequenced(through: l1, maxPool)
let level2 = inputLayer.sequenced(through: l2a, l2b)
let level3 = level2.sequenced(through: l3a, l3b)
Expand Down Expand Up @@ -221,7 +221,7 @@ struct PreActivatedResNet34: Layer {
}

@differentiable
func call(_ input: Input) -> Output {
func callAsFunction(_ input: Input) -> Output {
let inputLayer = input.sequenced(through: l1, maxPool)
let level2 = inputLayer.sequenced(through: l2a, l2b, l2c)
let level3 = level2.sequenced(through: l3a, l3b, l3c, l3d)
Expand Down
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