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// Copyright 2019 The TensorFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import TensorFlow
// Original Paper:
// SqueezeNet: AlexNet Level Accuracy with 50X Fewer Parameters
// Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally
// and Kurt Keutzer
// https://arxiv.org/pdf/1602.07360.pdf
public struct Fire: Layer {
public var squeeze: Conv2D<Float>
public var expand1: Conv2D<Float>
public var expand3: Conv2D<Float>
public init(
inputFilterCount: Int,
squeezeFilterCount: Int,
expand1FilterCount: Int,
expand3FilterCount: Int
) {
squeeze = Conv2D(
filterShape: (1, 1, inputFilterCount, squeezeFilterCount),
activation: relu)
expand1 = Conv2D(
filterShape: (1, 1, squeezeFilterCount, expand1FilterCount),
activation: relu)
expand3 = Conv2D(
filterShape: (3, 3, squeezeFilterCount, expand3FilterCount),
padding: .same,
activation: relu)
}
@differentiable
public func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
let squeezed = squeeze(input)
let expanded1 = expand1(squeezed)
let expanded3 = expand3(squeezed)
return expanded1.concatenated(with: expanded3, alongAxis: -1)
}
}
public struct SqueezeNetV1_0: Layer {
public var conv1 = Conv2D<Float>(
filterShape: (7, 7, 3, 96),
strides: (2, 2),
padding: .same,
activation: relu)
public var maxPool1 = MaxPool2D<Float>(poolSize: (3, 3), strides: (2, 2))
public var fire2 = Fire(
inputFilterCount: 96,
squeezeFilterCount: 16,
expand1FilterCount: 64,
expand3FilterCount: 64)
public var fire3 = Fire(
inputFilterCount: 128,
squeezeFilterCount: 16,
expand1FilterCount: 64,
expand3FilterCount: 64)
public var fire4 = Fire(
inputFilterCount: 128,
squeezeFilterCount: 32,
expand1FilterCount: 128,
expand3FilterCount: 128)
public var maxPool4 = MaxPool2D<Float>(poolSize: (3, 3), strides: (2, 2))
public var fire5 = Fire(
inputFilterCount: 256,
squeezeFilterCount: 32,
expand1FilterCount: 128,
expand3FilterCount: 128)
public var fire6 = Fire(
inputFilterCount: 256,
squeezeFilterCount: 48,
expand1FilterCount: 192,
expand3FilterCount: 192)
public var fire7 = Fire(
inputFilterCount: 384,
squeezeFilterCount: 48,
expand1FilterCount: 192,
expand3FilterCount: 192)
public var fire8 = Fire(
inputFilterCount: 384,
squeezeFilterCount: 64,
expand1FilterCount: 256,
expand3FilterCount: 256)
public var maxPool8 = MaxPool2D<Float>(poolSize: (3, 3), strides: (2, 2))
public var fire9 = Fire(
inputFilterCount: 512,
squeezeFilterCount: 64,
expand1FilterCount: 256,
expand3FilterCount: 256)
public var conv10: Conv2D<Float>
public var avgPool10 = AvgPool2D<Float>(poolSize: (13, 13), strides: (1, 1))
public var dropout = Dropout<Float>(probability: 0.5)
public init(classCount: Int) {
conv10 = Conv2D(filterShape: (1, 1, 512, classCount), strides: (1, 1), activation: relu)
}
@differentiable
public func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
let convolved1 = input.sequenced(through: conv1, maxPool1)
let fired1 = convolved1.sequenced(through: fire2, fire3, fire4, maxPool4, fire5, fire6)
let fired2 = fired1.sequenced(through: fire7, fire8, maxPool8, fire9)
let convolved2 = fired2.sequenced(through: dropout, conv10, avgPool10)
.reshaped(to: [input.shape[0], conv10.filter.shape[3]])
return convolved2
}
}
public struct SqueezeNetV1_1: Layer {
public var conv1 = Conv2D<Float>(
filterShape: (3, 3, 3, 64),
strides: (2, 2),
padding: .same,
activation: relu)
public var maxPool1 = MaxPool2D<Float>(poolSize: (3, 3), strides: (2, 2))
public var fire2 = Fire(
inputFilterCount: 64,
squeezeFilterCount: 16,
expand1FilterCount: 64,
expand3FilterCount: 64)
public var fire3 = Fire(
inputFilterCount: 128,
squeezeFilterCount: 16,
expand1FilterCount: 64,
expand3FilterCount: 64)
public var maxPool3 = MaxPool2D<Float>(poolSize: (3, 3), strides: (2, 2))
public var fire4 = Fire(
inputFilterCount: 128,
squeezeFilterCount: 32,
expand1FilterCount: 128,
expand3FilterCount: 128)
public var fire5 = Fire(
inputFilterCount: 256,
squeezeFilterCount: 32,
expand1FilterCount: 128,
expand3FilterCount: 128)
public var maxPool5 = MaxPool2D<Float>(poolSize: (3, 3), strides: (2, 2))
public var fire6 = Fire(
inputFilterCount: 256,
squeezeFilterCount: 48,
expand1FilterCount: 192,
expand3FilterCount: 192)
public var fire7 = Fire(
inputFilterCount: 384,
squeezeFilterCount: 48,
expand1FilterCount: 192,
expand3FilterCount: 192)
public var fire8 = Fire(
inputFilterCount: 384,
squeezeFilterCount: 64,
expand1FilterCount: 256,
expand3FilterCount: 256)
public var fire9 = Fire(
inputFilterCount: 512,
squeezeFilterCount: 64,
expand1FilterCount: 256,
expand3FilterCount: 256)
public var conv10: Conv2D<Float>
public var avgPool10 = AvgPool2D<Float>(poolSize: (13, 13), strides: (1, 1))
public var dropout = Dropout<Float>(probability: 0.5)
public init(classCount: Int) {
conv10 = Conv2D(filterShape: (1, 1, 512, classCount), strides: (1, 1), activation: relu)
}
@differentiable
public func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
let convolved1 = input.sequenced(through: conv1, maxPool1)
let fired1 = convolved1.sequenced(through: fire2, fire3, maxPool3, fire4, fire5)
let fired2 = fired1.sequenced(through: maxPool5, fire6, fire7, fire8, fire9)
let convolved2 = fired2.sequenced(through: dropout, conv10, avgPool10)
.reshaped(to: [input.shape[0], conv10.filter.shape[3]])
return convolved2
}
}