First Commit

.codecov.yml

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+ignore:
+  - "Source/library" # C library
+  - "Tests"          # Test files
+
+max_report_age: off

.gitattributes

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+# C libarary
+Source/library/* linguist-vendored

.github/ISSUE_TEMPLATE.md

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+<hr>
+<hr>
+
+## DO NOT Submit Non-bug Issue or Framework Question Here
+Please go to [pcpLiu/SerranoExplore](https://github.com/pcpLiu/SerranoExplore) opening an issue for general support. This repo only accepts bug-related issues.
+
+<hr>
+<hr>
+<!-- 
+Fill the form below. Replace content in `()` with your information. 
+All fields are required excepted optional fields.
+-->
+
+#### Description
+(Give a overall description of the situation you have met)
+
+
+#### Environment Information
+- Serrano version: (XXXXX)
+- Xcode Version: (XXXXX)
+- Platform: (macOS or iOS)
+- Platform System version: (iOSxxxx or macOSxxx)
+
+
+#### Reproduce Steps
+(A __gist__ or __github repo__ is prefered. Should be detailed enough to reproduce.)
+
+
+#### Expecting behavior
+(Describe what result or behavior should be expecting)
+
+
+#### Actual behavior
+(Describe actual result or behavior)
+
+
+#### Possible Implementation (Optional)
+(Optional implementation to fix this bug)

.github/PULL_REQUEST_TEMPLATE.md

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+<hr>
+<hr>
+
+## DO NOT Submit Non-bug PR Here
+Please go to [pcpLiu/SerranoExplore](https://github.com/pcpLiu/SerranoExplore) to discuss feature enhancements. This repo only accepts bug-related PRs.
+
+<hr>
+<hr>
+
+<!-- 
+Fill the form below. Replace content in `()` with your information. 
+All fields are required excepted optional fields.
+-->
+#### Description
+(Description of this PR)
+
+#### Related Issues
+(List related issues for this PR)
+
+#### How Has This Been Tested?
+(Give information how your tested the fixed code)

.gitignore

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+serrano.xcodeproj/xcuserdata
+.testing
+xcuserdata
+
+*.coverage.txt
+*.xccoverage.plist
+
+.cov/
+build/
+DerivedData/
+
+# auto generated local docs
+docs/generated_docs/*

.jazzy.yaml

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+## This configure used to generate guides docs only. Not for API
+
+
+xcodebuild_arguments: ['-scheme', 'TestingHostApp', '-project', 'serrano.xcodeproj']
+module: Serrano
+
+author: pcpLiu
+author_url: https://github.com/pcpLiu
+github_url: https://github.com/pcpLiu/Serrano
+module_version: v0.1.0-alpha
+github_file_prefix: https://github.com/pcpLiu/Serrano/tree/v0.1.0-alpha
+root_url: http://serrano-lib.org/docs/v0.1.0-alpha/guides
+theme: docs/fullwidth
+output: docs/generated_docs/v0.1.0-alpha/guides
+
+# Generate guides only
+exclude: Source/*
+
+
+documentation: docs/guides/**/*.md
+abstract: docs/sections/**/*.md'
+readme: docs/sections/Home.md
+
+
+# Layout
+custom_categories:
+  - name: 'Getting started'
+    children:
+      - 'What is Serrano'
+      - 'Core concepts'
+      - 'VGG16 Example'
+  - name: 'Extension'
+    children:
+      - 'Write your own operator'
+      - 'Write your own optimizer'
+  - name: 'Contribution'
+    children: [Contribution]
+  - name: 'API Reference'
+    children: [API Reference]

.travis.yml

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+os: osx
+osx_image: xcode9
+language: swift
+xcode_sdk: 
+  - iphonesimulator10.3
+  - macosx10.12
+
+branches:
+ only:
+ - master
+
+script:
+  - bash scripts/travisCI.sh

Examples/Graph/VGG16.swift

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+import XCTest
+import Serrano
+
+/**
+This code shows how to construct a VGG16 network using graph's low-level API.
+
+[vgg16](http://book.paddlepaddle.org/03.image_classification/image/vgg16.png)
+*/
+func configureVGG16() -> ComputationGraph {
+	let g = ComputationGraph()
+
+	// input [244, 244, 3]
+	let shape = TensorShape(dataType: .float, shape: [244, 244, 3])
+	let input = g.tensor(shape: shape)
+
+	// block 1
+	let convOp  = ConvOperator2D(numFilters: 64,
+	                             kernelSize: [3, 3],
+	                             padMode: PaddingMode.Same,
+	                             channelPosition: TensorChannelOrder.Last,
+	                             inputShape: input.shape)
+	let (out, _, _) = g.operation(inputs: [input], op: convOp)
+
+	let convOp1  = ConvOperator2D(numFilters: 64,
+	                              kernelSize: [3, 3],
+	                              padMode: PaddingMode.Same,
+	                              channelPosition: TensorChannelOrder.Last,
+	                              inputShape: out.first!.shape)
+	let (out1, _, _) = g.operation(inputs: out, op: convOp1)
+
+	let poo1 = MaxPool2DOperator(kernelSize: [2, 2],
+	                             channelPosition: TensorChannelOrder.Last,
+	                             paddingMode: PaddingMode.Valid)
+	let (out_block_1, _, _) = g.operation(inputs: out1, op: poo1)
+
+	// block 2
+	let convOp2  = ConvOperator2D(numFilters: 128,
+	                              kernelSize: [3, 3],
+	                              padMode: PaddingMode.Same,
+	                              channelPosition: TensorChannelOrder.Last,
+	                              inputShape: out_block_1.first!.shape)
+	let (out2, _, _) = g.operation(inputs: out_block_1, op: convOp2)
+
+	let convOp3  = ConvOperator2D(numFilters: 128,
+	                              kernelSize: [3, 3],
+	                              padMode: PaddingMode.Same,
+	                              channelPosition: TensorChannelOrder.Last,
+	                              inputShape: out2.first!.shape)
+	let (out3, _, _) = g.operation(inputs: out2, op: convOp3)
+
+	let poo2 = MaxPool2DOperator(kernelSize: [2, 2],
+	                             channelPosition: TensorChannelOrder.Last,
+	                             paddingMode: PaddingMode.Valid)
+	let (out_block_2, _, _) = g.operation(inputs: out3, op: poo2)
+
+	// block 3
+	let convOp4  = ConvOperator2D(numFilters: 256,
+	                              kernelSize: [3, 3],
+	                              padMode: PaddingMode.Same,
+	                              channelPosition: TensorChannelOrder.Last,
+	                              inputShape: out_block_2.first!.shape)
+	let (out4, _, _) = g.operation(inputs: out_block_2, op: convOp4)
+
+	let convOp5  = ConvOperator2D(numFilters: 256,
+	                              kernelSize: [3, 3],
+	                              padMode: PaddingMode.Same,
+	                              channelPosition: TensorChannelOrder.Last,
+	                              inputShape: out4.first!.shape)
+	let (out5, _, _) = g.operation(inputs: out4, op: convOp5)
+
+	let convOp6  = ConvOperator2D(numFilters: 256,
+	                              kernelSize: [3, 3],
+	                              padMode: PaddingMode.Same,
+	                              channelPosition: TensorChannelOrder.Last,
+	                              inputShape: out5.first!.shape)
+	let (out6, _, _) = g.operation(inputs: out5, op: convOp6)
+
+	let poo3 = MaxPool2DOperator(kernelSize: [2, 2],
+	                             channelPosition: TensorChannelOrder.Last,
+	                             paddingMode: PaddingMode.Valid)
+	let (out_block_3, _, _) = g.operation(inputs: out6, op: poo3)
+
+	// bloack 4
+	let convOp7  = ConvOperator2D(numFilters: 512,
+	                              kernelSize: [3, 3],
+	                              padMode: PaddingMode.Same,
+	                              channelPosition: TensorChannelOrder.Last,
+	                              inputShape: out_block_3.first!.shape)
+	let (out7, _, _) = g.operation(inputs: out_block_3, op: convOp7)
+
+	let convOp8  = ConvOperator2D(numFilters: 512,
+	                              kernelSize: [3, 3],
+	                              padMode: PaddingMode.Same,
+	                              channelPosition: TensorChannelOrder.Last,
+	                              inputShape: out7.first!.shape)
+	let (out8, _, _) = g.operation(inputs: out7, op: convOp8)
+
+	let convOp9  = ConvOperator2D(numFilters: 512,
+	                              kernelSize: [3, 3],
+	                              padMode: PaddingMode.Same,
+	                              channelPosition: TensorChannelOrder.Last,
+	                              inputShape: out8.first!.shape)
+	let (out9, _, _) = g.operation(inputs: out8, op: convOp9)
+
+	let poo4 = MaxPool2DOperator(kernelSize: [2, 2],
+	                             channelPosition: TensorChannelOrder.Last,
+	                             paddingMode: PaddingMode.Valid)
+	let (out_block_4, _, _) = g.operation(inputs: out9, op: poo4)
+
+	// block 5
+	let convOp10  = ConvOperator2D(numFilters: 512,
+	                               kernelSize: [3, 3],
+	                               padMode: PaddingMode.Same,
+	                               channelPosition: TensorChannelOrder.Last,
+	                               inputShape: out_block_4.first!.shape)
+	let (out10, _, _) = g.operation(inputs: out_block_4, op: convOp10)
+
+	let convOp11  = ConvOperator2D(numFilters: 512,
+	                               kernelSize: [3, 3],
+	                               padMode: PaddingMode.Same,
+	                               channelPosition: TensorChannelOrder.Last,
+	                               inputShape: out10.first!.shape)
+	let (out11, _, _) = g.operation(inputs: out10, op: convOp11)
+
+	let convOp12  = ConvOperator2D(numFilters: 512,
+	                               kernelSize: [3, 3],
+	                               padMode: PaddingMode.Same,
+	                               channelPosition: TensorChannelOrder.Last,
+	                               inputShape: out11.first!.shape)
+	let (out12, _, _) = g.operation(inputs: out11, op: convOp12)
+
+	let poo5 = MaxPool2DOperator(kernelSize: [2, 2],
+	                             channelPosition: TensorChannelOrder.Last,
+	                             paddingMode: PaddingMode.Valid)
+	let (out_block_5, _, _) = g.operation(inputs: out12, op: poo5)
+
+	// block 6
+	let fc13 = FullyconnectedOperator(inputDim: 25088, numUnits: 4096)
+	let (out13, _, _) = g.operation(inputs: out_block_5, op: fc13)
+
+	let fc14 = FullyconnectedOperator(inputDim: 4096, numUnits: 4096)
+	let (out14, _, _) = g.operation(inputs: out13, op: fc14)
+
+	let fc15 = FullyconnectedOperator(inputDim: 4096, numUnits: 4096)
+	let (out15, _, _) = g.operation(inputs: out14, op: fc15)
+
+	return g
+}
+
+
+
+class Example_VGG16: XCTestCase {
+	// Test vgg16 network forward
+	// Suggestion: run this function on macOS or real iOS devices supporting GPU. It goona be very slow on CPU mode.
+	func testVGG16Forawad() {
+		SerranoLogging.release = true
+
+		let _ = SerranoEngine.configuredEngine.configureEngine(computationMode: .GPU)
+		let vgg16 = configureVGG16()
+		vgg16.allocateAllTensors()
+		vgg16.forwardPrepare()
+
+		let start = CFAbsoluteTimeGetCurrent()
+//		vgg16.forward(mode: .CPU)
+		vgg16.forward(mode: .GPU)
+		print("Forward Execution Time : \((CFAbsoluteTimeGetCurrent() - start) * 100) ms")
+	}
+}

Examples/Graph/YOLO.swift

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+//
+//  YOLO.swift
+//  serrano
+//
+//  Created by ZHONGHAO LIU on 10/25/17.
+//  Copyright © 2017 ZHONGHAO LIU. All rights reserved.
+//
+
+import XCTest
+
+class YOLO: XCTestCase {
+
+    override func setUp() {
+        super.setUp()
+        // Put setup code here. This method is called before the invocation of each test method in the class.
+    }
+
+    override func tearDown() {
+        // Put teardown code here. This method is called after the invocation of each test method in the class.
+        super.tearDown()
+    }
+
+    func testExample() {
+        // This is an example of a functional test case.
+        // Use XCTAssert and related functions to verify your tests produce the correct results.
+    }
+
+    func testPerformanceExample() {
+        // This is an example of a performance test case.
+        self.measure {
+            // Put the code you want to measure the time of here.
+        }
+    }
+
+	//TODO:
+}

LICENSE

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+MIT License
+
+Copyright (c) 2017 Zhonghao LIU
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.