Initial commit of ONNX.js v0.1.0

.clang-format

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+---
+Language: JavaScript
+BasedOnStyle: Google
+ColumnLimit: 120
+---
+Language: Cpp
+BasedOnStyle: LLVM

.gitignore

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+dist/
+node_modules/
+types/
+
+lib/**/*.js
+lib/**/*.js.map
+test/**/*.js
+test/**/*.js.map
+tools/**/*.js
+tools/**/*.js.map
+
+npm-debug.log
+.DS_Store
+yarn-error.log

.gitmodules

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+[submodule "deps/emsdk"]
+	path = deps/emsdk
+	url = https://github.com/juj/emsdk.git
+	ignore = dirty
+[submodule "deps/eigen"]
+	path = deps/eigen
+	url = https://github.com/eigenteam/eigen-git-mirror.git
+	ignore = dirty
+[submodule "deps/data"]
+	path = deps/data
+	url = https://github.com/Microsoft/onnxjs-demo.git
+	branch = data

.huskyrc

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+{
+  "skipCI": false,
+  "hooks": {
+    "pre-commit": "npm run lint && npm run format && node -e \"var lines = require('child_process').execSync('git ls-files -m').toString(); var tsFound = lines.split('\\n').map(i => i.trim()).find(i => i.endsWith('.ts')); if (tsFound) { console.error('ERR File not formatted: ' + tsFound); process.exit(1); }\""
+  }
+}

.npmignore

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+benchmark/
+deps/
+examples/
+node_modules/
+src/
+test/
+tools/
+
+lib/**/*.ts
+
+.vscode
+
+.clang-format
+.huskyrc
+.gitmodules
+.npmrc
+.npmignore
+npm-debug.log
+
+karma.conf.js
+tsconfig.json
+tslint.json
+webpack.config.js

.vscode/launch.json

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+{
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "type": "chrome",
+      "request": "attach",
+      "name": "Attach to Chrome",
+      "port": 9333,
+      "webRoot": "${workspaceFolder}",
+      "sourceMaps": true,
+      "smartStep": true
+    },
+    {
+      "type": "node",
+      "request": "launch",
+      "name": "Launch Mocha Tests (node)",
+      "program": "${workspaceFolder}/node_modules/mocha/bin/_mocha",
+      "args": [
+        "-u",
+        "tdd",
+        "--timeout",
+        "999999",
+        "--colors",
+        "${workspaceFolder}/test/unittest"
+      ],
+      "internalConsoleOptions": "openOnSessionStart",
+      "sourceMaps": true,
+      "smartStep": true
+    }
+  ]
+}

.vscode/settings.json

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+// Place your settings in this file to overwrite default and user settings.
+{
+    "search.exclude": {
+      "**/node_modules": true,
+      "./dist": true,
+      "./types": true,
+    },
+    "files.exclude": {
+      "lib/**/*.js.map": true,
+      "lib/**/*.js": true,
+    },
+    "tslint.enable": true,
+    "tslint.run": "onType",
+    "tslint.configFile": "tslint.json",
+    "files.trimTrailingWhitespace": true,
+    "editor.tabSize": 2,
+    "editor.insertSpaces": true,
+    "[typescript]": {
+      "editor.formatOnSave": true
+    },
+    "editor.rulers": [120],
+    "clang-format.style": "file",
+    "files.insertFinalNewline": true,
+    "editor.detectIndentation": false,
+    "editor.wrappingIndent": "none",
+    "typescript.tsdk": "node_modules/typescript/lib",
+    "clang-format.executable": "${workspaceRoot}/node_modules/.bin/clang-format"
+  }

.vscode/tasks.json

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+{
+  // See https://go.microsoft.com/fwlink/?LinkId=733558
+  // for the documentation about the tasks.json format
+  "version": "2.0.0",
+  "tasks": [
+    {
+      "label": "test: suite0",
+      "group": "test",
+      "type": "shell",
+      "command": "${workspaceFolder}/tools/test-runner-cli suite0"
+    }
+  ]
+}

CONTRIBUTING.md

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+
+You are welcome to contribute to ONNX.js!
+
+You can contribute to ONNX.js in following ways:
+
+* Contribute to core module functionality
+    * For example- implement ONNX operators to support more models, implement optimizations to the model execution framework, etc.
+* Contribute bug fixes.
+* Review the [source code changes](https://github.com/Microsoft/onnxjs/pulls).
+* [Report issues](https://github.com/Microsoft/onnxjs/issues) and help us verify fixes as they are checked in.
+
+Any changes are required to be reviewed. Please submit a [pull request](https://help.github.com/articles/about-pull-requests/) so that changes can be reviewed.
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [email protected] with any additional questions or comments.

LICENSE

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+ONNX.js
+
+Copyright (c) Microsoft Corporation. All rights reserved.
+
+MIT License
+
+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.

README.md

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+# ONNX.js
+ONNX.js is a Javascript library for running ONNX models on browsers and on Node.js.
+
+ONNX.js has adopted WebAssembly and WebGL technologies for providing an optimized ONNX model inference runtime for both CPUs and GPUs.
+
+### Why ONNX models
+The [Open Neural Network Exchange](http://onnx.ai/) (ONNX) is an open standard for representing machine learning models. The biggest advantage of ONNX is that it allows interoperability across different open source AI frameworks, which itself offers more flexibility for AI frameworks adoption.  [This](#Getting-ONNX-models) is a great place to start getting acquainted with ONNX models.
+
+### Why ONNX.js
+With ONNX.js, web developers can score pre-trained ONNX models directly on browsers with various benefits of reducing server-client communication and protecting user privacy, as well as offering install-free and cross-platform in-browser ML experience.
+
+ONNX.js can run on both CPU and GPU. For running on CPU, [WebAssembly](https://developer.mozilla.org/en-US/docs/WebAssembly) is adopted to execute model at near-native speed. Furthermore, ONNX.js utilizes [Web Workers](https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API/Using_web_workers) to provide a "multi-threaded" environment to parallelize data processing. Empirical evaluation shows very promising performance gains on CPU by taking full advantage of WebAssembly and Web Workers. For running on GPUs, a popular standard for accessing GPU capabilities - WebGL is adopted. ONNX.js has further adopted several novel optimization techniques for reducing data transfer between CPU and GPU, as well as some techniques to reduce GPU processing cycles to further push the performance to the maximum.
+
+### Benchmarks
+
+Benchmarks have been run against the most prominent open source solutions in the same market. Below are the results collected for Chrome and Edge browsers on one sample machine (computations run on both CPU and GPU):
+
+![alt text](./docs/perf-resnet50.png "Resnet50 Perf numbers")
+
+> NOTE:
+>   1. Keras.js doesn't support WebGL usage on Edge
+>   2. Keras.js and TensorFlow.js doesn't support WebAssembly usage on any browser
+
+> The specs of the machine that was used to perform the benchmarking is listed below:
+> * OS:	Microsoft Windows 10 Enterprise Insider Preview
+> * Model: HP Z240 Tower Workstation
+> * Processor:	Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz, 3401 Mhz, 4 Core(s), 8 Logical Processor(s)
+> * Installed Physical Memory (RAM): 32.0 GB
+> * GPU make / Chip type: AMD FirePro W2100 / AMD FirePro SDI (0x6608)
+> * GPU Memory (approx.): 18.0 GB
+
+### Demo
+
+[ONNX.js demo website](https://microsoft.github.io/onnxjs-demo/) shows the capabilities of ONNX.js. Check the [code](https://github.com/Microsoft/onnxjs-demo).
+
+## Getting Started
+There are multiple ways to use ONNX.js in a project:
+
+### Using `<script>` tag
+
+This is the most straightforward way to use ONNX.js. The following HTML example shows how to use it:
+
+```html
+<html>
+  <head>
+  </head>
+
+  <body>
+    <!-- Load ONNX.js -->
+    <script src="https://cdn.jsdelivr.net/npm/onnxjs/dist/onnx.min.js"></script>
+    <!-- Code that consume ONNX.js -->
+    <script>
+      // create a session
+      const myOnnxSession = new onnx.InferenceSession();
+      // load the ONNX model file
+      myOnnxSession.loadModel("./my-model.onnx").then(()=>{
+        // generate model input
+        const inferenceInputs = getInputs();
+        // execute the model
+        session.run(inferenceInputs).then(output=>{
+          // consume the output
+          const outputTensor = output.values().next().value;
+          console.log(`model output tensor: ${outputTensor.data}.`);
+        });
+      })
+    </script>
+  </body>
+</html>
+```
+
+Refer to [browser/Add](./examples/browser/add) for an example.
+
+### Using NPM and bundling tools
+
+Modern browser based applications are usually built by frameworks like [Angular](https://angular.io/), [React](https://reactjs.org/), [Vue.js](https://vuejs.org/) and so on. This solution usually builds the source code into one or more bundle file(s). The following TypeScript example shows how to use ONNX.js in an async context:
+
+1. Import `Tensor` and `InferenceSession`.
+```ts
+import {Tensor, InferenceSession} from 'onnxjs';
+```
+
+2. Create an instance of `InferenceSession`.
+```ts
+const session = new InferenceSession();
+```
+
+3. Load the ONNX.js model
+```ts
+// use the following in an async method
+const url = './data/models/resnet/model.onnx';
+await session.loadModel(url);
+```
+
+4. Create your input Tensor(s) similar to the example below. You need to do any pre-processing required by
+your model at this stage. For that refer to the documentation of the model you have:
+```javascript
+// creating an array of input Tensors is the easiest way. For other options see the API documentation
+const inputs = [new Tensor(new Float32Array([1.0,2.0,3.0,4.0]), 'float32', [2,2])];
+```
+5. Run the model with the input Tensors. The output Tensor(s) are available once the run operation is complete:
+```javascript
+// run this in an async method:
+const outputMap = await session.run(inputs);
+const outputTensor = outputMap.values().next().value;
+```
+More verbose examples on how to use ONNX.js are located under the `examples` folder. For further info see [Examples](./examples/README.md)
+
+### Running in Node.js
+
+ONNX.js can run in Node.js as well. This is usually for testing purpose. Use the `require()` function to load ONNX.js:
+```js
+require('onnxjs');
+```
+
+Refer to [node/Add](./examples/node/add) for a detailed example.
+
+*NOTE: Currently, the supported platforms are Windows 10 + Edge/Chrome/Firefox/Electron/Node.js (support for other platforms is coming soon).*
+
+## Documents
+
+### Developers
+For information on development ONNX.js, please check [Development](./docs/development.md)
+
+For API reference, please check [API](./docs/api.md).
+
+### Getting ONNX models
+You could get ONNX models easily in multiple ways:
+- Choose a pre-trained ONNX model from the [ONNX Model Zoo](https://github.com/onnx/models)
+- Convert models from mainstream frameworks, e.g. PyTorch, TensorFlow and Keras, by following [ONNX tutorials](https://github.com/onnx/tutorials)
+- Use your data to generate a customized ONNX model from [Azure Custom Vision service](https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/home)
+- [Train a custom model in AzureML](https://github.com/Azure/MachineLearningNotebooks/tree/master/training) and save it in the ONNX format
+
+Learn more about ONNX
+- [ONNX website](http://onnx.ai/)
+- [ONNX on GitHub](https://github.com/onnx/onnx)
+
+### Operators supported
+The [list](./docs/operators.md) of ONNX operators supported by each of the 3 available builtin backends (cpu, wasm, and webgl).
+
+## Contribute
+We’d love to embrace your contribution to ONNX.js. Please refer to [CONTRIBUTING.md](./CONTRIBUTING.md).
+
+## License
+Copyright (c) Microsoft Corporation. All rights reserved.
+
+Licensed under the [MIT](./LICENSE) License.

benchmark/LICENSE

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+# Licenses
+
+TensorFlow.js:
+https://github.com/tensorflow/tfjs/blob/master/LICENSE
+
+Keras.js:
+https://github.com/transcranial/keras-js/blob/master/LICENSE
+
+WebDNN:
+https://github.com/mil-tokyo/webdnn/blob/master/LICENSE.txt
+

benchmark/README.md

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+# Benchmarks
+This sub project is to benchmark and compare ONNX.js peformance vs other leading in-browser AI Inference frameworks.
+
+## Frameworks
+- TensorFlow.js
+- Keras.js
+- WebDNN
+- ONNX.js
+
+## Backends
+(not all backends supported by all platforms)
+- WebGL
+- WebAssembly
+- CPU
+
+## Browsers
+(not all framework/backend combinations are supported by all browsers)
+- Chrome (WebGL 2)
+- Edge (WebGL 1)
+
+## Instructions
+Please download all the sub-folders (containing the model files and corresponding test data) under
+https://github.com/Microsoft/onnxjs-demo/tree/data/data/benchmark and place them in ./benchmark/data prior to running the benchmark tests
+
+1. Ensure that the ONNX.js project (the parent) is already installed and built:
+```bash
+npm ci
+npm run build
+```
+2. Change to `benchmark` subfolder and run npm ci and build in the benchmark folder
+```bash
+cd benchmark
+npm install
+npm run build
+```
+3. Run tests (Chrome)
+```bash
+npm run test
+```
+4. Run tests (Edge)
+
+Note that the Edge tests are likely to crash the broswer. A recommended way would be to comment out
+all Frameworks and backends except one and repeat this for all others. Look in the definition for
+`BenchmarkImageNetData` in src/index.js
+```bash
+npm run test-edge
+```