From 8c4497ffc9ca765a3aaf084d7c096239c89554f0 Mon Sep 17 00:00:00 2001
From: shiffman <daniel@shiffman.net>
Date: Tue, 22 Oct 2019 16:12:27 -0400
Subject: [PATCH 1/3] only lowercase if not a URL
---
src/ImageClassifier/index.js | 78 ++++++++++++++++++------------------
1 file changed, 39 insertions(+), 39 deletions(-)
diff --git a/src/ImageClassifier/index.js b/src/ImageClassifier/index.js
index 1359ca68b..8cf509b74 100644
--- a/src/ImageClassifier/index.js
+++ b/src/ImageClassifier/index.js
@@ -76,46 +76,46 @@ class ImageClassifier {
*/
async loadModel(modelUrl) {
if (modelUrl) this.model = await this.loadModelFrom(modelUrl);
- else this.model = await this.modelToUse.load({version: this.version, alpha: this.alpha});
+ else this.model = await this.modelToUse.load({ version: this.version, alpha: this.alpha });
return this;
}
async loadModelFrom(path = null) {
fetch(path)
- .then(r => r.json())
- .then((r) => {
- if (r.ml5Specs) {
- this.mapStringToIndex = r.ml5Specs.mapStringToIndex;
- }
- })
- // When loading model generated by Teachable Machine 2.0, the r.ml5Specs is missing,
- // which is causing imageClassifier failing to display lables.
- // In this case, labels are stored in path/./metadata.json
- // Therefore, I'm fetching the metadata and feeding the labels into this.mapStringToIndex
- // by Yang Yang, yy2473@nyu.edu, Oct 2, 2019
- .then(()=>{
- if (this.mapStringToIndex.length === 0) {
- const split = path.split("/");
- const prefix = split.slice(0, split.length - 1).join("/");
- const metadataUrl = `${prefix}/metadata.json`;
- fetch(metadataUrl)
- .then((res) => {
- if (!res.ok) {
- console.log("Tried to fetch metadata.json, but it seems to be missing.");
- throw Error(res.statusText);
- }
- return res;
- })
- .then(metadataJson => metadataJson.json())
- .then((metadataJson)=> {
- if (metadataJson.labels){
- this.mapStringToIndex = metadataJson.labels;
- }
- })
- .catch(() => console.log("Error when loading metadata.json"));
- }
- });
+ .then(r => r.json())
+ .then((r) => {
+ if (r.ml5Specs) {
+ this.mapStringToIndex = r.ml5Specs.mapStringToIndex;
+ }
+ })
+ // When loading model generated by Teachable Machine 2.0, the r.ml5Specs is missing,
+ // which is causing imageClassifier failing to display lables.
+ // In this case, labels are stored in path/./metadata.json
+ // Therefore, I'm fetching the metadata and feeding the labels into this.mapStringToIndex
+ // by Yang Yang, yy2473@nyu.edu, Oct 2, 2019
+ .then(() => {
+ if (this.mapStringToIndex.length === 0) {
+ const split = path.split("/");
+ const prefix = split.slice(0, split.length - 1).join("/");
+ const metadataUrl = `${prefix}/metadata.json`;
+ fetch(metadataUrl)
+ .then((res) => {
+ if (!res.ok) {
+ console.log("Tried to fetch metadata.json, but it seems to be missing.");
+ throw Error(res.statusText);
+ }
+ return res;
+ })
+ .then(metadataJson => metadataJson.json())
+ .then((metadataJson) => {
+ if (metadataJson.labels) {
+ this.mapStringToIndex = metadataJson.labels;
+ }
+ })
+ .catch(() => console.log("Error when loading metadata.json"));
+ }
+ });
// end of the Oct 2, 2019 fix
this.model = await tf.loadLayersModel(path);
return this.model;
@@ -206,7 +206,7 @@ class ImageClassifier {
|| inputNumOrCallback.elt instanceof HTMLCanvasElement
|| inputNumOrCallback.elt instanceof ImageData)
) {
- imgToPredict = inputNumOrCallback.elt; // Handle p5.js image
+ imgToPredict = inputNumOrCallback.elt; // Handle p5.js image
} else if (typeof inputNumOrCallback === 'object' && inputNumOrCallback.canvas instanceof HTMLCanvasElement) {
imgToPredict = inputNumOrCallback.canvas; // Handle p5.js image
} else if (!(this.video instanceof HTMLVideoElement)) {
@@ -242,15 +242,15 @@ class ImageClassifier {
}
const imageClassifier = (modelName, videoOrOptionsOrCallback, optionsOrCallback, cb) => {
- let model;
let video;
let options = {};
let callback = cb;
- if (typeof modelName === 'string') {
- model = modelName.toLowerCase();
- } else {
+ let model = modelName;
+ if (typeof model !== 'string') {
throw new Error('Please specify a model to use. E.g: "MobileNet"');
+ } else if (model.indexOf('http') === -1) {
+ model = modelName.toLowerCase();
}
if (videoOrOptionsOrCallback instanceof HTMLVideoElement) {
From b9535e78868f5c58cc3c8d6deca02e758dc9e868 Mon Sep 17 00:00:00 2001
From: shiffman <daniel@shiffman.net>
Date: Tue, 22 Oct 2019 16:57:38 -0400
Subject: [PATCH 2/3] same fix for sound classifier
---
src/SoundClassifier/index.js | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
diff --git a/src/SoundClassifier/index.js b/src/SoundClassifier/index.js
index e9a0a5334..b42449f34 100644
--- a/src/SoundClassifier/index.js
+++ b/src/SoundClassifier/index.js
@@ -59,7 +59,7 @@ class SoundClassifier {
/**
* Classifies the audio from microphone and takes a callback to handle the results
- * @param {function | number} numOrCallback -
+ * @param {function | number} numOrCallback -
* takes any of the following params
* @param {function} cb - a callback function that handles the results of the function.
* @return {function} a promise or the results of a given callback, cb.
@@ -82,14 +82,14 @@ class SoundClassifier {
}
const soundClassifier = (modelName, optionsOrCallback, cb) => {
- let model;
let options = {};
let callback = cb;
- if (typeof modelName === 'string') {
- model = modelName.toLowerCase();
- } else {
+ let model = modelName;
+ if (typeof model !== 'string') {
throw new Error('Please specify a model to use. E.g: "SpeechCommands18w"');
+ } else if (model.indexOf('http') === -1) {
+ model = modelName.toLowerCase();
}
if (typeof optionsOrCallback === 'object') {
From 36c0c428706c037da7ddd9721cbe59a53e32121a Mon Sep 17 00:00:00 2001
From: joeyklee <jsphknglee@gmail.com>
Date: Wed, 23 Oct 2019 17:38:25 -0400
Subject: [PATCH 3/3] only call modelInstantiation with beforeAll() rather than
beforeEach()
---
CONTRIBUTING.md | 2 +-
src/BodyPix/index_test.js | 2 +-
src/CharRNN/index_test.js | 16 ++--
src/FaceApi/index_test.js | 13 +--
src/ImageClassifier/index_test.js | 150 +++++++++++++++++++-----------
src/KMeans/index_test.js | 10 +-
src/NeuralNetwork/index_test.js | 34 +++----
7 files changed, 134 insertions(+), 93 deletions(-)
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index df592d8d5..ea8fd1b3c 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -249,7 +249,7 @@ We’re still rolling out all of our unit tests, but if you want to contribute t
```npm run test:single```
- To run a test on a single model
- ```npm run test -- --model:YourModelNameHere```
+ ```npm run test -- --model=YourModelNameHere```
This last one is case sensitive!
diff --git a/src/BodyPix/index_test.js b/src/BodyPix/index_test.js
index b1c767a31..bc4f6e29f 100644
--- a/src/BodyPix/index_test.js
+++ b/src/BodyPix/index_test.js
@@ -47,7 +47,7 @@ describe('bodyPix', () => {
return img;
}
- beforeEach(async () => {
+ beforeAll(async () => {
jasmine.DEFAULT_TIMEOUT_INTERVAL = 5000;
bp = await bodyPix();
});
diff --git a/src/CharRNN/index_test.js b/src/CharRNN/index_test.js
index 573c64e2c..5f9dde22e 100644
--- a/src/CharRNN/index_test.js
+++ b/src/CharRNN/index_test.js
@@ -33,20 +33,20 @@ describe('charRnn', () => {
rnn = await charRNN(RNN_MODEL_URL, undefined);
});
- it('instantiates an rnn with all the defaults', async () => {
- expect(rnn.ready).toBeTruthy();
- expect(rnn.defaults.seed).toBe(RNN_DEFAULTS.seed);
- expect(rnn.defaults.length).toBe(RNN_DEFAULTS.length);
- expect(rnn.defaults.temperature).toBe(RNN_DEFAULTS.temperature);
- expect(rnn.defaults.stateful).toBe(RNN_DEFAULTS.stateful);
- });
-
// it('loads the model with all the defaults', async () => {
// expect(rnn.cellsAmount).toBe(RNN_MODEL_DEFAULTS.cellsAmount);
// expect(rnn.vocabSize).toBe(RNN_MODEL_DEFAULTS.vocabSize);
// });
describe('generate', () => {
+ it('instantiates an rnn with all the defaults', async () => {
+ expect(rnn.ready).toBeTruthy();
+ expect(rnn.defaults.seed).toBe(RNN_DEFAULTS.seed);
+ expect(rnn.defaults.length).toBe(RNN_DEFAULTS.length);
+ expect(rnn.defaults.temperature).toBe(RNN_DEFAULTS.temperature);
+ expect(rnn.defaults.stateful).toBe(RNN_DEFAULTS.stateful);
+ });
+
it('Should generate content that follows default options if given an empty object', async() => {
const result = await rnn.generate({});
expect(result.sample.length).toBe(20);
diff --git a/src/FaceApi/index_test.js b/src/FaceApi/index_test.js
index 35b965733..5dfa6fe25 100644
--- a/src/FaceApi/index_test.js
+++ b/src/FaceApi/index_test.js
@@ -41,17 +41,18 @@ describe('faceApi', () => {
// return canvas;
// }
- beforeEach(async () => {
+ beforeAll(async () => {
jasmine.DEFAULT_TIMEOUT_INTERVAL = 15000;
faceapi = await faceApi();
});
-
- it('Should create faceApi with all the defaults', async () => {
- expect(faceapi.config.withLandmarks).toBe(FACEAPI_DEFAULTS.withLandmarks);
- expect(faceapi.config.withDescriptors).toBe(FACEAPI_DEFAULTS.withDescriptors);
- });
describe('landmarks', () => {
+
+ it('Should create faceApi with all the defaults', async () => {
+ expect(faceapi.config.withLandmarks).toBe(FACEAPI_DEFAULTS.withLandmarks);
+ expect(faceapi.config.withDescriptors).toBe(FACEAPI_DEFAULTS.withDescriptors);
+ });
+
it('Should get landmarks for Frida', async () => {
const img = await getImage();
await faceapi.detectSingle(img)
diff --git a/src/ImageClassifier/index_test.js b/src/ImageClassifier/index_test.js
index 31b0b4bdb..617f6478e 100644
--- a/src/ImageClassifier/index_test.js
+++ b/src/ImageClassifier/index_test.js
@@ -3,7 +3,11 @@
// This software is released under the MIT License.
// https://opensource.org/licenses/MIT
-const { imageClassifier } = ml5;
+const {
+ imageClassifier
+} = ml5;
+
+const TM_URL = 'https://storage.googleapis.com/tm-models/WfgKPytY/model.json';
const DEFAULTS = {
learningRate: 0.0001,
@@ -16,67 +20,109 @@ const DEFAULTS = {
version: 2,
};
+async function getImage() {
+ const img = new Image();
+ img.crossOrigin = true;
+ img.src = 'https://cdn.jsdelivr.net/gh/ml5js/ml5-library@development/assets/bird.jpg';
+ await new Promise((resolve) => {
+ img.onload = resolve;
+ });
+ return img;
+}
+
+async function getCanvas() {
+ const img = await getImage();
+ const canvas = document.createElement('canvas');
+ canvas.width = img.width;
+ canvas.height = img.height;
+ canvas.getContext('2d').drawImage(img, 0, 0);
+ return canvas;
+}
+
describe('imageClassifier', () => {
let classifier;
- async function getImage() {
- const img = new Image();
- img.crossOrigin = true;
- img.src = 'https://cdn.jsdelivr.net/gh/ml5js/ml5-library@development/assets/bird.jpg';
- await new Promise((resolve) => { img.onload = resolve; });
- return img;
- }
-
- async function getCanvas() {
- const img = await getImage();
- const canvas = document.createElement('canvas');
- canvas.width = img.width;
- canvas.height = img.height;
- canvas.getContext('2d').drawImage(img, 0, 0);
- return canvas;
- }
-
- beforeEach(async () => {
- jasmine.DEFAULT_TIMEOUT_INTERVAL = 15000;
- classifier = await imageClassifier('MobileNet', undefined, {});
- });
+ /**
+ * Test imageClassifier with teachable machine
+ */
+ // Teachable machine model
+ describe('with Teachable Machine model', () => {
+
+ beforeAll(async () => {
+ jasmine.DEFAULT_TIMEOUT_INTERVAL = 15000;
+ classifier = await imageClassifier(TM_URL, undefined, {});
+ });
+
+ describe('instantiate', () => {
+ it('Should create a classifier with all the defaults', async () => {
+ console.log(classifier)
+ expect(classifier.modelUrl).toBe(TM_URL);
+ });
+ });
- it('Should create a classifier with all the defaults', async () => {
- expect(classifier.version).toBe(DEFAULTS.version);
- expect(classifier.alpha).toBe(DEFAULTS.alpha);
- expect(classifier.topk).toBe(DEFAULTS.topk);
- expect(classifier.ready).toBeTruthy();
});
- describe('classify', () => {
- it('Should classify an image of a Robin', async () => {
- const img = await getImage();
- await classifier.classify(img)
- .then(results => expect(results[0].label).toBe('robin, American robin, Turdus migratorius'));
- });
- it('Should support p5 elements with an image on .elt', async () => {
- const img = await getImage();
- await classifier.classify({ elt: img })
- .then(results => expect(results[0].label).toBe('robin, American robin, Turdus migratorius'));
- });
- it('Should support HTMLCanvasElement', async () => {
- const canvas = await getCanvas();
- await classifier.classify(canvas)
- .then(results => expect(results[0].label).toBe('robin, American robin, Turdus migratorius'));
- });
+ /**
+ * Test imageClassifier with Mobilenet
+ */
+ describe('imageClassifier with Mobilenet', () => {
- it('Should support p5 elements with canvas on .canvas', async () => {
- const canvas = await getCanvas();
- await classifier.classify({ canvas })
- .then(results => expect(results[0].label).toBe('robin, American robin, Turdus migratorius'));
+ beforeAll(async () => {
+ jasmine.DEFAULT_TIMEOUT_INTERVAL = 15000;
+ classifier = await imageClassifier('MobileNet', undefined, {});
});
- it('Should support p5 elements with canvas on .elt', async () => {
- const canvas = await getCanvas();
- await classifier.classify({ elt: canvas })
- .then(results => expect(results[0].label).toBe('robin, American robin, Turdus migratorius'));
+ describe('instantiate', () => {
+
+ it('Should create a classifier with all the defaults', async () => {
+ expect(classifier.version).toBe(DEFAULTS.version);
+ expect(classifier.alpha).toBe(DEFAULTS.alpha);
+ expect(classifier.topk).toBe(DEFAULTS.topk);
+ expect(classifier.ready).toBeTruthy();
+ });
+ })
+
+ describe('classify', () => {
+
+ it('Should classify an image of a Robin', async () => {
+ const img = await getImage();
+ await classifier.classify(img)
+ .then(results => expect(results[0].label).toBe('robin, American robin, Turdus migratorius'));
+ });
+
+ it('Should support p5 elements with an image on .elt', async () => {
+ const img = await getImage();
+ await classifier.classify({
+ elt: img
+ })
+ .then(results => expect(results[0].label).toBe('robin, American robin, Turdus migratorius'));
+ });
+
+ it('Should support HTMLCanvasElement', async () => {
+ const canvas = await getCanvas();
+ await classifier.classify(canvas)
+ .then(results => expect(results[0].label).toBe('robin, American robin, Turdus migratorius'));
+ });
+
+ it('Should support p5 elements with canvas on .canvas', async () => {
+ const canvas = await getCanvas();
+ await classifier.classify({
+ canvas
+ })
+ .then(results => expect(results[0].label).toBe('robin, American robin, Turdus migratorius'));
+ });
+
+ it('Should support p5 elements with canvas on .elt', async () => {
+ const canvas = await getCanvas();
+ await classifier.classify({
+ elt: canvas
+ })
+ .then(results => expect(results[0].label).toBe('robin, American robin, Turdus migratorius'));
+ });
});
+
});
-});
+
+})
\ No newline at end of file
diff --git a/src/KMeans/index_test.js b/src/KMeans/index_test.js
index c90cb960c..a14bdecac 100644
--- a/src/KMeans/index_test.js
+++ b/src/KMeans/index_test.js
@@ -16,7 +16,8 @@ const KMEANS_DEFAULTS = {
describe('kMeans', () => {
let kmeansModel;
const dataurl = 'https://raw.githubusercontent.com/ml5js/ml5-examples/development/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_2clusters.csv'
- beforeEach(async () => {
+
+ beforeAll(async () => {
jasmine.DEFAULT_TIMEOUT_INTERVAL = 10000;
kmeansModel = await kmeans(dataurl, KMEANS_DEFAULTS, (err, result) => {
return;
@@ -43,11 +44,4 @@ describe('kMeans', () => {
expect(unique).toBe(2);
});
-
-
-
-
-
-
-
});
\ No newline at end of file
diff --git a/src/NeuralNetwork/index_test.js b/src/NeuralNetwork/index_test.js
index fdb079270..ca09f47ce 100644
--- a/src/NeuralNetwork/index_test.js
+++ b/src/NeuralNetwork/index_test.js
@@ -9,27 +9,27 @@ const {
} = ml5;
const NN_DEFAULTS = {
- task: 'regression',
- activationHidden: 'sigmoid',
- activationOutput: 'sigmoid',
- debug: false,
- learningRate: 0.25,
- inputs: 2,
- outputs: 1,
- noVal: null,
- hiddenUnits: 16,
- modelMetrics: ['accuracy'],
- modelLoss: 'meanSquaredError',
- modelOptimizer: null,
- batchSize: 64,
- epochs: 32,
+ task: 'regression',
+ activationHidden: 'sigmoid',
+ activationOutput: 'sigmoid',
+ debug: false,
+ learningRate: 0.25,
+ inputs: 2,
+ outputs: 1,
+ noVal: null,
+ hiddenUnits: 16,
+ modelMetrics: ['accuracy'],
+ modelLoss: 'meanSquaredError',
+ modelOptimizer: null,
+ batchSize: 64,
+ epochs: 32,
}
describe('neuralNetwork', () => {
let nn;
- beforeEach(async () => {
+ beforeAll(async () => {
jasmine.DEFAULT_TIMEOUT_INTERVAL = 15000;
nn = await neuralNetwork();
});
@@ -50,7 +50,7 @@ describe('neuralNetwork', () => {
// expect(nn.config.training.modelMetrics).toBe(NN_DEFAULTS.modelMetrics);
expect(nn.config.training.modelLoss).toBe(NN_DEFAULTS.modelLoss);
// expect(nn.config.training.modelOptimizer).toBe();
-
+
// data defaults
// expect(nn.config.dataOptions.dataUrl).toBe();
// expect(nn.config.dataOptions.inputs).toBe(NN_DEFAULTS.inputs);
@@ -60,4 +60,4 @@ describe('neuralNetwork', () => {
});
-});
+});
\ No newline at end of file