added describeWithNets test utility, which handles net creation and disposing of…

test/tests/e2e/faceDetectionNet.test.ts

 import * as faceapi from '../../../src';
 import { FaceDetection } from '../../../src/faceDetectionNet/FaceDetection';
 import { IRect } from '../../../src/Rect';
-import { expectAllTensorsReleased, expectMaxDelta } from '../../utils';
+import { describeWithNets, expectAllTensorsReleased, expectMaxDelta } from '../../utils';
 
 function expectRectClose(
   result: IRect,
@@ -33,19 +33,11 @@ describe('faceDetectionNet', () => {
     imgEl = await faceapi.bufferToImage(img)
   })
 
-  describe('uncompressed weights', () => {
-
-    let faceDetectionNet: faceapi.FaceDetectionNet
+  describeWithNets('uncompressed weights', { withFaceDetectionNet: { quantized: false } }, ({ faceDetectionNet }) => {
 
     const expectedScores = [0.98, 0.89, 0.82, 0.75, 0.58, 0.55]
     const maxBoxDelta = 1
 
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_detection_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceDetectionNet = faceapi.faceDetectionNet(weights)
-    })
-
     it('scores > 0.8', async () => {
       const detections = await faceDetectionNet.locateFaces(imgEl) as FaceDetection[]
 
@@ -72,18 +64,11 @@ describe('faceDetectionNet', () => {
 
   })
 
-  describe('quantized weights', () => {
-
-    let faceDetectionNet: faceapi.FaceDetectionNet
+  describeWithNets('quantized weights', { withFaceDetectionNet: { quantized: true } }, ({ faceDetectionNet }) => {
 
     const expectedScores = [0.97, 0.88, 0.83, 0.82, 0.59, 0.52]
     const maxBoxDelta = 5
 
-    beforeAll(async () => {
-      faceDetectionNet = new faceapi.FaceDetectionNet()
-      await faceDetectionNet.load('base/weights')
-    })
-
     it('scores > 0.8', async () => {
       const detections = await faceDetectionNet.locateFaces(imgEl) as FaceDetection[]
 

test/tests/e2e/faceLandmarkNet.test.ts

@@ -5,7 +5,7 @@ import { isTensor3D } from '../../../src/commons/isTensor';
 import { FaceLandmarks } from '../../../src/faceLandmarkNet/FaceLandmarks';
 import { Point } from '../../../src/Point';
 import { Dimensions, TMediaElement } from '../../../src/types';
-import { expectMaxDelta, expectAllTensorsReleased, tensor3D } from '../../utils';
+import { expectMaxDelta, expectAllTensorsReleased, tensor3D, describeWithNets } from '../../utils';
 import { NetInput } from '../../../src/NetInput';
 import { toNetInput } from '../../../src';
 
@@ -38,15 +38,7 @@ describe('faceLandmarkNet', () => {
     faceLandmarkPositionsRect = await (await fetch('base/test/data/faceLandmarkPositionsRect.json')).json()
   })
 
-  describe('uncompressed weights', () => {
-
-    let faceLandmarkNet: faceapi.FaceLandmarkNet
-
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_landmark_68_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceLandmarkNet = faceapi.faceLandmarkNet(weights)
-    })
+  describeWithNets('uncompressed weights', { withFaceLandmarkNet: { quantized: false } }, ({ faceLandmarkNet }) => {
 
     it('computes face landmarks for squared input', async () => {
       const { width, height } = imgEl1
@@ -78,14 +70,7 @@ describe('faceLandmarkNet', () => {
 
   })
 
-  describe('quantized weights', () => {
-
-    let faceLandmarkNet: faceapi.FaceLandmarkNet
-
-    beforeAll(async () => {
-      faceLandmarkNet = new faceapi.FaceLandmarkNet()
-      await faceLandmarkNet.load('base/weights')
-    })
+  describeWithNets('quantized weights', { withFaceLandmarkNet: { quantized: true } }, ({ faceLandmarkNet }) => {
 
     it('computes face landmarks for squared input', async () => {
       const { width, height } = imgEl1
@@ -117,15 +102,7 @@ describe('faceLandmarkNet', () => {
 
   })
 
-  describe('batch inputs', () => {
-
-    let faceLandmarkNet: faceapi.FaceLandmarkNet
-
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_landmark_68_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceLandmarkNet = faceapi.faceLandmarkNet(weights)
-    })
+  describeWithNets('batch inputs', { withFaceLandmarkNet: { quantized: false } }, ({ faceLandmarkNet }) => {
 
     it('computes face landmarks for batch of image elements', async () => {
       const inputs = [imgEl1, imgEl2, imgElRect]
@@ -229,14 +206,7 @@ describe('faceLandmarkNet', () => {
 
   })
 
-  describe('no memory leaks', () => {
-
-    let faceLandmarkNet: faceapi.FaceLandmarkNet
-
-    beforeAll(async () => {
-      faceLandmarkNet = new faceapi.FaceLandmarkNet()
-      await faceLandmarkNet.load('base/weights')
-    })
+  describeWithNets('no memory leaks', { withFaceLandmarkNet: { quantized: true } }, ({ faceLandmarkNet }) => {
 
     describe('NeuralNetwork, uncompressed model', () => {
 

test/tests/e2e/faceRecognitionNet.test.ts

@@ -2,7 +2,7 @@ import * as tf from '@tensorflow/tfjs-core';
 
 import * as faceapi from '../../../src';
 import { NetInput } from '../../../src/NetInput';
-import { expectAllTensorsReleased } from '../../utils';
+import { expectAllTensorsReleased, describeWithNets } from '../../utils';
 import { toNetInput } from '../../../src';
 
 describe('faceRecognitionNet', () => {
@@ -26,15 +26,7 @@ describe('faceRecognitionNet', () => {
     faceDescriptorRect = await (await fetch('base/test/data/faceDescriptorRect.json')).json()
   })
 
-  describe('uncompressed weights', () => {
-
-    let faceRecognitionNet: faceapi.FaceRecognitionNet
-
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_recognition_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceRecognitionNet = faceapi.faceRecognitionNet(weights)
-    })
+  describeWithNets('uncompressed weights', { withFaceRecognitionNet: { quantized: false } }, ({ faceRecognitionNet }) => {
 
     it('computes face descriptor for squared input', async () => {
       const result = await faceRecognitionNet.computeFaceDescriptor(imgEl1) as Float32Array
@@ -52,14 +44,7 @@ describe('faceRecognitionNet', () => {
 
   // TODO: figure out why descriptors return NaN in the test cases
   /*
-  describe('quantized weights', () => {
-
-    let faceRecognitionNet: faceapi.FaceRecognitionNet
-
-    beforeAll(async () => {
-      faceRecognitionNet = new faceapi.FaceRecognitionNet()
-      await faceRecognitionNet.load('base/weights')
-    })
+  describeWithNets('quantized weights', { withFaceRecognitionNet: { quantized: true } }, ({ faceRecognitionNet }) => {
 
     it('computes face descriptor for squared input', async () => {
       const result = await faceRecognitionNet.computeFaceDescriptor(imgEl1) as Float32Array
@@ -76,15 +61,7 @@ describe('faceRecognitionNet', () => {
   })
   */
 
-  describe('batch inputs', () => {
-
-    let faceRecognitionNet: faceapi.FaceRecognitionNet
-
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_recognition_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceRecognitionNet = faceapi.faceRecognitionNet(weights)
-    })
+  describeWithNets('batch inputs', { withFaceRecognitionNet: { quantized: false } }, ({ faceRecognitionNet }) => {
 
     it('computes face descriptors for batch of image elements', async () => {
       const inputs = [imgEl1, imgEl2, imgElRect]
@@ -156,19 +133,7 @@ describe('faceRecognitionNet', () => {
 
   })
 
-  describe('no memory leaks', () => {
-
-    let faceRecognitionNet: faceapi.FaceRecognitionNet
-
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_recognition_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceRecognitionNet = faceapi.faceRecognitionNet(weights)
-    })
-
-    afterAll(async () => {
-      faceRecognitionNet.dispose()
-    })
+  describeWithNets('no memory leaks', { withFaceRecognitionNet: { quantized: false } }, ({ faceRecognitionNet }) => {
 
     describe('NeuralNetwork, uncompressed model', () => {
 

test/utils.ts

 import * as tf from '@tensorflow/tfjs-core';
 
+import * as faceapi from '../src/';
+import { NeuralNetwork } from '../src/commons/NeuralNetwork';
+
 export function zeros(length: number): Float32Array {
   return new Float32Array(length)
 }
@@ -20,4 +23,85 @@ export async function expectAllTensorsReleased(fn: () => any) {
 
 export function tensor3D() {
   return tf.tensor3d([[[0]]])
-}
+}
\ No newline at end of file
+
+export type WithNetOptions = {
+  quantized?: boolean
+}
+
+export type InjectNetArgs = {
+  faceDetectionNet: faceapi.FaceDetectionNet
+  faceLandmarkNet: faceapi.FaceLandmarkNet
+  faceRecognitionNet: faceapi.FaceRecognitionNet
+}
+
+export type DescribeWithNetsOptions = {
+  withFaceDetectionNet?: WithNetOptions
+  withFaceLandmarkNet?: WithNetOptions
+  withFaceRecognitionNet?: WithNetOptions
+}
+
+async function loadNetWeights(uri: string): Promise<Float32Array> {
+  return new Float32Array(await (await fetch(uri)).arrayBuffer())
+}
+
+async function initNet<TNet extends NeuralNetwork<any>>(
+  net: TNet,
+  uncompressedFilename: string | boolean
+) {
+  await net.load(
+    uncompressedFilename
+      ? await loadNetWeights(`base/weights/uncompressed/${uncompressedFilename}`)
+      : 'base/weights'
+  )
+}
+
+export function describeWithNets(
+  description: string,
+  options: DescribeWithNetsOptions,
+  specDefinitions: (nets: InjectNetArgs) => void
+) {
+
+  describe(description, () => {
+
+    let faceDetectionNet: faceapi.FaceDetectionNet = new faceapi.FaceDetectionNet()
+    let faceLandmarkNet: faceapi.FaceLandmarkNet = new faceapi.FaceLandmarkNet()
+    let faceRecognitionNet: faceapi.FaceRecognitionNet = new faceapi.FaceRecognitionNet()
+
+    beforeAll(async () => {
+      const {
+        withFaceDetectionNet,
+        withFaceLandmarkNet,
+        withFaceRecognitionNet
+      } = options
+
+      if (withFaceDetectionNet) {
+        await initNet<faceapi.FaceDetectionNet>(
+          faceDetectionNet,
+          !withFaceDetectionNet.quantized && 'face_detection_model.weights'
+        )
+      }
+      if (withFaceLandmarkNet) {
+        await initNet<faceapi.FaceLandmarkNet>(
+          faceLandmarkNet,
+          !withFaceLandmarkNet.quantized && 'face_landmark_68_model.weights'
+        )
+      }
+      if (withFaceRecognitionNet) {
+        await initNet<faceapi.FaceRecognitionNet>(
+          faceRecognitionNet,
+          !withFaceRecognitionNet.quantized && 'face_recognition_model.weights'
+        )
+      }
+    })
+
+    afterAll(() => {
+      faceDetectionNet && faceDetectionNet.dispose()
+      faceLandmarkNet && faceLandmarkNet.dispose()
+      faceRecognitionNet && faceRecognitionNet.dispose()
+    })
+
+    specDefinitions({ faceDetectionNet, faceLandmarkNet, faceRecognitionNet })
+  })
+}
+