[add] tensorset now supports tensors created from buffer data

Author: filipecosta90

package-lock.json

@@ -40,14 +40,17 @@
     "@types/redis": "^2.8.21"
   },
   "devDependencies": {
+    "@babel/polyfill": "^7.8.7",
     "@istanbuljs/nyc-config-typescript": "^1.0.1",
     "@types/chai": "^4.2.11",
     "@types/mocha": "^7.0.2",
     "@types/node": "^13.13.4",
     "@types/redis": "^2.8.21",
     "chai": "^4.2.0",
     "codecov": "^3.6.5",
+    "core-js": "^2.5.7",
     "coveralls": "^3.1.0",
+    "jimp": "0.8.4",
     "mocha": "^7.1.2",
     "np": "^6.2.3",
     "nyc": "^15.0.1",

package.json

@@ -1,4 +1,4 @@
-import { Callback, RedisClient } from 'redis';
+import { RedisClient } from 'redis';
 import { Tensor } from './tensor';
 import { Model } from './model';
 import * as util from 'util';
@@ -29,8 +29,13 @@ export class Client {
     const args: any[] = [keName, t.dtype];
     t.shape.forEach((value) => args.push(value.toString()));
     if (t.data != null) {
-      args.push('VALUES');
-      t.data.forEach((value) => args.push(value.toString()));
+      if (t.data instanceof Buffer) {
+        args.push('BLOB');
+        args.push(t.data);
+      } else {
+        args.push('VALUES');
+        t.data.forEach((value) => args.push(value.toString()));
+      }
     }
     return this._sendCommand('ai.tensorset', args);
   }

src/client.ts

@@ -21,7 +21,7 @@ export class Helpers {
    * TODO: document
    * @param buffer
    */
-  normalizeRGB(buffer: Float32Array): Float32Array {
+  normalizeRGB(buffer: any): Float32Array {
     const npixels = buffer.length / 4;
     const out = new Float32Array(npixels * 3);
 

src/helpers.ts

@@ -4,55 +4,6 @@
 import { Backend } from './backend';
 
 export class Stats {
-  get backend(): Backend {
-    return this._backend;
-  }
-
-  set backend(value: Backend) {
-    this._backend = value;
-  }
-  get type(): string {
-    return this._type;
-  }
-
-  set type(value: string) {
-    this._type = value;
-  }
-  get key(): string {
-    return this._key;
-  }
-
-  set key(value: string) {
-    this._key = value;
-  }
-  get errors(): number {
-    return this._errors;
-  }
-
-  set errors(value: number) {
-    this._errors = value;
-  }
-  get calls(): number {
-    return this._calls;
-  }
-
-  set calls(value: number) {
-    this._calls = value;
-  }
-  get samples(): number {
-    return this._samples;
-  }
-
-  set samples(value: number) {
-    this._samples = value;
-  }
-  get duration(): number {
-    return this._duration;
-  }
-
-  set duration(value: number) {
-    this._duration = value;
-  }
   /**
    *
    * @param key - a String of the name of the key storing the model or script value
@@ -86,19 +37,77 @@ export class Stats {
   // the cumulative duration of executions in microseconds
   private _duration: number;
 
+  get duration(): number {
+    return this._duration;
+  }
+
+  set duration(value: number) {
+    this._duration = value;
+  }
+
   // the cumulative number of samples obtained from the 0th (batch) dimension (only applicable for RedisAI models)
   private _samples: number;
 
+  get samples(): number {
+    return this._samples;
+  }
+
+  set samples(value: number) {
+    this._samples = value;
+  }
+
   // the total number of executions
   private _calls: number;
 
+  get calls(): number {
+    return this._calls;
+  }
+
+  set calls(value: number) {
+    this._calls = value;
+  }
+
   //  the total number of errors generated by executions (excluding any errors generated during parsing commands)
   private _errors: number;
 
+  get errors(): number {
+    return this._errors;
+  }
+
+  set errors(value: number) {
+    this._errors = value;
+  }
+
   private _key: string;
+
+  get key(): string {
+    return this._key;
+  }
+
+  set key(value: string) {
+    this._key = value;
+  }
+
   private _type: string;
+
+  get type(): string {
+    return this._type;
+  }
+
+  set type(value: string) {
+    this._type = value;
+  }
+
   private _backend: Backend;
 
+  get backend(): Backend {
+    return this._backend;
+  }
+
+  set backend(value: Backend) {
+    this._backend = value;
+  }
+
   // tag is an optional string for tagging the model/script such as a version number or any arbitrary identifier
   private _tag: string | undefined;
 

src/stats.ts

@@ -10,7 +10,7 @@ export class Tensor {
    * @param shape one or more dimensions, or the number of elements per axis, for the tensor
    * @param data numeric data provided by one or more subsequent val arguments
    */
-  constructor(dtype: Dtype, shape: number[], data: number[] | null) {
+  constructor(dtype: Dtype, shape: number[], data: Buffer | number[] | null) {
     this._shape = shape;
     this._dtype = dtype;
     if (data != null) {
@@ -38,13 +38,13 @@ export class Tensor {
     this._shape = value;
   }
 
-  private _data: number[] | null;
+  private _data: Buffer | number[];
 
-  get data(): number[] | null {
+  get data(): Buffer | number[] | null {
     return this._data;
   }
 
-  set data(value: number[] | null) {
+  set data(value: Buffer | number[] | null) {
     this._data = value;
   }
 }

src/tensor.ts

@@ -8,6 +8,9 @@ import { Model } from '../src/model';
 import * as fs from 'fs';
 import { Backend } from '../src/backend';
 import { Script } from '../src/script';
+import { Helpers } from '../src';
+// tslint:disable-next-line:no-var-requires
+const Jimp = require('jimp');
 
 const mochaAsync = (fn: any) => {
   return (done: any) => {
@@ -88,6 +91,25 @@ it(
   }),
 );
 
+it(
+  'ai.tensorset/ai.tensorget positive testing with default data',
+  mochaAsync(async () => {
+    const nativeClient = createClient();
+    const aiclient = new Client(nativeClient);
+    const inputImage = await Jimp.read('./tests/test_data/panda-224x224.jpg');
+    const imageWidth = 224;
+    const imageHeight = 224;
+    const image = inputImage.cover(imageWidth, imageHeight);
+    const helpers = new Helpers();
+    const normalized = helpers.normalizeRGB(image.bitmap.data);
+    const buffer = Buffer.from(normalized.buffer);
+    const tensor = new Tensor(Dtype.float32, [imageWidth, imageHeight, 3], buffer);
+    const result = await aiclient.tensorset('tensor-image', tensor);
+    expect(result).to.equal('OK');
+    aiclient.end(true);
+  }),
+);
+
 it(
   'ai.tensorget negative testing',
   mochaAsync(async () => {

tests/test_client.ts

@@ -11,6 +11,11 @@
   "lib": [
     "es2015"
   ],
-  "include": ["src"],
-  "exclude": ["node_modules", "**/__tests__/*"]
+  "include": [
+    "src"
+  ],
+  "exclude": [
+    "node_modules",
+    "**/__tests__/*"
+  ]
 }

tsconfig.json

@@ -1,6 +1,9 @@
 {
   "defaultSeverity": "error",
-  "extends": ["tslint:recommended","tslint-config-prettier"],
+  "extends": [
+    "tslint:recommended",
+    "tslint-config-prettier"
+  ],
   "jsRules": {},
   "rules": {
     "no-console": false