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Merge pull request #1361 from lindapaiste/fix/xhr-to-axios
Rewrite XHR code using Axios
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,68 +1,67 @@ | ||
| /* eslint max-len: "off" */ | ||
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| import * as tf from '@tensorflow/tfjs'; | ||
| import axios from 'axios'; | ||
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| /** | ||
| * Pix2Pix loads data from a '.pict' file. | ||
| * File contains the properties (name and tensor shape) for each variable | ||
| * and a huge array of numbers for all of the variables. | ||
| * Numbers must be assigned to the correct variable. | ||
| */ | ||
| export default class CheckpointLoaderPix2pix { | ||
| /** | ||
| * @param {string} urlPath | ||
| */ | ||
| constructor(urlPath) { | ||
| /** | ||
| * @type {string} | ||
| */ | ||
| this.urlPath = urlPath; | ||
| } | ||
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| getAllVariables() { | ||
| return new Promise((resolve, reject) => { | ||
| const weightsCache = {}; | ||
| if (this.urlPath in weightsCache) { | ||
| resolve(weightsCache[this.urlPath]); | ||
| return; | ||
| } | ||
|
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| const xhr = new XMLHttpRequest(); | ||
| xhr.open('GET', this.urlPath, true); | ||
| xhr.responseType = 'arraybuffer'; | ||
| xhr.onload = () => { | ||
| if (xhr.status !== 200) { | ||
| reject(new Error('missing model')); | ||
| return; | ||
| } | ||
| const buf = xhr.response; | ||
| if (!buf) { | ||
| reject(new Error('invalid arraybuffer')); | ||
| return; | ||
| } | ||
| async getAllVariables() { | ||
| // Load the file as an ArrayBuffer. | ||
| const response = await axios.get(this.urlPath, { responseType: 'arraybuffer' }) | ||
| .catch(error => { | ||
| throw new Error(`No model found. Failed with error ${error}`); | ||
| }); | ||
| /** @type {ArrayBuffer} */ | ||
| const buf = response.data; | ||
|
|
||
| const parts = []; | ||
| let offset = 0; | ||
| while (offset < buf.byteLength) { | ||
| const b = new Uint8Array(buf.slice(offset, offset + 4)); | ||
| offset += 4; | ||
| const len = (b[0] << 24) + (b[1] << 16) + (b[2] << 8) + b[3]; // eslint-disable-line no-bitwise | ||
| parts.push(buf.slice(offset, offset + len)); | ||
| offset += len; | ||
| } | ||
| // Break data into three parts: shapes, index, and encoded. | ||
| /** @type {ArrayBuffer[]} */ | ||
| const parts = []; | ||
| let offset = 0; | ||
| while (offset < buf.byteLength) { | ||
| const b = new Uint8Array(buf.slice(offset, offset + 4)); | ||
| offset += 4; | ||
| const len = (b[0] << 24) + (b[1] << 16) + (b[2] << 8) + b[3]; // eslint-disable-line no-bitwise | ||
| parts.push(buf.slice(offset, offset + len)); | ||
| offset += len; | ||
| } | ||
|
|
||
| const shapes = JSON.parse((new TextDecoder('utf8')).decode(parts[0])); | ||
| const index = new Float32Array(parts[1]); | ||
| const encoded = new Uint8Array(parts[2]); | ||
| /** @type {Array<{ name: string, shape: number[] }>} */ | ||
| const shapes = JSON.parse((new TextDecoder('utf8')).decode(parts[0])); | ||
| const index = new Float32Array(parts[1]); | ||
| const encoded = new Uint8Array(parts[2]); | ||
|
|
||
| // decode using index | ||
| const arr = new Float32Array(encoded.length); | ||
| for (let i = 0; i < arr.length; i += 1) { | ||
| arr[i] = index[encoded[i]]; | ||
| } | ||
| // Dictionary of variables by name. | ||
| /** @type {Record<string, tf.Tensor>} */ | ||
| const weights = {}; | ||
|
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||
| const weights = {}; | ||
| offset = 0; | ||
| for (let i = 0; i < shapes.length; i += 1) { | ||
| const { shape } = shapes[i]; | ||
| const size = shape.reduce((total, num) => total * num); | ||
| const values = arr.slice(offset, offset + size); | ||
| const tfarr = tf.tensor1d(values, 'float32'); | ||
| weights[shapes[i].name] = tfarr.reshape(shape); | ||
| offset += size; | ||
| } | ||
| weightsCache[this.urlPath] = weights; | ||
| resolve(weights); | ||
| }; | ||
| xhr.send(null); | ||
| // Create a tensor for each shape. | ||
| offset = 0; | ||
| shapes.forEach(({ shape, name }) => { | ||
| const size = shape.reduce((total, num) => total * num); | ||
| // Get the raw data. | ||
| const raw = encoded.slice(offset, offset + size); | ||
| // Decode using index. | ||
| const values = new Float32Array(raw.length); | ||
| raw.forEach((value, i) => { | ||
| values[i] = index[value]; | ||
| }); | ||
| weights[name] = tf.tensor(values, shape, 'float32'); | ||
| offset += size; | ||
| }); | ||
| return weights; | ||
| } | ||
| } |