Rewrite CheckpointLoaderPix2pix using axios instead of xhr.

src/utils/checkpointLoaderPix2pix.js

@@ -1,68 +1,67 @@
-/* eslint max-len: "off" */
-
 import * as tf from '@tensorflow/tfjs';
+import axios from 'axios';
 
+/**
+ * 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;
   }
 
-  getAllVariables() {
-    return new Promise((resolve, reject) => {
-      const weightsCache = {};
-      if (this.urlPath in weightsCache) {
-        resolve(weightsCache[this.urlPath]);
-        return;
-      }
-
-      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 = {};
 
-        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;
   }
 }