Added sample custom tranform :)

Author: raghavmecheri

README.md

@@ -53,7 +53,7 @@ const results = await squeezeNet(loader);
 ```
 
 ### More Examples
-Additional examples of both setup and usage involving features like Torchvision Transforms and CUDA (in development) may be found [here](https://github.com/raghavmecheri/ptjs/tree/master/examples).
+Additional examples of both setup and usage involving features like Torchvision Transforms and CUDA (in development) may be found [here](https://github.com/raghavmecheri/pytorchjs/tree/master/examples/Usage).
 
 ## Key Features
 * Run your PyTorch models in a Javascript environment, without worrying about setting up Torchscript or downloading custom binaries

examples/Usage/Transforms.md

@@ -0,0 +1,75 @@
+# Using a custom ptjs transform in your workflow
+
+## Background
+This example assumes that you have a pre-trained torch model, that has been exported to [torchscript](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html), as well as an image dataset conforming to the structure that the ptjs DatasetFolder class expects. The structure may also be found below, for reference, and a sample script to export a torch model to torchscript can be found [here](../Exporting/Exporting.md)
+
+## Required File Structure
+```
+root/class_x/xxx.ext
+root/class_x/xxy.ext
+root/class_x/xxz.ext
+
+root/class_y/123.ext
+root/class_y/nsdf3.ext
+root/class_y/asd932_.ext
+```
+
+## Use Case
+To add a custom transform function to your `transforms.Compose` call
+
+## Sample Script
+### Import Dependancies
+```js
+import { torch, torchvision } from 'pytorchjs';
+
+const { load } = torch;
+const { DataLoader } = torch.utils.data;
+const { ImageFolder } = torchvision.datasets;
+const { Compose, Resize, InvertAxes, Normalize } = torchvision.transforms;
+```
+
+### Load Model
+```js
+const mymodel = load("mymodel.pt");
+```
+
+### Declare a custom transform
+Custom transform classes must conform to the following requirements. A sample may be found below:
+- Be callable. [This](https://hackernoon.com/creating-callable-objects-in-javascript-d21l3te1) is a good starting point, I prefer to just extend the Javascript `Function` interface, like how I've done in [`transforms.js`](https://github.com/raghavmecheri/pytorchjs/blob/master/src/torchvision/transforms/transforms.js)
+- Return a `numjs` object
+```js
+export class RandomAdd extends Function {
+  /**
+   * Create a new callable RandomAdd object
+   */
+  constructor() {
+    super();
+    // NOTE: This is just some template code I found at the linked Hackernoon page to make the function callable :)
+    return new Proxy(this, {
+      apply: (_target, _thisArg, argumentsList) => {
+        const x = argumentsList[0];
+        const output = this.__call__(x);
+        return output;
+      },
+    });
+  }
+
+  // All I'm doing here is adding a random integer to every value, because why not
+  __call__ = (x) => x.add(Math.floor(Math.random() * 10));
+}
+```
+
+### Implement this custom transform
+```js
+const transforms = new Compose([
+  new Resize({height: 224, width: 224}),
+  new RandomAdd(),
+  new InvertAxes()
+]);
+```
+
+### Load data and get inference results
+```js
+const loader = new DataLoader(new ImageFolder("dataset"), 1, transforms);
+const results = await mymodel(loader);
+```