While there's no Torch in the browser, there's still Candle. Candle.js is a GPU-accelerated library to make running PyTorch models in the browser a bit easier. It also serves as a tensor computation library.
Import gpu.js as Candle.js depends on it for accelerated computing:
<head>
...
<script src='libs/gpu-browser.min.js'></script>
</head>Import classes from Candle.js:
import {Model, Loader, Tensor, Linear, Conv2d, ConvTranspose2d, ReLU, Sigmoid} from "./candle.js";TL;DR: The layer names should match!
Your PyTorch model:
def __init__(...):
...
self.dconv1 = nn.Conv2d(1, 32, kernel_size=4, stride=2)
self.dconv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
self.dconv3 = nn.Conv2d(64, 128, kernel_size=4, stride=2)
self.dconv4 = nn.Conv2d(128, 256, kernel_size=4, stride=2)
self.proj_mu = nn.Linear(1024, 64)
self.proj_logvar = nn.Linear(1024, 64)
self.proj_z = nn.Linear(126, 1024)
self.uconv1 = nn.ConvTranspose2d(1024, 128, kernel_size=5, stride=2)
self.uconv2 = nn.ConvTranspose2d(128, 64, kernel_size=5, stride=2)
self.uconv3 = nn.ConvTranspose2d(64, 32, kernel_size=6, stride=2)
self.uconv4 = nn.ConvTranspose2d(32, 1, kernel_size=6, stride=2)Your Candle.js model:
constructor (...) {
...
this.dconv1 = new Conv2d(1, 32, 4, 2);
this.dconv2 = new Conv2d(32, 64, 4, 2);
this.dconv3 = new Conv2d(64, 128, 4, 2);
this.dconv4 = new Conv2d(128, 256, 4, 2);
this.proj_mu = new Linear(1024, 64);
this.proj_logvar = new Linear(1024, 64);
this.proj_z = new Linear(126, 1024);
this.uconv1 = new ConvTranspose2d(1024, 128, 5, 2);
this.uconv2 = new ConvTranspose2d(128, 64, 5, 2);
this.uconv3 = new ConvTranspose2d(64, 32, 6, 2);
this.uconv4 = new ConvTranspose2d(32, 1, 6, 2);
}Your PyTorch model:
def forward(x):
h = F.relu(self.dconv1(x))
...Your Candle.js model:
forward (x) {
let h = ReLU(this.dconv1.forward(x));
...
}Using Candle.js helper:
python candle-helper.py /path/to/your/model.pt
This will create a directory named /path/to/your/model-candle. In the directory will be a model.json that describes you model, and chunks of binary data that will be loaded by the Candle.js Loader accordingly.
Then, in your script:
let loader = new Loader('url/to/model-candle');
loader.load(vae, function () {
console.log('Everything loaded!');
});Et voila! You can now run your PyTorch model in the browser!
Note: currently no batch processing is supported in convolutional layers due to limits in GPU acceleration.
That is, Conv2d and ConvTranspose2d cannot accept inputs with size (n_samples, channels, height, width). Instead, pass in tensors one by one, with size (channels, height, width).
- Linear
- Conv2d (no dilation support)
- ConvTranspose2d (no padding and dilation support)
- ReLU
- Sigmoid