Torchscript support

Note

Try on [collab](https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_scripted_tensor_transforms.ipynb) or `go to the end ` to download the full example code.

This example illustrates torchscript support of the torchvision transforms <transforms> on Tensor images.

from pathlib import Path

import matplotlib.pyplot as plt

import torch
import torch.nn as nn

import torchvision.transforms as v1
from torchvision.io import read_image

plt.rcParams["savefig.bbox"] = 'tight'
torch.manual_seed(1)

# If you're trying to run that on collab, you can download the assets and the
# helpers from https://github.com/pytorch/vision/tree/main/gallery/
import sys
sys.path += ["../transforms"]
from helpers import plot
ASSETS_PATH = Path('../assets')

Most transforms support torchscript. For composing transforms, we use :class:torch.nn.Sequential instead of :class:~torchvision.transforms.v2.Compose:

dog1 = read_image(str(ASSETS_PATH / 'dog1.jpg'))
dog2 = read_image(str(ASSETS_PATH / 'dog2.jpg'))

transforms = torch.nn.Sequential(
    v1.RandomCrop(224),
    v1.RandomHorizontalFlip(p=0.3),
)

scripted_transforms = torch.jit.script(transforms)

plot([dog1, scripted_transforms(dog1), dog2, scripted_transforms(dog2)])

Warning

Above we have used transforms from the ``torchvision.transforms`` namespace, i.e. the "v1" transforms. The v2 transforms from the ``torchvision.transforms.v2`` namespace are the `recommended ` way to use transforms in your code.

The v2 transforms also support torchscript, but if you call
``torch.jit.script()`` on a v2 **class** transform, you'll actually end up
with its (scripted) v1 equivalent.  This may lead to slightly different
results between the scripted and eager executions due to implementation
differences between v1 and v2.

If you really need torchscript support for the v2 transforms, **we
recommend scripting the functionals** from the
``torchvision.transforms.v2.functional`` namespace to avoid surprises.</p></div>

Below we now show how to combine image transformations and a model forward pass, while using torch.jit.script to obtain a single scripted module.

Let’s define a Predictor module that transforms the input tensor and then applies an ImageNet model on it.

from torchvision.models import resnet18, ResNet18_Weights


class Predictor(nn.Module):

    def __init__(self):
        super().__init__()
        weights = ResNet18_Weights.DEFAULT
        self.resnet18 = resnet18(weights=weights, progress=False).eval()
        self.transforms = weights.transforms(antialias=True)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        with torch.no_grad():
            x = self.transforms(x)
            y_pred = self.resnet18(x)
            return y_pred.argmax(dim=1)

Now, let’s define scripted and non-scripted instances of Predictor and apply it on multiple tensor images of the same size

device = "cuda" if torch.cuda.is_available() else "cpu"

predictor = Predictor().to(device)
scripted_predictor = torch.jit.script(predictor).to(device)

batch = torch.stack([dog1, dog2]).to(device)

res = predictor(batch)
res_scripted = scripted_predictor(batch)

We can verify that the prediction of the scripted and non-scripted models are the same:

import json

with open(Path('../assets') / 'imagenet_class_index.json') as labels_file:
    labels = json.load(labels_file)

for i, (pred, pred_scripted) in enumerate(zip(res, res_scripted)):
    assert pred == pred_scripted
    print(f"Prediction for Dog {i + 1}: {labels[str(pred.item())]}")

Since the model is scripted, it can be easily dumped on disk and re-used

import tempfile

with tempfile.NamedTemporaryFile() as f:
    scripted_predictor.save(f.name)

    dumped_scripted_predictor = torch.jit.load(f.name)
    res_scripted_dumped = dumped_scripted_predictor(batch)
assert (res_scripted_dumped == res_scripted).all()