Torchscript support


Try on Colab or go to the end to download the full example code.

This example illustrates torchscript support of the torchvision 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 import read_image

plt.rcParams["savefig.bbox"] = 'tight'

# If you're trying to run that on Colab, you can download the assets and the
# helpers from
import sys
sys.path += ["../transforms"]
from helpers import plot
ASSETS_PATH = Path('../assets')

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

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

transforms = torch.nn.Sequential(

scripted_transforms = torch.jit.script(transforms)

plot([dog1, scripted_transforms(dog1), dog2, scripted_transforms(dog2)])
plot scripted tensor transforms


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.

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):
        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)
Downloading: "" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth

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())]}")
Prediction for Dog 1: ['n02113023', 'Pembroke']
Prediction for Dog 2: ['n02106662', 'German_shepherd']

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

import tempfile

with tempfile.NamedTemporaryFile() as f:

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

Total running time of the script: (0 minutes 1.574 seconds)

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