[BUG] - Incorrect ResNet18 preprocessing in Captum tutorial

Add Link

https://docs.pytorch.org/tutorials/beginner/introyt/captumyt.html

Describe the bug

The Captum tutorial uses the following preprocessing for a pretrained ResNet18 model:

transform = transforms.Compose([
    transforms.Resize(224),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
])

transform_normalize = transforms.Normalize(
    mean=[0.485, 0.456, 0.406],
    std=[0.229, 0.224, 0.225],
)

However, the preprocessing associated with ResNet18_Weights.IMAGENET1K_V1 uses a resize size of 256 followed by a center crop of 224:

weights = models.ResNet18_Weights.IMAGENET1K_V1
preprocess = weights.transforms()

print(preprocess)

Output:

ImageClassification(
    crop_size=[224]
    resize_size=[256]
    mean=[0.485, 0.456, 0.406]
    std=[0.229, 0.224, 0.225]
    interpolation=InterpolationMode.BILINEAR
)

Resize(224) and Resize(256) -> CenterCrop(224) do not produce the same model input. Resizing the shorter side directly to 224 changes the scale and visible image region compared with the preprocessing associated with the selected pretrained weights.

For my test image, the top-class predicted probability was approximately 95% with the tutorial preprocessing and 98% with ResNet18_Weights.IMAGENET1K_V1.transforms().

This is a difference in confidence for one image, not a measurement of dataset-level accuracy. However, it demonstrates that the current preprocessing materially changes the model output and may also change the resulting Captum attribution maps.

Sample code to reproduce

import torch
from torchvision import models, transforms

weights = models.ResNet18_Weights.IMAGENET1K_V1
model = models.resnet18(weights=weights).eval()

tutorial_preprocess = transforms.Compose([
    transforms.Resize(224),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225],
    ),
])

official_preprocess = weights.transforms()

x_tutorial = tutorial_preprocess(test_img).unsqueeze(0)
x_official = official_preprocess(test_img).unsqueeze(0)

with torch.inference_mode():
    tutorial_probabilities = model(x_tutorial).softmax(dim=1)
    official_probabilities = model(x_official).softmax(dim=1)

class_id = official_probabilities.argmax(dim=1).item()

print(
    "Tutorial preprocessing:",
    tutorial_probabilities[0, class_id].item(),
)
print(
    "Official preprocessing:",
    official_probabilities[0, class_id].item(),
)
print(
    "Maximum input difference:",
    (x_tutorial - x_official).abs().max().item(),
)

Expected Result:

The tutorial preprocessing should match the preprocessing associated with the pretrained weights. The tutorial could either use weights.transforms() or change Resize(224) to Resize(256) while retaining the separate normalization step needed by the tutorial.

Actual Result:

The tutorial resizes the shorter image side to 224 instead of 256. This produces a different input tensor, changes the predicted probabilities, and may change the Captum attribution visualization.

No exception or traceback is produced. This is a preprocessing correctness issue.

Describe your environment

Platform: Windows-11-10.0.26200-SP0 PyTorch: 2.13.0+cu132 Torchvision: 0.28.0+cu132 CUDA available: True CUDA version: 13.2