Add timm_wrapper support to AutoFeatureExtractor

[Factral]

What does this PR do?

A few days ago, the PR that adds timm_wrapper was merged #34564 blog post , enabling the use of timm models directly with Hugging Face interfaces, especially the Auto* ones. However, currently the AutoFeatureExtractor interface doesn't work with these models. This PR addresses that gap.

This PR adds timm_wrapper compatibility to AutoFeatureExtractor.from_pretrained(), enabling it to work with fine-tuned/trained timm model checkpoints.

Currently, when using a checkpoint from a trained/fine-tuned timm model (e.g., using examples/pytorch/image-classification/run_image_classification.py), AutoFeatureExtractor.from_pretrained() fails because timm_wrapper is not included in the interface.

While there's a warning about missing preprocessor_config.json in checkpoints, users can manually add it to their checkpoint following examples like https://huggingface.co/Factral/vit_large-model/blob/main/preprocessor_config.json. This PR ensures AutoFeatureExtractor works properly when this file is present.

Changes

• Added timm_wrapper to AutoFeatureExtractor interface
• Enables compatibility with timm model checkpoints when preprocessor_config.json is present
• Added is_timm kwarg in from_dict function

Before submitting

• Read contributor guidelines
• Updated documentation to reflect changes
• Added necessary tests for timm_wrapper functionality

Who can review?

@amyeroberts @qubvel - as this relates to vision models and timm integration

[qubvel]

Hi @Factral, thanks for submitting the PR!

I would recommend using AutoModel + AutoProcessor to get features from any Timm model. It works without adding preprocessing_config.json. Othrewise, we need to come up with the scheme to import FeatureExtractor the same way, without adding preprocessing_config.json to the repo on Hub, because preprocessing config is stored in config.json for timm models (please see how this was enabled for other AutoProcessors in original PR #34564)

[qubvel]

import torch
from PIL import Image
from transformers import AutoProcessor, AutoModel

checkpoint = "timm/resnet18.a1_in1k"

model = AutoModel.from_pretrained(checkpoint)
processor = AutoProcessor.from_pretrained(checkpoint)

# load your image here
image = Image.new("RGB", (224, 224), (255, 0, 0))

inputs = processor(image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

for k, v in outputs.items():
    print(k, v.shape)

# last_hidden_state torch.Size([1, 512, 7, 7])
# pooler_output torch.Size([1, 512])