chunked/nltk_chunking/_autoclass_tutorial/chunk_22.txt

For example, in the above diagram, to return the feature map from the first stage of the Swin backbone, you can set out_indices=(1,):

from transformers import AutoImageProcessor, AutoBackbone
import torch
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
model = AutoBackbone.from_pretrained("microsoft/swin-tiny-patch4-window7-224", out_indices=(1,))
inputs = processor(image, return_tensors="pt")
outputs = model(**inputs)
feature_maps = outputs.feature_maps

Now you can access the feature_maps object from the first stage of the backbone:

list(feature_maps[0].shape)
[1, 96, 56, 56]

AutoFeatureExtractor
For audio tasks, a feature extractor processes the audio signal the correct input format.