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1from transformers import MaskFormerImageProcessor, MaskFormerForInstanceSegmentation
2from PIL import Image
3import requests
4
5url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
6image = Image.open(requests.get(url, stream=True).raw)
7
8processor = MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-large-ade")
9inputs = processor(images=image, return_tensors="pt")
10
11model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-large-ade")
12outputs = model(**inputs)
13# model predicts class_queries_logits of shape `(batch_size, num_queries)`
14# and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
15class_queries_logits = outputs.class_queries_logits
16masks_queries_logits = outputs.masks_queries_logits
17
18# you can pass them to processor for postprocessing
19# we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
20predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]