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@ -1270,6 +1270,31 @@ class SamModel(SamPreTrainedModel):
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return_dict=None,
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**kwargs,
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) -> List[Dict[str, torch.Tensor]]:
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r"""
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Example:
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```python
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import AutoModel, AutoProcessor
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>>> model = AutoModel.from_pretrained("facebook/sam-vit-base")
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>>> processor = AutoProcessor.from_pretrained("facebook/sam-vit-base")
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>>> img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png"
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>>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
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>>> input_points = [[[400, 650]]] # 2D location of a window on the car
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>>> inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt")
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>>> # Get segmentation mask
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>>> outputs = model(**inputs)
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>>> # Postprocess masks
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>>> masks = processor.post_process_masks(
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... outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"]
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... )
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```
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"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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