Fix snippets

#7
by merve HF staff - opened
Files changed (1) hide show
  1. README.md +4 -4
README.md CHANGED
@@ -38,7 +38,7 @@ Here is how to use this model:
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  from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
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  from PIL import Image
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  import requests
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- url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/ade20k.jpeg"
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  image = Image.open(requests.get(url, stream=True).raw)
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  # Loading a single model for all three tasks
@@ -49,19 +49,19 @@ model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ad
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  semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
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  semantic_outputs = model(**semantic_inputs)
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  # pass through image_processor for postprocessing
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- predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
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  # Instance Segmentation
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  instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt")
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  instance_outputs = model(**instance_inputs)
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  # pass through image_processor for postprocessing
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- predicted_instance_map = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
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  # Panoptic Segmentation
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  panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt")
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  panoptic_outputs = model(**panoptic_inputs)
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  # pass through image_processor for postprocessing
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- predicted_semantic_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
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  ```
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  For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer).
 
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  from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
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  from PIL import Image
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  import requests
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+ url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/resolve/main/ade20k.jpeg"
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  image = Image.open(requests.get(url, stream=True).raw)
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  # Loading a single model for all three tasks
 
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  semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
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  semantic_outputs = model(**semantic_inputs)
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  # pass through image_processor for postprocessing
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+ predicted_semantic_map = processor.post_process_semantic_segmentation(semantic_outputs, target_sizes=[image.size[::-1]])[0]
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  # Instance Segmentation
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  instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt")
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  instance_outputs = model(**instance_inputs)
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  # pass through image_processor for postprocessing
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+ predicted_instance_map = processor.post_process_instance_segmentation(instance_outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
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  # Panoptic Segmentation
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  panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt")
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  panoptic_outputs = model(**panoptic_inputs)
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  # pass through image_processor for postprocessing
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+ predicted_semantic_map = processor.post_process_panoptic_segmentation(panoptic_outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
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  ```
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  For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer).