152 lines
6.7 KiB
Python
152 lines
6.7 KiB
Python
from typing import Union
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from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
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from .base import Pipeline, build_pipeline_init_args
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if is_vision_available():
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from PIL import Image
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from ..image_utils import load_image
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if is_torch_available():
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from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES
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logger = logging.get_logger(__name__)
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@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True, has_image_processor=True))
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class VisualQuestionAnsweringPipeline(Pipeline):
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"""
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Visual Question Answering pipeline using a `AutoModelForVisualQuestionAnswering`. This pipeline is currently only
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available in PyTorch.
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Example:
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```python
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>>> from transformers import pipeline
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>>> oracle = pipeline(model="dandelin/vilt-b32-finetuned-vqa")
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>>> image_url = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/lena.png"
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>>> oracle(question="What is she wearing ?", image=image_url)
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[{'score': 0.948, 'answer': 'hat'}, {'score': 0.009, 'answer': 'fedora'}, {'score': 0.003, 'answer': 'clothes'}, {'score': 0.003, 'answer': 'sun hat'}, {'score': 0.002, 'answer': 'nothing'}]
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>>> oracle(question="What is she wearing ?", image=image_url, top_k=1)
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[{'score': 0.948, 'answer': 'hat'}]
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>>> oracle(question="Is this a person ?", image=image_url, top_k=1)
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[{'score': 0.993, 'answer': 'yes'}]
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>>> oracle(question="Is this a man ?", image=image_url, top_k=1)
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[{'score': 0.996, 'answer': 'no'}]
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```
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Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
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This visual question answering pipeline can currently be loaded from [`pipeline`] using the following task
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identifiers: `"visual-question-answering", "vqa"`.
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The models that this pipeline can use are models that have been fine-tuned on a visual question answering task. See
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the up-to-date list of available models on
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[huggingface.co/models](https://huggingface.co/models?filter=visual-question-answering).
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.check_model_type(MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES)
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def _sanitize_parameters(self, top_k=None, padding=None, truncation=None, timeout=None, **kwargs):
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preprocess_params, postprocess_params = {}, {}
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if padding is not None:
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preprocess_params["padding"] = padding
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if truncation is not None:
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preprocess_params["truncation"] = truncation
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if timeout is not None:
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preprocess_params["timeout"] = timeout
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if top_k is not None:
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postprocess_params["top_k"] = top_k
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return preprocess_params, {}, postprocess_params
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def __call__(self, image: Union["Image.Image", str], question: str = None, **kwargs):
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r"""
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Answers open-ended questions about images. The pipeline accepts several types of inputs which are detailed
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below:
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- `pipeline(image=image, question=question)`
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- `pipeline({"image": image, "question": question})`
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- `pipeline([{"image": image, "question": question}])`
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- `pipeline([{"image": image, "question": question}, {"image": image, "question": question}])`
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Args:
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image (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
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The pipeline handles three types of images:
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- A string containing a http link pointing to an image
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- A string containing a local path to an image
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- An image loaded in PIL directly
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The pipeline accepts either a single image or a batch of images. If given a single image, it can be
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broadcasted to multiple questions.
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question (`str`, `List[str]`):
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The question(s) asked. If given a single question, it can be broadcasted to multiple images.
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top_k (`int`, *optional*, defaults to 5):
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The number of top labels that will be returned by the pipeline. If the provided number is higher than
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the number of labels available in the model configuration, it will default to the number of labels.
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timeout (`float`, *optional*, defaults to None):
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The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and
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the call may block forever.
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Return:
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A dictionary or a list of dictionaries containing the result. The dictionaries contain the following keys:
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- **label** (`str`) -- The label identified by the model.
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- **score** (`int`) -- The score attributed by the model for that label.
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"""
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if isinstance(image, (Image.Image, str)) and isinstance(question, str):
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inputs = {"image": image, "question": question}
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else:
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"""
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Supports the following format
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- {"image": image, "question": question}
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- [{"image": image, "question": question}]
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- Generator and datasets
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"""
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inputs = image
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results = super().__call__(inputs, **kwargs)
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return results
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def preprocess(self, inputs, padding=False, truncation=False, timeout=None):
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image = load_image(inputs["image"], timeout=timeout)
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model_inputs = self.tokenizer(
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inputs["question"], return_tensors=self.framework, padding=padding, truncation=truncation
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)
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image_features = self.image_processor(images=image, return_tensors=self.framework)
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model_inputs.update(image_features)
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return model_inputs
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def _forward(self, model_inputs, **generate_kwargs):
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if self.model.can_generate():
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model_outputs = self.model.generate(**model_inputs, **generate_kwargs)
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else:
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model_outputs = self.model(**model_inputs)
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return model_outputs
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def postprocess(self, model_outputs, top_k=5):
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if self.model.can_generate():
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return [
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{"answer": self.tokenizer.decode(output_ids, skip_special_tokens=True).strip()}
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for output_ids in model_outputs
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]
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else:
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if top_k > self.model.config.num_labels:
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top_k = self.model.config.num_labels
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if self.framework == "pt":
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probs = model_outputs.logits.sigmoid()[0]
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scores, ids = probs.topk(top_k)
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else:
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raise ValueError(f"Unsupported framework: {self.framework}")
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scores = scores.tolist()
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ids = ids.tolist()
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return [{"score": score, "answer": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]
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