58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import TYPE_CHECKING
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import torch
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from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
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from ..utils import requires_backends
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from .base import PipelineTool
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if TYPE_CHECKING:
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from PIL import Image
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class ImageQuestionAnsweringTool(PipelineTool):
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default_checkpoint = "dandelin/vilt-b32-finetuned-vqa"
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description = (
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"This is a tool that answers a question about an image. It takes an input named `image` which should be the "
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"image containing the information, as well as a `question` which should be the question in English. It "
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"returns a text that is the answer to the question."
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)
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name = "image_qa"
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pre_processor_class = AutoProcessor
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model_class = AutoModelForVisualQuestionAnswering
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inputs = ["image", "text"]
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outputs = ["text"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["vision"])
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super().__init__(*args, **kwargs)
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def encode(self, image: "Image", question: str):
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return self.pre_processor(image, question, return_tensors="pt")
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def forward(self, inputs):
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with torch.no_grad():
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return self.model(**inputs).logits
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def decode(self, outputs):
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idx = outputs.argmax(-1).item()
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return self.model.config.id2label[idx]
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