52 lines
1.7 KiB
Python
52 lines
1.7 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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from ..models.auto import AutoModelForVision2Seq
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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 ImageCaptioningTool(PipelineTool):
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default_checkpoint = "Salesforce/blip-image-captioning-base"
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description = (
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"This is a tool that generates a description of an image. It takes an input named `image` which should be the "
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"image to caption, and returns a text that contains the description in English."
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)
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name = "image_captioner"
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model_class = AutoModelForVision2Seq
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inputs = ["image"]
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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"):
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return self.pre_processor(images=image, return_tensors="pt")
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def forward(self, inputs):
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return self.model.generate(**inputs)
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def decode(self, outputs):
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return self.pre_processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
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