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--- |
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tags: |
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- image-to-text |
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- image-captioning |
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- endpoints-template |
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license: bsd-3-clause |
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library_name: generic |
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--- |
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# Fork of [salesforce/BLIP](https://github.com/salesforce/BLIP) for a `image-captioning` task on 🤗Inference endpoint. |
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This repository implements a `custom` task for `image-captioning` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/florentgbelidji/blip_captioning/blob/main/pipeline.py). |
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To use deploy this model a an Inference Endpoint you have to select `Custom` as task to use the `pipeline.py` file. -> _double check if it is selected_ |
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### expected Request payload |
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```json |
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{ |
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"image": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgICAgMC....", // base64 image as bytes |
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} |
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``` |
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below is an example on how to run a request using Python and `requests`. |
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## Run Request |
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1. prepare an image. |
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```bash |
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!wget https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg |
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``` |
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2.run request |
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```python |
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import json |
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from typing import List |
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import requests as r |
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import base64 |
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ENDPOINT_URL = "" |
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HF_TOKEN = "" |
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def predict(path_to_image: str = None): |
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with open(path_to_image, "rb") as i: |
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image = i.read() |
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payload = { |
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"inputs": [image], |
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"parameters": { |
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"do_sample": True, |
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"top_p":0.9, |
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"min_length":5, |
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"max_length":20 |
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} |
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} |
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response = r.post( |
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ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload |
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) |
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return response.json() |
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prediction = predict( |
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path_to_image="palace.jpg" |
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) |
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``` |
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Example parameters depending on the decoding strategy: |
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1. Beam search |
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``` |
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"parameters": { |
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"num_beams":5, |
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"max_length":20 |
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} |
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``` |
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2. Nucleus sampling |
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``` |
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"parameters": { |
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"num_beams":1, |
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"max_length":20, |
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"do_sample": True, |
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"top_k":50, |
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"top_p":0.95 |
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} |
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``` |
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3. Contrastive search |
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``` |
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"parameters": { |
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"penalty_alpha":0.6, |
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"top_k":4 |
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"max_length":512 |
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} |
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``` |
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See [generate()](https://huggingface.co/docs/transformers/v4.25.1/en/main_classes/text_generation#transformers.GenerationMixin.generate) doc for additional detail |
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expected output |
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```python |
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['buckingham palace with flower beds and red flowers'] |
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``` |
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