Spaces:
Build error
Build error
File size: 3,188 Bytes
0ae6b5c e67e20f 0ae6b5c e67e20f 2618f02 c6a9352 e67e20f 2618f02 e67e20f 0ae6b5c e67e20f c08ba31 e67e20f 0ae6b5c 2618f02 0ae6b5c 2ef97f1 0ae6b5c a703b1d 0ae6b5c 2ef97f1 0ae6b5c 4ba71c1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
from PIL import Image
import requests
import torch
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
import gradio as gr
from models.blip import blip_decoder
image_size = 384
transform = transforms.Compose([
transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
])
model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth'
model = blip_decoder(pretrained=model_url, image_size=384, vit='base')
model.eval()
model = model.to(device)
from models.blip_vqa import blip_vqa
image_size_vq = 480
transform_vq = transforms.Compose([
transforms.Resize((image_size_vq,image_size_vq),interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
])
model_url_vq = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth'
model_vq = blip_vqa(pretrained=model_url_vq, image_size=480, vit='base')
model_vq.eval()
model_vq = model_vq.to(device)
def inference(raw_image, model_n, question, strategy):
if model_n == 'Image Captioning':
image = transform(raw_image).unsqueeze(0).to(device)
with torch.no_grad():
if strategy == "beam search":
caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5)
else:
caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5)
return 'caption: '+caption[0]
else:
image_vq = transform_vq(raw_image).unsqueeze(0).to(device)
with torch.no_grad():
answer = model_vq(image_vq, question, train=False, inference='generate')
return 'answer: '+answer[0]
inputs = [gr.inputs.Image(type='pil'),gr.inputs.Radio(choices=['Image Captioning',"Visual Question Answering"], type="value", default="Image Captioning", label="Model"),"textbox",gr.inputs.Radio(choices=['Beam search','Nucleus sampling'], type="value", default="Nucleus sampling", label="Strategy")]
outputs = gr.outputs.Textbox(label="Output")
title = "BLIP"
description = "Gradio demo for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation by Salesforce Research. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.12086' target='_blank'>BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation</a> | <a href='https://github.com/salesforce/BLIP' target='_blank'>Github Repo</a></p>"
gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['starry.jpg',"Image Captioning","None","Nucleus sampling"]]).launch(enable_queue=True) |