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import numpy as np | |
import random | |
import torch | |
import torchvision.transforms as transforms | |
from PIL import Image | |
from models.tag2text import tag2text_caption | |
import gradio as gr | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
image_size = 384 | |
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], | |
std=[0.229, 0.224, 0.225]) | |
transform = transforms.Compose([transforms.Resize((image_size, image_size)),transforms.ToTensor(),normalize]) | |
#######Swin Version | |
pretrained = 'tag2text_swin_14m.pth' | |
model = tag2text_caption(pretrained=pretrained, image_size=image_size, vit='swin_b' ) | |
model.eval() | |
model = model.to(device) | |
def inference(raw_image, input_tag): | |
raw_image = raw_image.resize((image_size, image_size)) | |
image = transform(raw_image).unsqueeze(0).to(device) | |
model.threshold = 0.68 | |
if input_tag == '' or input_tag == 'none' or input_tag == 'None': | |
input_tag_list = None | |
else: | |
input_tag_list = [] | |
input_tag_list.append(input_tag.replace(',',' | ')) | |
with torch.no_grad(): | |
caption, tag_predict = model.generate(image,tag_input = input_tag_list, return_tag_predict = True) | |
if input_tag_list == None: | |
tag_1 = tag_predict | |
tag_2 = ['none'] | |
else: | |
_, tag_1 = model.generate(image,tag_input = None, return_tag_predict = True) | |
tag_2 = tag_predict | |
return tag_1[0],tag_2[0],caption[0] | |
inputs = [gr.inputs.Image(type='pil'),gr.inputs.Textbox(lines=2, label="User Specified Tags (Optional, Enter with commas)")] | |
outputs = [gr.outputs.Textbox(label="Model Identified Tags"),gr.outputs.Textbox(label="User Specified Tags"), gr.outputs.Textbox(label="Image Caption") ] | |
title = "Tag2Text" | |
description = "Welcome to Tag2Text demo! (Supported by Fudan University, OPPO Research Institute, International Digital Economy Academy) <br/> Upload your image to get the <b>tags</b> and <b>caption</b> of the image. Optional: You can also input specified tags to get the corresponding caption." | |
article = "<p style='text-align: center'>Tag2text training on open-source datasets, and we are persisting in refining and iterating upon it.<br/><a href='https://arxiv.org/abs/2303.05657' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a> | <a href='https://github.com/xinyu1205/Tag2Text' target='_blank'>Github Repo</a></p>" | |
demo = gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['images/COCO_val2014_000000483108.jpg',"none"], | |
['images/COCO_val2014_000000483108.jpg',"power line"], | |
['images/COCO_val2014_000000483108.jpg',"track, train"] , | |
['images/bdf391a6f4b1840a.jpg',"none"], | |
]) | |
demo.launch(enable_queue=True) | |