# --------------------------------------------------------
# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Xueyan Zou (xueyan@cs.wisc.edu), Jianwei Yang (jianwyan@microsoft.com)
# --------------------------------------------------------
import os
os.system("python -m pip install git+https://github.com/MaureenZOU/detectron2-xyz.git")
import gradio as gr
import torch
import argparse
from xdecoder.BaseModel import BaseModel
from xdecoder import build_model
from utils.distributed import init_distributed
from utils.arguments import load_opt_from_config_files
from tasks import *
def parse_option():
parser = argparse.ArgumentParser('X-Decoder All-in-One Demo', add_help=False)
parser.add_argument('--conf_files', default="configs/xdecoder/svlp_focalt_lang.yaml", metavar="FILE", help='path to config file', )
args = parser.parse_args()
return args
'''
build args
'''
args = parse_option()
opt = load_opt_from_config_files(args.conf_files)
opt = init_distributed(opt)
# META DATA
pretrained_pth_last = os.path.join("xdecoder_focalt_last.pt")
pretrained_pth_novg = os.path.join("xdecoder_focalt_last_novg.pt")
if not os.path.exists(pretrained_pth_last):
os.system("wget {}".format("https://projects4jw.blob.core.windows.net/x-decoder/release/xdecoder_focalt_last.pt"))
if not os.path.exists(pretrained_pth_novg):
os.system("wget {}".format("https://projects4jw.blob.core.windows.net/x-decoder/release/xdecoder_focalt_last_novg.pt"))
'''
build model
'''
model_last = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth_last).eval().cuda()
model_cap = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth_novg).eval().cuda()
with torch.no_grad():
model_last.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(["background", "background"], is_eval=True)
model_cap.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(["background", "background"], is_eval=True)
'''
inference model
'''
@torch.no_grad()
def inference(image, task, *args, **kwargs):
image = image.convert("RGB")
with torch.autocast(device_type='cuda', dtype=torch.float16):
if task == 'Referring Editing':
return referring_inpainting(model_last, image, *args, **kwargs)
elif task == 'Referring Segmentation':
return referring_segmentation(model_last, image, *args, **kwargs)
elif task == 'Open Vocabulary Semantic Segmentation':
return open_semseg(model_last, image, *args, **kwargs)
elif task == 'Open Vocabulary Panoptic Segmentation':
return open_panoseg(model_last, image, *args, **kwargs)
elif task == 'Open Vocabulary Instance Segmentation':
return open_instseg(model_last, image, *args, **kwargs)
elif task == 'Image Captioning':
return image_captioning(model_cap, image, *args, **kwargs)
elif task == 'Referring Captioning (Beta)':
return referring_captioning([model_last, model_cap], image, *args, **kwargs)
elif task == 'Text Retrieval':
return text_retrieval(model_cap, image, *args, **kwargs)
elif task == 'Image/Region Retrieval':
return region_retrieval([model_cap, model_last], image, *args, **kwargs)
'''
launch app
'''
title = "X-Decoder All-in-One Demo"
description = """ Project Page | Paper | Github Repo | Video
Skip the queue by duplicating this space and upgrading to GPU in settings
"""
article = "The Demo is Run on X-Decoder (Focal-T)."
inputs = [gr.inputs.Image(type='pil'), gr.inputs.Radio(choices=["Referring Segmentation", "Referring Editing", 'Open Vocabulary Semantic Segmentation','Open Vocabulary Instance Segmentation', "Open Vocabulary Panoptic Segmentation", "Image Captioning", "Text Retrieval", "Image/Region Retrieval", "Referring Captioning (Beta)"], type="value", default="OpenVocab Semantic Segmentation", label="Task"), gr.Textbox(label="xdecoder_text"), gr.Textbox(label="inpainting_text"), gr.Textbox(label="task_description")]
gr.Interface(
fn=inference,
inputs=inputs,
outputs=[
gr.outputs.Image(
type="pil",
label="segmentation results"),
gr.Textbox(label="text results"),
gr.outputs.Image(
type="pil",
label="editing results"),
],
examples=[
["./images/fruit.jpg", "Referring Segmentation", "The larger watermelon.,The front white flower.,White tea pot.,Flower bunch.,white vase.,The peach on the left.,The brown knife.,The handkerchief.", '', 'Format: s,s,s'],
["./images/apples.jpg", "Referring Editing", "the green apple", 'a red apple', 'x-decoder + ldm (inference takes ~20s), use inpainting_text "clean and empty scene" for image inpainting'],
["./images/animals.png", "Open Vocabulary Semantic Segmentation", "zebra,antelope,giraffe,ostrich,sky,water,grass,sand,tree", '', 'Format: x,x,x'],
["./images/street.jpg", "Open Vocabulary Panoptic Segmentation", "stuff:building,sky,street,tree,rock,sidewalk;thing:car,person,traffic light", '', 'Format: stuff:x,x,x;thing:y,y,y'],
["./images/owls.jpeg", "Open Vocabulary Instance Segmentation", "owl", '', 'Format: y,y,y'],
["./images/mountain.jpeg", "Image Captioning", "", '', ''],
["./images/rose.webp", "Text Retrieval", "lily,rose,peoney,tulip", '', 'Format: s,s,s'],
["./images/region_retrieval.png", "Image/Region Retrieval", "The tangerine on the plate.", '', 'Please describe the object in a detailed way (80 images in the pool).'],
["./images/landscape.jpg", "Referring Captioning (Beta)", "cloud", '', 'Please fill in a noun/noun phrase. (may start with a/the)'],
],
title=title,
description=description,
article=article,
allow_flagging='never',
cache_examples=True,
).launch()