app.py

import os 
import os
os.system("nvcc --version")
print(os.environ.get('CUDA_PATH'))
os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html')
os.system("git clone https://github.com/facebookresearch/Mask2Former.git")

os.chdir("Mask2Former")
os.system("pwd")
os.system("pip install git+https://github.com/cocodataset/panopticapi.git")
os.chdir("mask2former/modeling/pixel_decoder/ops")
os.system("pwd")

os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0"
os.environ["FORCE_CUDA"] = "1"
os.system("python setup.py build install")
os.chdir("/home/user/app/Mask2Former/")
os.system("pwd")
import gradio as gr
# check pytorch installation: 
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
setup_logger(name="mask2former")

# import some common libraries
import numpy as np
import cv2
import torch

# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer, ColorMode
from detectron2.data import MetadataCatalog
from detectron2.projects.deeplab import add_deeplab_config
coco_metadata = MetadataCatalog.get("coco_2017_val_panoptic")

# import Mask2Former project
from mask2former import add_maskformer2_config

cfg = get_cfg()
cfg.MODEL.DEVICE='cpu'
add_deeplab_config(cfg)
add_maskformer2_config(cfg)
cfg.merge_from_file("configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml")
cfg.MODEL.WEIGHTS = 'https://dl.fbaipublicfiles.com/maskformer/mask2former/coco/panoptic/maskformer2_swin_large_IN21k_384_bs16_100ep/model_final_f07440.pkl'
cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True
cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = True
cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = True
predictor = DefaultPredictor(cfg)
outputs = predictor(im)

def inference(img):
    im = cv2.imread(img)
    v = Visualizer(im[:, :, ::-1], coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
    panoptic_result = v.draw_panoptic_seg(outputs["panoptic_seg"][0].to("cpu"), outputs["panoptic_seg"][1]).get_image()
    v = Visualizer(im[:, :, ::-1], coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
    instance_result = v.draw_instance_predictions(outputs["instances"].to("cpu")).get_image()
    v = Visualizer(im[:, :, ::-1], coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
    semantic_result = v.draw_sem_seg(outputs["sem_seg"].argmax(0).to("cpu")).get_image()    
    return Image.fromarray(np.uint8(panoptic_result)).convert('RGB'),Image.fromarray(np.uint8(instance_result)).convert('RGB'),Image.fromarray(np.uint8(semantic_result)).convert('RGB')
    

title = "Detectron 2"
description = "Gradio demo for Detectron 2: A PyTorch-based modular object detection library. 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://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/' target='_blank'>Detectron2: A PyTorch-based modular object detection library</a> | <a href='https://github.com/facebookresearch/detectron2' target='_blank'>Github Repo</a></p>"

examples = [['airplane.png']]

gr.Interface(inference, inputs=gr.inputs.Image(type="filepath"), outputs=[gr.outputs.Image(label="Panoptic segmentation",type="pil"),gr.outputs.Image(label="instance segmentation",type="pil"),gr.outputs.Image(label="semantic segmentation",type="pil")],enable_queue=True, title=title,
    description=description,
    article=article,
    examples=examples).launch()