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/AK391/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 = "
Detectron2: A PyTorch-based modular object detection library | Github Repo
" 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()