import torch print("Installed the dependencies!") import numpy as np from PIL import Image import cv2 import imutils from detectron2.config import get_cfg from detectron2.projects.deeplab import add_deeplab_config from detectron2.data import MetadataCatalog from oneformer import ( add_oneformer_config, add_common_config, add_swin_config, add_dinat_config, ) from demo.defaults import DefaultPredictor from demo.visualizer import Visualizer, ColorMode import gradio as gr from huggingface_hub import hf_hub_download KEY_DICT = {"Cityscapes (19 classes)": "cityscapes",} SWIN_CFG_DICT = {"cityscapes": "configs/cityscapes/oneformer_swin_large_IN21k_384_bs16_90k.yaml"} SWIN_MODEL_DICT = {"cityscapes": hf_hub_download(repo_id="rubikk179/oneformer_cityscapes_Swin_40000ep", filename="model_final_swin.pth"), } print("-------------------------check hub: ",hf_hub_download(repo_id="rubikk179/oneformer_cityscapes_Swin_40000ep", filename="model_final_swin.pth")) CONVNEXT_CFG_DICT = {"cityscapes": "configs/cityscapes/oneformer_convnext_large_bs16_90k.yaml"} ConvNeXt_MODEL_DICT = {"cityscapes": hf_hub_download(repo_id="rubikk179/oneformer_cityscapes_ConvNeXt_40000ep", filename="model_final_convnext.pth"), } MODEL_DICT = {"ConvNeXt": CONVNEXT_MODEL_DICT, "Swin": SWIN_MODEL_DICT } CFG_DICT = {"ConvNeXt": CONVNEXT_CFG_DICT, "Swin": SWIN_CFG_DICT } WIDTH_DICT = {"cityscapes": 512} cpu_device = torch.device("cpu") PREDICTORS = { "ConvNeXt": { "Cityscapes (19 classes)": None, }, "Swin": { "Cityscapes (19 classes)": None, } } METADATA = { "ConvNeXt": { "Cityscapes (19 classes)": None, }, "Swin": { "Cityscapes (19 classes)": None, } } def setup_modules(): for dataset in ["Cityscapes (19 classes)"]: for backbone in ["ConvNeXt", "Swin"]: cfg = setup_cfg(dataset, backbone) metadata = MetadataCatalog.get( cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else "__unused" ) if 'cityscapes_fine_sem_seg_val' in cfg.DATASETS.TEST_PANOPTIC[0]: from cityscapesscripts.helpers.labels import labels stuff_colors = [k.color for k in labels if k.trainId != 255] metadata = metadata.set(stuff_colors=stuff_colors) PREDICTORS[backbone][dataset] = DefaultPredictor(cfg) METADATA[backbone][dataset] = metadata def setup_cfg(dataset, backbone): # load config from file and command-line arguments cfg = get_cfg() add_deeplab_config(cfg) add_common_config(cfg) add_swin_config(cfg) add_oneformer_config(cfg) add_dinat_config(cfg) dataset = KEY_DICT[dataset] cfg_path = CFG_DICT[backbone][dataset] cfg.merge_from_file(cfg_path) if torch.cuda.is_available(): cfg.MODEL.DEVICE = 'cuda' else: cfg.MODEL.DEVICE = 'cpu' cfg.MODEL.WEIGHTS = MODEL_DICT[backbone][dataset] cfg.freeze() return cfg # def setup_modules(dataset, backbone): # cfg = setup_cfg(dataset, backbone) # predictor = DefaultPredictor(cfg) # # predictor = PREDICTORS[backbone][dataset] # metadata = MetadataCatalog.get( # cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else "__unused" # ) # if 'cityscapes_fine_sem_seg_val' in cfg.DATASETS.TEST_PANOPTIC[0]: # from cityscapesscripts.helpers.labels import labels # stuff_colors = [k.color for k in labels if k.trainId != 255] # metadata = metadata.set(stuff_colors=stuff_colors) # return predictor, metadata def panoptic_run(img, predictor, metadata): visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE) predictions = predictor(img, "panoptic") panoptic_seg, segments_info = predictions["panoptic_seg"] out = visualizer.draw_panoptic_seg_predictions( panoptic_seg.to(cpu_device), segments_info, alpha=0.5 ) visualizer_map = Visualizer(img[:, :, ::-1], is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE) out_map = visualizer_map.draw_panoptic_seg_predictions( panoptic_seg.to(cpu_device), segments_info, alpha=1, is_text=False ) return out, out_map def instance_run(img, predictor, metadata): visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE) predictions = predictor(img, "instance") instances = predictions["instances"].to(cpu_device) out = visualizer.draw_instance_predictions(predictions=instances, alpha=0.5) visualizer_map = Visualizer(img[:, :, ::-1], is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE) out_map = visualizer_map.draw_instance_predictions(predictions=instances, alpha=1, is_text=False) return out, out_map def semantic_run(img, predictor, metadata): visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE) predictions = predictor(img, "semantic") out = visualizer.draw_sem_seg( predictions["sem_seg"].argmax(dim=0).to(cpu_device), alpha=0.5 ) visualizer_map = Visualizer(img[:, :, ::-1], is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE) out_map = visualizer_map.draw_sem_seg( predictions["sem_seg"].argmax(dim=0).to(cpu_device), alpha=1, is_text=False ) return out, out_map TASK_INFER = {"the task is panoptic": panoptic_run, "the task is instance": instance_run, "the task is semantic": semantic_run} def segment(path, task, dataset, backbone): # predictor, metadata = setup_modules(dataset, backbone) predictor = PREDICTORS[backbone][dataset] metadata = METADATA[backbone][dataset] img = cv2.imread(path) width = WIDTH_DICT[KEY_DICT[dataset]] img = imutils.resize(img, width=width) out, out_map = TASK_INFER[task](img, predictor, metadata) out = Image.fromarray(out.get_image()) out_map = Image.fromarray(out_map.get_image()) return out, out_map title = "

OneFormer: One Transformer to Rule Universal Image Segmentation

" # style='margin-bottom: -10px; description = "

Jitesh Jain, Jiachen Li*, MangTik Chiu*, Ali Hassani, Nikita Orlov, Humphrey Shi

" \ + "

Project Page | ArXiv Paper | Github Repo

" \ + "

\ OneFormer is the first multi-task universal image segmentation framework based on transformers. Our single OneFormer model achieves state-of-the-art performance across all three segmentation tasks with a single task-conditioned joint training process. OneFormer uses a task token to condition the model on the task in focus, making our architecture task-guided for training, and task-dynamic for inference, all with a single model. We believe OneFormer is a significant step towards making image segmentation more universal and accessible.\

" \ + "

[Note: Inference on CPU may take upto 2 minutes. On a single RTX A6000 GPU, OneFormer is able to inference at more than 15 FPS.]

" setup_modules() gradio_inputs = [gr.Image(source="upload", tool=None, label="Input Image",type="filepath"), gr.Radio(choices=["the task is panoptic" ,"the task is instance", "the task is semantic"], type="value", value="the task is panoptic", label="Task Token Input"), gr.Radio(choices=["Cityscapes (19 classes)"], type="value", value="Cityscapes (19 classes)", label="Model"), gr.Radio(choices=["ConvNeXt" ,"Swin"], type="value", value="Swin", label="Backbone"), ] gradio_outputs = [gr.Image(type="pil", label="Segmentation Overlay"), gr.Image(type="pil", label="Segmentation Map")] examples = [["examples/coco.jpeg", "the task is panoptic", "COCO (133 classes)", "DiNAT-L"], ["examples/cityscapes.png", "the task is panoptic", "Cityscapes (19 classes)", "DiNAT-L"], ["examples/ade20k.jpeg", "the task is panoptic", "ADE20K (150 classes)", "DiNAT-L"]] iface = gr.Interface(fn=segment, inputs=gradio_inputs, outputs=gradio_outputs, examples_per_page=5, allow_flagging="never", examples=examples, title=title, description=description) iface.launch(enable_queue=True, server_name="0.0.0.0")