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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",
"COCO (133 classes)": "coco",
"ADE20K (150 classes)": "ade20k",}
SWIN_CFG_DICT = {"cityscapes": "configs/cityscapes/oneformer_swin_large_IN21k_384_bs16_90k.yaml",
"coco": "configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml",
"ade20k": "configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml",}
SWIN_MODEL_DICT = {"cityscapes": hf_hub_download(repo_id="shi-labs/oneformer_cityscapes_swin_large",
filename="250_16_swin_l_oneformer_cityscapes_90k.pth"),
"coco": hf_hub_download(repo_id="shi-labs/oneformer_coco_swin_large",
filename="150_16_swin_l_oneformer_coco_100ep.pth"),
"ade20k": hf_hub_download(repo_id="shi-labs/oneformer_ade20k_swin_large",
filename="250_16_swin_l_oneformer_ade20k_160k.pth")
}
DINAT_CFG_DICT = {"cityscapes": "configs/cityscapes/oneformer_dinat_large_bs16_90k.yaml",
"coco": "configs/coco/oneformer_dinat_large_bs16_100ep.yaml",
"ade20k": "configs/ade20k/oneformer_dinat_large_IN21k_384_bs16_160k.yaml",}
DINAT_MODEL_DICT = {"cityscapes": hf_hub_download(repo_id="shi-labs/oneformer_cityscapes_dinat_large",
filename="250_16_dinat_l_oneformer_cityscapes_90k.pth"),
"coco": hf_hub_download(repo_id="shi-labs/oneformer_coco_dinat_large",
filename="150_16_dinat_l_oneformer_coco_100ep.pth"),
"ade20k": hf_hub_download(repo_id="shi-labs/oneformer_ade20k_dinat_large",
filename="250_16_dinat_l_oneformer_ade20k_160k.pth")
}
MODEL_DICT = {"DiNAT-L": DINAT_MODEL_DICT,
"Swin-L": SWIN_MODEL_DICT }
CFG_DICT = {"DiNAT-L": DINAT_CFG_DICT,
"Swin-L": SWIN_CFG_DICT }
WIDTH_DICT = {"cityscapes": 512,
"coco": 512,
"ade20k": 640}
cpu_device = torch.device("cpu")
PREDICTORS = {
"DiNAT-L": {
"Cityscapes (19 classes)": None,
"COCO (133 classes)": None,
"ADE20K (150 classes)": None
},
"Swin-L": {
"Cityscapes (19 classes)": None,
"COCO (133 classes)": None,
"ADE20K (150 classes)": None
}
}
METADATA = {
"DiNAT-L": {
"Cityscapes (19 classes)": None,
"COCO (133 classes)": None,
"ADE20K (150 classes)": None
},
"Swin-L": {
"Cityscapes (19 classes)": None,
"COCO (133 classes)": None,
"ADE20K (150 classes)": None
}
}
def setup_modules():
for dataset in ["Cityscapes (19 classes)", "COCO (133 classes)", "ADE20K (150 classes)"]:
for backbone in ["DiNAT-L", "Swin-L"]:
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 = "<h1 style='margin-bottom: -10px; text-align: center'>OneFormer: One Transformer to Rule Universal Image Segmentation</h1>"
description = "<p style='font-size: 14px; margin: 5px; font-weight: w300; text-align: center'> <a href='https://praeclarumjj3.github.io/' style='text-decoration:none' target='_blank'>Jitesh Jain, </a> <a href='https://chrisjuniorli.github.io/' style='text-decoration:none' target='_blank'>Jiachen Li<sup>*</sup>, </a> <a href='https://www.linkedin.com/in/mtchiu/' style='text-decoration:none' target='_blank'>MangTik Chiu<sup>*</sup>, </a> <a href='https://alihassanijr.com/' style='text-decoration:none' target='_blank'>Ali Hassani, </a> <a href='https://www.linkedin.com/in/nukich74/' style='text-decoration:none' target='_blank'>Nikita Orlov, </a> <a href='https://www.humphreyshi.com/home' style='text-decoration:none' target='_blank'>Humphrey Shi</a></p>" \
+ "<p style='font-size: 16px; margin: 5px; font-weight: w600; text-align: center'> <a href='https://praeclarumjj3.github.io/oneformer/' target='_blank'>Project Page</a> | <a href='https://arxiv.org/abs/2211.06220' target='_blank'>ArXiv Paper</a> | <a href='https://github.com/SHI-Labs/OneFormer' target='_blank'>Github Repo</a></p>" \
+ "<p style='text-align: center; margin: 5px; font-size: 14px; font-weight: w300;'> \
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.\
</p>" \
+ "<p style='text-align: center; font-size: 14px; margin: 5px; font-weight: w300;'> [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.]</p>"
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=["COCO (133 classes)" ,"Cityscapes (19 classes)", "ADE20K (150 classes)"], type="value", value="COCO (133 classes)", label="Model"),
gr.Radio(choices=["DiNAT-L" ,"Swin-L"], type="value", value="DiNAT-L", 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") |