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README.md

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+---
+title: ODISE
+emoji: 🤗
+colorFrom: indigo
+colorTo: pink
+sdk: gradio
+sdk_version: 3.20.0
+app_file: app.py
+pinned: true
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023, NVIDIA Corporation & Affiliates. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import os
+token = os.environ["GITHUB_TOKEN"]
+os.system(f"pip install git+https://xvjiarui:{token}@github.com/xvjiarui/ODISE_NV.git")
+
+import itertools
+import json
+from contextlib import ExitStack
+import gradio as gr
+import torch
+from mask2former.data.datasets.register_ade20k_panoptic import ADE20K_150_CATEGORIES
+from PIL import Image
+from torch.cuda.amp import autocast
+
+from detectron2.config import instantiate
+from detectron2.data import MetadataCatalog
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
+from detectron2.evaluation import inference_context
+from detectron2.utils.env import seed_all_rng
+from detectron2.utils.logger import setup_logger
+from detectron2.utils.visualizer import ColorMode, Visualizer, random_color
+
+from odise import model_zoo
+from odise.checkpoint import ODISECheckpointer
+from odise.config import instantiate_odise
+from odise.data import get_openseg_labels
+from odise.modeling.wrapper import OpenPanopticInference
+from odise.utils.file_io import ODISEHandler, PathManager
+from odise.model_zoo.model_zoo import _ModelZooUrls
+
+for k in ODISEHandler.URLS:
+    ODISEHandler.URLS[k] = ODISEHandler.URLS[k].replace("https://github.com/NVlabs/ODISE/releases/download/v1.0.0/", "https://huggingface.co/xvjiarui/download_cache/resolve/main/torch/odise/")
+PathManager.register_handler(ODISEHandler())
+_ModelZooUrls.PREFIX = _ModelZooUrls.PREFIX.replace("https://github.com/NVlabs/ODISE/releases/download/v1.0.0/", "https://huggingface.co/xvjiarui/download_cache/resolve/main/torch/odise/")
+
+setup_logger()
+logger = setup_logger(name="odise")
+
+COCO_THING_CLASSES = [
+    label
+    for idx, label in enumerate(get_openseg_labels("coco_panoptic", True))
+    if COCO_CATEGORIES[idx]["isthing"] == 1
+]
+COCO_THING_COLORS = [c["color"] for c in COCO_CATEGORIES if c["isthing"] == 1]
+COCO_STUFF_CLASSES = [
+    label
+    for idx, label in enumerate(get_openseg_labels("coco_panoptic", True))
+    if COCO_CATEGORIES[idx]["isthing"] == 0
+]
+COCO_STUFF_COLORS = [c["color"] for c in COCO_CATEGORIES if c["isthing"] == 0]
+
+ADE_THING_CLASSES = [
+    label
+    for idx, label in enumerate(get_openseg_labels("ade20k_150", True))
+    if ADE20K_150_CATEGORIES[idx]["isthing"] == 1
+]
+ADE_THING_COLORS = [c["color"] for c in ADE20K_150_CATEGORIES if c["isthing"] == 1]
+ADE_STUFF_CLASSES = [
+    label
+    for idx, label in enumerate(get_openseg_labels("ade20k_150", True))
+    if ADE20K_150_CATEGORIES[idx]["isthing"] == 0
+]
+ADE_STUFF_COLORS = [c["color"] for c in ADE20K_150_CATEGORIES if c["isthing"] == 0]
+
+LVIS_CLASSES = get_openseg_labels("lvis_1203", True)
+# use beautiful coco colors
+LVIS_COLORS = list(
+    itertools.islice(itertools.cycle([c["color"] for c in COCO_CATEGORIES]), len(LVIS_CLASSES))
+)
+
+
+class VisualizationDemo(object):
+    def __init__(self, model, metadata, aug, instance_mode=ColorMode.IMAGE):
+        """
+        Args:
+            model (nn.Module):
+            metadata (MetadataCatalog): image metadata.
+            instance_mode (ColorMode):
+            parallel (bool): whether to run the model in different processes from visualization.
+                Useful since the visualization logic can be slow.
+        """
+        self.model = model
+        self.metadata = metadata
+        self.aug = aug
+        self.cpu_device = torch.device("cpu")
+        self.instance_mode = instance_mode
+
+    def predict(self, original_image):
+        """
+        Args:
+            original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
+
+        Returns:
+            predictions (dict):
+                the output of the model for one image only.
+                See :doc:`/tutorials/models` for details about the format.
+        """
+        height, width = original_image.shape[:2]
+        aug_input = T.AugInput(original_image, sem_seg=None)
+        self.aug(aug_input)
+        image = aug_input.image
+        image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
+
+        inputs = {"image": image, "height": height, "width": width}
+        logger.info("forwarding")
+        with autocast():
+            predictions = self.model([inputs])[0]
+        logger.info("done")
+        return predictions
+
+    def run_on_image(self, image):
+        """
+        Args:
+            image (np.ndarray): an image of shape (H, W, C) (in BGR order).
+                This is the format used by OpenCV.
+        Returns:
+            predictions (dict): the output of the model.
+            vis_output (VisImage): the visualized image output.
+        """
+        vis_output = None
+        predictions = self.predict(image)
+        visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode)
+        if "panoptic_seg" in predictions:
+            panoptic_seg, segments_info = predictions["panoptic_seg"]
+            vis_output = visualizer.draw_panoptic_seg(
+                panoptic_seg.to(self.cpu_device), segments_info
+            )
+        else:
+            if "sem_seg" in predictions:
+                vis_output = visualizer.draw_sem_seg(
+                    predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
+                )
+            if "instances" in predictions:
+                instances = predictions["instances"].to(self.cpu_device)
+                vis_output = visualizer.draw_instance_predictions(predictions=instances)
+
+        return predictions, vis_output
+
+
+cfg = model_zoo.get_config("Panoptic/odise_label_coco_50e.py", trained=True)
+
+cfg.model.overlap_threshold = 0
+cfg.train.device = "cuda" if torch.cuda.is_available() else "cpu"
+seed_all_rng(42)
+
+dataset_cfg = cfg.dataloader.test
+wrapper_cfg = cfg.dataloader.wrapper
+
+aug = instantiate(dataset_cfg.mapper).augmentations
+
+model = instantiate_odise(cfg.model)
+model.to(torch.float16)
+model.to(cfg.train.device)
+ODISECheckpointer(model).load(cfg.train.init_checkpoint)
+
+
+title = "ODISE"
+description = """
+Gradio demo for ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models. \n
+You may click on of the examples or upload your own image. \n
+
+ODISE could perform open vocabulary segmentation, you may input more classes (separate by comma).
+The expected format is 'a1,a2;b1,b2', where a1,a2 are synonyms vocabularies for the first class. 
+The first word will be displayed as the class name.
+"""  # noqa
+
+article = """
+<p style='text-align: center'><a href='https://arxiv.org/abs/2303.04803' target='_blank'>Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models</a> | <a href='https://github.com/NVlab/ODISE' target='_blank'>Github Repo</a></p>
+"""  # noqa
+
+examples = [
+    [
+        "demo/examples/coco.jpg",
+        "black pickup truck, pickup truck; blue sky, sky",
+        ["COCO (133 categories)", "ADE (150 categories)", "LVIS (1203 categories)"],
+    ],
+    [
+        "demo/examples/ade.jpg",
+        "luggage, suitcase, baggage;handbag",
+        ["ADE (150 categories)"],
+    ],
+    [
+        "demo/examples/ego4d.jpg",
+        "faucet, tap; kitchen paper, paper towels",
+        ["COCO (133 categories)"],
+    ],
+]
+
+
+def build_demo_classes_and_metadata(vocab, label_list):
+    extra_classes = []
+
+    if vocab:
+        for words in vocab.split(";"):
+            extra_classes.append([word.strip() for word in words.split(",")])
+    extra_colors = [random_color(rgb=True, maximum=1) for _ in range(len(extra_classes))]
+
+    demo_thing_classes = extra_classes
+    demo_stuff_classes = []
+    demo_thing_colors = extra_colors
+    demo_stuff_colors = []
+
+    if any("COCO" in label for label in label_list):
+        demo_thing_classes += COCO_THING_CLASSES
+        demo_stuff_classes += COCO_STUFF_CLASSES
+        demo_thing_colors += COCO_THING_COLORS
+        demo_stuff_colors += COCO_STUFF_COLORS
+    if any("ADE" in label for label in label_list):
+        demo_thing_classes += ADE_THING_CLASSES
+        demo_stuff_classes += ADE_STUFF_CLASSES
+        demo_thing_colors += ADE_THING_COLORS
+        demo_stuff_colors += ADE_STUFF_COLORS
+    if any("LVIS" in label for label in label_list):
+        demo_thing_classes += LVIS_CLASSES
+        demo_thing_colors += LVIS_COLORS
+
+    MetadataCatalog.pop("odise_demo_metadata", None)
+    demo_metadata = MetadataCatalog.get("odise_demo_metadata")
+    demo_metadata.thing_classes = [c[0] for c in demo_thing_classes]
+    demo_metadata.stuff_classes = [
+        *demo_metadata.thing_classes,
+        *[c[0] for c in demo_stuff_classes],
+    ]
+    demo_metadata.thing_colors = demo_thing_colors
+    demo_metadata.stuff_colors = demo_thing_colors + demo_stuff_colors
+    demo_metadata.stuff_dataset_id_to_contiguous_id = {
+        idx: idx for idx in range(len(demo_metadata.stuff_classes))
+    }
+    demo_metadata.thing_dataset_id_to_contiguous_id = {
+        idx: idx for idx in range(len(demo_metadata.thing_classes))
+    }
+
+    demo_classes = demo_thing_classes + demo_stuff_classes
+
+    return demo_classes, demo_metadata
+
+
+def inference(image_path, vocab, label_list):
+
+    logger.info("building class names")
+    demo_classes, demo_metadata = build_demo_classes_and_metadata(vocab, label_list)
+    with ExitStack() as stack:
+        inference_model = OpenPanopticInference(
+            model=model,
+            labels=demo_classes,
+            metadata=demo_metadata,
+            semantic_on=False,
+            instance_on=False,
+            panoptic_on=True,
+        )
+        stack.enter_context(inference_context(inference_model))
+        stack.enter_context(torch.no_grad())
+
+        demo = VisualizationDemo(inference_model, demo_metadata, aug)
+        img = utils.read_image(image_path, format="RGB")
+        _, visualized_output = demo.run_on_image(img)
+        return Image.fromarray(visualized_output.get_image())
+
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" + title + "</h1>")
+    gr.Markdown(description)
+    input_components = []
+    output_components = []
+
+    with gr.Row():
+        output_image_gr = gr.outputs.Image(label="Panoptic Segmentation", type="pil")
+        output_components.append(output_image_gr)
+
+    with gr.Row().style(equal_height=True, mobile_collapse=True):
+        with gr.Column(scale=3, variant="panel") as input_component_column:
+            input_image_gr = gr.inputs.Image(type="filepath")
+            extra_vocab_gr = gr.inputs.Textbox(default="", label="Extra Vocabulary")
+            category_list_gr = gr.inputs.CheckboxGroup(
+                choices=["COCO (133 categories)", "ADE (150 categories)", "LVIS (1203 categories)"],
+                default=["COCO (133 categories)", "ADE (150 categories)", "LVIS (1203 categories)"],
+                label="Category to use",
+            )
+            input_components.extend([input_image_gr, extra_vocab_gr, category_list_gr])
+
+        with gr.Column(scale=2):
+            examples_handler = gr.Examples(
+                examples=examples,
+                inputs=[c for c in input_components if not isinstance(c, gr.State)],
+                outputs=[c for c in output_components if not isinstance(c, gr.State)],
+                fn=inference,
+                cache_examples=torch.cuda.is_available(),
+                examples_per_page=5,
+            )
+            with gr.Row():
+                clear_btn = gr.Button("Clear")
+                submit_btn = gr.Button("Submit", variant="primary")
+
+    gr.Markdown(article)
+
+    submit_btn.click(
+        inference,
+        input_components,
+        output_components,
+        api_name="predict",
+        scroll_to_output=True,
+    )
+
+    clear_btn.click(
+        None,
+        [],
+        (input_components + output_components + [input_component_column]),
+        _js=f"""() => {json.dumps(
+                    [component.cleared_value if hasattr(component, "cleared_value") else None
+                     for component in input_components + output_components] + (
+                        [gr.Column.update(visible=True)]
+                    )
+                    + ([gr.Column.update(visible=False)])
+                )}
+                """,
+    )
+
+demo.launch()

packages.txt

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+libtinfo5
+libsm6
+libxext6
+python3-opencv

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cu116
+torch==1.13.1+cu116
+torchvision==0.14.1+cu116
+xformers==0.0.16
+numpy<=1.21.5