Mask-Adapter / mask_adapter /data /datasets /register_ade20k_full.py
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"""
This file may have been modified by Bytedance Ltd. and/or its affiliates (“Bytedance's Modifications”).
All Bytedance's Modifications are Copyright (year) Bytedance Ltd. and/or its affiliates.
Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_full.py
"""
import os
import numpy as np
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.data.datasets import load_sem_seg
from . import openseg_classes
ADE20K_847_CATEGORIES = openseg_classes.get_ade20k_847_categories_with_prompt_eng()
ADE20k_847_COLORS = [np.random.randint(256, size=3).tolist() for k in ADE20K_847_CATEGORIES]
MetadataCatalog.get("openvocab_ade20k_full_sem_seg_train").set(
stuff_colors=ADE20k_847_COLORS[:],
)
MetadataCatalog.get("openvocab_ade20k_full_sem_seg_val").set(
stuff_colors=ADE20k_847_COLORS[:],
)
def _get_ade20k_847_meta():
# We only need class names
stuff_classes = [k["name"] for k in ADE20K_847_CATEGORIES]
assert len(stuff_classes) == 847, len(stuff_classes)
ret = {
"stuff_classes": stuff_classes,
}
return ret
def register_all_ade20k_847(root):
root = os.path.join(root, "ADE20K_2021_17_01")
meta = _get_ade20k_847_meta()
for name, dirname in [("train", "training"), ("val", "validation")]:
image_dir = os.path.join(root, "images_detectron2", dirname)
gt_dir = os.path.join(root, "annotations_detectron2", dirname)
name = f"openvocab_ade20k_full_sem_seg_{name}"
DatasetCatalog.register(
name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="tif", image_ext="jpg")
)
MetadataCatalog.get(name).set(
stuff_classes=meta["stuff_classes"][:],
image_root=image_dir,
sem_seg_root=gt_dir,
evaluator_type="sem_seg",
ignore_label=65535, # NOTE: gt is saved in 16-bit TIFF images
gt_ext="tif",
)
_root = os.getenv("DETECTRON2_DATASETS", "datasets")
register_all_ade20k_847(_root)