/
register_voc_seg.py
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/
register_voc_seg.py
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# Copyright (c) Facebook, Inc. and its affiliates.
import os
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.data.datasets import load_sem_seg
from .utils import load_binary_mask
CLASS_NAMES = (
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor",
)
BASE_CLASS_NAMES = [
c for i, c in enumerate(CLASS_NAMES) if i not in [15, 16, 17, 18, 19]
]
NOVEL_CLASS_NAMES = [c for i, c in enumerate(CLASS_NAMES) if i in [15, 16, 17, 18, 19]]
def _get_voc_meta(cat_list):
ret = {
"stuff_classes": cat_list,
}
return ret
def register_all_voc_11k(root):
root = os.path.join(root, "VOC2012")
meta = _get_voc_meta(CLASS_NAMES)
base_meta = _get_voc_meta(BASE_CLASS_NAMES)
novel_meta = _get_voc_meta(NOVEL_CLASS_NAMES)
for name, image_dirname, sem_seg_dirname in [
("train", "JPEGImages", "annotations_detectron2/train"),
("test", "JPEGImages", "annotations_detectron2/val"),
]:
image_dir = os.path.join(root, image_dirname)
gt_dir = os.path.join(root, sem_seg_dirname)
all_name = f"voc_sem_seg_{name}"
DatasetCatalog.register(
all_name,
lambda x=image_dir, y=gt_dir: load_sem_seg(
y, x, gt_ext="png", image_ext="jpg"
),
)
MetadataCatalog.get(all_name).set(
image_root=image_dir,
sem_seg_root=gt_dir,
evaluator_type="sem_seg",
ignore_label=255,
**meta,
)
MetadataCatalog.get(all_name).set(
evaluation_set={
"base": [
meta["stuff_classes"].index(n) for n in base_meta["stuff_classes"]
],
},
trainable_flag=[
1 if n in base_meta["stuff_classes"] else 0
for n in meta["stuff_classes"]
],
)
# classification
DatasetCatalog.register(
all_name + "_classification",
lambda x=image_dir, y=gt_dir: load_binary_mask(
y, x, gt_ext="png", image_ext="jpg"
),
)
MetadataCatalog.get(all_name + "_classification").set(
image_root=image_dir,
sem_seg_root=gt_dir,
evaluator_type="classification",
ignore_label=255,
evaluation_set={
"base": [
meta["stuff_classes"].index(n) for n in base_meta["stuff_classes"]
],
},
trainable_flag=[
1 if n in base_meta["stuff_classes"] else 0
for n in meta["stuff_classes"]
],
**meta,
)
# zero shot
image_dir = os.path.join(root, image_dirname)
gt_dir = os.path.join(root, sem_seg_dirname + "_base")
base_name = f"voc_base_sem_seg_{name}"
DatasetCatalog.register(
base_name,
lambda x=image_dir, y=gt_dir: load_sem_seg(
y, x, gt_ext="png", image_ext="jpg"
),
)
MetadataCatalog.get(base_name).set(
image_root=image_dir,
sem_seg_root=gt_dir,
evaluator_type="sem_seg",
ignore_label=255,
**base_meta,
)
# classification
DatasetCatalog.register(
base_name + "_classification",
lambda x=image_dir, y=gt_dir: load_binary_mask(
y, x, gt_ext="png", image_ext="jpg"
),
)
MetadataCatalog.get(base_name + "_classification").set(
image_root=image_dir,
sem_seg_root=gt_dir,
evaluator_type="classification",
ignore_label=255,
**base_meta,
)
# zero shot
image_dir = os.path.join(root, image_dirname)
gt_dir = os.path.join(root, sem_seg_dirname + "_novel")
novel_name = f"voc_novel_sem_seg_{name}"
DatasetCatalog.register(
novel_name,
lambda x=image_dir, y=gt_dir: load_sem_seg(
y, x, gt_ext="png", image_ext="jpg"
),
)
MetadataCatalog.get(novel_name).set(
image_root=image_dir,
sem_seg_root=gt_dir,
evaluator_type="sem_seg",
ignore_label=255,
**novel_meta,
)
def register_all_voc_pseudo(root, pseudo_sem_dir):
root = os.path.join(root, "VOC2012")
meta = _get_voc_meta(CLASS_NAMES)
base_meta = _get_voc_meta(BASE_CLASS_NAMES)
novel_meta = _get_voc_meta(NOVEL_CLASS_NAMES)
for name, image_dirname, sem_seg_dirname in [
("train", "JPEGImages", "annotations_detectron2/train"),
]:
image_dir = os.path.join(root, image_dirname)
all_name = f"voc_sem_seg_{name}_pseudo"
DatasetCatalog.register(
all_name,
lambda x=image_dir, y=pseudo_sem_dir: load_sem_seg(
y, x, gt_ext="png", image_ext="jpg"
),
)
MetadataCatalog.get(all_name).set(
image_root=image_dir,
sem_seg_root=pseudo_sem_dir,
evaluator_type="sem_seg",
ignore_label=255,
evaluation_set={
"base": [
meta["stuff_classes"].index(n) for n in base_meta["stuff_classes"]
],
},
trainable_flag=[
1 if n in base_meta["stuff_classes"] else 0
for n in meta["stuff_classes"]
],
**meta,
)
_root = os.getenv("DETECTRON2_DATASETS", "datasets")
register_all_voc_11k(_root)
_pseudo_dir = os.getenv("DETECTRON2_SEM_PSEUDO", "output/inference")
register_all_voc_pseudo(_root, _pseudo_dir)