.

detectron2/data/datasets/builtin.py

@@ -27,7 +27,7 @@ from .cityscapes_panoptic import register_all_cityscapes_panoptic
 from .coco import load_sem_seg, register_coco_instances, register_coco_instances_wsl
 from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated
 from .lvis import get_lvis_instances_meta, register_lvis_instances
-from .pascal_voc import register_pascal_voc, register_pascal_voc_wsl, register_pascal_voc_wsl_top1, register_pascal_voc_wsl_thres, register_pascal_voc_wsl_contain, register_pascal_voc_wsl_contain_total, register_pascal_voc_wsl_mist, register_pascal_voc_wsl_mist_contain, register_pascal_voc_wsl_contain_all, register_pascal_voc_wsl_contain_w2f, register_pascal_voc_wsl_oicr_contain, register_pascal_voc_wsl_oicr_contain_all, register_pascal_voc_wsl_w2f_overlap
+from .pascal_voc import register_pascal_voc, register_pascal_voc_wsl, register_pascal_voc_wsl_top1, register_pascal_voc_wsl_thres, register_pascal_voc_wsl_contain, register_pascal_voc_wsl_contain_total, register_pascal_voc_wsl_mist, register_pascal_voc_wsl_mist_contain, register_pascal_voc_wsl_contain_all, register_pascal_voc_wsl_contain_w2f, register_pascal_voc_wsl_oicr_contain, register_pascal_voc_wsl_oicr_contain_all, register_pascal_voc_wsl_w2f_overlap, register_pascal_voc_wsl_contain_all_adaptive
 
 # ==== Predefined datasets and splits for COCO ==========
 
@@ -405,6 +405,16 @@ def register_all_pascal_voc_wsl_contain_all(root):
         register_pascal_voc_wsl_contain_all(name, os.path.join(root, dirname), split, year)
         MetadataCatalog.get(name).evaluator_type = "pascal_voc"
 
+def register_all_pascal_voc_wsl_contain_all_adaptive(root):
+    SPLITS = [
+        ("voc_2007_train_wsl_contain_all_adaptive", "VOC2007", "train"),
+        ("voc_2007_val_wsl_contain_all_adaptive", "VOC2007", "val")
+    ]
+    for name, dirname, split in SPLITS:
+        year = 2007 if "2007" in name else 2012
+        register_pascal_voc_wsl_contain_all_adaptive(name, os.path.join(root, dirname), split, year)
+        MetadataCatalog.get(name).evaluator_type = "pascal_voc"
+
 def register_all_pascal_voc_w2f(root):
     SPLITS = [
         ("voc_2007_train_wsl_w2f", "VOC2007", "train"),
@@ -469,4 +479,5 @@ if __name__.endswith(".builtin"):
     register_all_pascal_voc_w2f_overlap(_root)
     register_all_pascal_voc_wsl_oicr_contain(_root)
     register_all_pascal_voc_wsl_oicr_contain_all(_root)
+    register_all_pascal_voc_wsl_contain_all_adaptive(_root)
     register_all_ade20k(_root)

detectron2/data/datasets/pascal_voc.py

@@ -12,7 +12,7 @@ from detectron2.data import DatasetCatalog, MetadataCatalog
 from detectron2.structures import BoxMode
 from detectron2.utils.file_io import PathManager
 
-__all__ = ["load_voc_instances", "register_pascal_voc", "register_pascal_voc_wsl", "register_pascal_voc_wsl_top1", "register_pascal_voc_wsl_thres", "register_pascal_voc_wsl_mist", "register_pascal_voc_wsl_mist_contain", "register_pascal_voc_wsl_contain_all", "register_pascal_voc_wsl_contain_w2f", "register_pascal_voc_wsl_oicr_contain", "register_pascal_voc_wsl_oicr_contain_all", "register_pascal_voc_wsl_w2f_overlap"]
+__all__ = ["load_voc_instances", "register_pascal_voc", "register_pascal_voc_wsl", "register_pascal_voc_wsl_top1", "register_pascal_voc_wsl_thres", "register_pascal_voc_wsl_mist", "register_pascal_voc_wsl_mist_contain", "register_pascal_voc_wsl_contain_all", "register_pascal_voc_wsl_contain_w2f", "register_pascal_voc_wsl_oicr_contain", "register_pascal_voc_wsl_oicr_contain_all", "register_pascal_voc_wsl_w2f_overlap", "register_pascal_voc_wsl_contain_all_adaptive"]
 
 
 # fmt: off
@@ -547,6 +547,83 @@ def load_voc_instances_wsl_contain_all(dirname: str, split: str, class_names: Un
         dicts.append(r)
     return dicts
 
+def load_voc_instances_wsl_contain_all_adaptive(dirname: str, split: str, class_names: Union[List[str], Tuple[str, ...]]):
+    # 获取 数据集对应划分(train, val, test) 图片 ids
+    with PathManager.open(os.path.join(dirname, "ImageSets", "Main", split + ".txt")) as f:
+        fileids = np.loadtxt(f, dtype=np.str)
+    
+    # 针对 single-input 的文件
+    # print("load from {}/single_voc07_wsl_{}_contain.json".format(dirname, split))
+    # annotation_wsl = json.load(open(
+    #    "{}/single_voc07_wsl_{}_contain.json".format(dirname, split), "r"
+    # ))
+    # 获取 annotations, wsl 预测之后的结果会保存为 json 的格式
+    if "07" in dirname:
+        annotation_wsl = json.load(open(
+            "{}/voc07_wsl_{}_contain_all_adaptive.json".format(dirname, split), "r"
+        ))
+    elif "12" in dirname:
+        annotation_wsl = json.load(open(
+            "{}/casd_voc12_wsl_{}_contain_all_adaptive.json".format(dirname, split), "r"
+        ))
+    else:
+        assert False, "Wrong dirname: {}".format(dirname)
+    multi_class_labels = None
+    if "multi_label" in annotation_wsl:
+        multi_class_labels = annotation_wsl.pop("multi_label")
+    annotation_dirname = PathManager.get_local_path(os.path.join(dirname, "Annotations/"))
+    dicts = []
+    for fileid in fileids:
+        anno = annotation_wsl[str(int(fileid))]
+        jpeg_file = os.path.join(dirname, "JPEGImages", fileid + ".jpg")
+        anno_file = os.path.join(annotation_dirname, fileid + ".xml")
+
+        if not os.path.isfile(anno_file):
+            with Image.open(jpeg_file) as img:
+                width, height = img.size
+            r = {"file_name": jpeg_file, "image_id": fileid, "height": height, "width": width}
+            instances = []
+            for obj in anno:
+                bbox = obj["bbox"]
+                bbox = [int(i) for i in bbox] # 因为 predict 出来的 bbox 是float, 要转化为 int list
+                category_id = obj["category_id"] - 1 # 因为保存统计时将 index + 1 了从而方便 TIDE 统计了, 因此这里需要 - 1
+                instances.append(
+                    {
+                        "category_id": category_id, "bbox": bbox, "bbox_mode": BoxMode.XYXY_ABS
+                    }
+                )
+            r["annotations"] = instances
+            if multi_class_labels is not None:
+                r["multi_label"] = multi_class_labels[str(int(fileid))]
+            dicts.append(r)
+            continue
+
+        with PathManager.open(anno_file) as f:
+            tree = ET.parse(f)
+
+        r = {
+            "file_name": jpeg_file,
+            "image_id": fileid,
+            "height": int(tree.findall("./size/height")[0].text),
+            "width": int(tree.findall("./size/width")[0].text),
+        }
+        instances = []
+        # 这里是从 annotation_wsl 中进行 gt 信息的提取, 而不是 从 anno file 中提取真正的 gt 信息出来
+        for obj in anno:
+            bbox = obj["bbox"]
+            bbox = [int(i) for i in bbox]
+            category_id = obj["category_id"] - 1
+            instances.append(
+                {
+                    "category_id": category_id, "bbox": bbox, "bbox_mode": BoxMode.XYXY_ABS
+                }
+            )
+        r["annotations"] = instances
+        if multi_class_labels is not None:
+            r["multi_label"] = multi_class_labels[str(int(fileid))]
+        dicts.append(r)
+    return dicts
+
 def load_voc_instances_wsl_w2f(dirname: str, split: str, class_names: Union[List[str], Tuple[str, ...]]):
     # 获取 数据集对应划分(train, val, test) 图片 ids
     with PathManager.open(os.path.join(dirname, "ImageSets", "Main", split + ".txt")) as f:
@@ -1027,6 +1104,12 @@ def register_pascal_voc_wsl_contain_all(name, dirname, split, year, class_names=
         thing_classes=list(class_names), dirname=dirname, year=year, split=split
     )
 
+def register_pascal_voc_wsl_contain_all_adaptive(name, dirname, split, year, class_names=CLASS_NAMES):
+    DatasetCatalog.register(name, lambda: load_voc_instances_wsl_contain_all_adaptive(dirname, split, class_names))
+    MetadataCatalog.get(name).set(
+        thing_classes=list(class_names), dirname=dirname, year=year, split=split
+    )
+
 def register_pascal_voc_wsl_contain_w2f(name, dirname, split, year, class_names=CLASS_NAMES):
     DatasetCatalog.register(name, lambda: load_voc_instances_wsl_w2f(dirname, split, class_names))
     MetadataCatalog.get(name).set(