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datasets/sun397.py

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+import os
+import pickle
+
+from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
+from dassl.utils import mkdir_if_missing
+
+from .oxford_pets import OxfordPets
+
+
+@DATASET_REGISTRY.register()
+class SUN397(DatasetBase):
+
+    dataset_dir = "sun397"
+
+    def __init__(self, cfg):
+        root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT))
+        self.dataset_dir = os.path.join(root, self.dataset_dir)
+        self.image_dir = os.path.join(self.dataset_dir, "SUN397")
+        self.split_path = os.path.join(self.dataset_dir, "split_zhou_SUN397.json")
+        self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot")
+        mkdir_if_missing(self.split_fewshot_dir)
+
+        if os.path.exists(self.split_path):
+            train, val, test = OxfordPets.read_split(self.split_path, self.image_dir)
+        else:
+            classnames = []
+            with open(os.path.join(self.dataset_dir, "ClassName.txt"), "r") as f:
+                lines = f.readlines()
+
+                for line in lines:
+                    line = line.strip()[1:]  # remove /
+                    classnames.append(line)
+            cname2lab = {c: i for i, c in enumerate(classnames)}
+            trainval = self.read_data(cname2lab, "Training_01.txt")
+            test = self.read_data(cname2lab, "Testing_01.txt")
+            train, val = OxfordPets.split_trainval(trainval)
+            OxfordPets.save_split(train, val, test, self.split_path, self.image_dir)
+
+        num_shots = cfg.DATASET.NUM_SHOTS
+        if num_shots >= 1:
+            seed = cfg.SEED
+            preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl")
+            
+            if os.path.exists(preprocessed):
+                print(f"Loading preprocessed few-shot data from {preprocessed}")
+                with open(preprocessed, "rb") as file:
+                    data = pickle.load(file)
+                    train, val = data["train"], data["val"]
+            else:
+                train = self.generate_fewshot_dataset(train, num_shots=num_shots)
+                val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4))
+                data = {"train": train, "val": val}
+                print(f"Saving preprocessed few-shot data to {preprocessed}")
+                with open(preprocessed, "wb") as file:
+                    pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL)
+
+        subsample = cfg.DATASET.SUBSAMPLE_CLASSES
+        train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample)
+
+        super().__init__(train_x=train, val=val, test=test)
+
+    def read_data(self, cname2lab, text_file):
+        text_file = os.path.join(self.dataset_dir, text_file)
+        items = []
+
+        with open(text_file, "r") as f:
+            lines = f.readlines()
+            for line in lines:
+                imname = line.strip()[1:]  # remove /
+                classname = os.path.dirname(imname)
+                label = cname2lab[classname]
+                impath = os.path.join(self.image_dir, imname)
+
+                names = classname.split("/")[1:]  # remove 1st letter
+                names = names[::-1]  # put words like indoor/outdoor at first
+                classname = " ".join(names)
+
+                item = Datum(impath=impath, label=label, classname=classname)
+                items.append(item)
+
+        return items