Release of PromptSRC with pretrained models.

datasets/imagenet.py

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+import os
+import pickle
+from collections import OrderedDict
+
+from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
+from dassl.utils import listdir_nohidden, mkdir_if_missing
+
+from .oxford_pets import OxfordPets
+
+
+@DATASET_REGISTRY.register()
+class ImageNet(DatasetBase):
+
+    dataset_dir = "imagenet"
+
+    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, "images")
+        self.preprocessed = os.path.join(self.dataset_dir, "preprocessed.pkl")
+        self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot")
+        mkdir_if_missing(self.split_fewshot_dir)
+
+        if os.path.exists(self.preprocessed):
+            with open(self.preprocessed, "rb") as f:
+                preprocessed = pickle.load(f)
+                train = preprocessed["train"]
+                test = preprocessed["test"]
+        else:
+            text_file = os.path.join(self.dataset_dir, "classnames.txt")
+            classnames = self.read_classnames(text_file)
+            train = self.read_data(classnames, "train")
+            # Follow standard practice to perform evaluation on the val set
+            # Also used as the val set (so evaluate the last-step model)
+            test = self.read_data(classnames, "val")
+
+            preprocessed = {"train": train, "test": test}
+            with open(self.preprocessed, "wb") as f:
+                pickle.dump(preprocessed, f, protocol=pickle.HIGHEST_PROTOCOL)
+
+        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 = data["train"]
+            else:
+                train = self.generate_fewshot_dataset(train, num_shots=num_shots)
+                data = {"train": train}
+                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, test = OxfordPets.subsample_classes(train, test, subsample=subsample)
+
+        super().__init__(train_x=train, val=test, test=test)
+
+    @staticmethod
+    def read_classnames(text_file):
+        """Return a dictionary containing
+        key-value pairs of <folder name>: <class name>.
+        """
+        classnames = OrderedDict()
+        with open(text_file, "r") as f:
+            lines = f.readlines()
+            for line in lines:
+                line = line.strip().split(" ")
+                folder = line[0]
+                classname = " ".join(line[1:])
+                classnames[folder] = classname
+        return classnames
+
+    def read_data(self, classnames, split_dir):
+        split_dir = os.path.join(self.image_dir, split_dir)
+        folders = sorted(f.name for f in os.scandir(split_dir) if f.is_dir())
+        items = []
+
+        for label, folder in enumerate(folders):
+            imnames = listdir_nohidden(os.path.join(split_dir, folder))
+            classname = classnames[folder]
+            for imname in imnames:
+                impath = os.path.join(split_dir, folder, imname)
+                item = Datum(impath=impath, label=label, classname=classname)
+                items.append(item)
+
+        return items