76 lines
3.3 KiB
Python
76 lines
3.3 KiB
Python
import os
|
|
import pickle
|
|
from scipy.io import loadmat
|
|
|
|
from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
|
|
from dassl.utils import mkdir_if_missing
|
|
|
|
from .oxford_pets import OxfordPets
|
|
import numpy as np
|
|
|
|
@DATASET_REGISTRY.register()
|
|
class StanfordCars(DatasetBase):
|
|
|
|
dataset_dir = "stanford_cars"
|
|
|
|
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.split_path = os.path.join(self.dataset_dir, "split_zhou_StanfordCars.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.dataset_dir)
|
|
else:
|
|
trainval_file = os.path.join(self.dataset_dir, "devkit", "cars_train_annos.mat")
|
|
test_file = os.path.join(self.dataset_dir, "cars_test_annos_withlabels.mat")
|
|
meta_file = os.path.join(self.dataset_dir, "devkit", "cars_meta.mat")
|
|
trainval = self.read_data("cars_train", trainval_file, meta_file)
|
|
test = self.read_data("cars_test", test_file, meta_file)
|
|
train, val = OxfordPets.split_trainval(trainval)
|
|
OxfordPets.save_split(train, val, test, self.split_path, self.dataset_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, image_dir, anno_file, meta_file):
|
|
anno_file = loadmat(anno_file)["annotations"][0]
|
|
meta_file = loadmat(meta_file)["class_names"][0]
|
|
items = []
|
|
|
|
for i in range(len(anno_file)):
|
|
imname = anno_file[i]["fname"][0]
|
|
impath = os.path.join(self.dataset_dir, image_dir, imname)
|
|
label = anno_file[i]["class"][0, 0]
|
|
label = int(label) - 1 # convert to 0-based index
|
|
classname = meta_file[label][0]
|
|
names = classname.split(" ")
|
|
year = names.pop(-1)
|
|
names.insert(0, year)
|
|
classname = " ".join(names)
|
|
item = Datum(impath=impath, label=label, classname=classname)
|
|
items.append(item)
|
|
|
|
return items
|