90 lines
3.7 KiB
Python
90 lines
3.7 KiB
Python
import os
|
|
import pickle
|
|
import random
|
|
from scipy.io import loadmat
|
|
from collections import defaultdict
|
|
|
|
from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
|
|
from dassl.utils import read_json, mkdir_if_missing
|
|
|
|
from .oxford_pets import OxfordPets
|
|
|
|
|
|
@DATASET_REGISTRY.register()
|
|
class OxfordFlowers(DatasetBase):
|
|
|
|
dataset_dir = "oxford_flowers"
|
|
|
|
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, "jpg")
|
|
self.label_file = os.path.join(self.dataset_dir, "imagelabels.mat")
|
|
self.lab2cname_file = os.path.join(self.dataset_dir, "cat_to_name.json")
|
|
self.split_path = os.path.join(self.dataset_dir, "split_zhou_OxfordFlowers.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:
|
|
train, val, test = self.read_data()
|
|
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):
|
|
tracker = defaultdict(list)
|
|
label_file = loadmat(self.label_file)["labels"][0]
|
|
for i, label in enumerate(label_file):
|
|
imname = f"image_{str(i + 1).zfill(5)}.jpg"
|
|
impath = os.path.join(self.image_dir, imname)
|
|
label = int(label)
|
|
tracker[label].append(impath)
|
|
|
|
print("Splitting data into 50% train, 20% val, and 30% test")
|
|
|
|
def _collate(ims, y, c):
|
|
items = []
|
|
for im in ims:
|
|
item = Datum(impath=im, label=y - 1, classname=c) # convert to 0-based label
|
|
items.append(item)
|
|
return items
|
|
|
|
lab2cname = read_json(self.lab2cname_file)
|
|
train, val, test = [], [], []
|
|
for label, impaths in tracker.items():
|
|
random.shuffle(impaths)
|
|
n_total = len(impaths)
|
|
n_train = round(n_total * 0.5)
|
|
n_val = round(n_total * 0.2)
|
|
n_test = n_total - n_train - n_val
|
|
assert n_train > 0 and n_val > 0 and n_test > 0
|
|
cname = lab2cname[str(label)]
|
|
train.extend(_collate(impaths[:n_train], label, cname))
|
|
val.extend(_collate(impaths[n_train : n_train + n_val], label, cname))
|
|
test.extend(_collate(impaths[n_train + n_val :], label, cname))
|
|
|
|
return train, val, test
|