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import os
import pickle
import math
import random
from collections import defaultdict
from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
from dassl.utils import read_json, write_json, mkdir_if_missing
@DATASET_REGISTRY.register()
class OxfordPets(DatasetBase):
dataset_dir = "oxford_pets"
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.anno_dir = os.path.join(self.dataset_dir, "annotations")
self.split_path = os.path.join(self.dataset_dir, "split_zhou_OxfordPets.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 = self.read_split(self.split_path, self.image_dir)
else:
trainval = self.read_data(split_file="trainval.txt")
test = self.read_data(split_file="test.txt")
train, val = self.split_trainval(trainval)
self.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, _, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample)
super().__init__(train_x=train, val=test, test=test)
# if cfg.TRAINER.NAME == "SuPr":
self.all_classnames = OxfordPets.get_all_classnames(train, val, test)
def read_data(self, split_file):
filepath = os.path.join(self.anno_dir, split_file)
items = []
with open(filepath, "r") as f:
lines = f.readlines()
for line in lines:
line = line.strip()
imname, label, species, _ = line.split(" ")
breed = imname.split("_")[:-1]
breed = "_".join(breed)
breed = breed.lower()
imname += ".jpg"
impath = os.path.join(self.image_dir, imname)
label = int(label) - 1 # convert to 0-based index
item = Datum(impath=impath, label=label, classname=breed)
items.append(item)
return items
@staticmethod
def split_trainval(trainval, p_val=0.2):
p_trn = 1 - p_val
print(f"Splitting trainval into {p_trn:.0%} train and {p_val:.0%} val")
tracker = defaultdict(list)
for idx, item in enumerate(trainval):
label = item.label
tracker[label].append(idx)
train, val = [], []
for label, idxs in tracker.items():
n_val = round(len(idxs) * p_val)
assert n_val > 0
random.shuffle(idxs)
for n, idx in enumerate(idxs):
item = trainval[idx]
if n < n_val:
val.append(item)
else:
train.append(item)
return train, val
@staticmethod
def save_split(train, val, test, filepath, path_prefix):
def _extract(items):
out = []
for item in items:
impath = item.impath
label = item.label
classname = item.classname
impath = impath.replace(path_prefix, "")
if impath.startswith("/"):
impath = impath[1:]
out.append((impath, label, classname))
return out
train = _extract(train)
val = _extract(val)
test = _extract(test)
split = {"train": train, "val": val, "test": test}
write_json(split, filepath)
print(f"Saved split to {filepath}")
@staticmethod
def read_split(filepath, path_prefix):
def _convert(items):
out = []
for impath, label, classname in items:
impath = os.path.join(path_prefix, impath)
item = Datum(impath=impath, label=int(label), classname=classname)
out.append(item)
return out
print(f"Reading split from {filepath}")
split = read_json(filepath)
train = _convert(split["train"])
val = _convert(split["val"])
test = _convert(split["test"])
return train, val, test
@staticmethod
def subsample_classes(*args, subsample="all"):
"""Divide classes into two groups. The first group
represents base classes while the second group represents
new classes.
Args:
args: a list of datasets, e.g. train, val and test.
subsample (str): what classes to subsample.
"""
assert subsample in ["all", "base", "new"]
if subsample == "all":
return args
dataset = args[0]
labels = set()
for item in dataset:
labels.add(item.label)
labels = list(labels)
labels.sort()
n = len(labels)
# Divide classes into two halves
m = math.ceil(n / 2)
print(f"SUBSAMPLE {subsample.upper()} CLASSES!")
if subsample == "base":
selected = labels[:m] # take the first half
else:
selected = labels[m:] # take the second half
relabeler = {y: y_new for y_new, y in enumerate(selected)}
output = []
for dataset in args:
dataset_new = []
for item in dataset:
if item.label not in selected:
continue
item_new = Datum(
impath=item.impath,
label=relabeler[item.label],
classname=item.classname
)
dataset_new.append(item_new)
output.append(dataset_new)
return output
@staticmethod
def get_all_classnames(*args):
classnames = []
for dataset in args:
for item in dataset:
classnames.append(item.classname)
return list(set(classnames)) |