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import os
import torch.nn as nn
from continuum import ClassIncremental, InstanceIncremental
from continuum.datasets import (
CIFAR100, ImageNet100, TinyImageNet200, ImageFolderDataset, Core50,
fgvc_aircraft, Caltech101, DTD, EuroSAT, flowers102, food101,
MNIST, OxfordPet, SUN397
)
from .utils import get_dataset_class_names
class ImageNet1000(ImageFolderDataset):
"""Continuum dataset for datasetsss with tree-like structure.
:param train_folder: The folder of the train data.
:param test_folder: The folder of the test data.
:param download: Dummy parameter.
"""
def __init__(
self,
data_path: str,
train: bool = True,
download: bool = False,
):
super().__init__(data_path=data_path, train=train, download=download)
def get_data(self):
if self.train:
self.data_path = os.path.join(self.data_path, "train")
else:
self.data_path = os.path.join(self.data_path, "val")
return super().get_data()
def get_dataset(cfg, is_train, transforms=None):
if cfg.dataset == "cifar100":
data_path = os.path.join(cfg.dataset_root, cfg.dataset)
dataset = CIFAR100(
data_path=data_path,
download=True,
train=is_train,
# transforms=transforms
)
classes_names = dataset.dataset.classes
# elif cfg.dataset == "tiny-imagenet-200":
elif cfg.dataset == "tinyimagenet":
# data_path = '/data/kangborui/'
data_path = os.path.join(cfg.dataset_root, cfg.dataset)
dataset = TinyImageNet200(
data_path,
train=is_train,
download=True
)
classes_names = get_dataset_class_names(cfg.workdir, cfg.dataset)
elif cfg.dataset == "imagenet100":
data_path = cfg.dataset_root
# data_path = os.path.join(cfg.dataset_root, "ImageNet")
dataset = ImageNet100(
data_path,
train=is_train,
data_subset=os.path.join('/home/kangborui/ClProject/MoE-Adapters4CL-cross-guild-fusion/cil/dataset_reqs/imagenet100_splits', "train_100.txt" if is_train else "val_100.txt")
)
classes_names = get_dataset_class_names(cfg.workdir, cfg.dataset)
elif cfg.dataset == "imagenet1000":
data_path = os.path.join(cfg.dataset_root, cfg.dataset)
dataset = ImageNet1000(
data_path,
train=is_train
)
classes_names = get_dataset_class_names(cfg.workdir, cfg.dataset)
elif cfg.dataset == "core50":
data_path = os.path.join(cfg.dataset_root, cfg.dataset)
dataset = dataset = Core50(
data_path,
scenario="domains",
classification="category",
train=is_train
)
classes_names = [
"plug adapters", "mobile phones", "scissors", "light bulbs", "cans",
"glasses", "balls", "markers", "cups", "remote controls"
]
else:
ValueError(f"'{cfg.dataset}' is a invalid dataset.")
return dataset, classes_names
def build_cl_scenarios(cfg, is_train, transforms) -> nn.Module:
dataset, classes_names = get_dataset(cfg, is_train)
if cfg.scenario == "class":
scenario = ClassIncremental(
dataset,
initial_increment=cfg.initial_increment,
increment=cfg.increment,
transformations=transforms.transforms, # Convert Compose into list
class_order=cfg.class_order,
)
elif cfg.scenario == "domain":
scenario = InstanceIncremental(
dataset,
transformations=transforms.transforms,
)
elif cfg.scenario == "task-agnostic":
NotImplementedError("Method has not been implemented. Soon be added.")
else:
ValueError(f"You have entered `{cfg.scenario}` which is not a defined scenario, "
"please choose from {{'class', 'domain', 'task-agnostic'}}.")
return scenario, classes_names |