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from torchvision import datasets, transforms
from utils.toolkit import split_images_labels
from torchvision.datasets import VisionDataset
from torch.utils.data import Dataset, DataLoader, random_split
from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
from typing import Any
import os
from PIL import Image
import logging
import torch
class iData(object):
train_trsf = []
test_trsf = []
common_trsf = []
class_order = None
class iCIFAR10(iData):
use_path = False
train_trsf = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ColorJitter(brightness=63 / 255),
transforms.ToTensor(),
]
test_trsf = [transforms.ToTensor()]
common_trsf = [
transforms.Normalize(
mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.2010)
),
]
class_order = np.arange(10).tolist()
def download_data(self):
train_dataset = datasets.cifar.CIFAR10("./data", train=True, download=True)
test_dataset = datasets.cifar.CIFAR10("./data", train=False, download=True)
self.train_data, self.train_targets = train_dataset.data, np.array(
train_dataset.targets
)
self.test_data, self.test_targets = test_dataset.data, np.array(
test_dataset.targets
)
class iCIFAR100(iData):
use_path = False
train_trsf = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
transforms.ToTensor()
]
test_trsf = [transforms.ToTensor()]
common_trsf = [
transforms.Normalize(
mean=(0.5071, 0.4867, 0.4408), std=(0.2675, 0.2565, 0.2761)
),
]
class_order = np.arange(100).tolist()
def download_data(self):
train_dataset = datasets.cifar.CIFAR100("./data", train=True, download=True)
test_dataset = datasets.cifar.CIFAR100("./data", train=False, download=True)
'''print(type(train_dataset))
print(type(test_dataset))
print('type of each pair of data', type(train_dataset[0]))
print(train_dataset.data[0].shape)
print(train_dataset.targets[0])'''
self.train_data, self.train_targets = train_dataset.data, np.array(
train_dataset.targets
)
self.test_data, self.test_targets = test_dataset.data, np.array(
test_dataset.targets
)
class iImageNet1000(iData):
use_path = True
train_trsf = [
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
]
test_trsf = [
transforms.Resize(256),
transforms.CenterCrop(224),
]
common_trsf = [
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
class_order = np.arange(1000).tolist()
def download_data(self):
assert 0, "You should specify the folder of your dataset"
train_dir = "[DATA-PATH]/train/"
test_dir = "[DATA-PATH]/val/"
train_dset = datasets.ImageFolder(train_dir)
test_dset = datasets.ImageFolder(test_dir)
self.train_data, self.train_targets = split_images_labels(train_dset.imgs)
self.test_data, self.test_targets = split_images_labels(test_dset.imgs)
class iImageNet100(iData):
use_path = True
train_trsf = [
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
]
test_trsf = [
transforms.Resize(256),
transforms.CenterCrop(224),
]
common_trsf = [
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
class_order = np.arange(1000).tolist()
def download_data(self):
assert 0, "You should specify the folder of your dataset"
train_dir = "[DATA-PATH]/train/"
test_dir = "[DATA-PATH]/val/"
train_dset = datasets.ImageFolder(train_dir)
test_dset = datasets.ImageFolder(test_dir)
self.train_data, self.train_targets = split_images_labels(train_dset.imgs)
self.test_data, self.test_targets = split_images_labels(test_dset.imgs)
class iData(object):
train_trsf = []
test_trsf = []
common_trsf = []
class_order = None
def split_images_labels(imgs):
images = []
labels = []
for item in imgs:
images.append(item[0])
labels.append(item[1])
return np.array(images), np.array(labels)
class CustomImageFolder(datasets.ImageFolder):
def find_classes(self, directory: str) -> Tuple[List[str], Dict[str, int]]:
classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
if not classes:
raise FileNotFoundError(f"Couldn't find any class folder in {directory}.")
class_to_idx = dict()
for i, cls_name in enumerate(classes):
if int(cls_name) > 5:
class_to_idx[cls_name] = int(cls_name) - 1
else:
class_to_idx[cls_name] = int(cls_name)
return classes, class_to_idx
class GNSS(iData):
use_path = True
common_trsf = [
transforms.ToTensor()
]
class_order = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
def download_data(self):
train_dir = "./Dataset/GNSS/Train/"
test_dir = "./Dataset/GNSS/Test/"
train_dset = CustomImageFolder(train_dir)
test_dset = CustomImageFolder(test_dir)
self.train_data, self.train_targets = split_images_labels(train_dset.imgs)
self.test_data, self.test_targets = split_images_labels(test_dset.imgs)
if __name__ == "__main__":
obj = GNSS()
obj.download_data()
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