Source code for datasets.seq_cifar100_224



import logging
from typing import Tuple

import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
from torchvision.transforms.functional import InterpolationMode
from torchvision.datasets import CIFAR100


from datasets.seq_cifar100 import TCIFAR100, MyCIFAR100
from datasets.transforms.denormalization import DeNormalize
from datasets.utils.continual_dataset import (ContinualDataset, fix_class_names_order,
                                              store_masked_loaders)
from utils.conf import base_path
from datasets.utils import set_default_from_args
from utils.prompt_templates import templates


[docs] class SequentialCIFAR100224(ContinualDataset): """ The Sequential CIFAR100 dataset with 224x224 resolution with ViT-B/16. Args: NAME (str): name of the dataset. SETTING (str): setting of the dataset. N_CLASSES_PER_TASK (int): number of classes per task. N_TASKS (int): number of tasks. N_CLASSES (int): number of classes. SIZE (tuple): size of the images. MEAN (tuple): mean of the dataset. STD (tuple): standard deviation of the dataset. TRANSFORM (torchvision.transforms): transformation to apply to the data. TEST_TRANSFORM (torchvision.transforms): transformation to apply to the test data. """ NAME = 'seq-cifar100-224' SETTING = 'class-il' N_CLASSES_PER_TASK = 10 N_TASKS = 10 N_CLASSES = 100 SIZE = (224, 224) MEAN, STD = (0.485, 0.456, 0.406), (0.229, 0.224, 0.225) TRANSFORM = transforms.Compose([ transforms.RandomResizedCrop(224, interpolation=InterpolationMode.BICUBIC), transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor(), transforms.Normalize(MEAN, STD) ]) TEST_TRANSFORM = transforms.Compose([ transforms.Resize(224, interpolation=InterpolationMode.BICUBIC), transforms.ToTensor(), transforms.Normalize(MEAN, STD) ]) def __init__(self, args, transform_type: str = 'weak'): super().__init__(args) assert transform_type in ['weak', 'strong'], "Transform type must be either 'weak' or 'strong'." if transform_type == 'strong': logging.info("Using strong augmentation for CIFAR100-224") self.TRANSFORM = transforms.Compose( [transforms.RandomResizedCrop(224, interpolation=InterpolationMode.BICUBIC), transforms.RandomHorizontalFlip(p=0.5), transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1), transforms.RandomRotation(15), transforms.ToTensor(), transforms.Normalize(SequentialCIFAR100224.MEAN, SequentialCIFAR100224.STD)] )
[docs] def get_data_loaders(self) -> Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader]: transform = self.TRANSFORM test_transform = self.TEST_TRANSFORM train_dataset = MyCIFAR100(base_path() + 'CIFAR100', train=True, download=True, transform=transform) test_dataset = TCIFAR100(base_path() + 'CIFAR100', train=False, download=True, transform=test_transform) train, test = store_masked_loaders(train_dataset, test_dataset, self) return train, test
[docs] @staticmethod def get_transform(): transform = transforms.Compose( [transforms.ToPILImage(), SequentialCIFAR100224.TRANSFORM]) return transform
[docs] @set_default_from_args("backbone") def get_backbone(): return "vit"
[docs] @staticmethod def get_loss(): return F.cross_entropy
[docs] @staticmethod def get_normalization_transform(): transform = transforms.Normalize(SequentialCIFAR100224.MEAN, SequentialCIFAR100224.STD) return transform
[docs] @staticmethod def get_denormalization_transform(): transform = DeNormalize(SequentialCIFAR100224.MEAN, SequentialCIFAR100224.STD) return transform
[docs] @set_default_from_args('n_epochs') def get_epochs(self): return 20
[docs] @set_default_from_args('batch_size') def get_batch_size(self): return 128
[docs] def get_class_names(self): if self.class_names is not None: return self.class_names classes = CIFAR100(base_path() + 'CIFAR100', train=True, download=True).classes classes = fix_class_names_order(classes, self.args) self.class_names = classes return self.class_names
[docs] @staticmethod def get_prompt_templates(): return templates['cifar100']