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load_data.py
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import torch
from torchvision.datasets import CIFAR10, CIFAR100, ImageFolder
import os
import numpy as np
from torch_datasets.configs import get_transforms
from torch_datasets.color_mnist import ColoredMNIST
from torch_datasets.imagenet import get_imagenet_dataset
from torch_datasets.cifar20 import get_coarse_labels
from torch_datasets.cifar10 import CIFAR10v2
from wilds.datasets.rxrx1_dataset import RxRx1Dataset
from wilds.datasets.amazon_dataset import AmazonDataset
from wilds.datasets.camelyon17_dataset import Camelyon17Dataset
from wilds.datasets.civilcomments_dataset import CivilCommentsDataset
from wilds.datasets.iwildcam_dataset import IWildCamDataset
from wilds.datasets.wilds_dataset import WILDSSubset
def load_train_dataset(dsname, iid_path, n_val_samples, seed=1, pretrained=True):
transform = get_transforms(dsname, 'train', pretrained)
if dsname == "CIFAR-10":
dataset = CIFAR10(iid_path, train=True, transform=transform, download=True)
elif dsname == "CIFAR-100":
dataset = CIFAR100(iid_path, train=True, transform=transform, download=True)
elif dsname == 'ImageNet':
dataset = ImageFolder(f"{iid_path}/imagenetv1/train/", transform=transform)
elif dsname in ['Living-17', 'Nonliving-26', 'Entity-13', 'Entity-30']:
dataset = get_breeds_dataset(iid_path, dsname, 'same', split='train', transform=transform)
elif dsname == 'Camelyon17':
dataset = Camelyon17Dataset(download=True, root_dir=iid_path)
elif dsname == 'iWildCam':
dataset = IWildCamDataset(download=True, root_dir=iid_path)
elif dsname == 'FMoW':
dataset = FMoWDataset(download=True, root_dir=iid_path, use_ood_val=True)
elif dsname == 'RxRx1':
dataset = RxRx1Dataset(download=True, root_dir=iid_path)
elif dsname == 'Amazon':
dataset = AmazonDataset(download=True, root_dir=iid_path)
elif dsname == 'CivilComments':
dataset = CivilCommentsDataset(download=True, root_dir=iid_path)
elif dsname == 'ColoredMNIST':
dataset = ColoredMNIST(root=iid_path, split='train', transform=transform)
else:
raise ValueError('unknown dataset')
if dsname in ['ImageNet', 'Living-17', 'Nonliving-26', 'Entity-13', 'Entity-30', 'ColoredMNIST']:
train_set = dataset
elif dsname == 'Camelyon17':
train_set = dataset.get_subset('train', transform=transform)
elif dsname == 'iWildCam':
train_set = dataset.get_subset('train', transform=transform)
elif dsname == 'FMoW':
train_set = dataset.get_subset('train', transform=transform)
elif dsname == 'RxRx1':
train_set = dataset.get_subset('train', transform=get_transforms(dsname, 'train', pretrained))
elif dsname == 'Amazon':
train_set = dataset.get_subset('train', transform=transform)
elif dsname == 'CivilComments':
train_set = dataset.get_subset('train', transform=transform)
else:
assert n_val_samples > 0, 'no validation set'
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
n_samples = len(dataset.data)
index_permute = torch.randperm(n_samples)
dataset.data = [dataset.data[i] for i in index_permute]
dataset.targets = [dataset.targets[i] for i in index_permute]
train_size = n_samples - n_val_samples
train_inds = index_permute[:train_size]
train_set = torch.utils.data.Subset(dataset, train_inds)
print("train size:", len(train_set))
return train_set
def load_val_dataset(dsname, iid_path, n_val_samples, seed=1, pretrained=True):
transform = get_transforms(dsname, 'train', pretrained)
if dsname == 'ImageNet':
val_set = ImageFolder(f"{iid_path}/imagenetv1/val/", transform=transform)
elif dsname in ['Living-17', 'Nonliving-26', 'Entity-13', 'Entity-30']:
val_set = get_breeds_dataset(iid_path, dsname, 'same', split='val', transform=transform)
elif dsname == 'Camelyon17':
dataset = Camelyon17Dataset(download=True, root_dir=iid_path)
val_set = dataset.get_subset('id_val', transform=transform)
elif dsname == 'iWildCam':
dataset = IWildCamDataset(download=True, root_dir=iid_path)
val_set = dataset.get_subset('id_val', transform=transform)
elif dsname == 'FMoW':
dataset = FMoWDataset(download=True, root_dir=iid_path, use_ood_val=True)
val_set = dataset.get_subset('id_val', transform=transform)
elif dsname == 'RxRx1':
dataset = RxRx1Dataset(download=True, root_dir=iid_path)
val_set = dataset.get_subset('id_test', transform=get_transforms(dsname, 'val', pretrained))
elif dsname == 'Amazon':
dataset = AmazonDataset(download=True, root_dir=iid_path)
val_set = dataset.get_subset('id_val', transform=transform)
elif dsname == 'CivilComments':
dataset = CivilCommentsDataset(download=True, root_dir=iid_path)
val_set = dataset.get_subset('val', transform=transform)
elif dsname == 'ColoredMNIST':
val_set = ColoredMNIST(root=iid_path, split='validate', transform=transform)
else:
if dsname == "CIFAR-10":
dataset = CIFAR10(iid_path, train=True, transform=transform, download=True)
elif dsname == "CIFAR-100":
dataset = CIFAR100(iid_path, train=True, transform=transform, download=True)
assert n_val_samples > 0, 'no validation set'
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
n_samples = len(dataset.data)
index_permute = torch.randperm(n_samples)
dataset.data = [dataset.data[i] for i in index_permute]
dataset.targets = [dataset.targets[i] for i in index_permute]
val_inds = index_permute[n_samples - n_val_samples:]
val_set = torch.utils.data.Subset(dataset, val_inds)
print("valid size:", len(val_set))
return val_set
def load_test_dataset(dsname, iid_path, subpopulation, corr_path, corr, corr_sev, n_test_sample, seed=1, pretrained=True):
transform = get_transforms(dsname, 'test', pretrained)
if dsname == "CIFAR-10":
dataset = CIFAR10(iid_path, train=False, transform=transform, download=True)
elif dsname == "CIFAR-100":
dataset = CIFAR100(iid_path, train=False, transform=transform, download=True)
elif dsname == 'ColoredMNIST':
dataset = ColoredMNIST(root=iid_path, split='test', transform=transform)
elif dsname == 'ImageNet':
dataset = ImageFolder(f"{iid_path}/imagenetv1/test/", transform=transform)
elif dsname in ['Living-17', 'Nonliving-26', 'Entity-13', 'Entity-30']:
dataset = get_breeds_dataset(iid_path, dsname, subpopulation, split='test', transform=transform)
elif dsname == 'Camelyon17':
dataset = Camelyon17Dataset(download=True, root_dir=iid_path)
elif dsname == 'FMoW':
dataset = FMoWDataset(download=True, root_dir=iid_path, use_ood_val=True)
elif dsname == 'iWildCam':
dataset = IWildCamDataset(download=True, root_dir=iid_path)
elif dsname == 'RxRx1':
dataset = RxRx1Dataset(download=True, root_dir=iid_path)
elif dsname == 'Amazon':
dataset = AmazonDataset(download=True, root_dir=iid_path)
elif dsname == 'CivilComments':
dataset = CivilCommentsDataset(download=True, root_dir=iid_path)
else:
raise ValueError('unknown dataset')
if corr != 'clean':
if dsname == 'CIFAR-10':
if corr == 'collection':
dataset = CIFAR10v2(root="./data/CIFAR-10-V2", train=True, download=True, transform=transform)
else:
path_images = os.path.join(corr_path, corr + '.npy')
path_labels = os.path.join(corr_path, 'labels.npy')
dataset.data = np.load(path_images)[(corr_sev - 1) * 10000:corr_sev * 10000]
dataset.targets = list(np.load(path_labels)[(corr_sev - 1) * 10000:corr_sev * 10000])
dataset.targets = [int(item) for item in dataset.targets]
elif dsname == 'CIFAR-100':
path_images = os.path.join(corr_path, corr + '.npy')
path_labels = os.path.join(corr_path, 'labels.npy')
dataset.data = np.load(path_images)[(corr_sev - 1) * 10000:corr_sev * 10000]
dataset.targets = list(np.load(path_labels)[(corr_sev - 1) * 10000:corr_sev * 10000])
dataset.targets = [int(item) for item in dataset.targets]
elif dsname == 'CIFAR-20':
path_images = os.path.join(corr_path, corr + '.npy')
path_labels = os.path.join(corr_path, 'labels.npy')
dataset.data = np.load(path_images)[(corr_sev - 1) * 10000:corr_sev * 10000]
dataset.targets = list(get_coarse_labels(np.load(path_labels))[(corr_sev - 1) * 10000:corr_sev * 10000])
dataset.targets = [int(item) for item in dataset.targets]
elif dsname == 'Tiny-ImageNet':
dataset = TinyImageNetCorrupted(corr_path, corr, corr_sev, transform=transform)
elif dsname == 'ImageNet':
dataset = get_imagenet_dataset(iid_path, subpopulation, transform, corr, corr_sev)
elif dsname in ['Living-17', 'Nonliving-26', 'Entity-13', 'Entity-30']:
dataset = get_breeds_dataset(
iid_path, dsname, subpopulation, split='test', transform=transform, corr=corr, corr_sev=corr_sev
)
elif dsname == 'FMoW':
full_dataset = FMoWDataset(download=True, root_dir=iid_path, use_ood_val=True)
assert corr in ['13-16', '16-18']
if corr == '13-16':
full_testset = full_dataset.get_subset('val', transform=transform)
elif corr == '16-18':
full_testset = full_dataset.get_subset('test', transform=transform)
if corr_sev == 0:
dataset = full_testset
else:
groups = full_dataset._eval_groupers['region'].metadata_to_group(full_testset.metadata_array)
ind = np.where( groups == (corr_sev - 1) )[0]
dataset = WILDSSubset(full_testset, ind, None)
elif dsname == 'RxRx1':
full_dataset = RxRx1Dataset(download=True, root_dir=iid_path)
assert corr in ['batch-1', 'batch-2']
if corr == 'batch-1':
dataset = full_dataset.get_subset('val', transform=transform)
elif corr == 'batch-2':
dataset = full_dataset.get_subset('test', transform=transform)
elif dsname == 'Amazon':
full_dataset = AmazonDataset(download=True, root_dir=iid_path)
assert corr in ['group-1', 'group-2']
if corr == 'group-1':
dataset = full_dataset.get_subset('val', transform=transform)
elif corr == 'group-2':
dataset = full_dataset.get_subset('test', transform=transform)
elif dsname == 'CivilComments':
full_dataset = CivilCommentsDataset(download=True, root_dir=iid_path)
assert corr_sev in [i for i in range(8)]
if corr_sev == 0:
dataset = full_dataset.get_subset('test', transform=transform)
else:
testset = full_dataset.get_subset('test', transform=transform)
groups = full_dataset._eval_groupers[corr_sev - 1].metadata_to_group(testset.metadata_array)
idx = np.concatenate((np.where(groups==1)[0], np.where(groups==3)[0]))
dataset = WILDSSubset(testset, idx, transform=None)
elif dsname == 'Camelyon17':
if corr == 'hospital-1':
dataset = dataset.get_subset('val', transform=transform)
elif corr == 'hospital-2':
dataset = dataset.get_subset('test', transform = transform)
else:
raise ValueError('unknown corruption')
return dataset