mirror of
https://github.com/AntoineHX/smart_augmentation.git
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160 lines
No EOL
7.1 KiB
Python
Executable file
160 lines
No EOL
7.1 KiB
Python
Executable file
""" Dataset definition.
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MNIST / CIFAR10
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"""
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import torch
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from torch.utils.data.dataset import ConcatDataset
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import torchvision
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#Train/Validation batch size.
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BATCH_SIZE = 300
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#Test batch size.
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TEST_SIZE = BATCH_SIZE
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#TEST_SIZE = 10000 #legerement +Rapide / + Consomation memoire !
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#Wether to download data.
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download_data=False
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#Number of worker to use.
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num_workers=2 #4
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#Pin GPU memory
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pin_memory=False #True :+ GPU memory / + Lent
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#Data storage folder
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dataroot="../data"
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#ATTENTION : Dataug (Kornia) Expect image in the range of [0, 1]
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#transform_train = torchvision.transforms.Compose([
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# torchvision.transforms.RandomHorizontalFlip(),
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# torchvision.transforms.ToTensor(),
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# torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
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#])
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transform = torchvision.transforms.Compose([
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torchvision.transforms.ToTensor(),
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# torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
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])
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transform_train = torchvision.transforms.Compose([
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#transforms.RandomHorizontalFlip(),
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#transforms.RandomVerticalFlip(),
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torchvision.transforms.ToTensor(),
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])
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#from RandAugment import RandAugment
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# Add RandAugment with N, M(hyperparameter)
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#transform_train.transforms.insert(0, RandAugment(n=2, m=30))
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### Classic Dataset ###
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#MNIST
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#data_train = torchvision.datasets.MNIST(dataroot, train=True, download=True, transform=transform_train)
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#data_val = torchvision.datasets.MNIST(dataroot, train=True, download=True, transform=transform)
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#data_test = torchvision.datasets.MNIST(dataroot, train=False, download=True, transform=transform)
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#CIFAR
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data_train = torchvision.datasets.CIFAR10(dataroot, train=True, download=download_data, transform=transform_train)
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#data_val = torchvision.datasets.CIFAR10(dataroot, train=True, download=download_data, transform=transform)
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data_test = torchvision.datasets.CIFAR10(dataroot, train=False, download=download_data, transform=transform)
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#data_train = torchvision.datasets.CIFAR100(dataroot, train=True, download=download_data, transform=transform_train)
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#data_val = torchvision.datasets.CIFAR100(dataroot, train=True, download=download_data, transform=transform)
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#data_test = torchvision.datasets.CIFAR100(dataroot, train=False, download=download_data, transform=transform)
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#SVHN
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#trainset = torchvision.datasets.SVHN(root=dataroot, split='train', download=download_data, transform=transform_train)
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#extraset = torchvision.datasets.SVHN(root=dataroot, split='extra', download=download_data, transform=transform_train)
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#data_train = ConcatDataset([trainset, extraset])
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#data_test = torchvision.datasets.SVHN(dataroot, split='test', download=download_data, transform=transform)
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#ImageNet
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#Necessite SciPy
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# Probleme ? : https://github.com/ildoonet/pytorch-randaugment/blob/48b8f509c4bbda93bbe733d98b3fd052b6e4c8ae/RandAugment/imagenet.py#L28
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#data_train = torchvision.datasets.ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'), split='train', transform=transform_train)
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#data_test = torchvision.datasets.ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'), split='val', transform=transform_test)
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#Validation set size [0, 1]
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valid_size=0.1
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#train_subset_indices=range(int(len(data_train)*(1-valid_size)))
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#val_subset_indices=range(int(len(data_train)*(1-valid_size)),len(data_train))
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#train_subset_indices=range(BATCH_SIZE*10)
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#val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)
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#from torch.utils.data import SubsetRandomSampler
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#dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
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#dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
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dl_test = torch.utils.data.DataLoader(data_test, batch_size=TEST_SIZE, shuffle=False, num_workers=num_workers, pin_memory=pin_memory)
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#Cross Validation
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'''
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from skorch.dataset import CVSplit
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import numpy as np
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cvs = CVSplit(cv=valid_size, stratified=True) #Stratified =True for unbalanced dataset #ShuffleSplit
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def next_CVSplit():
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train_subset, val_subset = cvs(data_train, y=np.array(data_train.targets))
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dl_train = torch.utils.data.DataLoader(train_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
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dl_val = torch.utils.data.DataLoader(val_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
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return dl_train, dl_val
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dl_train, dl_val = next_CVSplit()
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'''
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import numpy as np
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from sklearn.model_selection import ShuffleSplit
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from sklearn.model_selection import StratifiedShuffleSplit
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class CVSplit(object):
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"""Class that perform train/valid split on a dataset.
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Inspired from : https://skorch.readthedocs.io/en/latest/user/dataset.html
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Attributes:
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_stratified (bool): Wether the split should be stratified. Recommended to be True for unbalanced dataset.
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_val_size (float, int): If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the validation split.
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If int, represents the absolute number of validation samples.
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_data (Dataset): Dataset to split.
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_targets (np.array): Targets of the dataset used if _stratified is set to True.
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_cv (BaseShuffleSplit) : Scikit learn object used to split.
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"""
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def __init__(self, data, val_size=0.1, stratified=True):
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""" Intialize CVSplit.
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Args:
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data (Dataset): Dataset to split.
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val_size (float, int): If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the validation split.
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If int, represents the absolute number of validation samples. (Default: 0.1)
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stratified (bool): Wether the split should be stratified. Recommended to be True for unbalanced dataset.
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"""
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self._stratified=stratified
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self._val_size=val_size
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self._data=data
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if self._stratified:
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cv_cls = StratifiedShuffleSplit
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self._targets= np.array(data_train.targets)
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else:
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cv_cls = ShuffleSplit
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self._cv= cv_cls(test_size=val_size, random_state=0)
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def next_split(self):
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""" Get next cross-validation split.
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Returns:
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Train DataLoader, Validation DataLoader
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"""
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args=(np.arange(len(self._data)),)
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if self._stratified:
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args = args + (self._targets,)
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idx_train, idx_valid = next(iter(self._cv.split(*args)))
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train_subset = torch.utils.data.Subset(self._data, idx_train)
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val_subset = torch.utils.data.Subset(self._data, idx_valid)
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dl_train = torch.utils.data.DataLoader(train_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
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dl_val = torch.utils.data.DataLoader(val_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
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return dl_train, dl_val
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cvs = CVSplit(data_train, val_size=valid_size)
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dl_train, dl_val = cvs.next_split() |