smart_augmentation/higher/datasets.py

import torch
from torch.utils.data import SubsetRandomSampler
import torchvision

BATCH_SIZE = 300
#TEST_SIZE = 300
TEST_SIZE = 10000

#ATTENTION : Dataug (Kornia) Expect image in the range of [0, 1]
#transform_train = torchvision.transforms.Compose([
#    torchvision.transforms.RandomHorizontalFlip(),
#    torchvision.transforms.ToTensor(),
    #torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
#])
transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor(),
    #torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
])
'''
data_train = torchvision.datasets.MNIST(
    "./data", train=True, download=True, 
    transform=torchvision.transforms.Compose([
            #torchvision.transforms.RandomAffine(degrees=180, translate=None, scale=None, shear=None, resample=False, fillcolor=0),
            torchvision.transforms.ToTensor()
        ])
)
data_test = torchvision.datasets.MNIST(
    "./data", train=False, download=True, transform=torchvision.transforms.ToTensor()
)
'''

from torchvision.datasets.vision import VisionDataset
from PIL import Image
import augmentation_transforms
import numpy as np

class AugmentedDataset(VisionDataset):
    def __init__(self, root, train=True, transform=None, target_transform=None, download=False):

        super(AugmentedDataset, self).__init__(root, transform=transform, target_transform=target_transform)

        supervised_dataset = torchvision.datasets.CIFAR10(root, train=train, download=download, transform=transform)

        self.sup_data = supervised_dataset.data
        self.sup_targets = supervised_dataset.targets

        for idx, img in enumerate(self.sup_data):
            self.sup_data[idx]= Image.fromarray(img) #to PIL Image

        self.unsup_data=[]
        self.unsup_targets=[]

        self.data= self.sup_data
        self.targets= self.sup_targets


        self._TF = [
        'Invert', 'Cutout', 'Sharpness', 'AutoContrast', 'Posterize',
        'ShearX', 'TranslateX', 'TranslateY', 'ShearY', 'Rotate',
        'Equalize', 'Contrast', 'Color', 'Solarize', 'Brightness']
        self._op_list =[]
        self.prob=0.5
        for tf in self._TF:
            for mag in range(1, 10):
                self._op_list+=[(tf, self.prob, mag)]
        self._nb_op = len(self._op_list)

    def __getitem__(self, index):
        """
        Args:
            index (int): Index

        Returns:
            tuple: (image, target) where target is index of the target class.
        """
        img, target = self.data[index], self.targets[index]

        # doing this so that it is consistent with all other datasets
        # to return a PIL Image
        #img = Image.fromarray(img)

        if self.transform is not None:
            img = self.transform(img)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target

    def augement_data(self, aug_copy=1):

        policies = []
        for op_1 in self._op_list:
            for op_2 in self._op_list:
                policies += [[op_1, op_2]]

        for idx, image in enumerate(self.sup_data):
            for _ in range(aug_copy):
                chosen_policy = policies[np.random.choice(len(policies))]
                aug_image = augmentation_transforms.apply_policy(chosen_policy, image)
                #aug_image = augmentation_transforms.cutout_numpy(aug_image)

                self.unsup_data+=[aug_image]
                self.unsup_targets+=[self.sup_targets[idx]]

        print(type(self.data), type(self.sup_data), type(self.unsup_data))
        print(len(self.data), len(self.sup_data), len(self.unsup_data))
        #self.data= self.sup_data+self.unsup_data
        self.data= np.concatenate((self.sup_data, self.unsup_data), axis=0)
        print(len(self.data))
        self.targets= self.sup_targets+self.unsup_targets


    def len_supervised(self):
        return len(self.sup_data)

    def len_unsupervised(self):
        return len(self.unsup_data)

    def __len__(self):
        return len(self.data)


data_train = torchvision.datasets.CIFAR10("./data", train=True, download=True, transform=transform)
#print(len(data_train))
#data_train = AugmentedDataset("./data", train=True, download=True, transform=transform)
#print(len(data_train), data_train.len_supervised(), data_train.len_unsupervised())
#data_train.augement_data()
#print(len(data_train), data_train.len_supervised(), data_train.len_unsupervised())
#data_val = torchvision.datasets.CIFAR10(
#    "./data", train=True, download=True, transform=transform
#)
data_test = torchvision.datasets.CIFAR10(
    "./data", train=False, download=True, transform=transform
)
#'''
train_subset_indices=range(int(len(data_train)/2))
val_subset_indices=range(int(len(data_train)/2),len(data_train))
#train_subset_indices=range(BATCH_SIZE*10)
#val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)

dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))
dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices))
dl_test = torch.utils.data.DataLoader(data_test, batch_size=TEST_SIZE, shuffle=False)