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dataset_utils.py
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dataset_utils.py
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'''
Author: ----
Date: 2022-04-07 20:48:43
LastEditors: GhMa
LastEditTime: 2022-10-02 19:29:44
'''
import torch
import torchvision
import torchvision.transforms as transforms
import pandas as pd
import os
import random
import numpy as np
from tqdm import tqdm
class Cutout(object):
r"""
Forked from https://github.com/uoguelph-mlrg/Cutout
Randomly mask out one or more patches from an image.
Args:
n_holes (int): Number of patches to cut out of each image.
length (int): The length (in pixels) of each square patch.
"""
def __init__(self, n_holes, length):
self.n_holes = n_holes
self.length = length
def __call__(self, img):
"""
Args:
img (Tensor): Tensor image of size (C, H, W).
Returns:
Tensor: Image with n_holes of dimension length x length cut out of it.
"""
h = img.size(1)
w = img.size(2)
mask = np.ones((h, w), np.float32)
for n in range(self.n_holes):
y = np.random.randint(h)
x = np.random.randint(w)
y1 = np.clip(y - self.length // 2, 0, h)
y2 = np.clip(y + self.length // 2, 0, h)
x1 = np.clip(x - self.length // 2, 0, w)
x2 = np.clip(x + self.length // 2, 0, w)
mask[y1: y2, x1: x2] = 0.
mask = torch.from_numpy(mask)
mask = mask.expand_as(img)
img = img * mask
return img
def prepare_cifar10(args, autoaugment=True):
print('==> Preparing CIFAR-10 data..')
if autoaugment:
print('>>> Auto Augmentation')
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.autoaugment.AutoAugment(
policy=transforms.autoaugment.AutoAugmentPolicy.CIFAR10
),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
else:
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
trainset = torchvision.datasets.CIFAR10(
root='/home/ghma/exd1/data/cifar10',
train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(
trainset, batch_size=args.minibatch, shuffle=True, num_workers=args.workers)
testset = torchvision.datasets.CIFAR10(
root='/home/ghma/exd1/data/cifar10',
train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(
testset, batch_size=args.minibatch, shuffle=False, num_workers=args.workers)
return trainloader, testloader
class DVSCIFAR10(torch.utils.data.Dataset):
r'''
Forked from https://github.com/Gus-Lab/temporal_efficient_training
'''
def __init__(
self,
root,
train=True, transform=False, target_transform=None
):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
self.train = train
def __getitem__(self, index):
r"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
data, target = torch.load(self.root + '/{}.pt'.format(index))
if self.transform:
flip = random.random() > 0.5
if flip:
data = torch.flip(data, dims=(3,))
off1 = random.randint(-5, 5)
off2 = random.randint(-5, 5)
data = torch.roll(data, shifts=(off1, off2), dims=(2, 3))
if self.target_transform is not None:
target = self.target_transform(target)
return data, target.long().squeeze(-1)
def __len__(self):
return len(os.listdir(self.root))
def prepare_dvs_cifar10(args):
print('==> Preparing DVS-CIFAR-10..')
root = '/home/ghma/exd1/data/dvs-cifar10'
train_path = os.path.join(root, 'train')
val_path = os.path.join(root, 'test')
trainset = DVSCIFAR10(root=train_path, transform=True)
testset = DVSCIFAR10(root=val_path)
trainloader = torch.utils.data.DataLoader(
trainset, batch_size=args.minibatch, shuffle=True, num_workers=args.workers)
testloader = torch.utils.data.DataLoader(
testset, batch_size=args.minibatch, shuffle=False, num_workers=args.workers)
print('data loaded')
return trainloader, testloader