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167 lines (141 loc) · 7.9 KB
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Author : AI Partner
# @Email : ai.partner.cool@outlook.com
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
####### ---- CIFAR10 ---- #######
def Trainloader_cifar10(batch_size, train_dir, train_size) :
## train transform
if train_size == 32 :
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)), ## train set image statistic
])
else :
## for imagenet pretrained model: imagenet mean + std
transform_train = torchvision.transforms.Compose([
transforms.RandomResizedCrop(train_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
trainset = torchvision.datasets.CIFAR10(root=train_dir, train=True, download=True, transform=transform_train)
trainloader = DataLoader(
trainset,
batch_size=batch_size,
shuffle=True,
num_workers=2,
drop_last = True
) ## workers can surely be optimized...
return trainloader
def Testloader_cifar10(batch_size, test_dir, test_size) :
## test transform
if test_size == 32 :
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
else :
## for imagenet pretrained model: imagenet mean + std
transform_test = torchvision.transforms.Compose([
transforms.Resize(test_size),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
testset = torchvision.datasets.CIFAR10(root=test_dir, train=False, download=True, transform=transform_test)
testloader = DataLoader(
testset,
batch_size=batch_size,
shuffle=False,
num_workers=2,
drop_last = False
)
return testloader
####### ---- CIFAR100 ---- #######
def Trainloader_cifar100(batch_size, train_dir, train_size) :
## train transform
if train_size == 32 :
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)), ## train set image statistic
])
else :
## for imagenet pretrained model: imagenet mean + std
transform_train = torchvision.transforms.Compose([
transforms.RandomResizedCrop(train_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
trainset = torchvision.datasets.CIFAR100(root=train_dir, train=True, download=True, transform=transform_train)
trainloader = DataLoader(
trainset,
batch_size=batch_size,
shuffle=True,
num_workers=2,
drop_last = True
) ## workers can surely be optimized...
return trainloader
def Testloader_cifar100(batch_size, test_dir, test_size) :
## test transform
if test_size == 32:
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)), ## train set image statistic
])
else :
## for imagenet pretrained model: imagenet mean + std
transform_test = torchvision.transforms.Compose([
transforms.Resize(test_size),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
testset = torchvision.datasets.CIFAR100(root=test_dir, train=False, download=True, transform=transform_test)
testloader = DataLoader(
testset,
batch_size=batch_size,
shuffle=False,
num_workers=2,
drop_last = False
)
return testloader
####### ---- StanfordCars ---- #######
def Trainloader_ImageFolder(batch_size, train_dir, train_size) :
## imagenet mean + std
transform_train = torchvision.transforms.Compose([
transforms.RandomResizedCrop(train_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
trainloader = DataLoader(
ImageFolder(train_dir, transform_train),
batch_size=batch_size,
shuffle=True,
num_workers=2,
drop_last=True
)
return trainloader
def Testloader_ImageFolder(batch_size, test_dir, test_size) :
## imagenet mean + std
transform_test = torchvision.transforms.Compose([
transforms.Resize(int(test_size * 1.14)),
transforms.CenterCrop(test_size),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
testloader = DataLoader(
ImageFolder(test_dir, transform_test),
batch_size=batch_size,
shuffle=False,
num_workers=2,
drop_last=False
)
return testloader