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dataloader.py
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from __future__ import print_function
import os
import argparse
import socket
import time
import sys
from tqdm import tqdm
import torch
import torch.optim as optim
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
import torch.nn.functional as F
from dataset.mini_imagenet import ImageNet, MetaImageNet
from dataset.tiered_imagenet import TieredImageNet, MetaTieredImageNet
from dataset.cifar import CIFAR100, MetaCIFAR100, CIFAR100_toy
from dataset.transform_cfg import transforms_options, transforms_test_options, transforms_list
from dataset.dataset_selfsupervision import SSDatasetWrapper
import numpy as np
def get_dataloaders(opt):
# dataloader
train_partition = 'trainval' if opt.use_trainval else 'train'
if opt.dataset == 'toy':
train_trans, test_trans = transforms_options['D']
train_loader = DataLoader(CIFAR100_toy(args=opt, partition=train_partition, transform=train_trans),
batch_size=opt.batch_size, shuffle=True, drop_last=True,
num_workers=opt.num_workers)
val_loader = DataLoader(CIFAR100_toy(args=opt, partition='train', transform=test_trans),
batch_size=opt.batch_size // 2, shuffle=False, drop_last=False,
num_workers=opt.num_workers // 2)
# train_trans, test_trans = transforms_test_options[opt.transform]
# meta_testloader = DataLoader(MetaCIFAR100(args=opt, partition='test',
# train_transform=train_trans,
# test_transform=test_trans),
# batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
# num_workers=opt.num_workers)
# meta_valloader = DataLoader(MetaCIFAR100(args=opt, partition='val',
# train_transform=train_trans,
# test_transform=test_trans),
# batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
# num_workers=opt.num_workers)
n_cls = 5
return train_loader, val_loader, 5, 5, n_cls
if opt.dataset == 'miniImageNet':
train_trans, test_trans = transforms_options[opt.transform]
train_loader = DataLoader(ImageNet(args=opt, partition=train_partition, transform=train_trans),
batch_size=opt.batch_size, shuffle=True, drop_last=True,
num_workers=opt.num_workers)
val_loader = DataLoader(ImageNet(args=opt, partition='val', transform=test_trans),
batch_size=opt.batch_size // 2, shuffle=False, drop_last=False,
num_workers=opt.num_workers // 2)
train_trans, test_trans = transforms_test_options[opt.transform]
meta_testloader = DataLoader(MetaImageNet(args=opt, partition='test',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
meta_valloader = DataLoader(MetaImageNet(args=opt, partition='val',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
if opt.use_trainval:
n_cls = 80
else:
n_cls = 64
elif opt.dataset == 'tieredImageNet':
train_trans, test_trans = transforms_options[opt.transform]
train_loader = DataLoader(TieredImageNet(args=opt, partition=train_partition, transform=train_trans),
batch_size=opt.batch_size, shuffle=True, drop_last=True,
num_workers=opt.num_workers)
val_loader = DataLoader(TieredImageNet(args=opt, partition='train_phase_val', transform=test_trans),
batch_size=opt.batch_size // 2, shuffle=False, drop_last=False,
num_workers=opt.num_workers // 2)
train_trans, test_trans = transforms_test_options[opt.transform]
meta_testloader = DataLoader(MetaTieredImageNet(args=opt, partition='test',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
meta_valloader = DataLoader(MetaTieredImageNet(args=opt, partition='val',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
if opt.use_trainval:
n_cls = 448
else:
n_cls = 351
elif opt.dataset == 'CIFAR-FS' or opt.dataset == 'FC100':
train_trans, test_trans = transforms_options['D']
train_loader = DataLoader(CIFAR100(args=opt, partition=train_partition, transform=train_trans),
batch_size=opt.batch_size, shuffle=True, drop_last=True,
num_workers=opt.num_workers)
val_loader = DataLoader(CIFAR100(args=opt, partition='train', transform=test_trans),
batch_size=opt.batch_size // 2, shuffle=False, drop_last=False,
num_workers=opt.num_workers // 2)
train_trans, test_trans = transforms_test_options[opt.transform]
# ns = [opt.n_shots].copy()
# opt.n_ways = 32
# opt.n_shots = 5
# opt.n_aug_support_samples = 2
meta_trainloader = DataLoader(MetaCIFAR100(args=opt, partition='train',
train_transform=train_trans,
test_transform=test_trans),
batch_size=1, shuffle=True, drop_last=False,
num_workers=opt.num_workers)
# opt.n_ways = 5
# opt.n_shots = ns[0]
# print(opt.n_shots)
# opt.n_aug_support_samples = 5
meta_testloader = DataLoader(MetaCIFAR100(args=opt, partition='test',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
meta_valloader = DataLoader(MetaCIFAR100(args=opt, partition='val',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
if opt.use_trainval:
n_cls = 80
else:
if opt.dataset == 'CIFAR-FS':
n_cls = 64
elif opt.dataset == 'FC100':
n_cls = 60
else:
raise NotImplementedError('dataset not supported: {}'.format(opt.dataset))
# return train_loader, val_loader, meta_trainloader, meta_testloader, meta_valloader, n_cls
else:
raise NotImplementedError(opt.dataset)
return train_loader, val_loader, meta_testloader, meta_valloader, n_cls