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miniimagenetDataLoader.py
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import os
import cv2
import numpy as np
import torch.utils.data
from tqdm import tqdm
import math
import psutil
import gc
from torch.autograd import Variable
import torchvision.transforms as transforms
from option import Options
from dataset.mini_imagenet import MiniImagenet
from dataset.mini_imagenet import MiniImagenetPairs
from dataset.mini_imagenet import MiniImagenetOneShot
class miniImagenetDataLoader():
def __init__(self,type=MiniImagenet, fcn = None, opt=Options().parse()):
self.type = type
self.opt = opt
self.fcn = fcn
self.train_mean = np.array([120.45/255.0,115.74/255.0,104.65/255.0]) # RGB
self.train_std = np.array([127.5/255.0,127.5/255.0,127.5/255.0])
def getlstTransforms(self, train='train'):
lst_transforms = []
if not(self.opt.imageSize is None):
lst_transforms.append(transforms.Resize((self.opt.imageSize,self.opt.imageSize)))
if train == 'train':
lst_transforms.append(transforms.ColorJitter(brightness=0.4,contrast=0.4,saturation=0.4))
lst_transforms.append(transforms.RandomAffine(degrees=(0,10), translate=(0.1, 0.1), scale=(0.8, 1.2)))
lst_transforms.append(transforms.RandomVerticalFlip())
lst_transforms.append(transforms.RandomHorizontalFlip())
lst_transforms.append(transforms.ToTensor())
#lst_transforms.append(transforms.Lambda(lambda x: (x.float()-torch.from_numpy(self.train_mean).float())/torch.from_numpy(self.train_std).float()))
lst_transforms.append(transforms.Normalize(self.train_mean,self.train_std))
if self.opt.fcn_applyOnDataLoader:
lst_transforms.append(transforms.Lambda(lambda x: x.unsqueeze(0)))
if self.opt.cuda:
lst_transforms.append(transforms.Lambda(lambda x: Variable(x.cuda(), requires_grad=True)))
else:
lst_transforms.append(transforms.Lambda(lambda x: Variable(x, requires_grad=True)))
lst_transforms.append(transforms.Lambda(lambda x: self.fcn(x)))
return lst_transforms
def get(self, rnd_seed=None):
kwargs = {'num_workers': self.opt.nthread, 'pin_memory': True} if self.opt.cuda else {}
#kwargs = {}
#################################
# TRANSFORMATIONS: transformations for the TRAIN dataset
#################################
train_transform = transforms.Compose(self.getlstTransforms(train='train'))
if self.type == MiniImagenet:
datasetParams = self.type(root=self.opt.dataroot, train='train',
datasetCompactSize = self.opt.datasetCompactSize,
size = self.opt.imageSize,
transform=train_transform, target_transform=None)
elif self.type == MiniImagenetPairs:
datasetParams = self.type(root=self.opt.dataroot, train='train',
datasetCompactSize = self.opt.datasetCompactSize,
size = self.opt.imageSize,
transform=train_transform, target_transform=None,
numTrials=self.opt.batchSize)
elif self.type == MiniImagenetOneShot:
datasetParams = self.type(root=self.opt.dataroot, train='train',
datasetCompactSize = self.opt.datasetCompactSize,
size = self.opt.imageSize,
transform=train_transform, target_transform=None,
n_way = self.opt.one_shot_n_way, n_shot = self.opt.one_shot_n_shot,
numTrials=self.opt.batchSize)
train_loader = torch.utils.data.DataLoader(
datasetParams,
batch_size=self.opt.batchSize, shuffle=True, **kwargs)
eval_test_transform = transforms.Compose(self.getlstTransforms(train='eval_test'))
if self.type == MiniImagenet:
datasetParams = self.type(root=self.opt.dataroot, train='val',
datasetCompactSize = self.opt.datasetCompactSize,
size = self.opt.imageSize,
transform=eval_test_transform, target_transform=None)
elif self.type == MiniImagenetPairs:
datasetParams = self.type(root=self.opt.dataroot, train='val',
datasetCompactSize = self.opt.datasetCompactSize,
size = self.opt.imageSize,
transform=eval_test_transform, target_transform=None,
numTrials=self.opt.batchSize)
elif self.type == MiniImagenetOneShot:
datasetParams = self.type(root=self.opt.dataroot, train='val',
datasetCompactSize = self.opt.datasetCompactSize,
size = self.opt.imageSize,
transform=train_transform, target_transform=None,
n_way = self.opt.one_shot_n_way, n_shot = self.opt.one_shot_n_shot,
numTrials=self.opt.batchSize)
val_loader = torch.utils.data.DataLoader(
datasetParams,
batch_size=self.opt.batchSize, shuffle=False, **kwargs)
if self.type == MiniImagenet:
datasetParams = self.type(root=self.opt.dataroot, train='test',
datasetCompactSize = self.opt.datasetCompactSize,
size = self.opt.imageSize,
transform=eval_test_transform, target_transform=None)
elif self.type == MiniImagenetPairs:
datasetParams = self.type(root=self.opt.dataroot, train='test',
datasetCompactSize = self.opt.datasetCompactSize,
size = self.opt.imageSize,
transform=eval_test_transform, target_transform=None,
numTrials=self.opt.batchSize)
elif self.type == MiniImagenetOneShot:
datasetParams = self.type(root=self.opt.dataroot, train='test',
datasetCompactSize = self.opt.datasetCompactSize,
size = self.opt.imageSize,
transform=train_transform, target_transform=None,
n_way = self.opt.one_shot_n_way, n_shot = self.opt.one_shot_n_shot,
numTrials=self.opt.batchSize)
test_loader = torch.utils.data.DataLoader(
datasetParams,
batch_size=self.opt.batchSize, shuffle=False, **kwargs)
return train_loader, val_loader, test_loader