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load_data.py
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load_data.py
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import torch
from skimage import io, transform
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from pdb import set_trace as stop
import os, random
from dataloaders.voc2007_20 import Voc07Dataset
from dataloaders.vg500_dataset import VGDataset
from dataloaders.coco80_dataset import Coco80Dataset
from dataloaders.news500_dataset import NewsDataset
from dataloaders.coco1000_dataset import Coco1000Dataset
from dataloaders.cub312_dataset import CUBDataset
import warnings
warnings.filterwarnings("ignore")
def get_data(args):
dataset = args.dataset
data_root=args.dataroot
batch_size=args.batch_size
rescale=args.scale_size
random_crop=args.crop_size
attr_group_dict=args.attr_group_dict
workers=args.workers
n_groups=args.n_groups
normTransform = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
scale_size = rescale
crop_size = random_crop
if args.test_batch_size == -1:
args.test_batch_size = batch_size
trainTransform = transforms.Compose([transforms.Resize((scale_size, scale_size)),
transforms.RandomChoice([
transforms.RandomCrop(640),
transforms.RandomCrop(576),
transforms.RandomCrop(512),
transforms.RandomCrop(384),
transforms.RandomCrop(320)
]),
transforms.Resize((crop_size, crop_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normTransform])
testTransform = transforms.Compose([transforms.Resize((scale_size, scale_size)),
transforms.CenterCrop(crop_size),
transforms.ToTensor(),
normTransform])
test_dataset = None
test_loader = None
drop_last = False
if dataset == 'coco':
coco_root = os.path.join(data_root,'coco')
ann_dir = os.path.join(coco_root,'annotations_pytorch')
train_img_root = os.path.join(coco_root,'train2014')
test_img_root = os.path.join(coco_root,'val2014')
train_data_name = 'train.data'
val_data_name = 'val_test.data'
train_dataset = Coco80Dataset(
split='train',
num_labels=args.num_labels,
data_file=os.path.join(coco_root,train_data_name),
img_root=train_img_root,
annotation_dir=ann_dir,
max_samples=args.max_samples,
transform=trainTransform,
known_labels=args.train_known_labels,
testing=False)
valid_dataset = Coco80Dataset(split='val',
num_labels=args.num_labels,
data_file=os.path.join(coco_root,val_data_name),
img_root=test_img_root,
annotation_dir=ann_dir,
max_samples=args.max_samples,
transform=testTransform,
known_labels=args.test_known_labels,
testing=True)
elif dataset == 'coco1000':
ann_dir = os.path.join(data_root,'coco','annotations_pytorch')
data_dir = os.path.join(data_root,'coco')
train_img_root = os.path.join(data_dir,'train2014')
test_img_root = os.path.join(data_dir,'val2014')
train_dataset = Coco1000Dataset(ann_dir, data_dir, split = 'train', transform = trainTransform,known_labels=args.train_known_labels,testing=False)
valid_dataset = Coco1000Dataset(ann_dir, data_dir, split = 'val', transform = testTransform,known_labels=args.test_known_labels,testing=True)
elif dataset == 'vg':
vg_root = os.path.join(data_root,'VG')
train_dir=os.path.join(vg_root,'VG_100K')
train_list=os.path.join(vg_root,'train_list_500.txt')
test_dir=os.path.join(vg_root,'VG_100K')
test_list=os.path.join(vg_root,'test_list_500.txt')
train_label=os.path.join(vg_root,'vg_category_500_labels_index.json')
test_label=os.path.join(vg_root,'vg_category_500_labels_index.json')
train_dataset = VGDataset(
train_dir,
train_list,
trainTransform,
train_label,
known_labels=0,
testing=False)
valid_dataset = VGDataset(
test_dir,
test_list,
testTransform,
test_label,
known_labels=args.test_known_labels,
testing=True)
elif dataset == 'news':
drop_last=True
ann_dir = '/bigtemp/jjl5sw/PartialMLC/data/bbc_data/'
train_dataset = NewsDataset(ann_dir, split = 'train', transform = trainTransform,known_labels=0,testing=False)
valid_dataset = NewsDataset(ann_dir, split = 'test', transform = testTransform,known_labels=args.test_known_labels,testing=True)
elif dataset=='voc':
voc_root = os.path.join(data_root,'voc/VOCdevkit/VOC2007/')
img_dir = os.path.join(voc_root,'JPEGImages')
anno_dir = os.path.join(voc_root,'Annotations')
train_anno_path = os.path.join(voc_root,'ImageSets/Main/trainval.txt')
test_anno_path = os.path.join(voc_root,'ImageSets/Main/test.txt')
train_dataset = Voc07Dataset(
img_dir=img_dir,
anno_path=train_anno_path,
image_transform=trainTransform,
labels_path=anno_dir,
known_labels=args.train_known_labels,
testing=False,
use_difficult=False)
valid_dataset = Voc07Dataset(
img_dir=img_dir,
anno_path=test_anno_path,
image_transform=testTransform,
labels_path=anno_dir,
known_labels=args.test_known_labels,
testing=True)
elif dataset == 'cub':
drop_last=True
resol=299
resized_resol = int(resol * 256/224)
trainTransform = transforms.Compose([
#transforms.Resize((resized_resol, resized_resol)),
#transforms.RandomSizedCrop(resol),
transforms.ColorJitter(brightness=32/255, saturation=(0.5, 1.5)),
transforms.RandomResizedCrop(resol),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), #implicitly divides by 255
transforms.Normalize(mean = [0.5, 0.5, 0.5], std = [2, 2, 2])
])
testTransform = transforms.Compose([
#transforms.Resize((resized_resol, resized_resol)),
transforms.CenterCrop(resol),
transforms.ToTensor(), #implicitly divides by 255
transforms.Normalize(mean = [0.5, 0.5, 0.5], std = [2, 2, 2])
])
cub_root = os.path.join(data_root,'CUB_200_2011')
image_dir = os.path.join(cub_root,'images')
train_list = os.path.join(cub_root,'class_attr_data_10','train_valid.pkl')
valid_list = os.path.join(cub_root,'class_attr_data_10','train_valid.pkl')
test_list = os.path.join(cub_root,'class_attr_data_10','test.pkl')
train_dataset = CUBDataset(image_dir, train_list, trainTransform,known_labels=args.train_known_labels,attr_group_dict=attr_group_dict,testing=False,n_groups=n_groups)
valid_dataset = CUBDataset(image_dir, valid_list, testTransform,known_labels=args.test_known_labels,attr_group_dict=attr_group_dict,testing=True,n_groups=n_groups)
test_dataset = CUBDataset(image_dir, test_list, testTransform,known_labels=args.test_known_labels,attr_group_dict=attr_group_dict,testing=True,n_groups=n_groups)
else:
print('no dataset avail')
exit(0)
if train_dataset is not None:
train_loader = DataLoader(train_dataset, batch_size=batch_size,shuffle=True, num_workers=workers,drop_last=drop_last)
if valid_dataset is not None:
valid_loader = DataLoader(valid_dataset, batch_size=args.test_batch_size,shuffle=False, num_workers=workers)
if test_dataset is not None:
test_loader = DataLoader(test_dataset, batch_size=args.test_batch_size,shuffle=False, num_workers=workers)
return train_loader,valid_loader,test_loader