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dataset.py
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dataset.py
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"""
@author: Wei Han
Arrange information for complex scenes via dynamic clustering
Notes:
The flow of data is quite complex. It includes
- feeding all data into encoder for clustering,
- and taking clusters as data for localized tasks,
- and batches for encoder update
"""
import numpy as np
import torch
import config as cf
import copy
import torchvision
import torchvision.transforms as transforms
import os
import sys
from sklearn.preprocessing import OneHotEncoder
from PIL import Image
import os.path
import pickle
import torch.utils.data as data
class CIFAR10(data.Dataset):
"""`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
Args:
root (string): Root directory of dataset where directory
``cifar-10-batches-py`` exists or will be saved to if download is set to True.
train (bool, optional): If True, creates dataset from training set, otherwise
creates from test set.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool-batches-py, optional): If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
"""
base_folder = 'cifar-10-batches-py'
url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
filename = "cifar-10-python.tar.gz"
tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
train_list = [
['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
['data_batch_4', '634d18415352ddfa80567beed471001a'],
['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
]
test_list = [
['test_batch', '40351d587109b95175f43aff81a1287e'],
]
def __init__(self, root, train=True, valid=False, classes=np.arange(100), transform=None, target_transform=None):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
self.train = train # training set or test set
self.valid = valid
# now load the picked numpy arrays
if self.train:
self.train_data = []
self.train_labels = []
for fentry in self.train_list:
f = fentry[0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.train_data.append(entry['data'])
if 'labels' in entry:
self.train_labels += entry['labels']
else:
self.train_labels += entry['fine_labels']
fo.close()
self.train_data = np.concatenate(self.train_data)
self.train_data = self.train_data.reshape((50000, 3, 32, 32))
self.train_data = self.train_data.transpose((0, 2, 3, 1)) # convert to HWC
self.train_labels = np.array(self.train_labels)
train_data = np.empty((0, 32, 32, 3)).astype(np.uint8)
train_labels = np.empty((0,)).astype(np.int32)
same = classes == np.unique(self.train_labels)
same = same if isinstance(same, bool) else same.all()
if not same:
for class_label in classes:
train_data = np.vstack((train_data, self.train_data[self.train_labels==class_label]))
train_labels = np.hstack((train_labels, self.train_labels[self.train_labels==class_label]))
self.train_data = train_data
self.train_labels = train_labels
if self.valid:
labels, class_idx = np.unique(self.train_labels, return_inverse=True)
# Sample 20% data as validation set (each label)
temp_train_data = self.train_data
temp_train_labels = self.train_labels
self.train_data = np.empty((0, 32, 32, 3)).astype(np.uint8)
self.train_labels = np.empty((0,)).astype(np.int32)
self.valid_data = np.empty((0, 32, 32, 3)).astype(np.uint8)
self.valid_labels = np.empty((0,)).astype(np.int32)
for label in labels:
num_class = sum((class_idx == label).astype(int))
self.train_data = np.vstack((self.train_data, temp_train_data[class_idx == label][int(num_class * 0.2):, :, :, :]))
self.train_labels = np.hstack((self.train_labels, temp_train_labels[class_idx == label][int(num_class * 0.2):]))
self.valid_data = np.vstack((self.valid_data, temp_train_data[class_idx == label][:int(num_class * 0.2), :, :, :]))
self.valid_labels = np.hstack((self.valid_labels, temp_train_labels[class_idx == label][:int(num_class * 0.2)]))
else:
f = self.test_list[0][0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.test_data = entry['data']
if 'labels' in entry:
self.test_labels = entry['labels']
else:
self.test_labels = entry['fine_labels']
fo.close()
self.test_data = self.test_data.reshape((10000, 3, 32, 32))
self.test_data = self.test_data.transpose((0, 2, 3, 1)) # convert to HWC
test_data = np.empty((0, 32, 32, 3)).astype(np.uint8)
test_labels = np.empty((0,)).astype(np.int32)
same = classes == np.unique(self.test_labels)
same = same if isinstance(same, bool) else same.all()
if not same:
for class_label in classes:
test_data = np.vstack((test_data, self.test_data[self.test_labels == class_label]))
test_labels = np.hstack((test_labels, self.test_labels[self.test_labels == class_label]))
self.test_data = test_data
self.test_labels = test_labels
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
if self.train:
img, target = self.train_data[index], self.train_labels[index]
else:
img, target = self.test_data[index], self.test_labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
if self.train:
return len(self.train_data)
else:
return len(self.test_data)
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
tmp = 'train' if self.train is True else 'test'
fmt_str += ' Split: {}\n'.format(tmp)
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
class CIFAR100(CIFAR10):
"""`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
This is a subclass of the `CIFAR10` Dataset.
"""
base_folder = 'cifar-100-python'
url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
filename = "cifar-100-python.tar.gz"
tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
train_list = [
['train', '16019d7e3df5f24257cddd939b257f8d'],
]
test_list = [
['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
]
class Validset():
def __init__(self, trainset):
self.trainset = trainset
self.train_data = trainset.valid_data
self.train_labels = trainset.valid_labels
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.train_data[index], self.train_labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.trainset.transform is not None:
img = self.trainset.transform(img)
if self.trainset.target_transform is not None:
target = self.trainset.target_transform(target)
return img, target
def __len__(self):
return len(self.train_data)
def get_pretrain_dataLoders(args, valid=False, one_hot=True):
print('\nData Preparation')
# Data Uplaod
data_transform = transforms.Compose([
transforms.ToTensor(),
])
root_path = '/Users/changgang/Documents/DATA/Data For Research/CIFAR/'
if(args.dataset == 'cifar-10'):
trainset = CIFAR10(root=root_path, train=True, valid=valid, classes=np.arange(10), transform=data_transform)
testset = CIFAR10(root=root_path, train=False, classes=np.arange(10), transform=data_transform)
else:
assert args.dataset == 'cifar-100'
trainset = CIFAR100(root=root_path, train=True, valid=valid, classes=np.arange(100), transform=data_transform)
testset = CIFAR100(root=root_path, train=False, classes=np.arange(100), transform=data_transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.train_batch_size, shuffle=True, num_workers=0)
testloader = torch.utils.data.DataLoader(testset, batch_size=512, shuffle=False, num_workers=0)
trainbase = torch.utils.data.DataLoader(copy.deepcopy(trainset), batch_size=512, shuffle=False, num_workers=0)
if one_hot:
label_transformer = OneHotEncoder(sparse=False).fit(np.array(trainloader.dataset.train_labels).reshape(-1, 1)) #, categories='auto'
trainloader.dataset.train_labels = label_transformer.transform(np.array(trainloader.dataset.train_labels).reshape(-1, 1))
testloader.dataset.test_labels = label_transformer.transform(np.array(testloader.dataset.test_labels).reshape(-1, 1))
validloader = None
if valid:
validset = Validset(copy.deepcopy(trainset))
validloader = torch.utils.data.DataLoader(validset, batch_size=args.train_batch_size, shuffle=True, num_workers=0)
assert (np.unique(validloader.dataset.train_labels) == np.unique(trainbase.dataset.train_labels)).all()
if one_hot:
validloader.dataset.train_labels = label_transformer.transform(np.array(validloader.dataset.train_labels).reshape(-1, 1))
return trainloader, testloader, trainbase, validloader
def get_dataLoder(args, classes, one_hot=True):
# Data Uplaod
data_transform = transforms.Compose([
transforms.ToTensor(),
])
root_path = '/Users/changgang/Documents/DATA/Data For Research/CIFAR/'
if (args.dataset == 'cifar-10'):
trainset = CIFAR10(root=root_path, train=True, valid=False, classes=classes, transform=data_transform)
else:
assert args.dataset == 'cifar-100'
trainset = CIFAR100(root=root_path, train=True, valid=False, classes=classes, transform=data_transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.train_batch_size, shuffle=True, num_workers=0)
if one_hot:
label_transformer = OneHotEncoder(sparse=False).fit(np.array(trainloader.dataset.train_labels).reshape(-1, 1)) #, categories='auto'
trainloader.dataset.train_labels = label_transformer.transform(np.array(trainloader.dataset.train_labels).reshape(-1, 1))
return trainloader