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load_data.py
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from __future__ import print_function, division
import os
import glob
import torch
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from PIL import Image
import torchvision.transforms as T
import pickle
import itertools
class CelebaDataset(Dataset):
def __init__(self, list_IDs, labels, transform=T.ToTensor()):
self.labels = labels
self.list_IDs = list_IDs
self.transform = transform
def __len__(self):
return len(self.list_IDs)
def __getitem__(self, index):
ID = self.list_IDs[index]
img = Image.open(ID)
#print(transform)
X = self.transform(img)
y = self.labels[ID]
return X,y
class CelebaDataset_no_normalize(Dataset):
def __init__(self, list_IDs, labels):
self.labels = labels
self.list_IDs = list_IDs
def __len__(self):
return len(self.list_IDs)
def __getitem__(self, index):
'Generates one sample of data'
ID = self.list_IDs[index]
img = Image.open(ID)
transform = T.Compose([
T.ToTensor(),
])
X = transform(img)
y = self.labels[ID]
return X,y
def create_dataset_all(real_params, fake_params, params, augment, dataset, split='train'):
list_ids = []
labeldata = pickle.load(open(fake_params['attr_path'], 'rb'))
labeldata = np.tile(labeldata, 2)
domdata = pickle.load(open(fake_params['dom_path'], 'rb'))
domdata = domdata[fake_params['range_orig_image'][0]:fake_params['range_orig_image'][1]]
domdata = np.concatenate([(1-domdata), domdata])
labels = {}
label_val = fake_params['range_orig_label'][0]
for i in range(fake_params['range_orig_image'][0], fake_params['range_orig_image'][1]):
list_ids.append(fake_params['path_orig'] + 'gen_'+str(i)+'.jpg')
labels[fake_params['path_orig'] + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[label_val],domdata[label_val]])
label_val+=1
if (label_val!=fake_params['range_orig_label'][1]):
print('Help!')
for i in range(fake_params['range_new'][0], fake_params['range_new'][1]):
list_ids.append(fake_params['path_new'] + 'gen_'+str(i)+'.jpg')
labels[fake_params['path_new'] + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[i], domdata[i]])
img_path = real_params['path']+'/img_align_celeba/'
split_path = real_params['path']+'/list_eval_partition_celeba.txt'
attr_path = real_params['path']+'/list_attr_celeba.txt'
attribute = real_params['attribute']
protected_attribute = real_params['protected_attribute']
label_file = open(attr_path, 'r')
label_file = label_file.readlines()
train_beg = 0
valid_beg = 162770
test_beg = 182637
number = real_params['number']
if split=='train':
if number==0:
number = valid_beg - train_beg
beg = train_beg
elif split=='valid':
if number==0:
number = test_beg - valid_beg
beg = valid_beg
elif split=='test':
if number==0:
number = 202599 - test_beg
beg = test_beg
else:
print('Error')
return
# +1 for column names, +1 for CelebA naming convention
for i in range(beg+2, beg+number+2):
temp = label_file[i].strip().split()
list_ids.append(img_path+temp[0])
labels[img_path+temp[0]]=torch.Tensor([int((int(temp[attribute+1])+1)/2), int((int(temp[protected_attribute+1])+1)/2)])
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_xprimeonly(real_params, fake_params, params, augment, dataset, split='train'):
labeldata = pickle.load(open(fake_params['attr_path'], 'rb'))
labeldata = labeldata[15000:]
domdata = pickle.load(open(fake_params['dom_path'], 'rb'))
domdata = domdata[15000:]
domdata = 1 - domdata
list_ids = []
labels = {}
for i in range(160000):
list_ids.append(fake_params['path_new'] + 'gen_'+str(i)+'.jpg')
labels[fake_params['path_new'] + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[i], domdata[i]])
img_path = real_params['path']+'/img_align_celeba/'
split_path = real_params['path']+'/list_eval_partition_celeba.txt'
attr_path = real_params['path']+'/list_attr_celeba.txt'
attribute = real_params['attribute']
protected_attribute = real_params['protected_attribute']
label_file = open(attr_path, 'r')
label_file = label_file.readlines()
train_beg = 0
valid_beg = 162770
test_beg = 182637
number = real_params['number']
if split=='train':
if number==0:
number = valid_beg - train_beg
beg = train_beg
elif split=='valid':
if number==0:
number = test_beg - valid_beg
beg = valid_beg
elif split=='test':
if number==0:
number = 202599 - test_beg
beg = test_beg
else:
print('Error')
return
# +1 for column names, +1 for CelebA naming convention
for i in range(beg+2, beg+number+2):
temp = label_file[i].strip().split()
list_ids.append(img_path+temp[0])
labels[img_path+temp[0]]=torch.Tensor([int((int(temp[attribute+1])+1)/2), int((int(temp[protected_attribute+1])+1)/2)])
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_stargan(real_params, fake_params, params, augment, dataset, split='train'):
with open('list_attr_celeba.txt', "r") as f:
attr = pd.read_csv(f, delim_whitespace=True, header=1)
label_data = (attr['Smiling'].values[:162770] == 1).astype(np.float32)
list_ids = []
labels = {}
for i in range(162770):
# CelebA
img_path = 'data/celeba/img_align_celeba/{:06d}.jpg'.format(i+1)
list_ids.append(img_path)
labels[img_path] = torch.Tensor([label_data[i], 0.])
# Gender flipped
img_path = 'data/fake_images_raninv/Smiling/prime/gen_{}.jpg'.format(i)
list_ids.append(img_path)
labels[img_path] = torch.Tensor([label_data[i], 0.])
# Young flipped
img_path = 'data/fake_images_raninv/Smiling/young/gen_{}.jpg'.format(i)
list_ids.append(img_path)
labels[img_path] = torch.Tensor([label_data[i], 0.])
# Both flipped
img_path = 'data/fake_images_stargan/gen_{}.jpg'.format(i+162770*2)
list_ids.append(img_path)
labels[img_path] = torch.Tensor([label_data[i], 0.])
print('list_ids', len(list(set(list_ids))))
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_stargan_allsgd(real_params, fake_params, params, augment, dataset, split='train'):
with open('list_attr_celeba.txt', "r") as f:
attr = pd.read_csv(f, delim_whitespace=True, header=1)
label_data = (attr['Smiling'].values[:162770] == 1).astype(np.float32)
list_ids = []
labels = {}
for i in range(162770):
# CelebA
img_path = 'data/celeba/img_align_celeba/{:06d}.jpg'.format(i+1)
list_ids.append(img_path)
labels[img_path] = torch.Tensor([label_data[i], 0.])
# Gender flipped
img_path = 'data/fake_images_stargan/gen_{}.jpg'.format(i)
list_ids.append(img_path)
labels[img_path] = torch.Tensor([label_data[i], 0.])
# Young flipped
img_path = 'data/fake_images_stargan/gen_{}.jpg'.format(i+162770)
list_ids.append(img_path)
labels[img_path] = torch.Tensor([label_data[i], 0.])
# Both flipped
img_path = 'data/fake_images_stargan/gen_{}.jpg'.format(i+162770*2)
list_ids.append(img_path)
labels[img_path] = torch.Tensor([label_data[i], 0.])
print('list_ids', len(list(set(list_ids))))
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_trainsyn(real_params, fake_params, params, augment, dataset, split='train'):
labeldata = pickle.load(open(fake_params['attr_path'], 'rb'))
domdata = pickle.load(open(fake_params['dom_path'], 'rb'))
list_ids = []
labels = {}
for i in range(15000, 175000):
list_ids.append(fake_params['path_orig'] + 'gen_'+str(i)+'.jpg')
labels[fake_params['path_orig'] + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[i], domdata[i]])
img_path = real_params['path']+'/img_align_celeba/'
split_path = real_params['path']+'/list_eval_partition_celeba.txt'
attr_path = real_params['path']+'/list_attr_celeba.txt'
attribute = real_params['attribute']
protected_attribute = real_params['protected_attribute']
label_file = open(attr_path, 'r')
label_file = label_file.readlines()
train_beg = 0
valid_beg = 162770
test_beg = 182637
number = real_params['number']
if split=='train':
if number==0:
number = valid_beg - train_beg
beg = train_beg
elif split=='valid':
if number==0:
number = test_beg - valid_beg
beg = valid_beg
elif split=='test':
if number==0:
number = 202599 - test_beg
beg = test_beg
else:
print('Error')
return
# +1 for column names, +1 for CelebA naming convention
for i in range(beg+2, beg+number+2):
temp = label_file[i].strip().split()
list_ids.append(img_path+temp[0])
labels[img_path+temp[0]]=torch.Tensor([int((int(temp[attribute+1])+1)/2), int((int(temp[protected_attribute+1])+1)/2)])
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_inv(real_params, fake_params, params, augment, dataset, split='train'):
list_ids = []
labels = {}
labeldata = pickle.load(open(fake_params['attr_path'], 'rb'))
domdata = pickle.load(open(fake_params['dom_path'], 'rb'))
domdata = 1 - domdata # Reverse the gender label for z'
for i in range(162770):
list_ids.append(fake_params['path_new'] + 'gen_'+str(i)+'.jpg')
labels[fake_params['path_new'] + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[i], domdata[i]])
img_path = real_params['path']+'/img_align_celeba/'
split_path = real_params['path']+'/list_eval_partition_celeba.txt'
attr_path = real_params['path']+'/list_attr_celeba.txt'
attribute = real_params['attribute']
protected_attribute = real_params['protected_attribute']
label_file = open(attr_path, 'r')
label_file = label_file.readlines()
train_beg = 0
valid_beg = 162770
test_beg = 182637
number = real_params['number']
if split=='train':
if number==0:
number = valid_beg - train_beg
beg = train_beg
elif split=='valid':
if number==0:
number = test_beg - valid_beg
beg = valid_beg
elif split=='test':
if number==0:
number = 202599 - test_beg
beg = test_beg
else:
print('Error')
return
# +1 for column names, +1 for CelebA naming convention
for i in range(beg+2, beg+number+2):
temp = label_file[i].strip().split()
list_ids.append(img_path+temp[0])
labels[img_path+temp[0]]=torch.Tensor([int((int(temp[attribute+1])+1)/2), int((int(temp[protected_attribute+1])+1)/2)])
normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_inv_dist(real_params, fake_params, params, augment, dataset, split='train'):
list_ids = []
labels = {}
labeldata = pickle.load(open(fake_params['attr_path'], 'rb')) # GT labels for z
domdata = pickle.load(open(fake_params['dom_path'], 'rb')) # GT labels for z
for dirname in ['z0_minus1', 'z0_plus1']:
#for dirname in ['z0_minus3', 'z0_minus2', 'z0_minus1', 'on_hyperplane', 'z0_plus1', 'z0_plus2', 'z0_plus3']:
dir = fake_params['path_new'] + dirname + '/'
for i in range(162770):
if domdata[i] == 1 and dirname == 'z0_minus1':
list_ids.append(dir + 'gen_'+str(i)+'.jpg')
labels[dir + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[i], 0])
if domdata[i] == 0 and dirname == 'z0_plus1':
list_ids.append(dir + 'gen_'+str(i)+'.jpg')
labels[dir + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[i], 1])
#list_ids.append(dir + 'gen_'+str(i)+'.jpg')
#if dirname in ['z0_minus3', 'z0_minus2', 'z0_minus1']:
# labels[dir + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[i], 0])
#else:
# labels[dir + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[i], 1])
#print('list_ids', len(list_ids))
img_path = real_params['path']+'/img_align_celeba/'
split_path = real_params['path']+'/list_eval_partition_celeba.txt'
attr_path = real_params['path']+'/list_attr_celeba.txt'
attribute = real_params['attribute']
protected_attribute = real_params['protected_attribute']
label_file = open(attr_path, 'r')
label_file = label_file.readlines()
train_beg = 0
valid_beg = 162770
test_beg = 182637
number = real_params['number']
if split=='train':
if number==0:
number = valid_beg - train_beg
beg = train_beg
elif split=='valid':
if number==0:
number = test_beg - valid_beg
beg = valid_beg
elif split=='test':
if number==0:
number = 202599 - test_beg
beg = test_beg
else:
print('Error')
return
# +1 for column names, +1 for CelebA naming convention
for i in range(beg+2, beg+number+2):
temp = label_file[i].strip().split()
list_ids.append(img_path+temp[0])
labels[img_path+temp[0]]=torch.Tensor([int((int(temp[attribute+1])+1)/2), int((int(temp[protected_attribute+1])+1)/2)])
#print('list_ids', len(list_ids))
normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_fakezonly(real_params, fake_params, params, augment, dataset, split='train'):
labeldata = pickle.load(open(fake_params['attr_path'], 'rb'))
domdata = pickle.load(open(fake_params['dom_path'], 'rb'))
list_ids = []
labels = {}
for i in range(0, 160000):
list_ids.append(fake_params['path_orig'] + 'gen_' + str(i) + '.jpg')
labels[fake_params['path_orig'] + 'gen_' + str(i) + '.jpg'] = torch.Tensor([labeldata[i], domdata[i]])
print('\n', len(labels), 'images in fake z only\n')
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_reflections(path_orig, path_new, range_orig_image, range_orig_label, range_new, label_path, params, augment, dataset):
list_ids = []
labeldata = pickle.load(open(label_path, 'rb'))
labeldata = np.tile(labeldata, 2)
labels = {}
label_val = range_orig_label[0]
for i in range(range_orig_image[0], range_orig_image[1]):
list_ids.append(path_orig + 'gen_'+str(i)+'.jpg')
labels[path_orig + 'gen_'+str(i)+'.jpg'] = labeldata[label_val]
label_val+=1
if (label_val!=range_orig_label[1]):
print('Help!')
for i in range(range_new[0], range_new[1]):
list_ids.append(path_new + 'gen_'+str(i)+'.jpg')
labels[path_new + 'gen_'+str(i)+'.jpg'] = labeldata[i]
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_only_correct(path_orig, path_new, range_orig_image, range_orig_label, range_new, label_path_orig, label_path_new, params, augment, dataset):
list_ids = []
labeldata_orig = pickle.load(open(label_path_orig, 'rb'))
labeldata_new = pickle.load(open(label_path_new, 'rb'))
#labeldata = np.tile(labeldata, 2)
labels = {}
label_val = range_orig_label[0]
for (i_old, i_new) in itertools.zip_longest(range(range_orig_image[0], range_orig_image[1]), range(range_new[0], range_new[1])):
if labeldata_orig[label_val]!=labeldata_new[i_new]:
label_val+=1
continue
list_ids.append(path_orig + 'gen_'+str(i_old)+'.jpg')
labels[path_orig + 'gen_'+str(i_old)+'.jpg'] = labeldata_orig[label_val]
label_val+=1
list_ids.append(path_new + 'gen_'+str(i_new)+'.jpg')
labels[path_new + 'gen_'+str(i_new)+'.jpg'] = labeldata_new[i_new]
if (label_val!=range_orig_label[1]):
print('Help!')
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_actual(path, attribute, protected_attribute, params, augment, dataset, number=0, split='train'):
img_path = path+'/img_align_celeba/'
split_path = path+'/list_eval_partition_celeba.txt'
attr_path = path+'/list_attr_celeba.txt'
list_ids = []
label = open(attr_path, 'r')
label = label.readlines()
train_beg = 0
valid_beg = 162770
test_beg = 182637
if split=='train':
if number==0:
number = valid_beg - train_beg
beg = train_beg
elif split=='valid':
if number==0:
number = test_beg - valid_beg
beg = valid_beg
elif split=='test':
if number==0:
number = 202599 - test_beg
beg = test_beg
else:
print('Error')
return
attr = {}
for i in range(beg+2, beg+ number+2):
temp = label[i].strip().split()
list_ids.append(img_path+temp[0])
attr[img_path+temp[0]]=torch.Tensor([int((int(temp[attribute+1])+1)/2), int((int(temp[protected_attribute+1])+1)/2)])
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
#print(transform)
dset = dataset(list_ids, attr, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_actual_skew(path, attribute, protected_attribute, params, augment, dataset, skew=0.5, number=0, split='train'):
attr_list = ['5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes',
'Bald', 'Bangs', 'Big_Lips', 'Big_Nose', 'Black_Hair', 'Blond_Hair', 'Blurry',
'Brown_Hair', 'Bushy_Eyebrows', 'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee',
'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open',
'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin', 'Pointy_Nose',
'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair',
'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Lipstick',
'Wearing_Necklace', 'Wearing_Necktie', 'Young']
attr_name = attr_list[attribute]
#attr_name = 'Gray_Hair' # Uncomment for Male
print('skew', skew)
glob_pattern = os.path.join('celeba', '*.jpg')
image_filenames_all = sorted(glob.glob(glob_pattern))[:162770]
with open("list_attr_celeba.txt", "r") as f:
attr = pd.read_csv(f, delim_whitespace=True, header=1)
g = (attr['Male'][:162770].values == 1).astype(np.float16)
a = (attr[attr_name][:162770].values == 1).astype(np.float16)
MT = np.array(image_filenames_all)[np.logical_and(g==1, a==1)]
MF = np.array(image_filenames_all)[np.logical_and(g==1, a==0)]
FT = np.array(image_filenames_all)[np.logical_and(g==0, a==1)]
FF = np.array(image_filenames_all)[np.logical_and(g==0, a==0)]
print('Before selection: MT {}, MF {}, FT {}, FF {}'.format(MT.shape[0], MF.shape[0], FT.shape[0], FF.shape[0]))
np.random.seed(1)
if attr_name == 'Gray_Hair' and skew == 0.9:
FT = FT[np.random.choice(np.arange(946), size=661, replace=False)]
FF = FF[np.random.choice(np.arange(93563), size=88844, replace=False)]
elif attr_name == 'Gray_Hair' and skew == 0.7:
MT = MT[np.random.choice(np.arange(5950), size=2207, replace=False)]
FF = FF[np.random.choice(np.arange(93563), size=92302, replace=False)]
elif attr_name == 'Gray_Hair' and skew == 0.5:
MT = MT[np.random.choice(np.arange(5950), size=946, replace=False)]
elif attr_name == 'High_Cheekbones' and skew == 0.9:
MT = MT[np.random.choice(np.arange(20972), size=5853, replace=False)]
FF = FF[np.random.choice(np.arange(41836), size=25256, replace=False)]
elif attr_name == 'High_Cheekbones' and skew == 0.7:
MF = MF[np.random.choice(np.arange(47289), size=44562, replace=False)]
FT = FT[np.random.choice(np.arange(52673), size=48935, replace=False)]
FF = FF[np.random.choice(np.arange(41836), size=16600, replace=False)]
elif attr_name == 'High_Cheekbones' and skew == 0.5:
FT = FT[np.random.choice(np.arange(52673), size=20972, replace=False)]
print('After selection: MT {}, MF {}, FT {}, FF {}'.format(MT.shape[0], MF.shape[0], FT.shape[0], FF.shape[0]))
print()
list_ids = []
attr = {}
for i in range(FF.shape[0]):
list_ids.append(FF[i])
attr[FF[i]] = torch.Tensor([0, 0])
for i in range(FT.shape[0]):
list_ids.append(FT[i])
attr[FT[i]] = torch.Tensor([1, 0]) # Gray_Hair, High_Cheekbones
#attr[FT[i]] = torch.Tensor([0, 0]) # Male
for i in range(MF.shape[0]):
list_ids.append(MF[i])
attr[MF[i]] = torch.Tensor([0, 1]) # Gray_Hair, High_Cheekbones
#attr[MF[i]] = torch.Tensor([1, 1]) # Male
for i in range(MT.shape[0]):
list_ids.append(MT[i])
attr[MT[i]] = torch.Tensor([1, 1])
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, attr, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_all_skewdata(real_params, fake_params, params, augment, dataset, skew=0.5, split='train'):
list_ids = []
labeldata = pickle.load(open(fake_params['attr_path'], 'rb'))
labeldata = np.tile(labeldata, 2)
domdata = pickle.load(open(fake_params['dom_path'], 'rb'))
domdata = domdata[fake_params['range_orig_image'][0]:fake_params['range_orig_image'][1]]
domdata = np.concatenate([(1-domdata), domdata])
labels = {}
label_val = fake_params['range_orig_label'][0]
for i in range(fake_params['range_orig_image'][0], fake_params['range_orig_image'][1]):
list_ids.append(fake_params['path_orig'] + 'gen_'+str(i)+'.jpg')
labels[fake_params['path_orig'] + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[label_val],domdata[label_val]])
label_val+=1
if (label_val!=fake_params['range_orig_label'][1]):
print('Help!')
for i in range(fake_params['range_new'][0], fake_params['range_new'][1]):
list_ids.append(fake_params['path_new'] + 'gen_'+str(i)+'.jpg')
labels[fake_params['path_new'] + 'gen_'+str(i)+'.jpg'] = torch.Tensor([labeldata[i], domdata[i]])
attr_list = ['5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes',
'Bald', 'Bangs', 'Big_Lips', 'Big_Nose', 'Black_Hair', 'Blond_Hair', 'Blurry',
'Brown_Hair', 'Bushy_Eyebrows', 'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee',
'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open',
'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin', 'Pointy_Nose',
'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair',
'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Lipstick',
'Wearing_Necklace', 'Wearing_Necktie', 'Young']
attr_name = attr_list[real_params['attribute']]
print(attr_name, 'skew', skew)
glob_pattern = os.path.join('celeba', '*.jpg')
image_filenames_all = sorted(glob.glob(glob_pattern))[:162770]
with open("list_attr_celeba.txt", "r") as f:
attr = pd.read_csv(f, delim_whitespace=True, header=1)
g = (attr['Male'][:162770].values == 1).astype(np.float16)
a = (attr[attr_name][:162770].values == 1).astype(np.float16)
MT = np.array(image_filenames_all)[np.logical_and(g==1, a==1)]
MF = np.array(image_filenames_all)[np.logical_and(g==1, a==0)]
FT = np.array(image_filenames_all)[np.logical_and(g==0, a==1)]
FF = np.array(image_filenames_all)[np.logical_and(g==0, a==0)]
print('Before selection: MT {}, MF {}, FT {}, FF {}'.format(MT.shape[0], MF.shape[0], FT.shape[0], FF.shape[0]))
np.random.seed(1)
if attr_name == 'Gray_Hair' and skew == 0.9:
FT = FT[np.random.choice(np.arange(946), size=661, replace=False)]
FF = FF[np.random.choice(np.arange(93563), size=88844, replace=False)]
elif attr_name == 'Gray_Hair' and skew == 0.7:
MT = MT[np.random.choice(np.arange(5950), size=2207, replace=False)]
FF = FF[np.random.choice(np.arange(93563), size=92302, replace=False)]
elif attr_name == 'Gray_Hair' and skew == 0.5:
MT = MT[np.random.choice(np.arange(5950), size=946, replace=False)]
elif attr_name == 'High_Cheekbones' and skew == 0.9:
MT = MT[np.random.choice(np.arange(20972), size=5853, replace=False)]
FF = FF[np.random.choice(np.arange(41836), size=25256, replace=False)]
elif attr_name == 'High_Cheekbones' and skew == 0.7:
MF = MF[np.random.choice(np.arange(47289), size=44562, replace=False)]
FT = FT[np.random.choice(np.arange(52673), size=48935, replace=False)]
FF = FF[np.random.choice(np.arange(41836), size=16600, replace=False)]
elif attr_name == 'High_Cheekbones' and skew == 0.5:
FT = FT[np.random.choice(np.arange(52673), size=20972, replace=False)]
print('After selection: MT {}, MF {}, FT {}, FF {}'.format(MT.shape[0], MF.shape[0], FT.shape[0], FF.shape[0]))
print()
for i in range(FF.shape[0]):
list_ids.append(FF[i])
labels[FF[i]] = torch.Tensor([0, 0])
for i in range(FT.shape[0]):
list_ids.append(FT[i])
labels[FT[i]] = torch.Tensor([1, 0])
for i in range(MF.shape[0]):
list_ids.append(MF[i])
labels[MF[i]] = torch.Tensor([0, 1])
for i in range(MT.shape[0]):
list_ids.append(MT[i])
labels[MT[i]] = torch.Tensor([1, 1])
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dset = dataset(list_ids, labels, transform)
loader = DataLoader(dset, **params)
return loader
def create_dataset_actual_fivecrop(path, attribute, protected_attribute, params, augment, dataset, number=0, split='train'):
img_path = path+'/img_align_celeba/'
split_path = path+'/list_eval_partition_celeba.txt'
attr_path = path+'/list_attr_celeba.txt'
list_ids = []
label = open(attr_path, 'r')
label = label.readlines()
train_beg = 0
valid_beg = 162770
test_beg = 182637
if split=='train':
if number==0:
number = valid_beg - train_beg
beg = train_beg
elif split=='valid':
if number==0:
number = test_beg - valid_beg
beg = valid_beg
elif split=='test':
if number==0:
number = 202599 - test_beg
beg = test_beg
else:
print('Error')
return
attr = {}
for i in range(beg+2, beg+ number+2):
temp = label[i].strip().split()
list_ids.append(img_path+temp[0])
attr[img_path+temp[0]]=torch.Tensor([int((int(temp[attribute+1])+1)/2), int((int(temp[protected_attribute+1])+1)/2)])
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
if augment:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.RandomCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
transform = T.Compose([
T.Resize(64),
T.Resize(256),
T.FiveCrop(224),
#Lambda(lambda crops: torch.stack([ToTensor()(crop) for crop in crops])),