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data.py
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data.py
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# Copyright (C) 2018 Elvis Yu-Jing Lin <[email protected]>
#
# This work is licensed under the MIT License. To view a copy of this license,
# visit https://opensource.org/licenses/MIT.
"""Custom datasets for CelebA and CelebA-HQ."""
import numpy as np
import os
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image
class Custom(data.Dataset):
def __init__(self, data_path, attr_path, image_size, selected_attrs):
self.data_path = data_path
att_list = open(attr_path, 'r', encoding='utf-8').readlines()[1].split()
atts = [att_list.index(att) + 1 for att in selected_attrs]
self.images = np.loadtxt(attr_path, skiprows=2, usecols=[0], dtype=np.str)
self.labels = np.loadtxt(attr_path, skiprows=2, usecols=atts, dtype=np.int)
self.tf = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
def __getitem__(self, index):
img = self.tf(Image.open(os.path.join(self.data_path, self.images[index])))
att = torch.tensor((self.labels[index] + 1) // 2)
return img, att
def __len__(self):
return len(self.images)
class CelebA(data.Dataset):
def __init__(self, data_path, attr_path, image_size, mode, selected_attrs):
super(CelebA, self).__init__()
self.data_path = data_path
att_list = open(attr_path, 'r', encoding='utf-8').readlines()[1].split()
atts = [att_list.index(att) + 1 for att in selected_attrs]
images = np.loadtxt(attr_path, skiprows=2, usecols=[0], dtype=np.str)
labels = np.loadtxt(attr_path, skiprows=2, usecols=atts, dtype=np.int)
if mode == 'train':
self.images = images[:182000]
self.labels = labels[:182000]
if mode == 'valid':
self.images = images[182000:182637]
self.labels = labels[182000:182637]
if mode == 'test':
self.images = images[182637:]
self.labels = labels[182637:]
self.tf = transforms.Compose([
transforms.CenterCrop(170),
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
self.length = len(self.images)
def __getitem__(self, index):
img = self.tf(Image.open(os.path.join(self.data_path, self.images[index])))
att = torch.tensor((self.labels[index] + 1) // 2)
return img, att
def __len__(self):
return self.length
class CelebA_HQ(data.Dataset):
def __init__(self, data_path, attr_path, image_list_path, image_size, mode, selected_attrs):
super(CelebA_HQ, self).__init__()
self.data_path = data_path
att_list = open(attr_path, 'r', encoding='utf-8').readlines()[1].split()
atts = [att_list.index(att) + 1 for att in selected_attrs]
orig_images = np.loadtxt(attr_path, skiprows=2, usecols=[0], dtype=np.str)
orig_labels = np.loadtxt(attr_path, skiprows=2, usecols=atts, dtype=np.int)
indices = np.loadtxt(image_list_path, skiprows=1, usecols=[1], dtype=np.int)
images = ['{:d}.jpg'.format(i) for i in range(30000)]
labels = orig_labels[indices]
if mode == 'train':
self.images = images[:28000]
self.labels = labels[:28000]
if mode == 'valid':
self.images = images[28000:28500]
self.labels = labels[28000:28500]
if mode == 'test':
self.images = images[28500:]
self.labels = labels[28500:]
self.tf = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
self.length = len(self.images)
def __getitem__(self, index):
img = self.tf(Image.open(os.path.join(self.data_path, self.images[index])))
att = torch.tensor((self.labels[index] + 1) // 2)
return img, att
def __len__(self):
return self.length
def check_attribute_conflict(att_batch, att_name, att_names):
def _get(att, att_name):
if att_name in att_names:
return att[att_names.index(att_name)]
return None
def _set(att, value, att_name):
if att_name in att_names:
att[att_names.index(att_name)] = value
att_id = att_names.index(att_name)
for att in att_batch:
if att_name in ['Bald', 'Receding_Hairline'] and att[att_id] != 0:
if _get(att, 'Bangs') != 0:
_set(att, 1-att[att_id], 'Bangs')
elif att_name == 'Bangs' and att[att_id] != 0:
for n in ['Bald', 'Receding_Hairline']:
if _get(att, n) != 0:
_set(att, 1-att[att_id], n)
_set(att, 1-att[att_id], n)
elif att_name in ['Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Gray_Hair'] and att[att_id] != 0:
for n in ['Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Gray_Hair']:
if n != att_name and _get(att, n) != 0:
_set(att, 1-att[att_id], n)
elif att_name in ['Straight_Hair', 'Wavy_Hair'] and att[att_id] != 0:
for n in ['Straight_Hair', 'Wavy_Hair']:
if n != att_name and _get(att, n) != 0:
_set(att, 1-att[att_id], n)
elif att_name in ['Mustache', 'No_Beard'] and att[att_id] != 0:
for n in ['Mustache', 'No_Beard']:
if n != att_name and _get(att, n) != 0:
_set(att, 1-att[att_id], n)
return att_batch
if __name__ == '__main__':
import argparse
import matplotlib.pyplot as plt
import torchvision.utils as vutils
attrs_default = [
'Bald', 'Bangs', 'Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Bushy_Eyebrows',
'Eyeglasses', 'Male', 'Mouth_Slightly_Open', 'Mustache', 'No_Beard', 'Pale_Skin', 'Young'
]
parser = argparse.ArgumentParser()
parser.add_argument('--attrs', dest='attrs', default=attrs_default, nargs='+', help='attributes to test')
parser.add_argument('--data_path', dest='data_path', type=str, required=True)
parser.add_argument('--attr_path', dest='attr_path', type=str, required=True)
args = parser.parse_args()
dataset = CelebA(args.data_path, args.attr_path, 128, 'valid', args.attrs)
dataloader = data.DataLoader(
dataset, batch_size=64, shuffle=False, drop_last=False
)
print('Attributes:')
print(args.attrs)
for x, y in dataloader:
vutils.save_image(x, 'test.png', nrow=8, normalize=True, value_range=(-1., 1.))
print(y)
break
del x, y
dataset = CelebA(args.data_path, args.attr_path, 128, 'valid', args.attrs)
dataloader = data.DataLoader(
dataset, batch_size=16, shuffle=False, drop_last=False
)