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import numpy as np
import pickle
import os
import torch
from torch.utils.data import TensorDataset
from torchvision.datasets import ImageFolder
import torchvision.transforms as transforms
from sklearn.model_selection import train_test_split
def set_up_data(H):
shift_loss = -127.5
scale_loss = 1. / 127.5
if H.dataset == 'imagenet32':
trX, vaX, teX = imagenet32(H.data_root)
H.image_size = 32
H.image_channels = 3
shift = -116.2373
scale = 1. / 69.37404
elif H.dataset == 'imagenet64':
trX, vaX, teX = imagenet64(H.data_root)
H.image_size = 64
H.image_channels = 3
shift = -115.92961967
scale = 1. / 69.37404
elif H.dataset == 'ffhq_256':
trX, vaX, teX = ffhq256(H.data_root)
H.image_size = 256
H.image_channels = 3
shift = -112.8666757481
scale = 1. / 69.84780273
elif H.dataset == 'ffhq_1024':
trX, vaX, teX = ffhq1024(H.data_root)
H.image_size = 1024
H.image_channels = 3
shift = -0.4387
scale = 1.0 / 0.2743
shift_loss = -0.5
scale_loss = 2.0
elif H.dataset == 'cifar10':
(trX, _), (vaX, _), (teX, _) = cifar10(H.data_root, one_hot=False)
H.image_size = 32
H.image_channels = 3
shift = -120.63838
scale = 1. / 64.16736
else:
raise ValueError('unknown dataset: ', H.dataset)
do_low_bit = H.dataset in ['ffhq_256']
if H.test_eval:
print('DOING TEST')
eval_dataset = teX
else:
eval_dataset = vaX
shift = torch.tensor([shift]).cuda().view(1, 1, 1, 1)
scale = torch.tensor([scale]).cuda().view(1, 1, 1, 1)
shift_loss = torch.tensor([shift_loss]).cuda().view(1, 1, 1, 1)
scale_loss = torch.tensor([scale_loss]).cuda().view(1, 1, 1, 1)
if H.dataset == 'ffhq_1024':
train_data = ImageFolder(trX, transforms.ToTensor())
valid_data = ImageFolder(eval_dataset, transforms.ToTensor())
untranspose = True
else:
train_data = TensorDataset(torch.as_tensor(trX))
valid_data = TensorDataset(torch.as_tensor(eval_dataset))
untranspose = False
def preprocess_func(x):
nonlocal shift
nonlocal scale
nonlocal shift_loss
nonlocal scale_loss
nonlocal do_low_bit
nonlocal untranspose
'takes in a data example and returns the preprocessed input'
'as well as the input processed for the loss'
if untranspose:
x[0] = x[0].permute(0, 2, 3, 1)
inp = x[0].cuda(non_blocking=True).float()
out = inp.clone()
inp.add_(shift).mul_(scale)
if do_low_bit:
# 5 bits of precision
out.mul_(1. / 8.).floor_().mul_(8.)
out.add_(shift_loss).mul_(scale_loss)
return inp, out
return H, train_data, valid_data, preprocess_func
def mkdir_p(path):
os.makedirs(path, exist_ok=True)
def flatten(outer):
return [el for inner in outer for el in inner]
def unpickle_cifar10(file):
fo = open(file, 'rb')
data = pickle.load(fo, encoding='bytes')
fo.close()
data = dict(zip([k.decode() for k in data.keys()], data.values()))
return data
def imagenet32(data_root):
trX = np.load(os.path.join(data_root, 'imagenet32-train.npy'), mmap_mode='r')
np.random.seed(42)
tr_va_split_indices = np.random.permutation(trX.shape[0])
train = trX[tr_va_split_indices[:-5000]]
valid = trX[tr_va_split_indices[-5000:]]
test = np.load(os.path.join(data_root, 'imagenet32-valid.npy'), mmap_mode='r')
return train, valid, test
def imagenet64(data_root):
trX = np.load(os.path.join(data_root, 'imagenet64-train.npy'), mmap_mode='r')
np.random.seed(42)
tr_va_split_indices = np.random.permutation(trX.shape[0])
train = trX[tr_va_split_indices[:-5000]]
valid = trX[tr_va_split_indices[-5000:]]
test = np.load(os.path.join(data_root, 'imagenet64-valid.npy'), mmap_mode='r') # this is test.
return train, valid, test
def ffhq1024(data_root):
# we did not significantly tune hyperparameters on ffhq-1024, and so simply evaluate on the test set
return os.path.join(data_root, 'ffhq1024/train'), os.path.join(data_root, 'ffhq1024/valid'), os.path.join(data_root, 'ffhq1024/valid')
def ffhq256(data_root):
trX = np.load(os.path.join(data_root, 'ffhq-256.npy'), mmap_mode='r')
np.random.seed(5)
tr_va_split_indices = np.random.permutation(trX.shape[0])
train = trX[tr_va_split_indices[:-7000]]
valid = trX[tr_va_split_indices[-7000:]]
# we did not significantly tune hyperparameters on ffhq-256, and so simply evaluate on the test set
return train, valid, valid
def cifar10(data_root, one_hot=True):
tr_data = [unpickle_cifar10(os.path.join(data_root, 'cifar-10-batches-py/', 'data_batch_%d' % i)) for i in range(1, 6)]
trX = np.vstack(data['data'] for data in tr_data)
trY = np.asarray(flatten([data['labels'] for data in tr_data]))
te_data = unpickle_cifar10(os.path.join(data_root, 'cifar-10-batches-py/', 'test_batch'))
teX = np.asarray(te_data['data'])
teY = np.asarray(te_data['labels'])
trX = trX.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
teX = teX.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
trX, vaX, trY, vaY = train_test_split(trX, trY, test_size=5000, random_state=11172018)
if one_hot:
trY = np.eye(10, dtype=np.float32)[trY]
vaY = np.eye(10, dtype=np.float32)[vaY]
teY = np.eye(10, dtype=np.float32)[teY]
else:
trY = np.reshape(trY, [-1, 1])
vaY = np.reshape(vaY, [-1, 1])
teY = np.reshape(teY, [-1, 1])
return (trX, trY), (vaX, vaY), (teX, teY)
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