def prepare_cv_datasets(dataname, batch_size):
if dataname == 'mnist':
ordinary_train_dataset = dsets.MNIST(root='/DATA1/datasets/mnist',
train=True,
transform=transforms.ToTensor(),
download=False)
test_dataset = dsets.MNIST(root='/DATA1/datasets/mnist',
train=False,
transform=transforms.ToTensor())
elif dataname == 'kmnist':
ordinary_train_dataset = dsets.KMNIST(root='/DATA1/datasets/kmnist',
train=True,
transform=transforms.ToTensor(),
download=False)
test_dataset = dsets.KMNIST(root='/DATA1/datasets/kmnist',
train=False,
transform=transforms.ToTensor())
elif dataname == 'fashion':
ordinary_train_dataset = dsets.FashionMNIST(
root='/DATA1/datasets/fashion_mnist',
train=True,
transform=transforms.ToTensor(),
download=False)
test_dataset = dsets.FashionMNIST(root='/DATA1/datasets/fashion_mnist',
train=False,
transform=transforms.ToTensor())
elif dataname == 'cifar10':
train_transform = transforms.Compose([
transforms.ToTensor(
), # transforms.RandomHorizontalFlip(), transforms.RandomCrop(32,4),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.247, 0.243, 0.261))
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.247, 0.243, 0.261))
])
ordinary_train_dataset = dsets.CIFAR10(root='/DATA1/datasets/cifar10',
train=True,
transform=train_transform,
download=False)
test_dataset = dsets.CIFAR10(root='/DATA1/datasets/cifar10',
train=False,
transform=test_transform)
train_loader = torch.utils.data.DataLoader(dataset=ordinary_train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=0)
full_train_loader = torch.utils.data.DataLoader(
dataset=ordinary_train_dataset,
batch_size=len(ordinary_train_dataset.data),
shuffle=True,
num_workers=0)
num_classes = 10
return (full_train_loader, train_loader, test_loader,
ordinary_train_dataset, test_dataset, num_classes)
import torch.nn.functional as F
from torchvision import transforms, datasets
#여러 환경에서 돌아가야 하는 코드를 공유할 때
USE_CUDA = torch.cuda.is_available()
DEVICE = torch.device("cuda" if USE_CUDA else "cpu")
#학습 데이터 전체를 총 몇 번이나 볼 것인가
EPOCHS = 30
BATCH_SIZE = 64
transform = transforms.Compose([transforms.ToTensor()])
#학습용 트레이닝셋, 성능 평가용 테스트셋
trainset = datasets.FashionMNIST(root='./.data/',
train=True,
download=True,
transform=transform)
testset = datasets.FashionMNIST(root='./.data/',
train=False,
download=True,
transform=transform)
#매개변수에 앞서 불러온 데이터셋을 넣어주고 배치 크기를 지정해준다.
train_loader = torch.utils.data.DataLoader(dataset=trainset,
batch_size=BATCH_SIZE,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=testset,
batch_size=BATCH_SIZE,
shuffle=True)
def _get_data(self):
if self.args.dataset_name == 'rot_mnist':
data_obj_train = datasets.MNIST(self.root,
train=True,
download=self.download,
transform=transforms.ToTensor())
data_obj_test = datasets.MNIST(self.root,
train=False,
download=self.download,
transform=transforms.ToTensor())
mnist_imgs = torch.cat((data_obj_train.data, data_obj_test.data))
mnist_labels = torch.cat(
(data_obj_train.targets, data_obj_test.targets))
elif self.args.dataset_name == 'fashion_mnist':
data_obj_train = datasets.FashionMNIST(
self.root,
train=True,
download=self.download,
transform=transforms.ToTensor())
data_obj_test = datasets.FashionMNIST(
self.root,
train=False,
download=self.download,
transform=transforms.ToTensor())
mnist_imgs = torch.cat((data_obj_train.data, data_obj_test.data))
mnist_labels = torch.cat(
(data_obj_train.targets, data_obj_test.targets))
# Get total number of labeled examples
sup_inds = self.load_inds()
mnist_labels = mnist_labels[sup_inds]
mnist_imgs = mnist_imgs[sup_inds]
mnist_size = mnist_labels.shape[0]
print(type(mnist_imgs), mnist_labels.shape, mnist_imgs.shape)
to_pil = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((self.args.img_w, self.args.img_h))
])
to_augment = transforms.Compose([
transforms.RandomResizedCrop(self.args.img_w, scale=(0.7, 1.0)),
transforms.RandomHorizontalFlip(),
])
to_tensor = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
# Choose subsets that should be included into the training
training_list_img = []
training_list_labels = []
training_list_idx = []
training_list_size = []
# key: class labels, val: data indices
indices_dict = {}
for i in range(len(mnist_imgs)):
key = int(mnist_labels[i].numpy())
if key not in indices_dict.keys():
indices_dict[key] = []
indices_dict[key].append(i)
# if self.data_case == 'test':
# self.list_train_domains= ['30', '45']
for domain in self.list_train_domains:
# Run transforms
mnist_img_rot = torch.zeros(
(mnist_size, self.args.img_w, self.args.img_h))
mnist_idx = []
# Shuffling the images to create random across domains
curr_indices_dict = copy.deepcopy(indices_dict)
for key in curr_indices_dict.keys():
random.shuffle(curr_indices_dict[key])
for i in range(len(mnist_imgs)):
if domain == '0':
mnist_img_rot[i] = to_tensor(to_pil(mnist_imgs[i]))
else:
if self.data_case == 'train' and self.args.dataset_name == "fashion_mnist":
mnist_img_rot[i] = to_tensor(
to_augment(
transforms.functional.rotate(
to_pil(mnist_imgs[i]), int(domain))))
else:
mnist_img_rot[i] = to_tensor(
transforms.functional.rotate(
to_pil(mnist_imgs[i]), int(domain)))
mnist_idx.append(i)
print('Source Domain ', domain)
training_list_img.append(mnist_img_rot)
training_list_labels.append(mnist_labels)
training_list_idx.append(mnist_idx)
training_list_size.append(mnist_img_rot.shape[0])
# Making domain size equivalent everywhere by random sampling
if self.data_case == 'train':
num_classes = 10
for y_c in range(num_classes):
base_class_size = 0
base_class_idx = -1
for d_idx, domain in enumerate(self.list_train_domains):
class_idx = training_list_labels[d_idx] == y_c
curr_class_size = training_list_labels[d_idx][
class_idx].shape[0]
if base_class_size < curr_class_size:
base_class_size = curr_class_size
base_class_idx = d_idx
self.base_domain_size += base_class_size
print('Max Class Size: ', base_class_size, base_class_idx, y_c)
# Stack
train_imgs = torch.cat(training_list_img)
train_labels = torch.cat(training_list_labels)
train_indices = np.array(training_list_idx)
train_indices = np.reshape(
train_indices, (train_indices.shape[0] * train_indices.shape[1]))
self.training_list_size = training_list_size
print(train_imgs.shape, train_labels.shape, train_indices.shape)
print(self.training_list_size)
# Create domain labels
train_domains = torch.zeros(train_labels.size())
for idx in range(len(self.list_train_domains)):
train_domains[idx * mnist_size:(idx + 1) * mnist_size] += idx
# Shuffle everything one more time
inds = np.arange(train_labels.size()[0])
np.random.shuffle(inds)
train_imgs = train_imgs[inds]
train_labels = train_labels[inds]
train_domains = train_domains[inds].long()
train_indices = train_indices[inds]
# Convert to onehot
y = torch.eye(10)
train_labels = y[train_labels]
# Convert to onehot
d = torch.eye(len(self.list_train_domains))
train_domains = d[train_domains]
print(train_imgs.shape, train_labels.shape, train_domains.shape,
train_indices.shape)
return train_imgs.unsqueeze(
1), train_labels, train_domains, train_indices
def fashion_mnist():
return collect_download_configs(
lambda: datasets.FashionMNIST(ROOT, download=True),
name="FashionMNIST")
model.eval()
ret = np.empty(shape=0)
for data in input_loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
pred = torch.Tensor.cpu(pred).detach().numpy()[:, :]
ret = np.append(ret, pred)
return ret
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0, ), (1, )),
]) # normalize - zero mean and 1 std
batchSize = 64
train_loader = torch.utils.data.DataLoader(datasets.FashionMNIST(
'./data', train=True, download=True, transform=transform),
batch_size=batchSize,
shuffle=True)
# create the model for the run, my best - model C
model = Model_C(image_size=28 * 28)
lr = 0.001
optimizer = optim.Adam(model.parameters(), lr=lr)
epohcs_number = 10
for epoch in range(epohcs_number):
train(epoch, model)
# predict from a file
if (len(sys.argv) > 1):
fileName = sys.argv[1]
train_x = np.loadtxt(fileName) / 255 # divided by 255 as the traning set
plt.xticks([]), plt.yticks([])
if title is not None:
plt.title(title)
plt.savefig(path, dpi=100)
if __name__ == "__main__":
'''
================
Plot the dataset
================
'''
m_loader = torch.utils.data.DataLoader(datasets.MNIST(root='../data', train=False,\
download=False, transform=transforms.ToTensor()), batch_size=25)
f_loader = torch.utils.data.DataLoader(datasets.FashionMNIST(root='../data', train=False,\
download=False, transform=transforms.ToTensor()), batch_size=25)
m_data, m_label = iter(m_loader).next()
plot(m_data, m_label)
f_data, f_label = iter(f_loader).next()
plot(f_data, f_label)
'''
========================
Plot the reuslt of model
========================
'''
data = np.load('../data/xs_mnist.npy')
adv_data = np.load('../data/advs_mnist.npy')
labels = np.load('../data/ys_mnist.npy')
dataset = Dataset(data, adv_data, labels)
def __init__(self, args):
# parameters
self.epoch = args.epoch
self.sample_num = 64
self.batch_size = args.batch_size
self.save_dir = args.save_dir
self.result_dir = args.result_dir
self.dataset = args.dataset
self.log_dir = args.log_dir
self.gpu_mode = args.gpu_mode
self.model_name = args.gan_type
# EBGAN parameters
self.pt_loss_weight = 0.1
self.margin = max(1, self.batch_size / 64.) # margin for loss function
# usually margin of 1 is enough, but for large batch size it must be larger than 1
# networks init
self.G = generator(self.dataset)
self.D = discriminator(self.dataset)
self.G_optimizer = optim.Adam(self.G.parameters(),
lr=args.lrG,
betas=(args.beta1, args.beta2))
self.D_optimizer = optim.Adam(self.D.parameters(),
lr=args.lrD,
betas=(args.beta1, args.beta2))
if self.gpu_mode:
self.G.cuda()
self.D.cuda()
self.MSE_loss = nn.MSELoss().cuda()
else:
self.MSE_loss = nn.MSELoss()
print('---------- Networks architecture -------------')
utils.print_network(self.G)
utils.print_network(self.D)
print('-----------------------------------------------')
# load dataset
if self.dataset == 'mnist':
self.data_loader = DataLoader(datasets.MNIST(
'data/mnist',
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor()])),
batch_size=self.batch_size,
shuffle=True)
elif self.dataset == 'fashion-mnist':
self.data_loader = DataLoader(datasets.FashionMNIST(
'data/fashion-mnist',
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor()])),
batch_size=self.batch_size,
shuffle=True)
elif self.dataset == 'celebA':
train_set = get_training_set(
'/home/tmp_data_dir/zhaoyu/CelebA/img_align_celeba/',
'/home/tmp_data_dir/zhaoyu/CelebA/img_align_celeba/')
# train_set = get_training_set('/home/xujinchang/pytorch-CycleGAN-and-pix2pix/datasets/celeA_part/train/', '/home/xujinchang/pytorch-CycleGAN-and-pix2pix/datasets/celeA_part/train/')
self.data_loader = DataLoader(dataset=train_set,
batch_size=self.batch_size,
shuffle=True)
# self.data_loader = utils.load_celebA('data/celebA', transform=transforms.Compose(
# [transforms.CenterCrop(160), transforms.Scale(64), transforms.ToTensor()]), batch_size=self.batch_size,
# shuffle=True)
self.z_dim = 62
# fixed noise
if self.gpu_mode:
self.sample_z_ = Variable(torch.rand(
(self.batch_size, self.z_dim)).cuda(),
volatile=True)
else:
self.sample_z_ = Variable(torch.rand(
(self.batch_size, self.z_dim)),
volatile=True)
def load_data(dataset: str, iid: str):
"""Loads a dataset.
:param dataset: Name of the dataset
:param iid: True if the dataset must not be splitted by target value
:return: Train dataset, test dataset
"""
path_to_dataset = '{0}/dataset/'.format(get_path_to_datasets())
if dataset == "fake":
transform = transforms.ToTensor()
train_data = datasets.FakeData(size=200, transform=transform)
test_data = datasets.FakeData(size=200, transform=transform)
elif dataset == 'cifar10':
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform_train = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize,
])
transform_test = transforms.Compose([
transforms.ToTensor(),
normalize,
])
train_data = datasets.CIFAR10(root=path_to_dataset, train=True, download=True, transform=transform_train)
test_data = datasets.CIFAR10(root=path_to_dataset, train=False, download=True, transform=transform_test)
elif dataset == 'mnist':
# Normalization see : https://stackoverflow.com/a/67233938
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_data = datasets.MNIST(root=path_to_dataset, train=True, download=False, transform=transform)
test_data = datasets.MNIST(root=path_to_dataset, train=False, download=False, transform=transform)
elif dataset == "fashion_mnist":
train_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
val_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
# Download and load the training data
train_data = datasets.FashionMNIST(path_to_dataset, download=True, train=True, transform=train_transforms)
# Download and load the test data
test_data = datasets.FashionMNIST(path_to_dataset, download=True, train=False, transform=val_transforms)
elif dataset == "femnist":
transform = transforms.Compose([transforms.ToTensor()])
train_data = FEMNISTDataset(path_to_dataset, download=True, train=True, transform=transform)
test_data = FEMNISTDataset(path_to_dataset, download=True, train=False, transform=transform)
elif dataset == "a9a":
train_data = A9ADataset(train=True, iid=iid)
test_data = A9ADataset(train=False, iid=iid)
elif dataset == "mushroom":
train_data = MushroomDataset(train=True, iid=iid)
test_data = MushroomDataset(train=False, iid=iid)
elif dataset == "phishing":
train_data = PhishingDataset(train=True, iid=iid)
test_data = PhishingDataset(train=False, iid=iid)
elif dataset == "quantum":
train_data = QuantumDataset(train=True, iid=iid)
test_data = QuantumDataset(train=False, iid=iid)
return train_data, test_data
parser.add_argument('--repeat', type=int, default=200)
parser.add_argument('--ngpu' , type=int, default=1, help='number of GPUs to use')
parser.add_argument('--state_E', default='./saved_models/fmnist/netE_pixel.pth', help='path to encoder checkpoint')
parser.add_argument('--state_G', default='./saved_models/fmnist/netG_pixel.pth', help='path to encoder checkpoint')
parser.add_argument('--state_E_bg', default='./saved_models/fmnist/netE_pixel_bg.pth', help='path to encoder checkpoint')
parser.add_argument('--state_G_bg', default='./saved_models/fmnist/netG_pixel_bg.pth', help='path to encoder checkpoint')
opt = parser.parse_args()
cudnn.benchmark = True
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
dataset_fmnist = dset.FashionMNIST(root=opt.dataroot, train=False, download=True, transform=transforms.Compose([
transforms.Resize(opt.imageSize),
transforms.ToTensor()
]))
dataloader_fmnist = torch.utils.data.DataLoader(dataset_fmnist, batch_size=opt.batch_size,
shuffle=True, num_workers=int(opt.workers))
dataset_mnist = dset.MNIST(root=opt.dataroot, train=False, download=True, transform=transforms.Compose([
transforms.Resize(opt.imageSize),
transforms.ToTensor()
]))
dataloader_mnist = torch.utils.data.DataLoader(dataset_mnist, batch_size=opt.batch_size,
shuffle=True, num_workers=int(opt.workers))
#
# dataset_cifar_test = dset.CIFAR10(root=opt.dataroot, download=True,train = False,
# transform=transforms.Compose([
_, predictions = scores.max(
1) # output shape is 64x10 --> take max among the cols
num_correct += (predictions == y).sum()
num_samples += predictions.size(0)
acc = (float(num_correct) / float(num_samples)) * 100
print(f"Got {num_correct} / {num_samples} with accuracy {acc:.2f}")
model.train()
return acc
device = "cuda" if torch.cuda.is_available() else "cpu"
train_dataset = datasets.FashionMNIST(root='./dataset',
train=True,
transform=transforms.ToTensor(),
download=True)
train_loader = DataLoader(dataset=train_dataset,
batch_size=NetworkParameters.batch_size,
shuffle=True)
test_dataset = datasets.FashionMNIST(root='./dataset',
train=False,
transform=transforms.ToTensor(),
download=True)
test_loader = DataLoader(dataset=test_dataset,
batch_size=NetworkParameters.batch_size,
shuffle=True)
model = FFNN(input_size=NetworkParameters.input_size,
num_classes=NetworkParameters.num_class).to(device)
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}'.format(args.output_folder)
if args.dataset in ['mnist', 'fashion-mnist', 'cifar10']:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if args.dataset == 'mnist':
# Define the train & test datasets
train_dataset = datasets.MNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.MNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'fashion-mnist':
# Define the train & test datasets
train_dataset = datasets.FashionMNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.FashionMNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'cifar10':
# Define the train & test datasets
train_dataset = datasets.CIFAR10(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.CIFAR10(args.data_folder,
train=False,
transform=transform)
num_channels = 3
valid_dataset = test_dataset
elif args.dataset == 'miniimagenet':
transform = transforms.Compose([
transforms.RandomResizedCrop(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Define the train, valid & test datasets
train_dataset = MiniImagenet(args.data_folder,
train=True,
download=True,
transform=transform)
valid_dataset = MiniImagenet(args.data_folder,
valid=True,
download=True,
transform=transform)
test_dataset = MiniImagenet(args.data_folder,
test=True,
download=True,
transform=transform)
num_channels = 3
# Define the data loaders
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=args.num_workers,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16,
shuffle=True)
# Fixed images for Tensorboard
fixed_images, _ = next(iter(test_loader))
fixed_grid = make_grid(fixed_images, nrow=8, range=(-1, 1), normalize=True)
writer.add_image('original', fixed_grid, 0)
model = VectorQuantizedVAE(num_channels, args.hidden_size,
args.k).to(args.device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
# Generate the samples first once
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(),
nrow=8,
range=(-1, 1),
normalize=True)
writer.add_image('reconstruction', grid, 0)
best_loss = -1.
for epoch in range(args.num_epochs):
print(epoch)
train(train_loader, model, optimizer, args, writer)
loss, _ = test(valid_loader, model, args, writer)
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(),
nrow=8,
range=(-1, 1),
normalize=True)
writer.add_image('reconstruction', grid, epoch + 1)
if (epoch == 0) or (loss < best_loss):
best_loss = loss
with open('{0}/best.pt'.format(save_filename), 'wb') as f:
torch.save(model.state_dict(), f)
with open('{0}/model_{1}.pt'.format(save_filename, epoch + 1),
'wb') as f:
torch.save(model.state_dict(), f)
def load_data(dataset, batch_size, no_validation=False, shuffle=False, data_file=None):
"""
Loads training, validation and test data from the required dataset.
:param dataset: name of the dataset
:param batch_size: batch size for loaded dataset
:param shuffle: true if training and validation datasets should be shuffled each iteration
:param no_validation: true if empty validation set should be returned
:param data_file: directory of data files [needed for omniglot only]
:return: (train data loader, validation data loader, test data loader)
"""
if dataset == "omniglot":
all_data = torch.load(data_file)
x_train, x_val, x_test = all_data
# dummy y values since no classes for omniglot
y_train = torch.zeros(x_train.size(0), 1)
y_val = torch.zeros(x_val.size(0), 1)
y_test = torch.zeros(x_test.size(0), 1)
train = torch.utils.data.TensorDataset(x_train, y_train)
val = torch.utils.data.TensorDataset(x_val, y_val)
test = torch.utils.data.TensorDataset(x_test, y_test)
if no_validation:
train = torch.utils.data.ConcatDataset([train, val])
val = torch.utils.data.TensorDataset(torch.tensor([]), torch.tensor([]))
train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=shuffle, drop_last=True)
val_loader = torch.utils.data.DataLoader(val, batch_size=batch_size, shuffle=shuffle, drop_last=True)
test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=False)
elif dataset == "mnist":
train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transforms.ToTensor())
train_examples, val_examples = (50000, 10000)
if no_validation:
train_examples, val_examples = (60000, 0)
train_set, val_set = torch.utils.data.random_split(train_dataset, [train_examples, val_examples])
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=shuffle, drop_last=True)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=batch_size, shuffle=shuffle, drop_last=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('./data', train=False, transform=transforms.ToTensor()),
batch_size=batch_size, shuffle=False)
elif dataset == "fashion_mnist":
train_dataset = datasets.FashionMNIST('./data', train=True, download=True, transform=transforms.ToTensor())
train_examples, val_examples = (50000, 10000)
if no_validation:
train_examples, val_examples = (60000, 0)
train_set, val_set = torch.utils.data.random_split(train_dataset, [train_examples, val_examples])
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=shuffle, drop_last=True)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=batch_size, shuffle=shuffle, drop_last=True)
test_loader = torch.utils.data.DataLoader(
datasets.FashionMNIST('./data', train=False, transform=transforms.ToTensor()),
batch_size=batch_size, shuffle=False)
else:
print("Error: {} dataset not supported!")
quit()
print('--------------------------------')
print("{} dataset loaded".format(dataset))
print("batch size: {}".format(batch_size))
print("train batches: {}".format(len(train_loader)))
print("validation batches: {}".format(len(val_loader)))
print("test batches: {}".format(len(test_loader)))
if shuffle:
print("train and validation data will be shuffled")
return train_loader, val_loader, test_loader
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--batchSize', type=int, default=64)
parser.add_argument('--Epochs', type=int, default=175)
parser.add_argument('--no-cuda', action='store_true')
parser.add_argument('--save')
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--opt',
type=str,
default='sgd',
choices=('sgd', 'adam', 'rmsprop'))
args = parser.parse_args()
#Check for cuda
args.cuda = not args.no_cuda and torch.cuda.is_available()
args.save = args.save or 'postprocessing'
setproctitle.setproctitle(args.save)
#manual seed on CPU or GPU
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
#Path for saving the progress
if os.path.exists(args.save):
shutil.rmtree(args.save)
os.makedirs(args.save, exist_ok=True)
# mean and std of the Fashion-MNIST train dataset images
normMean = [0.2860405969887955]
normStd = [0.35302424451492237]
normTransform = transforms.Normalize(normMean, normStd)
# Transforms : Random crop, random horizontal flip
trainTransform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normTransform
])
# # Transforms : RandomRotation, RandomVerticalFlip
# trainTransform = transforms.Compose([
# transforms.RandomRotation(90),
# transforms.RandomVerticalFlip(),
# transforms.ToTensor(),
# normTransform
# ])
testTransform = transforms.Compose([transforms.ToTensor(), normTransform])
#Loading the datasets, if not found will be downloaded
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
loader_train = DataLoader(dataset.FashionMNIST(root='Fashion-MNIST',
train=True,
download=True,
transform=trainTransform),
batch_size=args.batchSize,
shuffle=True,
**kwargs)
loader_test = DataLoader(dataset.FashionMNIST(root='Fashion-MNIST',
train=False,
download=True,
transform=testTransform),
batch_size=args.batchSize,
shuffle=False,
**kwargs)
# Calling the Densenet
dense_net = densenet.DenseNet(growthRate=15,
depth=100,
reduction=0.5,
bottleneck=True,
nClasses=10)
print(' + Number of params: {}'.format(
sum([p.data.nelement() for p in dense_net.parameters()])))
if args.cuda:
dense_net = dense_net.cuda()
else:
print("no cuda")
#Choosing the optimizer
if args.opt == 'sgd':
optimizer = optim.SGD(dense_net.parameters(),
lr=1e-1,
momentum=0.9,
weight_decay=1e-4)
elif args.opt == 'adam':
optimizer = optim.Adam(dense_net.parameters(), weight_decay=1e-4)
elif args.opt == 'rmsprop':
optimizer = optim.RMSprop(dense_net.parameters(), weight_decay=1e-4)
#Progress being saved to csv files
pfile_train = open(os.path.join(args.save, 'train.csv'), 'w')
pfile_test = open(os.path.join(args.save, 'test.csv'), 'w')
# running the training loop
for epoch in range(1, args.Epochs + 1):
adjust_optimizer(args.opt, optimizer, epoch)
train(args, epoch, dense_net, loader_train, optimizer, pfile_train)
test(args, epoch, dense_net, loader_test, pfile_test)
torch.save(dense_net, os.path.join(args.save, 'latest.pth'))
os.system('./plot.py {} &'.format(args.save))
pfile_train.close()
pfile_test.close()
end = time.time()
print(end - start)
def get_dataset(name, data_dir, train=True, size=64, lsun_categories=None):
transform = transforms.Compose([
transforms.Resize(size),
transforms.CenterCrop(size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
transforms.Lambda(lambda x: x + 1. / 128 * torch.rand(x.size())),
])
transformMnist = transforms.Compose([
transforms.Resize(size),
transforms.CenterCrop(size),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
])
if name == 'image':
dataset = datasets.ImageFolder(data_dir, transform)
nlabels = len(dataset.classes)
elif name == 'npy':
# Only support normalization for now
dataset = datasets.DatasetFolder(data_dir, npy_loader, ['npy'])
nlabels = len(dataset.classes)
elif name == 'imagenet32':
dataset = ImageNetDataset(dir=data_dir, img_size=size)
nlabels = 1000
elif name == 'imagenet64':
dataset = ImageNetDataset(dir=data_dir, img_size=size)
nlabels = 1000
elif name == 'mnist':
dataset = datasets.MNIST(root=data_dir, train=train, download=True,
transform=transformMnist)
nlabels = 10
elif name == 'cifar10':
if train:
transform_train = transforms.Compose([
transforms.Resize(size),
transforms.RandomCrop(size, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
dataset = datasets.CIFAR10(root=data_dir, train=train, download=True,
transform=transform_train)
else:
transform_test = transforms.Compose([
transforms.Resize(size),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
dataset = datasets.CIFAR10(root=data_dir, train=train, download=True,
transform=transform_test)
nlabels = 10
elif name == 'fashionmnist':
dataset = datasets.FashionMNIST(root=data_dir, train=train, download=True,
transform=transformMnist)
nlabels = 10
elif name == 'lsun':
if lsun_categories is None:
lsun_categories = 'train'
dataset = datasets.LSUN(data_dir, lsun_categories, transform)
nlabels = len(dataset.classes)
elif name == 'lsun_class':
dataset = datasets.LSUNClass(data_dir, transform,
target_transform=(lambda t: 0))
nlabels = 1
elif name == 'celeba':
dataset = datasets.ImageFolder(root=data_dir, transform=transform)
nlabels = 1
else:
raise NotImplemented
return dataset, nlabels
test_dataloader = torch.utils.data.DataLoader(
datasets.MNIST('./data/mnist', train=False, download=True,
transform=transforms.Compose([
transforms.Resize(args.img_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])),
batch_size=args.batch_size, shuffle=True)
elif args.dataset == 'fmnist':
os.makedirs('./data/fmnist', exist_ok=True)
train_dataloader = torch.utils.data.DataLoader(
datasets.FashionMNIST('./data/fmnist', train=True, download=True,
transform=transforms.Compose([
transforms.Resize(args.img_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])),
batch_size=args.batch_size, shuffle=True)
test_dataloader = torch.utils.data.DataLoader(
datasets.FashionMNIST('./data/fmnist', train=False, download=True,
transform=transforms.Compose([
transforms.Resize(args.img_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])),
batch_size=args.batch_size, shuffle=True)
elif args.dataset == 'cifar10':
os.makedirs('./data/cifar10', exist_ok=True)
help='number of epochs to train (default: 100)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--data_dir', type=str, default='data',
help='path of data directory')
parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
parser.add_argument('--nf', type=int, default=32, help='number of features')
parser.add_argument('--result', type=str, default='results', help='dir of results')
args = parser.parse_args()
# prepare dataset
trans = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor()
])
train_dataset = datasets.FashionMNIST(root = args.data_dir, download=True, transform=trans)
train_loader = data_utils.DataLoader(train_dataset, args.batch_size, shuffle=True, num_workers=1)
test_dataset = datasets.FashionMNIST(root = args.data_dir, download=True, transform = trans, train=False)
test_loader = data_utils.DataLoader(test_dataset, args.batch_size, shuffle=False, num_workers=1)
# define the network
def decode_unit(input_features, output_features):
conv_unit = nn.Sequential(nn.ConvTranspose2d(input_features, output_features, 4, stride=2, padding=1),
nn.BatchNorm2d(output_features),
nn.LeakyReLU(0.2))
return conv_unit
def encode_unit(input_features, output_features):
conv_unit = nn.Sequential(nn.Conv2d(input_features, output_features, 6, stride=2, padding=2),
nn.BatchNorm2d(output_features),
nn.LeakyReLU(0.2))
def main():
# train args
parser = argparse.ArgumentParser(
description='Disributional Sliced Wasserstein Autoencoder')
parser.add_argument('--datadir', default='./', help='path to dataset')
parser.add_argument('--outdir',
default='./result',
help='directory to output images')
parser.add_argument('--batch-size',
type=int,
default=512,
metavar='N',
help='input batch size for training (default: 512)')
parser.add_argument('--epochs',
type=int,
default=200,
metavar='N',
help='number of epochs to train (default: 200)')
parser.add_argument('--lr',
type=float,
default=0.0005,
metavar='LR',
help='learning rate (default: 0.0005)')
parser.add_argument(
'--num-workers',
type=int,
default=16,
metavar='N',
help='number of dataloader workers if device is CPU (default: 16)')
parser.add_argument('--seed',
type=int,
default=16,
metavar='S',
help='random seed (default: 16)')
parser.add_argument('--g', type=str, default='circular', help='g')
parser.add_argument('--num-projection',
type=int,
default=1000,
help='number projection')
parser.add_argument('--lam',
type=float,
default=1,
help='Regularization strength')
parser.add_argument('--p', type=int, default=2, help='Norm p')
parser.add_argument('--niter',
type=int,
default=10,
help='number of iterations')
parser.add_argument('--r', type=float, default=1000, help='R')
parser.add_argument('--latent-size',
type=int,
default=32,
help='Latent size')
parser.add_argument('--dataset',
type=str,
default='MNIST',
help='(MNIST|FMNIST)')
parser.add_argument(
'--model-type',
type=str,
required=True,
help=
'(SWD|MSWD|DSWD|GSWD|DGSWD|JSWD|JMSWD|JDSWD|JGSWD|JDGSWD|CRAMER|JCRAMER|SINKHORN|JSINKHORN)'
)
args = parser.parse_args()
torch.random.manual_seed(args.seed)
if (args.g == 'circular'):
g_function = circular_function
model_type = args.model_type
latent_size = args.latent_size
num_projection = args.num_projection
dataset = args.dataset
model_dir = os.path.join(args.outdir, model_type)
assert dataset in ['MNIST', 'FMNIST']
assert model_type in [
'SWD', 'MSWD', 'DSWD', 'GSWD', 'DGSWD', 'JSWD', 'JMSWD', 'JDSWD',
'JGSWD', 'JDGSWD', 'CRAMER', 'JCRAMER', 'SINKHORN', 'JSINKHORN'
]
if not (os.path.isdir(args.datadir)):
os.makedirs(args.datadir)
if not (os.path.isdir(args.outdir)):
os.makedirs(args.outdir)
if not (os.path.isdir(args.outdir)):
os.makedirs(args.outdir)
if not (os.path.isdir(model_dir)):
os.makedirs(model_dir)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print('batch size {}\nepochs {}\nAdam lr {} \n using device {}\n'.format(
args.batch_size, args.epochs, args.lr, device.type))
# build train and test set data loaders
if (dataset == 'MNIST'):
image_size = 28
num_chanel = 1
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.datadir,
train=True,
download=True,
transform=transforms.Compose(
[transforms.ToTensor()])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
args.datadir,
train=False,
download=True,
transform=transforms.Compose([transforms.ToTensor()])),
batch_size=64,
shuffle=False,
num_workers=args.num_workers)
model = MnistAutoencoder(image_size=28,
latent_size=args.latent_size,
hidden_size=100,
device=device).to(device)
elif (dataset == 'FMNIST'):
image_size = 28
num_chanel = 1
train_loader = torch.utils.data.DataLoader(
datasets.FashionMNIST(args.datadir,
train=True,
download=True,
transform=transforms.Compose(
[transforms.ToTensor()])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)
test_loader = torch.utils.data.DataLoader(datasets.FashionMNIST(
args.datadir,
train=False,
download=True,
transform=transforms.Compose([transforms.ToTensor()])),
batch_size=64,
shuffle=False,
num_workers=args.num_workers)
model = MnistAutoencoder(image_size=28,
latent_size=args.latent_size,
hidden_size=100,
device=device).to(device)
if (model_type == 'DSWD' or model_type == 'DGSWD'):
transform_net = TransformNet(28 * 28).to(device)
op_trannet = optim.Adam(transform_net.parameters(),
lr=args.lr,
betas=(0.5, 0.999))
# train_net(28 * 28, 1000, transform_net, op_trannet)
elif (model_type == 'JDSWD' or model_type == 'JDSWD2'
or model_type == 'JDGSWD'):
transform_net = TransformNet(args.latent_size + 28 * 28).to(device)
op_trannet = optim.Adam(transform_net.parameters(),
lr=args.lr,
betas=(0.5, 0.999))
# train_net(args.latent_size + 28 * 28, 1000, transform_net, op_trannet)
optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(0.5, 0.999))
fixednoise = torch.randn((64, latent_size)).to(device)
for epoch in range(args.epochs):
total_loss = 0.0
for batch_idx, (data, y) in tqdm(enumerate(train_loader, start=0)):
if (model_type == 'SWD'):
loss = model.compute_loss_SWD(data,
torch.randn,
num_projection,
p=args.p)
elif (model_type == 'GSWD'):
loss = model.compute_loss_GSWD(data,
torch.randn,
g_function,
args.r,
num_projection,
p=args.p)
elif (model_type == 'MSWD'):
loss, v = model.compute_loss_MSWD(data,
torch.randn,
p=args.p,
max_iter=args.niter)
elif (model_type == 'DSWD'):
loss = model.compute_lossDSWD(data,
torch.randn,
num_projection,
transform_net,
op_trannet,
p=args.p,
max_iter=args.niter,
lam=args.lam)
elif (model_type == 'DGSWD'):
loss = model.compute_lossDGSWD(data,
torch.randn,
num_projection,
transform_net,
op_trannet,
g_function,
r=args.r,
p=args.p,
max_iter=args.niter,
lam=args.lam)
elif (model_type == 'JSWD'):
loss = model.compute_loss_JSWD(data,
torch.randn,
num_projection,
p=args.p)
elif (model_type == 'JGSWD'):
loss = model.compute_loss_JGSWD(data,
torch.randn,
g_function,
args.r,
num_projection,
p=args.p)
elif (model_type == 'JDSWD'):
loss = model.compute_lossJDSWD(data,
torch.randn,
num_projection,
transform_net,
op_trannet,
p=args.p,
max_iter=args.niter,
lam=args.lam)
elif (model_type == 'JDGSWD'):
loss = model.compute_lossJDGSWD(data,
torch.randn,
num_projection,
transform_net,
op_trannet,
g_function,
r=args.r,
p=args.p,
max_iter=args.niter,
lam=args.lam)
elif (model_type == 'JMSWD'):
loss, v = model.compute_loss_MSWD(data,
torch.randn,
p=args.p,
max_iter=args.niter)
elif (model_type == 'CRAMER'):
loss = model.compute_loss_cramer(data, torch.randn)
elif (model_type == 'JCRAMER'):
loss = model.compute_loss_join_cramer(data, torch.randn)
elif (model_type == 'SINKHORN'):
loss = model.compute_wasserstein_vi_loss(data,
torch.randn,
n_iter=args.niter,
p=args.p,
e=1)
elif (model_type == 'JSINKHORN'):
loss = model.compute_join_wasserstein_vi_loss(
data, torch.randn, n_iter=args.niter, p=args.p, e=1)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
total_loss /= (batch_idx + 1)
print("Epoch: " + str(epoch) + " Loss: " + str(total_loss))
if (epoch % 1 == 0):
model.eval()
sampling(model_dir + '/sample_epoch_' + str(epoch) + ".png",
fixednoise, model.decoder, 64, image_size, num_chanel)
if (model_type[0] == 'J'):
for _, (input, y) in enumerate(test_loader, start=0):
input = input.to(device)
input = input.view(-1, image_size**2)
reconstruct(
model_dir + '/reconstruction_epoch_' + str(epoch) +
".png", input, model.encoder, model.decoder,
image_size, num_chanel, device)
break
model.train()
def __init__(self,
which_set='Cifar-10',
root=None,
train=True,
download=True,
return_idxs=False,
num_classes=10,
aug=('random_order', 'random_h_flip', 'random_crop', 'random_rot_10', 'random_scale_0.9_1.1', 'random_shear_5', 'cutout'),
cut_n_holes=1,
cut_length=16,
):
assert which_set in ['Cifar-10', 'MNIST', 'Fashion-MNIST'], \
"Must be Cifar, MNIST, or Fashion MNIST, {} not allowed".format(which_set)
image_length = 32 if which_set == 'Cifar-10' else 28
normalizer = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
if which_set == 'MNIST':
normalizer = transforms.Normalize((0.1307,), (0.3081,))
elif which_set == 'Fashion-MNIST':
normalizer = transforms.Normalize((0.1307,), (0.3081,))
transforms_list = []
for augment in aug:
############### First do the things that don't change where the image is in the box
if augment == 'random_h_flip':
transforms_list.append(transforms.RandomHorizontalFlip())
if augment == 'random_v_flip':
transforms_list.append(transforms.RandomVerticalFlip())
############### Then mess with brightness etc.
if augment == 'color_jitter':
transforms_list.append(transforms.ColorJitter(brightness=0.3, contrast=0.3, hue=0.0))
############### Now do some sheering/cropping/rotation that changes where the images is
if augment == 'affine':
rot_degrees = 0
scale_low = None
scale_high = None
shear_degrees = None
for augment_inner in aug:
if 'random_rot' in augment_inner:
rot_degrees = int(augment_inner.split('_')[-1])
if 'random_scale' in augment_inner:
scale_low = float(augment_inner.split('_')[-2])
scale_high = float(augment_inner.split('_')[-1])
if 'random_shear' in augment_inner:
shear_degrees = int(augment_inner.split('_')[-1])
transforms_list.append(transforms.RandomAffine(degrees=rot_degrees,
scale=None if (scale_low == None) or (scale_high == None) else (scale_low, scale_high),
shear=shear_degrees))
if augment == 'random_crop':
transforms_list.append(transforms.RandomCrop(size=[image_length, image_length], padding=4))
transform = transforms.Compose(transforms_list) if 'random_order' not in aug else transforms.Compose([transforms.RandomOrder(transforms=transforms_list)])
transform.transforms.append(transforms.ToTensor())
transform.transforms.append(normalizer)
for augment in aug:
############### Finally do things that are related to regularisation
if augment == 'cutout':
transform.transforms.append(Cutout(n_holes=cut_n_holes, length=cut_length))
if augment == 'rainbowout':
transform.transforms.append(RainbowOut())
if augment == 'channelswap':
transform.transforms.append(ChannelSwap())
if which_set == 'MNIST':
self.dataset = datasets.MNIST(root='../data/MNIST' if root is None else root,
train=train,
download=download,
transform=transform)
elif which_set == 'Fashion-MNIST':
self.dataset = datasets.FashionMNIST(root='../data/Fashion-MNIST' if root is None else root,
train=train,
download=download,
transform=transform)
else:
self.dataset = datasets.CIFAR10(root='../data/Cifar-10' if root is None else root,
train=train, download=download,
transform=transform)
self.return_idxs = return_idxs
self.num_classes = num_classes
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
def get_data(dataset, batch_size):
# Get MNIST dataset.
if dataset == 'MNIST':
transform = transforms.Compose([
transforms.Resize(28),
transforms.CenterCrop(28),
transforms.ToTensor()
])
dataset = dsets.MNIST(root + 'mnist/',
train='train',
download=True,
transform=transform)
# Get SVHN dataset.
elif dataset == 'SVHN':
transform = transforms.Compose([
transforms.Resize(32),
transforms.CenterCrop(32),
transforms.ToTensor()
])
dataset = dsets.SVHN(root + 'svhn/',
split='train',
download=True,
transform=transform)
# Get FashionMNIST dataset.
elif dataset == 'FashionMNIST':
transform = transforms.Compose([
transforms.Resize(28),
transforms.CenterCrop(28),
transforms.ToTensor()
])
dataset = dsets.FashionMNIST(root + 'fashionmnist/',
train='train',
download=True,
transform=transform)
# Get CelebA dataset.
# MUST ALREADY BE DOWNLOADED IN THE APPROPRIATE DIRECTOR DEFINED BY ROOT PATH!
elif dataset == 'CelebA':
transform = transforms.Compose([
transforms.Resize(32),
transforms.CenterCrop(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = dsets.CelebA(root + 'celeba/',
download=True,
transform=transform)
# Create dataloader.
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=True)
return dataloader
def __init__(self, args):
# parameters
self.epoch = args.epoch
self.sample_num = 19
self.batch_size = args.batch_size
self.save_dir = args.save_dir
self.result_dir = args.result_dir
self.dataset = args.dataset
self.dataroot_dir = args.dataroot_dir
self.log_dir = args.log_dir
self.gpu_mode = args.gpu_mode
self.num_workers = args.num_workers
self.model_name = args.gan_type
self.loss_option = args.loss_option
if len(args.loss_option) > 0:
self.model_name = self.model_name + '_' + args.loss_option
self.loss_option = args.loss_option.split(',')
if len(args.comment) > 0:
self.model_name = self.model_name + '_' + args.comment
self.lambda_ = 0.25
if self.dataset == 'MultiPie' or self.dataset == 'miniPie':
self.Nd = 337 # 200
self.Np = 9
self.Ni = 20
self.Nz = 50
elif self.dataset == 'Bosphorus':
self.Nz = 50
elif self.dataset == 'CASIA-WebFace':
self.Nd = 10885
self.Np = 13
self.Ni = 20
self.Nz = 50
if not os.path.exists(self.result_dir + '/' + self.dataset + '/' + self.model_name):
os.makedirs(self.result_dir + '/' + self.dataset + '/' + self.model_name)
if not os.path.exists(os.path.join(self.save_dir, self.dataset, self.model_name)):
os.makedirs(os.path.join(self.save_dir, self.dataset, self.model_name))
# load dataset
data_dir = os.path.join( self.dataroot_dir, self.dataset )
if self.dataset == 'mnist':
self.data_loader = DataLoader(datasets.MNIST(data_dir, train=True, download=True,
transform=transforms.Compose(
[transforms.ToTensor()])),
batch_size=self.batch_size, shuffle=True)
elif self.dataset == 'fashion-mnist':
self.data_loader = DataLoader(
datasets.FashionMNIST(data_dir, train=True, download=True, transform=transforms.Compose(
[transforms.ToTensor()])),
batch_size=self.batch_size, shuffle=True)
elif self.dataset == 'celebA':
self.data_loader = utils.CustomDataLoader(data_dir, transform=transforms.Compose(
[transforms.CenterCrop(160), transforms.Scale(64), transforms.ToTensor()]), batch_size=self.batch_size,
shuffle=True)
elif self.dataset == 'MultiPie' or self.dataset == 'miniPie':
self.data_loader = DataLoader( utils.MultiPie(data_dir,
transform=transforms.Compose(
[transforms.Scale(100), transforms.RandomCrop(96), transforms.ToTensor()])),
batch_size=self.batch_size, shuffle=True)
elif self.dataset == 'CASIA-WebFace':
self.data_loader = utils.CustomDataLoader(data_dir, transform=transforms.Compose(
[transforms.Scale(100), transforms.RandomCrop(96), transforms.ToTensor()]), batch_size=self.batch_size,
shuffle=True)
elif self.dataset == 'Bosphorus':
self.data_loader = DataLoader( utils.Bosphorus(data_dir, use_image=True, skipCodes=['YR','PR','CR'],
transform=transforms.ToTensor(),
shape=128, image_shape=256),
batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers)
self.Nid = 105
self.Npcode = len(self.data_loader.dataset.posecodemap)
# fixed samples for reconstruction visualization
print( 'Generating fixed sample for visualization...' )
nPcodes = self.Npcode//4
nSamples = self.sample_num-nPcodes
sample_x2D_s = []
sample_x3D_s = []
for iB, (sample_x3D_,sample_y_,sample_x2D_) in enumerate(self.data_loader):
sample_x2D_s.append( sample_x2D_ )
sample_x3D_s.append( sample_x3D_ )
if iB > nSamples // self.batch_size:
break
sample_x2D_s = torch.cat( sample_x2D_s )[:nSamples,:,:,:]
sample_x3D_s = torch.cat( sample_x3D_s )[:nSamples,:,:,:]
sample_x2D_s = torch.split( sample_x2D_s, 1 )
sample_x3D_s = torch.split( sample_x3D_s, 1 )
sample_x2D_s += (sample_x2D_s[0],)*nPcodes
sample_x3D_s += (sample_x3D_s[0],)*nPcodes
self.sample_x2D_ = torch.cat( sample_x2D_s )
self.sample_x3D_ = torch.cat( sample_x3D_s )
self.sample_pcode_ = torch.zeros( nSamples+nPcodes, self.Npcode )
self.sample_pcode_[:nSamples,0]=1
for iS in range( nPcodes ):
ii = iS%self.Npcode
self.sample_pcode_[iS+nSamples,ii] = 1
self.sample_z_ = torch.rand( nSamples+nPcodes, self.Nz )
fname = os.path.join( self.result_dir, self.dataset, self.model_name, 'samples.png' )
nSpS = int(math.ceil( math.sqrt( nSamples+nPcodes ) )) # num samples per side
utils.save_images(self.sample_x2D_[:nSpS*nSpS,:,:,:].numpy().transpose(0,2,3,1), [nSpS,nSpS],fname)
fname = os.path.join( self.result_dir, self.dataset, self.model_name, 'sampleGT.npy')
self.sample_x3D_.numpy().squeeze().dump( fname )
if self.gpu_mode:
self.sample_x2D_ = Variable(self.sample_x2D_.cuda(), volatile=True)
self.sample_x3D_ = Variable(self.sample_x3D_.cuda(), volatile=True)
self.sample_z_ = Variable(self.sample_z_.cuda(), volatile=True)
self.sample_pcode_ = Variable(self.sample_pcode_.cuda(), volatile=True)
else:
self.sample_x2D_ = Variable(self.sample_x2D_, volatile=True)
self.sample_x3D_ = Variable(self.sample_x3D_, volatile=True)
self.sample_z_ = Variable(self.sample_z_, volatile=True)
self.sample_pcode_ = Variable(self.sample_pcode_, volatile=True)
# networks init
self.G_3Dto2D = generator3Dto2D(self.Nid, self.Npcode, self.Nz)
self.D_2D = discriminator2D(self.Nid, self.Npcode)
self.G_3Dto2D_optimizer = optim.Adam(self.G_3Dto2D.parameters(), lr=args.lrG, betas=(args.beta1, args.beta2))
self.D_2D_optimizer = optim.Adam(self.D_2D.parameters(), lr=args.lrD, betas=(args.beta1, args.beta2))
if self.gpu_mode:
self.G_3Dto2D.cuda()
self.D_2D.cuda()
self.CE_loss = nn.CrossEntropyLoss().cuda()
self.BCE_loss = nn.BCELoss().cuda()
self.MSE_loss = nn.MSELoss().cuda()
self.L1_loss = nn.L1Loss().cuda()
else:
self.CE_loss = nn.CrossEntropyLoss()
self.BCE_loss = nn.BCELoss()
self.MSE_loss = nn.MSELoss()
self.L1_loss = nn.L1Loss()
print('init done')
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST')
parser.add_argument('-m', '--model-directory', type=str, required=True,
help='location to store trained model')
parser.add_argument('-d', '--diagram-directory', type=str, required=False,
help='location to store homology info')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--up-to', type=int, default=500, metavar='N',
help='How many testing exmaples for creating diagrams')
parser.add_argument('--test-batch-size', type=int, default=64, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('-ct', '--create-diagrams', action='store_true', default=False,
help='Whether to compute homology on dynamic graph after training')
parser.add_argument('-ht', '--homology-train', action='store_true', default=False,
help='Whether to compute homology on static graph during training')
parser.add_argument('-da', '--dataset', type=str, required=True,
help='which dataset to train on (mnist or fashionmnist)')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
if args.dataset == 'mnist':
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True, transform=transforms.Compose([
transforms.ToTensor(),
])), batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, download=True, transform=transforms.Compose([
transforms.ToTensor(),
])), batch_size=args.test_batch_size, shuffle=False, **kwargs)
if args.dataset == 'fashion':
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data/fashion', train=True, download=True, transform=transforms.Compose([
transforms.ToTensor(),
])), batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.FashionMNIST('../data/fashion', train=False, download=True, transform=transforms.Compose([
transforms.ToTensor(),
])), batch_size=args.test_batch_size, shuffle=False, **kwargs)
model = FFF().to(device)
# optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
optimizer = optim.SGD(model.parameters(), lr=args.lr)
res_df = []
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
if args.homology_train:
res_df = test_homology(args,model,device,test_loader, epoch, res_df)
else:
test(args, model, device, test_loader)
if args.homology_train and args.diagram_directory is not None:
df_filename = model.save_string()
df_filename = 'train_homology_' + df_filename[:df_filename.find('.pt')] + '.pkl'
df_loc = os.path.join(args.diagram_directory, df_filename)
res_df = pd.DataFrame(res_df)
res_df.to_pickle(df_loc)
save_path = os.path.join(args.model_directory, model.save_string())
torch.save(model.state_dict(), save_path)
if args.diagram_directory is not None and args.create_diagrams:
create_diagrams(args, model)
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda
training_data = datasets.FashionMNIST(root="data",
train=True,
download=False,
transform=ToTensor())
test_data = datasets.FashionMNIST(root="data",
train=False,
download=False,
transform=ToTensor())
train_dataloader = DataLoader(training_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
class NeuralNetwork(nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__()
self.flatten = nn.Flatten()
self.linear_relu_stack = nn.Sequential(nn.Linear(28 * 28, 512),
nn.ReLU(), nn.Linear(512, 512),
nn.ReLU(), nn.Linear(512, 10),
nn.ReLU())
def forward(self, x):
x = self.flatten(x)
def get_dataset(data_name='mnist',
data_dir='data',
train=True,
label_id=None,
crop_flip=True):
"""
Get a dataset.
:param data_name: str, name of dataset.
:param data_dir: str, base directory of data.
:param train: bool, return train set if True, or test set if False.
:param label_id: None or int, return data with particular label_id.
:param crop_flip: bool, whether use crop_flip as data augmentation.
:return: pytorch dataset.
"""
transform_1d_crop_flip = transforms.Compose([
transforms.Resize((32, 32)),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
# transforms.Normalize((0.5,), (0.5,)) # 1-channel, scale to [-1, 1]
])
transform_1d = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
#transforms.Normalize((0.5,), (0.5, ))
])
transform_3d_crop_flip = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_3d = transforms.Compose([
transforms.ToTensor(),
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if data_name == 'mnist':
#if train:
# when train is True, we use transform_1d_crop_flip by default unless crop_flip is set to False
# transform = transform_1d if crop_flip is False else transform_1d_crop_flip
#else:
# transform = transform_1d
dataset = datasets.MNIST(data_dir,
train=train,
download=True,
transform=transform_1d)
elif data_name == 'fashion':
if train:
# when train is True, we use transform_1d_crop_flip by default unless crop_flip is set to False
transform = transform_1d if crop_flip is False else transform_1d_crop_flip
else:
transform = transform_1d
dataset = datasets.FashionMNIST(data_dir,
train=train,
download=True,
transform=transform)
elif data_name == 'cifar10':
if train:
# when train is True, we use transform_1d_crop_flip by default unless crop_flip is set to False
transform = transform_3d if crop_flip is False else transform_3d_crop_flip
else:
transform = transform_3d
dataset = datasets.CIFAR10(data_dir,
train=train,
download=True,
transform=transform)
elif data_name == 'svhn':
if train:
# when train is True, we use transform_1d_crop_flip by default unless crop_flip is set to False
transform = transform_3d if crop_flip is False else transform_3d_crop_flip
split = 'train'
else:
transform = transform_3d
split = 'test'
dataset = datasets.SVHN(data_dir,
split=split,
download=True,
transform=transform)
else:
print('dataset {} is not available'.format(data_name))
if label_id is not None:
# select samples with particular label
if data_name == 'cifar10': #isinstance(dataset.targets, list):
# for cifar10
targets = np.array(dataset.targets)
idx = targets == label_id
dataset.targets = list(targets[idx])
dataset.data = dataset.data[idx]
elif data_name == 'svhn':
idx = dataset.labels == label_id
dataset.labels = dataset.labels[idx]
dataset.data = dataset.data[idx]
else:
# for MNIST and FashionMNIST
targets = dataset.targets
data = dataset.data
idx = targets == label_id
dataset.targets = targets[idx]
dataset.data = data[idx]
return dataset
def load_fashion_mnist(batch_size, val_split=True, subset=[-1, -1, -1]):
transformations = [transforms.ToTensor()]
transformations.append(transforms.Normalize(
(0.1307, ), (0.3081, ))) # This makes a huge difference!
transform = transforms.Compose(transformations)
if val_split:
num_train = 50000 # Will split training set into 50,000 training and 10,000 validation images
# Train set
trainset = datasets.FashionMNIST(root='./data/fashion',
train=True,
download=True,
transform=transform)
trainset.data = trainset.train_data[:num_train, :, :]
trainset.targets = trainset.train_labels[:num_train]
# Validation set
valset = datasets.FashionMNIST(root='./data/fashion',
train=True,
download=True,
transform=transform)
valset.data = valset.train_data[num_train:, :, :]
valset.targets = valset.train_labels[num_train:]
# Test set
testset = datasets.FashionMNIST(root='./data/fashion',
train=False,
download=True,
transform=transform)
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
if subset[1] != -1:
valset = getSubset(valset, subset[1])
train_dataloader = DataLoader(trainset,
batch_size=batch_size,
shuffle=True,
num_workers=0) # 50,000 images
val_dataloader = DataLoader(valset,
batch_size=batch_size,
shuffle=True,
num_workers=0) # 10,000 images
test_dataloader = DataLoader(testset,
batch_size=batch_size,
shuffle=False,
num_workers=0) # 10,000 images
return train_dataloader, val_dataloader, test_dataloader
else:
trainset = datasets.FashionMNIST(root='./data/fashion',
train=True,
download=True,
transform=transform)
testset = datasets.FashionMNIST(root='./data/fashion',
train=False,
download=True,
transform=transform)
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
train_dataloader = DataLoader(trainset,
batch_size=batch_size,
shuffle=True,
num_workers=0) # 60,000 images
test_dataloader = DataLoader(testset,
batch_size=batch_size,
shuffle=False,
num_workers=0) # 10,000 images
return train_dataloader, None, test_dataloader
(data.squeeze().detach().cpu().numpy(), target.item()))
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--test-batch-size',
type=int,
default=1,
metavar='N',
help='input batch size for testing (default: 1)')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
args = parser.parse_args()
test_loader = torch.utils.data.DataLoader(datasets.FashionMNIST(
'../data',
train=False,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])),
batch_size=args.test_batch_size,
shuffle=True)
use_cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print("开始筛选----------------------------------")
filter(args, BlackNet, device, test_loader)
print("筛选完毕----------------------------------")
print("得到{}个正确预测的样本".format(len(filter_data)))
filter_data = np.array(filter_data)
np.save('filter_data.npy', filter_data)
return x
# model initalizatioin
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
hyperP = dict({'numEpoch': 10, 'lr': 1e-4, 'batchSize': 5})
if torch.cuda.is_available():
hyperP['batchSize'] = 600
myModel = VGG8()
myLoss = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(myModel.parameters(), lr=hyperP['lr'])
myModel.to(device)
# Download and load
train = DataLoader(datasets.FashionMNIST('data_fashionMNIST',
train=True,
download=True,
transform=transforms.ToTensor()),
shuffle=True,
batch_size=hyperP['batchSize'])
test = DataLoader(datasets.FashionMNIST('data_fashionMNIST',
train=False,
download=True,
transform=transforms.ToTensor()),
shuffle=False,
batch_size=hyperP['batchSize'])
print(sum(p.numel() for p in myModel.parameters() if p.requires_grad))
def testing(net, testloader):
correct = 0
import torch
from torchvision import datasets, transforms
from torch import nn, optim
import torch.nn.functional as F
import helper
import matplotlib.pyplot as plt
# Define a transform to normalize the data
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Download and load the training data
trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/',
download=True,
train=True,
transform=transform)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=64,
shuffle=True)
# Download and load the test data
testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/',
download=True,
train=False,
transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)
class Classifier(nn.Module):
def __init__(self):
super().__init__()
# 하이퍼파라미터
EPOCHS = 500
BATCH_SIZE = 100
USE_CUDA = torch.cuda.is_available()
DEVICE = torch.device("cuda" if USE_CUDA else "cpu")
print("Using Device:", DEVICE)
# 학습에 필요한 데이터셋을 로딩합니다.
# Fashion MNIST 데이터셋
trainset = datasets.FashionMNIST('./.data',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
train_loader = torch.utils.data.DataLoader(
dataset = trainset,
batch_size = BATCH_SIZE,
shuffle = True)
# 생성자는 64차원의 랜덤한 텐서를 입력받아 이에 행렬곱(Linear)과 활성화 함수(ReLU, Tanh) 연산을 실행합니다. 생성자의 결과값은 784차원, 즉 Fashion MNIST 속의 이미지와 같은 차원의 텐서입니다.
# 생성자 (Generator)
G = nn.Sequential(
nn.Linear(64, 256),
nn.ReLU(),
def add_noise(img):
noise = torch.randn(img.size()) * 0.2
noisy_img = img + noise
return noisy_img
def plot_sample_img(img, name):
img = img.view(1, 28, 28)
save_image(img, './sample_{}.png'.format(name))
img_transform = transforms.Compose([
transforms.ToTensor(),
])
dataset = datasets.FashionMNIST('./data', transform=img_transform, download=True)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
class autoencoder(nn.Module):
def __init__(self):
super(autoencoder, self).__init__()
self.encoder = nn.Sequential(
nn.Linear(28 * 28, 500),
nn.Tanh())
self.decoder = nn.Sequential(
nn.Linear(500, 28*28),
nn.Sigmoid())
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms
from torch.utils.data import Dataset, Subset, DataLoader, random_split
MNIST_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.1307], [0.3081])
])
MNIST_train = datasets.MNIST('.', download=True, train = True, transform=MNIST_transform)
MNIST_test = datasets.MNIST('.', download=True, train = False, transform=MNIST_transform)
FASHION_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.2859], [0.3530])
])
FASHION_train = datasets.FashionMNIST('.', download=True, train=True, transform=MNIST_transform)
FASHION_test = datasets.FashionMNIST('.', download=True, train=False, transform=FASHION_transform)
class GridDataset(Dataset):
def __init__(self, MNIST_dataset, FASHION_dataset): # pass in dataset
assert len(MNIST_dataset) == len(FASHION_dataset)
self.MNIST_dataset, self.FASHION_dataset = MNIST_dataset, FASHION_dataset
self.targets = FASHION_dataset.targets
torch.manual_seed(442) # Fix random seed for reproducibility
N = len(MNIST_dataset)
self.randpos = torch.randint(low=0,high=4,size=(N,)) # position of the FASHION-MNIST image
self.randidx = torch.randint(low=0,high=N,size=(N,3)) # indices of MNIST images
def __len__(self):
return len(self.MNIST_dataset)