def load_emnist(val_size=10000, seed=None):
"""Return the train (55k), val (5k, randomly drawn from the original test set) and test (10k) dataset for MNIST."""
image_transform = transforms.Compose([
# EMNIST images are flipped and rotated by default, fix this here.
transforms.RandomHorizontalFlip(1),
transforms.RandomRotation((90, 90)),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
target_transform = lambda x: x - 1 # make labels start at 0 instead of 1
raw_train_dataset = datasets.EMNIST('data/emnist',
split='letters',
train=True,
download=True,
transform=image_transform,
target_transform=target_transform)
test_dataset = datasets.EMNIST('data/emnist',
split='letters',
train=False,
download=True,
transform=image_transform,
target_transform=target_transform)
# Split 5k samples from the train dataset for validation (similar to Sacramento et al. 2018).
utils.seed_torch(seed)
train_dataset, val_dataset = torch.utils.data.dataset.random_split(
raw_train_dataset, (len(raw_train_dataset) - val_size, val_size))
return train_dataset, val_dataset, test_dataset
def _load(self):
"""
Load dataset
:rtype: Tuple[,]
:return: train and test dataset
"""
transformations = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1722, ), (0.3310, ))
])
train_loader = DataLoader(datasets.EMNIST(self.data_path,
split="byclass",
train=True,
download=True,
transform=transformations),
batch_size=self.batch_size,
shuffle=True,
pin_memory=True)
test_loader = DataLoader(datasets.EMNIST(self.data_path,
split="byclass",
train=False,
download=False,
transform=transformations),
batch_size=self.batch_size,
shuffle=True,
pin_memory=True)
dataset_test_len = len(test_loader.dataset)
dataset_train_len = len(train_loader.dataset)
print("Длина обучающего датасета {}\n Длина трениро"
"вочного датасета\n".format(dataset_train_len, dataset_test_len))
return train_loader, test_loader
def load_emnist_dataset():
import torchvision.datasets as datasets
mnist_train = datasets.EMNIST(root='../data/emnist',
split='balanced',
train=True,
download=True,
transform=None)
mnist_test = datasets.EMNIST(root='../data/emnist',
split='balanced',
train=False,
download=True,
transform=None)
test_labels = np.array(
[mnist_test[i][1].numpy() for i in range(len(mnist_test))],
dtype=np.int)
train_labels = np.array(
[mnist_train[i][1].numpy() for i in range(len(mnist_train))],
dtype=np.int)
test = np.array([
np.asarray(mnist_test[i][0]).reshape(28 * 28)
for i in range(len(mnist_test))
],
dtype=np.float)
train = np.array([
np.asarray(mnist_train[i][0]).reshape(28 * 28)
for i in range(len(mnist_train))
],
dtype=np.float)
train /= 255. # normalize data to be in range [0,1]
test /= 255.
return train, train_labels, test, test_labels, [28, 28]
def __init__(self):
super().__init__()
_path = Config().data.data_path
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomAffine(degrees=10,
translate=(0.2, 0.2),
scale=(0.8, 1.2)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
self.trainset = datasets.EMNIST(root=_path,
split='balanced',
train=True,
download=True,
transform=train_transform)
self.testset = datasets.EMNIST(root=_path,
split='balanced',
train=False,
download=True,
transform=test_transform)
def get_dataset(name, subset=None):
if name == 'EMNIST':
dataset = datasets.EMNIST('./data/EMNIST',
train=True,
download=True,
split='byclass',
transform=transforms.ToTensor())
elif name == 'MNIST':
dataset = datasets.EMNIST('./data/MNIST',
train=True,
download=True,
transform=transforms.ToTensor())
elif name == 'CIFAR10':
dataset = datasets.CIFAR10('./data/CIFAR10',
train=True,
download=True,
transform=transforms.ToTensor())
elif name == 'SVHN':
dataset = datasets.SVHN('./data/SVHN',
split='train',
download=True,
transform=transforms.ToTensor())
else:
raise Exception
if (subset is None) or (subset >= len(dataset)):
return dataset
else:
split = (subset, len(dataset) - subset)
subset, _ = torch.utils.data.random_split(dataset, split)
return subset
def main():
# curl https://www.itl.nist.gov/iaui/vip/cs_links/EMNIST/gzip.zip -o ../data/EMNIST/raw/emnist.zip
train_loader = torch.utils.data.DataLoader(
datasets.EMNIST('../data', 'balanced', train=True,
transform=transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=300, shuffle=False)
test_loader = torch.utils.data.DataLoader(
datasets.EMNIST('../data', 'balanced', train=False,
transform=transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=500, shuffle=False)
model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.1)
for epoch in range(3):
train(model, train_loader, optimizer, epoch)
test(model, test_loader)
torch.save(model.state_dict(), "emnist.pt")
def load_emnist(batch_size):
transformations = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1722, ), (0.3310, ))])
train_loader = torch.utils.data.DataLoader(datasets.EMNIST(
DATAPATHS['EMNIST'],
split="byclass",
train=True,
download=True,
transform=transformations),
batch_size=batch_size,
shuffle=True,
pin_memory=True)
labels_loader = torch.utils.data.DataLoader(datasets.EMNIST(
DATAPATHS['EMNIST'],
split="byclass",
train=False,
download=False,
transform=transformations),
batch_size=batch_size,
shuffle=True,
pin_memory=True)
dataset_test_len = len(labels_loader.dataset)
dataset_train_len = len(train_loader.dataset)
print("Длина обучающего датасета {}\n Длина трениро"
"вочного датасета\n".format(dataset_train_len, dataset_test_len))
return train_loader, labels_loader
def get_data(
self, data_filepath, val_set_percentage, random_split_seed, download=False
):
train_set = datasets.EMNIST(
root=data_filepath,
split="balanced",
train=True,
download=download,
transform=self.transform_train,
)
num_training_items = int(len(train_set) * (1.0 - val_set_percentage))
num_val_items = len(train_set) - num_training_items
train_set, val_set = torch.utils.data.random_split(
train_set,
[num_training_items, num_val_items],
generator=torch.Generator().manual_seed(random_split_seed),
)
test_set = datasets.EMNIST(
root=data_filepath,
split="balanced",
train=False,
transform=self.transform_validate,
)
num_labels = 47
return train_set, val_set, test_set, num_labels
def __init__(self, dataset_name: str, root_dir: Path) -> None:
self.root_dir = root_dir
self.dataset_name = dataset_name
if self.dataset_name == "MNIST":
## Reference: https://stackoverflow.com/a/66816284
new_mnist_mirror = 'https://ossci-datasets.s3.amazonaws.com/mnist'
datasets.MNIST.resources = [
('/'.join([new_mnist_mirror,
url.split('/')[-1]]), md5)
for url, md5 in datasets.MNIST.resources
]
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, )),
transforms.Lambda(
lambda x: torch.flatten(x, start_dim=1).squeeze())
])
self.train_data = datasets.MNIST(root_dir / "raw/",
train=True,
download=True,
transform=transform)
self.test_data = datasets.MNIST(root_dir / "raw/",
train=False,
download=True,
transform=transform)
self.num_train_data = len(self.train_data)
self.num_classes = 10
elif self.dataset_name == "EMNIST":
self.train_data = datasets.EMNIST(root_dir / "raw/",
split="letters",
train=True,
download=True)
self.test_data = datasets.EMNIST(root_dir / "raw/",
split="letters",
train=False,
download=True)
elif self.dataset_name == "CIFAR10":
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
self.train_data = datasets.CIFAR10(root_dir / "raw/",
train=True,
download=True,
transform=transform)
self.test_data = datasets.CIFAR10(root_dir / "raw/",
train=False,
download=True,
transform=transform)
self.num_train_data = len(self.train_data)
self.num_classes = 10
else:
raise ValueError("Unknown dataset_name")
def download_EMNIST(split='letters'):
"""
Download EMNIST dataset and save it into data folder.
:param split: ['balanced', 'byclass', 'bymerge', 'digits', 'letters', 'mnist']
"""
data_folder = os.path.join(ROOT_DIR, DATA_DIR)
dsets.EMNIST(root=data_folder, train=True, transform=transforms.ToTensor(), download=True, split=split)
dsets.EMNIST(root=data_folder, train=False, transform=transforms.ToTensor(), download=True, split=split)
def get_data_loader():
# Get Data
root = './data'
transform_labeled = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(size=28,
padding=int(28 * 0.125),
padding_mode='reflect'),
transforms.ToTensor(),
transforms.Normalize(mean=(0.1307, ), std=(0.3081, ))
])
transform_val = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
base_dataset = datasets.EMNIST(root,
train=True,
split='balanced',
download=True)
label_size = int(args.basicLabelRatio * len(base_dataset))
train_labeled_idxs, train_unlabeled_idxs = x_u_split(base_dataset.targets,
label_size,
args.k_img,
7 * args.k_img,
num_classes=47)
labeled_dataset = EMNISTSSL(root,
train_labeled_idxs,
train=True,
transform=transform_labeled)
unlabeled_dataset = EMNISTSSL(root,
train_unlabeled_idxs,
train=True,
transform=TransformFix(mean=(0.1307, ),
std=(0.3081, ),
size=28))
test_dataset = datasets.EMNIST(root,
train=False,
split='balanced',
transform=transform_val,
download=True)
labeled_loader = DataLoader(labeled_dataset,
args.bs,
num_workers=4,
pin_memory=True,
shuffle=True)
unlabeled_loader = DataLoader(unlabeled_dataset,
args.bs * 7,
num_workers=4,
pin_memory=True,
shuffle=True)
test_loader = DataLoader(test_dataset,
args.bs,
shuffle=True,
num_workers=4,
pin_memory=True)
return labeled_loader, unlabeled_loader, test_loader
def getdata_emnist(batch_size):
transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
data_train=datasets.EMNIST("./data/emnist",split='balanced',transform=transform,train=True,download=True)
data_test=datasets.EMNIST("./data/emnist",split='balanced',transform=transform,train=False,download=True)
data_loader_train=torch.utils.data.DataLoader(dataset=data_train,batch_size=batch_size,shuffle=True)
data_loader_test=torch.utils.data.DataLoader(dataset=data_test,batch_size=batch_size,shuffle=True)
return data_loader_train,data_loader_test
def __init__(self, args):
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
self.train_loader = torch.utils.data.DataLoader(
datasets.EMNIST('data/emnist', train=True, download=True, split='byclass',
transform=transforms.ToTensor()),
batch_size=args.batch_size, shuffle=True, **kwargs)
self.test_loader = torch.utils.data.DataLoader(
datasets.EMNIST('data/emnist', train=False, split='byclass',
transform=transforms.ToTensor()),
batch_size=args.batch_size, shuffle=True, **kwargs)
def foo(Model, Name):
print(Name)
args = Args()
torch.manual_seed(args.seed)
use_cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 16, 'pin_memory': True} if use_cuda else {}
transformer = transforms.Compose([
transforms.Lambda(to_tensor),
])
model = Model().to(device)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
test_loader = torch.utils.data.DataLoader(
datasets.EMNIST(args.data_path, split='mnist', train=False, transform=transformer, download = True),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
if len(sys.argv) > 1 and sys.argv[1] == 'test':
model.load_state_dict(torch.load(args.save_path + Name, map_location=lambda storage, loc: storage))
failed = {}
test(args, model, device, test_loader, failed)
for k, v in failed.items():
im = Image.new(mode='L', size=(len(v) * 28, 28))
for i, f in enumerate(map(lambda h: hashmap.get(h, None), v)):
if f != None:
im.paste(f, (i * 28, 0))
im.save('./result/' + k + '.png')
else:
train_loader = torch.utils.data.DataLoader(
datasets.EMNIST(args.data_path, split='mnist', train=True, transform=transformer, download=True),
batch_size=args.batch_size, shuffle=True, **kwargs)
accuracies = []
for epoch in range(args.epochs):
train(args, model, device, train_loader, optimizer, epoch)
test(args, model, device, test_loader, accuracies)
torch.save(model.state_dict(), args.save_path + Name)
accuracieses.append((accuracies, Name))
plt.plot(accuracies)
plt.xlabel('epoch')
plt.ylabel('accuracy')
# plt.show()
plt.title(Name)
plt.savefig(Name)
plt.clf()
def get_emnist_semi(root, num_expand_x, num_expand_u, device_ids, server_idxs):
root = './data'
transform_labeled = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(size=28,
padding=int(28 * 0.125),
padding_mode='reflect'),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
transform_val = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
base_dataset = datasets.EMNIST(root,
train=True,
split='balanced',
download=True)
train_labeled_idxs, train_unlabeled_idxs = x_u_split_semi(
base_dataset.targets, num_expand_x, num_expand_u, device_ids,
server_idxs)
train_unlabeled_dataset_list = []
train_labeled_dataset_list = []
train_unlabeled_idxs_tmp = copy.deepcopy(train_unlabeled_idxs[0])
for id in range(len(train_unlabeled_idxs)):
train_unlabeled_dataset = EMNIST(root,
train_unlabeled_idxs[id],
train=True,
transform=TransformFix(
size=28,
mean=(0.1307, ),
std=(0.3081, )))
train_unlabeled_dataset_list.append(train_unlabeled_dataset)
train_labeled_dataset = EMNIST(root,
train_labeled_idxs[id],
train=True,
transform=transform_labeled)
train_labeled_dataset_list.append(train_labeled_dataset)
test_dataset = datasets.EMNIST(root,
train=False,
split='balanced',
transform=transform_val,
download=True)
return train_labeled_dataset_list, train_unlabeled_dataset_list, test_dataset
def load_mnist_dataset(dataset, data_dir, training_data_ratio=1):
# data_dir = '../data/mnist/'
apply_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
if dataset == "mnist":
train_dataset = datasets.MNIST(data_dir,
train=True,
download=True,
transform=apply_transform)
test_dataset = datasets.MNIST(data_dir,
train=False,
download=True,
transform=apply_transform)
elif dataset == "fmnist":
train_dataset = datasets.FashionMNIST(data_dir,
train=True,
download=True,
transform=apply_transform)
test_dataset = datasets.FashionMNIST(data_dir,
train=False,
download=True,
transform=apply_transform)
elif dataset == "emnist":
train_dataset = datasets.EMNIST(data_dir,
train=True,
download=True,
transform=apply_transform,
split="balanced")
test_dataset = datasets.EMNIST(data_dir,
train=False,
download=True,
transform=apply_transform,
split="balanced")
else:
raise NotImplementedError
if training_data_ratio != 1:
select_len = int(len(train_dataset) * training_data_ratio)
train_dataset.data = train_dataset.data[:select_len]
train_dataset.targets = train_dataset.targets[:select_len]
return train_dataset, test_dataset
def EMNIST(train=False, batch_size=None, augm_flag=False, val_size=None):
if batch_size==None:
if train:
batch_size=train_batch_size
else:
batch_size=test_batch_size
transform_base = [transforms.ToTensor(), pre.Transpose()] #EMNIST is rotated 90 degrees from MNIST
transform_train = transforms.Compose([
transforms.RandomCrop(28, padding=4),
] + transform_base)
transform_test = transforms.Compose(transform_base)
transform_train = transforms.RandomChoice([transform_train, transform_test])
transform = transform_train if (augm_flag and train) else transform_test
dataset = datasets.EMNIST(path, split='letters',
train=train, transform=transform, download=True)
if train or val_size is None:
loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
shuffle=train, num_workers=1)
return loader
else:
# Split into val and test sets
test_size = len(dataset) - val_size
dataset_val, dataset_test = data_utils.random_split(dataset, (val_size, test_size))
val_loader = torch.utils.data.DataLoader(dataset_val, batch_size=batch_size,
shuffle=train, num_workers=1)
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=batch_size,
shuffle=train, num_workers=1)
return val_loader, test_loader
def EMNIST(train=False, batch_size=None, augm_flag=False):
if batch_size == None:
if train:
batch_size = train_batch_size
else:
batch_size = test_batch_size
transform_base = [transforms.ToTensor(),
pre.Transpose()
] #EMNIST is rotated 90 degrees from MNIST
transform_train = transforms.Compose([
transforms.RandomCrop(28, padding=4),
] + transform_base)
transform_test = transforms.Compose(transform_base)
transform_train = transforms.RandomChoice(
[transform_train, transform_test])
transform = transform_train if (augm_flag and train) else transform_test
dataset = datasets.EMNIST(path,
split='letters',
train=train,
transform=transform,
download=True)
loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=train,
num_workers=1)
return loader
def __init__(self, batch_size, augm_flag):
super().__init__(batch_size, augm_flag)
self.n_train, self.n_test = 60000, 10000
# TODO: actually, these numbers are smaller than the real ones.
self.n_classes = 10
self.height, self.width, self.n_colors = 28, 28, 1
self.data_dir = self.base_path + 'emnist/'
transform_base = [transforms.Lambda(lambda x: np.array(x).T / 255.0)]
transform_train = transforms.Compose([
transforms.RandomCrop(self.height, padding=4),
] + transform_base)
transform_test = transforms.Compose(transform_base)
transform_train = transform_train if self.augm_flag else transform_test
self.train_dataset = datasets.EMNIST(self.data_dir, split='letters', train=True, transform=transform_train, download=True)
self.test_dataset = datasets.EMNIST(self.data_dir, split='letters', train=False, transform=transform_test, download=True)
def get_dataset(data_name, data_root, image_size, train):
transform = transforms.Compose(
[transforms.Resize(image_size),
transforms.ToTensor()])
if data_name == "mnist":
dataset = datasets.MNIST(root=data_root,
train=train,
transform=transform,
download=True)
elif data_name == "fushion-mnist":
dataset = datasets.FashionMNIST(root=data_root,
train=train,
transform=transform,
download=True)
elif data_name == "kmnist":
dataset = datasets.KMNIST(root=data_root,
train=train,
transform=transform,
download=True)
elif data_name == "emnist":
dataset = datasets.EMNIST(root=data_root,
split="byclass",
train=train,
transform=transform,
download=True)
else:
dataset = None
return dataset
def get_loader(_dir,
_batch_size=1,
_train=True,
_portion=None,
_download=False,
**_args):
dataset = datasets.EMNIST(
_dir,
split='letters',
train=_train,
download=_download,
transform=transforms.ToTensor(),
target_transform=transforms.Lambda(
lambda x: x - 1
) # Necessary because labels are mapped 1 to 26 instead of 0 to 25
)
indices = list(torch.randperm(len(dataset)))
if _portion is not None:
indices = indices[0:math.floor(_portion * len(dataset))]
sampler = torch.utils.data.SubsetRandomSampler(indices)
batch_sampler = torch.utils.data.BatchSampler(sampler,
batch_size=_batch_size,
drop_last=False)
loader = torch.utils.data.DataLoader(dataset,
batch_sampler=batch_sampler,
**_args)
return loader
def rnn_train():
train_data = dsets.EMNIST(
root='./mnist',
split='mnist',
train=True,
transform=transforms.ToTensor(),
download=DOWNLOADS
)
train_load = Data.DataLoader(dataset=train_data,batch_size=BATCH_SIZE,shuffle=True,num_workers=2)
rnn = RNN()
optimizer = op.Adam(params=rnn.parameters(),lr=LR)
loss_fun = nn.CrossEntropyLoss()
for epoch in range(EPOCH):
for step ,(t_x ,t_y) in enumerate(train_load):
y = rnn(t_x)
loss = loss_fun(y,t_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("step",step,"| loss=",loss)
torch.save(rnn,"./rnn.pkl")
def raw_dataset(self, data_dir: str, download: bool, train: bool, transform):
if self.split == 'letters':
target_transform = (lambda x: x - 1)
else:
target_transform = None
return datasets.EMNIST(data_dir, split=self.split, download=download, train=train, transform=transform,
target_transform=target_transform)
def __init__(self, options):
transform_list = []
if options.image_size is not None:
transform_list.append(
transforms.Resize((options.image_size, options.image_size)))
# transform_list.append(transforms.CenterCrop(options.image_size))
transform_list.append(transforms.ToTensor())
if options.image_colors == 1:
transform_list.append(transforms.Normalize(mean=[0.5], std=[0.5]))
elif options.image_colors == 3:
transform_list.append(
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5,
0.5]))
transform = transforms.Compose(transform_list)
if options.dataset == 'mnist':
dataset = datasets.MNIST(options.data_dir,
train=True,
download=True,
transform=transform)
elif options.dataset == 'emnist':
# Updated URL from https://www.westernsydney.edu.au/bens/home/reproducible_research/emnist
datasets.EMNIST.url = 'https://cloudstor.aarnet.edu.au/plus/s/ZNmuFiuQTqZlu9W/download'
dataset = datasets.EMNIST(options.data_dir,
split=options.image_class,
train=True,
download=True,
transform=transform)
elif options.dataset == 'fashion-mnist':
dataset = datasets.FashionMNIST(options.data_dir,
train=True,
download=True,
transform=transform)
elif options.dataset == 'lsun':
training_class = options.image_class + '_train'
dataset = datasets.LSUN(options.data_dir,
classes=[training_class],
transform=transform)
elif options.dataset == 'cifar10':
dataset = datasets.CIFAR10(options.data_dir,
train=True,
download=True,
transform=transform)
elif options.dataset == 'cifar100':
dataset = datasets.CIFAR100(options.data_dir,
train=True,
download=True,
transform=transform)
else:
dataset = datasets.ImageFolder(root=options.data_dir,
transform=transform)
self.dataloader = DataLoader(dataset,
batch_size=options.batch_size,
num_workers=options.loader_workers,
shuffle=True,
drop_last=True,
pin_memory=options.pin_memory)
self.iterator = iter(self.dataloader)
def main():
train_batch_size = 100
dataset = datasets.EMNIST(
'../dataEMNIST',
split = 'balanced',
train = True,
download = True,
transform=transforms.ToTensor()
)
shuffle_dataset = True
val_split = 0.002
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(val_split * dataset_size))
if shuffle_dataset :
np.random.seed(2)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_indices)
val_sampler = SubsetRandomSampler(val_indices)
train_loader = torch.utils.data.DataLoader(
dataset,
sampler = train_sampler,
batch_size = 100
)
val_loader = torch.utils.data.DataLoader(
dataset,
sampler = val_sampler,
batch_size = 100
)
model = model_root().to(torch.device("cuda"))
model.load_state_dict(torch.load('root_emnist.pth'))
model_one = model_1().to(torch.device("cuda"))
model_one.load_state_dict(torch.load('emnist_1.pth'))
model_one_zero = model_1_0().to(torch.device("cuda"))
model_one_zero.load_state_dict(torch.load('emnist_1_0.pth'))
learning_rate = 0.0001
optimizer = torch.optim.Adam(model_one_zero.parameters(), lr=learning_rate)
loss_fn = nn.CrossEntropyLoss()
max = train(model, model_one, model_one_zero, optimizer, loss_fn, train_loader, val_loader, torch.device("cuda"), 0)
for i in range(2):
model_one_zero.load_state_dict(torch.load('emnist_1_0.pth'))
learning_rate /= 10
optimizer = torch.optim.Adam(model_one_zero.parameters(), lr=learning_rate, weight_decay = 5e-4)
loss_fn = nn.CrossEntropyLoss()
max = train(model, model_one, model_one_zero, optimizer, loss_fn, train_loader, val_loader, torch.device("cuda"), max)
def __init__(self):
super(CustomDataset, self).__init__()
self.trans = transforms.Compose([
transforms.Resize(64),
transforms.ToTensor(),
transforms.Normalize(mean = [0.5], std = [0.5])
]
)
self.letter_images = datasets.EMNIST('data_letters', 'letters', train=True, download = False, transform = self.trans)
def get_train_test_queues(args, train_transform, valid_transform):
print("Getting",args.dataset,"data")
if args.dataset == 'cifar10':
print("Using CIFAR10")
train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)
elif args.dataset == 'mnist':
print("Using MNIST")
train_data = dset.MNIST(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.MNIST(root=args.data, train=False, download=True, transform=valid_transform)
elif args.dataset == 'emnist':
print("Using EMNIST")
train_data = dset.EMNIST(root=args.data, split='balanced', train=True, download=True, transform=train_transform)
valid_data = dset.EMNIST(root=args.data, split='balanced', train=False, download=True, transform=valid_transform)
elif args.dataset == 'fashion':
print("Using Fashion")
train_data = dset.FashionMNIST(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.FashionMNIST(root=args.data, train=False, download=True, transform=valid_transform)
elif args.dataset == 'svhn':
print("Using SVHN")
train_data = dset.SVHN(root=args.data, split='train', download=True, transform=train_transform)
valid_data = dset.SVHN(root=args.data, split='test', download=True, transform=valid_transform)
elif args.dataset == 'stl10':
print("Using STL10")
train_data = dset.STL10(root=args.data, split='train', download=True, transform=train_transform)
valid_data = dset.STL10(root=args.data, split='test', download=True, transform=valid_transform)
elif args.dataset == 'devanagari':
print("Using DEVANAGARI")
# Ensure dataset is present in the directory args.data. Does not support auto download
print(args.data)
train_data = dset.ImageFolder(root=os.path.join(args.data,"Train"), transform=train_transform, loader = grey_pil_loader)
valid_data = dset.ImageFolder(root=os.path.join(args.data, "Test"), transform=valid_transform, loader = grey_pil_loader)
else:
assert False, "Cannot get training queue for dataset"
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=1)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=1)
return train_queue, valid_queue
def get_test_loader(data_dir, batch_size, num_workers=4, pin_memory=False):
"""
Utility function for loading and returning a multi-process
test iterator over the MNIST dataset.
If using CUDA, num_workers should be set to 1 and pin_memory to True.
Args
----
- data_dir: path directory to the dataset.
- batch_size: how many samples per batch to load.
- num_workers: number of subprocesses to use when loading the dataset.
- pin_memory: whether to copy tensors into CUDA pinned memory. Set it to
True if using GPU.
Returns
-------
- data_loader: test set iterator.
"""
# define transforms
normalize = transforms.Normalize((0.1307, ), (0.3081, ))
trans = transforms.Compose([
transforms.ToTensor(),
normalize,
])
# load dataset
# dataset = datasets.MNIST(
# data_dir, train=False, download=False, transform=trans
# )
#gb changes*********************************************************************************************
emnist_dir = "data"
# load dataset
dataset = datasets.EMNIST(
emnist_dir,
download=True,
split='letters',
train=True,
transform=transforms.Compose([
lambda img: transforms.functional.rotate(img, -90),
lambda img: transforms.functional.hflip(img),
transforms.ToTensor()
]))
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=pin_memory,
)
return data_loader
def __init__(self, train: bool = True,
max_seq_len: int = 1,
blank_label = 11,
pad_label = 10,
img_size: [int, int] = (28, 28)):
self.dataset = datasets.EMNIST(root='./data',
split='mnist',
train=train,
download=True)
self.max_seq_len = max_seq_len
self.img_size = img_size
def make_dataset(dataset, dataroot, imageSize):
"""
:param dataset: must be in 'cifar10 | lsun | imagenet | folder | lfw | fake'
:return: pytorch dataset for DataLoader to utilize
"""
if dataset in ['imagenet', 'folder', 'lfw']:
# folder dataset
dataset = dset.ImageFolder(root=dataroot,
transform=transforms.Compose([
transforms.Resize(imageSize),
transforms.CenterCrop(imageSize),
transforms.ToTensor(),
transforms.Normalize(
(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
elif dataset == 'lsun':
dataset = dset.LSUN(db_path=dataroot, classes=['bedroom_train'],
transform=transforms.Compose([
transforms.Resize(imageSize),
transforms.CenterCrop(imageSize),
transforms.ToTensor(),
transforms.Normalize(
(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
elif dataset == 'cifar10':
dataset = dset.CIFAR10(root=dataroot, download=True,
transform=transforms.Compose([
transforms.Resize(imageSize),
transforms.CenterCrop(imageSize),
transforms.ToTensor(),
transforms.Normalize(
(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
elif dataset == 'emnist':
dataset = dset.EMNIST(root=dataroot,download=True,split='letters',
transform=transforms.Compose([
transforms.Resize(imageSize),
transforms.ToTensor(),
transforms.Normalize(
(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
elif dataset == 'celeba':
dataset = dset.ImageFolder(root=dataroot,
transform=transforms.Compose([
transforms.CenterCrop(138),
transforms.Resize(imageSize),
transforms.ToTensor(),
transforms.Normalize(
(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
else:
raise Exception('--dataset must be in cifar10 | lsun | imagenet | folder | lfw | fake')
assert dataset
return dataset
