def add_query(self):
print('\nSelect queries... (query type: {})'.format(
self.args.query_type))
start_time = time.time()
self.netG.eval()
self.netD.eval()
# get query & update train/pool
if self.args.query_type == 'random':
query_idx = np.random.permutation(len(
self.pool))[:self.args.per_size]
elif self.args.query_type == 'gold':
query_idx = query.gold_acquistiion(self.pool, self.netD, self.args,
self.device)
else:
raise NotImplementedError
self.train_idx = list(set(self.train_idx) | set(query_idx))
self.pool_idx = list(set(self.pool_idx) - set(query_idx))
self.trainset = data_utils.Subset(self.base_dataset, self.train_idx)
self.pool = data_utils.Subset(self.base_dataset, self.pool_idx)
# print computation time
query_time = int(time.time() - start_time)
print('{:d}s elapsed'.format(query_time))
def get_train_valid_cifar10_dataloader(data_dir,
batch_size=100,
train_portion=0.99):
trans = get_img_tranformation()
full_dataset = torchvision.datasets.CIFAR10(root=data_dir,
train=True,
download=False,
transform=trans)
num_train = len(full_dataset)
train_size = int(train_portion * num_train)
valid_size = len(full_dataset) - train_size
# splite the dataset to train and validation set non-randomly
idxs = list(range(num_train))
train_idxs = idxs[valid_size:]
valid_idxs = idxs[:valid_size]
#
train_set = data.Subset(full_dataset, train_idxs)
valid_set = data.Subset(full_dataset, valid_idxs)
trainloader = data.DataLoader(train_set,
batch_size=batch_size,
shuffle=False,
num_workers=2)
validloader = data.DataLoader(valid_set,
batch_size=batch_size,
shuffle=False,
num_workers=2)
return trainloader, validloader
def get_AwA2(augment, dataroot):
num_classes = 85
image_shape = (64, 64, 3)
test_transform = transforms.Compose(
[transforms.Resize((64, 64)),
transforms.ToTensor()])
if augment:
transformations = [
transforms.Resize((64, 64)),
transforms.RandomCrop(size=64, padding=4, padding_mode='reflect'),
transforms.RandomHorizontalFlip()
]
else:
transformations = [transforms.Resize((64, 64))]
transformations.extend([transforms.ToTensor()])
train_transform = transforms.Compose(transformations)
path = Path(dataroot) / 'data' / 'AwA2' / 'Animals_with_Attributes2'
dataset = AwA2(path,
train_transform=train_transform,
test_transform=test_transform)
train_dataset = data.Subset(dataset, list(dataset.train_idxs))
test_dataset = data.Subset(dataset, list(dataset.test_idxs))
pred_bin_mat = torch.tensor(dataset.pred_bin_mat).float()
return image_shape, num_classes, train_dataset, test_dataset, pred_bin_mat
def split(self, condition):
"""
Splits the dataset according to the given boolean condition. When :code:`pyblaze.nn` is
imported, this method is available on all :code:`torch.utils.data.Dataset` objects.
Attention
---------
Do not call this method on iterable datasets.
Parameters
----------
condition: callable (object) -> bool
The condition which splits the dataset.
Returns
-------
torch.utils.data.Subset
The dataset with the items for which the condition evaluated to `true`.
torch.utils.data.Subset
The dataset with the items for which the condition evaluated to `false`.
"""
filter_ = np.array([condition(item) for item in self])
true_indices = np.where(filter_)[0]
false_indices = np.where(~filter_)[0]
return data.Subset(self, true_indices), data.Subset(self, false_indices)
def init_data(self):
print('Initialize dataset...')
self.train_transform = data.get_transform(self.args.image_size,
self.args.train_transform)
self.test_transform = data.get_transform(self.args.image_size,
self.args.test_transform)
# load base dataset
self.base_dataset, self.test_dataset = data.load_base_dataset(
self.args)
self.base_dataset.transform = self.train_transform
self.test_dataset.transform = self.test_transform
# split to train/val/pool set
if self.args.init_size is None:
self.train_idx = list(range(len(self.base_dataset)))
self.val_idx = []
self.pool_idx = []
self.args.init_size = len(self.base_dataset)
self.args.per_size = 0
self.args.max_size = len(self.base_dataset)
else:
self.train_idx, self.val_idx, self.pool_idx = data.split_dataset(
self.base_dataset, self.args.ny, self.args.init_size,
self.args.val_size)
if self.args.max_size is None:
self.args.per_size = 0
self.args.max_size = self.args.init_size
# define trainset and pool
self.trainset = data_utils.Subset(self.base_dataset, self.train_idx)
self.valset = data_utils.Subset(self.base_dataset, self.val_idx)
self.pool = data_utils.Subset(self.base_dataset, self.pool_idx)
def __init__(self, context, dataset, criterion):
self.ctx = context
self.criterion = criterion
ds_size = len(dataset)
test_count = ds_size // 10
train_count = ds_size - test_count
test_indices = torch.arange(start=0, end=test_count)
train_indices = torch.arange(start=test_count + 1, end=ds_size - 1)
self.train_dataset = td.Subset(dataset, indices=train_indices)
self.test_dataset = td.Subset(dataset, indices=test_indices)
log_dir = os.path.join(
'runs',
self.ctx.params['meta_name'],
)
log_path = os.path.join(
log_dir,
'run_{}'.format(datetime.datetime.now().isoformat()),
)
if not os.path.exists('runs'):
os.mkdir('runs')
if not os.path.exists(log_dir):
os.mkdir(log_dir)
print('Writing Tensorboard logs to {}'.format(log_path))
self.writer = SummaryWriter(log_path)
print('Writing graph')
dummy_input = torch.empty(size=self.ctx.model_shape,
dtype=torch.float32).to(self.ctx.device)
self.writer.add_graph(self.ctx.net, dummy_input)
del dummy_input
print('done')
def get_dataloader(self,split,verbose=0): if split == 'all_train': dataset = self.get_dataset(split='train') ; shuffle = True ; if self.unlab_samples_per_class != -1: labels = torch.tensor([y for x,y in dataset]) indices = torch.arange(len(labels)) indices = torch.cat([indices[labels==x][:self.unlab_samples_per_class] for x in torch.unique(labels)]) dataset = data.Subset(dataset, indices) elif split == 'lab_train': dataset = self.get_dataset(split='train') ; shuffle = True ; if self.samples_per_class != -1: labels = torch.tensor([y for x,y in dataset]) indices = torch.arange(len(labels)) indices = torch.cat([indices[labels==x][:self.samples_per_class] for x in torch.unique(labels)]) dataset = data.Subset(dataset, indices) elif split == 'test': dataset = self.get_dataset(split='test') ; shuffle = False ; elif split == 'valid': dataset = self.get_dataset(split='valid') ; shuffle = False ; dataloader = data.DataLoader(dataset, batch_size=self.batch_size, shuffle=shuffle, sampler=None, batch_sampler=None, num_workers=16) if verbose > 0: print(split,len(dataloader)) labels = torch.cat([y for x,y in dataloader]) labels = labels.numpy() print(np.unique(labels,return_counts=True)) return dataloader
def train_data(bs):
"""Get data loader for trainning & validating, bs means batch_size."""
train_ds = VideoZoomDataset(train_dataset_rootdir, VIDEO_SEQUENCE_LENGTH,
get_transform(train=True))
print(train_ds)
# Split train_ds in train and valid set
# xxxx--modify here
valid_len = int(0.2 * len(train_ds))
indices = [i for i in range(len(train_ds) - valid_len, len(train_ds))]
valid_ds = data.Subset(train_ds, indices)
indices = [i for i in range(len(train_ds) - valid_len)]
train_ds = data.Subset(train_ds, indices)
# Define training and validation data loaders
n_threads = min(4, bs)
train_dl = data.DataLoader(train_ds,
batch_size=bs,
shuffle=True,
num_workers=n_threads)
valid_dl = data.DataLoader(valid_ds,
batch_size=bs,
shuffle=False,
num_workers=n_threads)
return train_dl, valid_dl
def train_test_split(dset):
length = len(dset)
n_train = int(length * 0.6)
n_test = int(length * 0.1)
idx = list(range(length))
train_idx = idx[:n_train]
val_idx = idx[n_train:(n_train + n_test)]
test_idx = idx[(n_train + n_test):]
train_set = data.Subset(dset, train_idx)
val_set = data.Subset(dset, val_idx)
test_set = data.Subset(dset, test_idx)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=100,
shuffle=False)
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=100,
shuffle=False)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=100,
shuffle=False)
#print(len(train_loader), len(val_loader), len(test_loader))
return train_loader, val_loader, test_loader
def main(opt):
model = LSTM(opt, batch_first=True, dropout=opt.dropout)
if opt.pre_train:
model.load_state_dict(torch.load(opt.save_path))
optimizer = optim.Adam(model.parameters(), opt.learning_rate)
mseloss = nn.MSELoss()
dataset = PowerDataset(opt,
prepocess_path=opt.prepocess_path,
transform=transforms.Compose(
[transforms.ToTensor()]))
train_dataset = data.Subset(dataset, indices=range(8664))
test_dataset = data.Subset(dataset, indices=range(8664, len(dataset)))
train_dataloader = data.dataloader.DataLoader(train_dataset,
num_workers=opt.n_threads,
batch_size=opt.batch_size,
shuffle=True)
test_sampler = data.SequentialSampler(test_dataset)
test_dataloader = data.dataloader.DataLoader(
test_dataset,
num_workers=opt.n_threads,
batch_size=opt.test_batch_size,
shuffle=False,
sampler=test_sampler)
for e in range(opt.epochs):
if opt.test_only:
test(model, test_dataloader)
break
print('epoch: ', e)
train(model, mseloss, optimizer, train_dataloader)
test(model, test_dataloader)
torch.save(model.state_dict(), opt.save_path)
def train_data(bs):
"""Get data loader for trainning & validating, bs means batch_size."""
train_ds = ImageColorDataset(train_dataset_rootdir,
get_transform(train=True))
print(train_ds)
# Split train_ds in train and valid set
valid_len = int(0.2 * len(train_ds))
indices = [i for i in range(len(train_ds) - valid_len, len(train_ds))]
valid_ds = data.Subset(train_ds, indices)
indices = [i for i in range(len(train_ds) - valid_len)]
train_ds = data.Subset(train_ds, indices)
# Define training and validation data loaders
train_dl = data.DataLoader(train_ds,
batch_size=bs,
shuffle=True,
num_workers=4)
valid_dl = data.DataLoader(valid_ds,
batch_size=bs,
shuffle=False,
num_workers=4)
return train_dl, valid_dl
def get_loader(BATCH_SIZE):
data_set = Img_data()
indices = [i for i in range(0, len(data_set))]
train_data = data.Subset(data_set, indices[:15120])
val_data = data.Subset(data_set, indices[15120:])
transform = get_transforms()
train_data.dataset.set_transform(transform["train"])
val_data.dataset.set_transform(transform["val"])
train_loader = data.DataLoader(train_data,
batch_size=BATCH_SIZE,
num_workers=2,
shuffle=True,
pin_memory=True)
val_loader = data.DataLoader(val_data,
batch_size=BATCH_SIZE,
num_workers=2,
shuffle=False,
pin_memory=True)
return train_loader, val_loader
def _split_train_val_create_loaders(self, dataset, train_idxs, test_idxs
) -> Tuple[torch_data.DataLoader, torch_data.DataLoader]:
train_dataset = torch_data.Subset(dataset, train_idxs)
val_dataset = torch_data.Subset(dataset, test_idxs)
train_loader = self._create_loader_with_answers(train_dataset)
test_loader = self._create_loader_with_answers(val_dataset)
return train_loader, test_loader
def split(full_dataset, n_features, n_attributes):
"""Split dataset making sure that each symbol is represented with equal frequency"""
assert n_attributes == 2, 'Only implemented for 2 attrs'
first_dim_indices, second_dim_indices = list(range(n_features)), list(range(n_features))
random.shuffle(second_dim_indices)
test_indices = [a * n_features + b for a, b in zip(first_dim_indices, second_dim_indices)]
train_indices = [i for i in range(n_features * n_features) if i not in test_indices]
return data.Subset(full_dataset, train_indices), data.Subset(full_dataset, test_indices)
def train_test_split_dataset(full_dataset, test_size=0.2):
""" Splits the given dataset in a train and test set using stratified sampling """
full_indices = np.arange(len(full_dataset))
full_targets = np.array([target for _, target in full_dataset.samples])
train_indices, test_indices = train_test_split(full_indices, test_size=test_size, random_state=41,
stratify=full_targets)
return data.Subset(full_dataset, train_indices), data.Subset(full_dataset, test_indices)
def _fixed_split(self, dataset, ratio=0.5):
assert isinstance(dataset, data.dataset.Dataset)
n_total = len(dataset)
thres = int(n_total * ratio)
id_a = range(len(dataset))[:thres]
id_b = range(len(dataset))[thres:]
data_a = data.Subset(dataset, id_a)
data_b = data.Subset(dataset, id_b)
return data_a, data_b
def prepare_data(self):
dataset = SplitDataset(self.text_dataset, self.labeler, 500, 800, 20)
train_indices, valid_indices = train_test_split(
np.arange(len(dataset)),
test_size=self.hparams.test_size,
random_state=1234)
self.train_dataset = data.Subset(dataset, train_indices)
self.valid_dataset = data.Subset(dataset, valid_indices)
def load_mnist(args, **kwargs):
"""
Dataloading function for mnist. Outputs image data in vectorized form: each image is a vector of size 784
"""
args.dynamic_binarization = False
args.input_type = "binary"
flatten = kwargs.get("flatten", False)
# start processing
transforms_list = [
tv.transforms.ToTensor(),
# tv.transforms.Normalize((0.5,), (0.5,)),
]
args.xdim = (28, 28)
if flatten:
transforms_list.append(tv.transforms.Lambda(lambda x: x.view(-1)))
args.xdim = (784,)
preprocess = tv.transforms.Compose(transforms_list)
preprocess = kwargs.get("preprocess", preprocess)
train_dataset = tv.datasets.MNIST(
"./data", transform=preprocess, download=True, train=True
)
train_len = len(train_dataset)
if args.val_frac:
train_indices, validation_indices = train_test_split(
np.arange(train_len), test_size=args.val_frac, random_state=args.manual_seed
)
train_indices = train_indices.tolist()
validation_indices = validation_indices.tolist()
else:
train_indices = np.arange(train_len).tolist()
val_indices = []
train = utdata.Subset(train_dataset, train_indices)
validation = utdata.Subset(train_dataset, validation_indices)
# pytorch data loader
train_loader = data_utils.DataLoader(
train, batch_size=args.batch_size, shuffle=True, pin_memory=args.pin_memory
)
val_loader = data_utils.DataLoader(
validation,
batch_size=args.batch_size,
shuffle=False,
pin_memory=args.pin_memory,
)
test = tv.datasets.MNIST("./data", transform=preprocess, download=True, train=False)
test_loader = data_utils.DataLoader(
test, batch_size=args.batch_size, shuffle=False, pin_memory=args.pin_memory
)
return train_loader, val_loader, test_loader, args
def k_fold_split_dataset(full_dataset, n_splits=5):
""" Splits the dataset `n_splits` times using CV and yields tuples (train_dataset, test_dataset) """
skf = StratifiedKFold(n_splits, random_state=42, shuffle=True)
full_targets = np.array([target for _, target in full_dataset.samples])
full_inputs = np.zeros(len(full_dataset))
for train_indices, test_indices in skf.split(full_inputs, full_targets):
yield data.Subset(full_dataset, train_indices), data.Subset(full_dataset, test_indices)
def __init__(self, dataset: data.Dataset):
super().__init__()
self.dataset = dataset
self.training_mask = np.full((len(dataset), ), False)
self.pool_mask = np.full((len(dataset), ), True)
self.training_dataset = data.Subset(self.dataset, None)
self.pool_dataset = data.Subset(self.dataset, None)
self._update_indices()
def __init__(self, dataset: data.Dataset):
super().__init__()
self.dataset = dataset
self.active_mask = np.full((len(dataset), ), False)
self.available_mask = np.full((len(dataset), ), True)
self.active_dataset = data.Subset(self.dataset, None)
self.available_dataset = data.Subset(self.dataset, None)
self._update_indices()
def prepare_data(self):
text_data = MemoryMapDataset("texts.txt", "slices.pkl")
dataset = SplitDataset(text_data, 500, 800, 20)
train_indices, valid_indeces = train_test_split(np.arange(
len(dataset)),
test_size=200_000,
random_state=1234)
self.train_dataset = data.Subset(dataset, train_indices)
self.valid_dataset = data.Subset(dataset, valid_indeces)
def train_test_split(torch_dataset, test_size):
dataset_size = len(torch_dataset)
indices = list(range(dataset_size))
n_test = int(dataset_size * test_size)
n_train = dataset_size - n_test
train_indices, test_indices = (indices[:n_train], indices[n_train:])
train_set = data.Subset(torch_dataset, train_indices)
test_set = data.Subset(torch_dataset, test_indices)
return train_set, test_set
def split(self, dataset):
"""
Splits the dataset into dev, train and test
:param dataset: the dataset to split
:return: DataSets named tupple (dev, train, test)
"""
dev = data_utils.Subset(dataset, range(len(dataset) * 2 // 10))
train = data_utils.Subset(dataset, range(0, len(dataset) * 9 // 10))
test = data_utils.Subset(
dataset, range(len(dataset) * 9 // 10 + 1, len(dataset)))
return DataSets(dev=dev, train=train, test=test)
def __init__(self, dataset: data.Dataset):
self.dataset = dataset
self.total_size = len(dataset)
self.train_mask = np.full((self.total_size, ), False)
self.pool_mask = np.full((self.total_size, ), True)
self.train_data = data.Subset(dataset, None)
self.pool_data = data.Subset(dataset, None)
self._update_indices()
def load_cifar10(args, **kwargs):
flatten = kwargs.get("flatten", False)
# start processing
transforms_list = [
tv.transforms.ToTensor(),
# tv.transforms.Normalize((0.5,), (0.5,)),
]
args.xdim = (3, 32, 32)
if flatten:
transforms_list.append(tv.transforms.Lambda(lambda x: x.view(-1)))
args.xdim = (1024,)
preprocess = tv.transforms.Compose(transforms_list)
preprocess = kwargs.get("preprocess", preprocess)
train_dataset = tv.datasets.CIFAR10(
"./data", transform=preprocess, download=True, train=True
)
train_len = len(train_dataset)
if args.val_frac:
train_indices, validation_indices = train_test_split(
np.arange(train_len), test_size=args.val_frac, random_state=args.manual_seed
)
train_indices = train_indices.tolist()
validation_indices = validation_indices.tolist()
else:
train_indices = np.arange(train_len).tolist()
val_indices = []
train = utdata.Subset(train_dataset, train_indices)
validation = utdata.Subset(train_dataset, validation_indices)
# pytorch data loader
train_loader = data_utils.DataLoader(
train, batch_size=args.batch_size, shuffle=True, pin_memory=args.pin_memory
)
val_loader = data_utils.DataLoader(
validation,
batch_size=args.batch_size,
shuffle=False,
pin_memory=args.pin_memory,
)
test = tv.datasets.CIFAR10(
"./data", transform=preprocess, download=True, train=False
)
test_loader = data_utils.DataLoader(
test, batch_size=args.batch_size, shuffle=False, pin_memory=args.pin_memory
)
return train_loader, val_loader, test_loader, args
def train_test_split(self, dataset: torch_data.Dataset, test_size=0.2):
dataset_indexes = list(range(len(dataset)))
train_idxs, test_idxs = train_test_split(dataset_indexes,
test_size=test_size)
train_dataset = torch_data.Subset(dataset, train_idxs)
val_dataset = torch_data.Subset(dataset, test_idxs)
train_loader = self.loader_builder.build(train_dataset,
has_answers=True)
test_loader = self.loader_builder.build(val_dataset)
return train_loader, test_loader
def cifar_loaders(batch_size, shuffle_train = True, shuffle_test = False, train_random_transform = False, normalize_input = False, num_examples = None, test_batch_size=None):
if normalize_input:
# lly slightly changed the std and mean
mean = [0.485, 0.456, 0.406]
std = [0.225, 0.225, 0.225]
# std = [0.2023, 0.1994, 0.2010]
# mean = [0.4914, 0.4822, 0.4465]
normalize = transforms.Normalize(mean = mean, std = std)
else:
std = [1.0, 1.0, 1.0]
mean = [0, 0, 0]
normalize = transforms.Normalize(mean = mean, std = std)
if train_random_transform:
if normalize_input:
train = datasets.CIFAR10('./data', train=True, download=True,
transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize,
]))
else:
train = datasets.CIFAR10('./data', train=True, download=True,
transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
]))
else:
train = datasets.CIFAR10('./data', train=True, download=True,
transform=transforms.Compose([transforms.ToTensor(),normalize]))
test = datasets.CIFAR10('./data', train=False,
transform=transforms.Compose([transforms.ToTensor(), normalize]))
if num_examples:
indices = list(range(num_examples))
train = data.Subset(train, indices)
test = data.Subset(test, indices)
train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size,
shuffle=shuffle_train, pin_memory=True, num_workers=min(multiprocessing.cpu_count(),6))
if test_batch_size:
batch_size = test_batch_size
test_loader = torch.utils.data.DataLoader(test, batch_size=max(batch_size, 1),
shuffle=shuffle_test, pin_memory=True, num_workers=min(multiprocessing.cpu_count(),6))
train_loader.std = std
test_loader.std = std
train_loader.mean = mean
test_loader.mean = mean
return train_loader, test_loader
def _split(self, valid_rate, shuffle_seed):
self.indices = list(range(self.dataset_size))
random.seed(shuffle_seed)
random.shuffle(self.indices)
split = int(np.floor((1 - valid_rate) * self.dataset_size))
self.train_indices, self.valid_indices = self.indices[:split], self.indices[split:]
self.train_dataset = data.Subset(self, self.train_indices)
self.valid_dataset = data.Subset(self, self.valid_indices)
self.train_sampler = data.RandomSampler(self.train_dataset)
self.valid_sampler = data.SequentialSampler(self.valid_dataset)
self.test_sampler = data.SequentialSampler(self)
def create_train_val_slice(self,
image_datasets,
sample_size=None,
val_same_as_train=False):
img_dataset = image_datasets # reminder - this is a generator
# clone the image_datasets_reduced[train] generator for the val
if val_same_as_train:
img_dataset['val'] = list(img_dataset['train'])
# copy the train to val (so the tranformations won't occur again)
# image_datasets_reduced['train'] = image_datasets_reduced['val']
dataset_sizes = {x: len(img_dataset[x]) for x in ['train', 'val']}
if sample_size: # return the whole data set
sample_n = {
x: random.sample(list(range(dataset_sizes[x])), sample_size)
for x in ['train', 'val']
}
img_dataset = {
x: data.Subset(img_dataset[x], sample_n[x])
for x in ['train', 'val']
}
dataset_sizes = {x: len(img_dataset[x]) for x in ['train', 'val']}
dataloaders = {
x: data.DataLoader(img_dataset[x],
batch_size=self.batch_size,
shuffle=True,
num_workers=0)
for x in ['train', 'val']
}
return dataloaders, dataset_sizes
