def get_radio_ml_loader(batch_size, train, **kwargs):
data_dir = kwargs['data_dir']
min_snr = kwargs.get('min_snr', 6)
max_snr = kwargs.get('max_snr', 30)
per_h5_frac = kwargs.get('per_h5_frac', 0.5)
train_frac = kwargs.get('train_frac', 0.9)
per_sample_frac = kwargs.get('per_sample_frac', 1.0)
normalize = kwargs.get('normalize', True)
skip_1 = kwargs.get('skip_1', False)
fake_height = kwargs.get('fake_height', False)
classes = kwargs.get('classes', 24)
dataset = RadioMLDataset(data_dir, train,
normalize=normalize,
fake_height=fake_height,
min_snr=min_snr,
max_snr=max_snr,
per_h5_frac=per_h5_frac,
train_frac=train_frac,
skip_1=skip_1,
per_sample_frac=per_sample_frac,
classes=classes)
identifier = 'train' if train else 'test'
print('[%s] dataset size: %d' % (identifier, len(dataset)))
loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=train)
loader.name = 'RadioML_{}'.format(identifier)
return loader
def get_radio_ml_loader_2016(batch_size, X, Y, train, normalize):
dataset = RadioMLDataset2016(X, Y, normalize=normalize)
identifier = 'train' if train else 'test'
print('[%s] dataset size: %d' % (identifier, len(dataset)))
loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=train)
loader.name = 'RadioML2016_{}'.format(identifier)
return loader
def get_mnist_loader(batch_size, train, taskid=0, **kwargs):
transform = transforms.Compose([
transforms.Grayscale(),
transforms.ToTensor(),
transforms.Normalize((0.0, ), (1.0, )),
transforms.Lambda(lambda x: x.view([28, 28]))
])
dataset = datasets.MNIST(root='./data',
download=True,
transform=transform,
train=train)
loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=train)
loader.taskid = taskid
loader.name = 'MNIST_{}'.format(taskid)
loader.short_name = 'MNIST'
return loader
def get_radio_ml_loader(batch_size, train, **kwargs):
data_dir = kwargs['data_dir']
min_snr = kwargs.get('min_snr', 6)
max_snr = kwargs.get('max_snr', 30)
per_h5_frac = kwargs.get('per_h5_frac', 0.5)
train_frac = kwargs.get('train_frac', 0.9)
dataset = RadioMLDataset(data_dir,
train,
normalize=False,
min_snr=min_snr,
max_snr=max_snr,
per_h5_frac=per_h5_frac,
train_frac=train_frac)
identifier = 'train' if train else 'test'
print('[%s] dataset size: %d' % (identifier, len(dataset)))
loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=train)
loader.name = 'RadioML_{}'.format(identifier)
return loader
