def cook_svhn_complete(location, verbose=1, **kwargs):
"""
Wrapper to cook svhn dataset that creates the whole thing. Will take as input,
Args:
location: Where are the matlab files.
save_directory: which directory to save the cooked dataset onto.
dataset_parms: default is the dictionary. Refer to :mod:`setup_dataset`
preprocess_params: default is the dictionary. Refer to :mod:`setup_dataset`
Notes:
This will also have the split parameter.
"""
if not 'data_params' in kwargs.keys():
data_params = {
"source": 'matlab',
# "name" : 'yann_svhn', # some name.
"location":
location, # some location to load from.
"height": 32,
"width": 32,
"channels": 3,
"batches2test": 13,
"batches2train": 100,
"mini_batches_per_batch": (10, 10, 10),
"batches2validate": 13,
"mini_batch_size": 500
}
else:
data_params = kwargs['data_params']
if not 'preprocess_params' in kwargs.keys():
# parameters relating to preprocessing.
preprocess_params = {
"normalize": True,
"ZCA": False,
"grayscale": False,
"zero_mean": True,
}
else:
preprocess_params = kwargs['preprocess_params']
if not 'save_directory' in kwargs.keys():
save_directory = '_datasets'
else:
save_directory = kwargs['save_directory']
if not 'splits' in kwargs.keys():
splits = {"base": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "shot": [], "p": 0}
else:
splits = kwargs['splits']
dataset = split_only_train(dataset_init_args=data_params,
save_directory=save_directory,
preprocess_init_args=preprocess_params,
split_args=splits,
verbose=3)
return dataset
def cook_split_inc(verbose=1, **kwargs):
"""
Wrapper to cook mnist dataset that also creates the rest of the dataset. Will take as input,
Args:
save_directory: which directory to save the cooked dataset onto.
dataset_parms: default is the dictionary. Refer to :mod:`setup_dataset`
preprocess_params: default is the dictionary. Refer to :mod:`setup_dataset`
Notes:
The base of this dataset will be classes 0,1,2,4,5,7,9 and the split will be classes
3,6,8.
"""
if not 'data_params' in kwargs.keys():
data_params = {
"source": 'skdata',
"name": 'cifar10',
"location": '',
"mini_batch_size": 100,
"mini_batches_per_batch": (400, 100, 100),
"batches2train": 1,
"batches2test": 1,
"batches2validate": 1,
"height": 32,
"width": 32,
"channels": 3
}
else:
data_params = kwargs['data_params']
if not 'preprocess_params' in kwargs.keys():
# parameters relating to preprocessing.
preprocess_params = {
"normalize": True,
"ZCA": False,
"grayscale": False,
"zero_mean": True,
}
else:
preprocess_params = kwargs['preprocess_params']
if not 'save_directory' in kwargs.keys():
save_directory = '_datasets'
else:
save_directory = kwargs['save_directory']
if not 'splits' in kwargs.keys():
splits = {"base": [6, 7, 8, 9], "shot": [0, 1, 2, 3, 4, 5], "p": 0}
else:
splits = kwargs['splits']
dataset = split_only_train(dataset_init_args=data_params,
save_directory=save_directory,
preprocess_init_args=preprocess_params,
split_args=splits,
verbose=3)
return dataset
def cook_mnist_complete(verbose = 1, **kwargs):
"""
Wrapper to cook mnist dataset that creates the whole thing. Will take as input,
Args:
save_directory: which directory to save the cooked dataset onto.
dataset_parms: default is the dictionary. Refer to :mod:`setup_dataset`
preprocess_params: default is the dictionary. Refer to :mod:`setup_dataset`
Notes:
This will also have the split parameter.
"""
if not 'data_params' in kwargs.keys():
data_params = {
"source" : 'skdata',
"name" : 'mnist_rotated',
"location" : '',
"mini_batch_size" : 500,
"mini_batches_per_batch" : (80, 20, 20),
"batches2train" : 1,
"batches2test" : 1,
"batches2validate" : 1,
"height" : 28,
"width" : 28,
"channels" : 1 }
else:
data_params = kwargs['data_params']
if not 'preprocess_params' in kwargs.keys():
# parameters relating to preprocessing.
preprocess_params = {
"normalize" : True,
"ZCA" : False,
"grayscale" : False,
"zero_mean" : True,
}
else:
preprocess_params = kwargs['preprocess_params']
if not 'save_directory' in kwargs.keys():
save_directory = '_datasets'
else:
save_directory = kwargs ['save_directory']
if not 'splits' in kwargs.keys():
splits = {
"base" : [0,1,2,3,4,5,6,7,8,9],
"shot" : [],
"p" : 0
}
else:
splits = kwargs ['splits']
dataset = split_only_train(dataset_init_args = data_params,
save_directory = save_directory,
preprocess_init_args = preprocess_params,
split_args = splits,
verbose = 3)
return dataset