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dataBlocks.py
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dataBlocks.py
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# author is He Zhao
# The time to create is 2:18 PM, 7/12/16
import numpy as np
from multiprocessing import Process, Queue
class DataIterator(object):
def __init__(self, *data, **params):
'''
PARAMS:
fullbatch (bool): decides if the number of examples return after every
iteration should be always a full batch.
'''
self.data = data
self.batchsize = params['batchsize']
if 'fullbatch' in params:
self.fullbatch = params['fullbatch']
else:
self.fullbatch = False
def __iter__(self):
self.first = 0
return self
def __len__(self):
return len(self.data[0])
def __getitem__(self, key):
outs = []
for val in self.data:
outs.append(val[key])
return self.__class__(*outs, batchsize=self.batchsize, fullbatch=self.fullbatch)
class SequentialIterator(DataIterator):
'''
batchsize = 3
[0, 1, 2], [3, 4, 5], [6, 7, 8]
'''
def __next__(self):
if self.fullbatch and self.first+self.batchsize > len(self):
raise StopIteration()
elif self.first >= len(self):
raise StopIteration()
outs = []
for val in self.data:
outs.append(val[self.first:self.first+self.batchsize])
self.first += self.batchsize
return outs
class StepIterator(DataIterator):
'''
batchsize = 3
step = 1
[0, 1, 2], [1, 2, 3], [2, 3, 4]
'''
def __init__(self, *data, **params):
super(self, StepIterator).__init__(self, *data, **params)
self.step = params['step']
def __next__(self):
if self.fullbatch and self.first+self.batchsize > len(self):
raise StopIteration()
elif self.first >= len(self):
raise StopIteration()
outs = []
for val in self.data:
outs.append(val[self.first:self.first+self.batchsize])
self.first += self.step
return outs
def np_load_func(path):
arr = np.load(path)
return arr
class DataBlocks(object):
def __init__(self, data_paths, batchsize=32, load_func=np_load_func, allow_preload=False):
"""
DESCRIPTION:
This is class for processing blocks of data, whereby dataset is loaded
and unloaded into memory one block at a time.
PARAM:
data_paths (list or list of list): contains list of paths for data loading,
example:
[f1a.npy, f1b.npy, f1c.npy] or
[(f1a.npy, f1b.npy, f1c.npy), (f2a.npy, f2b.npy, f2c.npy)]
load_func (function): function for loading the data_paths, default to
numpy file loader
allow_preload (bool): by allowing preload, it will preload the next data block
while training at the same time on the current datablock,
this will reduce time but will also cost more memory.
"""
assert isinstance(data_paths, (list)), "data_paths is not a list"
self.data_paths = data_paths
self.batchsize = batchsize
self.load_func = load_func
self.allow_preload = allow_preload
self.q = Queue()
def __iter__(self):
self.files = iter(self.data_paths)
if self.allow_preload:
self.lastblock = False
bufile = next(self.files)
self.load_file(bufile, self.q)
return self
def __next__(self):
if self.allow_preload:
if self.lastblock:
raise StopIteration
try:
arr = self.q.get(block=True, timeout=None)
self.iterator = SequentialIterator(*arr, batchsize=self.batchsize)
bufile = next(self.files)
p = Process(target=self.load_file, args=(bufile, self.q))
p.start()
except:
self.lastblock = True
else:
fpath = next(self.files)
arr = self.load_file(fpath)
self.iterator = SequentialIterator(*arr, batchsize=self.batchsize)
return self.iterator
def load_file(self, paths, queue=None):
'''
paths (list or str): []
'''
data = []
if isinstance(paths, (list, tuple)):
for path in paths:
data.append(self.load_func(path))
else:
data.append(self.load_func(paths))
if queue:
queue.put(data)
return data
@property
def nblocks(self):
return len(self.data_paths)
class SimpleBlocks(object):
def __init__(self, data_paths, batchsize=32, load_func=np_load_func, allow_preload=False):
"""
DESCRIPTION:
This is class for processing blocks of data, whereby dataset is loaded
and unloaded into memory one block at a time.
PARAM:
data_paths (list or list of list): contains list of paths for data loading,
example:
[f1a.npy, f2a.npy, f3a.npy] ==> 1 col, 3 blocks or
[(f1a.npy, f1b.npy, f1c.npy), (f2a.npy, f2b.npy, f2c.npy)] ==> 3 cols, 2 blocks
load_func (function): function for loading the data_paths, default to
numpy file loader
allow_preload (bool): by allowing preload, it will preload the next data block
while training at the same time on the current datablock,
this will reduce time but will also cost more memory.
"""
assert isinstance(data_paths, (list)), "data_paths is not a list"
self.data_paths = data_paths
self.batchsize = batchsize
self.load_func = load_func
self.allow_preload = allow_preload
self.q = Queue()
def __iter__(self):
self.files = iter(self.data_paths)
if self.allow_preload:
self.lastblock = False
bufile = next(self.files)
self.load_file(bufile, self.q)
return self
def __next__(self):
if self.allow_preload:
if self.lastblock:
raise StopIteration
try:
arr = self.q.get(block=True, timeout=None)
self.iterator = SequentialIterator(*arr, batchsize=self.batchsize)
bufile = next(self.files)
p = Process(target=self.load_file, args=(bufile, self.q))
p.start()
except:
self.lastblock = True
else:
fpath = next(self.files)
arr = self.load_file(fpath)
self.iterator = SequentialIterator(*arr, batchsize=self.batchsize)
return self.iterator
def load_file(self, paths, queue=None):
'''
paths (list or str): []
'''
data = []
if isinstance(paths, (list, tuple)):
for path in paths:
data.append(self.load_func(path))
else:
data.append(self.load_func(paths))
if queue:
queue.put(data)
return data
@property
def nblocks(self):
return len(self.data_paths)
class DataBlocks(SimpleBlocks):
def __init__(self, data_paths, train_valid_ratio=[5,1], batchsize=32, load_func=np_load_func, allow_preload=False):
"""
DESCRIPTION:
This is class for processing blocks of data, whereby dataset is loaded
and unloaded into memory one block at a time.
PARAM:
data_paths (list or list of list): contains list of paths for data loading,
example:
[f1a.npy, f1b.npy, f1c.npy] or
[(f1a.npy, f1b.npy, f1c.npy), (f2a.npy, f2b.npy, f2c.npy)]
load_func (function): function for loading the data_paths, default to
numpy file loader
allow_preload (bool): by allowing preload, it will preload the next data block
while training at the same time on the current datablock,
this will reduce time but will also cost more memory.
"""
assert isinstance(data_paths, (list)), "data_paths is not a list"
self.data_paths = data_paths
self.train_valid_ratio = train_valid_ratio
self.batchsize = batchsize
self.load_func = load_func
self.allow_preload = allow_preload
self.q = Queue()
def __next__(self):
if self.allow_preload:
if self.lastblock:
raise StopIteration
try:
train, valid = self.q.get(block=True, timeout=None)
self.train_iterator = SequentialIterator(*train, batchsize=self.batchsize)
self.valid_iterator = SequentialIterator(*valid, batchsize=self.batchsize)
bufile = next(self.files)
p = Process(target=self.load_file, args=(bufile, self.q))
p.start()
except:
self.lastblock = True
else:
fpath = next(self.files)
train, valid = self.load_file(fpath)
self.train_iterator = SequentialIterator(*train, batchsize=self.batchsize, fullbatch=True)
self.valid_iterator = SequentialIterator(*valid, batchsize=self.batchsize, fullbatch=True)
return self.train_iterator, self.valid_iterator
def load_file(self, paths, queue=None):
'''
paths (list or str): []
'''
train = []
valid = []
if isinstance(paths, (list, tuple)):
for path in paths:
X = self.load_func(path)
num_train = len(X) * self.train_valid_ratio[0] * 1.0 / sum(self.train_valid_ratio)
num_train = int(num_train)
train.append(X[:num_train])
valid.append(X[num_train:])
else:
X = self.load_func(paths)
# np.random.shuffle(X)
num_train = len(X) * self.train_valid_ratio[0] * 1.0 / sum(self.train_valid_ratio)
num_train = int(num_train)
train.append(X[:num_train])
valid.append(X[num_train:])
data = [train, valid]
if queue:
queue.put(data)
return data