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mlpractical/data_providers.py

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update gcloud instructions
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# -*- coding: utf-8 -*-
"""Data providers.
This module provides classes for loading datasets and iterating over batches of
data points.
"""
from __future__ import print_function
import pickle
import gzip
import numpy as np
import os
DEFAULT_SEED = 20112018
from PIL import Image
import os
import os.path
import numpy as np
import sys
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
import torch.utils.data as data
from torchvision.datasets.utils import download_url, check_integrity
class DataProvider(object):
"""Generic data provider."""
def __init__(self, inputs, targets, batch_size, max_num_batches=-1,
shuffle_order=True, rng=None):
"""Create a new data provider object.
Args:
inputs (ndarray): Array of data input features of shape
(num_data, input_dim).
targets (ndarray): Array of data output targets of shape
(num_data, output_dim) or (num_data,) if output_dim == 1.
batch_size (int): Number of data points to include in each batch.
max_num_batches (int): Maximum number of batches to iterate over
in an epoch. If `max_num_batches * batch_size > num_data` then
only as many batches as the data can be split into will be
used. If set to -1 all of the data will be used.
shuffle_order (bool): Whether to randomly permute the order of
the data before each epoch.
rng (RandomState): A seeded random number generator.
"""
self.inputs = inputs
self.targets = targets
if batch_size < 1:
raise ValueError('batch_size must be >= 1')
self._batch_size = batch_size
if max_num_batches == 0 or max_num_batches < -1:
raise ValueError('max_num_batches must be -1 or > 0')
self._max_num_batches = max_num_batches
self._update_num_batches()
self.shuffle_order = shuffle_order
self._current_order = np.arange(inputs.shape[0])
if rng is None:
rng = np.random.RandomState(DEFAULT_SEED)
self.rng = rng
self.new_epoch()
@property
def batch_size(self):
"""Number of data points to include in each batch."""
return self._batch_size
@batch_size.setter
def batch_size(self, value):
if value < 1:
raise ValueError('batch_size must be >= 1')
self._batch_size = value
self._update_num_batches()
@property
def max_num_batches(self):
"""Maximum number of batches to iterate over in an epoch."""
return self._max_num_batches
@max_num_batches.setter
def max_num_batches(self, value):
if value == 0 or value < -1:
raise ValueError('max_num_batches must be -1 or > 0')
self._max_num_batches = value
self._update_num_batches()
def _update_num_batches(self):
"""Updates number of batches to iterate over."""
# maximum possible number of batches is equal to number of whole times
# batch_size divides in to the number of data points which can be
# found using integer division
possible_num_batches = self.inputs.shape[0] // self.batch_size
if self.max_num_batches == -1:
self.num_batches = possible_num_batches
else:
self.num_batches = min(self.max_num_batches, possible_num_batches)
def __iter__(self):
"""Implements Python iterator interface.
This should return an object implementing a `next` method which steps
through a sequence returning one element at a time and raising
`StopIteration` when at the end of the sequence. Here the object
returned is the DataProvider itself.
"""
return self
def new_epoch(self):
"""Starts a new epoch (pass through data), possibly shuffling first."""
self._curr_batch = 0
if self.shuffle_order:
self.shuffle()
def __next__(self):
return self.next()
def reset(self):
"""Resets the provider to the initial state."""
inv_perm = np.argsort(self._current_order)
self._current_order = self._current_order[inv_perm]
self.inputs = self.inputs[inv_perm]
self.targets = self.targets[inv_perm]
self.new_epoch()
def shuffle(self):
"""Randomly shuffles order of data."""
perm = self.rng.permutation(self.inputs.shape[0])
self._current_order = self._current_order[perm]
self.inputs = self.inputs[perm]
self.targets = self.targets[perm]
def next(self):
"""Returns next data batch or raises `StopIteration` if at end."""
if self._curr_batch + 1 > self.num_batches:
# no more batches in current iteration through data set so start
# new epoch ready for another pass and indicate iteration is at end
self.new_epoch()
raise StopIteration()
# create an index slice corresponding to current batch number
batch_slice = slice(self._curr_batch * self.batch_size,
(self._curr_batch + 1) * self.batch_size)
inputs_batch = self.inputs[batch_slice]
targets_batch = self.targets[batch_slice]
self._curr_batch += 1
return inputs_batch, targets_batch
class MNISTDataProvider(DataProvider):
"""Data provider for MNIST handwritten digit images."""
def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
shuffle_order=True, rng=None):
"""Create a new MNIST data provider object.
Args:
which_set: One of 'train', 'valid' or 'eval'. Determines which
portion of the MNIST data this object should provide.
batch_size (int): Number of data points to include in each batch.
max_num_batches (int): Maximum number of batches to iterate over
in an epoch. If `max_num_batches * batch_size > num_data` then
only as many batches as the data can be split into will be
used. If set to -1 all of the data will be used.
shuffle_order (bool): Whether to randomly permute the order of
the data before each epoch.
rng (RandomState): A seeded random number generator.
"""
# check a valid which_set was provided
assert which_set in ['train', 'valid', 'test'], (
'Expected which_set to be either train, valid or eval. '
'Got {0}'.format(which_set)
)
self.which_set = which_set
self.num_classes = 10
# construct path to data using os.path.join to ensure the correct path
# separator for the current platform / OS is used
# MLP_DATA_DIR environment variable should point to the data directory
data_path = os.path.join(
"data", 'mnist-{0}.npz'.format(which_set))
assert os.path.isfile(data_path), (
'Data file does not exist at expected path: ' + data_path
)
# load data from compressed numpy file
loaded = np.load(data_path)
inputs, targets = loaded['inputs'], loaded['targets']
inputs = inputs.astype(np.float32)
# pass the loaded data to the parent class __init__
super(MNISTDataProvider, self).__init__(
inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
def next(self):
"""Returns next data batch or raises `StopIteration` if at end."""
inputs_batch, targets_batch = super(MNISTDataProvider, self).next()
return inputs_batch, self.to_one_of_k(targets_batch)
def to_one_of_k(self, int_targets):
"""Converts integer coded class target to 1 of K coded targets.
Args:
int_targets (ndarray): Array of integer coded class targets (i.e.
where an integer from 0 to `num_classes` - 1 is used to
indicate which is the correct class). This should be of shape
(num_data,).
Returns:
Array of 1 of K coded targets i.e. an array of shape
(num_data, num_classes) where for each row all elements are equal
to zero except for the column corresponding to the correct class
which is equal to one.
"""
one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
return one_of_k_targets
class EMNISTDataProvider(DataProvider):
"""Data provider for EMNIST handwritten digit images."""
def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
shuffle_order=True, rng=None, flatten=False):
"""Create a new EMNIST data provider object.
Args:
which_set: One of 'train', 'valid' or 'eval'. Determines which
portion of the EMNIST data this object should provide.
batch_size (int): Number of data points to include in each batch.
max_num_batches (int): Maximum number of batches to iterate over
in an epoch. If `max_num_batches * batch_size > num_data` then
only as many batches as the data can be split into will be
used. If set to -1 all of the data will be used.
shuffle_order (bool): Whether to randomly permute the order of
the data before each epoch.
rng (RandomState): A seeded random number generator.
"""
# check a valid which_set was provided
assert which_set in ['train', 'valid', 'test'], (
'Expected which_set to be either train, valid or eval. '
'Got {0}'.format(which_set)
)
self.which_set = which_set
self.num_classes = 47
# construct path to data using os.path.join to ensure the correct path
# separator for the current platform / OS is used
# MLP_DATA_DIR environment variable should point to the data directory
data_path = os.path.join(
"data", 'emnist-{0}.npz'.format(which_set))
assert os.path.isfile(data_path), (
'Data file does not exist at expected path: ' + data_path
)
# load data from compressed numpy file
loaded = np.load(data_path)
print(loaded.keys())
inputs, targets = loaded['inputs'], loaded['targets']
inputs = inputs.astype(np.float32)
if flatten:
inputs = np.reshape(inputs, newshape=(-1, 28*28))
else:
inputs = np.reshape(inputs, newshape=(-1, 1, 28, 28))
inputs = inputs / 255.0
# pass the loaded data to the parent class __init__
super(EMNISTDataProvider, self).__init__(
inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
def __len__(self):
return self.num_batches
def next(self):
"""Returns next data batch or raises `StopIteration` if at end."""
inputs_batch, targets_batch = super(EMNISTDataProvider, self).next()
return inputs_batch, self.to_one_of_k(targets_batch)
def to_one_of_k(self, int_targets):
"""Converts integer coded class target to 1 of K coded targets.
Args:
int_targets (ndarray): Array of integer coded class targets (i.e.
where an integer from 0 to `num_classes` - 1 is used to
indicate which is the correct class). This should be of shape
(num_data,).
Returns:
Array of 1 of K coded targets i.e. an array of shape
(num_data, num_classes) where for each row all elements are equal
to zero except for the column corresponding to the correct class
which is equal to one.
"""
one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
return one_of_k_targets
class MetOfficeDataProvider(DataProvider):
"""South Scotland Met Office weather data provider."""
def __init__(self, window_size, batch_size=10, max_num_batches=-1,
shuffle_order=True, rng=None):
"""Create a new Met Office data provider object.
Args:
window_size (int): Size of windows to split weather time series
data into. The constructed input features will be the first
`window_size - 1` entries in each window and the target outputs
the last entry in each window.
batch_size (int): Number of data points to include in each batch.
max_num_batches (int): Maximum number of batches to iterate over
in an epoch. If `max_num_batches * batch_size > num_data` then
only as many batches as the data can be split into will be
used. If set to -1 all of the data will be used.
shuffle_order (bool): Whether to randomly permute the order of
the data before each epoch.
rng (RandomState): A seeded random number generator.
"""
data_path = os.path.join(
os.environ['DATASET_DIR'], 'HadSSP_daily_qc.txt')
assert os.path.isfile(data_path), (
'Data file does not exist at expected path: ' + data_path
)
raw = np.loadtxt(data_path, skiprows=3, usecols=range(2, 32))
assert window_size > 1, 'window_size must be at least 2.'
self.window_size = window_size
# filter out all missing datapoints and flatten to a vector
filtered = raw[raw >= 0].flatten()
# normalise data to zero mean, unit standard deviation
mean = np.mean(filtered)
std = np.std(filtered)
normalised = (filtered - mean) / std
# create a view on to array corresponding to a rolling window
shape = (normalised.shape[-1] - self.window_size + 1, self.window_size)
strides = normalised.strides + (normalised.strides[-1],)
windowed = np.lib.stride_tricks.as_strided(
normalised, shape=shape, strides=strides)
# inputs are first (window_size - 1) entries in windows
inputs = windowed[:, :-1]
# targets are last entry in windows
targets = windowed[:, -1]
super(MetOfficeDataProvider, self).__init__(
inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
class CCPPDataProvider(DataProvider):
def __init__(self, which_set='train', input_dims=None, batch_size=10,
max_num_batches=-1, shuffle_order=True, rng=None):
"""Create a new Combined Cycle Power Plant data provider object.
Args:
which_set: One of 'train' or 'valid'. Determines which portion of
data this object should provide.
input_dims: Which of the four input dimension to use. If `None` all
are used. If an iterable of integers are provided (consisting
of a subset of {0, 1, 2, 3}) then only the corresponding
input dimensions are included.
batch_size (int): Number of data points to include in each batch.
max_num_batches (int): Maximum number of batches to iterate over
in an epoch. If `max_num_batches * batch_size > num_data` then
only as many batches as the data can be split into will be
used. If set to -1 all of the data will be used.
shuffle_order (bool): Whether to randomly permute the order of
the data before each epoch.
rng (RandomState): A seeded random number generator.
"""
data_path = os.path.join(
os.environ['DATASET_DIR'], 'ccpp_data.npz')
assert os.path.isfile(data_path), (
'Data file does not exist at expected path: ' + data_path
)
# check a valid which_set was provided
assert which_set in ['train', 'valid'], (
'Expected which_set to be either train or valid '
'Got {0}'.format(which_set)
)
# check input_dims are valid
if not input_dims is not None:
input_dims = set(input_dims)
assert input_dims.issubset({0, 1, 2, 3}), (
'input_dims should be a subset of {0, 1, 2, 3}'
)
loaded = np.load(data_path)
inputs = loaded[which_set + '_inputs']
if input_dims is not None:
inputs = inputs[:, input_dims]
targets = loaded[which_set + '_targets']
super(CCPPDataProvider, self).__init__(
inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
class AugmentedMNISTDataProvider(MNISTDataProvider):
"""Data provider for MNIST dataset which randomly transforms images."""
def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
shuffle_order=True, rng=None, transformer=None):
"""Create a new augmented MNIST data provider object.
Args:
which_set: One of 'train', 'valid' or 'test'. Determines which
portion of the MNIST data this object should provide.
batch_size (int): Number of data points to include in each batch.
max_num_batches (int): Maximum number of batches to iterate over
in an epoch. If `max_num_batches * batch_size > num_data` then
only as many batches as the data can be split into will be
used. If set to -1 all of the data will be used.
shuffle_order (bool): Whether to randomly permute the order of
the data before each epoch.
rng (RandomState): A seeded random number generator.
transformer: Function which takes an `inputs` array of shape
(batch_size, input_dim) corresponding to a batch of input
images and a `rng` random number generator object (i.e. a
call signature `transformer(inputs, rng)`) and applies a
potentiall random set of transformations to some / all of the
input images as each new batch is returned when iterating over
the data provider.
"""
super(AugmentedMNISTDataProvider, self).__init__(
which_set, batch_size, max_num_batches, shuffle_order, rng)
self.transformer = transformer
def next(self):
"""Returns next data batch or raises `StopIteration` if at end."""
inputs_batch, targets_batch = super(
AugmentedMNISTDataProvider, self).next()
transformed_inputs_batch = self.transformer(inputs_batch, self.rng)
return transformed_inputs_batch, targets_batch
class CIFAR10(data.Dataset):
"""`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
Args:
root (string): Root directory of dataset where directory
``cifar-10-batches-py`` exists or will be saved to if download is set to True.
train (bool, optional): If True, creates dataset from training set, otherwise
creates from test set.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
"""
base_folder = 'cifar-10-batches-py'
url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
filename = "cifar-10-python.tar.gz"
tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
train_list = [
['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
['data_batch_4', '634d18415352ddfa80567beed471001a'],
['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
]
test_list = [
['test_batch', '40351d587109b95175f43aff81a1287e'],
]
def __init__(self, root, set_name,
transform=None, target_transform=None,
download=False):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
self.set_name = set_name # training set or test set
if download:
self.download()
if not self._check_integrity():
raise RuntimeError('Dataset not found or corrupted.' +
' You can use download=True to download it')
# now load the picked numpy arrays
rng = np.random.RandomState(seed=0)
train_sample_idx = rng.choice(a=[i for i in range(50000)], size=47500, replace=False)
val_sample_idx = [i for i in range(50000) if i not in train_sample_idx]
if self.set_name=='train':
self.data = []
self.labels = []
for fentry in self.train_list:
f = fentry[0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.data.append(entry['data'])
if 'labels' in entry:
self.labels += entry['labels']
else:
self.labels += entry['fine_labels']
fo.close()
self.data = np.concatenate(self.data)
self.data = self.data.reshape((50000, 3, 32, 32))
self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC
self.data = self.data[train_sample_idx]
self.labels = np.array(self.labels)[train_sample_idx]
print(set_name, self.data.shape)
print(set_name, self.labels.shape)
elif self.set_name=='val':
self.data = []
self.labels = []
for fentry in self.train_list:
f = fentry[0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.data.append(entry['data'])
if 'labels' in entry:
self.labels += entry['labels']
else:
self.labels += entry['fine_labels']
fo.close()
self.data = np.concatenate(self.data)
self.data = self.data.reshape((50000, 3, 32, 32))
self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC
self.data = self.data[val_sample_idx]
self.labels = np.array(self.labels)[val_sample_idx]
print(set_name, self.data.shape)
print(set_name, self.labels.shape)
else:
f = self.test_list[0][0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.data = entry['data']
if 'labels' in entry:
self.labels = entry['labels']
else:
self.labels = entry['fine_labels']
fo.close()
self.data = self.data.reshape((10000, 3, 32, 32))
self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC
self.labels = np.array(self.labels)
print(set_name, self.data.shape)
print(set_name, self.labels.shape)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
return len(self.data)
def _check_integrity(self):
root = self.root
for fentry in (self.train_list + self.test_list):
filename, md5 = fentry[0], fentry[1]
fpath = os.path.join(root, self.base_folder, filename)
if not check_integrity(fpath, md5):
return False
return True
def download(self):
import tarfile
if self._check_integrity():
print('Files already downloaded and verified')
return
root = self.root
download_url(self.url, root, self.filename, self.tgz_md5)
# extract file
cwd = os.getcwd()
tar = tarfile.open(os.path.join(root, self.filename), "r:gz")
os.chdir(root)
tar.extractall()
tar.close()
os.chdir(cwd)
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
tmp = self.set_name
fmt_str += ' Split: {}\n'.format(tmp)
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
class CIFAR100(CIFAR10):
"""`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
This is a subclass of the `CIFAR10` Dataset.
"""
base_folder = 'cifar-100-python'
url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
filename = "cifar-100-python.tar.gz"
tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
train_list = [
['train', '16019d7e3df5f24257cddd939b257f8d'],
]
test_list = [
['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
]
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