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data_preprocessing.py
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data_preprocessing.py
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# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import numpy as np
import pickle
import tensorflow as tf
_EPSILON = 1e-8
class DataPreprocessing(object):
""" Data Preprocessing.
Base class for applying common real-time data preprocessing.
This class is meant to be used as an argument of `input_data`. When training
a model, the defined pre-processing methods will be applied at both
training and testing time. Note that DataAugmentation is similar to
DataPreprocessing, but only applies at training time.
Arguments:
None.
Parameters:
methods: `list of function`. Augmentation methods to apply.
args: A `list` of arguments to use for these methods.
"""
def __init__(self, name="DataPreprocessing"):
self.methods = []
self.args = []
self.session = None
# Data Persistence
with tf.name_scope(name) as scope:
self.scope = scope
self.global_mean = self.PersistentParameter(scope, name="mean")
self.global_std = self.PersistentParameter(scope, name="std")
self.global_pc = self.PersistentParameter(scope, name="pc")
def apply(self, batch):
for i, m in enumerate(self.methods):
if self.args[i]:
batch = m(batch, *self.args[i])
else:
batch = m(batch)
return batch
def restore_params(self, session):
self.global_mean.is_restored(session)
self.global_std.is_restored(session)
self.global_pc.is_restored(session)
def initialize(self, dataset, session, limit=None):
""" Initialize preprocessing methods that pre-requires
calculation over entire dataset. """
if self.global_mean.is_required:
# If a value is already provided, it has priority
if self.global_mean.value is not None:
self.global_mean.assign(self.global_mean.value, session)
# Otherwise, if it has not been restored, compute it
if not self.global_mean.is_restored(session):
print("---------------------------------")
print("Preprocessing... Calculating mean over all dataset "
"(this may take long)...")
self._compute_global_mean(dataset, session, limit)
print("Mean: " + str(self.global_mean.value) + " (To avoid "
"repetitive computation, add it to argument 'mean' of "
"`add_featurewise_zero_center`)")
if self.global_std.is_required:
# If a value is already provided, it has priority
if self.global_std.value is not None:
self.global_std.assign(self.global_std.value, session)
# Otherwise, if it has not been restored, compute it
if not self.global_std.is_restored(session):
print("---------------------------------")
print("Preprocessing... Calculating std over all dataset "
"(this may take long)...")
self._compute_global_std(dataset, session, limit)
print("STD: " + str(self.global_std.value) + " (To avoid "
"repetitive computation, add it to argument 'std' of "
"`add_featurewise_stdnorm`)")
if self.global_pc.is_required:
# If a value is already provided, it has priority
if self.global_pc.value is not None:
self.global_pc.assign(self.global_pc.value, session)
# Otherwise, if it has not been restored, compute it
if not self.global_pc.is_restored(session):
print("---------------------------------")
print("Preprocessing... PCA over all dataset "
"(this may take long)...")
self._compute_global_pc(dataset, session, limit)
with open('PC.pkl', 'wb') as f:
pickle.dump(self.global_pc.value, f)
print("PC saved to 'PC.pkl' (To avoid repetitive computation, "
"load this pickle file and assign its value to 'pc' "
"argument of `add_zca_whitening`)")
# -----------------------
# Preprocessing Methods
# -----------------------
def add_samplewise_zero_center(self):
""" add_samplewise_zero_center.
Zero center each sample by subtracting it by its mean.
Returns:
Nothing.
"""
self.methods.append(self._samplewise_zero_center)
self.args.append(None)
def add_samplewise_stdnorm(self):
""" add_samplewise_stdnorm.
Scale each sample with its standard deviation.
Returns:
Nothing.
"""
self.methods.append(self._samplewise_stdnorm)
self.args.append(None)
def add_featurewise_zero_center(self, mean=None):
""" add_samplewise_zero_center.
Zero center every sample with specified mean. If not specified,
the mean is evaluated over all samples.
Arguments:
mean: `float` (optional). Provides a custom mean. If none
provided, it will be automatically caluclated based on
the training dataset. Default: None.
Returns:
Nothing.
"""
self.global_mean.is_required = True
self.global_mean.value = mean
self.methods.append(self._featurewise_zero_center)
self.args.append(None)
def add_featurewise_stdnorm(self, std=None):
""" add_featurewise_stdnorm.
Scale each sample by the specified standard deviation. If no std
specified, std is evaluated over all samples data.
Arguments:
std: `float` (optional). Provides a custom standard derivation.
If none provided, it will be automatically caluclated based on
the training dataset. Default: None.
Returns:
Nothing.
"""
self.global_std.is_required = True
self.global_std.value = std
self.methods.append(self._featurewise_stdnorm)
self.args.append(None)
def add_zca_whitening(self, pc=None):
""" add_zca_whitening.
Apply ZCA Whitening to data.
Arguments:
pc: `array` (optional). Use the provided pre-computed principal
component instead of computing it.
Returns:
Nothing.
"""
self.global_pc.is_required = True
self.global_pc.value = pc
self.methods.append(self._zca_whitening)
self.args.append(None)
# ---------------------------
# Preprocessing Calculation
# ---------------------------
def _samplewise_zero_center(self, batch):
for i in range(len(batch)):
batch[i] -= np.mean(batch[i], axis=0)
return batch
def _samplewise_stdnorm(self, batch):
for i in range(len(batch)):
batch[i] /= (np.std(batch[i], axis=0) + _EPSILON)
return batch
def _featurewise_zero_center(self, batch):
for i in range(len(batch)):
batch[i] -= self.global_mean.value
return batch
def _featurewise_stdnorm(self, batch):
for i in range(len(batch)):
batch[i] /= (self.global_std.value + _EPSILON)
return batch
def _zca_whitening(self, batch):
for i in range(len(batch)):
flat = np.reshape(batch[i], batch[i].size)
white = np.dot(flat, self.global_pc.value)
s1, s2, s3 = batch[i].shape[0], batch[i].shape[1], batch[i].shape[2]
batch[i] = np.reshape(white, (s1, s2, s3))
return batch
# ---------------------------------------
# Calulation with Persistent Parameters
# ---------------------------------------
def _compute_global_mean(self, dataset, session, limit=None):
""" Compute mean of a dataset. A limit can be specified for faster
computation, considering only 'limit' first elements. """
_dataset = dataset
mean = 0.
if isinstance(limit, int):
_dataset = _dataset[:limit]
if isinstance(_dataset, np.ndarray):
mean = np.mean(_dataset)
else:
# Iterate in case of non numpy data
for i in range(len(dataset)):
mean += np.mean(dataset[i]) / len(dataset)
self.global_mean.assign(mean, session)
return mean
def _compute_global_std(self, dataset, session, limit=None):
""" Compute std of a dataset. A limit can be specified for faster
computation, considering only 'limit' first elements. """
_dataset = dataset
std = 0.
if isinstance(limit, int):
_dataset = _dataset[:limit]
if isinstance(_dataset, np.ndarray):
std = np.std(_dataset)
else:
for i in range(len(dataset)):
std += np.std(dataset[i]) / len(dataset)
self.global_std.assign(std, session)
return std
def _compute_global_pc(self, dataset, session, limit=None):
""" Compute the Principal Component. """
_dataset = dataset
if isinstance(limit, int):
_dataset = _dataset[:limit]
d = _dataset
s0, s1, s2, s3 = d.shape[0], d.shape[1], d.shape[2], d.shape[3]
flat = np.reshape(d, (s0, s1 * s2 * s3))
sigma = np.dot(flat.T, flat) / flat.shape[1]
U, S, V = np.linalg.svd(sigma)
pc = np.dot(np.dot(U, np.diag(1. / np.sqrt(S + _EPSILON))), U.T)
self.global_pc.assign(pc, session)
return pc
# -----------------------
# Persistent Parameters
# -----------------------
class PersistentParameter:
""" Create a persistent variable that will be stored into the Graph.
"""
def __init__(self, scope, name):
self.is_required = False
with tf.name_scope(scope):
with tf.device('/cpu:0'):
# One variable contains the value
self.var = tf.Variable(0., trainable=False, name=name,
validate_shape=False)
# Another one check if it has been restored or not
self.var_r = tf.Variable(False, trainable=False,
name=name+"_r")
# RAM saved vars for faster access
self.restored = False
self.value = None
def is_restored(self, session):
if self.var_r.eval(session=session):
self.value = self.var.eval(session=session)
return True
else:
return False
def assign(self, value, session):
session.run(tf.assign(self.var, value, validate_shape=False))
self.value = value
session.run(self.var_r.assign(True))
self.restored = True
class ImagePreprocessing(DataPreprocessing):
""" Image Preprocessing.
Base class for applying real-time image related pre-processing.
This class is meant to be used as an argument of `input_data`. When training
a model, the defined pre-processing methods will be applied at both
training and testing time. Note that ImageAugmentation is similar to
ImagePreprocessing, but only applies at training time.
"""
def __init__(self):
super(ImagePreprocessing, self).__init__()
self.global_mean_pc = False
self.global_std_pc = False
# -----------------------
# Preprocessing Methods
# -----------------------
def add_image_normalization(self):
""" add_image_normalization.
Normalize a picture pixel to 0-1 float (instead of 0-255 int).
Returns:
Nothing.
"""
self.methods.append(self._normalize_image)
self.args.append(None)
def add_crop_center(self, shape):
""" add_crop_center.
Crop the center of an image.
Arguments:
shape: `tuple` of `int`. The croping shape (height, width).
Returns:
Nothing.
"""
self.methods.append(self._crop_center)
self.args.append([shape])
def resize(self, height, width):
raise NotImplementedError
def blur(self):
raise NotImplementedError
# -----------------------
# Preprocessing Methods
# -----------------------
def _normalize_image(self, batch):
return np.array(batch) / 255.
def _crop_center(self, batch, shape):
oshape = np.shape(batch[0])
nh = int((oshape[0] - shape[0]) * 0.5)
nw = int((oshape[1] - shape[1]) * 0.5)
new_batch = []
for i in range(len(batch)):
new_batch.append(batch[i][nh: nh + shape[0], nw: nw + shape[1]])
return new_batch
# ----------------------------------------------
# Preprocessing Methods (Overwrited from Base)
# ----------------------------------------------
def add_samplewise_zero_center(self, per_channel=False):
""" add_samplewise_zero_center.
Zero center each sample by subtracting it by its mean.
Arguments:
per_channel: `bool`. If True, apply per channel mean.
Returns:
Nothing.
"""
self.methods.append(self._samplewise_zero_center)
self.args.append([per_channel])
def add_samplewise_stdnorm(self, per_channel=False):
""" add_samplewise_stdnorm.
Scale each sample with its standard deviation.
Arguments:
per_channel: `bool`. If True, apply per channel std.
Returns:
Nothing.
"""
self.methods.append(self._samplewise_stdnorm)
self.args.append([per_channel])
def add_featurewise_zero_center(self, mean=None, per_channel=False):
""" add_samplewise_zero_center.
Zero center every sample with specified mean. If not specified,
the mean is evaluated over all samples.
Arguments:
mean: `float` (optional). Provides a custom mean. If none
provided, it will be automatically caluclated based on
the training dataset. Default: None.
per_channel: `bool`. If True, compute mean per color channel.
Returns:
Nothing.
"""
self.global_mean.is_required = True
self.global_mean.value = mean
if per_channel:
self.global_mean_pc = True
self.methods.append(self._featurewise_zero_center)
self.args.append(None)
def add_featurewise_stdnorm(self, std=None, per_channel=False):
""" add_featurewise_stdnorm.
Scale each sample by the specified standard deviation. If no std
specified, std is evaluated over all samples data.
Arguments:
std: `float` (optional). Provides a custom standard derivation.
If none provided, it will be automatically caluclated based on
the training dataset. Default: None.
per_channel: `bool`. If True, compute std per color channel.
Returns:
Nothing.
"""
self.global_std.is_required = True
self.global_std.value = std
if per_channel:
self.global_std_pc = True
self.methods.append(self._featurewise_stdnorm)
self.args.append(None)
# --------------------------------------------------
# Preprocessing Calculation (Overwrited from Base)
# --------------------------------------------------
def _samplewise_zero_center(self, batch, per_channel=False):
for i in range(len(batch)):
if not per_channel:
batch[i] -= np.mean(batch[i])
else:
batch[i] -= np.mean(batch[i], axis=(0, 1, 2), keepdims=True)
return batch
def _samplewise_stdnorm(self, batch, per_channel=False):
for i in range(len(batch)):
if not per_channel:
batch[i] /= (np.std(batch[i]) + _EPSILON)
else:
batch[i] /= (np.std(batch[i], axis=(0, 1, 2),
keepdims=True) + _EPSILON)
return batch
# --------------------------------------------------------------
# Calulation with Persistent Parameters (Overwrited from Base)
# --------------------------------------------------------------
def _compute_global_mean(self, dataset, session, limit=None):
""" Compute mean of a dataset. A limit can be specified for faster
computation, considering only 'limit' first elements. """
_dataset = dataset
mean = 0.
if isinstance(limit, int):
_dataset = _dataset[:limit]
if isinstance(_dataset, np.ndarray) and not self.global_mean_pc:
mean = np.mean(_dataset)
else:
# Iterate in case of non numpy data
for i in range(len(dataset)):
if not self.global_mean_pc:
mean += np.mean(dataset[i]) / len(dataset)
else:
mean += (np.mean(dataset[i], axis=(0, 1),
keepdims=True) / len(dataset))[0][0]
self.global_mean.assign(mean, session)
return mean
def _compute_global_std(self, dataset, session, limit=None):
""" Compute std of a dataset. A limit can be specified for faster
computation, considering only 'limit' first elements. """
_dataset = dataset
std = 0.
if isinstance(limit, int):
_dataset = _dataset[:limit]
if isinstance(_dataset, np.ndarray) and not self.global_std_pc:
std = np.std(_dataset)
else:
for i in range(len(dataset)):
if not self.global_std_pc:
std += np.std(dataset[i]) / len(dataset)
else:
std += (np.std(dataset[i], axis=(0, 1),
keepdims=True) / len(dataset))[0][0]
self.global_std.assign(std, session)
return std
class SequencePreprocessing(DataPreprocessing):
def __init__(self):
super(SequencePreprocessing, self).__init__()
def sequence_padding(self):
raise NotImplementedError