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generate_data.py
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import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.datasets import cifar100,mnist,cifar10,fashion_mnist
from scipy.io import loadmat
import numpy as onp #original numpy
import jax.numpy as jnp #jax numpy
import itertools
#import custom_datasets
# TODO: Setup this function to take in a string for the data set
def setupMNIST():
classes = 10
subtract_pixel_mean = True
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#for MNIST
x_train = onp.expand_dims(x_train,axis=3)
x_test = onp.expand_dims(x_test,axis=3)
y_train = y_train.reshape([-1])
y_test = y_test.reshape([-1])
# Input image dimensions.
input_shape = x_train.shape[1:]
# Normalize data.
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# If subtract pixel mean is enabled
if subtract_pixel_mean:
x_train_mean = onp.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
orig_x_train = onp.array(x_train)
orig_y_train = onp.array(y_train)
datagen = ImageDataGenerator(
# set input mean to 0 over the dataset
featurewise_center=False,
# set each sample mean to 0
samplewise_center=False,
# divide inputs by std of dataset
featurewise_std_normalization=False,
# divide each input by its std
samplewise_std_normalization=False,
# apply ZCA whitening
zca_whitening=False,
# epsilon for ZCA whitening
zca_epsilon=1e-06,
# randomly rotate images in the range (deg 0 to 180)
rotation_range=0,
# randomly shift images horizontally
width_shift_range=0.1,
# randomly shift images vertically
height_shift_range=0.1,
# set range for random shear
shear_range=0.,
# set range for random zoom
zoom_range=0.,
# set range for random channel shifts
channel_shift_range=0.,
# set mode for filling points outside the input boundaries
fill_mode='nearest',
# value used for fill_mode = "constant"
cval=0.,
# randomly flip images
horizontal_flip=True,
# randomly flip images
vertical_flip=False,
# set rescaling factor (applied before any other transformation)
rescale=None,
# set function that will be applied on each input
preprocessing_function=None,
# image data format, either "channels_first" or "channels_last"
data_format=None,
# fraction of images reserved for validation (strictly between 0 and 1)
validation_split=0.0)
datagen = ImageDataGenerator()
datagen.fit(x_train)
train_flow = datagen.flow(x_train, y_train, batch_size=128)
train_ds = map(lambda x: {'image': x[0].astype(onp.float32),
'label': x[1].astype(onp.int32)},train_flow)
test_ds = {'image': x_test.astype(jnp.float32),
'label': y_test.astype(jnp.int32)}
full_train_ds = {'image': x_train.astype(jnp.float32),
'label': y_train.astype(jnp.int32)}
return x_train, full_train_ds, train_ds, test_ds, classes
def setupFashionMNIST():
classes = 10
subtract_pixel_mean = True
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
# Add 3rd dimension to correspond to color in RGB images
x_train = onp.expand_dims(x_train,axis=3)
x_test = onp.expand_dims(x_test,axis=3)
y_train = y_train.reshape([-1])
y_test = y_test.reshape([-1])
# Input image dimensions.
input_shape = x_train.shape[1:]
# Normalize data.
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# If subtract pixel mean is enabled
if subtract_pixel_mean:
x_train_mean = onp.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
orig_x_train = onp.array(x_train)
orig_y_train = onp.array(y_train)
datagen = ImageDataGenerator()
datagen.fit(x_train)
train_flow = datagen.flow(x_train, y_train, batch_size=128)
train_ds = map(lambda x: {'image': x[0].astype(onp.float32),
'label': x[1].astype(onp.int32)},train_flow)
full_train_ds = {'image': x_train.astype(jnp.float32),
'label': y_train.astype(jnp.int32)}
test_ds = {'image': x_test.astype(jnp.float32),
'label': y_test.astype(jnp.int32)}
return x_train, full_train_ds, train_ds, test_ds, classes
def setupCIFAR10():
classes = 10
subtract_pixel_mean = True
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = y_train.reshape([-1])
y_test = y_test.reshape([-1])
# Input image dimensions.
input_shape = x_train.shape[1:]
# Normalize data.
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# If subtract pixel mean is enabled
if subtract_pixel_mean:
x_train_mean = onp.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
orig_x_train = onp.array(x_train)
orig_y_train = onp.array(y_train)
datagen = ImageDataGenerator()
datagen.fit(x_train)
train_flow = datagen.flow(x_train, y_train, batch_size=128)
train_ds = map(lambda x: {'image': x[0].astype(onp.float32),
'label': x[1].astype(onp.int32)},train_flow)
full_train_ds = {'image': x_train.astype(jnp.float32),
'label': y_train.astype(jnp.int32)}
test_ds = {'image': x_test.astype(jnp.float32),
'label': y_test.astype(jnp.int32)}
return x_train, full_train_ds, train_ds, test_ds, classes
def setupCIFAR100():
classes = 100
subtract_pixel_mean = True
(x_train, y_train), (x_test, y_test) = cifar100.load_data()
y_train = y_train.reshape([-1])
y_test = y_test.reshape([-1])
# Input image dimensions.
input_shape = x_train.shape[1:]
# Normalize data.
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# If subtract pixel mean is enabled
if subtract_pixel_mean:
x_train_mean = onp.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
orig_x_train = onp.array(x_train)
orig_y_train = onp.array(y_train)
datagen = ImageDataGenerator()
datagen.fit(x_train)
train_flow = datagen.flow(x_train, y_train, batch_size=128)
train_ds = map(lambda x: {'image': x[0].astype(onp.float32),
'label': x[1].astype(onp.int32)},train_flow)
full_train_ds = {'image': x_train.astype(jnp.float32),
'label': y_train.astype(jnp.int32)}
test_ds = {'image': x_test.astype(jnp.float32),
'label': y_test.astype(jnp.int32)}
return x_train, full_train_ds, train_ds, test_ds, classes
def setupSVHN():
classes = 10
subtract_pixel_mean = True
def load_data(path):
""" Helper function for loading a MAT-File"""
data = loadmat(path)
return data['X'], data['y']
x_train, y_train = load_data('train_32x32.mat')
x_test, y_test = load_data('test_32x32.mat')
# Gets rid of the extra dimension on the training labels
y_train = y_train.reshape([-1])
y_test = y_test.reshape([-1])
x_train = onp.moveaxis(x_train, -1, 0)
x_test = onp.moveaxis(x_test, -1, 0)
# Input image dimensions.
input_shape = x_train.shape[1:]
# Normalize data.
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# If subtract pixel mean is enabled
if subtract_pixel_mean:
x_train_mean = onp.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
orig_x_train = onp.array(x_train)
orig_y_train = onp.array(y_train)
datagen = ImageDataGenerator()
datagen.fit(x_train)
train_flow = datagen.flow(x_train, y_train, batch_size=128)
train_ds = map(lambda x: {'image': x[0].astype(onp.float32),
'label': x[1].astype(onp.int32)},train_flow)
full_train_ds = {'image': x_train.astype(jnp.float32),
'label': y_train.astype(jnp.int32)}
test_ds = {'image': x_test.astype(jnp.float32),
'label': y_test.astype(jnp.int32)}
return x_train, full_train_ds, train_ds, test_ds, classes
def setupTinyImageNet():
classes = 200
subtract_pixel_mean = True
dataset = custom_datasets.TINYIMAGENET('Data', train=True, download=True)
print(dataset)
#(x_train, y_train), (x_test, y_test) = cifar100.load_data()
y_train = y_train.reshape([-1])
y_test = y_test.reshape([-1])
# Input image dimensions.
input_shape = x_train.shape[1:]
# Normalize data.
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# If subtract pixel mean is enabled
if subtract_pixel_mean:
x_train_mean = onp.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
orig_x_train = onp.array(x_train)
orig_y_train = onp.array(y_train)
datagen = ImageDataGenerator()
datagen.fit(x_train)
train_flow = datagen.flow(x_train, y_train, batch_size=128)
train_ds = map(lambda x: {'image': x[0].astype(onp.float32),
'label': x[1].astype(onp.int32)},train_flow)
full_train_ds = {'image': x_train.astype(jnp.float32),
'label': y_train.astype(jnp.int32)}
test_ds = {'image': x_test.astype(jnp.float32),
'label': y_test.astype(jnp.int32)}
return x_train, full_train_ds, train_ds, test_ds, classes