def return_dataset(data, scale=False, usps=False, all_use='no', directory="."):
if data == 'svhn':
train_image, train_label, \
test_image, test_label = load_svhn(directory)
if data == 'mnist':
train_image, train_label, \
test_image, test_label = load_mnist(directory)
# print(train_image.shape)
if data == 'mnistm':
train_image, train_label, \
test_image, test_label = load_mnistm(directory)
# print(train_image.shape)
if data == 'usps':
train_image, train_label, \
test_image, test_label = load_usps(directory)
if data == 'synth':
train_image, train_label, \
test_image, test_label = load_syntraffic(directory)
if data == 'gtsrb':
train_image, train_label, \
test_image, test_label = load_gtsrb(directory)
if data == 'syn':
train_image, train_label, \
test_image, test_label = load_syn(directory)
return train_image, train_label, test_image, test_label
def return_dataset(data, scale=False, usps=False, all_use='no'):
if data == 'svhn':
train_image, train_label, test_image, test_label = load_svhn()
print('The size of {} training dataset: {} and testing dataset: {}'.
format(data, train_image.shape, test_image.shape))
if data == 'mnist':
train_image, train_label, test_image, test_label = load_mnist(
scale=scale, usps=usps, all_use=all_use)
print('The size of {} training dataset: {} and testing dataset: {}'.
format(data, train_image.shape, test_image.shape))
if data == 'usps':
train_image, train_label, test_image, test_label = load_usps(
all_use=all_use)
print('The size of {} training dataset: {} and testing dataset: {}'.
format(data, train_image.shape, test_image.shape))
if data == 'synth':
train_image, train_label, test_image, test_label = load_syntraffic()
print('The size of {} training dataset: {} and testing dataset: {}'.
format(data, train_image.shape, test_image.shape))
if data == 'gtsrb':
train_image, train_label, test_image, test_label = load_gtsrb()
print('The size of {} training dataset: {} and testing dataset: {}'.
format(data, train_image.shape, test_image.shape))
return train_image, train_label, test_image, test_label
def return_dataset(data, scale=False, usps=False, all_use=False):
if data == 'svhn':
train_image, train_label, \
test_image, test_label = load_svhn()
if data == 'mnist':
train_image, train_label, \
test_image, test_label = load_mnist(scale=scale, usps=usps, all_use=all_use)
print(train_image.shape)
if data == 'usps':
train_image, train_label, \
test_image, test_label = load_usps(all_use=all_use)
return train_image, train_label, test_image, test_label
def get_ndarray(self):
(x_train, y_train), (x_test, y_test) = load_svhn(True)
# y is integer at this moment
#y_train = np.identity(10)[y_train]
#y_test = np.identity(10)[y_test ]
print(x_train[:2])
print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
return (x_train, y_train), (x_test, y_test)
def return_dataset(data, scale=False, usps=False, all_use='no'):
if data == 'svhn':
train_image, train_label, \
test_image, test_label = load_svhn()
if data == 'mnist':
train_image, train_label, \
test_image, test_label = load_mnist(scale=scale, usps=usps, all_use=all_use)
if data == 'usps':
train_image, train_label, \
test_image, test_label = load_usps(all_use=all_use)
if data == 'synth':
train_image, train_label, \
test_image, test_label = load_syntraffic()
if data == 'gtsrb':
train_image, train_label, \
test_image, test_label = load_gtsrb()
return train_image, train_label, test_image, test_label
def __init__(self, dataset, batch_size):
self.dataset = dataset
# image width,height
if self.dataset == "MNIST":
from tensorflow.examples.tutorials import mnist
_h, _w, _c = 28,28,1
img_size = _h*_w*_c # the canvas size
_l = 10
elif self.dataset == "CIFAR10":
_h, _w, _c = 32,32,3
img_size = _h*_w*_c
_l = 10
elif self.dataset == "SVHN":
#import loadSVHNKingma as svhn
#PCA_DIM = 768
PCA_DIM = -1 # no compressed raw data
#img_size = PCA_DIM # PCA
_h, _w, _c = 32,32,3
img_size = _h*_w*_c
_l = 10
elif self.dataset == "KaggleBreastHistology":
_h, _w, _c = 50,50,3
img_size = _h*_w*_c
_l = 2
elif self.dataset == "BreaKHis":
_h, _w, _c = 460,700,3
img_size = _h*_w*_c
_l = 2
elif self.dataset == "Kyoto2006":
from loadKyoto2006 import loadKyoto2006
_h, _w, _c = None,None,None
img_size = None # dummy
_l = 2
else: sys.exit("invalid dataset")
self.h = _h
self.w = _w
self.c = _c
self.l = _l
self.img_size = img_size
self.batch_size = batch_size
if self.dataset == "MNIST":
PATH_OF_MNIST = "D:/data/img/MNIST/"
data_directory = PATH_OF_MNIST
if not os.path.exists(data_directory): os.makedirs(data_directory)
mnist_datasets = mnist.input_data.read_data_sets(data_directory, one_hot=True)
dataset_train, dataset_test = mnist_datasets.train, mnist_datasets.test # binarized (0-1) mnist data
n_examples_train = dataset_train.images.shape[0]
n_examples_test = dataset_test.images.shape[0]
elif self.dataset == "CIFAR10":
#from cifar10 import load_cifar10
from keras.datasets import cifar10
(data_train, labels_train), (data_test, labels_test) = cifar10.load_data() # [0-255] integer
data_train = data_train / 255.
data_test = data_test / 255.
if IS_NHWC_or_1D == '1D':
data_train, data_test = data_train.reshape((-1, img_size)), data_test.reshape((-1, img_size)) # NHWC to 1d
data_train, data_test = data_train.astype(np.float32), data_test.astype(np.float32)
labels_train, labels_test = labels_train.reshape((-1, )), labels_test.reshape((-1, )) # flatten
# if normalized or zca-ed one is preferable,
#data_train, labels_train, data_test, labels_test = cifar10.loadCIFAR10( PATH_OF_CIFAR10, use_cache=True)
labels_train = self._one_hot_encoded(labels_train, 10)
labels_test = self._one_hot_encoded(labels_test, 10)
n_examples_train = len(data_train)
n_examples_test = len(data_test)
elif self.dataset == "SVHN":
from svhn import load_svhn, NUM_EXAMPLES_TRAIN, NUM_EXAMPLES_TEST
# data_train.shape is (604388,3072) w/ extra and (73257,3072) w/o extra
#data_train, labels_train, data_test, labels_test = svhn.loadSVHN(cutoffdim=PCA_DIM, use_cache=False, use_extra=False)
(data_train, labels_train), (data_test, labels_test) = load_svhn()
labels_train = self._one_hot_encoded(labels_train, 10)
labels_test = self._one_hot_encoded(labels_test, 10)
"""
n_examples_train = NUM_EXAMPLES_TRAIN
n_examples_test = NUM_EXAMPLES_TEST
"""
n_examples_train = (data_train.shape[0]//self.batch_size) * self.batch_size # discard residual
data_train, labels_train = data_train[0:n_examples_train, :], labels_train[0:n_examples_train, :]
n_examples_test = data_test.shape[0]//self.batch_size * self.batch_size
data_test, labels_test = data_test[0:n_examples_test, :], labels_test[0:n_examples_test, :]
elif self.dataset == "KaggleBreastHistology":
from HandleIIDDataTFRecord import HandleIIDDataTFRecord
K = 10
TEST_IDXES = [9]
PATHS = ( ['D:/data/img/KaggleBreastHistology'], None)
d = HandleIIDDataTFRecord( self.dataset, self.batch_size, K, PATHS, is_debug=False)
(data_train, labels_train), (data_test, labels_test) = d.get_ndarrays(TEST_IDXES)
labels_train = self._one_hot_encoded(labels_train, self.l)
labels_test = self._one_hot_encoded(labels_test, self.l)
#print('x:', data_train[0])
#print('y:', labels_train[0])
#sys.exit('kokomade')
n_examples_train = len(data_train)
n_examples_test = len(data_test)
n_examples_train = (data_train.shape[0]//self.batch_size) * self.batch_size # discard residual
n_examples_test = data_test.shape[0]//self.batch_size * self.batch_size
elif self.dataset == "BreaKHis":
from HandleIIDDataTFRecord import HandleIIDDataTFRecord
K = 10
TEST_IDXES = [9]
PATHS = ( ['D:/data/img/BreaKHis/BreaKHis_v1/histology_slides/breast'], None)
d = HandleIIDDataTFRecord( self.dataset, self.batch_size, K, PATHS, is_debug=False)
(data_train, labels_train), (data_test, labels_test) = d.get_ndarrays(TEST_IDXES)
labels_train = self._one_hot_encoded(labels_train, self.l)
labels_test = self._one_hot_encoded(labels_test, self.l)
n_examples_train = len(data_train)
n_examples_test = len(data_test)
#n_examples_train = (data_train.shape[0]//self.batch_size) * self.batch_size # discard residual
#data_train, labels_train = data_train[0:n_examples_train, :], labels_train[0:n_examples_train, :]
#n_examples_test = data_test.shape[0]//self.batch_size * self.batch_size
#data_test, labels_test = data_test[0:n_examples_test, :], labels_test[0:n_examples_test, :]
elif self.dataset == "Kyoto2006":
data_train, labels_train = loadKyoto2006('train', use_sval=False, use_cache=True, as_onehot=True)
data_test, labels_test = loadKyoto2006( 'test', use_sval=False, use_cache=True, as_onehot=True)
print(data_train.shape, labels_train.shape)
n_examples_train = (data_train.shape[0]//self.batch_size) * self.batch_size # discard residual
data_train, labels_train = data_train[0:n_examples_train, :], labels_train[0:n_examples_train, :]
n_examples_test = data_test.shape[0]//self.batch_size * self.batch_size
data_test, labels_test = data_test[0:n_examples_test, :], labels_test[0:n_examples_test, :]
self.img_size = data_train.shape[1]
# ugly work waround for ImageInterface
self.h, self.w, self.c = 1,1,self.img_size
if self.dataset == "SVHN":
pass
else:
assert(n_examples_train%self.batch_size ==0)
assert(n_examples_test%self.batch_size ==0)
if self.dataset == "MNIST":
# following two properties are for MNIST.
self.dataset_train = dataset_train
self.dataset_test = dataset_test
# bellow is just trial for crafting adv examples in eval.py
self.data_train, self.labels_train = dataset_train.next_batch(55000) # x: (BATCH_SIZE x img_size)
self.data_test, self.labels_test = dataset_test.next_batch(10000) # x: (BATCH_SIZE x img_size)
else:
self.data_train = data_train
self.labels_train = labels_train
self.data_test = data_test
self.labels_test = labels_test
#if IS_NHWC_or_1D == 'NHWC':
# self.dataset_train = np.reshape( self.dataset_train, (self.batch_size, self.h, self.w, self.c))
# self.dataset_test = np.reshape( self.dataset_test, (self.batch_size, self.h, self.w, self.c))
self.n_examples_train = n_examples_train
self.n_examples_test = n_examples_test
self.n_batches_train = int( self.n_examples_train/self.batch_size )
self.n_batches_test = int( self.n_examples_test/self.batch_size )
print('n_examples_train:%d, n_batches_train:%d, n_batches_test:%d' % \
(self.n_examples_train, self.n_batches_train, self.n_batches_test))
#!/usr/bin/env python3
"""
Implementation of self-ensembling for visual domain adaptation
"""
import tensorflow as tf
from mnist import load_mnist
from usps import load_usps
from svhn import load_svhn
from load_data import load_dataset
if __name__ == "__main__":
# Note: "It is worth noting that only the training sets of the small image
# datasets were used during training; the test sets usedfor reporting scores
# only." -- so, only use *_test for evaluation.
# Note 2: "The USPS images were up-scaled using bilinear interpolation from
# 16×16 to 28×28 resolution to match that of MNIST."
# Note 3: "The MNIST images were padded to 32×32 resolution and converted
# to RGB by replicating the greyscale channel into the three RGB channels
# to match the format of SVHN."
usps_train, usps_test = load_dataset(*load_usps())
mnist_train, mnist_test = load_dataset(*load_mnist())
svhn_train, svhn_test = load_dataset(*load_svhn())
