def test_setup():
global mandrill, mandrill_t, rowdfilt, tf
tf = import_module('tensorflow')
lowlevel = import_module('dtcwt.tf.lowlevel')
rowdfilt = getattr(lowlevel, 'rowdfilt')
mandrill = datasets.mandrill()
mandrill_t = tf.expand_dims(tf.constant(mandrill, dtype=tf.float32),axis=0)
def test_setup():
global mandrill, mandrill_t, tf, colfilter
tf = import_module('tensorflow')
lowlevel = import_module('dtcwt.tf.lowlevel')
colfilter = getattr(lowlevel, 'colfilter')
mandrill = datasets.mandrill()
mandrill_t = tf.expand_dims(tf.constant(mandrill, dtype=tf.float32),axis=0)
def setup():
global mandrill
mandrill = datasets.mandrill()
global qbgn
qbgn = np.load(os.path.join(os.path.dirname(__file__), 'qbgn.npz'))['qbgn']
global verif
verif = np.load(os.path.join(os.path.dirname(__file__), 'verification.npz'))
def setup():
global mandrill
mandrill = datasets.mandrill()
global qbgn
qbgn = np.load(os.path.join(os.path.dirname(__file__), 'qbgn.npz'))['qbgn']
global verif
verif = np.load(os.path.join(os.path.dirname(__file__),
'verification.npz'))
def setup():
global mandrill, in_p, pyramid_ops
global tf, Transform1d, dtwavexfm2, dtwaveifm2, Pyramid_tf
global np_dtypes, tf_dtypes, stats
# Import the tensorflow modules
tf = import_module('tensorflow')
dtcwt_tf = import_module('dtcwt.tf')
dtcwt_tf_xfm1 = import_module('dtcwt.tf.transform1d')
Transform1d = getattr(dtcwt_tf, 'Transform1d')
Pyramid_tf = getattr(dtcwt_tf, 'Pyramid')
np_dtypes = getattr(dtcwt_tf_xfm1, 'np_dtypes')
tf_dtypes = getattr(dtcwt_tf_xfm1, 'tf_dtypes')
mandrill = datasets.mandrill()
# Make sure we run tests on cpu rather than gpus
os.environ["CUDA_VISIBLE_DEVICES"] = ""
dtcwt.push_backend('tf')
def setup():
global mandrill, in_p, pyramid_ops
global tf, Transform1d, dtwavexfm2, dtwaveifm2, Pyramid_tf
global np_dtypes, tf_dtypes, stats
# Import the tensorflow modules
tf = import_module('tensorflow')
dtcwt_tf = import_module('dtcwt.tf')
dtcwt_tf_xfm1 = import_module('dtcwt.tf.transform1d')
Transform1d = getattr(dtcwt_tf, 'Transform1d')
Pyramid_tf = getattr(dtcwt_tf, 'Pyramid')
np_dtypes = getattr(dtcwt_tf_xfm1, 'np_dtypes')
tf_dtypes = getattr(dtcwt_tf_xfm1, 'tf_dtypes')
mandrill = datasets.mandrill()
# Make sure we run tests on cpu rather than gpus
os.environ["CUDA_VISIBLE_DEVICES"] = ""
dtcwt.push_backend('tf')
def setup():
# Import some tf only dependencies
global mandrill, Transform2d, Pyramid
global tf, np_dtypes, tf_dtypes, dtwavexfm2, dtwaveifm2
# Import the tensorflow modules
tf = import_module('tensorflow')
dtcwt.push_backend('tf')
Transform2d = getattr(dtcwt, 'Transform2d')
Pyramid = getattr(dtcwt, 'Pyramid')
compat = import_module('dtcwt.compat')
dtwavexfm2 = getattr(compat, 'dtwavexfm2')
dtwaveifm2 = getattr(compat, 'dtwaveifm2')
import dtcwt.tf.transform2d as transform2d
np_dtypes = getattr(transform2d, 'np_dtypes')
tf_dtypes = getattr(transform2d, 'tf_dtypes')
mandrill = datasets.mandrill()
# Make sure we run tests on cpu rather than gpus
os.environ["CUDA_VISIBLE_DEVICES"] = ""
def setup():
global mandrill
mandrill = datasets.mandrill()
def setup():
global mandrill, mandrill_crop
mandrill = datasets.mandrill().astype(np.float64)
mandrill_crop = mandrill[:233, :301]
def setup():
global mandrill
mandrill = datasets.mandrill()
assert x.dtype in tf_dtypes
xfm = Transform2d()
X = np.random.randn(5,100,100,4)
p = xfm.forward_channels(X,data_format="nhwc")
x = xfm.inverse_channels(p,data_format="nhwc")
assert x.dtype in np_dtypes
xfm = Transform2d()
X = tf.placeholder(tf.float32, [None, 100,100,4])
p = xfm.forward_channels(X,data_format="nhwc")
x = xfm.inverse_channels(p,data_format="nhwc")
assert x.dtype in tf_dtypes
@skip_if_no_tf
@pytest.mark.parametrize("test_input,biort,qshift", [
(datasets.mandrill(),'antonini','qshift_a'),
(datasets.mandrill()[100:400,40:450],'legall','qshift_a'),
(datasets.mandrill(),'near_sym_a','qshift_c'),
(datasets.mandrill()[100:375,30:322],'near_sym_b','qshift_d'),
(datasets.mandrill(),'near_sym_b_bp', 'qshift_b_bp')
])
def test_results_match(test_input, biort, qshift):
"""
Compare forward transform with numpy forward transform for mandrill image
"""
im = test_input
f_np = Transform2d_np(biort=biort,qshift=qshift)
p_np = f_np.forward(im, include_scale=True)
f_tf = Transform2d(biort=biort,qshift=qshift)
p_tf = f_tf.forward(im, include_scale=True)
p = xfm.forward(X)
x = xfm.inverse(p)
assert x.dtype in tf_dtypes
X = np.random.randn(5,100,100)
p = xfm.forward_channels(X)
x = xfm.inverse_channels(p)
assert x.dtype in np_dtypes
X = tf.placeholder(tf.float32, [None, 100,100])
p = xfm.forward_channels(X)
x = xfm.inverse_channels(p)
assert x.dtype in tf_dtypes
@skip_if_no_tf
@pytest.mark.parametrize("test_input,biort,qshift", [
(datasets.mandrill(),'antonini','qshift_a'),
(datasets.mandrill()[100:400,40:450],'legall','qshift_a'),
(datasets.mandrill(),'near_sym_a','qshift_c'),
(datasets.mandrill()[100:374,30:322],'near_sym_b','qshift_d'),
])
def test_results_match(test_input, biort, qshift):
"""
Compare forward transform with numpy forward transform for mandrill image
"""
im = test_input
f_np = Transform1d_np(biort=biort,qshift=qshift)
p_np = f_np.forward(im, include_scale=True)
f_tf = Transform1d(biort=biort,qshift=qshift)
p_tf = f_tf.forward(im, include_scale=True)
def setup():
global mandrill, mandrill_crop
mandrill = datasets.mandrill().astype(np.float64)
mandrill_crop = mandrill[:233, :301]
