def get_suite(plugin, plugin_kwargs=None, tags=None):
if plugin_kwargs is None:
plugin_kwargs = {}
if tags is None:
tags = []
def test_input_d_1_default_remove_mean_threshold_stretch():
arr_in = np.zeros((20, 30, 1), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 1), dtype=DTYPE)
np.random.seed(42)
data = np.random.randn(np.prod(arr_in.shape))
arr_in[:] = data.reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[0.20177312], [0.21249016]], dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_input_d_4_div_euclidean_remove_mean_false_default_rest():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[ 0.13273999, -0.09456467,
-0.01975331, -0.04648187],
[ 0.00148955, -0.00257985,
0.02118244, -0.01543736]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_div_euclidean_remove_mean_false_threshold_1_stretch_1e_2():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[ 0.01255756, -0.00894607,
-0.00186872, -0.00439731],
[ 0.00013929, -0.00024125,
0.00198085, -0.0014436 ]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=False, threshold=1, stretch=1e-2,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=False, threshold=1, stretch=1e-2,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_div_euclidean_remove_mean_true():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[ 1.27813682e-01, -9.97862518e-02,
-2.48777084e-02, -5.16409911e-02],
[ -2.00690944e-02, -2.42322776e-02,
7.76741435e-05, -3.73861268e-02]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_div_std_remove_mean_false():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 8], [20, 12]]
gt = np.array([[ 1.32899761, -0.94678491,
-0.19777086, -0.46537822],
[ 1.67757177, 0.42027149,
-0.70711917, -0.05593578]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='std', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='std', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_div_std_remove_mean_true():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 8], [20, 12]]
gt = np.array([[ 1.27801514, -0.99776751,
-0.2487534 , -0.51636076],
[ 1.42037416, 0.16307378,
-0.9643169 , -0.31313351]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='std', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='std', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_outker_shape_0_div_mag_remove_mean_false():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
outker_shape = 0, 0
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[ 0.24052431, -0.18180957,
-0.04978044, -0.0898783 ],
[ 0.00301287, -0.00500357,
0.04109935, -0.03260877]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='euclidean', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='euclidean', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_outker_shape_0_div_mag_remove_mean_true():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
outker_shape = 0, 0
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[ 0.18866782, -0.17986178,
-0.05663793, -0.06177634],
[-0.00420652, -0.03951693,
-0.0673274 , -0.05859426]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='euclidean', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='euclidean', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_outker_shape_0_div_std_remove_mean_false():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
outker_shape = 0, 0
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 8], [20, 12]]
gt = np.array([[ 1.26222396, -0.90901738,
-0.24902068, -0.45406818],
[ 1.54160333, 0.49371463,
-0.80440265, -0.05310058]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='std', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='std', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_outker_shape_0_div_std_remove_mean_true():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
outker_shape = 0, 0
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 8], [20, 12]]
gt = np.array([[ 0.94326323, -0.89923584,
-0.28315943, -0.30885619],
[ 1.27807069, 0.63492846,
-1.23798132, -0.50979644]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='std', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='std', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_lena_npy_array():
arr_in = lena()[::32, ::32].astype(DTYPE)
idx = [[4, 2], [4, 2]]
gt = np.array([0.2178068, 0.30647671],
dtype=DTYPE)
arr_out = lnorm(arr_in,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_lena_npy_array_non_C_contiguous():
arr_in = lena()[::32, ::32].astype(DTYPE)
arr_in = np.asfortranarray(arr_in)
idx = [[4, 2], [4, 2]]
gt = np.array([0.2178068, 0.30647671],
dtype=DTYPE)
try:
arr_out = lnorm(arr_in,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
except NotImplementedError:
raise SkipTest
def test_lena_pt3_array():
lena32 = lena()[::32, ::32].astype(DTYPE)/255.
arr_in = Array(lena32.shape, dtype=DTYPE)
arr_in[:] = lena32
idx = [[4, 2], [4, 2]]
gt = np.array([0.21779411, 0.30645376],
dtype=DTYPE)
arr_out = lnorm(arr_in,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
suite = {}
if plugin_kwargs:
plugin_kwarg_hash = '__kwargs_hash_' + get_pkl_sha1(plugin_kwargs)
else:
plugin_kwarg_hash = ""
for key, value in locals().iteritems():
if isinstance(value, types.FunctionType) and key.startswith('test_'):
func = value
func.__name__ += '__plugin_%s' % plugin + plugin_kwarg_hash
func = attr(*tags)(info(plugin, plugin_kwargs, func))
suite[func.__name__] = func
return suite
def get_suite(plugin, plugin_kwargs=None, tags=None):
if plugin_kwargs is None:
plugin_kwargs = {}
if tags is None:
tags = []
def test_default_lena():
arr_in = lena().astype(np.float32) / 1.0
idx = [[4, 2], [4, 2]]
gt = np.array([1442.0, 1455.0], dtype=DTYPE)
arr_out = lpool(arr_in, plugin=plugin, plugin_kwargs=plugin_kwargs)
assert_equals(arr_out.ndim, arr_in.ndim)
gv = arr_out[idx]
print gv.shape
print gv
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
def test_default_lena_non_C_contiguous():
arr_in = lena() / 1.0
arr_in = np.asfortranarray(arr_in)
idx = [[4, 2], [4, 2]]
gt = np.array([1442.0, 1455.0], dtype=DTYPE)
try:
arr_out = lpool(arr_in, plugin=plugin, plugin_kwargs=plugin_kwargs)
assert_equals(arr_out.ndim, arr_in.ndim)
gv = arr_out[idx]
print gv.shape
print gv
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
except NotImplementedError:
raise SkipTest
def test_default_input_d_1():
arr_in = np.zeros((20, 30, 1), dtype=DTYPE)
ker_shape = 5, 5
arr_out = np.zeros((16, 26, 1), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 8], [20, 12]]
gt = np.array([[-3.59586287], [9.16217232]], dtype=DTYPE)
lpool(arr_in, arr_out=arr_out, ker_shape=ker_shape, plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lpool(arr_in, ker_shape=ker_shape, plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
def test_ker_shape_5():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
ker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 8], [20, 12]]
gt = np.array(
[[7.93315649, -0.24066222, 0.64046526, -3.51567388], [7.14803362, -2.99622989, 9.5001564, 11.99116325]],
dtype=DTYPE,
)
lpool(arr_in, arr_out=arr_out, ker_shape=ker_shape, plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lpool(arr_in, ker_shape=ker_shape, plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
def test_ker_shape_5_order_2():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
ker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
order = 2
idx = [[4, 8], [20, 12]]
gt = np.array(
[[5.22081137, 4.9204731, 3.75381684, 4.892416], [5.60951042, 4.28514147, 4.77592659, 5.77252817]],
dtype=DTYPE,
)
lpool(arr_in, arr_out=arr_out, ker_shape=ker_shape, order=order, plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lpool(arr_in, ker_shape=ker_shape, order=order, plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
def test_ker_shape_5_order_2_stride_2():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
ker_shape = 5, 5
arr_out = np.zeros((8, 13, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
order = 2
stride = 2
idx = [[4, 3], [10, 6]]
gt = np.array(
[[4.81159449, 5.68032312, 5.07941389, 6.04614496], [4.87372255, 4.47074938, 4.07878876, 4.43441534]],
dtype=DTYPE,
)
lpool(
arr_in,
arr_out=arr_out,
ker_shape=ker_shape,
order=order,
stride=stride,
plugin=plugin,
plugin_kwargs=plugin_kwargs,
)
gv = arr_out[idx]
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lpool(
arr_in, ker_shape=ker_shape, order=order, stride=stride, plugin=plugin, plugin_kwargs=plugin_kwargs
)
gv = arr_out[idx]
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
def test_strides_self():
arr_in = lena() / 1.0
arr_in = arr_in[:12, :12]
for stride in xrange(1, 12):
try:
arr_out = lpool(arr_in, plugin=plugin, plugin_kwargs=plugin_kwargs)
gt = arr_out[::stride, ::stride]
gv = lpool(arr_in, stride=stride, plugin=plugin, plugin_kwargs=plugin_kwargs)
assert_array_equal(gv, gt)
except NotImplementedError:
raise SkipTest
def test_strides_scipy_naive():
arr_in = lena() / 1.0
arr_in = arr_in[:12, :12]
for stride in xrange(1, 12):
try:
gt = lpool(arr_in, stride=stride, plugin="scipy_naive")
gv = lpool(arr_in, stride=stride, plugin=plugin, plugin_kwargs=plugin_kwargs)
assert_array_equal(gv, gt)
except NotImplementedError:
raise SkipTest
def test_test_lena_npy_array():
arr_in = lena()[::32, ::32].astype(DTYPE)
idx = [[4, 2], [4, 2]]
gt = np.array([1280.99987793, 992.0], dtype=DTYPE)
arr_out = lpool(arr_in, plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
def test_test_lena_pt3_array():
lena32 = lena()[::32, ::32].astype(DTYPE) / 255.0
arr_in = Array(lena32.shape, dtype=DTYPE)
arr_in[:] = lena32
idx = [[4, 2], [4, 2]]
gt = np.array([5.02353001, 3.89019608], dtype=DTYPE)
arr_out = lpool(arr_in, plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose_round(gv, gt, rtol=RTOL, atol=ATOL)
suite = {}
if plugin_kwargs:
plugin_kwarg_hash = "__kwargs_hash_" + get_pkl_sha1(plugin_kwargs)
else:
plugin_kwarg_hash = ""
for key, value in locals().iteritems():
if isinstance(value, types.FunctionType) and key.startswith("test_"):
func = value
func.__name__ += "__plugin_%s" % plugin + plugin_kwarg_hash
func = attr(*tags)(info(plugin, plugin_kwargs, func))
suite[func.__name__] = func
return suite