def test_xcov_derivative_cc2tensor():
from neon.backends.cc2 import GPU, GPUTensor
be = GPU(rng_seed=0)
np.random.seed(0)
n = 10
k = 8
(k1, k2) = (3, 5)
a = np.array(np.random.randn(k, n), dtype='float32', order='C')
s = np.zeros_like(a)
acc = xcc(a[:k1], a[k1:]) # k1 x k2
c1 = a[k1:] - a[k1:].mean(1, keepdims=True) # k2 x n
c2 = a[:k1] - a[:k1].mean(1, keepdims=True) # k1 x n
s[:k1] = acc.dot(c1) / n
s[k1:] = acc.T.dot(c2) / n
outputs = GPUTensor(a.copy())
tempbuf1 = be.empty((k1, n))
tempbuf2 = be.empty((k2, n))
tempbuf3 = be.empty((k1, k2))
tempbuf4 = be.empty(outputs.shape)
temp = [tempbuf1, tempbuf2, tempbuf3, tempbuf4]
my_result = xcov_cost_derivative(be, outputs, [], temp, k1)
expected_result = GPUTensor(s)
assert_tensor_near_equal(expected_result, my_result)
def test_xcov_derivative_cc2tensor():
from neon.backends.cc2 import GPU, GPUTensor
be = GPU(rng_seed=0)
np.random.seed(0)
n = 10
k = 8
(k1, k2) = (3, 5)
a = np.array(np.random.randn(k, n), dtype='float32', order='C')
s = np.zeros_like(a)
acc = xcc(a[:k1], a[k1:]) # k1 x k2
c1 = a[k1:] - a[k1:].mean(1, keepdims=True) # k2 x n
c2 = a[:k1] - a[:k1].mean(1, keepdims=True) # k1 x n
s[:k1] = acc.dot(c1)/n
s[k1:] = acc.T.dot(c2)/n
outputs = GPUTensor(a.copy())
tempbuf1 = be.empty((k1, n))
tempbuf2 = be.empty((k2, n))
tempbuf3 = be.empty((k1, k2))
tempbuf4 = be.empty(outputs.shape)
temp = [tempbuf1, tempbuf2, tempbuf3, tempbuf4]
my_result = xcov_cost_derivative(be, outputs, [], temp, k1)
expected_result = GPUTensor(s)
assert_tensor_near_equal(expected_result, my_result)
def test_cc2_rectleaky_derivative_slope_zero_rectlin_equiv():
from neon.backends.cc2 import GPU
be = GPU()
inputs = be.uniform(low=-5.0, high=10.0, size=(10, 10))
lin_buf = be.empty(inputs.shape)
leaky_buf = be.empty(inputs.shape)
be.rectlin_derivative(inputs, out=lin_buf)
be.rectleaky_derivative(inputs, slope=0.0, out=leaky_buf)
assert_tensor_equal(lin_buf, leaky_buf)
def test_xcov_cc2tensor():
np.random.seed(0)
n = 10
k = 8
(k1, k2) = (3, 5)
a = np.array(np.random.randn(k, n) * 10, dtype='float32', order='C')
acc = xcc(a[:k1], a[k1:])
expected_result = 0.5 * (acc**2.).sum()
from neon.backends.cc2 import GPU, GPUTensor
be = GPU(rng_seed=0) # to ensure cublas_init() is called.
outputs = GPUTensor(a.copy())
tempbuf1 = be.empty((k1, n))
tempbuf2 = be.empty((k2, n))
tempbuf3 = be.empty((k1, k2))
tempbuf4 = be.empty(outputs.shape)
temp = [tempbuf1, tempbuf2, tempbuf3, tempbuf4]
my_result = xcov_cost(be, outputs, [], temp, k1)
assert_tensor_near_equal(expected_result, my_result, tolerance=1e-3)
def test_xcov_cc2tensor():
np.random.seed(0)
n = 10
k = 8
(k1, k2) = (3, 5)
a = np.array(np.random.randn(k, n)*10, dtype='float32', order='C')
acc = xcc(a[:k1], a[k1:])
expected_result = 0.5 * (acc**2.).sum()
from neon.backends.cc2 import GPU, GPUTensor
be = GPU(rng_seed=0) # to ensure cublas_init() is called.
outputs = GPUTensor(a.copy())
tempbuf1 = be.empty((k1, n))
tempbuf2 = be.empty((k2, n))
tempbuf3 = be.empty((k1, k2))
tempbuf4 = be.empty(outputs.shape)
temp = [tempbuf1, tempbuf2, tempbuf3, tempbuf4]
my_result = xcov_cost(be, outputs, [], temp, k1)
assert_tensor_near_equal(expected_result, my_result, tolerance=1e-3)
class TestGPU(object):
def setup(self):
from neon.backends.cc2 import GPU, GPUTensor
# this code gets called prior to each test
self.be = GPU(rng_seed=0)
self.gpt = GPUTensor
def test_empty_creation(self):
tns = self.be.empty((4, 3))
assert tns.shape == (4, 3)
def test_array_creation(self):
tns = self.be.array([[1, 2], [3, 4]])
assert tns.shape == (2, 2)
assert_tensor_equal(tns, self.gpt([[1, 2], [3, 4]]))
def test_zeros_creation(self):
tns = self.be.zeros([3, 1])
assert tns.shape == (3, 1)
assert_tensor_equal(tns, self.gpt([[0], [0], [0]]))
def test_ones_creation(self):
tns = self.be.ones([1, 4])
assert tns.shape == (1, 4)
assert_tensor_equal(tns, self.gpt([[1, 1, 1, 1]]))
def test_all_equal(self):
left = self.be.ones([2, 2])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [1, 1]]))
def test_some_equal(self):
left = self.be.ones([2, 2])
right = self.be.array([[0, 1], [0, 1]])
out = self.be.empty([2, 2])
self.be.equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 1], [0, 1]]))
def test_none_equal(self):
left = self.be.ones([2, 2])
right = self.be.zeros([2, 2])
out = self.be.empty([2, 2])
self.be.equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 0], [0, 0]]))
def test_all_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.zeros([2, 2])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [1, 1]]))
def test_some_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.array([[0, 1], [0, 1]])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 0], [1, 0]]))
def test_none_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 0], [0, 0]]))
def test_greater(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.greater(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 0], [0, 1]]))
def test_greater_equal(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.greater_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 0], [1, 1]]))
def test_less(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.less(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [0, 0]]))
def test_less_equal(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.less_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [1, 0]]))
@nottest # TODO: cudanet doesn't currently support noaxis argmin
def test_argmin_noaxis(self):
tsr = self.be.array([[-1, 0], [1, 92]])
out = self.be.empty([1, 1])
self.be.argmin(tsr, None, out)
assert_tensor_equal(out, self.gpt([[0]]))
def test_argmin_axis0(self):
tsr = self.be.array([[-1, 0], [1, 92]])
out = self.be.empty((1, 2))
self.be.argmin(tsr, 0, out)
assert_tensor_equal(out, self.gpt([[0, 0]]))
def test_argmin_axis1(self):
tsr = self.be.array([[-1, 10], [11, 9]])
out = self.be.empty((2, 1))
self.be.argmin(tsr, 1, out)
assert_tensor_equal(out, self.gpt([[0], [1]]))
@nottest # TODO: cudanet doesn't currently support noaxis argmax
def test_argmax_noaxis(self):
tsr = self.be.array([[-1, 0], [1, 92]])
out = self.be.empty([1, 1])
self.be.argmax(tsr, None, out)
assert_tensor_equal(out, self.gpt(3))
def test_argmax_axis0(self):
tsr = self.be.array([[-1, 0], [1, 92]])
out = self.be.empty((1, 2))
self.be.argmax(tsr, 0, out)
assert_tensor_equal(out, self.gpt([[1, 1]]))
def test_argmax_axis1(self):
tsr = self.be.array([[-1, 10], [11, 9]])
out = self.be.empty((2, 1))
self.be.argmax(tsr, 1, out)
assert_tensor_equal(out, self.gpt([[1], [0]]))
def test_2norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 2
# -> sum([[1, 0], [1, 9]], axis=0)**.5 -> sqrt([2, 9])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=2, axis=0, out=out),
self.gpt([[2**rpow, 9**rpow]]))
# -> sum([[1, 0], [1, 9]], axis=1)**.5 -> sqrt([1, 10])
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=2, axis=1, out=out),
self.gpt([1**rpow, 10**rpow]))
def test_1norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum([[1, 0], [1, 3]], axis=0)**1 -> [2, 3]
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=1, axis=0, out=out),
self.gpt([[2, 3]]))
# -> sum([[1, 0], [1, 3]], axis=1)**1 -> [1, 4]
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=1, axis=1, out=out),
self.gpt([1, 4]))
def test_0norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum(tsr != 0, axis=0) -> [2, 1]
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=0, axis=0, out=out),
self.gpt([[2, 1]]))
# -> sum(tsr != 0, axis=1) -> [1, 2]
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=0, axis=1, out=out),
self.gpt([1, 2]))
def test_infnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> max(abs(tsr), axis=0) -> [1, 3]
assert_tensor_equal(self.be.norm(tsr, order=float('inf'), axis=0),
self.gpt([[1, 3]]))
# -> max(abs(tsr), axis=1) -> [1, 3]
assert_tensor_equal(self.be.norm(tsr, order=float('inf'), axis=1),
self.gpt([1, 3]))
def test_neginfnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> min(abs(tsr), axis=0) -> [1, 0]
assert_tensor_equal(self.be.norm(tsr, order=float('-inf'), axis=0),
self.gpt([[1, 0]]))
# -> min(abs(tsr), axis=1) -> [0, 1]
assert_tensor_equal(self.be.norm(tsr, order=float('-inf'), axis=1),
self.gpt([0, 1]))
def test_lrgnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 5
# -> sum([[1, 0], [1, 243]], axis=0)**rpow -> rpow([2, 243])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=5, axis=0, out=out),
self.gpt([[2**rpow, 243**rpow]]))
# -> sum([[1, 0], [1, 243]], axis=1)**rpow -> rpow([1, 244])
# 244**.2 == ~3.002465 hence the near_equal test
out = self.be.empty((2, 1))
assert_tensor_near_equal(self.be.norm(tsr, order=5, axis=1, out=out),
self.gpt([1**rpow, 244**rpow]), 1e-6)
def test_negnorm(self):
tsr = self.be.array([[-1, -2], [1, 3]])
rpow = -1. / 3
# -> sum([[1, .125], [1, .037037]], axis=0)**rpow -> rpow([2, .162037])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=-3, axis=0, out=out),
self.gpt([[2**rpow, .162037037037**rpow]]))
# -> sum([[1, .125], [1, .037037]], axis=1)**rpow ->
# rpow([1.125, 1.037037])
out = self.be.empty((2, 1))
assert_tensor_near_equal(self.be.norm(tsr, order=-3, axis=1, out=out),
self.gpt([1.125**rpow, 1.037037**rpow]), 1e-6)
class TestGPU(object):
def setup(self):
from neon.backends.cc2 import GPU, GPUTensor
# this code gets called prior to each test
self.be = GPU(rng_seed=0)
self.gpt = GPUTensor
def test_empty_creation(self):
tns = self.be.empty((4, 3))
assert tns.shape == (4, 3)
def test_array_creation(self):
tns = self.be.array([[1, 2], [3, 4]])
assert tns.shape == (2, 2)
assert_tensor_equal(tns, self.gpt([[1, 2], [3, 4]]))
def test_zeros_creation(self):
tns = self.be.zeros([3, 1])
assert tns.shape == (3, 1)
assert_tensor_equal(tns, self.gpt([[0], [0], [0]]))
def test_ones_creation(self):
tns = self.be.ones([1, 4])
assert tns.shape == (1, 4)
assert_tensor_equal(tns, self.gpt([[1, 1, 1, 1]]))
def test_all_equal(self):
left = self.be.ones([2, 2])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [1, 1]]))
def test_some_equal(self):
left = self.be.ones([2, 2])
right = self.be.array([[0, 1], [0, 1]])
out = self.be.empty([2, 2])
self.be.equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 1], [0, 1]]))
def test_none_equal(self):
left = self.be.ones([2, 2])
right = self.be.zeros([2, 2])
out = self.be.empty([2, 2])
self.be.equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 0], [0, 0]]))
def test_all_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.zeros([2, 2])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [1, 1]]))
def test_some_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.array([[0, 1], [0, 1]])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 0], [1, 0]]))
def test_none_not_equal(self):
left = self.be.ones([2, 2])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.not_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 0], [0, 0]]))
def test_greater(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.greater(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 0], [0, 1]]))
def test_greater_equal(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.greater_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[0, 0], [1, 1]]))
def test_less(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.less(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [0, 0]]))
def test_less_equal(self):
left = self.be.array([[-1, 0], [1, 92]])
right = self.be.ones([2, 2])
out = self.be.empty([2, 2])
self.be.less_equal(left, right, out)
assert out.shape == (2, 2)
assert_tensor_equal(out, self.gpt([[1, 1], [1, 0]]))
@nottest # TODO: cudanet doesn't currently support noaxis argmin
def test_argmin_noaxis(self):
tsr = self.be.array([[-1, 0], [1, 92]])
out = self.be.empty([1, 1])
self.be.argmin(tsr, None, out)
assert_tensor_equal(out, self.gpt([[0]]))
def test_argmin_axis0(self):
tsr = self.be.array([[-1, 0], [1, 92]])
out = self.be.empty((1, 2))
self.be.argmin(tsr, 0, out)
assert_tensor_equal(out, self.gpt([[0, 0]]))
def test_argmin_axis1(self):
tsr = self.be.array([[-1, 10], [11, 9]])
out = self.be.empty((2, 1))
self.be.argmin(tsr, 1, out)
assert_tensor_equal(out, self.gpt([[0], [1]]))
@nottest # TODO: cudanet doesn't currently support noaxis argmax
def test_argmax_noaxis(self):
tsr = self.be.array([[-1, 0], [1, 92]])
out = self.be.empty([1, 1])
self.be.argmax(tsr, None, out)
assert_tensor_equal(out, self.gpt(3))
def test_argmax_axis0(self):
tsr = self.be.array([[-1, 0], [1, 92]])
out = self.be.empty((1, 2))
self.be.argmax(tsr, 0, out)
assert_tensor_equal(out, self.gpt([[1, 1]]))
def test_argmax_axis1(self):
tsr = self.be.array([[-1, 10], [11, 9]])
out = self.be.empty((2, 1))
self.be.argmax(tsr, 1, out)
assert_tensor_equal(out, self.gpt([[1], [0]]))
def test_2norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 2
# -> sum([[1, 0], [1, 9]], axis=0)**.5 -> sqrt([2, 9])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=2, axis=0, out=out),
self.gpt([[2**rpow, 9**rpow]]))
# -> sum([[1, 0], [1, 9]], axis=1)**.5 -> sqrt([1, 10])
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=2, axis=1, out=out),
self.gpt([1**rpow, 10**rpow]))
def test_1norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum([[1, 0], [1, 3]], axis=0)**1 -> [2, 3]
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=1, axis=0, out=out),
self.gpt([[2, 3]]))
# -> sum([[1, 0], [1, 3]], axis=1)**1 -> [1, 4]
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=1, axis=1, out=out),
self.gpt([1, 4]))
def test_0norm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> sum(tsr != 0, axis=0) -> [2, 1]
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=0, axis=0, out=out),
self.gpt([[2, 1]]))
# -> sum(tsr != 0, axis=1) -> [1, 2]
out = self.be.empty((2, 1))
assert_tensor_equal(self.be.norm(tsr, order=0, axis=1, out=out),
self.gpt([1, 2]))
def test_infnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> max(abs(tsr), axis=0) -> [1, 3]
assert_tensor_equal(self.be.norm(tsr, order=float('inf'), axis=0),
self.gpt([[1, 3]]))
# -> max(abs(tsr), axis=1) -> [1, 3]
assert_tensor_equal(self.be.norm(tsr, order=float('inf'), axis=1),
self.gpt([1, 3]))
def test_neginfnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
# -> min(abs(tsr), axis=0) -> [1, 0]
assert_tensor_equal(self.be.norm(tsr, order=float('-inf'), axis=0),
self.gpt([[1, 0]]))
# -> min(abs(tsr), axis=1) -> [0, 1]
assert_tensor_equal(self.be.norm(tsr, order=float('-inf'), axis=1),
self.gpt([0, 1]))
def test_lrgnorm(self):
tsr = self.be.array([[-1, 0], [1, 3]])
rpow = 1. / 5
# -> sum([[1, 0], [1, 243]], axis=0)**rpow -> rpow([2, 243])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=5, axis=0, out=out),
self.gpt([[2**rpow, 243**rpow]]))
# -> sum([[1, 0], [1, 243]], axis=1)**rpow -> rpow([1, 244])
# 244**.2 == ~3.002465 hence the near_equal test
out = self.be.empty((2, 1))
assert_tensor_near_equal(self.be.norm(tsr, order=5, axis=1, out=out),
self.gpt([1**rpow, 244**rpow]), 1e-6)
def test_negnorm(self):
tsr = self.be.array([[-1, -2], [1, 3]])
rpow = -1. / 3
# -> sum([[1, .125], [1, .037037]], axis=0)**rpow -> rpow([2, .162037])
out = self.be.empty((1, 2))
assert_tensor_equal(self.be.norm(tsr, order=-3, axis=0, out=out),
self.gpt([[2**rpow, .162037037037**rpow]]))
# -> sum([[1, .125], [1, .037037]], axis=1)**rpow ->
# rpow([1.125, 1.037037])
out = self.be.empty((2, 1))
assert_tensor_near_equal(self.be.norm(tsr, order=-3, axis=1, out=out),
self.gpt([1.125**rpow, 1.037037**rpow]),
1e-6)
