def test_data(self):
# confirm type as class tuple.
a = dc.zeros(2, 3).asTypeInt()
adata = a.data()
assert type(adata) == type((1, ))
# load new data
new_data_list = [10, 11, 12, 13, 14, 15]
a.load(dc.vectorInt(new_data_list))
assert a[0] == 10
# load one element with flat index
a[0] = 777
assert a[0] == 777
# reshape, fetch and load with multi indices
a = dc.arange(12).asTypeInt()
a.reshape(dc.vectorSizeT([2, 2, 3]))
assert a[0, 1, 1] == 4
a[1, 1, 1] = 200
assert a[1, 1, 1] == 200
# negative test
try:
# This throws ValueError
print(a[0, 0, 9, 9, 9])
except ValueError as e:
assert e
def setUp(self):
self.nullT = dc.array(0)
self.zeros = dc.zeros(2, 3).asTypeInt()
self.ones = dc.ones(2, 3).asTypeInt()
self.f0_4 = dc.arange(5)
self.f5_9 = dc.arange(10, 5)
self.np_f0_4 = np.arange(5)
self.np_f5_9 = np.arange(10, 5)
self.i0_4 = self.f0_4.asTypeInt()
self.i5_9 = self.f5_9.asTypeInt()
self.np_i0_4 = self.np_f0_4.astype(np.int)
self.np_i5_9 = self.np_f5_9.astype(np.int)
self.b0_4 = self.f0_4.asTypeBool()
self.b5_9 = self.f5_9.asTypeBool()
def test_non_binary():
t1 = dc.array(2, 3)
t2 = dc.array(3, 2)
mul = dc.matmul(t1, t2)
#print ("multiplication : " , mul.to_string())
t3 = dc.array(2, 3, 4)
#print("old shape", t1.shape())
new_shape = dc.vectorSizeT([2, 12])
t3.reshape(new_shape)
t3.reshape(4, 6)
t3.reshape((4, 6))
t3.reshape([4, 6])
#print("new shape", t1.shape())
py_list = list(t3)
# convert tensor to python list
py_tuple = tuple(t3)
# convert tensor to python tuple
np_ary = t3.numpy()
# convert to numpy array
#t4 = dc.thresholded_relu(t1);
#print("relu", t4.to_string())
#replace first few values in tensor with new values.
data = dc.vectorFloat([1.0, 2.0, 3.0, 4.0])
t3.load(data)
#print(t3.to_string())
arr = dc.array([1, 2])
#print(arr)
arr2D = dc.array([[1, 2], [10, 20]]).asTypeInt()
#print(arr2D)
arrRand = dc.random(2, 3)
#print(arrRand)
empty = dc.empty(3, 2)
#print(empty)
zeros = dc.zeros(2, 2)
#print(zeros);
ones = dc.ones(2, 2)
#print(ones)
ranges = dc.arange(15, 3, 2)
#print(ranges)
dc.reshape(arr2D, (1, 4))
#3D MatMul Test1
a = dc.array(2, 2, 2)
b = dc.array(2, 2, 2)
adata = dc.vectorFloat([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])
bdata = dc.vectorFloat([8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0])
a.load(adata)
b.load(bdata)
test_multiply(a, b)
#3D MatMul Test2
a = dc.array(2, 2, 3)
b = dc.array(2, 3, 2)
adata = dc.vectorFloat(
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0])
bdata = dc.vectorFloat(
[12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0])
a.load(adata)
b.load(bdata)
test_multiply(a, b)
def test_create(self):
# null tensor test
a = dc.array(0)
assert a.isnull() == True
assert a.empty() == True
# test assignment is shallow copy of memory
b = a
assert a.sameas(b) == True
assert a.identifier() == b.identifier()
# tensor without initiliaztion
a = dc.array(2, 3, 4, 5)
assert a.length() == 120
# tensor random initiliaztion
a = dc.random(2, 3, 4, 5)
assert a.length() == 120
# tensor without initiliaztion
a = dc.empty(2, 3, 4, 5)
assert a.length() == 120
# zero tensor
a = dc.zeros(2, 3, 4, 5)
assert np.array(list(a.data())).sum().astype(np.int) == 0
# one tensor
a = dc.ones(2, 3, 4, 5)
assert np.array(list(a.data())).sum().astype(np.int) == 120
# tensor from python list
l1D = [1, 3, 5]
a = dc.array(l1D).asTypeInt()
np.testing.assert_equal(np.array(l1D), np.array(list(a.data())))
# tensor from python list of lists
l2D = [[1, 3, 5], [2, 4, 6]]
a = dc.array(l2D).asTypeInt()
assert a.rank() == 2
assert a.shape() == (2, 3)
np.testing.assert_equal(np.array(l2D).flatten(), \
np.array(list(a.data())))
# copy tensor
b = a.copy()
assert a.sameas(b) == False
assert a.identifier() != b.identifier()
# arange
a = dc.arange(10)
assert a.length() == 10
# add start and step
a = dc.arange(10, 5, 3).asTypeInt()
assert a.data() == (5, 8)
# swap start and stop.
a = dc.arange(5, 10, 3).asTypeInt()
assert a.data() == (5, 8)
def test_assignments(self):
assert not self.nullT
# The truth value of an array with more than one element is ambiguous. Use dc.any() or dc.all()
# assert self.zeros
# assert self.ones
# Add
temp_zeros = self.zeros.copy()
temp_zeros += self.ones
dnnc_testing.utils.assert_equal(temp_zeros, self.ones)
temp_zeros = self.zeros.copy()
temp_zeros += 1
dnnc_testing.utils.assert_equal(temp_zeros, self.ones)
# Sub
temp_ones = self.ones.copy()
temp_ones -= self.ones
dnnc_testing.utils.assert_equal(temp_ones, self.zeros)
temp = self.f5_9
temp -= dc.array([5])
dnnc_testing.utils.assert_allclose(temp, self.f0_4)
temp = self.i5_9
temp -= dc.array([5]).asTypeInt()
dnnc_testing.utils.assert_equal(temp, self.i0_4)
temp = self.b5_9
temp -= dc.array([5]).asTypeBool()
dnnc_testing.utils.assert_equal(temp, dc.zeros(5).asTypeBool())
# Mul
temp_zeros = self.zeros.copy()
temp_zeros *= self.ones
dnnc_testing.utils.assert_equal(temp_zeros, self.zeros)
temp_ones = self.ones.copy()
temp_ones *= 0
dnnc_testing.utils.assert_equal(temp_ones, self.zeros)
# TrueDiv
temp_zeros = self.zeros.copy().asTypeFloat()
temp_zeros /= self.ones.asTypeFloat()
dnnc_testing.utils.assert_equal(temp_zeros.asTypeFloat(),
self.zeros.asTypeFloat())
temp_zeros = self.zeros.copy()
temp_zeros /= 1
dnnc_testing.utils.assert_equal(temp_zeros.asTypeFloat(),
self.zeros.asTypeFloat())
# FloorDiv
temp_zeros = self.zeros.copy()
temp_zeros //= self.ones
dnnc_testing.utils.assert_equal(temp_zeros, self.zeros)
temp_zeros = self.zeros.copy()
temp_zeros //= 1
dnnc_testing.utils.assert_equal(temp_zeros, self.zeros)
# Pow
temp_zeros = self.zeros.copy()
temp_zeros **= self.ones
dnnc_testing.utils.assert_equal(temp_zeros, self.zeros)
temp_ones = self.ones.copy()
temp_ones **= 0
dnnc_testing.utils.assert_equal(temp_ones, self.ones)
# Mod
temp_zeros = self.zeros.copy()
temp_zeros %= self.ones
dnnc_testing.utils.assert_equal(temp_zeros, self.zeros)
temp_zeros = self.zeros.copy()
temp_zeros %= 1
dnnc_testing.utils.assert_equal(temp_zeros, self.zeros)
# Left Shift
temp_zeros = self.zeros.copy()
temp_zeros <<= self.ones
dnnc_testing.utils.assert_equal(temp_zeros, self.zeros << self.ones)
temp_ones = self.ones.copy()
temp_ones <<= 0
dnnc_testing.utils.assert_equal(temp_ones, self.ones << 0)
# Right Shift
temp_zeros = self.zeros.copy()
temp_zeros <<= self.ones
dnnc_testing.utils.assert_equal(temp_zeros, self.zeros >> self.ones)
temp_ones = self.ones.copy()
temp_ones <<= 0
dnnc_testing.utils.assert_equal(temp_ones, self.ones >> 0)
# And
temp_zeros = self.zeros.copy()
temp_zeros &= self.ones
dnnc_testing.utils.assert_equal(temp_zeros, self.zeros)
temp_ones = self.ones.copy()
temp_ones &= 0
dnnc_testing.utils.assert_equal(temp_ones, self.zeros)
# Or
temp_zeros = self.zeros.copy()
temp_zeros |= self.ones
dnnc_testing.utils.assert_equal(temp_zeros, self.ones)
temp_ones = self.ones.copy()
temp_ones |= 0
dnnc_testing.utils.assert_equal(temp_ones, self.ones)
# Xor
temp_zeros = self.zeros.copy()
temp_zeros ^= self.ones
dnnc_testing.utils.assert_equal(temp_zeros, self.ones)
temp_ones = self.ones.copy()
temp_ones ^= 1
dnnc_testing.utils.assert_equal(temp_ones, self.zeros)
def setUp(self):
## random testcase
self.channels = 1
self.featuremaps = 1
self.batchsize = 1
#self.oneK = 1024
self.oneK = 50
self.X_h = self.oneK + np.random.randint(self.oneK * 3)
self.X_w = self.oneK + np.random.randint(self.oneK * 3)
self.K_h = 3 + np.random.randint(97)
self.K_w = 3 + np.random.randint(97)
self.np_strides = np.zeros(2).astype(np.float32)
self.np_strides[0] = 1 + np.random.randint(self.K_w - 1)
self.np_strides[1] = 1 + np.random.randint(self.K_h - 1)
self.np_B = np.zeros(self.featuremaps).astype(np.float32)
self.np_X_data = np.random.randn(self.X_w * self.X_h).astype(
np.float32)
self.np_K_data = np.random.randn(self.K_w * self.K_h).astype(
np.float32)
self.np_X = np.reshape(self.np_X_data, (self.X_h, self.X_w))
self.np_K = np.reshape(self.np_K_data, (self.K_h, self.K_w))
self.dc_X = dc.reshape(
dc.array(list(self.np_X_data)),
(self.batchsize, self.channels, self.X_h, self.X_w)).asTypeFloat()
self.dc_K = dc.reshape(dc.array(list(self.np_K_data)),
(self.featuremaps, self.channels, self.K_h,
self.K_w)).asTypeFloat()
self.dc_B = dc.zeros(self.featuremaps).asTypeFloat()
self.dc_strides = dc.reshape(dc.array(list(self.np_strides)),
(2)).asTypeInt()
self.dc_nullT = dc.array(0)
## onnx conv example testcase
self.onnx_dc_X = dc.reshape(
dc.array([
0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,
14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.
]), (1, 1, 5, 5))
self.onnx_dc_X2 = dc.reshape(
dc.array([
0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,
14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
26., 27., 28., 29., 30., 31., 32., 33., 34.
]), (1, 1, 7, 5))
self.onnx_dc_W = dc.reshape(
dc.array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), (1, 1, 3, 3))
self.onnx_npr_su = np.array([
12., 21., 27., 33., 24., 33., 54., 63., 72., 51., 63., 99., 108.,
117., 81., 93., 144., 153., 162., 111., 72., 111., 117., 123., 84.
])
self.onnx_npr_vl = np.array(
[54., 63., 72., 99., 108., 117., 144., 153., 162.])
self.onnx_npr_vl_s2 = np.array([54., 72., 144., 162., 234., 252.])
self.onnx_npr_sp_s2 = np.array([
12., 27., 24., 63., 108., 81., 123., 198., 141., 112., 177., 124.
])
self.onnx_npr_ap_s2 = np.array(
[21., 33., 99., 117., 189., 207., 171., 183.])
self.onnx_dc_BIGW = dc.reshape(dc.array(list(np.ones(900))),
(1, 1, 30, 30))