def test_asfarray():
test_case = Cases()
for array in test_case.array_sets:
# Check for dtype matching
actual = onp.asfarray(array)
expected = mnp.asfarray(array).asnumpy()
# Since we set float32/int32 as the default dtype in mindspore, we need
# to make a conversion between numpy.asarray and mindspore.numpy.asarray
if actual.dtype is onp.dtype('float64'):
assert expected.dtype == onp.dtype('float32')
else:
assert actual.dtype == expected.dtype
match_array(actual, expected, error=7)
for i in range(len(test_case.onp_dtypes)):
actual = onp.asfarray(array, test_case.onp_dtypes[i])
expected = mnp.asfarray(array, test_case.mnp_dtypes[i]).asnumpy()
match_array(actual, expected, error=7)
# Additional tests for nested tensor/numpy_array mixture
mnp_input = [(mnp.ones(3, ), mnp.ones(3)), [[1, 1, 1], (1, 1, 1)]]
onp_input = [(onp.ones(3, ), onp.ones(3)), [[1, 1, 1], (1, 1, 1)]]
actual = onp.asfarray(onp_input)
expected = mnp.asfarray(mnp_input).asnumpy()
match_array(actual, expected, error=7)
def test_ones():
test_case = Cases()
for shape in test_case.all_shapes:
for i in range(len(test_case.onp_dtypes)):
actual = onp.ones(shape, test_case.onp_dtypes[i])
expected = mnp.ones(shape, test_case.mnp_dtypes[i]).asnumpy()
match_array(actual, expected)
actual = onp.ones(shape)
expected = mnp.ones(shape).asnumpy()
match_array(actual, expected)
def test_tri_triu_tril():
x = mnp.ones((16, 32), dtype="bool")
match_array(mnp.tril(x).asnumpy(), onp.tril(x.asnumpy()))
match_array(mnp.tril(x, -1).asnumpy(), onp.tril(x.asnumpy(), -1))
match_array(mnp.triu(x).asnumpy(), onp.triu(x.asnumpy()))
match_array(mnp.triu(x, -1).asnumpy(), onp.triu(x.asnumpy(), -1))
x = mnp.ones((64, 64), dtype="uint8")
match_array(mnp.tril(x).asnumpy(), onp.tril(x.asnumpy()))
match_array(mnp.tril(x, 25).asnumpy(), onp.tril(x.asnumpy(), 25))
match_array(mnp.triu(x).asnumpy(), onp.triu(x.asnumpy()))
match_array(mnp.triu(x, 25).asnumpy(), onp.triu(x.asnumpy(), 25))
match_array(mnp.tri(64, 64).asnumpy(), onp.tri(64, 64))
match_array(mnp.tri(64, 64, -10).asnumpy(), onp.tri(64, 64, -10))
def __init__(self,
input_dim,
hidden_num,
hidden_dim,
output_dim,
mu,
lamb,
nonlinear="leaky-relu",
norm_prod='paths',
square_prod=False):
super(BaseModel, self).__init__()
self.input_dim = input_dim
self.hidden_num = hidden_num
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.mu = mu
self.lamb = lamb
self.nonlinear = nonlinear
self.norm_prod = norm_prod
self.square_prod = square_prod
self.normal = msd.Normal(dtype=mstype.float32)
self.extra_params = []
# initialize current adjacency matrix
self.adjacency = msnp.ones(
(self.input_dim, self.input_dim), dtype=mstype.float32) - msnp.eye(
self.input_dim, dtype=mstype.float32)
# Generate layer_list
layer_list = [self.hidden_dim] * self.hidden_num
layer_list.insert(0, self.input_dim)
layer_list.append(self.output_dim)
# Instantiate the parameters of each layer in the model of each variable
tmp_weights = list()
tmp_biases = list()
for i, item in enumerate(layer_list[:-1]):
in_dim = item
out_dim = layer_list[i + 1]
tmp_weights.append(
Parameter(msnp.zeros((self.input_dim, out_dim, in_dim),
dtype=mstype.float32),
requires_grad=True,
name='w' + str(i)))
tmp_biases.append(
Parameter(msnp.zeros((self.input_dim, out_dim),
dtype=mstype.float32),
requires_grad=True,
name='b' + str(i)))
self.weights = ParameterTuple(tmp_weights)
self.biases = ParameterTuple(tmp_biases)
# reset initialization parameters
self.reset_params()
def test_array():
# array's function is very similar to asarray, so we mainly test the
# `copy` argument.
test_case = Cases()
for array in test_case.array_sets:
arr1 = mnp.asarray(array)
arr2 = mnp.array(arr1, copy=False)
arr3 = mnp.array(arr1)
arr4 = mnp.asarray(array, dtype='int32')
arr5 = mnp.asarray(arr4, dtype=mnp.int32)
assert arr1 is arr2
assert arr1 is not arr3
assert arr4 is arr5
# Additional tests for nested tensor/numpy_array mixture
mnp_input = [(mnp.ones(3, ), mnp.ones(3)), [[1, 1, 1], (1, 1, 1)]]
onp_input = [(onp.ones(3, ), onp.ones(3)), [[1, 1, 1], (1, 1, 1)]]
actual = onp.array(onp_input)
expected = mnp.array(mnp_input).asnumpy()
match_array(actual, expected, error=7)
def test_cumsum():
x = mnp.ones((16, 16), dtype="bool")
match_array(mnp.cumsum(x).asnumpy(), onp.cumsum(x.asnumpy()))
match_array(
mnp.cumsum(x, axis=0).asnumpy(), onp.cumsum(x.asnumpy(), axis=0))
match_meta(mnp.cumsum(x).asnumpy(), onp.cumsum(x.asnumpy()))
x = rand_int(3, 4, 5)
match_array(
mnp.cumsum(mnp.asarray(x), dtype="bool").asnumpy(),
onp.cumsum(x, dtype="bool"))
match_array(
mnp.cumsum(mnp.asarray(x), axis=-1).asnumpy(), onp.cumsum(x, axis=-1))
def test_swapaxes_exception():
with pytest.raises(ValueError):
mnp.swapaxes(mnp.ones((3, 3)), 1, 10)
def test_expand_dims_exception():
with pytest.raises(TypeError):
mnp.expand_dims(mnp.ones((3, 3)), 1.2)
def __init__(self):
self.all_shapes = [
0, 1, 2, (), (1,), (2,), (1, 2, 3), [], [1], [2], [1, 2, 3]
]
self.onp_dtypes = [onp.int32, 'int32', int,
onp.float32, 'float32', float,
onp.uint32, 'uint32',
onp.bool_, 'bool', bool]
self.mnp_dtypes = [mnp.int32, 'int32', int,
mnp.float32, 'float32', float,
mnp.uint32, 'uint32',
mnp.bool_, 'bool', bool]
self.array_sets = [1, 1.1, True, [1, 0, True], [1, 1.0, 2], (1,),
[(1, 2, 3), (4, 5, 6)], onp.random.random( # pylint: disable=no-member
(100, 100)).astype(onp.float32),
onp.random.random((100, 100)).astype(onp.bool)]
self.arrs = [
rand_int(2),
rand_int(2, 3),
rand_int(2, 3, 4),
rand_int(2, 3, 4, 5),
]
# scalars expanded across the 0th dimension
self.scalars = [
rand_int(),
rand_int(1),
rand_int(1, 1),
rand_int(1, 1, 1),
]
# arrays of the same size expanded across the 0th dimension
self.expanded_arrs = [
rand_int(2, 3),
rand_int(1, 2, 3),
rand_int(1, 1, 2, 3),
rand_int(1, 1, 1, 2, 3),
]
# arrays with dimensions of size 1
self.nested_arrs = [
rand_int(1),
rand_int(1, 2),
rand_int(3, 1, 8),
rand_int(1, 3, 9, 1),
]
# arrays which can be broadcast
self.broadcastables = [
rand_int(5),
rand_int(6, 1),
rand_int(7, 1, 5),
rand_int(8, 1, 6, 1)
]
# boolean arrays which can be broadcast
self.bool_broadcastables = [
rand_bool(),
rand_bool(1),
rand_bool(5),
rand_bool(6, 1),
rand_bool(7, 1, 5),
rand_bool(8, 1, 6, 1),
]
self.mnp_prototypes = [
mnp.ones((2, 3, 4)),
mnp.ones((0, 3, 0, 2, 5)),
onp.ones((2, 7, 0)),
onp.ones(()),
[mnp.ones(3), (1, 2, 3), onp.ones(3), [4, 5, 6]],
([(1, 2), mnp.ones(2)], (onp.ones(2), [3, 4])),
]
self.onp_prototypes = [
onp.ones((2, 3, 4)),
onp.ones((0, 3, 0, 2, 5)),
onp.ones((2, 7, 0)),
onp.ones(()),
[onp.ones(3), (1, 2, 3), onp.ones(3), [4, 5, 6]],
([(1, 2), onp.ones(2)], (onp.ones(2), [3, 4])),
]
class RollSwap(Cell):
def __init__(self):
super(RollSwap, self).__init__()
def construct(self, x):
x = mnp.rollaxis(x, 2)
x = mnp.swapaxes(x, 0, 1)
return x
test_case_array_ops = [
('ReshapeExpandSqueeze', {
'block': ReshapeExpandSqueeze(),
'desc_inputs': [mnp.ones((2, 3, 4))]}),
('TransposeConcatRavel', {
'block': TransposeConcatRavel(),
'desc_inputs': [mnp.ones((2, 3, 4)),
mnp.ones((2, 3, 4)),
mnp.ones((2, 4, 1))]}),
('RollSwap', {
'block': RollSwap(),
'desc_inputs': [mnp.ones((2, 3, 4))]})
]
test_case_lists = [test_case_array_ops]
test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
# use -k to select certain testcast
def test_swapaxes_exception():
with pytest.raises(TypeError):
swapaxes_exception(mnp.ones((3, 3)))
class RollSwap(Cell):
def __init__(self):
super(RollSwap, self).__init__()
def construct(self, x):
x = mnp.rollaxis(x, 2)
x = mnp.swapaxes(x, 0, 1)
return x
test_case_array_ops = [
('ReshapeExpandSqueeze', {
'block': ReshapeExpandSqueeze(),
'desc_inputs': [mnp.ones((2, 3, 4))]
}),
('TransposeConcatRavel', {
'block':
TransposeConcatRavel(),
'desc_inputs':
[mnp.ones((2, 3, 4)),
mnp.ones((2, 3, 4)),
mnp.ones((2, 4, 1))]
}),
('RollSwap', {
'block': RollSwap(),
'desc_inputs': [mnp.ones((2, 3, 4))]
})
]
def ones(shape, dtype=mstype.float32):
return mnp.ones(shape, dtype)
