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_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)