def test_full_like():
test_case = Cases()
for mnp_proto, onp_proto in zip(test_case.mnp_prototypes, test_case.onp_prototypes):
shape = onp.zeros_like(onp_proto).shape
fill_value = rand_int()
actual = mnp.full_like(mnp_proto, mnp.array(fill_value)).asnumpy()
expected = onp.full_like(onp_proto, fill_value)
match_array(actual, expected)
for i in range(len(shape) - 1, 0, -1):
fill_value = rand_int(*shape[i:])
actual = mnp.full_like(mnp_proto, mnp.array(fill_value)).asnumpy()
expected = onp.full_like(onp_proto, fill_value)
match_array(actual, expected)
fill_value = rand_int(1, *shape[i + 1:])
actual = mnp.full_like(mnp_proto, mnp.array(fill_value)).asnumpy()
expected = onp.full_like(onp_proto, fill_value)
match_array(actual, expected)
def construct(self, inputs: Tensor, mask: Tensor):
if mask.dtype != mindspore.bool_:
mask = self.cast(mask, mindspore.bool_)
masked = mnp.full_like(inputs, self.value)
outputs = self.select(mask, masked, inputs)
return outputs