def test_unary(symbol, dims):
sizes = {'a': 3, 'b': 4}
shape = tuple(sizes[d] for d in dims)
inputs = OrderedDict((d, bint(sizes[d])) for d in dims)
dtype = 'real'
data = torch.rand(shape) + 0.5
if symbol == '~':
data = data.byte()
dtype = 2
expected_data = unary_eval(symbol, data)
x = Tensor(data, inputs, dtype)
actual = unary_eval(symbol, x)
check_funsor(actual, inputs, funsor.Domain((), dtype), expected_data)
def test_unary(symbol, dims):
sizes = {'a': 3, 'b': 4}
shape = tuple(sizes[d] for d in dims)
inputs = OrderedDict((d, bint(sizes[d])) for d in dims)
dtype = 'real'
data = rand(shape) + 0.5
if symbol == '~':
data = ops.astype(data, 'uint8')
dtype = 2
if get_backend() != "torch" and symbol in [
"abs", "sqrt", "exp", "log", "log1p", "sigmoid"
]:
expected_data = getattr(ops, symbol)(data)
else:
expected_data = unary_eval(symbol, data)
x = Tensor(data, inputs, dtype)
actual = unary_eval(symbol, x)
check_funsor(actual, inputs, funsor.Domain((), dtype), expected_data)