def test_masked_select_broadcast(test_case):
x = flow.ones(2, 3, 3)
mask = flow.triu(flow.ones(3, 3), 1)
flow_res = flow.masked_select(x, mask)
np_res = [1, 1, 1, 1, 1, 1]
test_case.assertTrue(
np.allclose(flow_res.numpy(), np_res, 1e-05, 1e-05))
def _test_masked_select(test_case, device):
x = flow.tensor(
np.array([[-0.462, 0.3139], [0.3898, -0.7197], [0.0478, -0.1657]]),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
mask = x.gt(0.05)
of_out = flow.masked_select(x, mask)
np_out = np.array([0.3139, 0.3898])
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05))
of_out = of_out.sum()
of_out.backward()
np_grad = np.array([[0, 1], [1, 0], [0, 0]])
test_case.assertTrue(np.allclose(x.grad.numpy(), np_grad, 1e-05, 1e-05))
def _test_masked_select_broadcast(test_case, device):
x = flow.tensor(
np.array([[[-0.462, 0.3139], [0.3898, -0.7197], [0.0478, -0.1657]]]),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
mask = flow.tensor(
np.array([
[[1.0, 0.0], [1.0, 1.0], [0.0, 1.0]],
[[1.0, 0], [1.0, 1.0], [0.0, 1.0]],
[[1.0, 1.0], [0.0, 1.0], [1.0, 1.0]],
]),
dtype=flow.int8,
device=flow.device(device),
)
of_out = flow.masked_select(x, mask)
np_out = [
-0.462,
0.3898,
-0.7197,
-0.1657,
-0.462,
0.3898,
-0.7197,
-0.1657,
-0.462,
0.3139,
-0.7197,
0.0478,
-0.1657,
]
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05))
of_out = of_out.sum()
of_out.backward()
np_grad = [[[3.0, 1.0], [2.0, 3.0], [1.0, 3.0]]]
test_case.assertTrue(np.allclose(x.grad.numpy(), np_grad, 1e-05, 1e-05))
def _masked_select(self, mask):
return flow.masked_select(self, mask)
def __init__(self, tensor):
self.floating_dtype = tensor.dtype.is_floating_point
self.int_mode = True
self.sci_mode = False
self.max_width = 1
self.random_sample_num = 50
tensor = _try_convert_to_local_tensor(tensor)
with flow.no_grad():
tensor_view = tensor.reshape(-1)
if not self.floating_dtype:
for value in tensor_view:
value_str = "{}".format(value)
self.max_width = max(self.max_width, len(value_str))
else:
nonzero_finite_vals = flow.masked_select(tensor_view, tensor_view.ne(0))
if nonzero_finite_vals.numel() == 0:
# no valid number, do nothing
return
nonzero_finite_abs = nonzero_finite_vals.abs()
nonzero_finite_min = nonzero_finite_abs.min().numpy().astype(np.float64)
nonzero_finite_max = nonzero_finite_abs.max().numpy().astype(np.float64)
for value in nonzero_finite_abs.numpy():
if value != np.ceil(value):
self.int_mode = False
break
if self.int_mode:
# Check if scientific representation should be used.
if (
nonzero_finite_max / nonzero_finite_min > 1000.0
or nonzero_finite_max > 1.0e8
):
self.sci_mode = True
for value in nonzero_finite_vals:
value_str = (
("{{:.{}e}}").format(PRINT_OPTS.precision).format(value)
)
self.max_width = max(self.max_width, len(value_str))
else:
for value in nonzero_finite_vals:
value_str = ("{:.0f}").format(value)
self.max_width = max(self.max_width, len(value_str) + 1)
else:
if (
nonzero_finite_max / nonzero_finite_min > 1000.0
or nonzero_finite_max > 1.0e8
or nonzero_finite_min < 1.0e-4
):
self.sci_mode = True
for value in nonzero_finite_vals:
value_str = (
("{{:.{}e}}").format(PRINT_OPTS.precision).format(value)
)
self.max_width = max(self.max_width, len(value_str))
else:
for value in nonzero_finite_vals:
value_str = (
("{{:.{}f}}").format(PRINT_OPTS.precision).format(value)
)
self.max_width = max(self.max_width, len(value_str))
if PRINT_OPTS.sci_mode is not None:
self.sci_mode = PRINT_OPTS.sci_mode