Skip to content

Improve detection of NaN values in Select op #1663

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Aug 11, 2021
Merged

Improve detection of NaN values in Select op #1663

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
c0e1e08
Improve detection of NaN values in Select op
TomWildenhain-Microsoft Aug 10, 2021
da2b13a
Merge branch 'master' into tom/select_isnan
guschmue Aug 11, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 10 additions & 0 deletions tests/test_backend.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3295,6 +3295,16 @@ def func(x):
return tf.identity(picks, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})

@check_opset_min_version(9, "IsNaN")
def test_where_isnan(self):
x_val = np.array([1, 2, -3, float('nan'), -5, -6, float('nan'), 8, 9, 0], dtype=np.float32)
true_result = np.array([111, 222, 333, 444, 555, 666, 777, 888, 999, 1000],
dtype=np.float32)
def func(x):
picks = tf.where(is_nan(x), true_result, x)
return tf.identity(picks, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})

@check_opset_min_version(9, "Where for strings needs opset 9")
@skip_tfjs("Technically tf where doesn't support strings and tfjs doesn't like it")
def test_where_string(self):
Expand Down
9 changes: 9 additions & 0 deletions tf2onnx/onnx_opset/controlflow.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -184,6 +184,15 @@ def version_9(cls, ctx, node, **kwargs):
# We can't use the mul/add trick if a NaN is involved. handles_nan is added earlier in the converter.
handles_nan = node.get_attr_value("handles_nan", False)
if ctx.get_dtype(node.output[0]) in [TensorProto.FLOAT, TensorProto.DOUBLE]:
cond_node = node.inputs[0]
if cond_node.type == "IsNaN":
handles_nan = True
if cond_node.type == "NotEqual" and cond_node.input[0] == cond_node.input[1]:
handles_nan = True
if cond_node.type == "Not" and cond_node.inputs[0].type == "Equal":
eq_node = cond_node.inputs[0]
if eq_node.input[0] == eq_node.input[1]:
handles_nan = True
for inp in node.inputs[1:]:
if inp.is_const() and np.any(np.isnan(inp.get_tensor_value(as_list=False))):
handles_nan = True
Expand Down