def _merge_shapes_for_tf(shape1, shape2):
"""
Merge 2 shapes, return merged shape, set unknown for dims with different values.
Raise exception for mismatch.
"""
if shape1 is None:
return shape2
if shape2 is None:
return shape1
utils.make_sure(utils.is_list_or_tuple(shape1), "invalid type for shape1")
utils.make_sure(utils.is_list_or_tuple(shape2), "invalid type for shape2")
utils.make_sure(
len(shape1) == len(shape2),
"shapes rank mismatch: shape1=%s, shape2=%s", shape1, shape2)
merged = []
for d1, d2 in zip(shape1, shape2):
d = d1
if d1 is None:
d = d2
elif d2 is not None:
# None means unknown in tensorflow
d = None
merged.append(d)
return merged
def check_opset_constraints(self, opset, extra_opset=None):
""" Return (condition, reason) tuple, condition is True if constraints are met. """
if not self.opset_constraints:
return True, None
opsets = {"onnx": opset}
if extra_opset:
for e in extra_opset:
opsets[e.domain] = e.version
for constraint in self.opset_constraints:
domain = constraint.domain
opset_version = opsets.get(domain)
if not opset_version:
return False, "conversion requires opset {}".format(domain)
if constraint.min_version and opset_version < constraint.min_version:
reason = "conversion requires opset {} >= {}".format(domain, constraint.min_version)
return False, reason
if constraint.max_version and opset_version > constraint.max_version:
reason = "conversion requires opset {} <= {}".format(domain, constraint.max_version)
return False, reason
if constraint.excluded_version:
if utils.is_list_or_tuple(constraint.excluded_version):
skip = opset_version in constraint.excluded_version
else:
skip = opset_version == constraint.excluded_version
if skip:
reason = "conversion requires opset {} != {}".format(domain, constraint.excluded_version)
return False, reason
return True, None
