def test_is_attr_legal(self):
# no name, no field
attr = AttributeProto()
self.assertFalse(helper.is_attribute_legal(attr))
# name, but no field
attr = AttributeProto()
attr.name = "test"
self.assertFalse(helper.is_attribute_legal(attr))
# name, with two fields
attr = AttributeProto()
attr.name = "test"
attr.f = 1.0
attr.i = 2
self.assertFalse(helper.is_attribute_legal(attr))
def test_is_attr_legal(self):
# no name, no field
attr = AttributeProto()
self.assertRaises(checker.ValidationError, checker.check_attribute,
attr)
# name, but no field
attr = AttributeProto()
attr.name = "test"
self.assertRaises(checker.ValidationError, checker.check_attribute,
attr)
# name, with two fields
attr = AttributeProto()
attr.name = "test"
attr.f = 1.0
attr.i = 2
self.assertRaises(checker.ValidationError, checker.check_attribute,
attr)
def map_attr(attr):
if attr.name in depluralizer:
# TODO: replace this with a version test
if not can_be_singular(attr.ints):
raise "Caffe2 doesn't support plural kernel_shape/strides/pads prior to 6cb4d1ecb0dfb553f797f6a8a61dd6966909cb0b; if you know your Caffe2 is recent enough, comment out this test"
# In fact, this code is MANDATORY, because prior to
# https://github.com/caffe2/caffe2/commit/6cb4d1ecb0dfb553f797f6a8a61dd6966909cb0b
# the pluralized versions were not supported.
# You'll get an error like
# "[enforce fail at conv_transpose_unpool_op_base.h:54] kernel_h_ > 0"
# if your Caffe2 is too old and you actually use the plural
# version
singular_attr = AttributeProto()
singular_attr.name = depluralizer[attr.name]
singular_attr.i = attr.ints[0]
return cls._onnx_arg_to_caffe2_arg(op_def.type, singular_attr)
else:
return cls._onnx_arg_to_caffe2_arg(op_def.type, attr)
def test_is_attr_legal_verbose(self):
ATTR_FUNCTIONS = [
(lambda attr: setattr(attr, "f", 1.0)),
(lambda attr: setattr(attr, "i", 1)),
(lambda attr: setattr(attr, "s", b"str")),
(lambda attr: attr.floats.extend([1.0, 2.0])),
(lambda attr: attr.ints.extend([1, 2])),
(lambda attr: attr.strings.extend([b"a", b"b"])),
(lambda attr: attr.tensors.extend([TensorProto(), TensorProto()])),
(lambda attr: attr.graphs.extend([GraphProto(), GraphProto()])),
]
# Randomly set one field, and the result should be legal.
for i in range(100):
attr = AttributeProto()
attr.name = "test"
random.choice(ATTR_FUNCTIONS)(attr)
self.assertTrue(helper.is_attribute_legal(attr))
# Randomly set two fields, and then ensure helper function catches it.
for i in range(100):
attr = AttributeProto()
attr.name = "test"
for func in random.sample(ATTR_FUNCTIONS, 2):
func(attr)
self.assertFalse(helper.is_attribute_legal(attr))
def test_is_attr_legal_verbose(self):
SET_ATTR = [
(lambda attr: setattr(attr, "f", 1.0) or \
setattr(attr, 'type', AttributeProto.FLOAT)),
(lambda attr: setattr(attr, "i", 1) or \
setattr(attr, 'type', AttributeProto.INT)),
(lambda attr: setattr(attr, "s", b"str") or \
setattr(attr, 'type', AttributeProto.STRING)),
(lambda attr: attr.floats.extend([1.0, 2.0]) or \
setattr(attr, 'type', AttributeProto.FLOATS)),
(lambda attr: attr.ints.extend([1, 2]) or \
setattr(attr, 'type', AttributeProto.INTS)),
(lambda attr: attr.strings.extend([b"a", b"b"]) or \
setattr(attr, 'type', AttributeProto.STRINGS)),
]
# Randomly set one field, and the result should be legal.
for _i in range(100):
attr = AttributeProto()
attr.name = "test"
random.choice(SET_ATTR)(attr)
checker.check_attribute(attr)
# Randomly set two fields, and then ensure helper function catches it.
for _i in range(100):
attr = AttributeProto()
attr.name = "test"
for func in random.sample(SET_ATTR, 2):
func(attr)
self.assertRaises(checker.ValidationError, checker.check_attribute,
attr)
def make_attribute(key, value):
"""Makes an AttributeProto based on the value type."""
attr = AttributeProto()
attr.name = key
is_iterable = isinstance(value, collections.Iterable)
bytes_or_false = _to_bytes_or_false(value)
# First, singular cases
# float
if isinstance(value, float):
attr.f = value
# integer
elif isinstance(value, numbers.Integral):
attr.i = value
# string
elif bytes_or_false:
attr.s = bytes_or_false
elif isinstance(value, TensorProto):
attr.t.CopyFrom(value)
elif isinstance(value, GraphProto):
attr.g.CopyFrom(value)
# third, iterable cases
elif is_iterable:
byte_array = [_to_bytes_or_false(v) for v in value]
if all(isinstance(v, float) for v in value):
attr.floats.extend(value)
elif all(isinstance(v, numbers.Integral) for v in value):
# Turn np.int32/64 into Python built-in int.
attr.ints.extend(int(v) for v in value)
elif all(byte_array):
attr.strings.extend(byte_array)
elif all(isinstance(v, TensorProto) for v in value):
attr.tensors.extend(value)
elif all(isinstance(v, GraphProto) for v in value):
attr.graphs.extend(value)
else:
raise ValueError(
"You passed in an iterable attribute but I cannot figure out "
"its applicable type.")
else:
raise ValueError(
'Value "{}" is not valid attribute data type.'.format(value))
return attr
