class TestMILDefaultValues:
@mb.program(
input_specs=[mb.TensorSpec(shape=[1]),
mb.TensorSpec(shape=[1])])
def basic_network(x, y):
return mb.add(x=x, y=y, name="output")
def test_mil_default_value_to_proto(self):
program_input_spec = [
ct.TensorType(name="x",
shape=[1],
default_value=np.array([1.0]).astype(np.float32)),
ct.TensorType(name="y", shape=[1])
]
mlmodel = ct.convert(self.basic_network,
convert_to="mlprogram",
inputs=program_input_spec)
input_spec = mlmodel.get_spec().description.input
assert len(input_spec) == 2, "2 input expected, got {} instead".format(
len(input_spec))
assert input_spec[
0].name == "x", "input name in MLModel is {}, 'x' is expected".format(
input_spec[0].name)
assert input_spec[0].type.WhichOneof(
"Type"
) == "multiArrayType", "Expected multiArrayType, got {}".format(
input_spec[0].type.WhichOneof("Type"))
assert input_spec[0].type.multiArrayType.WhichOneof(
"defaultOptionalValue"
) == "floatDefaultValue", "Expected floatDefaultValue, got {} instead".format(
input_spec[0].type.multiArrayType.WhichOneof(
"defaultOptionalValue"))
assert input_spec[0].type.multiArrayType.floatDefaultValue == 1.0
def test_mil_default_value_runtime(self):
program_input_spec = [
ct.TensorType(name="x",
shape=[1],
default_value=np.array([1.0]).astype(np.float32)),
ct.TensorType(name="y", shape=[1])
]
mlmodel = ct.convert(self.basic_network,
convert_to="mlprogram",
inputs=program_input_spec)
if _macos_version() < (12, 0):
# Can only get predictions for ml program on macOS 12+
return
res = mlmodel.predict({"x": np.array([3.]), "y": np.array([2.])})
assert res["output"][0] == 5.0
res = mlmodel.predict({"y": np.array([2.])})
assert res["output"][0] == 3.0
def basic_network(x, y):
return mb.add(x=x, y=y, name="output")
def basic_network(x):
return mb.relu(x=x)
class TestMILFlexibleShapes:
from coremltools.converters.mil.mil.program import Symbol
@mb.program(input_specs=[
mb.TensorSpec(shape=[1, 3, Symbol("H"), Symbol("W")])
])
def basic_network(x):
return mb.relu(x=x)
def test_mil_enumerated_multiarray(self):
enumerated_shapes = tuple([(1, 3, 10, 10), (1, 3, 10, 20),
(1, 3, 10, 30)])
input_shape = [
ct.TensorType(name="x",
shape=ct.EnumeratedShapes(shapes=enumerated_shapes))
]
mlmodel = ct.convert(self.basic_network,
source="milinternal",
convert_to="mlprogram",
inputs=input_shape)
input_spec = mlmodel.get_spec().description.input
assert len(input_spec) == 1, "1 input expected, got {} instead".format(
len(input_spec))
assert input_spec[
0].name == "x", "input name in MLModel is {}, 'x' is expected".format(
input_spec[0].name)
assert input_spec[0].type.WhichOneof(
"Type"
) == "multiArrayType", "Expected multiArrayType, got {}".format(
input_spec[0].type.WhichOneof("Type"))
assert input_spec[0].type.multiArrayType.WhichOneof(
"ShapeFlexibility"
) == "enumeratedShapes", "Expected enumeratedShapes in ShapeFlexibility"
spec_default_shape = [
s for s in input_spec[0].type.multiArrayType.shape
]
spec_enumerated_shapes = set()
for enumerated in input_spec[
0].type.multiArrayType.enumeratedShapes.shapes:
spec_enumerated_shapes.add(tuple([s for s in enumerated.shape]))
assert spec_default_shape == [
1, 3, 10, 10
], "Expected default shape to be [1, 3, 10, 10], got {} instead".format(
str(spec_default_shape))
assert spec_enumerated_shapes == set(
enumerated_shapes), "Enumerated shape mismatch"
def test_mil_enumerated_multiarray_with_default(self):
enumerated_shapes = tuple([(1, 3, 10, 10), (1, 3, 10, 20),
(1, 3, 10, 30)])
input_shape = [
ct.TensorType(name="x",
shape=ct.EnumeratedShapes(shapes=enumerated_shapes,
default=(1, 3, 10, 30)))
]
mlmodel = ct.convert(self.basic_network,
source="milinternal",
convert_to="mlprogram",
inputs=input_shape)
input_spec = mlmodel.get_spec().description.input
assert len(input_spec) == 1, "1 input expected, got {} instead".format(
len(input_spec))
assert input_spec[
0].name == "x", "input name in MLModel is {}, 'x' is expected".format(
input_spec[0].name)
assert input_spec[0].type.WhichOneof(
"Type"
) == "multiArrayType", "Expected multiArrayType, got {}".format(
input_spec[0].type.WhichOneof("Type"))
assert input_spec[0].type.multiArrayType.WhichOneof(
"ShapeFlexibility"
) == "enumeratedShapes", "Expected enumeratedShapes in ShapeFlexibility"
spec_default_shape = [
s for s in input_spec[0].type.multiArrayType.shape
]
spec_enumerated_shapes = set()
for enumerated in input_spec[
0].type.multiArrayType.enumeratedShapes.shapes:
spec_enumerated_shapes.add(tuple([s for s in enumerated.shape]))
assert spec_default_shape == [
1, 3, 10, 30
], "Expected default shape to be [1, 3, 10, 10], got {} instead".format(
str(spec_default_shape))
assert spec_enumerated_shapes == set(
enumerated_shapes), "Enumerated shape mismatch"
def test_mil_enumerated_image(self):
enumerated_shapes = tuple([(1, 3, 10, 10), (1, 3, 10, 20),
(1, 3, 10, 30)])
input_shape = [
ct.ImageType(name="x",
shape=ct.EnumeratedShapes(shapes=enumerated_shapes))
]
mlmodel = ct.convert(self.basic_network,
source="milinternal",
convert_to="mlprogram",
inputs=input_shape)
input_spec = mlmodel.get_spec().description.input
assert len(input_spec) == 1, "1 input expected, got {} instead".format(
len(input_spec))
assert input_spec[
0].name == "x", "input name in MLModel is {}, 'x' is expected".format(
input_spec[0].name)
assert input_spec[0].type.WhichOneof(
"Type") == "imageType", "Expected imageType, got {}".format(
input_spec[0].type.WhichOneof("Type"))
assert input_spec[0].type.imageType.WhichOneof(
"SizeFlexibility"
) == "enumeratedSizes", "Expected enumeratedShapes in ShapeFlexibility"
spec_H = input_spec[0].type.imageType.height
spec_W = input_spec[0].type.imageType.width
assert spec_H == 10 and spec_W == 10, "expected [H, W] == [10, 10], got [{}, {}] instead".format(
spec_H, spec_W)
spec_enumerated_shapes = set()
for enumerated in input_spec[0].type.imageType.enumeratedSizes.sizes:
spec_enumerated_shapes.add(
tuple([1, 3, enumerated.height, enumerated.width]))
assert spec_enumerated_shapes == set(
enumerated_shapes), "Enumerated shape mismatch"
def test_mil_enumerated_image_with_default(self):
enumerated_shapes = tuple([(1, 3, 10, 10), (1, 3, 10, 20),
(1, 3, 10, 30)])
input_shape = [
ct.ImageType(name="x",
shape=ct.EnumeratedShapes(shapes=enumerated_shapes,
default=(1, 3, 10, 30)))
]
mlmodel = ct.convert(self.basic_network,
source="milinternal",
convert_to="mlprogram",
inputs=input_shape)
input_spec = mlmodel.get_spec().description.input
assert len(input_spec) == 1, "1 input expected, got {} instead".format(
len(input_spec))
assert input_spec[
0].name == "x", "input name in MLModel is {}, 'x' is expected".format(
input_spec[0].name)
assert input_spec[0].type.WhichOneof(
"Type") == "imageType", "Expected imageType, got {}".format(
input_spec[0].type.WhichOneof("Type"))
assert input_spec[0].type.imageType.WhichOneof(
"SizeFlexibility"
) == "enumeratedSizes", "Expected enumeratedShapes in ShapeFlexibility"
spec_H = input_spec[0].type.imageType.height
spec_W = input_spec[0].type.imageType.width
assert spec_H == 10 and spec_W == 30, "expected [H, W] == [10, 30], got [{}, {}] instead".format(
spec_H, spec_W)
spec_enumerated_shapes = set()
for enumerated in input_spec[0].type.imageType.enumeratedSizes.sizes:
spec_enumerated_shapes.add(
tuple([1, 3, enumerated.height, enumerated.width]))
assert spec_enumerated_shapes == set(
enumerated_shapes), "Enumerated shape mismatch"
def test_mil_ranged_multiarray(self):
input_shape = [
ct.TensorType(name="x", shape=(1, 3, 10, ct.RangeDim(10, 30)))
]
mlmodel = ct.convert(self.basic_network,
source="milinternal",
convert_to="mlprogram",
inputs=input_shape)
input_spec = mlmodel.get_spec().description.input
assert len(input_spec) == 1, "1 input expected, got {} instead".format(
len(input_spec))
assert input_spec[
0].name == "x", "input name in MLModel is {}, 'x' is expected".format(
input_spec[0].name)
assert input_spec[0].type.WhichOneof(
"Type"
) == "multiArrayType", "Expected multiArrayType, got {}".format(
input_spec[0].type.WhichOneof("Type"))
assert input_spec[0].type.multiArrayType.WhichOneof(
"ShapeFlexibility"
) == "shapeRange", "Expected shapeRange in ShapeFlexibility"
spec_default_shape = [
s for s in input_spec[0].type.multiArrayType.shape
]
ranged_shapes = [(1, 1), (3, 3), (10, 10), (10, 30)]
spec_ranged_shapes = []
for range_dim in input_spec[
0].type.multiArrayType.shapeRange.sizeRanges:
spec_ranged_shapes.append(
tuple([range_dim.lowerBound, range_dim.upperBound]))
assert spec_default_shape == [
1, 3, 10, 10
], "Expected default shape to be [1, 3, 10, 10], got {} instead".format(
str(spec_default_shape))
assert spec_ranged_shapes == ranged_shapes, "Enumerated shape mismatch"
def test_mil_ranged_multiarray_with_default(self):
input_shape = [
ct.TensorType(name="x",
shape=(1, 3, 10, ct.RangeDim(10, 30, default=20)))
]
mlmodel = ct.convert(self.basic_network,
source="milinternal",
convert_to="mlprogram",
inputs=input_shape)
input_spec = mlmodel.get_spec().description.input
assert len(input_spec) == 1, "1 input expected, got {} instead".format(
len(input_spec))
assert input_spec[
0].name == "x", "input name in MLModel is {}, 'x' is expected".format(
input_spec[0].name)
assert input_spec[0].type.WhichOneof(
"Type"
) == "multiArrayType", "Expected multiArrayType, got {}".format(
input_spec[0].type.WhichOneof("Type"))
assert input_spec[0].type.multiArrayType.WhichOneof(
"ShapeFlexibility"
) == "shapeRange", "Expected shapeRange in ShapeFlexibility"
spec_default_shape = [
s for s in input_spec[0].type.multiArrayType.shape
]
ranged_shapes = [(1, 1), (3, 3), (10, 10), (10, 30)]
spec_ranged_shapes = []
for range_dim in input_spec[
0].type.multiArrayType.shapeRange.sizeRanges:
spec_ranged_shapes.append(
tuple([range_dim.lowerBound, range_dim.upperBound]))
assert spec_default_shape == [
1, 3, 10, 20
], "Expected default shape to be [1, 3, 10, 20], got {} instead".format(
str(spec_default_shape))
assert spec_ranged_shapes == ranged_shapes, "Enumerated shape mismatch"
def test_mil_ranged_image(self):
input_shape = [
ct.ImageType(name="x", shape=(1, 3, 10, ct.RangeDim(10, 30)))
]
mlmodel = ct.convert(self.basic_network,
source="milinternal",
convert_to="mlprogram",
inputs=input_shape)
input_spec = mlmodel.get_spec().description.input
assert len(input_spec) == 1, "1 input expected, got {} instead".format(
len(input_spec))
assert input_spec[
0].name == "x", "input name in MLModel is {}, 'x' is expected".format(
input_spec[0].name)
assert input_spec[0].type.WhichOneof(
"Type") == "imageType", "Expected imageType, got {}".format(
input_spec[0].type.WhichOneof("Type"))
assert input_spec[0].type.imageType.WhichOneof(
"SizeFlexibility"
) == "imageSizeRange", "Expected imageSizeRange in ShapeFlexibility"
spec_H = input_spec[0].type.imageType.height
spec_W = input_spec[0].type.imageType.width
assert spec_H == 10 and spec_W == 10, "expected [H, W] == [10, 10], got [{}, {}] instead".format(
spec_H, spec_W)
spec_H_range = [
input_spec[0].type.imageType.imageSizeRange.heightRange.lowerBound,
input_spec[0].type.imageType.imageSizeRange.heightRange.upperBound
]
spec_W_range = [
input_spec[0].type.imageType.imageSizeRange.widthRange.lowerBound,
input_spec[0].type.imageType.imageSizeRange.widthRange.upperBound
]
assert spec_H_range == [10, 10], "Ranged height mismatch"
assert spec_W_range == [10, 30], "Ranged width mismatch"
def test_mil_ranged_image_with_default(self):
input_shape = [
ct.ImageType(name="x",
shape=(1, 3, 10, ct.RangeDim(10, 30, default=20)))
]
mlmodel = ct.convert(self.basic_network,
source="milinternal",
convert_to="mlprogram",
inputs=input_shape)
input_spec = mlmodel.get_spec().description.input
assert len(input_spec) == 1, "1 input expected, got {} instead".format(
len(input_spec))
assert input_spec[
0].name == "x", "input name in MLModel is {}, 'x' is expected".format(
input_spec[0].name)
assert input_spec[0].type.WhichOneof(
"Type") == "imageType", "Expected imageType, got {}".format(
input_spec[0].type.WhichOneof("Type"))
assert input_spec[0].type.imageType.WhichOneof(
"SizeFlexibility"
) == "imageSizeRange", "Expected imageSizeRange in ShapeFlexibility"
spec_H = input_spec[0].type.imageType.height
spec_W = input_spec[0].type.imageType.width
assert spec_H == 10 and spec_W == 20, "expected [H, W] == [10, 20], got [{}, {}] instead".format(
spec_H, spec_W)
spec_H_range = [
input_spec[0].type.imageType.imageSizeRange.heightRange.lowerBound,
input_spec[0].type.imageType.imageSizeRange.heightRange.upperBound
]
spec_W_range = [
input_spec[0].type.imageType.imageSizeRange.widthRange.lowerBound,
input_spec[0].type.imageType.imageSizeRange.widthRange.upperBound
]
assert spec_H_range == [10, 10], "Ranged height mismatch"
assert spec_W_range == [10, 30], "Ranged width mismatch"