def get_shape(inp: dict):
if 'shape' not in inp:
raise ValueError("shape key not found")
shape = inp['shape']
default_shape = inp.get('default_shape')
if not isinstance(shape, list):
raise ValueError("shape should be list, found {}, type {}".format(
shape, type(shape)))
list_items = 0
for s in shape:
if isinstance(s, list):
list_items += 1
is_enum_shapes = list_items == len(shape)
# All or None shapes element shoud be list
# Regular shape [1,20,20,3]
# Enumerated Shapes [[1,20,20,3], [1,40,40,3]]
user_assert(list_items == 0 or is_enum_shapes,
"Can not parse shape {}".format(shape))
if is_enum_shapes:
enum_shapes = [transform_shape(s) for s in shape]
print("enum_shapes:", enum_shapes)
return ct.EnumeratedShapes(shapes=enum_shapes, default=default_shape)
# Replace string elements such as "1..50" with RangeDim(1, 50)
tx_shape = transform_shape(shape)
print("tx_shape:", tx_shape)
return ct.Shape(shape=tx_shape, default=default_shape)
def test_multiarray_input_enumerated(self, convert_to):
if convert_to == "mlprogram" and ct.utils._macos_version() < (12, 0):
return
example_input = torch.rand(1, 3, 50, 50) * 100
traced_model = torch.jit.trace(TestConvModule().eval(), example_input)
input_shape = ct.EnumeratedShapes(shapes=[[1, 3, 25,
25], [1, 3, 50, 50],
[1, 3, 67, 67]],
default=[1, 3, 67, 67])
model = ct.convert(traced_model,
inputs=[ct.TensorType(shape=input_shape)],
convert_to=convert_to)
spec = model.get_spec()
assert list(spec.description.input[0].type.multiArrayType.shape) == [
1, 3, 67, 67
]
assert list(spec.description.input[0].type.multiArrayType.
enumeratedShapes.shapes[0].shape) == [1, 3, 67, 67]
assert len(spec.description.input[0].type.multiArrayType.
enumeratedShapes.shapes) == 3
_assert_torch_coreml_output_shapes(model, spec, traced_model,
example_input)
def _convert_to_mil_type(shape, dtype, name: str):
mil_shape = shape
if _check_enumerated_shape(shape):
mil_shape = ct.EnumeratedShapes(shape)
ml_type = TensorType(shape=mil_shape, dtype=torch_to_mil_types[dtype])
ml_type.name = name
return ml_type
def test_image_input_enumerated(self, convert_to):
if convert_to == "mlprogram" and ct.utils._macos_version() < (12, 0):
return
example_input = torch.rand(1, 3, 50, 50) * 255
traced_model = torch.jit.trace(TestConvModule().eval(), example_input)
input_shape = ct.EnumeratedShapes(shapes=[[1, 3, 25,
25], [1, 3, 50, 50],
[1, 3, 67, 67]],
default=[1, 3, 67, 67])
model = ct.convert(traced_model,
inputs=[ct.ImageType(shape=input_shape)],
convert_to=convert_to)
spec = model.get_spec()
assert spec.description.input[0].type.imageType.width == 67
assert spec.description.input[0].type.imageType.height == 67
assert len(spec.description.input[0].type.imageType.enumeratedSizes.
sizes) == 3
assert spec.description.input[0].type.imageType.enumeratedSizes.sizes[
0].width == 25
assert spec.description.input[0].type.imageType.enumeratedSizes.sizes[
0].height == 25
_assert_torch_coreml_output_shapes(model,
spec,
traced_model,
example_input,
is_image_input=True)
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_torch_enumerated_shapes():
import torch
in_channels = 3
out_channels = 2
kernel_size = 3
class TestModule(torch.nn.Module):
def __init__(self):
super(TestModule, self).__init__()
self.conv = torch.nn.Conv2d(in_channels, out_channels,
kernel_size)
def forward(self, x):
return self.conv(x)
model = TestModule()
model.eval()
example_input = torch.randn(1, 3, 28, 28)
traced_model = torch.jit.trace(model, example_input)
shapes = [(1, 3, 28, 28), (1, 3, 56, 56)]
enumerated_shapes = ct.EnumeratedShapes(shapes=shapes)
tensor_input = ct.TensorType(name="input", shape=enumerated_shapes)
mlmodel = ct.convert(
traced_model,
inputs=[tensor_input],
)
if ct.utils._is_macos():
result = mlmodel.predict(
{"input": example_input.detach().numpy().astype(np.float32)},
useCPUOnly=True,
)
# Verify outputs
expected = model(example_input)
name = list(result.keys())[0]
np.testing.assert_allclose(result[name],
expected.detach().numpy(),
rtol=1e-3,
atol=1e-4)
# Test (1, 3, 56, 56) shape (can't verify numerical parity with Torch
# which doesn't support enumerated shape)
test_input_x = np.random.rand(*shapes[1]).astype(np.float32)
results = mlmodel.predict({"input": test_input_x})
# Test with a wrong shape
with pytest.raises(
RuntimeError,
match=r"not compatible with the model\'s feature"):
test_input_x = np.random.rand(1, 3, 29, 29).astype(np.float32)
results = mlmodel.predict({"input": test_input_x})
def test_torch_image_enumerated_shapes():
import torch
import torchvision
torch_model = torchvision.models.mobilenet_v2().features
torch_model.eval()
example_input = torch.rand(1, 3, 256, 256)
traced_model = torch.jit.trace(torch_model, example_input)
input_shapes = ct.EnumeratedShapes(shapes=[(1, 3, 256, 256), (1, 3, 224, 224)])
image_input = ct.ImageType(shape=input_shapes,
bias=[-1, -1, -1], scale=1 / 127)
model = ct.convert(traced_model, inputs=[image_input])
assert model is not None
spec = model.get_spec()
assert len(spec.description.input[0].type.imageType.enumeratedSizes.sizes) == 2
def test_tf2_image_enumerated_shapes():
import tensorflow as tf
keras_model = tf.keras.applications.MobileNetV2(
input_shape=(None, None, 3,),
classes=1000,
include_top=False,
)
input_shapes = ct.EnumeratedShapes(shapes=[(1, 192, 192, 3), (1, 224, 224, 3)])
image_input = ct.ImageType(shape=input_shapes,
bias=[-1,-1,-1], scale=1/127)
model = ct.convert(keras_model, inputs=[image_input])
assert model is not None
spec = model.get_spec()
assert len(spec.description.input[0].type.imageType.enumeratedSizes.sizes) == 2
def test_tf2keras_enumerated_shapes():
# Test examples in https://coremltools.readme.io/docs/flexible-inputs
import tensorflow as tf
input_shape = (28, 28, 3)
# None denotes seq_len dimension
x = tf.keras.Input(shape=input_shape, name="input")
C_out = 2
kHkW = 3
y = tf.keras.layers.Conv2D(C_out,
kHkW,
activation='relu',
input_shape=input_shape)(x)
keras_model = tf.keras.Model(inputs=[x], outputs=[y])
# One RangeDim shared by two inputs
shapes = [(1, 28, 28, 3), (1, 56, 56, 3)]
enumerated_shapes = ct.EnumeratedShapes(shapes=shapes)
tensor_input = ct.TensorType(name="input", shape=enumerated_shapes)
mlmodel = ct.convert(keras_model, inputs=[tensor_input])
# Test (1, 28, 28, 3) shape
test_input_x = np.random.rand(*shapes[0]).astype(np.float32)
expected_val = keras_model([test_input_x])
if ct.utils._is_macos():
results = mlmodel.predict({"input": test_input_x})
np.testing.assert_allclose(results["Identity"],
expected_val,
rtol=1e-2)
# Test (1, 56, 56, 3) shape (can't verify numerical parity with Keras
# which doesn't support enumerated shape)
test_input_x = np.random.rand(*shapes[1]).astype(np.float32)
results = mlmodel.predict({"input": test_input_x})
# Test with a wrong shape
with pytest.raises(
RuntimeError,
match=r"not compatible with the model\'s feature"):
test_input_x = np.random.rand(1, 29, 29, 3).astype(np.float32)
results = mlmodel.predict({"input": test_input_x})
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"
