def assert_model_is_valid(
program, inputs, backend="nn_proto", verbose=True, expected_output_shapes=None
):
"""
Assert Core ML model is valid.
Inputs:
- input: str -> shape tuple. All program input names need to appear in str.
shape tuple can only contain positive integers.
"""
input_dict = dict()
for name, shape in inputs.items():
input_dict[name] = np.random.rand(*shape)
# Avoid circular import
from coremltools.converters._converters_entry import convert
mlmodel = convert(program, source="mil", convert_to=backend)
assert mlmodel is not None
if verbose:
from coremltools.models.neural_network.printer import print_network_spec
print_network_spec(mlmodel.get_spec(), style="coding")
if _IS_MACOS:
prediction = mlmodel.predict(input_dict, useCPUOnly=True)
assert prediction is not None
if expected_output_shapes is not None:
for out_name, out_shape in expected_output_shapes.items():
assert out_name in prediction
assert out_shape == prediction[out_name].shape
def test_fusion_with_image_full(self):
from coremltools.converters._converters_entry import convert
@mb.program(input_specs=[mb.TensorSpec(shape=(10, 20, 30, 3))])
def prog(x):
x1 = mb.transpose(x=x, perm=[0, 3, 1, 2])
x2 = mb.relu(x=x)
x3 = mb.transpose(x=x2, perm=[0, 3, 1, 2])
x4 = mb.add(x=x1, y=x3)
return mb.relu(x=x4)
mlmodel = convert(prog,
inputs=[
ImageType(name="x",
shape=(10, 20, 30, 3),
channel_first=False)
],
source="mil",
convert_to="nn_proto")
assert mlmodel is not None
assert len(mlmodel.get_spec().neuralNetwork.layers) == 3
def get_core_ml_prediction(
build, input_placeholders, input_values, use_cpu_only=False,
backend="nn_proto"):
"""
Return predictions of the given model.
"""
program = Program()
with Function(input_placeholders) as ssa_func:
output_vars = build(**ssa_func.inputs)
if isinstance(output_vars, tuple):
output_vars = list(output_vars)
elif not isinstance(output_vars, list):
output_vars = [output_vars]
ssa_func.set_outputs(output_vars)
program.add_function("main", ssa_func)
# Avoid circular import
from coremltools.converters._converters_entry import convert
mlmodel = convert(program, source="mil",
convert_to=backend, useCPUOnly=use_cpu_only)
return mlmodel.predict(input_values, useCPUOnly=use_cpu_only)
def convert_to_mlmodel(model_spec, tensor_inputs, backend="nn_proto"):
# Avoid circular dependency
from coremltools.converters._converters_entry import convert
def _convert_to_inputtype(inputs):
if isinstance(inputs, list):
return [_convert_to_inputtype(x) for x in inputs]
elif isinstance(inputs, tuple):
return tuple([_convert_to_inputtype(x) for x in inputs])
elif isinstance(inputs, TensorType):
return inputs
elif isinstance(inputs, torch.Tensor):
return TensorType(shape=inputs.shape,
dtype=torch_to_mil_types[inputs.dtype])
else:
raise ValueError("Unable to parse type {} into InputType.".format(
type(inputs)))
inputs = list(_convert_to_inputtype(tensor_inputs))
return convert(model_spec,
inputs=inputs,
convert_to=backend,
source="pytorch")
def tf_graph_to_mlmodel(
graph, feed_dict, output_nodes, frontend="tensorflow", backend="nn_proto"
):
"""
Parameters
----------
graph: tf.Graph
TensorFlow 1.x model in tf.Graph format.
feed_dict: dict of {tf.placeholder -> np.array or python primitive)
Dict of placeholder and value pairs representing inputs.
output_nodes: tf.node or list[tf.node]
List of names representing outputs.
frontend: str
Frontend to convert from.
backend: str
Backend to convert to.
-----------
Returns MLModel, Input Values, Output Names
"""
# Avoid circular dependency
from coremltools.converters._converters_entry import convert
if isinstance(output_nodes, tuple):
output_nodes = list(output_nodes)
if not isinstance(output_nodes, list):
output_nodes = [output_nodes]
# Convert TF graph.
input_names = get_tf_node_names(list(feed_dict.keys()), mode="inputs")
output_names = get_tf_node_names(output_nodes, mode="outputs")
input_values = {name: val for name, val in zip(input_names, feed_dict.values())}
mlmodel = convert(
graph, inputs=None, outputs=output_names, source=frontend, convert_to=backend
)
return mlmodel, input_values, output_names, output_nodes
def run_compare_builder(
build,
input_placeholders,
input_values,
expected_output_types=None,
expected_outputs=None,
use_cpu_only=False,
frontend_only=False,
backend="nn_proto",
atol=1e-04,
rtol=1e-05,
inputs=None,
also_compare_shapes=False,
):
"""
Inputs:
- build: python function taking input of Vars and returning Var or
list[Var]. Each input argument in build must match a key in
input_values / input_placeholders.
- input_placeholders: str -> placeholder. It may not be an empty
dict as MLModel doesn't support function with
no input.
- input_values: str -> np.array or PIL.Image. Keys must match those in
input_placeholders.
- expected_output_types: list[(shape, builtin_type)] or (shape,
builtin_type). None skips type inference validation.
- expected_outputs: list[np.array] or np.array. Required iff
frontend_only == False
- frontend_only: True to test up to proto generation.
- inputs: type of inputs (either None (defaults to tensor) or [ct.ImageType])
"""
from coremltools.converters._converters_entry import convert
if not isinstance(expected_output_types, list):
expected_output_types = [expected_output_types]
if expected_outputs is not None and not isinstance(expected_outputs, list):
expected_outputs = [expected_outputs]
prog = Program()
with Function(input_placeholders) as ssa_func:
output_vars = build(**ssa_func.inputs)
if isinstance(output_vars, tuple):
output_vars = list(output_vars)
elif not isinstance(output_vars, list):
output_vars = [output_vars]
ssa_func.set_outputs(output_vars)
prog.add_function("main", ssa_func)
# get output names for output_vars
output_names = [x.name for x in output_vars]
# Validate type inference
msg = (
"Provided expected outputs types {} should match number of output"
+ " variables {}"
)
assert_msg = msg.format(len(expected_output_types), len(output_vars))
assert len(output_vars) == len(expected_output_types), assert_msg
for out_var, s in zip(output_vars, expected_output_types):
if out_var.dtype != s[-1]:
raise ValueError(
"Output {} type: expect {}, got {}. Program:\n{}".format(
out_var.name, s[-1].__type_info__(),
out_var.dtype.__type_info__(), prog
)
)
if UNK_VARIADIC in s[:-1]:
msg = "Skip type checking for UNK_VARIADIC. Output shape: {} vs expected shape: {}"
logging.debug(msg.format(out_var.shape, s[:-1]))
continue
expected_shape = s[:-1]
msg = "Output {} shape: expect {}, got {}. Program:\n{}".format(
out_var.name, expected_shape, out_var.shape, prog
)
# No more variadic here.
if len(out_var.shape) != len(expected_shape):
raise ValueError(msg)
# replace UNK_SYM in out_var.shape.
output_shape = [
0 if es == UNK_SYM else os for os, es in zip(out_var.shape, expected_shape)
]
expected_shape = [0 if es == UNK_SYM else es for es in expected_shape]
# convert float etc to int.
output_shape = [i if is_symbolic(i) else int(i) for i in output_shape]
expected_shape = [i if is_symbolic(i) else int(i) for i in expected_shape]
if output_shape != expected_shape:
raise ValueError(msg)
mlmodel = convert(prog, source="mil", convert_to=backend, inputs=inputs)
if frontend_only:
return
if expected_outputs:
assert len(output_vars) == len(expected_outputs), (
"Provided expected_outputs {}"
" should match number of output"
" variables {}".format(len(expected_outputs), len(output_vars))
)
expected_outputs = {
name: val for name, val in zip(output_names, expected_outputs)
}
compare_backend(
mlmodel=mlmodel,
input_key_values=input_values,
expected_outputs=expected_outputs,
use_cpu_only=use_cpu_only,
atol=atol,
rtol=rtol,
also_compare_shapes=also_compare_shapes,
)
def run_compare_tf_keras(
model,
input_values,
use_cpu_only=False,
frontend_only=False,
frontend="tensorflow",
backend="nn_proto",
atol=1e-04,
rtol=1e-05,
):
"""
Parameters
----------
model: TensorFlow 2.x model
TensorFlow 2.x model annotated with @tf.function.
input_values: list of np.array
List of input values in the same order as the input signature.
use_cpu_only: bool
If true, use CPU only for prediction, otherwise, use GPU also.
frontend_only: bool
If true, skip the prediction call, only validate conversion.
frontend: str
Frontend to convert from.
backend: str
Backend to convert to.
atol: float
The absolute tolerance parameter.
rtol: float
The relative tolerance parameter.
"""
# Avoid circular dependency
from coremltools.converters._converters_entry import convert
mlmodel = convert(model, source=frontend, convert_to=backend)
# assumes conversion preserve the i/o names
proto = mlmodel.get_spec()
inputs = sorted([str(i.name) for i in proto.description.input])
outputs = [str(o.name) for o in proto.description.output]
if frontend_only:
return
# get tf.keras model output as reference and run comparison
keras_outputs = model(input_values)
if not isinstance(keras_outputs, list):
keras_outputs = [keras_outputs]
ref = [output.numpy() for output in keras_outputs]
expected_outputs = {n: v for n, v in zip(outputs, ref)}
input_key_values = {n: v for n, v in zip(inputs, input_values)}
compare_backend(
mlmodel,
input_key_values,
expected_outputs,
use_cpu_only,
atol=atol,
rtol=rtol,
also_compare_shapes=True,
)
pred = None
if not coremltoolsutils._has_custom_layer(proto):
pred = run_core_ml_predict(mlmodel, input_key_values, use_cpu_only)
else:
print('Skipping model prediction as it has a custom nn layer!')
return proto, mlmodel, input_key_values, pred