# Copyright (c) 2017, Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can be
# found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
import atexit as _atexit
import json
import os as _os
import shutil as _shutil
import tempfile as _tempfile
import warnings as _warnings
from copy import deepcopy as _deepcopy
from typing import Dict as _Dict
from typing import List as _List
from typing import Optional as _Optional
import numpy as _np
import numpy as _numpy
from coremltools import ComputeUnit as _ComputeUnit
from coremltools import ReshapeFrequency as _ReshapeFrequency
from coremltools import SpecializationStrategy as _SpecializationStrategy
from coremltools import _logger as logger
from coremltools import proto as _proto
from coremltools._deps import _HAS_TF_1, _HAS_TF_2, _HAS_TORCH
from coremltools.converters.mil.mil.program import Program as _Program
from coremltools.converters.mil.mil.scope import ScopeSource as _ScopeSource
from .utils import (
_MLMODEL_EXTENSION,
_MLPACKAGE_EXTENSION,
_create_mlpackage,
_get_model_spec_path,
_has_custom_layer,
_is_macos,
_macos_version,
_try_get_weights_dir_path,
)
from .utils import load_spec as _load_spec
from .utils import save_spec as _save_spec
if _HAS_TORCH:
import torch as _torch
if _HAS_TF_1 or _HAS_TF_2:
import tensorflow as _tf
try:
from ..libmodelpackage import ModelPackage as _ModelPackage
except:
_ModelPackage = None
try:
from ..libcoremlpython import _MLModelProxy
except Exception as e:
logger.warning(f"Failed to load _MLModelProxy: {e}")
_MLModelProxy = None
try:
from ..libcoremlpython import _MLModelAssetProxy
except Exception as e:
logger.warning(f"Failed to load _MLModelAssetProxy: {e}")
_MLModelAssetProxy = None
_HAS_PIL = True
try:
from PIL import Image as _PIL_IMAGE
except:
_HAS_PIL = False
_MLMODEL_FULL_PRECISION = "float32"
_MLMODEL_HALF_PRECISION = "float16"
_MLMODEL_QUANTIZED = "quantized_model"
_VALID_MLMODEL_PRECISION_TYPES = [
_MLMODEL_FULL_PRECISION,
_MLMODEL_HALF_PRECISION,
_MLMODEL_QUANTIZED,
]
# Linear quantization
_QUANTIZATION_MODE_LINEAR_QUANTIZATION = "_linear_quantization"
# Linear quantization represented as a lookup table
_QUANTIZATION_MODE_LOOKUP_TABLE_LINEAR = "_lookup_table_quantization_linear"
# Lookup table quantization generated by K-Means
_QUANTIZATION_MODE_LOOKUP_TABLE_KMEANS = "_lookup_table_quantization_kmeans"
# Custom lookup table quantization
_QUANTIZATION_MODE_CUSTOM_LOOKUP_TABLE = "_lookup_table_quantization_custom"
# Dequantization
_QUANTIZATION_MODE_DEQUANTIZE = "_dequantize_network" # used for testing
# Symmetric linear quantization
_QUANTIZATION_MODE_LINEAR_SYMMETRIC = "_linear_quantization_symmetric"
_SUPPORTED_QUANTIZATION_MODES = [
_QUANTIZATION_MODE_LINEAR_QUANTIZATION,
_QUANTIZATION_MODE_LOOKUP_TABLE_LINEAR,
_QUANTIZATION_MODE_LOOKUP_TABLE_KMEANS,
_QUANTIZATION_MODE_CUSTOM_LOOKUP_TABLE,
_QUANTIZATION_MODE_DEQUANTIZE,
_QUANTIZATION_MODE_LINEAR_SYMMETRIC,
]
_LUT_BASED_QUANTIZATION = [
_QUANTIZATION_MODE_LOOKUP_TABLE_LINEAR,
_QUANTIZATION_MODE_LOOKUP_TABLE_KMEANS,
_QUANTIZATION_MODE_CUSTOM_LOOKUP_TABLE,
]
_METADATA_VERSION = "com.github.apple.coremltools.version"
_METADATA_SOURCE = "com.github.apple.coremltools.source"
_METADATA_SOURCE_DIALECT = "com.github.apple.coremltools.source_dialect"
from .compute_device import MLComputeDevice as _MLComputeDevice
def _verify_optimization_hint_input(optimization_hint_input: _Optional[dict] = None) -> None:
"""
Throws an exception if ``optimization_hint_input`` is not valid.
"""
if optimization_hint_input is None:
return
if not isinstance(optimization_hint_input, dict):
raise TypeError('"optimization_hint_input" must be a dictionary or None')
if optimization_hint_input != {} and _macos_version() < (15, 0):
raise ValueError('Optimization hints are only available on macOS >= 15.0')
for k in optimization_hint_input.keys():
if k not in ('allowLowPrecisionAccumulationOnGPU', 'reshapeFrequency', 'specializationStrategy'):
raise ValueError(f"Unrecognized key in optimization_hint dictionary: {k}")
if "allowLowPrecisionAccumulationOnGPU" in optimization_hint_input and not isinstance(optimization_hint_input["allowLowPrecisionAccumulationOnGPU"], bool):
raise TypeError('"allowLowPrecisionAccumulationOnGPU" value of "optimization_hint_input" dictionary must be of type bool')
if "specializationStrategy" in optimization_hint_input and not isinstance(optimization_hint_input["specializationStrategy"], _SpecializationStrategy):
raise TypeError('"specializationStrategy" value of "optimization_hint_input" dictionary must be of type coremltools.SpecializationStrategy')
if "reshapeFrequency" in optimization_hint_input and not isinstance(optimization_hint_input["reshapeFrequency"], _ReshapeFrequency):
raise TypeError('"reshapeFrequency" value of "optimization_hint_input" dictionary must be of type coremltools.ReshapeFrequency')
class _FeatureDescription:
def __init__(self, fd_spec):
self._fd_spec = fd_spec
def __repr__(self):
return "Features(%s)" % ",".join(map(lambda x: x.name, self._fd_spec))
def __len__(self):
return len(self._fd_spec)
def __getitem__(self, key):
for f in self._fd_spec:
if key == f.name:
return f.shortDescription
raise KeyError("No feature with name %s." % key)
def __contains__(self, key):
for f in self._fd_spec:
if key == f.name:
return True
return False
def __setitem__(self, key, value):
for f in self._fd_spec:
if key == f.name:
f.shortDescription = value
return
raise AttributeError("No feature with name %s." % key)
def __iter__(self):
for f in self._fd_spec:
yield f.name
class MLState:
def __init__(self, proxy) -> None:
"""
Holds state for an MLModel.
This is an opaque object. Nothing can be done with it except pass it to MLModel.predict.
See Also
--------
ct.MLModel.predict
"""
self.__proxy__ = proxy
[docs]
class MLModelAsset:
"""
A class representing a compiled model asset.
It supports two initialization methods:
- From a compiled model directory: The directory should have a '.mlmodelc' extension.
- From memory: Allows direct initialization using in-memory model data.
"""
def __init__(self, proxy) -> None:
if _MLModelAssetProxy is None or not isinstance(proxy, _MLModelAssetProxy):
raise TypeError("The proxy parameter must be of type _MLModelAssetProxy.")
self.__proxy__ = proxy
[docs]
@classmethod
def from_path(
cls,
compiled_model_path: str,
) -> "MLModelAsset":
"""
Create an MLModelAsset instance from a compiled model path.
Parameters
----------
compiled_model_path : str
The file path to the compiled model.
Returns
-------
MLModelAsset
An instance of MLModelAsset created from the specified path.
"""
return _MLModelProxy.create_model_asset_from_path(compiled_model_path)
[docs]
@classmethod
def from_memory(
cls,
spec_data: bytes,
blob_mapping: _Dict[str, bytes] = {},
) -> "MLModelAsset":
"""
Create an MLModelAsset instance from in-memory data.
Parameters
----------
spec_data : bytes
The specification data of the model.
blob_mapping : Dict[str, bytes])
A dictionary with blob path as the key and blob data as the value.
Returns
-------
MLModelAsset
An instance of MLModelAsset created from the provided memory data.
"""
return _MLModelProxy.create_model_asset_from_memory(spec_data, blob_mapping)
[docs]
class MLModel:
"""
This class defines the minimal interface to a Core ML object in Python.
At a high level, the protobuf specification consists of:
- Model description: Encodes names and type information of the inputs and outputs to the model.
- Model parameters: The set of parameters required to represent a specific instance of the model.
- Metadata: Information about the origin, license, and author of the model.
With this class, you can inspect a Core ML model, modify metadata, and make
predictions for the purposes of testing (on select platforms).
Examples
--------
.. sourcecode:: python
# Load the model
model = MLModel("HousePricer.mlmodel")
# Set the model metadata
model.author = "Author"
model.license = "BSD"
model.short_description = "Predicts the price of a house in the Seattle area."
# Get the interface to the model
model.input_description
model.output_description
# Set feature descriptions manually
model.input_description["bedroom"] = "Number of bedrooms"
model.input_description["bathrooms"] = "Number of bathrooms"
model.input_description["size"] = "Size (in square feet)"
# Set
model.output_description["price"] = "Price of the house"
# Make predictions
predictions = model.predict({"bedroom": 1.0, "bath": 1.0, "size": 1240})
# Get the spec of the model
spec = model.get_spec()
# Save the model
model.save("HousePricer.mlpackage")
# Load the model from the spec object
spec = model.get_spec()
# modify spec (e.g. rename inputs/outputs etc)
model = MLModel(spec)
# if model type is mlprogram, i.e. spec.WhichOneof('Type') == "mlProgram", then:
model = MLModel(spec, weights_dir=model.weights_dir)
# Load a non-default function from a multifunction .mlpackage
model = MLModel("MultifunctionModel.mlpackage", function_name="deep_features")
See Also
--------
predict
"""
[docs]
def __init__(
self,
model,
is_temp_package=False,
mil_program=None,
skip_model_load=False,
compute_units=_ComputeUnit.ALL,
weights_dir=None,
function_name=None,
optimization_hints: _Optional[dict] = None,
) -> None:
"""
Construct an MLModel from an ``.mlmodel``.
Parameters
----------
model: str or Model_pb2
For an ML program (``mlprogram``), the model can be a path string (``.mlpackage``) or ``Model_pb2``.
If it is a path string, it must point to a directory containing bundle
artifacts (such as ``weights.bin``).
If it is of type ``Model_pb2`` (spec), then you must also provide ``weights_dir`` if the model
has weights, because both the proto spec and the weights are
required to initialize and load the model.
The proto spec for an ``mlprogram``, unlike a neural network (``neuralnetwork``),
does not contain the weights; they are stored separately.
If the model does not have weights, you can provide an empty ``weights_dir``.
For non- ``mlprogram`` model types, the model can be a path string (``.mlmodel``)
or type ``Model_pb2``, such as a spec object.
is_temp_package: bool
Set to ``True`` if the input model package dir is temporary and can be deleted upon interpreter termination.
mil_program: coremltools.converters.mil.Program
Set to the MIL program object, if available.
It is available whenever an MLModel object is constructed using
the unified converter API `coremltools.convert() <https://apple.github.io/coremltools/source/coremltools.converters.convert.html>`_.
skip_model_load: bool
Set to ``True`` to prevent Core ML Tools from calling into the Core ML framework
to compile and load the model. In that case, the returned model object cannot
be used to make a prediction. This flag may be used to load a newer model
type on an older Mac, to inspect or load/save the spec.
Example: Loading an ML program model type on a macOS 11, since an ML program can be
compiled and loaded only from macOS12+.
Defaults to ``False``.
compute_units: coremltools.ComputeUnit
The set of processing units the model can use to make predictions.
An enum with four possible values:
- ``coremltools.ComputeUnit.ALL``: Use all compute units available, including the
neural engine.
- ``coremltools.ComputeUnit.CPU_ONLY``: Limit the model to only use the CPU.
- ``coremltools.ComputeUnit.CPU_AND_GPU``: Use both the CPU and GPU,
but not the neural engine.
- ``coremltools.ComputeUnit.CPU_AND_NE``: Use both the CPU and neural engine, but
not the GPU. Available only for macOS >= 13.0.
weights_dir: str
Path to the weight directory, required when loading an MLModel of type ``mlprogram``,
from a spec object, such as when the argument ``model`` is of type ``Model_pb2``.
function_name : str
The name of the function from ``model`` to load.
If not provided, ``function_name`` will be set to the ``defaultFunctionName`` in the proto.
optimization_hints : dict or None
Keys are the names of the optimization hint: 'allowLowPrecisionAccumulationOnGPU', 'reshapeFrequency'
or 'specializationStrategy'.
- 'allowLowPrecisionAccumulationOnGPU' value must have ``bool`` type.
- 'reshapeFrequency' value must have ``coremltools.ReshapeFrequency`` type.
- 'specializationStrategy' must have``coremltools.SpecializationStrategy`` type.
Notes
-----
Internally this maintains the following:
- ``_MLModelProxy``: A pybind wrapper around
CoreML::Python::Model (see
`coremltools/coremlpython/CoreMLPython.mm <https://github.com/apple/coremltools/blob/main/coremlpython/CoreMLPython.mm>`_)
- ``package_path`` (mlprogram only): Directory containing all artifacts (``.mlmodel``,
weights, and so on).
- ``weights_dir`` (mlprogram only): Directory containing weights inside the package_path.
Examples
--------
.. sourcecode:: python
loaded_model = MLModel("my_model.mlmodel")
loaded_model = MLModel("my_model.mlpackage")
"""
def cleanup(package_path):
if _os.path.exists(package_path):
_shutil.rmtree(package_path)
def does_model_contain_mlprogram(model) -> bool:
"""
Is this an mlprogram or is it a pipeline with at least one mlprogram?
"""
model_type = model.WhichOneof("Type")
if model_type == "mlProgram":
return True
elif model_type not in ("pipeline", "pipelineClassifier", "pipelineRegressor"):
return False
# Does this pipeline contain an mlprogram?
if model_type == "pipeline":
pipeline_models = model.pipeline.models
elif model_type == "pipelineClassifier":
pipeline_models = model.pipelineClassifier.pipeline.models
else:
assert model_type == "pipelineRegressor"
pipeline_models = model.pipelineRegressor.pipeline.models
for m in pipeline_models:
if does_model_contain_mlprogram(m):
return True
return False
if not isinstance(compute_units, _ComputeUnit):
raise TypeError('"compute_units" parameter must be of type: coremltools.ComputeUnit')
elif (compute_units == _ComputeUnit.CPU_AND_NE
and _is_macos()
and _macos_version() < (13, 0)
):
raise ValueError(
'coremltools.ComputeUnit.CPU_AND_NE is only available on macOS >= 13.0'
)
_verify_optimization_hint_input(optimization_hints)
self.compute_unit = compute_units
self.function_name = function_name
if optimization_hints is not None:
self.optimization_hints = optimization_hints.copy()
else:
self.optimization_hints = None
self.is_package = False
self.is_temp_package = False
self.package_path = None
self._weights_dir = None
if mil_program is not None and not isinstance(mil_program, _Program):
raise ValueError('"mil_program" must be of type "coremltools.converters.mil.Program"')
self._mil_program = mil_program
if isinstance(model, str):
model = _os.path.abspath(_os.path.expanduser(_os.path.expandvars(model)))
if _os.path.isdir(model):
self.is_package = True
self.package_path = model
self.is_temp_package = is_temp_package
self._weights_dir = _try_get_weights_dir_path(model)
self.__proxy__, self._spec, self._framework_error = self._get_proxy_and_spec(
model, compute_units, skip_model_load=skip_model_load, optimization_hints=optimization_hints,
)
elif isinstance(model, _proto.Model_pb2.Model):
if does_model_contain_mlprogram(model):
if model.WhichOneof("Type") == "mlProgram" and weights_dir is None:
raise Exception(
"MLModel of type mlProgram cannot be loaded just from the model spec object. "
"It also needs the path to the weights file. Please provide that as well, "
"using the 'weights_dir' argument."
)
self.is_package = True
self.is_temp_package = True
filename = _create_mlpackage(model, weights_dir)
self.package_path = filename
self._weights_dir = _try_get_weights_dir_path(filename)
else:
filename = _tempfile.NamedTemporaryFile(suffix=_MLMODEL_EXTENSION, delete=False).name
_save_spec(model, filename)
self.__proxy__, self._spec, self._framework_error = self._get_proxy_and_spec(
filename, compute_units, skip_model_load=skip_model_load, optimization_hints=optimization_hints
)
try:
_os.remove(filename)
except OSError:
pass
else:
raise TypeError(
"Expected model to be a .mlmodel file, .mlpackage file or a Model_pb2 object"
)
self._input_description = _FeatureDescription(self._spec.description.input)
self._output_description = _FeatureDescription(self._spec.description.output)
self._model_input_names_set = set([i.name for i in self._spec.description.input])
if self.is_package and self.is_temp_package:
_atexit.register(cleanup, self.package_path)
# If function_name is not passed, self.function_name defaults to defaultFunctionName in the proto.
default_function_name = self._spec.description.defaultFunctionName
if self.function_name is None and len(default_function_name) > 0:
self.function_name = default_function_name
if self.function_name is not None:
if not self._is_multifunction() and self.function_name != "main":
raise ValueError('function_name must be "main" for non multifunction model')
# Updated self._model_input_names_set based on self.function_name.
# self._model_input_names_set defines the allowed input keys for the data dictionary passed to self.predict().
if self.function_name is not None and self._is_multifunction():
f = self._get_function_description(self.function_name)
self._model_input_names_set = set([i.name for i in f.input])
def _get_proxy_and_spec(
self,
filename: str,
compute_units: _ComputeUnit,
skip_model_load: _Optional[bool] = False,
optimization_hints: _Optional[dict] = None,
):
filename = _os.path.expanduser(filename)
specification = _load_spec(filename)
if _MLModelProxy and not skip_model_load:
# check if the version is supported
engine_version = _MLModelProxy.maximum_supported_specification_version()
if specification.specificationVersion > engine_version:
# in this case the specification is a newer kind of .mlmodel than this
# version of the engine can support so we'll not try to have a proxy object
return None, specification, None
function_name = "" if self.function_name is None else self.function_name
optimization_hints_str_vals = {}
if optimization_hints is not None:
for k, v in optimization_hints.items():
if isinstance(v, bool):
optimization_hints_str_vals[k] = str(v)
else:
optimization_hints_str_vals[k] = v.name
try:
return (
_MLModelProxy(
filename,
compute_units.name,
function_name,
optimization_hints_str_vals,
None,
),
specification,
None,
)
except RuntimeError as e:
_warnings.warn(
"You will not be able to run predict() on this Core ML model."
+ " Underlying exception message was: "
+ str(e),
RuntimeWarning,
)
return None, specification, e
return None, specification, None
@property
def short_description(self):
return self._spec.description.metadata.shortDescription
@short_description.setter
def short_description(self, short_description):
self._spec.description.metadata.shortDescription = short_description
@property
def input_description(self):
return self._input_description
@property
def output_description(self):
return self._output_description
@property
def user_defined_metadata(self):
return self._spec.description.metadata.userDefined
@property
def author(self):
return self._spec.description.metadata.author
@author.setter
def author(self, author):
self._spec.description.metadata.author = author
@property
def license(self):
return self._spec.description.metadata.license
@license.setter
def license(self, license):
self._spec.description.metadata.license = license
@property
def version(self):
return self._spec.description.metadata.versionString
@property
def weights_dir(self):
return self._weights_dir
@version.setter
def version(self, version_string):
self._spec.description.metadata.versionString = version_string
def __repr__(self):
return self._spec.description.__repr__()
def __str__(self):
return self.__repr__()
[docs]
def save(self, save_path: str):
"""
Save the model to an ``.mlmodel`` format. For an MIL program, the ``save_path`` is
a package directory containing the ``mlmodel`` and weights.
Parameters
----------
save_path: Target file path / bundle directory for the model.
Examples
--------
.. sourcecode:: python
model.save("my_model_file.mlmodel")
loaded_model = MLModel("my_model_file.mlmodel")
"""
save_path = _os.path.expanduser(save_path)
# Clean up existing file or directory.
if _os.path.exists(save_path):
if _os.path.isdir(save_path):
_shutil.rmtree(save_path)
else:
_os.remove(save_path)
if self.is_package:
name, ext = _os.path.splitext(save_path)
if not ext:
save_path = "{}{}".format(save_path, _MLPACKAGE_EXTENSION)
elif ext != _MLPACKAGE_EXTENSION:
raise Exception(
"For an ML Program, extension must be {} (not {}). Please see https://coremltools.readme.io/docs/unified-conversion-api#target-conversion-formats to see the difference between neuralnetwork and mlprogram model types.".format(
_MLPACKAGE_EXTENSION, ext
)
)
_shutil.copytree(self.package_path, save_path)
if self._mil_program is not None and all(
[
_ScopeSource.EXIR_DEBUG_HANDLE in function._essential_scope_sources for function in self._mil_program.functions.values()
]
):
debug_handle_to_ops_mapping = (
self._mil_program.construct_debug_handle_to_ops_mapping()
)
if len(debug_handle_to_ops_mapping) > 0:
debug_handle_to_ops_mapping_as_json = json.dumps(
{
"version" : self.user_defined_metadata[_METADATA_VERSION],
"mapping" : debug_handle_to_ops_mapping,
}
)
saved_debug_handle_to_ops_mapping_path = _os.path.join(
save_path, "executorch_debug_handle_mapping.json"
)
with open(saved_debug_handle_to_ops_mapping_path, "w") as f:
f.write(debug_handle_to_ops_mapping_as_json)
saved_spec_path = _get_model_spec_path(save_path)
_save_spec(self._spec, saved_spec_path)
else:
_save_spec(self._spec, save_path)
[docs]
def get_compiled_model_path(self):
"""
Returns the path for the underlying compiled ML Model.
**Important**: This path is available only for the lifetime of this Python object. If you want
the compiled model to persist, you need to make a copy.
"""
if self.__proxy__ is None:
raise Exception("This model was not loaded or compiled with the Core ML Framework.")
return self.__proxy__.get_compiled_model_path()
[docs]
def get_spec(self):
"""
Get a deep copy of the protobuf specification of the model.
Returns
-------
model: Model_pb2
Protobuf specification of the model.
Examples
--------
.. sourcecode:: python
spec = model.get_spec()
"""
return _deepcopy(self._spec)
[docs]
def predict(self, data, state: _Optional[MLState] = None):
"""
Return predictions for the model.
Parameters
----------
data: dict[str, value] or list[dict[str, value]]
Dictionary of data to use for predictions, where the keys are the names of the input features.
For batch predictons, use a list of such dictionaries.
The following dictionary values types are acceptable: list, array, numpy.ndarray, tensorflow.Tensor
and torch.Tensor.
state : MLState
Optional state object as returned by ``make_state()``.
Returns
-------
dict[str, value]
Predictions as a dictionary where each key is the output feature name.
list[dict[str, value]]
For batch prediction, returns a list of the above dictionaries.
Examples
--------
.. sourcecode:: python
data = {"bedroom": 1.0, "bath": 1.0, "size": 1240}
predictions = model.predict(data)
data = [
{"bedroom": 1.0, "bath": 1.0, "size": 1240},
{"bedroom": 4.0, "bath": 2.5, "size": 2400},
]
batch_predictions = model.predict(data)
"""
def verify_and_convert_input_dict(d):
self._verify_input_dict(d)
self._convert_tensor_to_numpy(d)
# TODO: remove the following call when this is fixed: rdar://92239209
self._update_float16_multiarray_input_to_float32(d)
if self.is_package and _is_macos() and _macos_version() < (12, 0):
raise Exception(
"predict() for .mlpackage is not supported in macOS version older than 12.0."
)
MLModel._check_predict_data(data)
if self.__proxy__:
return self._get_predictions(self.__proxy__,
verify_and_convert_input_dict,
data,
state)
else: # Error case
if _macos_version() < (10, 13):
raise Exception(
"Model prediction is only supported on macOS version 10.13 or later."
)
if not _MLModelProxy:
raise Exception("Unable to load CoreML.framework. Cannot make predictions.")
elif (
_MLModelProxy.maximum_supported_specification_version()
< self._spec.specificationVersion
):
engineVersion = _MLModelProxy.maximum_supported_specification_version()
raise Exception(
"The specification has version "
+ str(self._spec.specificationVersion)
+ " but the Core ML framework version installed only supports Core ML model specification version "
+ str(engineVersion)
+ " or older."
)
elif _has_custom_layer(self._spec):
raise Exception(
"This model contains a custom neural network layer, so predict is not supported."
)
else:
if self._framework_error:
raise self._framework_error
else:
raise Exception("Unable to load CoreML.framework. Cannot make predictions.")
@staticmethod
def _check_predict_data(data):
if type(data) not in (list, dict):
raise TypeError(
f'"data" parameter must be either a dict or list of dict, but got {type(data)}.'
)
if type(data) == list and not all(map(lambda x: type(x) == dict, data)):
raise TypeError("\"data\" list must contain only dictionaries")
@staticmethod
def _get_predictions(proxy, preprocess_method, data, state):
if type(data) == dict:
preprocess_method(data)
state = None if state is None else state.__proxy__
return proxy.predict(data, state)
else:
assert type(data) == list
assert state is None, "State can only be used for unbatched predictions"
for i in data:
preprocess_method(i)
return proxy.batchPredict(data)
def _is_stateful(self) -> bool:
model_desc = self._spec.description
# For a single function model, we check if len(state) > 0
if len(model_desc.functions) == 0:
return len(model_desc.state) > 0
# For a multifunction model, we first get the corresponding function description,
# and check the state field.
f = list(filter(lambda f: f.name == self.function_name, model_desc.functions))
return len(f.state) > 0
def _is_multifunction(self) -> bool:
return len(self._spec.description.functions) > 0
def _get_function_description(
self, function_name: str
) -> "_proto.Model_pb2.FunctionDescription":
f = list(filter(lambda f: f.name == function_name, self._spec.description.functions))
if len(f) == 0:
raise ValueError(f"function_name {function_name} not found in the model.")
assert len(f) == 1, f"Invalid proto: two functions with the same name {function_name}."
return f[0]
[docs]
def make_state(self) -> MLState:
"""
Returns a new state object, which can be passed to the ``predict`` method.
Returns
_______
state: MLState
Holds state for an MLModel.
State functionality is only supported on macOS 15+.
Examples
--------
.. sourcecode:: python
state = model.make_state()
predictions = model.predict(x, state)
See Also
--------
predict
"""
if not _is_macos() or _macos_version() < (15, 0):
raise Exception("State functionality is only supported on macOS 15+")
if self.__proxy__ is None:
raise Exception("This model was not loaded with the Core ML Framework. Cannot get state.")
return MLState(self.__proxy__.newState())
def _input_has_infinite_upper_bound(self) -> bool:
"""Check if any input has infinite upper bound (-1)."""
for input_spec in self.input_description._fd_spec:
for size_range in input_spec.type.multiArrayType.shapeRange.sizeRanges:
if size_range.upperBound == -1:
return True
return False
def _set_build_info_mil_attributes(self, metadata):
if self._spec.WhichOneof('Type') != "mlProgram":
# No MIL attributes to set
return
ml_program_attributes = self._spec.mlProgram.attributes
build_info_proto = ml_program_attributes["buildInfo"]
# Set ValueType to dictionary of string to string
str_type = _proto.MIL_pb2.ValueType()
str_type.tensorType.dataType = _proto.MIL_pb2.DataType.STRING
dict_type_str_to_str = _proto.MIL_pb2.ValueType()
dict_type_str_to_str.dictionaryType.keyType.CopyFrom(str_type)
dict_type_str_to_str.dictionaryType.valueType.CopyFrom(str_type)
build_info_proto.type.CopyFrom(dict_type_str_to_str)
# Copy the metadata
build_info_dict = build_info_proto.immediateValue.dictionary
for k, v in metadata.items():
key_pair = _proto.MIL_pb2.DictionaryValue.KeyValuePair()
key_pair.key.immediateValue.tensor.strings.values.append(k)
key_pair.key.type.CopyFrom(str_type)
key_pair.value.immediateValue.tensor.strings.values.append(v)
key_pair.value.type.CopyFrom(str_type)
build_info_dict.values.append(key_pair)
def _get_mil_internal(self):
"""
Get a deep copy of the MIL program object, if available.
It's available whenever an MLModel object is constructed using
the unified converter API [``coremltools.convert()``](https://apple.github.io/coremltools/source/coremltools.converters.mil.html#coremltools.converters._converters_entry.convert).
Returns
-------
program: coremltools.converters.mil.Program
Examples
--------
.. sourcecode:: python
mil_prog = model._get_mil_internal()
"""
return _deepcopy(self._mil_program)
def _verify_input_dict(self, input_dict):
# Check if the input name given by the user is valid.
# Although this is checked during prediction inside CoreML Framework,
# we still check it here to return early and
# return a more verbose error message
self._verify_input_name_exists(input_dict)
# verify that the pillow image modes are correct, for image inputs
self._verify_pil_image_modes(input_dict)
def _verify_pil_image_modes(self, input_dict):
if not _HAS_PIL:
return
for input_desc in self._spec.description.input:
if input_desc.type.WhichOneof("Type") == "imageType":
input_val = input_dict.get(input_desc.name, None)
if not isinstance(input_val, _PIL_IMAGE.Image):
msg = "Image input, '{}' must be of type PIL.Image.Image in the input dict"
raise TypeError(msg.format(input_desc.name))
if input_desc.type.imageType.colorSpace in (
_proto.FeatureTypes_pb2.ImageFeatureType.BGR,
_proto.FeatureTypes_pb2.ImageFeatureType.RGB,
):
if input_val.mode != "RGB":
msg = "RGB/BGR image input, '{}', must be of type PIL.Image.Image with mode=='RGB'"
raise TypeError(msg.format(input_desc.name))
elif (
input_desc.type.imageType.colorSpace
== _proto.FeatureTypes_pb2.ImageFeatureType.GRAYSCALE
):
if input_val.mode != "L":
msg = "GRAYSCALE image input, '{}', must be of type PIL.Image.Image with mode=='L'"
raise TypeError(msg.format(input_desc.name))
elif (
input_desc.type.imageType.colorSpace
== _proto.FeatureTypes_pb2.ImageFeatureType.GRAYSCALE_FLOAT16
):
if input_val.mode != "F":
msg = "GRAYSCALE_FLOAT16 image input, '{}', must be of type PIL.Image.Image with mode=='F'"
raise TypeError(msg.format(input_desc.name))
def _verify_input_name_exists(self, input_dict):
for given_input in input_dict.keys():
if given_input not in self._model_input_names_set:
err_msg = "Provided key \"{}\", in the input dict, " \
"does not match any of the model input name(s), which are: {}"
raise KeyError(err_msg.format(given_input, self._model_input_names_set))
@staticmethod
def _update_float16_multiarray_input_to_float32(input_data: dict):
for k, v in input_data.items():
if isinstance(v, _np.ndarray) and v.dtype == _np.float16:
input_data[k] = v.astype(_np.float32)
def _convert_tensor_to_numpy(self, input_dict):
def convert(given_input):
if isinstance(given_input, _numpy.ndarray):
sanitized_input = given_input
elif _HAS_TORCH and isinstance(given_input, _torch.Tensor):
sanitized_input = given_input.detach().numpy()
elif (_HAS_TF_1 or _HAS_TF_2) and isinstance(given_input, _tf.Tensor):
sanitized_input = given_input.eval(session=_tf.compat.v1.Session())
else:
sanitized_input = _numpy.array(given_input)
return sanitized_input
model_input_to_types = {}
for inp in self._spec.description.input:
type_value = inp.type.multiArrayType.dataType
type_name = inp.type.multiArrayType.ArrayDataType.Name(type_value)
if type_name != "INVALID_ARRAY_DATA_TYPE":
model_input_to_types[inp.name] = type_name
for given_input_name, given_input in input_dict.items():
if given_input_name not in model_input_to_types:
continue
input_dict[given_input_name] = convert(given_input)
[docs]
@classmethod
def get_available_compute_devices(cls) -> _List[_MLComputeDevice]:
"""
The list of available compute devices for CoreML.
Use the method to get the list of compute devices that MLModel's predict method can use.
Some compute devices on the hardware are exclusive to the domain ML frameworks such as Vision and SoundAnalysis and
not available to Core ML framework. See also ``MLComputeDevice.get_all_compute_devices()``.
Returns
-------
The list of compute devices MLModel's predict method can use.
Examples
--------
.. sourcecode:: python
compute_devices = coremltools.MLModel.get_available_compute_devices()
"""
return _MLModelProxy.get_available_compute_devices()
@property
def load_duration_in_nano_seconds(self) -> _Optional[int]:
"""
Retrieves the duration of the model loading process in nanoseconds.
Notes
-----
Calculates the time elapsed during the model loading process, specifically
measuring the execution time of ``[MLModel loadContentsOfURL:configuration:error:]`` method
of the Core ML framework.
Returns
-------
Optional[int]:
The duration of the model loading process in nanoseconds.
Returns None if duration is not available.
"""
return self.__proxy__.get_load_duration_in_nano_seconds()
@property
def last_predict_duration_in_nano_seconds(self) -> _Optional[int]:
"""
Retrieves the duration of the last predict operation in nanoseconds.
This method returns the time taken for the most recent prediction made by
the model, measured in nanoseconds.
Notes
-----
Calculates the time elapsed during the model predict call, specifically
measuring the execution time of ``[MLModel predictionFromFeatures:error:]``
or ``[MLModel predictionFromBatch:error:]`` method of the Core ML framework.
Returns
-------
Optional[int]:
The duration of the last prediction operation in nanoseconds.
Returns None if no prediction has been made yet.
"""
return self.__proxy__.get_last_predict_duration_in_nano_seconds()