# Copyright (c) 2021, 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 collections from datetime import date import gc import os from typing import List, Optional, Text, Union from coremltools import ( _LOWEST_ALLOWED_SPECIFICATION_VERSION_FOR_MILPROGRAM, _LOWEST_ALLOWED_SPECIFICATION_VERSION_FOR_NEURALNETWORK, ) from coremltools import ComputeUnit as _ComputeUnit from coremltools import __version__ as _ct_version from coremltools import _logger as logger from coremltools._deps import _HAS_TF_1, _HAS_TF_2, _HAS_TORCH, _HAS_TORCH_EXPORT_API from coremltools.converters._profile_utils import _profile from coremltools.converters.mil._deployment_compatibility import ( AvailableTarget, check_deployment_compatibility, ) from coremltools.converters.mil.converter import mil_convert from coremltools.converters.mil.input_types import ( ClassifierConfig, EnumeratedShapes, ImageType, InputType, RangeDim, Shape, StateType, TensorType, ) from coremltools.converters.mil.mil import Program, types from coremltools.converters.mil.mil.passes.defs.quantization import ComputePrecision as precision from coremltools.converters.mil.mil.passes.defs.quantization import FP16ComputePrecision from coremltools.converters.mil.mil.passes.graph_pass import PassOption as _PassOption from coremltools.converters.mil.mil.passes.pass_pipeline import PassPipeline from coremltools.models import ( _METADATA_CONVERSION_DATE, _METADATA_SOURCE, _METADATA_SOURCE_DIALECT, _METADATA_VERSION ) from coremltools.models.utils import _MLPACKAGE_EXTENSION if _HAS_TF_1: import tensorflow as tf from coremltools.converters.mil.frontend.tensorflow.load import TF1Loader if _HAS_TF_2: import tensorflow as tf from coremltools.converters.mil.frontend.tensorflow2.load import TF2Loader if _HAS_TORCH: import torch from coremltools.converters.mil.frontend.torch.load import is_torch_model if _HAS_TORCH_EXPORT_API: from torch.export import ExportedProgram @_profile def convert( model, source="auto", inputs=None, outputs=None, classifier_config=None, minimum_deployment_target=None, convert_to=None, compute_precision=None, skip_model_load=False, compute_units=_ComputeUnit.ALL, package_dir=None, debug=False, pass_pipeline: Optional[PassPipeline] = None, states=None, ): """ Convert a TensorFlow or PyTorch model to the Core ML model format as either a neural network or an `ML program `_. Some parameters and requirements differ for TensorFlow and PyTorch conversions. Parameters ---------- model : TensorFlow 1, TensorFlow 2, or PyTorch model in one of the following formats: * TensorFlow versions 1.x - Frozen `tf.Graph `_ - Frozen graph (``.pb``) file path - `tf.keras.Model `_ - `HDF5 `_ file path (``.h5``) - `SavedModel `_ directory path * TensorFlow versions 2.x - `tf.keras.Model `_ - `HDF5 file path `_ (``.h5``) - `SavedModel `_ directory path - A `concrete function `_ - A `GraphDef `_ * PyTorch - TorchScript Models: - A `TorchScript `_ object - Path to a ``.pt`` file - Torch Exported Models: - An `ExportedProgram `_ object with ``EDGE`` dialect. source : str (optional) One of [``auto``, ``tensorflow``, ``pytorch``, ``milinternal``]. ``auto`` determines the framework automatically for most cases. Raises ``ValueError`` if it fails to determine the source framework. inputs : list of ``TensorType`` or ``ImageType`` * If you specify ``dtype`` with ``TensorType`` or ``ImageType``, it will be applied to the input of the converted model. For example, the following code snippet will produce a Core ML model with float 16 typed inputs. .. sourcecode:: python import coremltools as ct mlmodel = ct.convert( keras_model, inputs=[ct.TensorType(dtype=np.float16)], minimum_deployment_target=ct.target.macOS13, ) * The following code snippet will produce a Core ML model with the ``GRAYSCALE_FLOAT16`` input image type: .. sourcecode:: python import coremltools as ct # H : image height, W: image width mlmodel = ct.convert( torch_model, inputs=[ ct.ImageType(shape=(1, 1, H, W), color_layout=ct.colorlayout.GRAYSCALE_FLOAT16) ], minimum_deployment_target=ct.target.macOS13, ) * TensorFlow 1 and 2 (including tf.keras): - The ``inputs`` parameter is optional. If not provided, the inputs are placeholder nodes in the model (if the model is a frozen graph) or function inputs (if the model is a ``tf.function``). - If ``inputs`` is provided, it must be a flat list. - The ``inputs`` must correspond to all or some of the placeholder nodes in the TF model. - If ``name`` is specified with ``TensorType`` and ``ImageType``, it must correspond to a placeholder op in the TF graph. The input names in the converted Core ML model can later be modified using the ``ct.utils.rename_feature`` API. - If ``dtype`` is not specified, it defaults to the ``dtype`` of the inputs in the TF model. - For ``minimum_deployment_target >= ct.target.macOS13``, and with ``compute_precision`` in float 16 precision. When ``inputs`` not provided or ``dtype`` not specified, the float 32 inputs default to float 16. * PyTorch: - TorchScript Models: - The ``inputs`` parameter is required. - Number of elements in ``inputs`` must match the number of inputs of the PyTorch model. - ``inputs`` may be a nested list or tuple. - ``TensorType`` and ``ImageType`` must have the ``shape`` specified. - If the ``name`` argument is specified with ``TensorType`` or ``ImageType``, the converted Core ML model will have inputs with the same name. - If ``dtype`` is missing: * For ``minimum_deployment_target <= ct.target.macOS12``, it defaults to float 32. * For ``minimum_deployment_target >= ct.target.macOS13``, and with ``compute_precision`` in float 16 precision. It defaults to float 16. - Torch Exported Models: - The ``inputs`` parameter is not supported. - The ``inputs`` parameter is inferred from the Torch `ExportedProgram `_. outputs : list of ``TensorType`` or ``ImageType`` (optional) * If you specify ``dtype`` with ``TensorType`` or ``ImageType``, it will be applied to the output of the converted model. For example, to produce float 16 typed inputs and outputs: .. sourcecode:: python import coremltools as ct mlmodel = ct.convert( keras_model, inputs=[ct.TensorType(dtype=np.float16)], outputs=[ct.TensorType(dtype=np.float16)], minimum_deployment_target=ct.target.macOS13, ) * To produce image inputs and outputs: .. sourcecode:: python import coremltools as ct # H: image height, W: image width mlmodel = ct.convert( torch_model, inputs=[ct.ImageType(shape=(1, 3, H, W), color_layout=ct.colorlayout.RGB)], outputs=[ct.ImageType(color_layout=ct.colorlayout.RGB)], minimum_deployment_target=ct.target.macOS13, ) * TensorFlow 1 and 2 (including tf.keras): - If ``outputs`` is not specified, the converter infers outputs from the sink nodes in the graph. - If specified, the ``name`` with ``TensorType`` or ``ImageType`` must correspond to a node in the TF graph. In this case, the model will be converted up to that node. - For ``minimum_deployment_target >= ct.target.macOS13``, and with ``compute_precision`` in float 16 precision. If ``dtype`` not specified, the outputs inferred of type float 32 default to float 16. * PyTorch: TorchScript Models - If specified, the length of the list must match the number of outputs returned by the PyTorch model. - If ``name`` is specified, it is applied to the output names of the converted Core ML model. - For ``minimum_deployment_target >= ct.target.macOS13``, and with ``compute_precision`` in float 16 precision. - If ``dtype`` not specified, the outputs inferred of type float 32 default to float 16. * PyTorch: Torch Exported Models: - The ``outputs`` parameter is not supported. - The ``outputs`` parameter is inferred from Torch `ExportedProgram `_. classifier_config : ClassifierConfig class (optional) The configuration if the MLModel is intended to be a classifier. minimum_deployment_target : coremltools.target enumeration (optional) A member of the ``coremltools.target`` enum. The value of this parameter determines the type of the model representation produced by the converter. To learn about the differences between ML programs and neural networks, see `ML Programs `_. - The converter produces a neural network (``neuralnetwork``) if: .. sourcecode:: python ( minimum_deployment_target <= coremltools.target.iOS14 or minimum_deployment_target <= coremltools.target.macOS11 or minimum_deployment_target <= coremltools.target.watchOS7 or minimum_deployment_target <= coremltools.target.tvOS14 ) - The converter produces an ML program (``mlprogram``) if: .. sourcecode:: python ( minimum_deployment_target >= coremltools.target.iOS15 or minimum_deployment_target >= coremltools.target.macOS12 or minimum_deployment_target >= coremltools.target.watchOS8 or minimum_deployment_target >= coremltools.target.tvOS15 ) - If neither the ``minimum_deployment_target`` nor the ``convert_to`` parameter is specified, the converter produces an ML program model type with as minimum of a deployment target as possible. - If this parameter is specified and ``convert_to`` is also specified, they must be compatible. The following are examples of invalid values: .. sourcecode:: python # Invalid: convert_to = "mlprogram", minimum_deployment_target = coremltools.target.iOS14 # Invalid: convert_to = "neuralnetwork", minimum_deployment_target = coremltools.target.iOS15 convert_to : str (optional) Must be one of [``'mlprogram'``, ``'neuralnetwork'``, ``'milinternal'``]. The value of this parameter determines the type of the model representation produced by the converter. To learn about the differences between ML programs and neural networks, see `ML Programs `_. - ``'mlprogram'`` : Returns an MLModel (``coremltools.models.MLModel``) containing a MILSpec.Program proto, which is the Core ML program format. The model saved from this returned object is executable on iOS15, macOS12, watchOS8, and tvOS15. - ``'neuralnetwork'``: Returns an MLModel (``coremltools.models.MLModel``) containing a NeuralNetwork proto, which is the original Core ML format. The model saved from this returned object is executable either on iOS13/macOS10.15/watchOS6/tvOS13 and newer, or on iOS14/macOS11/watchOS7/tvOS14 and newer, depending on the layers used in the model. - ``'milinternal'``: Returns an MIL program object (``coremltools.converters.mil.Program``). An MIL program is primarily used for debugging and inspection. It can be converted to an MLModel for execution by using one of the following: .. sourcecode:: python ct.convert(mil_program, convert_to="neuralnetwork") ct.convert(mil_program, convert_to="mlprogram") - If neither the ``minimum_deployment_target`` nor the ``convert_to`` parameter is specified, the converter produces the ML programs model type with as minimum of a deployment target as possible. compute_precision : coremltools.precision enumeration or ct.transform.FP16ComputePrecision() (optional) Use this argument to control the storage precision of the tensors in the ML program. Must be one of the following. - ``coremltools.precision.FLOAT16`` enum: The following transform is applied to produce a float 16 program; that is, a program in which all the intermediate float tensors are of type float 16 (for ops that support that type). .. sourcecode:: python coremltools.transform.FP16ComputePrecision(op_selector=lambda op: True) The above transform iterates through all the ops, looking at each op's inputs and outputs. If they are of type float 32, ``cast`` ops are injected to convert those tensors (also known as `vars`) to type float 16. Similarly, int32 vars will also be cast to int16. - ``coremltools.precision.FLOAT32`` enum: No transform is applied. The original float32 tensor dtype in the source model is preserved. Opt into this option if the default converted model is displaying numerical precision issues. - ``coremltools.transform.FP16ComputePrecision(op_selector=...)`` Use this option to control which tensors are cast to float 16. Before casting the inputs/outputs of any op from float32 to float 16, the op_selector function is invoked on the op object. This function must return a boolean value. By default it returns ``True`` for every op, but you can customize this. For example: .. sourcecode:: python coremltools.transform.FP16ComputePrecision( op_selector=lambda op: op.op_type != "linear" ) The above casts all the float32 tensors to be float 16, except the input/output tensors to any ``linear`` op. See more examples below. - ``None``: The default - When ``convert_to="mlprogram"``, the ``compute_precision`` parameter defaults to ``coremltools.precision.FLOAT16``. - When ``convert_to="neuralnetwork"``, the ``compute_precision`` parameter needs to be ``None`` and has no meaning. - For example, you can customize the float 16 precision transform to prevent casting all the ``real_div`` ops in the program to float 16 precision: .. sourcecode:: python def skip_real_div_ops(op): if op.op_type == "real_div": return False return True model = ct.convert( source_model, compute_precision=ct.transform.FP16ComputePrecision(op_selector=skip_real_div_ops), minimum_deployment_target=ct.target.iOS15, ) skip_model_load : bool Set to ``True`` to prevent coremltools from calling into the Core ML framework to compile and load the model, post-conversion. In that case, the returned model object cannot be used to make a prediction, but can be used to save with ``model.save()``. This flag may be used to convert to a newer model type on an older Mac, which may raise a runtime warning if done without turning this flag on. Example: Use this flag to suppress a runtime warning when converting to an ML program model on macOS 11, since an ML program can only be compiled and loaded from macOS12+. Defaults to ``False``. compute_units: coremltools.ComputeUnit The set of processing units the model can use to make predictions. After conversion, the model is loaded with the provided set of compute units and returned. An enum with the following 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. package_dir : str Post conversion, the model is saved at a temporary location and loaded to form the MLModel object ready for prediction. * If ``package_dir`` is provided, model will be saved at this location rather than creating a temporary directory. * If not ``None``, this must be a path to a directory with the extension ``.mlpackage``. debug : bool This flag should generally be ``False`` except for debugging purposes. Setting this flag to ``True`` produces the following behavior: * For Torch conversion, it will print the list of supported and unsupported ops found in the model if conversion fails due to an unsupported op. * For Tensorflow conversion, it will cause to display extra logging and visualizations. pass_pipeline : PassPipeline Manage graph passes. You can control which graph passes to run and the order of the graph passes. You can also specify options for each pass. See the details in the docstring of PassPipeline (``coremltools/converters/mil/mil/passes/pass_pipeline.py``). * To avoid fusing the ``conv`` and ``batchnorm`` ops, skip the corresponding pass as shown in the following example: .. sourcecode:: python pipeline = ct.PassPipeline() pipeline.remove_passes({"common::fuse_conv_batchnorm"}) mlmodel = ct.convert(model, pass_pipeline=pipeline) * To avoid folding too-large ``const`` ops that lead to a large model, set pass option as shown in the following example: .. sourcecode:: python pipeline = ct.PassPipeline() pipeline.set_options("common::const_elimination", {"skip_const_by_size": "1e6"}) mlmodel = ct.convert(model, pass_pipeline=pipeline) We also provide a set of predefined pass pipelines that you can directly call. * To avoid running all graph pass, you can use: .. sourcecode:: python mlmodel = ct.convert(model, pass_pipeline=ct.PassPipeline.EMPTY) * To only run the cleanup graph passes, like constant_elimination, dead_code_elimination, etc. You can use: .. sourcecode:: python mlmodel = ct.convert(model, pass_pipeline=ct.PassPipeline.CLEANUP) * To convert a source model with sparse weights to a sparse format Core ML model, you can use: .. sourcecode:: python mlmodel = ct.convert(model, pass_pipeline=ct.PassPipeline.DEFAULT_PRUNING) * To convert a source model with palettized weights to a compressed format Core ML model, you can use: .. sourcecode:: python mlmodel = ct.convert(model, pass_pipeline=ct.PassPipeline.DEFAULT_PALETTIZATION) states: Create a stateful ``mlprogram`` model by providing the ``StateType`` in the ``states`` argument (for details see `MIL Input Types `_). The stateful model is useful when converting a large language model with KV-Cache. The name of ``StateType`` must match the key of the PyTorch ``named_buffers()`` method in the source traced model. The following example converts a torch model with a buffer called ``state_1``. .. sourcecode:: python class UpdateBufferModel(torch.nn.Module): def __init__(self): super(UpdateBufferModel, self).__init__() self.register_buffer( "state_1", torch.tensor(np.array([0, 0, 0], dtype=np.float32)) ) def forward(self, x): # In place update of the model state self.state_1.add_(x) return self.state_1 model = UpdateBufferModel() traced_model = torch.jit.trace(model, torch.tensor([1, 2, 3], dtype=torch.float32)) inputs = [ ct.TensorType(shape=(1, 2)), ] states = [ ct.StateType( wrapped_type=ct.TensorType( shape=(1, 2), ), name="state_1", ), ] mlmodel = ct.convert( traced_model, inputs=inputs, states=states, minimum_deployment_target=ct.target.iOS18, ) Returns ------- model : ``coremltools.models.MLModel`` or ``coremltools.converters.mil.Program`` A Core ML MLModel object or MIL program object (see ``convert_to``). Examples -------- TensorFlow 1, 2 (``model`` is a frozen graph): >>> with tf.Graph().as_default() as graph: >>> x = tf.placeholder(tf.float32, shape=(1, 2, 3), name="input") >>> y = tf.nn.relu(x, name="output") Automatically infer inputs and outputs: >>> mlmodel = ct.convert(graph) >>> test_input = np.random.rand(1, 2, 3) - 0.5 >>> results = mlmodel.predict({"input": test_input}) >>> print(results['output']) TensorFlow 2 (``model`` is a tf.Keras model path): >>> x = tf.keras.Input(shape=(32,), name='input') >>> y = tf.keras.layers.Dense(16, activation='softmax')(x) >>> keras_model = tf.keras.Model(x, y) >>> keras_model.save(h5_path) >>> mlmodel = ct.convert(h5_path) >>> test_input = np.random.rand(2, 32) >>> results = mlmodel.predict({'input': test_input}) >>> print(results['Identity']) PyTorch: TorchScript Models: >>> model = torchvision.models.mobilenet_v2() >>> model.eval() >>> example_input = torch.rand(1, 3, 256, 256) >>> traced_model = torch.jit.trace(model, example_input) >>> input = ct.TensorType(name='input_name', shape=(1, 3, 256, 256)) >>> mlmodel = ct.convert(traced_model, inputs=[input]) >>> results = mlmodel.predict({"input": example_input.numpy()}) >>> print(results['1651']) # 1651 is the node name given by PyTorch's JIT For more options see `Conversion Options `_. """ _check_deployment_target(minimum_deployment_target) outputs_as_strings, outputs_as_tensor_or_image_types = _validate_outputs_argument(outputs) exact_source = _determine_source(model, source, outputs_as_strings, outputs_as_tensor_or_image_types, outputs) source_dialect = _determine_source_dialect(model, exact_source) exact_target = _determine_target(convert_to, minimum_deployment_target) _validate_conversion_arguments( model, exact_source, exact_target, inputs, outputs_as_tensor_or_image_types, classifier_config, compute_precision, exact_target, minimum_deployment_target, ) need_fp16_cast_pass = _need_fp16_cast_pass(compute_precision, exact_target) if pass_pipeline is None: pass_pipeline = PassPipeline() if not need_fp16_cast_pass: pass_pipeline.remove_passes({"common::add_fp16_cast", "common::add_int16_cast"}) if isinstance(compute_precision, FP16ComputePrecision): # For backward compatibility with the `op_selector` param in FP16ComputePrecision. pass_pipeline._pass_options["common::add_fp16_cast"] = [ _PassOption(option_name="op_selector", option_val=compute_precision.op_selector) ] if package_dir is not None: _, ext = os.path.splitext(package_dir) if ext != _MLPACKAGE_EXTENSION: raise ValueError( f"`package_dir` must have extension {_MLPACKAGE_EXTENSION} (not {ext})" ) specification_version = minimum_deployment_target.value if minimum_deployment_target is not None else None if specification_version is None: specification_version = _set_default_specification_version(exact_target) use_default_fp16_io = ( specification_version is not None and specification_version >= AvailableTarget.iOS16 and need_fp16_cast_pass ) # Verify the inputs cannot contains state if states is None: states = [] _verify_inputs_doesnot_contains_states(inputs) # states can only passed if the source is pytorch if len(states) > 0 and exact_source != "pytorch": raise ValueError("'states' can only be passed with pytorch source model.") mlmodel = mil_convert( model, convert_from=exact_source, convert_to=exact_target, inputs=inputs, outputs=outputs_as_tensor_or_image_types, # None or list[ct.ImageType/ct.TensorType] classifier_config=classifier_config, skip_model_load=skip_model_load, compute_units=compute_units, package_dir=package_dir, debug=debug, specification_version=specification_version, main_pipeline=pass_pipeline, use_default_fp16_io=use_default_fp16_io, states=states, ) if exact_target == "mlprogram" and mlmodel._input_has_infinite_upper_bound(): raise ValueError( "For mlprogram, inputs with infinite upper_bound is not allowed. Please set upper_bound" ' to a positive value in "RangeDim()" for the "inputs" param in ct.convert().' ) if exact_target == 'milinternal': return mlmodel # Returns the MIL program if minimum_deployment_target is not None: check_deployment_compatibility( spec=mlmodel.get_spec(), representation=exact_target, deployment_target=minimum_deployment_target, ) gc.collect() mlmodel = _record_build_metadata(mlmodel, exact_source, source_dialect=source_dialect) return mlmodel def _need_fp16_cast_pass( compute_precision: Optional[Union[precision, FP16ComputePrecision]], convert_to: Text ) -> bool: if convert_to not in ("mlprogram", "neuralnetwork", "milinternal", "milpython"): raise NotImplementedError(f"Backend converter {convert_to} not implemented") if compute_precision is None: return convert_to != "neuralnetwork" elif compute_precision == precision.FLOAT32: return False elif compute_precision == precision.FLOAT16 or isinstance( compute_precision, FP16ComputePrecision ): return True else: raise ValueError(f"Invalid value of the argument 'compute_precision': {compute_precision}") def _set_default_specification_version(target) -> Optional[AvailableTarget]: if target == "neuralnetwork": return _LOWEST_ALLOWED_SPECIFICATION_VERSION_FOR_NEURALNETWORK elif target == "mlprogram": return _LOWEST_ALLOWED_SPECIFICATION_VERSION_FOR_MILPROGRAM elif target in ("milinternal", "milpython"): return None else: raise NotImplementedError("Backend converter {} not implemented".format(target)) def _check_deployment_target(minimum_deployment_target): if minimum_deployment_target is not None and not isinstance( minimum_deployment_target, AvailableTarget ): msg = ( "Unrecognized value of argument 'minimum_deployment_target': {}. " "It needs to be a member of 'coremltools.target' enumeration. " "For example, coremltools.target.iOS13" ) raise TypeError(msg.format(minimum_deployment_target)) def _verify_inputs_doesnot_contains_states( inputs: List[InputType], ) -> None: """ Verify that StateType is not present in the inputs. """ if inputs is None: return for val in inputs: if isinstance(val, StateType): raise ValueError("'inputs' cannot contain an instance of StateType.") def _validate_outputs_argument(outputs): """ - validate properties that the "outputs" argument must satisfy, for instance, it should either be a list of ct.ImageType/ct.TensorType or a list of strings, etc. - return : tuple - (outputs_as_strings, outputs_as_tensor_or_image_types) - outputs_as_strings: list[str] - outputs_as_tensor_or_image_types : list[ct.ImageType] or list[ct.TensorType] """ if outputs is None: return None, None else: if not isinstance(outputs, list): raise ValueError('"outputs" must be of type list') if len(outputs) == 0: return None, None if not all(map(lambda t: ((isinstance(t, InputType) and t.can_be_output()) or isinstance(t, str)), outputs)): raise ValueError('Elements in "outputs" must be ct.TensorType or ct.ImageType or str') msg_inconsistent_types = 'all elements of "outputs" must either be of type str ' \ 'or of types ct.ImageType/ct.TensorType' if isinstance(outputs[0], str): # if one of the elements is a string, all elements must be strings if not all(isinstance(t, str) for t in outputs): raise ValueError(msg_inconsistent_types) return outputs, [TensorType(name=name) for name in outputs] if isinstance(outputs[0], InputType) and outputs[0].can_be_output(): if not all((isinstance(t, InputType) and t.can_be_output()) for t in outputs): raise ValueError(msg_inconsistent_types) if any((isinstance(t, TensorType) or isinstance(t, ImageType)) and t.shape is not None for t in outputs): msg = "The 'shape' argument must not be specified for the outputs, since it is " \ "automatically inferred from the input shapes and the ops in the model" raise ValueError(msg) for out_ in outputs: if isinstance(out_, TensorType): if out_.default_value is not None: raise ValueError( "The 'default_value' argument must not be specified for the outputs" ) if isinstance(out_, ImageType): if out_.scale != 1.0: raise ValueError("'scale' must be 1.0 for a output of ImageType") if not (out_.bias is None or out_.bias == 0.0 or out_.bias == [0.0, 0.0, 0.0]): raise ValueError("'bias' must be None or 0 for an output of ImageType") if out_.channel_first is not None: raise ValueError("'channel_first' must be None for an output of ImageType") output_names = [t.name for t in outputs] # verify that either all of the entries in output_names is "None" or none of them is "None" msg_consistent_names = 'Either none or all the outputs must have the "name" argument specified' if output_names[0] is None and not all(name is None for name in output_names): raise ValueError(msg_consistent_names) if output_names[0] is not None and not all(name is not None for name in output_names): raise ValueError(msg_consistent_names) if output_names[0] is not None: if len(set(output_names)) != len(output_names): raise ValueError("Duplicate names provided in 'outputs'") if output_names[0] is None: return None, outputs else: return output_names, outputs def _validate_enumerated_shape_inputs(inputs, convert_to, minimum_deployment_target) -> None: if convert_to.lower() != "mlprogram": return enumerated_shape_inputs = list( filter(lambda input: isinstance(input.shape, EnumeratedShapes), inputs) ) if len(enumerated_shape_inputs) <= 1: return if minimum_deployment_target < AvailableTarget.iOS18: raise ValueError( f"Expected a single enumerated shape input for deployment targets below iOS 18, " f"but found {len(enumerated_shape_inputs)}. " "Please ensure only one input with EnumeratedShapes type is present." ) for current_input, next_input in zip(enumerated_shape_inputs, enumerated_shape_inputs[1:]): if len(current_input.shape.shapes) != len(next_input.shape.shapes): raise ValueError( f"Enumerated shape input mismatch. All enumerated shape inputs must have the same number of shapes for deployment targets iOS 18 or above." ) def _validate_conversion_arguments( model, exact_source, exact_target, inputs, outputs, classifier_config, compute_precision, convert_to, minimum_deployment_target, ) -> None: """ Validate and process model, inputs, classifier_config based on `exact_source` (which cannot be `auto`) and `exact_target`. """ def raise_if_duplicated(input_list): # Detect duplicated inputs input_names = [t.name for t in input_list if t.name is not None] dups = [ item for item, count in collections.Counter(input_names).items() if count > 1 ] if len(dups) > 0: raise ValueError("Duplicated inputs: {}".format(dups)) def _flatten_list(_inputs): ret = [] for _input in _inputs: if isinstance(_input, (list, tuple)): ret.extend(_flatten_list(_input)) elif isinstance(_input, InputType): ret.append(_input) else: raise ValueError( "Unknown type {} for flattening into InputType.".format( type(_input) ) ) return ret flat_inputs = None if inputs is not None: if not isinstance(inputs, list): raise ValueError("`inputs` must be of type list") # get flattened inputs flat_inputs = _flatten_list(inputs) for flat_input in flat_inputs: if not isinstance(flat_input, InputType): raise ValueError( "inputs must be a list of type ct.TensorType, ct.ImageType, or ct.StateType" ) if flat_input.dtype == types.fp16: if not ( minimum_deployment_target is not None and minimum_deployment_target >= AvailableTarget.iOS16 ): raise TypeError( "float16 dtype for inputs is only supported for deployment " "target >= iOS16/macOS13/watchOS9/tvOS16" ) elif flat_input.dtype == types.int8: if not ( minimum_deployment_target is not None and minimum_deployment_target >= AvailableTarget.iOS26 ): raise TypeError( "int8 dtype for inputs is only supported for deployment target >= iOS26" ) _validate_enumerated_shape_inputs( flat_inputs, convert_to=exact_target, minimum_deployment_target=minimum_deployment_target, ) if exact_target == "mlprogram": err_msg_infinite_bound = ( "For mlprogram, inputs with infinite upper_bound is not allowed. Please set upper_bound" ' to a positive value in "RangeDim()" for the "inputs" param in ct.convert().' ) if inputs is not None: for flat_input in _flatten_list(inputs): tensor_shapes: List[Optional[Shape]] = ( flat_input.shape.shapes if isinstance(flat_input.shape, EnumeratedShapes) else [flat_input.shape] ) for tensor_shape in tensor_shapes: if tensor_shape is not None: for shape in tensor_shape.shape: if isinstance(shape, RangeDim) and shape.upper_bound < 0: raise ValueError(err_msg_infinite_bound) if outputs is not None: for t in outputs: if t.dtype == types.fp16: if not ( minimum_deployment_target is not None and minimum_deployment_target >= AvailableTarget.iOS16 ): raise TypeError( "float16 dtype for outputs is only supported for deployment " "target >= iOS16/macOS13/watchOS9/tvOS16" ) elif t.dtype == types.int8: if not ( minimum_deployment_target is not None and minimum_deployment_target >= AvailableTarget.iOS26 ): raise TypeError( "int8 dtype for outpus is only supported for deployment target >= iOS26" ) if classifier_config is not None: if not isinstance(classifier_config, ClassifierConfig): raise ValueError("`classifier_config` must be of type ClassifierConfig") if convert_to.lower() == "neuralnetwork" and compute_precision is not None: raise ValueError( "compute_precision is only supported for mlprogram target and must be " "None if target=='neuralnetwork'. Note that target may be implicitly set " "depending on the minimum_deployment_target. See " "minimum_deployment_target for more details." ) if compute_precision is not None: if compute_precision not in [precision.FLOAT32, precision.FLOAT16]: if not isinstance(compute_precision, FP16ComputePrecision): raise ValueError( "'compute_precision' must be either coremltools.precision.FLOAT32 " "or coremltools.precision.FLOAT16 or of type " "coremltools.transform.FP16ComputePrecision()" ) if exact_source in {"tensorflow", "tensorflow2"}: if exact_source == "tensorflow" and not _HAS_TF_1: raise ValueError( 'Converter was called with source="tensorflow", but missing ' "tensorflow package" ) if inputs is not None: raise_if_duplicated(inputs) if inputs is not None and not all(isinstance(_input, InputType) for _input in inputs): raise ValueError("Input should be a list of TensorType or ImageType") elif exact_source == "pytorch": if _HAS_TORCH_EXPORT_API and isinstance(model, ExportedProgram): if model.dialect not in ("ATEN", "EDGE"): raise NotImplementedError( f"Conversion for models with only ATEN or EDGE dialect is supported/tested. Provided Dialect: {model.dialect}." " Run '.run_decompositions({})' on your exported PyTorch Model prior to conversion." ) else: if is_torch_model(model): if inputs is None: raise ValueError( 'Expected argument "inputs" for TorchScript models not provided' ) raise_if_duplicated(flat_inputs) if inputs is not None and not all( [isinstance(_input, InputType) for _input in flat_inputs] ): raise ValueError( "Input should be a list/tuple (or nested lists/tuples) of TensorType or ImageType" ) else: raise TypeError( "Model must either be a TorchScript object (or .pt or .pth file) or an ExportedProgram object (if using torch.export based API), received: {}".format( type(model) ) ) elif exact_source == "milinternal": if not isinstance(model, Program): raise ValueError( "Converter was asked to convert MIL input, but input is not a MIL " "program!" ) def _determine_source_dialect(model, exact_source): source_dialect = None if exact_source == "pytorch": if _HAS_TORCH_EXPORT_API and isinstance(model, ExportedProgram): return f"TorchExport::{model.dialect}" else: return "TorchScript" return source_dialect def _determine_source(model, source, output_names, outputs_as_tensor_or_image_types, output_argument_as_specified_by_user) -> str: """ Infer source (which can be auto) to the precise framework. """ source = source.lower() if source not in {"auto", "tensorflow", "pytorch", "milinternal"}: raise ValueError( f'Unrecognized value of argument "source": {source}. It must be one of ["auto", "tensorflow", "pytorch", "milinternal"].' ) # Determine tensorflow version if source == "tensorflow" and _HAS_TF_2: return "tensorflow2" if source != 'auto': return source # Determine `auto` source if source == "auto" and _HAS_TF_1: try: loader = TF1Loader(model, outputs=outputs_as_tensor_or_image_types) loader._graph_def_from_model(output_names=output_names) return "tensorflow" except: pass if source == "auto" and _HAS_TF_2: try: loader = TF2Loader(model, outputs=outputs_as_tensor_or_image_types) loader._graph_def_from_model(output_names=output_names) return "tensorflow2" except: pass if source == "auto" and _HAS_TORCH: if _HAS_TORCH_EXPORT_API and isinstance(model, ExportedProgram): return "pytorch" if is_torch_model(model): # validate that the outputs passed by the user are of type ImageType/TensorType if output_argument_as_specified_by_user is not None and not all( [ (isinstance(t, InputType) and t.can_be_output()) for t in output_argument_as_specified_by_user ] ): raise ValueError( '"outputs" must be a list of type ct.TensorType or ct.ImageType ' "for pytorch conversion" ) return "pytorch" if source == "auto" and isinstance(model, Program): return "milinternal" msg = ( "Unable to determine the type of the model, i.e. the source framework. " 'Please provide the value of argument "source", from one of ' '["tensorflow", "pytorch", "milinternal"]. Note that model conversion requires the ' "source package that generates the model. Please make sure you have " "the appropriate version of source package installed. E.g., if you're " "converting model originally trained with TensorFlow 1.14, make sure " "you have `tensorflow==1.14` installed." ) raise ValueError(msg) def _determine_target(convert_to, minimum_deployment_target) -> str: """ Infer the precise backend target, which could be one of ``milinternal``, ``neuralnetwork`` or ``mlprogram`` """ if minimum_deployment_target is None and convert_to is None: logger.warning( "When both 'convert_to' and 'minimum_deployment_target' not specified, " "'convert_to' is set to \"mlprogram\" and 'minimum_deployment_target' is set to " "ct.target.iOS15 (which is same as ct.target.macOS12). " "Note: the model will not run on systems older than iOS15/macOS12/watchOS8/tvOS15. " "In order to make your model run on older system, please set the 'minimum_deployment_target' to iOS14/iOS13. " "Details please see the link: https://apple.github.io/coremltools/docs-guides/source/target-conversion-formats.html" ) if minimum_deployment_target is not None: if convert_to == "mlprogram" and minimum_deployment_target < AvailableTarget.iOS15: raise ValueError( f"When 'convert_to' is {convert_to}, the minimum deployment target " f"must be at least iOS15/macOS12/watchOS8/tvOS15" ) if convert_to == "neuralnetwork" and minimum_deployment_target >= AvailableTarget.iOS15: raise ValueError( f"If minimum deployment target is iOS15/macOS12/watchOS8/tvOS15 or " f"higher, then 'convert_to' cannot be {convert_to}. It must be " f"'mlprogram'" ) if convert_to is not None: return convert_to else: if minimum_deployment_target is None: return "mlprogram" elif minimum_deployment_target <= AvailableTarget.iOS14: return "neuralnetwork" else: return "mlprogram" def _get_metadata_from_mlmodel(mlmodel): # Copy from source mlmodel if metadata info exists src_pkg_version = mlmodel.user_defined_metadata[_METADATA_SOURCE] coremltools_version = mlmodel.user_defined_metadata[_METADATA_VERSION] src_dialect = ( None if _METADATA_SOURCE_DIALECT not in mlmodel.user_defined_metadata else mlmodel.user_defined_metadata[_METADATA_SOURCE_DIALECT] ) src_pkg_version_list = src_pkg_version.split("==") if len(src_pkg_version_list) == 0: src_pkg, pkg_ver = None, None elif len(src_pkg_version_list) == 1: src_pkg, pkg_ver = src_pkg_version_list[0], "" elif len(src_pkg_version_list) == 2: src_pkg, pkg_ver = src_pkg_version_list else: raise AssertionError("Unable to parse src_pkg_version") build_info = { "coremltools-version": _ct_version if not coremltools_version else coremltools_version } if src_pkg is not None and pkg_ver is not None: build_info['coremltools-component-' + src_pkg] = str(pkg_ver) if src_dialect is not None: build_info["coremltools-source-dialect"] = src_dialect return build_info def _record_build_metadata(mlmodel, exact_source, source_dialect=None): # recording metadata: coremltools version, source framework and version if exact_source in {"tensorflow", "tensorflow2"} and (_HAS_TF_1 or _HAS_TF_2): src_pkg_version = "tensorflow=={0}".format(tf.__version__) elif exact_source == "pytorch" and _HAS_TORCH: src_pkg_version = "torch=={0}".format(torch.__version__) elif exact_source == 'milinternal': src_pkg_version = "milinternal" else: raise ValueError('Unsupported source {}'.format(exact_source)) mlmodel.user_defined_metadata[_METADATA_SOURCE] = src_pkg_version mlmodel.user_defined_metadata[_METADATA_VERSION] = _ct_version if source_dialect is not None: mlmodel.user_defined_metadata[_METADATA_SOURCE_DIALECT] = source_dialect current_date = str(date.today()) mlmodel.user_defined_metadata[_METADATA_CONVERSION_DATE] = current_date build_info = _get_metadata_from_mlmodel(mlmodel) mlmodel._set_build_info_mil_attributes(build_info) return mlmodel