Source code for mindspore_lite.model

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"""
Model API.
"""
import os
from enum import Enum

from ._checkparam import check_isinstance
from .context import Context
from .lib import _c_lite_wrapper
from .tensor import Tensor

__all__ = ['ModelType', 'Model', 'RunnerConfig', 'ModelParallelRunner']


[文档]class ModelType(Enum): """ The MoedelType is used to define the model type. """ MINDIR = 0 MINDIR_LITE = 4
model_type_py_cxx_map = { ModelType.MINDIR: _c_lite_wrapper.ModelType.kMindIR, ModelType.MINDIR_LITE: _c_lite_wrapper.ModelType.kMindIR_Lite, } model_type_cxx_py_map = { _c_lite_wrapper.ModelType.kMindIR: ModelType.MINDIR, _c_lite_wrapper.ModelType.kMindIR_Lite: ModelType.MINDIR_LITE, }
[文档]class Model: """ The Model class is used to define a MindSpore model, facilitating computational graph management. Examples: >>> import mindspore_lite as mslite >>> model = mslite.Model() >>> print(model) model_path: . """ def __init__(self): self._model = _c_lite_wrapper.ModelBind() self.model_path_ = "" def __str__(self): res = f"model_path: {self.model_path_}." return res
[文档] def build_from_file(self, model_path, model_type, context): """ Load and build a model from file. Args: model_path (str): Define the model path, include model file. model_type (ModelType): Define The type of model file. Options: ModelType::MINDIR | ModelType::MINDIR_LITE. - ModelType::MINDIR: An intermediate representation of the MindSpore model. The recommended model file suffix is ".mindir". - ModelType::MINDIR_LITE: An intermediate representation of the MindSpore Lite model. The recommended model file suffix is ".ms". context (Context): Define the context used to store options during execution. Raises: TypeError: `model_path` is not a str. TypeError: `model_type` is not a ModelType. TypeError: `context` is not a Context. RuntimeError: `model_path` does not exist. RuntimeError: build from file failed. Examples: >>> import mindspore_lite as mslite >>> model = mslite.Model() >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> model.build_from_file("mobilenetv2.ms", mslite.ModelType.MINDIR_LITE, context) >>> print(model) model_path: mobilenetv2.ms. """ check_isinstance("model_path", model_path, str) check_isinstance("model_type", model_type, ModelType) check_isinstance("context", context, Context) if not os.path.exists(model_path): raise RuntimeError(f"build_from_file failed, model_path does not exist!") self.model_path_ = model_path model_type_ = _c_lite_wrapper.ModelType.kMindIR_Lite if model_type is ModelType.MINDIR: model_type_ = _c_lite_wrapper.ModelType.kMindIR ret = self._model.build_from_file(self.model_path_, model_type_, context._context) if not ret.IsOk(): raise RuntimeError(f"build_from_file failed! Error is {ret.ToString()}")
[文档] def resize(self, inputs, dims): """ Resizes the shapes of inputs. Args: inputs (list[Tensor]): A list that includes all input tensors in order. dims (list[list[int]]): A list that includes the new shapes of inputs, should be consistent with inputs. Raises: TypeError: `inputs` is not a list. TypeError: `inputs` is a list, but the elements are not Tensor. TypeError: `dims` is not a list. TypeError: `dims` is a list, but the elements are not list. TypeError: `dims` is a list, the elements are list, but the element's elements are not int. ValueError: The size of `inputs` is not equal to the size of `dims` . RuntimeError: resize input failed. Examples: >>> import mindspore_lite as mslite >>> model = mslite.Model() >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> model.build_from_file("mobilenetv2.ms", mslite.ModelType.MINDIR_LITE, context) >>> inputs = model.get_inputs() >>> print("Before resize, the first input shape: ", inputs[0].get_shape()) Before resize, the first input shape: [1, 224, 224, 3] >>> model.resize(inputs, [[1, 112, 112, 3]]) >>> print("After resize, the first input shape: ", inputs[0].get_shape()) After resize, the first input shape: [1, 112, 112, 3] """ if not isinstance(inputs, list): raise TypeError("inputs must be list, but got {}.".format(type(inputs))) _inputs = [] if not isinstance(dims, list): raise TypeError("dims must be list, but got {}.".format(type(dims))) for i, element in enumerate(inputs): if not isinstance(element, Tensor): raise TypeError(f"inputs element must be Tensor, but got " f"{type(element)} at index {i}.") for i, element in enumerate(dims): if not isinstance(element, list): raise TypeError(f"dims element must be list, but got " f"{type(element)} at index {i}.") for j, dim in enumerate(element): if not isinstance(dim, int): raise TypeError(f"dims element's element must be int, but got " f"{type(dim)} at {i}th dims element's {j}th element.") if len(inputs) != len(dims): raise ValueError(f"inputs' size does not match dims's size, but got " f"inputs: {len(inputs)} and dims: {len(dims)}.") for i, element in enumerate(inputs): _inputs.append(element._tensor) ret = self._model.resize(_inputs, dims) if not ret.IsOk(): raise RuntimeError(f"resize failed! Error is {ret.ToString()}")
[文档] def predict(self, inputs, outputs): """ Inference model. Args: inputs (list[Tensor]): A list that includes all input tensors in order. outputs (list[Tensor]): The model outputs are filled in the container in sequence. Raises: TypeError: `inputs` is not a list. TypeError: `inputs` is a list, but the elements are not Tensor. TypeError: `outputs` is not a list. TypeError: `outputs` is a list, but the elements are not Tensor. RuntimeError: predict model failed. Examples: >>> # 1. predict which indata is from file >>> import mindspore_lite as mslite >>> import numpy as np >>> model = mslite.Model() >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> model.build_from_file("mobilenetv2.ms", mslite.ModelType.MINDIR_LITE, context) >>> inputs = model.get_inputs() >>> outputs = model.get_outputs() >>> in_data = np.fromfile("input.bin", dtype=np.float32) >>> inputs[0].set_data_from_numpy(in_data) >>> model.predict(inputs, outputs) >>> for output in outputs: ... data = output.get_data_to_numpy() ... print("outputs: ", data) ... outputs: [[1.02271215e-05 9.92699006e-06 1.69684317e-05 ... 6.69087376e-06 2.16263197e-06 1.24009384e-04]] >>> # 2. predict which indata is numpy array >>> import mindspore_lite as mslite >>> import numpy as np >>> model = mslite.Model() >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> model.build_from_file("mobilenetv2.ms", mslite.ModelType.MINDIR_LITE, context) >>> inputs = model.get_inputs() >>> outputs = model.get_outputs() >>> for input in inputs: ... in_data = np.arange(1 * 224 * 224 * 3, dtype=np.float32).reshape((1, 224, 224, 3)) ... input.set_data_from_numpy(in_data) ... >>> model.predict(inputs, outputs) >>> for output in outputs: ... data = output.get_data_to_numpy() ... print("outputs: ", data) ... outputs: [[0.00035889 0.00065501 0.00052925 ... 0.00018388 0.00148316 0.00116824]] >>> # 3. predict which indata is new mslite tensor with numpy array >>> import mindspore_lite as mslite >>> import numpy as np >>> model = mslite.Model() >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> model.build_from_file("mobilenetv2.ms", mslite.ModelType.MINDIR_LITE, context) >>> inputs = model.get_inputs() >>> outputs = model.get_outputs() >>> input_tensors = [] >>> for input in inputs: ... input_tensor = mslite.Tensor() ... input_tensor.set_data_type(input.get_data_type()) ... input_tensor.set_shape(input.get_shape()) ... input_tensor.set_format(input.get_format()) ... input_tensor.set_tensor_name(input.get_tensor_name()) ... in_data = np.arange(1 * 224 * 224 * 3, dtype=np.float32).reshape((1, 224, 224, 3)) ... input_tensor.set_data_from_numpy(in_data) ... input_tensors.append(input_tensor) ... >>> model.predict(input_tensors, outputs) >>> for output in outputs: ... data = output.get_data_to_numpy() ... print("outputs: ", data) ... outputs: [[0.00035889 0.00065501 0.00052925 ... 0.00018388 0.00148316 0.00116824]] """ if not isinstance(inputs, list): raise TypeError("inputs must be list, but got {}.".format(type(inputs))) if not isinstance(outputs, list): raise TypeError("outputs must be list, but got {}.".format(type(outputs))) _inputs = [] for i, element in enumerate(inputs): if not isinstance(element, Tensor): raise TypeError(f"inputs element must be Tensor, but got " f"{type(element)} at index {i}.") _inputs.append(element._tensor) _outputs = [] for i, element in enumerate(outputs): if not isinstance(element, Tensor): raise TypeError(f"outputs element must be Tensor, but got " f"{type(element)} at index {i}.") _outputs.append(element._tensor) ret = self._model.predict(_inputs, _outputs, None, None) if not ret.IsOk(): raise RuntimeError(f"predict failed! Error is {ret.ToString()}")
[文档] def get_inputs(self): """ Obtains all input tensors of the model. Returns: list[Tensor], the inputs tensor list of the model. Examples: >>> import mindspore_lite as mslite >>> model = mslite.Model() >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> model.build_from_file("mobilenetv2.ms", mslite.ModelType.MINDIR_LITE, context) >>> inputs = model.get_inputs() """ inputs = [] for _tensor in self._model.get_inputs(): inputs.append(Tensor(_tensor)) return inputs
[文档] def get_outputs(self): """ Obtains all output tensors of the model. Returns: list[Tensor], the outputs tensor list of the model. Examples: >>> import mindspore_lite as mslite >>> model = mslite.Model() >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> model.build_from_file("mobilenetv2.ms", mslite.ModelType.MINDIR_LITE, context) >>> outputs = model.get_outputs() """ outputs = [] for _tensor in self._model.get_outputs(): outputs.append(Tensor(_tensor)) return outputs
[文档] def get_input_by_tensor_name(self, tensor_name): """ Obtains the input tensor of the model by name. Args: tensor_name (str): tensor name. Returns: Tensor, the input tensor of the tensor name. Raises: TypeError: `tensor_name` is not a str. RuntimeError: get input by tensor name failed. Examples: >>> import mindspore_lite as mslite >>> model = mslite.Model() >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> model.build_from_file("mobilenetv2.ms", mslite.ModelType.MINDIR_LITE, context) >>> input_tensor = model.get_input_by_tensor_name("graph_input-173") >>> print(input_tensor) tensor_name: graph_input-173, data_type: DataType.FLOAT32, shape: [1, 224, 224, 3], format: Format.NHWC, element_num: 150528, data_size: 602112. """ check_isinstance("tensor_name", tensor_name, str) _tensor = self._model.get_input_by_tensor_name(tensor_name) if _tensor.is_null(): raise RuntimeError(f"get_input_by_tensor_name failed!") return Tensor(_tensor)
[文档] def get_output_by_tensor_name(self, tensor_name): """ Obtains the output tensor of the model by name. Args: tensor_name (str): tensor name. Returns: Tensor, the output tensor of the tensor name. Raises: TypeError: `tensor_name` is not a str. RuntimeError: get output by tensor name failed. Examples: >>> import mindspore_lite as mslite >>> model = mslite.Model() >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> model.build_from_file("mobilenetv2.ms", mslite.ModelType.MINDIR_LITE, context) >>> output_tensor = model.get_output_by_tensor_name("Softmax-65") >>> print(output_tensor) tensor_name: Softmax-65, data_type: DataType.FLOAT32, shape: [1, 1001], format: Format.NHWC, element_num: 1001, data_size: 4004. """ check_isinstance("tensor_name", tensor_name, str) _tensor = self._model.get_output_by_tensor_name(tensor_name) if _tensor.is_null(): raise RuntimeError(f"get_output_by_tensor_name failed!") return Tensor(_tensor)
[文档]class RunnerConfig: """ RunnerConfig Class defines runner config of one or more servables. The class can be used to make model parallel runner which corresponds to the service provided by a model. The client sends inference tasks and receives inference results through server. Args: context (Context, optional): Define the context used to store options during execution. Default: None. workers_num (int, optional): the num of workers. Default: None. config_info (dict{str, dict{str, str}}, optional): Nested map for passing model weight paths. e.g. {"weight": {"weight_path": "/home/user/weight.cfg"}}. Default: None. key currently supports ["weight"]; value is in dict format, key of it currently supports ["weight_path"], value of it is the path of weight, e.g. "/home/user/weight.cfg". Raises: TypeError: `context` is neither a Context nor None. TypeError: `workers_num` is neither an int nor None. TypeError: `config_info` is neither a dict nor None. TypeError: `config_info` is a dict, but the key is not str. TypeError: `config_info` is a dict, the key is str, but the value is not dict. TypeError: `config_info` is a dict, the key is str, the value is dict, but the key of value is not str. TypeError: `config_info` is a dict, the key is str, the value is dict, the key of the value is str, but the value of the value is not str. ValueError: `workers_num` is an int, but it is less than 0. Examples: >>> # only for serving inference >>> import mindspore_lite as mslite >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> config_info = {"weight": {"weight_path": "path of model weight"}} >>> runner_config = mslite.RunnerConfig(context=context, workers_num=0, config_info=config_info) >>> print(runner_config) workers num: 0, config info: weight: weight_path path of model weight , context: thread num: 0, bind mode: 1. """ def __init__(self, context=None, workers_num=None, config_info=None, config_path=None): if context is not None: check_isinstance("context", context, Context) if workers_num is not None: check_isinstance("workers_num", workers_num, int) if workers_num < 0: raise ValueError(f"RunnerConfig's init failed! workers_num must be positive.") if config_info is not None: check_isinstance("config_info", config_info, dict) for k, v in config_info.items(): check_isinstance("config_info_key", k, str) check_isinstance("config_info_value", v, dict) for v_k, v_v in v.items(): check_isinstance("config_info_value_key", v_k, str) check_isinstance("config_info_value_value", v_v, str) self._runner_config = _c_lite_wrapper.RunnerConfigBind() if context is not None: self._runner_config.set_context(context._context) if workers_num is not None: self._runner_config.set_workers_num(workers_num) if config_info is not None: for k, v in config_info.items(): self._runner_config.set_config_info(k, v) if config_path is not None: if not os.path.exists(config_path): raise ValueError(f"RunnerConfig's init failed, config_path does not exist!") check_isinstance("config_path", config_path, str) self._runner_config.set_config_path(config_path) def __str__(self): res = f"workers num: {self._runner_config.get_workers_num()},\n" \ f"config info: {self._runner_config.get_config_info_string()},\n" \ f"context: {self._runner_config.get_context_info()},\n" \ f"config path: {self._runner_config.get_config_path()}." return res
[文档]class ModelParallelRunner: """ The ModelParallelRunner class is used to define a MindSpore ModelParallelRunner, facilitating Model management. Examples: >>> # only for serving inference >>> import mindspore_lite as mslite >>> model_parallel_runner = mslite.ModelParallelRunner() >>> print(model_parallel_runner) model_path: . """ def __init__(self): self._model = _c_lite_wrapper.ModelParallelRunnerBind() self.model_path_ = "" def __str__(self): return f"model_path: {self.model_path_}."
[文档] def init(self, model_path, runner_config=None): """ build a model parallel runner from model path so that it can run on a device. Args: model_path (str): Define the model path. runner_config (RunnerConfig, optional): Define the config used to store options during model pool init. Default: None. Raises: TypeError: `model_path` is not a str. TypeError: `runner_config` is neither a RunnerConfig nor None. RuntimeError: `model_path` does not exist. RuntimeError: ModelParallelRunner's init failed. Examples: >>> import mindspore_lite as mslite >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> runner_config = mslite.RunnerConfig(context=context, workers_num=4) >>> model_parallel_runner = mslite.ModelParallelRunner() >>> model_parallel_runner.init(model_path="mobilenetv2.ms", runner_config=runner_config) >>> print(model_parallel_runner) model_path: mobilenetv2.ms. """ check_isinstance("model_path", model_path, str) if not os.path.exists(model_path): raise RuntimeError(f"ModelParallelRunner's init failed, model_path does not exist!") self.model_path_ = model_path if runner_config is not None: check_isinstance("runner_config", runner_config, RunnerConfig) ret = self._model.init(self.model_path_, runner_config._runner_config) else: ret = self._model.init(self.model_path_, None) if not ret.IsOk(): raise RuntimeError(f"ModelParallelRunner's init failed! Error is {ret.ToString()}")
[文档] def predict(self, inputs, outputs): """ Inference ModelParallelRunner. Args: inputs (list[Tensor]): A list that includes all input tensors in order. outputs (list[Tensor]): The model outputs are filled in the container in sequence. Raises: TypeError: `inputs` is not a list. TypeError: `inputs` is a list, but the elements are not Tensor. TypeError: `outputs` is not a list. TypeError: `outputs` is a list, but the elements are not Tensor. RuntimeError: predict model failed. Examples: >>> import mindspore_lite as mslite >>> import numpy as np >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> runner_config = mslite.RunnerConfig(context=context, workers_num=4) >>> model_parallel_runner = mslite.ModelParallelRunner() >>> model_parallel_runner.init(model_path="mobilenetv2.ms", runner_config=runner_config) >>> inputs = model_parallel_runner.get_inputs() >>> in_data = np.fromfile("input.bin", dtype=np.float32) >>> inputs[0].set_data_from_numpy(in_data) >>> outputs = model_parallel_runner.get_outputs() >>> model_parallel_runner.predict(inputs, outputs) >>> for output in outputs: ... data = output.get_data_to_numpy() ... print("outputs: ", data) ... outputs: [[1.02271215e-05 9.92699006e-06 1.69684317e-05 ... 6.69087376e-06 2.16263197e-06 1.24009384e-04]] """ if not isinstance(inputs, list): raise TypeError("inputs must be list, but got {}.".format(type(inputs))) if not isinstance(outputs, list): raise TypeError("outputs must be list, but got {}.".format(type(outputs))) _inputs = [] for i, element in enumerate(inputs): if not isinstance(element, Tensor): raise TypeError(f"inputs element must be Tensor, but got " f"{type(element)} at index {i}.") _inputs.append(element._tensor) _outputs = [] for i, element in enumerate(outputs): if not isinstance(element, Tensor): raise TypeError(f"outputs element must be Tensor, but got " f"{type(element)} at index {i}.") _outputs.append(element._tensor) predict_output = self._model.predict(_inputs, _outputs, None, None) if not isinstance(predict_output, list) or len(predict_output) == 0: raise RuntimeError(f"predict failed!") outputs.clear() for _, element in enumerate(predict_output): outputs.append(Tensor(element))
[文档] def get_inputs(self): """ Obtains all input tensors of the model. Returns: list[Tensor], the inputs tensor list of the model. Examples: >>> import mindspore_lite as mslite >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> runner_config = mslite.RunnerConfig(context=context, workers_num=4) >>> model_parallel_runner = mslite.ModelParallelRunner() >>> model_parallel_runner.init(model_path="mobilenetv2.ms", runner_config=runner_config) >>> inputs = model_parallel_runner.get_inputs() """ inputs = [] for _tensor in self._model.get_inputs(): inputs.append(Tensor(_tensor)) return inputs
[文档] def get_outputs(self): """ Obtains all output tensors of the model. Returns: list[Tensor], the outputs tensor list of the model. Examples: >>> import mindspore_lite as mslite >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) >>> runner_config = mslite.RunnerConfig(context=context, workers_num=4) >>> model_parallel_runner = mslite.ModelParallelRunner() >>> model_parallel_runner.init(model_path="mobilenetv2.ms", runner_config=runner_config) >>> outputs = model_parallel_runner.get_outputs() """ outputs = [] for _tensor in self._model.get_outputs(): outputs.append(Tensor(_tensor)) return outputs