diff --git a/mindspore/lite/python/OWNERS b/mindspore/lite/python/OWNERS new file mode 100644 index 00000000000..46847de247b --- /dev/null +++ b/mindspore/lite/python/OWNERS @@ -0,0 +1,7 @@ +approvers: +- zhaizhiqiang +- zhanghaibo5 +- zhang_xue_tong +- jpc_chenjianping +- sunsuodong +- wang_shaocong diff --git a/mindspore/lite/python/api/__init__.py b/mindspore/lite/python/api/__init__.py index 1119eed817f..de3a3fabd80 100644 --- a/mindspore/lite/python/api/__init__.py +++ b/mindspore/lite/python/api/__init__.py @@ -17,10 +17,12 @@ MindSpore Lite Python API. """ from .context import Context, DeviceInfo, CPUDeviceInfo, GPUDeviceInfo, AscendDeviceInfo +from .converter import FmkType, Converter from .model import ModelType, Model, RunnerConfig, ModelParallelRunner from .tensor import DataType, Format, Tensor __all__ = [] __all__.extend(context.__all__) +__all__.extend(converter.__all__) __all__.extend(model.__all__) __all__.extend(tensor.__all__) diff --git a/mindspore/lite/python/api/context.py b/mindspore/lite/python/api/context.py index d145ee8f618..5d3ac2546bf 100644 --- a/mindspore/lite/python/api/context.py +++ b/mindspore/lite/python/api/context.py @@ -36,24 +36,32 @@ class Context: Raises: TypeError: type of input parameters are invalid. + ValueError: value of input parameters are invalid. Examples: >>> import mindspore_lite as mslite - >>> context = mslite.Context(thread_num=1, thread_affinity_core_list=[1,2], enable_parallel=False) - >>> context.append_device_info(mslite.CPUDeviceInfo()) + >>> context = mslite.Context(thread_num=1, thread_afffinity_mode=1, enable_parallel=False) + >>> print(context) + thread_num: 1, thread_affinity_mode: 1, thread_affinity_core_list: [], enable_parallel: False, \ + device_list: 0, . """ - def __init__(self, thread_num=2, thread_affinity_mode=1, thread_affinity_core_list=None, enable_parallel=False): - check_isinstance("thread_num", thread_num, int) - check_isinstance("thread_affinity_mode", thread_affinity_mode, int) + def __init__(self, thread_num=None, thread_affinity_mode=None, thread_affinity_core_list=None, \ + enable_parallel=False): + if thread_num is not None: + check_isinstance("thread_num", thread_num, int) + if thread_num < 0: + raise ValueError(f"Context's init failed, thread_num must be positive.") + if thread_affinity_mode is not None: + check_isinstance("thread_affinity_mode", thread_affinity_mode, int) check_list_of_element("thread_affinity_core_list", thread_affinity_core_list, int, enable_none=True) check_isinstance("enable_parallel", enable_parallel, bool) - if thread_num < 0: - raise ValueError(f"Context's init failed! thread_num must be positive.") core_list = [] if thread_affinity_core_list is None else thread_affinity_core_list self._context = _c_lite_wrapper.ContextBind() - self._context.set_thread_num(thread_num) - self._context.set_thread_affinity_mode(thread_affinity_mode) + if thread_num is not None: + self._context.set_thread_num(thread_num) + if thread_affinity_mode is not None: + self._context.set_thread_affinity_mode(thread_affinity_mode) self._context.set_thread_affinity_core_list(core_list) self._context.set_enable_parallel(enable_parallel) @@ -62,7 +70,7 @@ class Context: f"thread_affinity_mode: {self._context.get_thread_affinity_mode()}, " \ f"thread_affinity_core_list: {self._context.get_thread_affinity_core_list()}, " \ f"enable_parallel: {self._context.get_enable_parallel()}, " \ - f"device_list: {self._context.get_device_list()}" + f"device_list: {self._context.get_device_list()}." return res def append_device_info(self, device_info): @@ -79,6 +87,9 @@ class Context: >>> import mindspore_lite as mslite >>> context = mslite.Context() >>> context.append_device_info(mslite.CPUDeviceInfo()) + >>> print(context) + thread_num: 2, thread_affinity_mode: 0, thread_affinity_core_list: [], enable_parallel: False, \ + device_list: 0, . """ if not isinstance(device_info, DeviceInfo): raise TypeError("device_info must be CPUDeviceInfo, GPUDeviceInfo or AscendDeviceInfo, but got {}.".format( @@ -107,7 +118,11 @@ class CPUDeviceInfo(DeviceInfo): Examples: >>> import mindspore_lite as mslite - >>> device_info = mslite.CPUDeviceInfo() + >>> cpu_device_info = mslite.CPUDeviceInfo(enable_fp16=True) + >>> print(cpu_device_info) + device_type: DeviceType.kCPU, enable_fp16: True. + >>> context = mslite.Context() + >>> context.append_device_info(cpu_device_info) """ def __init__(self, enable_fp16=False): @@ -132,17 +147,27 @@ class GPUDeviceInfo(DeviceInfo): Raises: TypeError: type of input parameters are invalid. + ValueError: value of input parameters are invalid. Examples: >>> import mindspore_lite as mslite - >>> device_info = mslite.GPUDeviceInfo(enable_fp16=True) + >>> gpu_device_info = mslite.GPUDeviceInfo(device_id=1, enable_fp16=False) + >>> print(gpu_device_info) + device_type: DeviceType.kGPU, device_id: 1, enable_fp16: False. + >>> cpu_device_info = mslite.CPUDeviceInfo(enable_fp16=False) + >>> context = mslite.Context() + >>> context.append_device_info(mslite.CPUDeviceInfo(gpu_device_info)) + >>> context.append_device_info(mslite.CPUDeviceInfo(cpu_device_info)) + >>> print(context) + thread_num: 2, thread_affinity_mode: 0, thread_affinity_core_list: [], enable_parallel: False, \ + device_list: 1, 0, . """ def __init__(self, device_id=0, enable_fp16=False): super(GPUDeviceInfo, self).__init__() check_isinstance("device_id", device_id, int) if device_id < 0: - raise ValueError(f"GPUDeviceInfo's init failed! device_id must be positive.") + raise ValueError(f"GPUDeviceInfo's init failed, device_id must be positive.") check_isinstance("enable_fp16", enable_fp16, bool) self._device_info = _c_lite_wrapper.GPUDeviceInfoBind() self._device_info.set_device_id(device_id) @@ -162,7 +187,11 @@ class GPUDeviceInfo(DeviceInfo): int, the rank id of the context. Examples: - >>> rank_id = context.get_rank_id() + >>> import mindspore_lite as mslite + >>> device_info = mslite.GPUDeviceInfo(device_id=1, enable_fp16=True) + >>> rank_id = device_info.get_rank_id() + >>> print(rank_id) + 1 """ return self._device_info.get_rank_id() @@ -174,7 +203,11 @@ class GPUDeviceInfo(DeviceInfo): int, the group size of the context. Examples: - >>> group_size = context.get_group_size() + >>> import mindspore_lite as mslite + >>> device_info = mslite.GPUDeviceInfo(device_id=1, enable_fp16=True) + >>> group_size = device_info.get_group_size() + >>> print(group_size) + 1 """ return self._device_info.get_group_size() @@ -200,10 +233,24 @@ class AscendDeviceInfo(DeviceInfo): Raises: TypeError: type of input parameters are invalid. + ValueError: value of input parameters are invalid. + RuntimeError: file path does not exist + Examples: >>> import mindspore_lite as mslite - >>> device_info = mslite.AscendDeviceInfo(input_format="NHWC") + >>> ascend_device_info = mslite.AscendDeviceInfo(device_id=0, input_format="NCHW", \ + ... input_shape={1: [1, 3, 28, 28]}, precision_mode="force_fp16", \ + ... op_select_impl_mode="high_performance", dynamic_batch_size=None, \ + ... dynamic_image_size="", fusion_switch_config_path="", insert_op_cfg_path="") + >>> print(ascend_device_info) + >>> cpu_device_info = mslite.CPUDeviceInfo(enable_fp16=False) + >>> context = mslite.Context() + >>> context.append_device_info(mslite.CPUDeviceInfo(gpu_device_info)) + >>> context.append_device_info(mslite.CPUDeviceInfo(ascend_device_info)) + >>> print(context) + thread_num: 2, thread_affinity_mode: 0, thread_affinity_core_list: [], enable_parallel: False, \ + device_list: 3, 0, . """ def __init__(self, device_id=0, input_format=None, input_shape=None, precision_mode="force_fp16", @@ -220,13 +267,13 @@ class AscendDeviceInfo(DeviceInfo): check_isinstance("fusion_switch_config_path", fusion_switch_config_path, str) check_isinstance("insert_op_cfg_path", insert_op_cfg_path, str) if device_id < 0: - raise ValueError(f"AscendDeviceInfo's init failed! device_id must be positive.") + raise ValueError(f"AscendDeviceInfo's init failed, device_id must be positive.") if fusion_switch_config_path != "": if not os.path.exists(fusion_switch_config_path): - raise RuntimeError(f"AscendDeviceInfo's init failed! fusion_switch_config_path is not exist!") + raise RuntimeError(f"AscendDeviceInfo's init failed, fusion_switch_config_path does not exist!") if insert_op_cfg_path != "": if not os.path.exists(insert_op_cfg_path): - raise RuntimeError(f"AscendDeviceInfo's init failed! insert_op_cfg_path is not exist!") + raise RuntimeError(f"AscendDeviceInfo's init failed, insert_op_cfg_path does not exist!") input_format_list = "" if input_format is None else input_format input_shape_list = {} if input_shape is None else input_shape batch_size_list = [] if dynamic_batch_size is None else dynamic_batch_size diff --git a/mindspore/lite/python/api/converter.py b/mindspore/lite/python/api/converter.py index 719a3b4b743..7af127ab8a0 100644 --- a/mindspore/lite/python/api/converter.py +++ b/mindspore/lite/python/api/converter.py @@ -15,18 +15,33 @@ """ Converter API. """ +from enum import Enum from .lib import _c_lite_wrapper +__all__ = ['FmkType', 'Converter'] + + +class FmkType(Enum): + """ + The FmkType is used to define Input model framework type. + """ + kFmkTypeTf = 0 + kFmkTypeCaffe = 1 + kFmkTypeOnnx = 2 + kFmkTypeMs = 3 + kFmkTypeTflite = 4 + kFmkTypePytorch = 5 + class Converter: """ Converter is used to convert third-party models. Args: - fmk_type(Enum, optional): Input model framework type. TF | TFLITE | CAFFE | MINDIR | ONNX. - model_file (str, optional): Input model file. - TF: *.pb | TFLITE: *.tflite | CAFFE: *.prototxt | MINDIR: *.mindir | ONNX: *.onnx. - output_file (list, optional): Output model file path. Will add .ms automatically. + fmk_type(Enum): Input model framework type. TF | TFLITE | CAFFE | MINDIR | ONNX. + model_file (str): Input model file. + TF: *.pb | TFLITE: *.tflite | CAFFE: *.prototxt | MINDIR: *.mindir | ONNX: *.onnx. + output_file (list): Output model file path. Will add .ms automatically. weight_file (str, optional): Input model weight file. Needed when fmk is CAFFE. CAFFE: *.caffemodel, config_file (str, optional): Configuration for post-training, offline split op to parallel, disable op fusion ability and set plugin so path. @@ -57,7 +72,7 @@ class Converter: Examples: >>> import mindspore_lite as mslite - >>> device_info = mslite.context.AscendDeviceInfo(input_format="NHWC") + >>> converter = mslite.Converter(mslite.FmkType.kFmkTypeTflite, "mobilenetv2.tflite", "mobilenetv2.tflite") """ def __init__(self, fmk_type, model_file, output_file, weight_file="", config_file=None, weight_fp16=None, @@ -84,4 +99,9 @@ class Converter: return res def converter(self): - pass + """ + Converter is used to convert third-party models. + """ + ret = self._converter.converter + if not ret.IsOk(): + raise RuntimeError(f"build_from_file failed! Error is {ret.ToString()}") diff --git a/mindspore/lite/python/api/model.py b/mindspore/lite/python/api/model.py index 9207d8f34c9..2baf8949d90 100644 --- a/mindspore/lite/python/api/model.py +++ b/mindspore/lite/python/api/model.py @@ -43,8 +43,8 @@ class Model: Examples: >>> import mindspore_lite as mslite >>> model = mslite.Model() - >>> context.append_device_info(mslite.CPUDeviceInfo()) - >>> model.build_from_file("mnist.tflite.ms", mslite.ModelType.MINDIR_LITE, context) + >>> print(model) + model_path: . """ def __init__(self): @@ -70,14 +70,20 @@ class Model: RuntimeError: build model failed. Examples: - >>> model.build_from_file("mnist.tflite.ms", mslite.ModelType.MINDIR_LITE, context) + >>> 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 model_path != "": if not os.path.exists(model_path): - raise RuntimeError(f"build_from_file failed! model_path is not exist!") + 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 @@ -100,8 +106,17 @@ class Model: RuntimeError: resize model 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() - >>> model.resize(inputs, [[1, 224, 224, 3]]) + >>> 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))) @@ -121,11 +136,11 @@ class Model: 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's size does not match dims's size, but got " + 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): if len(element.get_shape()) != len(dims[i]): - raise ValueError(f"one of inputs's size does not match one of dims's size, but got " + raise ValueError(f"one of inputs' size does not match one of dims's size, but got " f"input: {element.get_shape()} and dim: {len(dims[i])} at {i} index.") _inputs.append(element._tensor) ret = self._model.resize(_inputs, dims) @@ -142,12 +157,71 @@ class Model: Raises: TypeError: type of input parameters are invalid. - RuntimeError: resize model failed. + RuntimeError: predict model failed. Examples: + >>> # predict which indata is from file + >>> import mindspore_lite as mslite + >>> import numpy ad 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("mobilenetv2.ms.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: [[8.9401474e-05 4.4536911e-05 1.0089713e-04 ... 3.2687691e-05 \ + 3.6021424e-04 8.3650106e-05]] + + >>> # predict which indata is numpy array + >>> import mindspore_lite as mslite + >>> import numpy ad 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 0.00052926 ... 0.00018387 0.00148318 0.00116824]] + + >>> # predict which indata is new mslite tensor with numpy array + >>> import mindspore_lite as mslite + >>> import numpy ad 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_data_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 0.00052926 ... 0.00018387 0.00148318 0.00116824]] """ if not isinstance(inputs, list): raise TypeError("inputs must be list, but got {}.".format(type(inputs))) @@ -178,6 +252,11 @@ class Model: 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 = [] @@ -193,6 +272,11 @@ class Model: 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 = [] @@ -214,7 +298,15 @@ class Model: TypeError: type of input parameters are invalid. Examples: - >>> input = model.get_input_by_tensor_name("tensor_in") + >>> 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) @@ -236,7 +328,15 @@ class Model: TypeError: type of input parameters are invalid. Examples: - >>> output = model.get_output_by_tensor_name("tensor_out") + >>> 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) @@ -247,8 +347,9 @@ class Model: class RunnerConfig: """ - RunnerConfig Class - + 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): Define the context used to store options during execution. workers_num (int): the num of workers. @@ -257,18 +358,27 @@ class RunnerConfig: TypeError: type of input parameters are invalid. Examples: + >>> # only for serving inference >>> import mindspore_lite as mslite - >>> runner_config = mslite.RunnerConfig(context, 4) + >>> context = mslite.Context() + >>> context.append_device_info(mslite.CPUDeviceInfo()) + >>> runner_config = mslite.RunnerConfig(context=context, workers_num=4) + >>> print(runner_config) + workers num: 4, context: 0, . """ - def __init__(self, context, workers_num): - check_isinstance("context", context, Context) - check_isinstance("workers_num", workers_num, int) - if workers_num < 0: - raise ValueError(f"RunnerConfig's init failed! workers_num must be positive.") + def __init__(self, context=None, workers_num=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.") self._runner_config = _c_lite_wrapper.RunnerConfigBind() - self._runner_config.set_workers_num(workers_num) - self._runner_config.set_context(context._context) + 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) def __str__(self): res = f"workers num: {self._runner_config.get_workers_num()}, " \ @@ -284,8 +394,11 @@ class ModelParallelRunner: None Examples: + >>> # only for serving inference >>> import mindspore_lite as mslite >>> model_parallel_runner = mslite.ModelParallelRunner() + >>> print(model_parallel_runner) + model_path: . """ def __init__(self): @@ -295,7 +408,7 @@ class ModelParallelRunner: def __str__(self): return f"model_path: {self.model_path_}." - def init(self, model_path, runner_config): + def init(self, model_path, runner_config=None): """ build a model parallel runner from model path so that it can run on a device. @@ -308,15 +421,25 @@ class ModelParallelRunner: RuntimeError: init ModelParallelRunner failed. Examples: - >>> model_parallel_runner.init("mnist.tflite.ms", runner_config) + >>> 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) - check_isinstance("runner_config", runner_config, RunnerConfig) if model_path != "": if not os.path.exists(model_path): - raise RuntimeError(f"ModelParallelRunner's init failed! model_path is not exist!") + raise RuntimeError(f"ModelParallelRunner's init failed, model_path does not exist!") self.model_path_ = model_path - ret = self._model.init(self.model_path_, runner_config._runner_config) + 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_) if not ret.IsOk(): raise RuntimeError(f"ModelParallelRunner's init failed! Error is {ret.ToString()}") @@ -330,12 +453,25 @@ class ModelParallelRunner: Raises: TypeError: type of input parameters are invalid. - RuntimeError: resize model failed. + RuntimeError: predict model 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) >>> inputs = model_parallel_runner.get_inputs() + >>> in_data = np.fromfile("mobilenetv2.ms.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: [[8.9401474e-05 4.4536911e-05 1.0089713e-04 ... 3.2687691e-05 \ + 3.6021424e-04 8.3650106e-05]] """ if not isinstance(inputs, list): raise TypeError("inputs must be list, but got {}.".format(type(inputs))) @@ -366,6 +502,12 @@ class ModelParallelRunner: 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 = [] @@ -381,6 +523,12 @@ class ModelParallelRunner: 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 = [] diff --git a/mindspore/lite/python/api/tensor.py b/mindspore/lite/python/api/tensor.py index 96e0df50865..565928db506 100644 --- a/mindspore/lite/python/api/tensor.py +++ b/mindspore/lite/python/api/tensor.py @@ -83,6 +83,10 @@ class Tensor: Examples: >>> import mindspore_lite as mslite >>> tensor = mslite.Tensor() + >>> tensor.set_data_type(mslite.DataType.FLOAT32) + >>> print(tensor) + tensor_name: , data_type: DataType.FLOAT32, shape: [], format: Format.NCHW, \ + element_num: 1, data_size: 0. """ def __init__(self, tensor=None): @@ -105,6 +109,7 @@ class Tensor: TypeError: type of input parameters are invalid. Examples: + >>> import mindspore_lite as mslite >>> tensor = mslite.Tensor() >>> tensor.set_tensor_name("tensor0") """ @@ -120,7 +125,12 @@ class Tensor: str, the name of the tensor. Examples: - >>> name = tensor.get_tensor_name() + >>> import mindspore_lite as mslite + >>> tensor = mslite.Tensor() + >>> tensor.set_tensor_name("tensor0") + >>> tensor_name = tensor.get_tensor_name() + >>> print(tenser_name) + tensor0 """ return self._tensor.get_tensor_name() @@ -135,6 +145,7 @@ class Tensor: TypeError: type of input parameters are invalid. Examples: + >>> import mindspore_lite as mslite >>> tensor = mslite.Tensor() >>> tensor.set_data_type(mslite.DataType.FLOAT32) """ @@ -166,7 +177,12 @@ class Tensor: DataType, the data type of the tensor. Examples: + >>> import mindspore_lite as mslite + >>> tensor = mslite.Tensor() + >>> tensor.set_data_type(mslite.DataType.FLOAT32) >>> data_type = tensor.get_data_type() + >>> print(data_type) + DataType.FLOAT32 """ data_type_map = { _c_lite_wrapper.DataType.kTypeUnknown: DataType.UNKNOWN, @@ -197,6 +213,7 @@ class Tensor: TypeError: type of input parameters are invalid. Examples: + >>> import mindspore_lite as mslite >>> tensor = mslite.Tensor() >>> tensor.set_shape([1, 112, 112, 3]) """ @@ -215,7 +232,12 @@ class Tensor: list[int], the shape of the tensor. Examples: + >>> import mindspore_lite as mslite + >>> tensor = mslite.Tensor() + >>> tensor.set_shape([1, 112, 112, 3]) >>> shape = tensor.get_shape() + >>> print(shape) + [1, 112, 112, 3] """ return self._tensor.get_shape() @@ -230,6 +252,7 @@ class Tensor: TypeError: type of input parameters are invalid. Examples: + >>> import mindspore_lite as mslite >>> tensor = mslite.Tensor() >>> tensor.set_format(mslite.Format.NHWC) """ @@ -267,7 +290,12 @@ class Tensor: Format, the format of the tensor. Examples: + >>> import mindspore_lite as mslite + >>> tensor = mslite.Tensor() + >>> tensor.set_format(mslite.Format.NHWC) >>> tensor_format = tensor.get_format() + >>> print(tensor_format) + Format,NHWC """ format_map = { _c_lite_wrapper.Format.DEFAULT_FORMAT: Format.DEFAULT, @@ -301,19 +329,29 @@ class Tensor: int, the element num of the tensor data. Examples: + >>> import mindspore_lite as mslite + >>> tensor = mslite.Tensor() >>> num = tensor.get_element_num() + >>> print(num) + 1 """ return self._tensor.get_element_num() def get_data_size(self): """ - Get the data size of the tensor. + Get the data size of the tensor. data_size = element_num * data_type Returns: int, the data size of the tensor data. Examples: + >>> # data_size is related to data_type + >>> import mindspore_lite as mslite + >>> tensor = mslite.Tensor() + >>> tensor.set_data_type(mslite.DataType.FLOAT32) >>> size = tensor.get_data_size() + >>> print(size) + 4 """ return self._tensor.get_data_size() @@ -328,7 +366,21 @@ class Tensor: TypeError: type of input parameters are invalid. Examples: - >>> in_data = numpy.fromfile("mnist.tflite.ms.bin", dtype=np.float32) + >>> # data is from file + >>> import mindspore_lite as mslite + >>> import numpy ad np + >>> tensor = mslite.Tensor() + >>> tensor.set_shape([1, 224, 224, 3]) + >>> tensor.set_data_type(mslite.DataType.FLOAT32) + >>> in_data = np.fromfile("mobilenetv2.ms.bin", dtype=np.float32) + >>> tensor.set_data_from_numpy(in_data) + >>> # data is numpy arrange + >>> import mindspore_lite as mslite + >>> import numpy ad np + >>> tensor = mslite.Tensor() + >>> tensor.set_shape([1, 2, 2, 3]) + >>> tensor.set_data_type(mslite.DataType.FLOAT32) + >>> in_data = np.arrange(1 * 2 * 2 * 3, dtype=np.float32) >>> tensor.set_data_from_numpy(in_data) """ if not isinstance(numpy_obj, numpy.ndarray): @@ -364,7 +416,19 @@ class Tensor: numpy.ndarray, the numpy object from tensor data. Examples: + >>> import mindspore_lite as mslite + >>> import numpy ad np + >>> tensor = mslite.Tensor() + >>> tensor.set_shape([1, 2, 2, 3]) + >>> tensor.set_data_type(mslite.DataType.FLOAT32) + >>> in_data = np.arrange(1 * 2 * 2 * 3, dtype=np.float32) + >>> tensor.set_data_from_numpy(in_data) >>> data = tensor.get_data_to_numpy() + >>> print(data) + [[[[ 0. 1. 2.] + [ 3. 4. 5.]] + [[ 6. 7. 8.] + [ 9. 10. 11.]]]] """ return self._tensor.get_data_to_numpy() @@ -373,6 +437,6 @@ class Tensor: f"data_type: {self.get_data_type()}, " \ f"shape: {self.get_shape()}, " \ f"format: {self.get_format()}, " \ - f"element_num, {self.get_element_num()}, " \ - f"data_size, {self.get_data_size()}." + f"element_num: {self.get_element_num()}, " \ + f"data_size: {self.get_data_size()}." return res diff --git a/mindspore/lite/python/src/model_pybind.cc b/mindspore/lite/python/src/model_pybind.cc index a30d61def50..7f522c2c62d 100644 --- a/mindspore/lite/python/src/model_pybind.cc +++ b/mindspore/lite/python/src/model_pybind.cc @@ -61,18 +61,16 @@ void ModelPyBind(const py::module &m) { py::class_>(m, "ModelBind") .def(py::init<>()) - .def( - "build_from_buff", - static_cast &)>(&Model::Build)) - .def( - "build_from_file", - static_cast &)>(&Model::Build)) + .def("build_from_buff", + py::overload_cast &>(&Model::Build)) + .def("build_from_file", + py::overload_cast &>(&Model::Build)) .def("build_from_file_with_decrypt", - static_cast &, const Key &, - const std::string &, const std::string &)>(&Model::Build)) + py::overload_cast &, const Key &, + const std::string &, const std::string &>(&Model::Build)) .def("resize", &Model::Resize) - .def("predict", static_cast &, std::vector *, - const MSKernelCallBack &, const MSKernelCallBack &)>(&Model::Predict)) + .def("predict", py::overload_cast &, std::vector *, const MSKernelCallBack &, + const MSKernelCallBack &>(&Model::Predict)) .def("get_inputs", &Model::GetInputs) .def("get_outputs", &Model::GetOutputs) .def("get_input_by_tensor_name", diff --git a/mindspore/lite/test/runtest.sh b/mindspore/lite/test/runtest.sh index f917650f86f..311e751c4d5 100644 --- a/mindspore/lite/test/runtest.sh +++ b/mindspore/lite/test/runtest.sh @@ -113,3 +113,41 @@ echo 'Runtime config file test' echo 'run c api ut test' ./lite-test --gtest_filter="TensorCTest.*" ./lite-test --gtest_filter="ContextCTest.*" + +echo "lite Python API ut test" +mindspore_lite_whl=`ls ${CUR_DIR}/../../../output/*.whl` +if [ ! -f "${mindspore_lite_whl}" ]; then + echo -e "\e[31mPython-API Whl not found, so lite Python API ut test will not be run. \e[0m" +else + export PYTHONPATH=${CUR_DIR}/../build/package/:${PYTHONPATH} + + # prepare model and inputdata for Python-API ut test + if [ ! -e mobilenetv2.ms ]; then + MODEL_DOWNLOAD_URL="https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms" + wget -c -O mobilenetv2.ms --no-check-certificate ${MODEL_DOWNLOAD_URL} + fi + + if [ ! -e mobilenetv2.ms.bin ]; then + BIN_DOWNLOAD_URL="https://download.mindspore.cn/model_zoo/official/lite/quick_start/micro/mobilenetv2.tar.gz" + wget -c --no-check-certificate ${BIN_DOWNLOAD_URL} + tar -zxf mobilenetv2.tar.gz + cp mobilenetv2/*.ms.bin ./mobilenetv2.ms.bin + rm -rf mobilenetv2.tar.gz mobilenetv2/ + fi + + # run Python-API ut test + pytest ${CUR_DIR}/ut/python/test_inference_api.py -s + RET=$? + if [ ${RET} -ne 0 ]; then + exit ${RET} + fi + + # run CPU Python-API st test + echo "run CPU Python API st test" + pytest ${CUR_DIR}/st/python/test_inference.py::test_cpu_inference_01 -s + RET=$? + if [ ${RET} -ne 0 ]; then + exit ${RET} + fi +fi + diff --git a/mindspore/lite/test/st/python/test_inference.py b/mindspore/lite/test/st/python/test_inference.py index a8b5390503f..62b0ad26dee 100644 --- a/mindspore/lite/test/st/python/test_inference.py +++ b/mindspore/lite/test/st/python/test_inference.py @@ -19,13 +19,13 @@ import mindspore_lite as mslite import numpy as np -def common_predict(context): +def common_predict(context, model_path, in_data_path): model = mslite.Model() - model.build_from_file("mnist.tflite.ms", mslite.ModelType.MINDIR_LITE, context) + model.build_from_file(model_path, mslite.ModelType.MINDIR_LITE, context) inputs = model.get_inputs() outputs = model.get_outputs() - in_data = np.fromfile("mnist.tflite.ms.bin", dtype=np.float32) + in_data = np.fromfile(in_data_path, dtype=np.float32) inputs[0].set_data_from_numpy(in_data) model.predict(inputs, outputs) for output in outputs: @@ -39,7 +39,9 @@ def test_cpu_inference_01(): print("cpu_device_info: ", cpu_device_info) context = mslite.Context(thread_num=1, thread_affinity_mode=2) context.append_device_info(cpu_device_info) - common_predict(context) + cpu_model_path = "mobilenetv2.ms" + cpu_in_data_path = "mobilenetv2.ms.bin" + common_predict(context, cpu_model_path, cpu_in_data_path) # ============================ gpu inference ============================ @@ -51,7 +53,9 @@ def test_gpu_inference_01(): context = mslite.Context(thread_num=1, thread_affinity_mode=2) context.append_device_info(gpu_device_info) context.append_device_info(cpu_device_info) - common_predict(context) + gpu_model_path = "mobilenetv2.ms" + gpu_in_data_path = "mobilenetv2.ms.bin" + common_predict(context, gpu_model_path, gpu_in_data_path) # ============================ ascend inference ============================ @@ -70,7 +74,9 @@ def test_ascend_inference_01(): context = mslite.Context(thread_num=1, thread_affinity_mode=2) context.append_device_info(ascend_device_info) context.append_device_info(cpu_device_info) - common_predict(context) + ascend_model_path = "mnist.tflite.ms" + ascend_in_data_path = "mnist.tflite.ms.bin" + common_predict(context, ascend_model_path, ascend_in_data_path) # ============================ server inference ============================ @@ -81,10 +87,12 @@ def test_server_inference_01(): context.append_device_info(cpu_device_info) runner_config = mslite.RunnerConfig(context, 4) model_parallel_runner = mslite.ModelParallelRunner() - model_parallel_runner.init(model_path="mnist.tflite.ms", runner_config=runner_config) + cpu_model_path = "mobilenetv2.ms" + cpu_in_data_path = "mobilenetv2.ms.bin" + model_parallel_runner.init(model_path=cpu_model_path, runner_config=runner_config) inputs = model_parallel_runner.get_inputs() - in_data = np.fromfile("mnist.tflite.ms.bin", dtype=np.float32) + in_data = np.fromfile(cpu_in_data_path, dtype=np.float32) inputs[0].set_data_from_numpy(in_data) outputs = model_parallel_runner.get_outputs() model_parallel_runner.predict(inputs, outputs) diff --git a/mindspore/lite/test/ut/python/test_inference_api.py b/mindspore/lite/test/ut/python/test_inference_api.py index f12cb0fa70a..f91f64884c3 100644 --- a/mindspore/lite/test/ut/python/test_inference_api.py +++ b/mindspore/lite/test/ut/python/test_inference_api.py @@ -194,7 +194,7 @@ def test_ascend_device_info_21(): def test_ascend_device_info_22(): with pytest.raises(RuntimeError) as raise_info: device_info = mslite.AscendDeviceInfo(fusion_switch_config_path="fusion_switch.cfg") - assert "fusion_switch_config_path is not exist" in str(raise_info.value) + assert "fusion_switch_config_path does not exist" in str(raise_info.value) def test_ascend_device_info_23(): @@ -206,79 +206,84 @@ def test_ascend_device_info_23(): def test_ascend_device_info_24(): with pytest.raises(RuntimeError) as raise_info: device_info = mslite.AscendDeviceInfo(insert_op_cfg_path="insert_op.cfg") - assert "insert_op_cfg_path is not exist" in str(raise_info.value) + assert "insert_op_cfg_path does not exist" in str(raise_info.value) # ============================ Context ============================ def test_context_01(): + context = mslite.Context() + assert "thread_num:" in str(context) + + +def test_context_02(): with pytest.raises(TypeError) as raise_info: context = mslite.Context(thread_num="1") assert "thread_num must be int" in str(raise_info.value) -def test_context_02(): +def test_context_03(): with pytest.raises(ValueError) as raise_info: context = mslite.Context(thread_num=-1) assert "thread_num must be positive" in str(raise_info.value) -def test_context_03(): +def test_context_04(): context = mslite.Context(thread_num=4) assert "thread_num: 4" in str(context) -def test_context_04(): +def test_context_05(): with pytest.raises(TypeError) as raise_info: context = mslite.Context(thread_affinity_mode="1") assert "thread_affinity_mode must be int" in str(raise_info.value) -def test_context_05(): +def test_context_06(): context = mslite.Context(thread_affinity_mode=2) assert "thread_affinity_mode: 2" in str(context) -def test_context_06(): +def test_context_07(): with pytest.raises(TypeError) as raise_info: context = mslite.Context(thread_affinity_core_list=2) assert "thread_affinity_core_list must be list" in str(raise_info.value) -def test_context_07(): +def test_context_08(): context = mslite.Context(thread_affinity_core_list=[2]) assert "thread_affinity_core_list: [2]" in str(context) -def test_context_08(): +def test_context_09(): with pytest.raises(TypeError) as raise_info: context = mslite.Context(thread_affinity_core_list=["1", "0"]) assert "thread_affinity_core_list element must be int" in str(raise_info.value) -def test_context_09(): +def test_context_10(): context = mslite.Context(thread_affinity_core_list=[1, 0]) assert "thread_affinity_core_list: [1, 0]" in str(context) -def test_context_10(): +def test_context_11(): with pytest.raises(TypeError) as raise_info: context = mslite.Context(enable_parallel=1) assert "enable_parallel must be bool" in str(raise_info.value) -def test_context_11(): +def test_context_12(): context = mslite.Context(enable_parallel=True) assert "enable_parallel: True" in str(context) -def test_context_12(): +def test_context_13(): with pytest.raises(TypeError) as raise_info: context = mslite.Context() context.append_device_info("CPUDeviceInfo") assert "device_info must be CPUDeviceInfo, GPUDeviceInfo or AscendDeviceInfo" in str(raise_info.value) -def test_context_13(): +def test_context_14(): gpu_device_info = mslite.GPUDeviceInfo() cpu_device_info = mslite.CPUDeviceInfo() context = mslite.Context() @@ -433,7 +438,7 @@ def test_model_build_04(): context = mslite.Context() model = mslite.Model() model.build_from_file(model_path="test.ms", model_type=mslite.ModelType.MINDIR_LITE, context=context) - assert "model_path is not exist" in str(raise_info.value) + assert "model_path does not exist" in str(raise_info.value) def get_model(): @@ -489,7 +494,7 @@ def test_model_resize_06(): model = get_model() inputs = model.get_inputs() model.resize(inputs, [[1, 112, 112, 3], [1, 112, 112, 3]]) - assert "inputs's size does not match dims's size" in str(raise_info.value) + assert "inputs' size does not match dims's size" in str(raise_info.value) def test_model_resize_07(): @@ -497,7 +502,7 @@ def test_model_resize_07(): model = get_model() inputs = model.get_inputs() model.resize(inputs, [[1, 112, 112]]) - assert "one of inputs's size does not match one of dims's size" in str(raise_info.value) + assert "one of inputs' size does not match one of dims's size" in str(raise_info.value) def test_model_resize_08(): @@ -569,6 +574,7 @@ def test_model_predict_07(): input_tensor.set_data_type(inputs[0].get_data_type()) input_tensor.set_shape(inputs[0].get_shape()) input_tensor.set_format(inputs[0].get_format()) + input_tensor.set_tensor_name(inputs[0].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) outputs = model.get_outputs() diff --git a/mindspore/lite/test/ut/python/test_server_inference_api.py b/mindspore/lite/test/ut/python/test_server_inference_api.py index 85a01f67460..d0ce50ecc7f 100644 --- a/mindspore/lite/test/ut/python/test_server_inference_api.py +++ b/mindspore/lite/test/ut/python/test_server_inference_api.py @@ -76,7 +76,7 @@ def test_model_parallel_runner_init_02(): context = mslite.Context() context.append_device_info(cpu_device_info) model_parallel_runner = mslite.ModelParallelRunner() - model_parallel_runner.init(model_path="test.ms", runner_config=context) + model_parallel_runner.init(model_path="mobilenetv2.ms", runner_config=context) assert "runner_config must be RunnerConfig" in str(raise_info.value) @@ -88,12 +88,12 @@ def test_model_parallel_runner_init_03(): runner_config = mslite.RunnerConfig(context, 4) model_parallel_runner = mslite.ModelParallelRunner() model_parallel_runner.init(model_path="test.ms", runner_config=runner_config) - assert "model_path is not exist" in str(raise_info.value) + assert "model_path does not exist" in str(raise_info.value) def test_model_parallel_runner_init_04(): with pytest.raises(RuntimeError) as raise_info: - context = mslite.context.Context() + context = mslite.Context() runner_config = mslite.model.RunnerConfig(context, 4) model_parallel_runner = mslite.model.ModelParallelRunner() model_parallel_runner.init(model_path="mobilenetv2.ms", runner_config=runner_config) diff --git a/mindspore/lite/tools/converter/anf_transform.cc b/mindspore/lite/tools/converter/anf_transform.cc index abe46d30268..ba4cf83bb85 100644 --- a/mindspore/lite/tools/converter/anf_transform.cc +++ b/mindspore/lite/tools/converter/anf_transform.cc @@ -477,7 +477,7 @@ FuncGraphPtr AnfTransform::TransformFuncGraph(const FuncGraphPtr &old_graph, bool AnfTransform::StoreBuiltinPass(const std::shared_ptr ¶m) { if (param == nullptr) { - MS_LOG(ERROR) << "config is nullptr"; + MS_LOG(ERROR) << "param is nullptr"; return false; } auto fmk = param->fmk_type;