[MS][LITE] code docs lite python api fix v3
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@ -24,18 +24,23 @@ mindspore_lite.Context
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- **enable_parallel** (bool，可选) - 设置状态是否启用并行执行模型推理或并行训练。默认值：False。
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异常：
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- **TypeError** - `thread_num` 不是int类型或None。
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- **TypeError** - `inter_op_parallel_num` 不是int类型或None。
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- **TypeError** - `thread_affinity_mode` 不是int类型或None。
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- **TypeError** - `thread_affinity_core_list` 不是list类型或None。
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- **TypeError** - `thread_affinity_core_list` 是list类型，但元素不是int类型或None。
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- **TypeError** - `thread_num` 既不是int类型也不是None。
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- **TypeError** - `inter_op_parallel_num` 既不是int类型也不是None。
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- **TypeError** - `thread_affinity_mode` 既不是int类型也不是None。
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- **TypeError** - `thread_affinity_core_list` 既不是list类型也不是None。
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- **TypeError** - `thread_affinity_core_list` 是list类型，但元素既不是int类型也不是None。
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- **TypeError** - `enable_parallel` 不是bool类型。
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- **ValueError** - `thread_num` 小于0。
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- **ValueError** - `inter_op_parallel_num` 小于0。
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.. py:method:: append_device_info(device_info)
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设置张量的数据类型。
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将一个用户定义的设备信息附加到上下文中。
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.. note::
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添加GPU设备信息后，必须在调用上下文之前添加CPU设备信息。因为当GPU不支持算子时，系统将尝试CPU是否支持该算子。此时，需要切换至带有CPU设备信息的上下文中。
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添加Ascend设备信息后，必须在调用上下文之前添加CPU设备信息。因为当在Ascend上不支持算子时，系统将尝试CPU是否支持算子。此时，需要切换至带有CPU设备信息的上下文中。
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参数：
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- **device_info** (DeviceInfo) - 实例化的设备信息。
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@ -11,11 +11,11 @@ mindspore_lite.Converter
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参数：
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- **fmk_type** (FmkType) - 输入模型框架类型。选项：FmkType.TF | FmkType.CAFFE | FmkType.ONNX | FmkType.MINDIR | FmkType.TFLITE。
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- **model_file** (str) - 输入模型文件路径。e.g. "/home/user/model.prototxt"。选项：TF: "\*.pb" | CAFFE: "\*.prototxt" | ONNX: "\*.onnx" | MINDIR: "\*.mindir" | TFLITE: "\*.tflite"。
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- **output_file** (str) - 输出模型文件路径。不需加后缀，可自动生成.ms后缀。e.g. "/home/user/model.prototxt"，它将生成名为model.prototxt.ms的模型在/home/user/路径下。
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- **output_file** (str) - 输出模型文件路径。可自动生成.ms后缀。e.g. "/home/user/model.prototxt"，它将生成名为model.prototxt.ms的模型在/home/user/路径下。
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- **weight_file** (str，可选) - 输入模型权重文件。仅当输入模型框架类型为FmkType.CAFFE时必选。e.g. "/home/user/model.caffemodel"。默认值：""。
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- **config_file** (str，可选) - 作为训练后量化或离线拆分算子并行的配置文件路径，禁用算子融合功能并将插件设置为so路径。默认值：""。
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- **weight_fp16** (bool，可选) - 在Float16数据类型中序列化常量张量，仅对Float32数据类型中的常量张量有效。默认值：""。
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- **input_shape** (dict{string:list[int]}，可选) - 设置模型输入的维度，输入维度的顺序与原始模型一致。对于某些模型，模型结构可以进一步优化，但转换后的模型可能会失去动态形状的特征。e.g. {"inTensor1": [1, 32, 32, 32], "inTensor2": [1, 1, 32, 32]}。默认值：""。
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- **input_shape** (dict{str: list[int]}，可选) - 设置模型输入的维度，输入维度的顺序与原始模型一致。对于某些模型，模型结构可以进一步优化，但转换后的模型可能会失去动态形状的特征。e.g. {"inTensor1": [1, 32, 32, 32], "inTensor2": [1, 1, 32, 32]}。默认值：""。
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- **input_format** (Format，可选) - 指定导出模型的输入格式。仅对四维输入有效。选项：Format.NHWC | Format.NCHW。默认值：Format.NHWC。
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- **input_data_type** (DataType，可选) - 输入张量的数据类型，默认与模型中定义的类型相同。默认值：DataType.FLOAT32。
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- **output_data_type** (DataType，可选) - 输出张量的数据类型，默认与模型中定义的类型相同。默认值：DataType.FLOAT32。
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@ -23,7 +23,7 @@ mindspore_lite.Converter
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- **decrypt_key** (str，可选) - 用于解密文件的密钥，以十六进制字符表示。仅当fmk_type为FmkType.MINDIR时有效。默认值：""。
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- **decrypt_mode** (str，可选) - MindIR文件的解密方法。仅在设置decrypt_key时有效。选项："AES-GCM" | "AES-CBC"。默认值："AES-GCM"。
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- **enable_encryption** (bool，可选) - 是否导出加密模型。默认值：False。
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- **encrypt_key** (str，可选) - 用于加密文件的密钥，以十六进制字符表示。仅支持AES-GCM，密钥长度为16。默认值：""。
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- **encrypt_key** (str，可选) - 用于加密文件的密钥，以十六进制字符表示。仅支持decrypt_mode是"AES-GCM"，密钥长度为16。默认值：""。
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- **infer** (bool，可选) - 转换后是否进行预推理。默认值：False。
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- **train_model** (bool，可选) - 模型是否将在设备上进行训练。默认值：False。
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- **no_fusion** (bool，可选) - 避免融合优化，默认允许融合优化。默认值：False。
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@ -34,12 +34,11 @@ mindspore_lite.Converter
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- **TypeError** - `output_file` 不是str类型。
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- **TypeError** - `weight_file` 不是str类型。
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- **TypeError** - `config_file` 不是str类型。
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- **TypeError** - `config_info` 是dict类型，但dict的键不是str类型。
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- **TypeError** - `config_info` 是dict类型，但dict的值不是str类型。
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- **TypeError** - `weight_fp16` 不是bool类型。
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- **TypeError** - `input_shape` 不是dict类型或None。
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- **TypeError** - `input_shape` 是dict类型，但dict的值不是list类型。
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- **TypeError** - `input_shape` 是dict类型，dict的值是list类型，但dict的值的元素不是int类型。
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- **TypeError** - `input_shape` 既不是dict类型也不是None。
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- **TypeError** - `input_shape` 是dict类型，但key不是str类型。
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- **TypeError** - `input_shape` 是dict类型，key是str类型，但value不是list类型。
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- **TypeError** - `input_shape` 是dict类型，key是str类型，value是list类型，但value的元素不是int类型。
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- **TypeError** - `input_format` 不是Format类型。
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- **TypeError** - `input_data_type` 不是DataType类型。
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- **TypeError** - `output_data_type` 不是DataType类型。
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@ -68,6 +67,9 @@ mindspore_lite.Converter
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获取转换的配置信息。配套set_config_info方法使用，用于在线推理场景。在get_config_info前，请先用set_config_info方法赋值。
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返回：
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dict{str: dict{str: str}}，在转换中设置的配置信息。
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.. py:method:: set_config_info(section, config_info)
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设置转换时的配置信息。配套get_config_info方法使用，用于在线推理场景。
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@ -81,12 +83,14 @@ mindspore_lite.Converter
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- "mixed_bit_weight_quant_param"：混合位权重量化参数部分。量化的配置参数之一。
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- "full_quant_param"： 全量化参数部分。量化的配置参数之一。
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- "data_preprocess_param"：数据预处理参数部分。量化的配置参数之一。
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- "registry"：扩展配置参数部分。量化的配置参数之一。
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- "registry"：扩展配置参数部分。扩展的配置参数之一。
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- **config_info** (dict{str}，可选) - 配置参数列表。配合section一起，设置confile的个别参数。e.g. 对于section是"common_quant_param"，config_info是{"quant_type":"WEIGHT_QUANT"}。默认值：None。
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- **config_info** (dict{str: str}，可选) - 配置参数列表。配合section一起，设置confile的个别参数。e.g. 对于section是"common_quant_param"，config_info是{"quant_type":"WEIGHT_QUANT"}。默认值：None。
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有关训练后量化的配置参数，请参见 `quantization <https://www.mindspore.cn/lite/docs/zh-CN/master/use/post_training_quantization.html>`_。
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有关扩展的配置参数，请参见 `extension <https://www.mindspore.cn/lite/docs/zh-CN/master/use/nnie.html#%E6%89%A9%E5%B1%95%E9%85%8D%E7%BD%AE>`_。
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异常：
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- **TypeError** - `section` 不是str类型。
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- **TypeError** - `config_info` 不是dict类型。
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- **TypeError** - `config_info` 是dict类型，但key不是str类型。
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- **TypeError** - `config_info` 是dict类型，key是str类型，但value不是str类型。
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@ -38,7 +38,7 @@ mindspore_lite.DataType
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* **用法**
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由于Python API中的 `mindspore_lite.Tensor` 是直接使用pybind11技术包装C++ API，"DataType"在Python API和C++ API之间有一对一的对应关系，修改 `DataType` 的方法在 `tensor` 类的set和get方法中。
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由于Python API中的 `mindspore_lite.Tensor` 是直接使用pybind11技术包装C++ API， `DataType` 在Python API和C++ API之间有一对一的对应关系，修改 `DataType` 的方法在 `tensor` 类的set和get方法中。
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- `set_data_type`: 在 `data_type_py_cxx_map` 中以Python API中的 `DataType` 为关键字进行查询，并获取C++ API中的 `DataType` ，将其传递给C++ API中的 `set_data_type` 方法。
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- `get_data_type`: 通过C++ API中的 `get_data_type` 方法在C++ API中获取 `DataType` ，以C++ API中的 `DataType` 为关键字在 `data_type_cxx_py_map` 中查询，返回在Python API中的 `DataType` 。
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@ -43,7 +43,7 @@ mindspore_lite.Format
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* **用法**
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由于Python API中的 `mindspore_lite.Tensor` 是直接使用pybind11技术包装C++ API，"Format"在Python API和C++ API之间有一对一的对应关系，修改 `Format` 的方法在 `tensor` 类的set和get方法中。
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由于Python API中的 `mindspore_lite.Tensor` 是直接使用pybind11技术包装C++ API， `Format` 在Python API和C++ API之间有一对一的对应关系，修改 `Format` 的方法在 `tensor` 类的set和get方法中。
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- `set_format`: 在 `format_py_cxx_map` 中以Python API中的 `Format` 为关键字进行查询，并获取C++ API中的 `Format` ，将其传递给C++ API中的 `set_format` 方法。
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- `get_format`: 通过C++ API中的 `get_format` 方法在C++ API中获取 `Format` ，以C++ API中的 `Format` 为关键字在 `format_cxx_py_map` 中查询，返回在Python API中的 `Format` 。
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@ -15,7 +15,7 @@ mindspore_lite.ModelParallelRunner
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异常：
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- **TypeError** - `model_path` 不是str类型。
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- **TypeError** - `runner_config` 不是RunnerConfig类型或None。
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- **TypeError** - `runner_config` 既不是RunnerConfig类型也不是None。
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- **RuntimeError** - `model_path` 文件路径不存在。
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- **RuntimeError** - 初始化模型并行Runner失败。
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@ -10,12 +10,12 @@ mindspore_lite.RunnerConfig
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参数：
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- **context** (Context，可选) - 定义用于在执行期间存储选项的上下文。默认值：None。
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- **workers_num** (int，可选) - workers的数量。默认值：None。
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- **config_info** (dict{str, dict{str, str}}，可选) - 传递模型权重文件路径的嵌套映射。例如：{"weight": {"weight_path": "/home/user/weight.cfg"}}。默认值：None。key当前支持["weight"]；value为dict格式，其中的key当前支持["weight_path"]，其中的value为权重的路径，例如"/home/user/weight.cfg"。
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- **config_info** (dict{str: dict{str: str}}，可选) - 传递模型权重文件路径的嵌套映射。例如：{"weight": {"weight_path": "/home/user/weight.cfg"}}。默认值：None。key当前支持["weight"]；value为dict格式，其中的key当前支持["weight_path"]，其中的value为权重的路径，例如"/home/user/weight.cfg"。
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异常：
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- **TypeError** - `context` 不是Context类型或None。
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- **TypeError** - `workers_num` 不是int类型或None。
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- **TypeError** - `config_info` 不是dict类型或None。
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- **TypeError** - `context` 既不是Context类型也不是None。
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- **TypeError** - `workers_num` 既不是int类型也不是None。
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- **TypeError** - `config_info` 既不是dict类型也不是None。
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- **TypeError** - `config_info` 是dict类型，但key不是str类型。
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- **TypeError** - `config_info` 是dict类型，key是str类型，但value不是dict类型。
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- **TypeError** - `config_info` 是dict类型，key是str类型，value是dict类型，但value的key不是str类型。
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@ -9,7 +9,7 @@ mindspore_lite.Tensor
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- **tensor** (Tensor，可选) - 被存储在新张量中的数据，可以是其它Tensor。默认值：None。
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异常：
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- **TypeError** - `tensor` 不是Tensor类型或None。
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- **TypeError** - `tensor` 既不是Tensor类型也不是None。
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.. py:method:: get_data_size()
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@ -51,7 +51,7 @@ mindspore_lite.Tensor
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获取张量的形状。
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返回：
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list[int]，张量的形状。
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list[int]，张量的形状。
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.. py:method:: get_tensor_name()
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@ -47,18 +47,18 @@ class Context:
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Default: False.
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Raises:
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TypeError: `thread_num` is not an int or None.
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TypeError: `inter_op_parallel_num` is not an int or None.
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TypeError: `thread_affinity_mode` is not an int or None.
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TypeError: `thread_affinity_core_list` is not a list or None.
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TypeError: `thread_affinity_core_list` is a list, but the elements are not int or None.
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TypeError: `thread_num` is neither an int nor None.
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TypeError: `inter_op_parallel_num` is neither an int nor None.
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TypeError: `thread_affinity_mode` is neither an int nor None.
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TypeError: `thread_affinity_core_list` is neither a list nor None.
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TypeError: `thread_affinity_core_list` is a list, but the elements are neither int nor None.
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TypeError: `enable_parallel` is not a bool.
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ValueError: `thread_num` is less than 0.
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ValueError: `inter_op_parallel_num` is less than 0.
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Examples:
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>>> import mindspore_lite as mslite
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>>> context = mslite.Context(thread_num=1, inter_op_parallel_num=1, thread_afffinity_mode=1,
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>>> context = mslite.Context(thread_num=1, inter_op_parallel_num=1, thread_affinity_mode=1,
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... enable_parallel=False)
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>>> print(context)
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thread_num: 1,
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@ -66,7 +66,7 @@ class Context:
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thread_affinity_mode: 1,
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thread_affinity_core_list: [],
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enable_parallel: False,
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device_list: 0, .
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device_list: .
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"""
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def __init__(self, thread_num=None, inter_op_parallel_num=None, thread_affinity_mode=None, \
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@ -107,6 +107,15 @@ class Context:
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"""
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Append one user-defined device info to the context.
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Note:
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After gpu device info is added, cpu device info must be added before call context.
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Because when ops are not supported on GPU, The system will try whether the CPU supports it.
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At that time, need to switch to the context with cpu device info.
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After Ascend device info is added, cpu device info must be added before call context.
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Because when ops are not supported on Ascend, The system will try whether the CPU supports it.
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At that time, need to switch to the context with cpu device info.
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Args:
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device_info (DeviceInfo): the instance of device info.
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@ -118,8 +127,8 @@ class Context:
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>>> context = mslite.Context()
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>>> context.append_device_info(mslite.CPUDeviceInfo())
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>>> print(context)
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thread_num: 2,
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inter_op_parallel_num: 1,
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thread_num: 0,
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inter_op_parallel_num: 0,
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thread_affinity_mode: 0,
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thread_affinity_core_list: [],
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enable_parallel: False,
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@ -158,6 +167,13 @@ class CPUDeviceInfo(DeviceInfo):
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enable_fp16: True.
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>>> context = mslite.Context()
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>>> context.append_device_info(cpu_device_info)
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>>> print(context)
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thread_num: 0,
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inter_op_parallel_num: 0,
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thread_affinity_mode: 0,
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thread_affinity_core_list: [],
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enable_parallel: False,
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device_list: 0, .
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"""
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def __init__(self, enable_fp16=False):
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@ -194,11 +210,11 @@ class GPUDeviceInfo(DeviceInfo):
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enable_fp16: False.
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>>> cpu_device_info = mslite.CPUDeviceInfo(enable_fp16=False)
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>>> context = mslite.Context()
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>>> context.append_device_info(mslite.CPUDeviceInfo(gpu_device_info))
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>>> context.append_device_info(mslite.CPUDeviceInfo(cpu_device_info))
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>>> context.append_device_info(gpu_device_info)
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>>> context.append_device_info(cpu_device_info)
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>>> print(context)
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thread_num: 2,
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inter_op_parallel_num: 1,
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thread_num: 0,
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inter_op_parallel_num: 0,
|
||||
thread_affinity_mode: 0,
|
||||
thread_affinity_core_list: [],
|
||||
enable_parallel: False,
|
||||
|
|
@ -233,7 +249,7 @@ class GPUDeviceInfo(DeviceInfo):
|
|||
>>> device_info = mslite.GPUDeviceInfo(device_id=1, enable_fp16=True)
|
||||
>>> rank_id = device_info.get_rank_id()
|
||||
>>> print(rank_id)
|
||||
1
|
||||
0
|
||||
"""
|
||||
return self._device_info.get_rank_id()
|
||||
|
||||
|
|
@ -276,8 +292,8 @@ class AscendDeviceInfo(DeviceInfo):
|
|||
>>> context.append_device_info(ascend_device_info)
|
||||
>>> context.append_device_info(cpu_device_info)
|
||||
>>> print(context)
|
||||
thread_num: 2,
|
||||
inter_op_parallel_num: 1,
|
||||
thread_num: 0,
|
||||
inter_op_parallel_num: 0,
|
||||
thread_affinity_mode: 0,
|
||||
thread_affinity_core_list: [],
|
||||
enable_parallel: False
|
||||
|
|
|
|||
|
|
@ -75,7 +75,7 @@ class Converter:
|
|||
Options: "AES-GCM" | "AES-CBC". Default: "AES-GCM".
|
||||
enable_encryption (bool, optional): Whether to export the encryption model. Default: False.
|
||||
encrypt_key (str, optional): The key used to encrypt the file, expressed in hexadecimal characters.
|
||||
Only support AES-GCM and the key length is 16. Default: "".
|
||||
Only support decrypt_mode is "AES-GCM", the key length is 16. Default: "".
|
||||
infer (bool, optional): Whether to do pre-inference after convert. Default: False.
|
||||
train_model (bool, optional): whether the model is going to be trained on device. Default: False.
|
||||
no_fusion(bool, optional): Avoid fusion optimization, fusion optimization is allowed by default. Default: False.
|
||||
|
|
@ -86,12 +86,11 @@ class Converter:
|
|||
TypeError: `output_file` is not a str.
|
||||
TypeError: `weight_file` is not a str.
|
||||
TypeError: `config_file` is not a str.
|
||||
TypeError: `config_info` is a dict, but the keys are not str.
|
||||
TypeError: `config_info` is a dict, but the values are not str.
|
||||
TypeError: `weight_fp16` is not a bool.
|
||||
TypeError: `input_shape` is not a dict or None.
|
||||
TypeError: `input_shape` is a dict, but the values are not list.
|
||||
TypeError: `input_shape` is a dict, the values are list, but the value's elements are not int.
|
||||
TypeError: `input_shape` is neither a dict nor None.
|
||||
TypeError: `input_shape` is a dict, but the keys are not str.
|
||||
TypeError: `input_shape` is a dict, the keys are str, but the values are not list.
|
||||
TypeError: `input_shape` is a dict, the keys are str, the values are list, but the value's elements are not int.
|
||||
TypeError: `input_format` is not a Format.
|
||||
TypeError: `input_data_type` is not a DataType.
|
||||
TypeError: `output_data_type` is not a DataType.
|
||||
|
|
@ -110,19 +109,22 @@ class Converter:
|
|||
RuntimeError: `config_file` is not "", but `config_file` does not exist.
|
||||
|
||||
Examples:
|
||||
>>> # Download the model package and extract it, model download link:
|
||||
>>> # https://download.mindspore.cn/model_zoo/official/lite/quick_start/micro/mobilenetv2.tar.gz
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> converter = mslite.Converter(mslite.FmkType.TFLITE, "mobilenetv2.tflite", "mobilenetv2.tflite")
|
||||
>>> converter = mslite.Converter(mslite.FmkType.kFmkTypeTflite, "./mobilenetv2/mobilenet_v2_1.0_224.tflite",
|
||||
... "mobilenet_v2_1.0_224.tflite")
|
||||
>>> print(converter)
|
||||
config_file: ,
|
||||
config_info: ,
|
||||
config_info: {},
|
||||
weight_fp16: False,
|
||||
input_shape: {},
|
||||
input_format: Format.NHWC,
|
||||
input_data_type: DataType.FLOAT32,
|
||||
output_data_type: DataType.FLOAT32,
|
||||
export_mindir: MINDIR_LITE,
|
||||
export_mindir: ModelType.MINDIR_LITE,
|
||||
decrypt_key: ,
|
||||
decrypt_mode: ,
|
||||
decrypt_mode: AES-GCM,
|
||||
enable_encryption: False,
|
||||
encrypt_key: ,
|
||||
infer: False,
|
||||
|
|
@ -253,10 +255,15 @@ class Converter:
|
|||
Raises:
|
||||
TypeError: `section` is not a str.
|
||||
TypeError: `config_info` is not a dict.
|
||||
TypeError: `config_info` is a dict, but the keys are not str.
|
||||
TypeError: `config_info` is a dict, the keys are str, but the values are not str.
|
||||
|
||||
Examples:
|
||||
>>> # Download the model package and extract it, model download link:
|
||||
>>> # https://download.mindspore.cn/model_zoo/official/lite/quick_start/micro/mobilenetv2.tar.gz
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> converter = mslite.Converter(mslite.FmkType.TFLITE, "mobilenetv2.tflite", "mobilenetv2.tflite")
|
||||
>>> converter = mslite.Converter(mslite.FmkType.kFmkTypeTflite, "./mobilenetv2/mobilenet_v2_1.0_224.tflite",
|
||||
... "mobilenet_v2_1.0_224.tflite")
|
||||
>>> section = "common_quant_param"
|
||||
>>> config_info = {"quant_type":"WEIGHT_QUANT"}
|
||||
>>> converter.set_config_info(section, config_info)
|
||||
|
|
@ -272,11 +279,14 @@ class Converter:
|
|||
Please use set_config_info method before get_config_info.
|
||||
|
||||
Returns:
|
||||
dict{str, dict{str, str}, the config info which has been set in converter.
|
||||
dict{str, dict{str, str}}, the config info which has been set in converter.
|
||||
|
||||
Examples:
|
||||
>>> # Download the model package and extract it, model download link:
|
||||
>>> # https://download.mindspore.cn/model_zoo/official/lite/quick_start/micro/mobilenetv2.tar.gz
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> converter = mslite.Converter(mslite.FmkType.TFLITE, "mobilenetv2.tflite", "mobilenetv2.tflite")
|
||||
>>> converter = mslite.Converter(mslite.FmkType.kFmkTypeTflite, "./mobilenetv2/mobilenet_v2_1.0_224.tflite",
|
||||
... "mobilenet_v2_1.0_224.tflite")
|
||||
>>> section = "common_quant_param"
|
||||
>>> config_info_in = {"quant_type":"WEIGHT_QUANT"}
|
||||
>>> converter.set_config_info(section, config_info_in)
|
||||
|
|
@ -294,9 +304,13 @@ class Converter:
|
|||
RuntimeError: converter model failed.
|
||||
|
||||
Examples:
|
||||
>>> # Download the model package and extract it, model download link:
|
||||
>>> # https://download.mindspore.cn/model_zoo/official/lite/quick_start/micro/mobilenetv2.tar.gz
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> converter = mslite.Converter(mslite.FmkType.TFLITE, "mobilenetv2.tflite", "mobilenetv2.tflite")
|
||||
>>> converter = mslite.Converter(mslite.FmkType.kFmkTypeTflite, "./mobilenetv2/mobilenet_v2_1.0_224.tflite",
|
||||
... "mobilenet_v2_1.0_224.tflite")
|
||||
>>> converter.converter()
|
||||
CONVERT RESULT SUCCESS:0
|
||||
"""
|
||||
ret = self._converter.converter()
|
||||
if not ret.IsOk():
|
||||
|
|
|
|||
|
|
@ -86,6 +86,7 @@ class Model:
|
|||
RuntimeError: `model_path` does not exist.
|
||||
|
||||
Examples:
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> model = mslite.Model()
|
||||
>>> context = mslite.Context()
|
||||
|
|
@ -125,6 +126,7 @@ class Model:
|
|||
ValueError: The size of the elements of `inputs` is not equal to the size of the elements of `dims` .
|
||||
|
||||
Examples:
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> model = mslite.Model()
|
||||
>>> context = mslite.Context()
|
||||
|
|
@ -182,27 +184,29 @@ class Model:
|
|||
RuntimeError: predict model failed.
|
||||
|
||||
Examples:
|
||||
>>> # predict which indata is from file
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> # in_data download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/input.bin
|
||||
>>> # 1. predict which indata is from file
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> import numpy ad np
|
||||
>>> 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("mobilenetv2.ms.bin", dtype=np.float32)
|
||||
>>> 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: [[8.9401474e-05 4.4536911e-05 1.0089713e-04 ... 3.2687691e-05
|
||||
3.6021424e-04 8.3650106e-05]]
|
||||
|
||||
>>> # predict which indata is numpy array
|
||||
...
|
||||
outputs: [[1.0227193e-05 9.9270510e-06 1.6968443e-05 ... 6.6909502e-06
|
||||
2.1626458e-06 1.2400946e-04]]
|
||||
>>> # 2. predict which indata is numpy array
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> import numpy ad np
|
||||
>>> import numpy as np
|
||||
>>> model = mslite.Model()
|
||||
>>> context = mslite.Context()
|
||||
>>> context.append_device_info(mslite.CPUDeviceInfo())
|
||||
|
|
@ -212,16 +216,16 @@ class Model:
|
|||
>>> 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
|
||||
>>> # 3. predict which indata is new mslite tensor with numpy array
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> import numpy ad np
|
||||
>>> import numpy as np
|
||||
>>> model = mslite.Model()
|
||||
>>> context = mslite.Context()
|
||||
>>> context.append_device_info(mslite.CPUDeviceInfo())
|
||||
|
|
@ -234,15 +238,16 @@ class Model:
|
|||
... 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())
|
||||
... 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 0.00052926 ... 0.00018387 0.00148318 0.00116824]]
|
||||
"""
|
||||
if not isinstance(inputs, list):
|
||||
|
|
@ -274,6 +279,7 @@ class Model:
|
|||
list[Tensor], the inputs tensor list of the model.
|
||||
|
||||
Examples:
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> model = mslite.Model()
|
||||
>>> context = mslite.Context()
|
||||
|
|
@ -294,6 +300,7 @@ class Model:
|
|||
list[Tensor], the outputs tensor list of the model.
|
||||
|
||||
Examples:
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> model = mslite.Model()
|
||||
>>> context = mslite.Context()
|
||||
|
|
@ -321,6 +328,7 @@ class Model:
|
|||
RuntimeError: get input by tensor name failed.
|
||||
|
||||
Examples:
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> model = mslite.Model()
|
||||
>>> context = mslite.Context()
|
||||
|
|
@ -356,6 +364,7 @@ class Model:
|
|||
RuntimeError: get output by tensor name failed.
|
||||
|
||||
Examples:
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> model = mslite.Model()
|
||||
>>> context = mslite.Context()
|
||||
|
|
@ -393,9 +402,9 @@ class RunnerConfig:
|
|||
value of it is the path of weight, e.g. "/home/user/weight.cfg".
|
||||
|
||||
Raises:
|
||||
TypeError: `context` is not a Context or None.
|
||||
TypeError: `workers_num` is not an int or None.
|
||||
TypeError: `config_info` is not a dict or None.
|
||||
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.
|
||||
|
|
@ -477,11 +486,12 @@ class ModelParallelRunner:
|
|||
|
||||
Raises:
|
||||
TypeError: `model_path` is not a str.
|
||||
TypeError: `runner_config` is not a RunnerConfig or None.
|
||||
TypeError: `runner_config` is neither a RunnerConfig nor None.
|
||||
RuntimeError: `model_path` does not exist.
|
||||
RuntimeError: ModelParallelRunner's init failed.
|
||||
|
||||
Examples:
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> context = mslite.Context()
|
||||
>>> context.append_device_info(mslite.CPUDeviceInfo())
|
||||
|
|
@ -519,6 +529,8 @@ class ModelParallelRunner:
|
|||
RuntimeError: predict model failed.
|
||||
|
||||
Examples:
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> # in_data download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/input.bin
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> context = mslite.Context()
|
||||
>>> context.append_device_info(mslite.CPUDeviceInfo())
|
||||
|
|
@ -526,13 +538,14 @@ class ModelParallelRunner:
|
|||
>>> 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)
|
||||
>>> 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: [[8.9401474e-05 4.4536911e-05 1.0089713e-04 ... 3.2687691e-05
|
||||
3.6021424e-04 8.3650106e-05]]
|
||||
"""
|
||||
|
|
@ -567,6 +580,7 @@ class ModelParallelRunner:
|
|||
list[Tensor], the inputs tensor list of the model.
|
||||
|
||||
Examples:
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> context = mslite.Context()
|
||||
>>> context.append_device_info(mslite.CPUDeviceInfo())
|
||||
|
|
@ -588,6 +602,7 @@ class ModelParallelRunner:
|
|||
list[Tensor], the outputs tensor list of the model.
|
||||
|
||||
Examples:
|
||||
>>> # model download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/mobilenetv2.ms
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> context = mslite.Context()
|
||||
>>> context.append_device_info(mslite.CPUDeviceInfo())
|
||||
|
|
|
|||
|
|
@ -159,7 +159,7 @@ class Tensor:
|
|||
tensor(Tensor): The data to be stored in a new tensor. It can be another Tensor. Default: None.
|
||||
|
||||
Raises:
|
||||
TypeError: `tensor` is not a Tensor or None.
|
||||
TypeError: `tensor` is neither a Tensor nor None.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore_lite as mslite
|
||||
|
|
@ -171,7 +171,7 @@ class Tensor:
|
|||
shape: [],
|
||||
format: Format.NCHW,
|
||||
element_num: 1,
|
||||
data_size: 0.
|
||||
data_size: 4.
|
||||
"""
|
||||
|
||||
def __init__(self, tensor=None):
|
||||
|
|
@ -214,7 +214,7 @@ class Tensor:
|
|||
>>> tensor = mslite.Tensor()
|
||||
>>> tensor.set_tensor_name("tensor0")
|
||||
>>> tensor_name = tensor.get_tensor_name()
|
||||
>>> print(tenser_name)
|
||||
>>> print(tensor_name)
|
||||
tensor0
|
||||
"""
|
||||
return self._tensor.get_tensor_name()
|
||||
|
|
@ -379,22 +379,37 @@ class Tensor:
|
|||
RuntimeError: The data size of `numpy_obj` is not equal to the data size of the tensor.
|
||||
|
||||
Examples:
|
||||
>>> # data is from file
|
||||
>>> # in_data download link: https://download.mindspore.cn/model_zoo/official/lite/quick_start/input.bin
|
||||
>>> # 1. set tensor data which is from file
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> import numpy ad np
|
||||
>>> import numpy as 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)
|
||||
>>> in_data = np.fromfile("input.bin", dtype=np.float32)
|
||||
>>> tensor.set_data_from_numpy(in_data)
|
||||
>>> # data is numpy arrange
|
||||
>>> print(tensor)
|
||||
tensor_name: ,
|
||||
data_type: DataType.FLOAT32,
|
||||
shape: [1, 224, 224, 3],
|
||||
format: Format.NCHW,
|
||||
element_num: 150528,
|
||||
data_size: 602112.
|
||||
>>> # 2. set tensor data which is numpy arange
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> import numpy ad np
|
||||
>>> import numpy as 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)
|
||||
>>> in_data = np.arange(1 * 2 * 2 * 3, dtype=np.float32)
|
||||
>>> tensor.set_data_from_numpy(in_data)
|
||||
>>> print(tensor)
|
||||
tensor_name: ,
|
||||
data_type: DataType.FLOAT32,
|
||||
shape: [1, 2, 2, 3],
|
||||
format: Format.NCHW,
|
||||
element_num: 12,
|
||||
data_size: 48.
|
||||
"""
|
||||
if not isinstance(numpy_obj, numpy.ndarray):
|
||||
raise TypeError(f"numpy_obj must be numpy.ndarray, but got {type(numpy_obj)}.")
|
||||
|
|
@ -430,11 +445,11 @@ class Tensor:
|
|||
|
||||
Examples:
|
||||
>>> import mindspore_lite as mslite
|
||||
>>> import numpy ad np
|
||||
>>> import numpy as 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)
|
||||
>>> in_data = np.arange(1 * 2 * 2 * 3, dtype=np.float32)
|
||||
>>> tensor.set_data_from_numpy(in_data)
|
||||
>>> data = tensor.get_data_to_numpy()
|
||||
>>> print(data)
|
||||
|
|
|
|||
Loading…
Reference in New Issue