# Copyright 2022-2023 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Converter API.
"""
from __future__ import absolute_import
import os
from enum import Enum
from mindspore_lite._checkparam import check_isinstance, check_input_shape, check_config_info
from mindspore_lite.lib import _c_lite_wrapper
from mindspore_lite.tensor import DataType, Format, data_type_py_cxx_map, data_type_cxx_py_map, format_py_cxx_map, \
format_cxx_py_map
from mindspore_lite.model import ModelType, model_type_py_cxx_map, model_type_cxx_py_map
__all__ = ['FmkType', 'Converter']
[文档]class FmkType(Enum):
"""
When Converter, the `FmkType` is used to define Input model framework type.
Currently, the following model framework types are supported:
=========================== ============================================================================
Definition Description
=========================== ============================================================================
`FmkType.TF` TensorFlow model's framework type, and the model uses .pb as suffix.
`FmkType.CAFFE` Caffe model's framework type, and the model uses .prototxt as suffix.
`FmkType.ONNX` ONNX model's framework type, and the model uses .onnx as suffix.
`FmkType.MINDIR` MindSpore model's framework type, and the model uses .mindir as suffix.
`FmkType.TFLITE` TensorFlow Lite model's framework type, and the model uses .tflite as suffix.
`FmkType.PYTORCH` PyTorch model's framework type, and the model uses .pt or .pth as suffix.
=========================== ============================================================================
Examples:
>>> # Method 1: Import mindspore_lite package
>>> import mindspore_lite as mslite
>>> print(mslite.FmkType.TF)
FmkType.TF
>>> # Method 2: from mindspore_lite package import FmkType
>>> from mindspore_lite import FmkType
>>> print(FmkType.TF)
FmkType.TF
"""
TF = 0
CAFFE = 1
ONNX = 2
MINDIR = 3
TFLITE = 4
PYTORCH = 5
[文档]class Converter:
r"""
Constructs a `Converter` class.
Used in the following scenarios:
1. Convert the third-party model into MindSpore model or MindSpore Lite model.
2. Convert MindSpore model into MindSpore model or MindSpore Lite model.
Convert to MindSpore model is recommended. Currently, Convert to MindSpore Lite model is supported,
but it will be deprecated in the future. If you want to convert to MindSpore Lite model, please use
`converter_tool <https://www.mindspore.cn/lite/docs/en/master/use/cloud_infer/converter_tool.html>`_ instead of
The Python interface. The Model api and ModelParallelRunner api only support MindSpore model.
Note:
Please construct the `Converter` class first, and then generate the model by executing the Converter.convert()
method.
The encryption and decryption function is only valid when it is set to `MSLITE_ENABLE_MODEL_ENCRYPTION=on` at
compile time, and only supports Linux x86 platforms. `decrypt_key` and `encrypt_key` are string expressed in
hexadecimal. For example, if encrypt_key is set as "30313233343637383939414243444546", the corresponding
hexadecimal expression is '(b)0123456789ABCDEF' . Linux platform users can use the' xxd 'tool to convert the
key expressed in bytes into hexadecimal expressions. It should be noted that the encryption and decryption
algorithm has been updated in version 1.7, resulting in the new Python interface does not support the conversion
of MindSpore Lite's encryption exported models in version 1.6 and earlier.
Examples:
>>> # testcase based on cloud inference package.
>>> import mindspore_lite as mslite
>>> converter = mslite.Converter()
>>> # The ms model may be generated only after converter.convert() is executed after the class is constructed.
>>> converter.weight_fp16 = True
>>> converter.input_shape = {"inTensor1": [1, 3, 32, 32]}
>>> converter.input_format = mslite.Format.NHWC
>>> converter.input_data_type = mslite.DataType.FLOAT32
>>> converter.output_data_type = mslite.DataType.FLOAT32
>>> converter.save_type = mslite.ModelType.MINDIR
>>> converter.decrypt_key = "30313233343637383939414243444546"
>>> converter.decrypt_mode = "AES-GCM"
>>> converter.enable_encryption = True
>>> converter.encrypt_key = "30313233343637383939414243444546"
>>> converter.infer = True
>>> converter.optimize = "general"
>>> converter.device = "Ascend"
>>> section = "common_quant_param"
>>> config_info_in = {"quant_type": "WEIGHT_QUANT"}
>>> converter.set_config_info(section, config_info_in)
>>> print(converter.get_config_info())
{'common_quant_param': {'quant_type': 'WEIGHT_QUANT'}}
>>> print(converter)
config_info: {'common_quant_param': {'quant_type': 'WEIGHT_QUANT'}},
weight_fp16: True,
input_shape: {'inTensor1': [1, 3, 32, 32]},
input_format: Format.NHWC,
input_data_type: DataType.FLOAT32,
output_data_type: DataType.FLOAT32,
save_type: ModelType.MINDIR,
decrypt_key: 30313233343637383939414243444546,
decrypt_mode: AES-GCM,
enable_encryption: True,
encrypt_key: 30313233343637383939414243444546,
infer: True,
optimize: general,
device: Ascend.
"""
def __init__(self):
self._converter = _c_lite_wrapper.ConverterBind()
self.optimize_user_defined = "general"
def __str__(self):
res = f"config_info: {self.get_config_info()},\n" \
f"weight_fp16: {self.weight_fp16},\n" \
f"input_shape: {self.input_shape},\n" \
f"input_format: {self.input_format},\n" \
f"input_data_type: {self.input_data_type},\n" \
f"output_data_type: {self.output_data_type},\n" \
f"save_type: {self.save_type},\n" \
f"decrypt_key: {self.decrypt_key},\n" \
f"decrypt_mode: {self.decrypt_mode},\n" \
f"enable_encryption: {self.enable_encryption},\n" \
f"encrypt_key: {self.encrypt_key},\n" \
f"infer: {self.infer},\n" \
f"optimize: {self.optimize},\n" \
f"device: {self.device}."
return res
@property
def decrypt_key(self):
"""
Get the key used to decrypt the encrypted MindIR file.
Returns:
str, the key used to decrypt the encrypted MindIR file, expressed in hexadecimal characters. Only valid when
fmk_type is FmkType.MINDIR.
"""
return self._converter.get_decrypt_key()
@decrypt_key.setter
def decrypt_key(self, decrypt_key):
"""
Set the key used to decrypt the encrypted MindIR file
Args:
decrypt_key (str): Set the key used to decrypt the encrypted MindIR file, expressed in hexadecimal
characters. Only valid when fmk_type is FmkType.MINDIR.
Raises:
TypeError: `decrypt_key` is not a str.
"""
check_isinstance("decrypt_key", decrypt_key, str)
self._converter.set_decrypt_key(decrypt_key)
@property
def decrypt_mode(self):
"""
Get decryption mode for the encrypted MindIR file.
Returns:
str, decryption mode for the encrypted MindIR file. Only valid when dec_key is set. Options are "AES-GCM" |
"AES-CBC".
"""
return self._converter.get_decrypt_mode()
@decrypt_mode.setter
def decrypt_mode(self, decrypt_mode):
"""
Set decryption mode for the encrypted MindIR file.
Args:
decrypt_mode (str): Set decryption mode for the encrypted MindIR file. Only valid when dec_key is set.
Options are "AES-GCM" | "AES-CBC".
Raises:
TypeError: `decrypt_mode` is not a str.
ValueError: `decrypt_mode` is neither "AES-GCM" nor "AES-CBC" when it is a str.
"""
check_isinstance("decrypt_mode", decrypt_mode, str)
if decrypt_mode not in ["AES-GCM", "AES-CBC"]:
raise ValueError(f"decrypt_mode must be in [AES-GCM, AES-CBC], but got {decrypt_mode}.")
self._converter.set_decrypt_mode(decrypt_mode)
@property
def device(self):
"""
Get target device when converter model.
Returns:
str, target device when converter model. Only valid for Ascend. The use case is when on the Ascend device,
if you need to the converted model to have the ability to use Ascend backend to perform inference,
you can set the parameter. If it is not set, the converted model will use CPU backend to perform
inference by default. Option is "Ascend".
"""
return self._converter.get_device()
@device.setter
def device(self, device):
"""
Set target device when converter model.
Args:
device (str): Set target device when converter model. Only valid for Ascend. The use case is when on the
Ascend device, if you need to the converted model to have the ability to use Ascend backend to perform
inference, you can set the parameter. If it is not set, the converted model will use CPU backend to
perform inference by default. Option is "Ascend".
Raises:
TypeError: `device` is not a str.
ValueError: `device` is not "Ascend" when it is a str.
"""
check_isinstance("device", device, str)
if device not in ["Ascend"]:
raise ValueError(f"device must be in [Ascend], but got {device}.")
self._converter.set_device(device)
@property
def enable_encryption(self):
"""
Get the status whether to encrypt the model when exporting.
Returns:
bool, whether to encrypt the model when exporting. Export encryption can protect the integrity of the model,
but it will increase the initialization time at runtime.
"""
return self._converter.get_enable_encryption()
@enable_encryption.setter
def enable_encryption(self, enable_encryption):
"""
Set whether to encrypt the model when exporting.
Args:
enable_encryption (bool): Whether to encrypt the model when exporting. Export encryption can protect the
integrity of the model, but it will increase the initialization time at runtime.
Raises:
TypeError: `enable_encryption` is not a bool.
"""
check_isinstance("enable_encryption", enable_encryption, bool)
self._converter.set_enable_encryption(enable_encryption)
@property
def encrypt_key(self):
"""
Get the key used to encrypt the model when exporting.
Returns:
str, the key used to encrypt the model when exporting, expressed in hexadecimal characters. Only support to
use it when `decrypt_mode` is "AES-GCM", the key length is 16.
"""
return self._converter.get_encrypt_key()
@encrypt_key.setter
def encrypt_key(self, encrypt_key):
"""
Set the key used to encrypt the model when exporting, expressed in hexadecimal characters.
Args:
encrypt_key (str): Set the key used to encrypt the model when exporting, expressed in hexadecimal
characters. Only support when `decrypt_mode` is "AES-GCM", the key length is 16.
Raises:
TypeError: `encrypt_key` is not a str.
"""
check_isinstance("encrypt_key", encrypt_key, str)
self._converter.set_encrypt_key(encrypt_key)
@property
def infer(self):
"""
Get the status whether to perform pre-inference at the completion of the conversion.
Returns:
bool, whether to perform pre-inference at the completion of the conversion.
"""
return self._converter.get_infer()
@infer.setter
def infer(self, infer):
"""
Set whether to perform pre-inference at the completion of the conversion.
Args:
infer (bool): whether to perform pre-inference at the completion of the conversion.
Raises:
TypeError: `infer` is not a bool.
"""
check_isinstance("infer", infer, bool)
self._converter.set_infer(infer)
@property
def input_data_type(self):
"""
Get the data type of the quantization model input Tensor.
Returns:
DataType, the data type of the quantization model input Tensor. It is only valid when the quantization
parameters ( `scale` and `zero point` ) of the model input Tensor are available. The following 4
DataTypes are supported: DataType.FLOAT32 | DataType.INT8 | DataType.UINT8 | DataType.UNKNOWN.
For details, see
`DataType <https://mindspore.cn/lite/api/en/r2.0/mindspore_lite/mindspore_lite.DataType.html>`_ .
- DataType.FLOAT32: 32-bit floating-point number.
- DataType.INT8: 8-bit integer.
- DataType.UINT8: unsigned 8-bit integer.
- DataType.UNKNOWN: Set the Same DataType as the model input Tensor.
"""
return data_type_cxx_py_map.get(self._converter.get_input_data_type())
@input_data_type.setter
def input_data_type(self, input_data_type):
"""
Set the data type of the quantization model input Tensor.
Args:
input_data_type (DataType): Set the data type of the quantization model input Tensor. It is only valid when
the quantization parameters ( `scale` and `zero point` ) of the model input Tensor are available. The
following 4 DataTypes are supported: DataType.FLOAT32 | DataType.INT8 | DataType.UINT8 |
DataType.UNKNOWN. For details, see
`DataType <https://mindspore.cn/lite/api/en/r2.0/mindspore_lite/mindspore_lite.DataType.html>`_ .
- DataType.FLOAT32: 32-bit floating-point number.
- DataType.INT8: 8-bit integer.
- DataType.UINT8: unsigned 8-bit integer.
- DataType.UNKNOWN: Set the Same DataType as the model input Tensor.
Raises:
TypeError: `input_data_type` is not a DataType.
ValueError: `input_data_type` is not in [DataType.FLOAT32, DataType.INT8, DataType.UINT8, DataType.UNKNOWN]
when `input_data_type` is a DataType.
"""
check_isinstance("input_data_type", input_data_type, DataType)
if input_data_type not in [DataType.FLOAT32, DataType.INT8, DataType.UINT8, DataType.UNKNOWN]:
raise ValueError(f"input_data_type must be in [DataType.FLOAT32, DataType.INT8, DataType.UINT8, "
f"DataType.UNKNOWN].")
self._converter.set_input_data_type(data_type_py_cxx_map.get(input_data_type))
@property
def input_format(self):
"""
Get the input format of model.
Returns:
Format, the input format of model. Only Valid for 4-dimensional input. The following 2 input formats are
supported: Format.NCHW | Format.NHWC. For details,
see `Format <https://mindspore.cn/lite/api/en/r2.0/mindspore_lite/mindspore_lite.Format.html>`_ .
- Format.NCHW: Store Tensor data in the order of batch N, channel C, height H and width W.
- Format.NHWC: Store Tensor data in the order of batch N, height H, width W and channel C.
"""
return format_cxx_py_map.get(self._converter.get_input_format())
@input_format.setter
def input_format(self, input_format):
"""
Set the input format of model.
Args:
input_format (Format): Set the input format of model. Only Valid for 4-dimensional input.The
following 2 input formats are supported: Format.NCHW | Format.NHWC. For details,
see `Format <https://mindspore.cn/lite/api/en/r2.0/mindspore_lite/mindspore_lite.Format.html>`_ .
- Format.NCHW: Store Tensor data in the order of batch N, channel C, height H and width W.
- Format.NHWC: Store Tensor data in the order of batch N, height H, width W and channel C.
Raises:
TypeError: `input_format` is not a Format.
ValueError: `input_format` is neither Format.NCHW nor Format.NHWC when it is a Format.
"""
check_isinstance("input_format", input_format, Format)
if input_format not in [Format.NCHW, Format.NHWC]:
raise ValueError(f"input_format must be in [Format.NCHW, Format.NHWC].")
self._converter.set_input_format(format_py_cxx_map.get(input_format))
@property
def input_shape(self):
"""
Get the dimension of the model input.
Returns:
dict{str, list[int]}, the dimension of the model input. The order of input dimensions is consistent with the
original model. In the following scenarios, users may need to set the parameter.
For example, {"inTensor1": [1, 32, 32, 32], "inTensor2": [1, 1, 32, 32]}.
- Usage 1:The input of the model to be converted is dynamic shape, but prepare to use fixed shape for
inference, then set the parameter to fixed shape. After setting, when inferring on the converted
model, the default input shape is the same as the parameter setting, no need to resize.
- Usage 2: No matter whether the original input of the model to be converted is dynamic shape or not,
but prepare to use fixed shape for inference, and the performance of the model is expected to be
optimized as much as possible, then set the parameter to fixed shape. After setting, the model
structure will be further optimized, but the converted model may lose the characteristics of dynamic
shape(some operators strongly related to shape will be merged).
- Usage 3: When using the converter function to generate code for Micro inference execution, it is
recommended to set the parameter to reduce the probability of errors during deployment. When the model
contains a Shape ops or the input of the model to be converted is a dynamic shape, you must set the
parameter to fixed shape to support the relevant shape optimization and code generation.
"""
return self._converter.get_input_shape()
@input_shape.setter
def input_shape(self, input_shape):
"""
Set the dimension of the model input.
Args:
input_shape (dict{str, list[int]}): Set the dimension of the model input. The order of input dimensions is
consistent with the original model. In the following scenarios, users may need to set the parameter.
For example, {"inTensor1": [1, 32, 32, 32], "inTensor2": [1, 1, 32, 32]}.
- Usage 1:The input of the model to be converted is dynamic shape, but prepare to use fixed shape for
inference, then set the parameter to fixed shape. After setting, when inferring on the converted
model, the default input shape is the same as the parameter setting, no need to resize.
- Usage 2: No matter whether the original input of the model to be converted is dynamic shape or not,
but prepare to use fixed shape for inference, and the performance of the model is expected to be
optimized as much as possible, then set the parameter to fixed shape. After setting, the model
structure will be further optimized, but the converted model may lose the characteristics of dynamic
shape(some operators strongly related to shape will be merged).
- Usage 3: When using the converter function to generate code for Micro inference execution, it is
recommended to set the parameter to reduce the probability of errors during deployment. When the model
contains a Shape ops or the input of the model to be converted is a dynamic shape, you must set the
parameter to fixed shape to support the relevant shape optimization and code generation.
Raises:
TypeError: `input_shape` is not a dict.
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.
"""
check_input_shape("input_shape", input_shape)
self._converter.set_input_shape(input_shape)
@property
def optimize(self):
"""
Get the status whether avoid fusion optimization.
optimize is used to set the mode of optimization during the offline conversion. If this parameter is set to
"none", no relevant graph optimization operations will be performed during the offline conversion phase of
the model, and the relevant graph optimization operations will be performed during the execution of the
inference phase. The advantage of this parameter is that the converted model can be deployed directly to any
CPU/GPU/Ascend hardware backend since it is not optimized in a specific way, while the disadvantage is that
the initialization time of the model increases during inference execution. If this parameter is set to
"general", general optimization will be performed, such as constant folding and operator fusion (the
converted model only supports CPU/GPU hardware backend, not Ascend backend). If this parameter is set to
"ascend_oriented", the optimization for Ascend hardware will be performed (the converted model only supports
Ascend hardware backend).
For the MindSpore model, since it is already a `mindir` model, two approaches are suggested:
1. Inference is performed directly without offline conversion.
2. Setting `optimize` to "general" in CPU/GPU hardware backend and setting `optimize` to "ascend_oriented" in
Ascend hardware when using offline conversion. The relevant optimization is done in the offline phase to reduce
the initialization time of inference execution.
Returns:
str, whether avoid fusion optimization. Options are "none" | "general" | "ascend_oriented". "none" means
fusion optimization is not allowed. "general" and "ascend_oriented" means fusion optimization is allowed.
"""
return self.optimize_user_defined
@optimize.setter
def optimize(self, optimize):
"""
Set whether avoid fusion optimization.
Args:
optimize(str): Whether avoid fusion optimization. Options are "none" | "general" | "ascend_oriented".
"none" means fusion optimization is not allowed. "general" and "ascend_oriented" means fusion
optimization is allowed.
Raises:
TypeError: `optimize` is not a str.
ValueError: `optimize` is not in ["none", "general", "ascend_oriented"] when it is a str.
"""
check_isinstance("optimize", optimize, str)
if optimize == "none":
self._converter.set_no_fusion(True)
self.optimize_user_defined = "none"
elif optimize == "general":
self._converter.set_no_fusion(False)
self.optimize_user_defined = "general"
elif optimize == "ascend_oriented":
self._converter.set_no_fusion(False)
self.device = "Ascend"
self.optimize_user_defined = "ascend_oriented"
else:
raise ValueError(f"optimize must be 'none', 'general' or 'ascend_oriented'.")
@property
def output_data_type(self):
"""
Get the data type of the quantization model output Tensor.
Returns:
DataType, the data type of the quantization model output Tensor. It is only valid when the quantization
parameters ( `scale` and `zero point` ) of the model output Tensor are available. The following 4
DataTypes are supported: DataType.FLOAT32 | DataType.INT8 | DataType.UINT8 | DataType.UNKNOWN.
For details, see
`DataType <https://mindspore.cn/lite/api/en/r2.0/mindspore_lite/mindspore_lite.DataType.html>`_ .
- DataType.FLOAT32: 32-bit floating-point number.
- DataType.INT8: 8-bit integer.
- DataType.UINT8: unsigned 8-bit integer.
- DataType.UNKNOWN: Set the Same DataType as the model output Tensor.
"""
return data_type_cxx_py_map.get(self._converter.get_output_data_type())
@output_data_type.setter
def output_data_type(self, output_data_type):
"""
Set the data type of the quantization model output Tensor.
Args:
output_data_type (DataType): Set the data type of the quantization model output Tensor. It is only valid
when the quantization parameters ( `scale` and `zero point` ) of the model output Tensor are available.
The following 4 DataTypes are supported: DataType.FLOAT32 | DataType.INT8 | DataType.UINT8 |
DataType.UNKNOWN. For details, see
`DataType <https://mindspore.cn/lite/api/en/r2.0/mindspore_lite/mindspore_lite.DataType.html>`_ .
- DataType.FLOAT32: 32-bit floating-point number.
- DataType.INT8: 8-bit integer.
- DataType.UINT8: unsigned 8-bit integer.
- DataType.UNKNOWN: Set the Same DataType as the model output Tensor.
Raises:
TypeError: `output_data_type` is not a DataType.
ValueError: `output_data_type` is not in [DataType.FLOAT32, DataType.INT8, DataType.UINT8, DataType.UNKNOWN]
when `output_data_type` is a DataType.
"""
check_isinstance("output_data_type", output_data_type, DataType)
if output_data_type not in [DataType.FLOAT32, DataType.INT8, DataType.UINT8, DataType.UNKNOWN]:
raise ValueError(f"output_data_type must be in [DataType.FLOAT32, DataType.INT8, DataType.UINT8, "
f"DataType.UNKNOWN].")
self._converter.set_output_data_type(data_type_py_cxx_map.get(output_data_type))
@property
def save_type(self):
"""
Get the model type needs to be export.
Returns:
ModelType, the model type needs to be export. Options are ModelType.MINDIR | ModelType.MINDIR_LITE.
Convert to MindSpore model is recommended. Currently, Convert to MindSpore Lite model is supported,
but it will be deprecated in the future. For details, see
`ModelType <https://mindspore.cn/lite/api/en/r2.0/mindspore_lite/mindspore_lite.ModelType.html>`_ .
"""
return model_type_cxx_py_map.get(self._converter.get_save_type())
@save_type.setter
def save_type(self, save_type):
"""
Set the model type needs to be export.
Args:
save_type (ModelType): Set the model type needs to be export. Options are ModelType.MINDIR |
ModelType.MINDIR_LITE. Convert to MindSpore model is recommended. Currently, Convert to MindSpore Lite
model is supported, but it will be deprecated in the future. For details, see
`ModelType <https://mindspore.cn/lite/api/en/r2.0/mindspore_lite/mindspore_lite.ModelType.html>`_ .
Raises:
TypeError: `save_type` is not a ModelType.
"""
check_isinstance("save_type", save_type, ModelType)
self._converter.set_save_type(model_type_py_cxx_map.get(save_type))
@property
def weight_fp16(self):
"""
Get the status whether the model will be saved as the Float16 data type.
Returns:
bool, whether the model will be saved as the Float16 data type. If it is True, the const Tensor of the
Float32 in the model will be saved as the Float16 data type during Converter, and the generated model
size will be compressed. Then, according to `Context.CPU` 's `precision_mode` parameter determines the
inputs' data type to perform inference. The priority of `weight_fp16` is very low. For example, if
quantization is enabled, for the weight of the quantized, `weight_fp16` will not take effect again.
`weight_fp16` only effective for the const Tensor in Float32 data type.
"""
return self._converter.get_weight_fp16()
@weight_fp16.setter
def weight_fp16(self, weight_fp16):
"""
Set whether the model will be saved as the Float16 data type.
Args:
weight_fp16 (bool): If it is True, the const Tensor of the Float32 in the model will be saved as the Float16
data type during Converter, and the generated model size will be compressed. Then, according to
`Context.CPU` 's `precision_mode` parameter determines the inputs' data type to perform inference. The
priority of `weight_fp16` is very low. For example, if quantization is enabled, for the weight of the
quantized, `weight_fp16` will not take effect again. `weight_fp16` only effective for the const Tensor
in Float32 data type.
Raises:
TypeError: `weight_fp16` is not a bool.
"""
check_isinstance("weight_fp16", weight_fp16, bool)
self._converter.set_weight_fp16(weight_fp16)
[文档] def convert(self, fmk_type, model_file, output_file, weight_file="", config_file=""):
"""
Perform conversion, and convert the third-party model to the MindSpore model or MindSpore Lite model.
Args:
fmk_type (FmkType): Input model framework type. Options are FmkType.TF | FmkType.CAFFE |
FmkType.ONNX | FmkType.MINDIR | FmkType.TFLITE | FmkType.PYTORCH. For details, see
`FmkType <https://mindspore.cn/lite/api/en/r2.0/mindspore_lite/mindspore_lite.FmkType.html>`_ .
model_file (str): Set the path of the input model when convert. For example, "/home/user/model.prototxt".
Options are TF: "model.pb" | CAFFE: "model.prototxt" | ONNX: "model.onnx" | MINDIR: "model.mindir" |
TFLITE: "model.tflite" | PYTORCH: "model.pt or model.pth".
output_file (str): Set the path of the output model. The suffix .ms or .mindir can be automatically
generated. If set `save_type` to ModelType.MINDIR, then MindSpore's model will be generated, which uses
.mindir as suffix. If set `save_type` to ModelType.MINDIR_LITE, then MindSpore Lite's model will be
generated, which uses .ms as suffix. For example, the input model is "/home/user/model.prototxt", set
`save_type` to ModelType.MINDIR, it will generate the model named model.prototxt.mindir in /home/user/.
weight_file (str, optional): Set the path of input model weight file. Required only when fmk_type is
FmkType.CAFFE. The Caffe model is generally divided into two files: 'model.prototxt' is model structure,
corresponding to `model_file` parameter; 'model.Caffemodel' is model weight value file, corresponding to
`weight_file` parameter. For example, "/home/user/model.caffemodel". Default: "", indicating no weight
file.
config_file (str, optional): Set the path of the configuration file of Converter can be used to
post-training, offline split op to parallel, disable op fusion ability and set plugin so path.
`config_file` uses the `key = value` method to define the related parameters.
For the configuration parameters related to post training quantization, please refer to
`quantization <https://www.mindspore.cn/lite/docs/en/r2.0/use/post_training_quantization.html>`_ .
For the configuration parameters related to extension, please refer to
`extension <https://www.mindspore.cn/lite/docs/en/r2.0/use/nnie.html#extension-configuration>`_ .
For example, "/home/user/model.cfg". Default: "", indicating that no configuration file.
Raises:
TypeError: `fmk_type` is not a FmkType.
TypeError: `model_file` is not a str.
TypeError: `output_file` is not a str.
TypeError: `weight_file` is not a str.
TypeError: `config_file` is not a str.
RuntimeError: `model_file` does not exist.
RuntimeError: `weight_file` is not "", but `weight_file` does not exist.
RuntimeError: `config_file` is not "", but `config_file` does not exist.
RuntimeError: convert model failed.
Examples:
>>> import mindspore_lite as mslite
>>> converter = mslite.Converter()
>>> converter.save_type = mslite.ModelType.MINDIR
>>> converter.convert(mslite.FmkType.TFLITE, "./mobilenetv2/mobilenet_v2_1.0_224.tflite",
... "mobilenet_v2_1.0_224.tflite")
CONVERT RESULT SUCCESS:0
>>> # mobilenet_v2_1.0_224.tflite.mindir model will be generated.
"""
check_isinstance("fmk_type", fmk_type, FmkType)
check_isinstance("model_file", model_file, str)
check_isinstance("output_file", output_file, str)
check_isinstance("weight_file", weight_file, str)
check_isinstance("config_file", config_file, str)
if not os.path.exists(model_file):
raise RuntimeError(f"Perform convert method failed, model_file does not exist!")
if weight_file != "":
if not os.path.exists(weight_file):
raise RuntimeError(f"Perform convert method failed, weight_file does not exist!")
if config_file != "":
if not os.path.exists(config_file):
raise RuntimeError(f"Perform convert method failed, config_file does not exist!")
self._converter.set_config_file(config_file)
fmk_type_py_cxx_map = {
FmkType.TF: _c_lite_wrapper.FmkType.kFmkTypeTf,
FmkType.CAFFE: _c_lite_wrapper.FmkType.kFmkTypeCaffe,
FmkType.ONNX: _c_lite_wrapper.FmkType.kFmkTypeOnnx,
FmkType.MINDIR: _c_lite_wrapper.FmkType.kFmkTypeMs,
FmkType.TFLITE: _c_lite_wrapper.FmkType.kFmkTypeTflite,
FmkType.PYTORCH: _c_lite_wrapper.FmkType.kFmkTypePytorch,
}
ret = self._converter.convert(fmk_type_py_cxx_map.get(fmk_type), model_file, output_file, weight_file)
if not ret.IsOk():
raise RuntimeError(f"Converter model failed! model_file is {model_file}, error is {ret.ToString()}")
[文档] def get_config_info(self):
r"""
Get config info of converter.It is used together with `set_config_info` method for online converter.
Please use `set_config_info` method before `get_config_info` .
Returns:
:obj:`dict{str: dict{str: str}}`, the config info which has been set in converter.
Examples:
>>> import mindspore_lite as mslite
>>> converter = mslite.Converter()
>>> section = "common_quant_param"
>>> config_info_in = {"quant_type": "WEIGHT_QUANT"}
>>> converter.set_config_info(section, config_info_in)
>>> config_info_out = converter.get_config_info()
>>> print(config_info_out)
{'common_quant_param': {'quant_type': 'WEIGHT_QUANT'}}
"""
return self._converter.get_config_info()
[文档] def set_config_info(self, section="", config_info=None):
r"""
Set config info for Converter.It is used together with `get_config_info` method for online converter.
Args:
section (str, optional): The category of the configuration parameter.
Set the individual parameters of the configfile together with `config_info` .
For example, for `section` = "common_quant_param", `config_info` = {"quant_type": "WEIGHT_QUANT"}.
Default: "".
For the configuration parameters related to post training quantization, please refer to
`quantization <https://www.mindspore.cn/lite/docs/en/r2.0/use/post_training_quantization.html>`_ .
For the configuration parameters related to extension, please refer to
`extension <https://www.mindspore.cn/lite/docs/en/r2.0/use/nnie.html#extension-configuration>`_ .
- "common_quant_param": Common quantization parameter.
- "mixed_bit_weight_quant_param": Mixed bit weight quantization parameter.
- "full_quant_param": Full quantization parameter.
- "data_preprocess_param": Data preprocess quantization parameter.
- "registry": Extension configuration parameter.
config_info (:obj:`dict{str: str}`, optional): List of configuration parameters.
Set the individual parameters of the configfile together with `section` .
For example, for `section` = "common_quant_param", `config_info` = {"quant_type": "WEIGHT_QUANT"}.
Default: None, None is equivalent to {}.
For the configuration parameters related to post training quantization, please refer to
`quantization <https://www.mindspore.cn/lite/docs/en/r2.0/use/post_training_quantization.html>`_ .
For the configuration parameters related to extension, please refer to
`extension <https://www.mindspore.cn/lite/docs/en/r2.0/use/nnie.html#extension-configuration>`_ .
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:
>>> import mindspore_lite as mslite
>>> converter = mslite.Converter()
>>> section = "common_quant_param"
>>> config_info = {"quant_type": "WEIGHT_QUANT"}
>>> converter.set_config_info(section, config_info)
"""
check_isinstance("section", section, str)
check_config_info("config_info", config_info, enable_none=True)
if section != "" and config_info is not None:
self._converter.set_config_info(section, config_info)