OpenVINO™ モデル変換 API¶
この Jupyter ノートブックは、ローカルへのインストール後にのみ起動できます。
このノートブックでは、モデルを元のフレームワーク形式から OpenVINO 中間表現 (IR) に変換する方法を示します。
目次¶
# Required imports. Please execute this cell first.
%pip install -q --extra-index-url https://download.pytorch.org/whl/cpu \
"openvino-dev>=2023.1.0" "requests" "tqdm" "transformers[onnx]>=4.21.1" "torch" "torchvision"
Note: you may need to restart the kernel to use updated packages.
OpenVINO IR 形式¶
OpenVINO 中間表現 (IR) は、OpenVINO 独自のモデル形式です。モデル変換 API によってモデルを変換して作成します。モデル変換 API は、頻繁に使用されるディープラーニング操作を OpenVINO の同様の表現に変換し、トレーニングされたモデルからの関連する重みとバイアスを使用して調整します。結果の IR には 2 つのファイルが含まれます: ネットワーク・トポロジーに関する情報を含む .xml ファイルと、重みとバイアスのバイナリーデータを含む .bin ファイル。
Python 変換 API とモデル・オプティマイザー・コマンドライン・ツールを使用した IR の準備¶
モデルを元のフレームワーク形式から OpenVINO IR に変換するには、Python 変換 API とモデル・オプティマイザー・コマンドライン・ツールの 2 つの方法があります。あなたにとって最も便利なものに基づいて、いずれかを選択できます。同じパラメーター・セットを使用した場合、モデル変換の結果に違いは生じません。詳細については、モデル準備のドキュメントを参照してください。
# Model Optimizer CLI tool parameters description
! mo --help
usage: main.py [options]
optional arguments:
-h, --help show this help message and exit
--framework FRAMEWORK
Name of the framework used to train the input model.
Framework-agnostic parameters:
--model_name MODEL_NAME, -n MODEL_NAME
Model_name parameter passed to the final create_ir
transform. This parameter is used to name a network in
a generated IR and output .xml/.bin files.
--output_dir OUTPUT_DIR, -o OUTPUT_DIR
Directory that stores the generated IR. By default, it
is the directory from where the Model Conversion is
launched.
--freeze_placeholder_with_value FREEZE_PLACEHOLDER_WITH_VALUE
Replaces input layer with constant node with provided
value, for example: "node_name->True". It will be
DEPRECATED in future releases. Use "input" option to
specify a value for freezing.
--static_shape Enables IR generation for fixed input shape (folding
ShapeOf operations and shape-calculating sub-graphs
to Constant). Changing model input shape using the
OpenVINO Runtime API in runtime may fail for such an
IR.
--use_new_frontend Force the usage of new Frontend for model conversion
into IR. The new Frontend is C++ based and is
available for ONNX* and PaddlePaddle* models. Model
Conversion API uses new Frontend for ONNX* and
PaddlePaddle* by default that means use_new_frontend
and use_legacy_frontend options are not specified.
--use_legacy_frontend
Force the usage of legacy Frontend for model
conversion into IR. The legacy Frontend is Python
based and is available for TensorFlow*, ONNX*, MXNet*,
Caffe*, and Kaldi* models.
--input_model INPUT_MODEL, -m INPUT_MODEL, -w INPUT_MODEL
Tensorflow*: a file with a pre-trained model (binary
or text .pb file after freezing). Caffe*: a model
proto file with model weights.
--input INPUT Quoted list of comma-separated input nodes names with
shapes, data types, and values for freezing. The order
of inputs in converted model is the same as order of
specified operation names. The shape and value are
specified as comma-separated lists. The data type of
input node is specified in braces and can have one of
the values: f64 (float64), f32 (float32), f16
(float16), i64 (int64), i32 (int32), u8 (uint8),
boolean (bool). Data type is optional. If it's not
specified explicitly then there are two options: if
input node is a parameter, data type is taken from the
original node dtype, if input node is not a parameter,
data type is set to f32. Example, to set input_1
with shape [1,100], and Parameter node sequence_len
with scalar input with value 150, and boolean input
is_training with False value use the following
format:
"input_1[1,100],sequence_len->150,is_training->False".
Another example, use the following format to set input
port 0 of the node node_name1 with the shape [3,4]
as an input node and freeze output port 1 of the node
"node_name2" with the value [20,15] of the int32 type
and shape [2]:
"0:node_name1[3,4],node_name2:1[2]{i32}->[20,15]".
--output OUTPUT The name of the output operation of the model or list
of names. For TensorFlow*, do not add :0 to this
name.The order of outputs in converted model is the
same as order of specified operation names.
--input_shape INPUT_SHAPE
Input shape(s) that should be fed to an input node(s)
of the model. Shape is defined as a comma-separated
list of integer numbers enclosed in parentheses or
square brackets, for example [1,3,227,227] or
(1,227,227,3), where the order of dimensions depends
on the framework input layout of the model. For
example, [N,C,H,W] is used for ONNX* models and
[N,H,W,C] for TensorFlow* models. The shape can
contain undefined dimensions (? or -1) and should fit
the dimensions defined in the input operation of the
graph. Boundaries of undefined dimension can be
specified with ellipsis, for example
[1,1..10,128,128]. One boundary can be undefined, for
example [1,..100] or [1,3,1..,1..]. If there are
multiple inputs in the model, --input_shape should
contain definition of shape for each input separated
by a comma, for example: [1,3,227,227],[2,4] for a
model with two inputs with 4D and 2D shapes.
Alternatively, specify shapes with the --input option.
--example_input EXAMPLE_INPUT
Sample of model input in original framework. For
PyTorch it can be torch.Tensor. For Tensorflow it can
be tf.Tensor or numpy.ndarray. For PaddlePaddle it can
be Paddle Variable.
--batch BATCH, -b BATCH
Set batch size. It applies to 1D or higher dimension
inputs. The default dimension index for the batch is
zero. Use a label 'n' in --layout or --source_layout
option to set the batch dimension. For example,
"x(hwnc)" defines the third dimension to be the batch.
--mean_values MEAN_VALUES
Mean values to be used for the input image per
channel. Values to be provided in the (R,G,B) or
[R,G,B] format. Can be defined for desired input of
the model, for example: "--mean_values
data[255,255,255],info[255,255,255]". The exact
meaning and order of channels depend on how the
original model was trained.
--scale_values SCALE_VALUES
Scale values to be used for the input image per
channel. Values are provided in the (R,G,B) or [R,G,B]
format. Can be defined for desired input of the model,
for example: "--scale_values
data[255,255,255],info[255,255,255]". The exact
meaning and order of channels depend on how the
original model was trained. If both --mean_values and
--scale_values are specified, the mean is subtracted
first and then scale is applied regardless of the
order of options in command line.
--scale SCALE, -s SCALE
All input values coming from original network inputs
will be divided by this value. When a list of inputs
is overridden by the --input parameter, this scale is
not applied for any input that does not match with the
original input of the model. If both --mean_values and
--scale are specified, the mean is subtracted first
and then scale is applied regardless of the order of
options in command line.
--reverse_input_channels [REVERSE_INPUT_CHANNELS]
Switch the input channels order from RGB to BGR (or
vice versa). Applied to original inputs of the model
if and only if a number of channels equals 3. When
--mean_values/--scale_values are also specified,
reversing of channels will be applied to user's input
data first, so that numbers in --mean_values and
--scale_values go in the order of channels used in the
original model. In other words, if both options are
specified, then the data flow in the model looks as
following: Parameter -> ReverseInputChannels -> Mean
apply-> Scale apply -> the original body of the model.
--source_layout SOURCE_LAYOUT
Layout of the input or output of the model in the
framework. Layout can be specified in the short form,
e.g. nhwc, or in complex form, e.g. "[n,h,w,c]".
Example for many names: "in_name1([n,h,w,c]),in_name2(
nc),out_name1(n),out_name2(nc)". Layout can be
partially defined, "?" can be used to specify
undefined layout for one dimension, "..." can be used
to specify undefined layout for multiple dimensions,
for example "?c??", "nc...", "n...c", etc.
--target_layout TARGET_LAYOUT
Same as --source_layout, but specifies target layout
that will be in the model after processing by
ModelOptimizer.
--layout LAYOUT Combination of --source_layout and --target_layout.
Can't be used with either of them. If model has one
input it is sufficient to specify layout of this
input, for example --layout nhwc. To specify layouts
of many tensors, names must be provided, for example:
--layout "name1(nchw),name2(nc)". It is possible to
instruct ModelOptimizer to change layout, for example:
--layout "name1(nhwc->nchw),name2(cn->nc)". Also "*"
in long layout form can be used to fuse dimensions,
for example "[n,c,...]->[n*c,...]".
--compress_to_fp16 [COMPRESS_TO_FP16]
If the original model has FP32 weights or biases, they
are compressed to FP16. All intermediate data is kept
in original precision. Option can be specified alone
as "--compress_to_fp16", or explicit True/False values
can be set, for example: "--compress_to_fp16=False",
or "--compress_to_fp16=True"
--extensions EXTENSIONS
Paths or a comma-separated list of paths to libraries
(.so or .dll) with extensions. For the legacy MO path
(if --use_legacy_frontend is used), a directory or a
comma-separated list of directories with extensions
are supported. To disable all extensions including
those that are placed at the default location, pass an
empty string.
--transform TRANSFORM
Apply additional transformations. Usage: "--transform
transformation_name1[args],transformation_name2..."
where [args] is key=value pairs separated by
semicolon. Examples: "--transform LowLatency2" or "--
transform Pruning" or "--transform
LowLatency2[use_const_initializer=False]" or "--
transform "MakeStateful[param_res_names= {'input_name_
1':'output_name_1','input_name_2':'output_name_2'}]"
Available transformations: "LowLatency2",
"MakeStateful", "Pruning"
--transformations_config TRANSFORMATIONS_CONFIG
Use the configuration file with transformations
description. Transformations file can be specified as
relative path from the current directory, as absolute
path or as arelative path from the mo root directory.
--silent [SILENT] Prevent any output messages except those that
correspond to log level equals ERROR, that can be set
with the following option: --log_level. By default,
log level is already ERROR.
--log_level {CRITICAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}
Logger level of logging massages from MO. Expected one
of ['CRITICAL', 'ERROR', 'WARN', 'WARNING', 'INFO',
'DEBUG', 'NOTSET'].
--version Version of Model Optimizer
--progress [PROGRESS]
Enable model conversion progress display.
--stream_output [STREAM_OUTPUT]
Switch model conversion progress display to a
multiline mode.
--share_weights [SHARE_WEIGHTS]
Map memory of weights instead reading files or share
memory from input model. Currently, mapping feature is
provided only for ONNX models that do not require
fallback to the legacy ONNX frontend for the
conversion.
TensorFlow*-specific parameters:
--input_model_is_text [INPUT_MODEL_IS_TEXT]
TensorFlow*: treat the input model file as a text
protobuf format. If not specified, the Model Optimizer
treats it as a binary file by default.
--input_checkpoint INPUT_CHECKPOINT
TensorFlow*: variables file to load.
--input_meta_graph INPUT_META_GRAPH
Tensorflow*: a file with a meta-graph of the model
before freezing
--saved_model_dir SAVED_MODEL_DIR
TensorFlow*: directory with a model in SavedModel
format of TensorFlow 1.x or 2.x version.
--saved_model_tags SAVED_MODEL_TAGS
Group of tag(s) of the MetaGraphDef to load, in string
format, separated by ','. For tag-set contains
multiple tags, all tags must be passed in.
--tensorflow_custom_operations_config_update TENSORFLOW_CUSTOM_OPERATIONS_CONFIG_UPDATE
TensorFlow*: update the configuration file with node
name patterns with input/output nodes information.
--tensorflow_object_detection_api_pipeline_config TENSORFLOW_OBJECT_DETECTION_API_PIPELINE_CONFIG
TensorFlow*: path to the pipeline configuration file
used to generate model created with help of Object
Detection API.
--tensorboard_logdir TENSORBOARD_LOGDIR
TensorFlow*: dump the input graph to a given directory
that should be used with TensorBoard.
--tensorflow_custom_layer_libraries TENSORFLOW_CUSTOM_LAYER_LIBRARIES
TensorFlow*: comma separated list of shared libraries
with TensorFlow* custom operations implementation.
Caffe*-specific parameters:
--input_proto INPUT_PROTO, -d INPUT_PROTO
Deploy-ready prototxt file that contains a topology
structure and layer attributes
--caffe_parser_path CAFFE_PARSER_PATH
Path to Python Caffe* parser generated from
caffe.proto
--k K Path to CustomLayersMapping.xml to register custom
layers
--disable_omitting_optional [DISABLE_OMITTING_OPTIONAL]
Disable omitting optional attributes to be used for
custom layers. Use this option if you want to transfer
all attributes of a custom layer to IR. Default
behavior is to transfer the attributes with default
values and the attributes defined by the user to IR.
--enable_flattening_nested_params [ENABLE_FLATTENING_NESTED_PARAMS]
Enable flattening optional params to be used for
custom layers. Use this option if you want to transfer
attributes of a custom layer to IR with flattened
nested parameters. Default behavior is to transfer the
attributes without flattening nested parameters.
MXNet-specific parameters:
--input_symbol INPUT_SYMBOL
Symbol file (for example, model-symbol.json) that
contains a topology structure and layer attributes
--nd_prefix_name ND_PREFIX_NAME
Prefix name for args.nd and argx.nd files.
--pretrained_model_name PRETRAINED_MODEL_NAME
Name of a pretrained MXNet model without extension and
epoch number. This model will be merged with args.nd
and argx.nd files
--save_params_from_nd [SAVE_PARAMS_FROM_ND]
Enable saving built parameters file from .nd files
--legacy_mxnet_model [LEGACY_MXNET_MODEL]
Enable MXNet loader to make a model compatible with
the latest MXNet version. Use only if your model was
trained with MXNet version lower than 1.0.0
--enable_ssd_gluoncv [ENABLE_SSD_GLUONCV]
Enable pattern matchers replacers for converting
gluoncv ssd topologies.
Kaldi-specific parameters:
--counts COUNTS Path to the counts file
--remove_output_softmax [REMOVE_OUTPUT_SOFTMAX]
Removes the SoftMax layer that is the output layer
--remove_memory [REMOVE_MEMORY]
Removes the Memory layer and use additional inputs
outputs instead
# Python conversion API parameters description
from openvino.tools import mo
mo.convert_model(help=True)
Optional parameters:
--help
Print available parameters.
--framework
Name of the framework used to train the input model.
Framework-agnostic parameters:
--input_model
Model object in original framework (PyTorch, Tensorflow) or path to
model file.
Tensorflow*: a file with a pre-trained model (binary or text .pb file
after freezing).
Caffe*: a model proto file with model weights
Supported formats of input model:
PaddlePaddle
paddle.hapi.model.Model
paddle.fluid.dygraph.layers.Layer
paddle.fluid.executor.Executor
PyTorch
torch.nn.Module
torch.jit.ScriptModule
torch.jit.ScriptFunction
TF
tf.compat.v1.Graph
tf.compat.v1.GraphDef
tf.compat.v1.wrap_function
tf.compat.v1.session
TF2 / Keras
tf.keras.Model
tf.keras.layers.Layer
tf.function
tf.Module
tf.train.checkpoint
--input
Input can be set by passing a list of InputCutInfo objects or by a list
of tuples. Each tuple can contain optionally input name, input
type or input shape. Example: input=("op_name", PartialShape([-1,
3, 100, 100]), Type(np.float32)). Alternatively input can be set by
a string or list of strings of the following format. Quoted list of comma-separated
input nodes names with shapes, data types, and values for freezing.
If operation names are specified, the order of inputs in converted
model will be the same as order of specified operation names (applicable
for TF2, ONNX, MxNet).
The shape and value are specified as comma-separated lists. The data
type of input node is specified
in braces and can have one of the values: f64 (float64), f32 (float32),
f16 (float16), i64
(int64), i32 (int32), u8 (uint8), boolean (bool). Data type is optional.
If it's not specified explicitly then there are two options: if input
node is a parameter, data type is taken from the original node dtype,
if input node is not a parameter, data type is set to f32. Example, to set
input_1 with shape [1,100], and Parameter node sequence_len with
scalar input with value 150, and boolean input is_training with
False value use the following format: "input_1[1,100],sequence_len->150,is_training->False".
Another example, use the following format to set input port 0 of the node
node_name1 with the shape [3,4] as an input node and freeze output
port 1 of the node node_name2 with the value [20,15] of the int32 type
and shape [2]: "0:node_name1[3,4],node_name2:1[2]{i32}->[20,15]".
--output
The name of the output operation of the model or list of names. For TensorFlow*,
do not add :0 to this name.The order of outputs in converted model is the
same as order of specified operation names.
--input_shape
Input shape(s) that should be fed to an input node(s) of the model. Input
shapes can be defined by passing a list of objects of type PartialShape,
Shape, [Dimension, ...] or [int, ...] or by a string of the following
format. Shape is defined as a comma-separated list of integer numbers
enclosed in parentheses or square brackets, for example [1,3,227,227]
or (1,227,227,3), where the order of dimensions depends on the framework
input layout of the model. For example, [N,C,H,W] is used for ONNX* models
and [N,H,W,C] for TensorFlow* models. The shape can contain undefined
dimensions (? or -1) and should fit the dimensions defined in the input
operation of the graph. Boundaries of undefined dimension can be specified
with ellipsis, for example [1,1..10,128,128]. One boundary can be
undefined, for example [1,..100] or [1,3,1..,1..]. If there are multiple
inputs in the model, --input_shape should contain definition of shape
for each input separated by a comma, for example: [1,3,227,227],[2,4]
for a model with two inputs with 4D and 2D shapes. Alternatively, specify
shapes with the --input option.
--example_input
Sample of model input in original framework.
For PyTorch it can be torch.Tensor.
For Tensorflow it can be tf.Tensor or numpy.ndarray.
For PaddlePaddle it can be Paddle Variable.
--batch
Set batch size. It applies to 1D or higher dimension inputs.
The default dimension index for the batch is zero.
Use a label 'n' in --layout or --source_layout option to set the batch
dimension.
For example, "x(hwnc)" defines the third dimension to be the batch.
--mean_values
Mean values to be used for the input image per channel. Mean values can
be set by passing a dictionary, where key is input name and value is mean
value. For example mean_values={'data':[255,255,255],'info':[255,255,255]}.
Or mean values can be set by a string of the following format. Values to
be provided in the (R,G,B) or [R,G,B] format. Can be defined for desired
input of the model, for example: "--mean_values data[255,255,255],info[255,255,255]".
The exact meaning and order of channels depend on how the original model
was trained.
--scale_values
Scale values to be used for the input image per channel. Scale values
can be set by passing a dictionary, where key is input name and value is
scale value. For example scale_values={'data':[255,255,255],'info':[255,255,255]}.
Or scale values can be set by a string of the following format. Values
are provided in the (R,G,B) or [R,G,B] format. Can be defined for desired
input of the model, for example: "--scale_values data[255,255,255],info[255,255,255]".
The exact meaning and order of channels depend on how the original model
was trained. If both --mean_values and --scale_values are specified,
the mean is subtracted first and then scale is applied regardless of
the order of options in command line.
--scale
All input values coming from original network inputs will be divided
by this value. When a list of inputs is overridden by the --input parameter,
this scale is not applied for any input that does not match with the original
input of the model. If both --mean_values and --scale are specified,
the mean is subtracted first and then scale is applied regardless of
the order of options in command line.
--reverse_input_channels
Switch the input channels order from RGB to BGR (or vice versa). Applied
to original inputs of the model if and only if a number of channels equals
3. When --mean_values/--scale_values are also specified, reversing
of channels will be applied to user's input data first, so that numbers
in --mean_values and --scale_values go in the order of channels used
in the original model. In other words, if both options are specified,
then the data flow in the model looks as following: Parameter -> ReverseInputChannels
-> Mean apply-> Scale apply -> the original body of the model.
--source_layout
Layout of the input or output of the model in the framework. Layout can
be set by passing a dictionary, where key is input name and value is LayoutMap
object. Or layout can be set by string of the following format. Layout
can be specified in the short form, e.g. nhwc, or in complex form, e.g.
"[n,h,w,c]". Example for many names: "in_name1([n,h,w,c]),in_name2(nc),out_name1(n),out_name2(nc)".
Layout can be partially defined, "?" can be used to specify undefined
layout for one dimension, "..." can be used to specify undefined layout
for multiple dimensions, for example "?c??", "nc...", "n...c", etc.
--target_layout
Same as --source_layout, but specifies target layout that will be in
the model after processing by ModelOptimizer.
--layout
Combination of --source_layout and --target_layout. Can't be used
with either of them. If model has one input it is sufficient to specify
layout of this input, for example --layout nhwc. To specify layouts
of many tensors, names must be provided, for example: --layout "name1(nchw),name2(nc)".
It is possible to instruct ModelOptimizer to change layout, for example:
--layout "name1(nhwc->nchw),name2(cn->nc)".
Also "*" in long layout form can be used to fuse dimensions, for example
"[n,c,...]->[n*c,...]".
--compress_to_fp16
If the original model has FP32 weights or biases, they are compressed
to FP16. All intermediate data is kept in original precision. Option
can be specified alone as "--compress_to_fp16", or explicit True/False
values can be set, for example: "--compress_to_fp16=False", or "--compress_to_fp16=True"
--extensions
Paths to libraries (.so or .dll) with extensions, comma-separated
list of paths, objects derived from BaseExtension class or lists of
objects. For the legacy MO path (if --use_legacy_frontend is used),
a directory or a comma-separated list of directories with extensions
are supported. To disable all extensions including those that are placed
at the default location, pass an empty string.
--transform
Apply additional transformations. 'transform' can be set by a list
of tuples, where the first element is transform name and the second element
is transform parameters. For example: [('LowLatency2', {{'use_const_initializer':
False}}), ...]"--transform transformation_name1[args],transformation_name2..."
where [args] is key=value pairs separated by semicolon. Examples:
"--transform LowLatency2" or
"--transform Pruning" or
"--transform LowLatency2[use_const_initializer=False]" or
"--transform "MakeStateful[param_res_names=
{'input_name_1':'output_name_1','input_name_2':'output_name_2'}]""
Available transformations: "LowLatency2", "MakeStateful", "Pruning"
--transformations_config
Use the configuration file with transformations description or pass
object derived from BaseExtension class. Transformations file can
be specified as relative path from the current directory, as absolute
path or as relative path from the mo root directory.
--silent
Prevent any output messages except those that correspond to log level
equals ERROR, that can be set with the following option: --log_level.
By default, log level is already ERROR.
--log_level
Logger level of logging massages from MO.
Expected one of ['CRITICAL', 'ERROR', 'WARN', 'WARNING', 'INFO',
'DEBUG', 'NOTSET'].
--version
Version of Model Optimizer
--progress
Enable model conversion progress display.
--stream_output
Switch model conversion progress display to a multiline mode.
--share_weights
Map memory of weights instead reading files or share memory from input
model.
Currently, mapping feature is provided only for ONNX models
that do not require fallback to the legacy ONNX frontend for the conversion.
PaddlePaddle-specific parameters:
--example_output
Sample of model output in original framework. For PaddlePaddle it can
be Paddle Variable.
TensorFlow*-specific parameters:
--input_model_is_text
TensorFlow*: treat the input model file as a text protobuf format. If
not specified, the Model Optimizer treats it as a binary file by default.
--input_checkpoint
TensorFlow*: variables file to load.
--input_meta_graph
Tensorflow*: a file with a meta-graph of the model before freezing
--saved_model_dir
TensorFlow*: directory with a model in SavedModel format of TensorFlow
1.x or 2.x version.
--saved_model_tags
Group of tag(s) of the MetaGraphDef to load, in string format, separated
by ','. For tag-set contains multiple tags, all tags must be passed in.
--tensorflow_custom_operations_config_update
TensorFlow*: update the configuration file with node name patterns
with input/output nodes information.
--tensorflow_object_detection_api_pipeline_config
TensorFlow*: path to the pipeline configuration file used to generate
model created with help of Object Detection API.
--tensorboard_logdir
TensorFlow*: dump the input graph to a given directory that should be
used with TensorBoard.
--tensorflow_custom_layer_libraries
TensorFlow*: comma separated list of shared libraries with TensorFlow*
custom operations implementation.
MXNet-specific parameters:
--input_symbol
Symbol file (for example, model-symbol.json) that contains a topology
structure and layer attributes
--nd_prefix_name
Prefix name for args.nd and argx.nd files.
--pretrained_model_name
Name of a pretrained MXNet model without extension and epoch number.
This model will be merged with args.nd and argx.nd files
--save_params_from_nd
Enable saving built parameters file from .nd files
--legacy_mxnet_model
Enable MXNet loader to make a model compatible with the latest MXNet
version. Use only if your model was trained with MXNet version lower
than 1.0.0
--enable_ssd_gluoncv
Enable pattern matchers replacers for converting gluoncv ssd topologies.
Caffe*-specific parameters:
--input_proto
Deploy-ready prototxt file that contains a topology structure and
layer attributes
--caffe_parser_path
Path to Python Caffe* parser generated from caffe.proto
--k
Path to CustomLayersMapping.xml to register custom layers
--disable_omitting_optional
Disable omitting optional attributes to be used for custom layers.
Use this option if you want to transfer all attributes of a custom layer
to IR. Default behavior is to transfer the attributes with default values
and the attributes defined by the user to IR.
--enable_flattening_nested_params
Enable flattening optional params to be used for custom layers. Use
this option if you want to transfer attributes of a custom layer to IR
with flattened nested parameters. Default behavior is to transfer
the attributes without flattening nested parameters.
Kaldi-specific parameters:
--counts
Path to the counts file
--remove_output_softmax
Removes the SoftMax layer that is the output layer
--remove_memory
Removes the Memory layer and use additional inputs outputs instead
サンプルモデルの取得¶
このノートブックでは、変換例に 2 つのモデルを使用します。
Hugging Face の distilbert NLP モデル
Torchvision の Resnet50 CV 分類モデル
from pathlib import Path
# create a directory for models files
MODEL_DIRECTORY_PATH = Path("model")
MODEL_DIRECTORY_PATH.mkdir(exist_ok=True)
Hugging Face から distilbert NLP モデルを取得し、ONNX 形式でエクスポートします。
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers.onnx import export, FeaturesManager
ONNX_NLP_MODEL_PATH = MODEL_DIRECTORY_PATH / "distilbert.onnx"
# download model
hf_model = AutoModelForSequenceClassification.from_pretrained(
"distilbert-base-uncased-finetuned-sst-2-english"
)
# initialize tokenizer
tokenizer = AutoTokenizer.from_pretrained(
"distilbert-base-uncased-finetuned-sst-2-english"
)
# get model onnx config function for output feature format sequence-classification
model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(
hf_model, feature="sequence-classification"
)
# fill onnx config based on pytorch model config
onnx_config = model_onnx_config(hf_model.config)
# export to onnx format
export(
preprocessor=tokenizer,
model=hf_model,
config=onnx_config,
opset=onnx_config.default_onnx_opset,
output=ONNX_NLP_MODEL_PATH,
)
2024-02-09 23:08:18.586507: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0. 2024-02-09 23:08:18.621399: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-02-09 23:08:19.256172: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:246: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
mask, torch.tensor(torch.finfo(scores.dtype).min)
(['input_ids', 'attention_mask'], ['logits'])
Torchvision から Resnet50 CV 分類モデルを取得します。
from torchvision.models import resnet50, ResNet50_Weights
# create model object
pytorch_model = resnet50(weights=ResNet50_Weights.DEFAULT)
# switch model from training to inference mode
pytorch_model.eval()
ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=2048, out_features=1000, bias=True)
)
PyTorch モデルから ONNX 形式へ変換します。
import torch
import warnings
ONNX_CV_MODEL_PATH = MODEL_DIRECTORY_PATH / "resnet.onnx"
if ONNX_CV_MODEL_PATH.exists():
print(f"ONNX model {ONNX_CV_MODEL_PATH} already exists.")
else:
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
torch.onnx.export(
model=pytorch_model, args=torch.randn(1, 3, 780, 520), f=ONNX_CV_MODEL_PATH
)
print(f"ONNX model exported to {ONNX_CV_MODEL_PATH}")
ONNX model model/resnet.onnx already exists.
基本変換¶
モデルを OpenVINO IR に変換するには、次のコマンドを使用します。
# Model Optimizer CLI
! mo --input_model model/distilbert.onnx --output_dir model
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.bin
# Python conversion API
from openvino.tools import mo
# mo.convert_model returns an openvino.runtime.Model object
ov_model = mo.convert_model(ONNX_NLP_MODEL_PATH)
# then model can be serialized to *.xml & *.bin files
from openvino.runtime import serialize
serialize(ov_model, xml_path=MODEL_DIRECTORY_PATH / "distilbert.xml")
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
モデル変換パラメーター¶
Python 変換 API とモデル・オプティマイザー・コマンドライン・ツールはどちらも次の機能を提供します。
inputおよびinput_shapeパラメーターを使用して、モデル変換の元の入力形状を上書きします。入力形状の設定ガイド。inputとoutputパラメーターを使用してモデルの不要な部分 (サポートされていない操作やトレーニング・サブグラフなど) を切り取り、変換されたモデルの新しい入力と出力を定義します。モデルの一部を切り取りガイド。mean_values、scales_values、layout、およびその他のパラメーターを使用して、変換されたモデルに追加の入力前処理サブグラフを挿入します。前処理計算の埋め込みガイド。- モデルの重み (例えば、畳み込みや行列乗算の重み) を、
compress_to_fp16圧縮パラメーターを使用して FP16 データタイプに圧縮します。モデルを FP16 に圧縮するガイド。
すぐに使用できる変換 (input_model パラメーターのみを指定) が成功しない場合は、上記のパラメーターを使用して入力形状をオーバーライドし、モデルをカットする必要がある場合があります。
入力形状の設定¶
モデル変換は、未定義の次元を含む動的入力形状を持つモデルに対してサポートされます。ただし、データの形状が推論要求ごとに変わらない場合は、入力に対して静的な形状を設定することを推奨します (すべての次元が完全に定義されている場合)。実行時の推論中ではなく、このステージでこれを実行すると、パフォーマンスとメモリー消費の点で有利になる可能性があります。静的形状を設定するため、モデル変換 API は input と input_shape パラメーターを提供します。
詳細については、入力形状の設定ガイドを参照してください。
# Model Optimizer CLI
! mo --input_model model/distilbert.onnx --input input_ids,attention_mask --input_shape [1,128],[1,128] --output_dir model
# alternatively
! mo --input_model model/distilbert.onnx --input input_ids[1,128],attention_mask[1,128] --output_dir model
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.bin
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.bin
# Python conversion API
from openvino.tools import mo
ov_model = mo.convert_model(
ONNX_NLP_MODEL_PATH,
input=["input_ids", "attention_mask"],
input_shape=[[1, 128], [1, 128]],
)
# alternatively specify input shapes, using the input parameter
ov_model = mo.convert_model(
ONNX_NLP_MODEL_PATH, input=[("input_ids", [1, 128]), ("attention_mask", [1, 128])]
)
input_shape パラメーターを使用すると、元の入力形状を、指定されたモデルと互換性のある形状に上書きできます。動的形状、つまり動的な次元を使用すると、元のモデルを変換後のモデルの静的な形状に置き換えることができ、またその逆も可能です。動的次元は、モデル変換 API パラメーターで -1 または ? としてマークできます。例えば、ONNX Bert モデルのモデル変換を起動し、入力の動的シーケンス長次元を指定します。
# Model Optimizer CLI
! mo --input_model model/distilbert.onnx --input input_ids,attention_mask --input_shape [1,-1],[1,-1] --output_dir model
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.bin
# Python conversion API
from openvino.tools import mo
ov_model = mo.convert_model(
ONNX_NLP_MODEL_PATH,
input=["input_ids", "attention_mask"],
input_shape=[[1, -1], [1, -1]],
)
実行時に次元が定義されていないモデルのメモリー消費を最適化するため、モデル変換 API には次元の境界を定義する機能が用意されています。未定義の次元境界は省略記号で指定できます。例えば、ONNX Bert モデルのモデル変換を起動し、シーケンス長の次元の境界を指定します。
# Model Optimizer CLI
! mo --input_model model/distilbert.onnx --input input_ids,attention_mask --input_shape [1,10..128],[1,10..128] --output_dir model
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.bin
# Python conversion API
from openvino.tools import mo
ov_model = mo.convert_model(
ONNX_NLP_MODEL_PATH,
input=["input_ids", "attention_mask"],
input_shape=[[1, "10..128"], [1, "10..128"]],
)
モデルの一部を切り取り¶
次の例は、モデルカットが役立つ場合、または必要な場合を示しています。
モデルには、既存の OpenVINO 操作に変換できない前処理部分または後処理部分が含まれています。
モデルにはトレーニング部分があり、モデル内に保持しておくと便利ですが、推論時には使用されないデータがあります。
モデルには、カスタムレイヤーとして簡単に実装できずサポートされない操作が多数含まれているため、複雑すぎて一度に変換できません。
OpenVINO ランタイムでのモデル変換や推論で問題が発生することがあります。問題を特定するには、モデル内で問題のある領域を反復検索して変換範囲を制限します。
単一のカスタムレイヤーまたはカスタムレイヤーの組み合わせは、デバッグの目的で分離されます。
詳細については、モデルの一部を切り取るガイドを参照してください。
# Model Optimizer CLI
# cut at the end
! mo --input_model model/distilbert.onnx --output /classifier/Gemm --output_dir model
# cut from the beginning
! mo --input_model model/distilbert.onnx --input /distilbert/embeddings/LayerNorm/Add_1,attention_mask --output_dir model
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.bin
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.bin
# Python conversion API
from openvino.tools import mo
# cut at the end
ov_model = mo.convert_model(ONNX_NLP_MODEL_PATH, output="/classifier/Gemm")
# cut from the beginning
ov_model = mo.convert_model(
ONNX_NLP_MODEL_PATH,
input=["/distilbert/embeddings/LayerNorm/Add_1", "attention_mask"],
)
前処理計算の埋め込み¶
推論用の入力データはトレーニングのデータセットとは異なる場合があり、推論の前に追加の前処理が必要になります。前処理や推論を含むパイプライン全体を高速化するため、モデル変換 API は、mean_values、scale_values、reverse_input_channels、layout など特別なパラメーターを提供します。これらのパラメーターに基づいて、モデル変換 API は、定義された前処理を実行するために挿入されたサブグラフを含む OpenVINO IR を生成します。この前処理ブロックは、入力データの平均スケール正規化、チャネル次元に沿ったデータの反転、およびデータ・レイアウトの変更を実行できます。前処理の詳細については、埋め込み前処理計算の記事を参照してください。
レイアウトを指定¶
レイアウトは形状の次元の平均を定義し、入力と出力の両方に指定できます。一部の前処理では、バッチの設定、平均値またはスケールの適用、入力チャネル (BGR<->RGB) の反転など、入力レイアウトの設定が必要です。レイアウト構文の詳細については、レイアウト API の概要を参照してください。レイアウトを指定するには、レイアウトオプションの後にレイアウト値を使用します。
次のコマンドでは、ONNX 形式にエクスポートされた Pytorch Resnet50 モデルの NCHW レイアウトを指定します。
# Model Optimizer CLI
! mo --input_model model/resnet.onnx --layout nchw --output_dir model
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.bin
# Python conversion API
from openvino.tools import mo
ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, layout="nchw")
モデルのレイアウトを変更¶
モデルのレイアウトが入力データと異なる場合、モデルのレイアウトの変更が必要になる場合があります。レイアウトを変更するには、layout または source_layout を target_layout とともに使用します。
# Model Optimizer CLI
! mo --input_model model/resnet.onnx --layout "nchw->nhwc" --output_dir model
# alternatively use source_layout and target_layout parameters
! mo --input_model model/resnet.onnx --source_layout nchw --target_layout nhwc --output_dir model
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.bin
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.bin
# Python conversion API
from openvino.tools import mo
ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, layout="nchw->nhwc")
# alternatively use source_layout and target_layout parameters
ov_model = mo.convert_model(
ONNX_CV_MODEL_PATH, source_layout="nchw", target_layout="nhwc"
)
平均とスケール値の指定¶
モデル変換 API には、値を指定するための次のパラメーターがあります: mean_values、scale_values、scale。これらのパラメーターを使用して、モデル変換 API は入力データの平均値の正規化に対応する前処理ブロックを埋め込み、前処理にかかる時間が推論で無視できる程度になるようにブロックを最適化します。
# Model Optimizer CLI
! mo --input_model model/resnet.onnx --mean_values [123,117,104] --scale 255 --output_dir model
! mo --input_model model/resnet.onnx --mean_values [123,117,104] --scale_values [255,255,255] --output_dir model
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.bin
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.bin
# Python conversion API
from openvino.tools import mo
ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, mean_values=[123, 117, 104], scale=255)
ov_model = mo.convert_model(
ONNX_CV_MODEL_PATH, mean_values=[123, 117, 104], scale_values=[255, 255, 255]
)
入力チャネルの反転¶
状況によっては、アプリケーションの入力画像が RGB (または BGR) 形式である場合があり、モデルはカラーチャネルの順序が逆である BGR (または RGB) 形式の画像でトレーニングされることがあります。この場合、推論前にカラーチャネルを元に戻すことで入力画像を前処理することが重要です。
# Model Optimizer CLI
! mo --input_model model/resnet.onnx --reverse_input_channels --output_dir model
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.bin
# Python conversion API
from openvino.tools import mo
ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, reverse_input_channels=True)
オプションで、モデル変換中に関連するすべての浮動小数点重みを FP16 データ型に圧縮し、圧縮された FP16 モデルを作成することもできます。この小さなモデルは、ファイルシステム内の元のスペースの約半分を占めます。圧縮によって精度が低下する可能性がありますが、ほとんどのモデルではこの低下はごくわずかです。
# Model Optimizer CLI
! mo --input_model model/resnet.onnx --compress_to_fp16=True --output_dir model
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/resnet.bin
# Python conversion API
from openvino.tools import mo
ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, compress_to_fp16=True)
Python オブジェクトとして表現されたモデルに変換¶
Python 変換 API は、Pytorch モデルや TensorFlow Keras モデルなどの Python モデル・オブジェクトを、ファイルに保存したり、トレーニング環境 (Jupyter ノートブックまたはトレーニングスクリプト) を離れたりすることなく、直接渡すことができます。
# Python conversion API
from openvino.tools import mo
ov_model = mo.convert_model(pytorch_model)
WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
convert_model() MO コマンドライン・ツールで使用可能なすべてのパラメーターを受け入れます。パラメーターは、コマンドライン・ツールと同様に、Python クラスまたは文字列アナログによって指定できます。
# Python conversion API
from openvino.tools import mo
ov_model = mo.convert_model(
pytorch_model,
input_shape=[1, 3, 100, 100],
mean_values=[127, 127, 127],
layout="nchw",
)
ov_model = mo.convert_model(pytorch_model, source_layout="nchw", target_layout="nhwc")
ov_model = mo.convert_model(
pytorch_model, compress_to_fp16=True, reverse_input_channels=True
)