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README.md
Model optimization component
This component is executing model optimization process using OpenVINO Toolkit and generate as output the model in Intermediate Representation format.
Component takes the following arguments:
- model input GCS path
- model optimizer parameters
- model output GCS path
usage: convert_model.py [-h] [--input_path INPUT_PATH]
[--mo_options MO_OPTIONS] [--output_path OUTPUT_PATH]
Model converter to OpenVINO Intermediate Representation format
optional arguments:
-h, --help show this help message and exit
--input_path INPUT_PATH
GCS path of input model file or folder
--mo_options MO_OPTIONS
OpenVINO Model Optimizer options
--output_path OUTPUT_PATH
GCS path of output folder
Component parameters
It takes as input GCS path to the input model in any of the OpenVINO supported frameworks:
- Tensorflow
- Caffe
- MXNET
- Kaldi
- ONNX
Input model path can be a folder or an individual file which will be copied to a component working directory
Model optimizer options can include any of the parameters supported by OpenVINO toolkit model optimizer.
Refer to OpenVINO documentation for details.
mo.py --help
usage: mo.py [-h] [--framework {tf,caffe,mxnet,kaldi,onnx}]
[--input_model INPUT_MODEL] [--model_name MODEL_NAME]
[--output_dir OUTPUT_DIR] [--input_shape INPUT_SHAPE]
[--scale SCALE] [--reverse_input_channels]
[--log_level {CRITICAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}]
[--input INPUT] [--output OUTPUT] [--mean_values MEAN_VALUES]
[--scale_values SCALE_VALUES]
[--data_type {FP16,FP32,half,float}] [--disable_fusing]
[--disable_resnet_optimization]
[--finegrain_fusing FINEGRAIN_FUSING] [--disable_gfusing]
[--move_to_preprocess] [--extensions EXTENSIONS] [--batch BATCH]
[--version] [--silent]
[--freeze_placeholder_with_value FREEZE_PLACEHOLDER_WITH_VALUE]
[--generate_deprecated_IR_V2] [--input_model_is_text]
[--input_checkpoint INPUT_CHECKPOINT]
[--input_meta_graph INPUT_META_GRAPH]
[--saved_model_dir SAVED_MODEL_DIR]
[--saved_model_tags SAVED_MODEL_TAGS]
[--offload_unsupported_operations_to_tf]
[--tensorflow_subgraph_patterns TENSORFLOW_SUBGRAPH_PATTERNS]
[--tensorflow_operation_patterns TENSORFLOW_OPERATION_PATTERNS]
[--tensorflow_custom_operations_config_update TENSORFLOW_CUSTOM_OPERATIONS_CONFIG_UPDATE]
[--tensorflow_use_custom_operations_config TENSORFLOW_USE_CUSTOM_OPERATIONS_CONFIG]
[--tensorflow_object_detection_api_pipeline_config TENSORFLOW_OBJECT_DETECTION_API_PIPELINE_CONFIG]
[--tensorboard_logdir TENSORBOARD_LOGDIR]
[--tensorflow_custom_layer_libraries TENSORFLOW_CUSTOM_LAYER_LIBRARIES]
[--disable_nhwc_to_nchw] [--input_proto INPUT_PROTO] [-k K]
[--mean_file MEAN_FILE] [--mean_file_offsets MEAN_FILE_OFFSETS]
[--disable_omitting_optional] [--enable_flattening_nested_params]
[--input_symbol INPUT_SYMBOL] [--nd_prefix_name ND_PREFIX_NAME]
[--pretrained_model_name PRETRAINED_MODEL_NAME]
[--save_params_from_nd] [--legacy_mxnet_model] [--counts COUNTS]
[--remove_output_softmax]
optional arguments:
-h, --help show this help message and exit
--framework {tf,caffe,mxnet,kaldi,onnx}
Name of the framework used to train the input model.
Framework-agnostic parameters:
--input_model INPUT_MODEL, -w INPUT_MODEL, -m 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
--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 Optimizer is
launched.
--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 Caffe* models and
[N,H,W,C] for TensorFlow* models. Model Optimizer
performs necessary transformations to convert the
shape to the layout required by Inference Engine
(N,C,H,W). The shape should not contain undefined
dimensions (? or -1) and should fit the dimensions
defined in the input operation of the graph. 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.
--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.
--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. Applied
after application of --mean_values and --scale_values
options, so numbers in --mean_values and
--scale_values go in the order of channels used in the
original model.
--log_level {CRITICAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}
Logger level
--input INPUT The name of the input operation of the given model.
Usually this is a name of the input placeholder of the
model.
--output OUTPUT The name of the output operation of the model. For
TensorFlow*, do not add :0 to this name.
--mean_values MEAN_VALUES, -ms 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.
--data_type {FP16,FP32,half,float}
Data type for all intermediate tensors and weights. If
original model is in FP32 and --data_type=FP16 is
specified, all model weights and biases are quantized
to FP16.
--disable_fusing Turn off fusing of linear operations to Convolution
--disable_resnet_optimization
Turn off resnet optimization
--finegrain_fusing FINEGRAIN_FUSING
Regex for layers/operations that won't be fused.
Example: --finegrain_fusing Convolution1,.*Scale.*
--disable_gfusing Turn off fusing of grouped convolutions
--move_to_preprocess Move mean values to IR preprocess section
--extensions EXTENSIONS
Directory or a comma separated list of directories
with extensions. To disable all extensions including
those that are placed at the default location, pass an
empty string.
--batch BATCH, -b BATCH
Input batch size
--version Version of Model Optimizer
--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.
--freeze_placeholder_with_value FREEZE_PLACEHOLDER_WITH_VALUE
Replaces input layer with constant node with provided
value, e.g.: "node_name->True"
--generate_deprecated_IR_V2
Force to generate legacy/deprecated IR V2 to work with
previous versions of the Inference Engine. The
resulting IR may or may not be correctly loaded by
Inference Engine API (including the most recent and
old versions of Inference Engine) and provided as a
partially-validated backup option for specific
deployment scenarios. Use it at your own discretion.
By default, without this option, the Model Optimizer
generates IR V3.
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 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 representing non frozen model
--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.
--offload_unsupported_operations_to_tf
TensorFlow*: automatically offload unsupported
operations to TensorFlow*
--tensorflow_subgraph_patterns TENSORFLOW_SUBGRAPH_PATTERNS
TensorFlow*: a list of comma separated patterns that
will be applied to TensorFlow* node names to infer a
part of the graph using TensorFlow*.
--tensorflow_operation_patterns TENSORFLOW_OPERATION_PATTERNS
TensorFlow*: a list of comma separated patterns that
will be applied to TensorFlow* node type (ops) to
infer these operations using TensorFlow*.
--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_use_custom_operations_config TENSORFLOW_USE_CUSTOM_OPERATIONS_CONFIG
TensorFlow*: use the configuration file with custom
operation description.
--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.
--disable_nhwc_to_nchw
Disables default translation from NHWC to NCHW
Caffe*-specific parameters:
--input_proto INPUT_PROTO, -d INPUT_PROTO
Deploy-ready prototxt file that contains a topology
structure and layer attributes
-k K Path to CustomLayersMapping.xml to register custom
layers
--mean_file MEAN_FILE, -mf MEAN_FILE
Mean image to be used for the input. Should be a
binaryproto file
--mean_file_offsets MEAN_FILE_OFFSETS, -mo MEAN_FILE_OFFSETS
Mean image offsets to be used for the input
binaryproto file. When the mean image is bigger than
the expected input, it is cropped. By default, centers
of the input image and the mean image are the same and
the mean image is cropped by dimensions of the input
image. The format to pass this option is the
following: "-mo (x,y)". In this case, the mean file is
cropped by dimensions of the input image with offset
(x,y) from the upper left corner of the mean image
--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.
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
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
Kaldi-specific parameters:
--counts COUNTS Path to the counts file
--remove_output_softmax
Removes the SoftMax layer that is the output layer
The output folder specify then should be uploaded the generated model file in IR format with .bin and .xml extensions.
The component also creates 3 files including the paths to generated model:
/tmp/output.txt- GSC path to the folder including the generated model files./tmp/bin_path.txt- GSC path to weights model file/tmp/xml_path.txt- GSC path to graph model file They can be used as parameters to be passed to other jobs in ML pipelines.
Examples
Input path - gs://tensorflow_model_path/resnet/1/saved_model.pb
MO options - --saved_model_dir .
Output path - gs://tensorflow_model_path/resnet/1
Input path - gs://tensorflow_model_path/resnet/1
MO options - --saved_model_dir 1
Output path - gs://tensorflow_model_path/resnet/dldt/1
Building docker image
docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy .
Starting and testing the component locally
This component requires GCP authentication token in json format generated for the service account, which has access to GCS location. In the example below it is in key.json in the current path.
COMMAND="convert_model.py --mo_options \"--saved_model_dir .\" --input_path gs://tensorflow_model_path/resnet/1/saved_model.pb --output_path gs://tensorflow_model_path/resnet/1"
docker run --rm -it -v $(pwd)/key.json:/etc/credentials/gcp-key.json \
-e GOOGLE_APPLICATION_CREDENTIALS=/etc/credentials/gcp-key.json <image_name> $COMMAND