To simply convert a model trained by any supported framework, run the Model Optimizer launch script
mo.py with specifying a path to the input model file:
python3 mo.py --input_model INPUT_MODEL
NOTE: The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the
RGB<->BGR conversion specifying the command-line parameter:
--reverse_input_channels. Otherwise, inference results may be incorrect. For details, refer to When to Reverse Input Channels.
To adjust the conversion process, you can also use the general (framework-agnostic) parameters:
-h, --help show this help message and exit
Name of the framework used to train the input model.
--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
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.
Alternatively, specify shapes with the --input
--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.
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
--input INPUT Quoted list of comma-separated input nodes names with
shapes, data types, and values for freezing. The shape
and value are specified as space-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. For 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 : "0:node_name1[3
--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 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 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
--disable_fusing Turn off fusing of linear operations to Convolution
Turn off resnet optimization
Regex for layers/operations that won't be fused.
Example: --finegrain_fusing Convolution1,.*Scale.*
--disable_gfusing Turn off fusing of grouped convolutions
Turn on Concat optimization.
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
--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.
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 Inference Engine API in runtime may fail for such an IR.
Disable compression and store weights with original
--progress Enable model conversion progress display.
--stream_output Switch model conversion progress display to a
Use the configuration file with transformations
The sections below provide details on using particular parameters and examples of CLI commands.
When to Specify Mean and Scale Values
Usually neural network models are trained with the normalized input data. This means that the input data values are converted to be in a specific range, for example,
[0, 1] or
[-1, 1]. Sometimes the mean values (mean images) are subtracted from the input data values as part of the pre-processing. There are two cases how the input data pre-processing is implemented.
- The input pre-processing operations are a part of a topology. In this case, the application that uses the framework to infer the topology does not pre-process the input.
- The input pre-processing operations are not a part of a topology and the pre-processing is performed within the application which feeds the model with an input data.
In the first case, the Model Optimizer generates the IR with required pre-processing layers and Inference Engine samples may be used to infer the model.
In the second case, information about mean/scale values should be provided to the Model Optimizer to embed it to the generated IR. Model Optimizer provides a number of command line parameters to specify them:
If both mean and scale values are specified, the mean is subtracted first and then scale is applied. Input values are divided by the scale value(s).
There is no a universal recipe for determining the mean/scale values for a particular model. The steps below could help to determine them:
- Read the model documentation. Usually the documentation describes mean/scale value if the pre-processing is required.
- Open the example script/application executing the model and track how the input data is read and passed to the framework.
- Open the model in a visualization tool and check for layers performing subtraction or multiplication (like
Eltwise etc) of the input data. If such layers exist, the pre-processing is most probably the part of the model.
When to Specify Input Shapes
There are situations when the input data shape for the model is not fixed, like for the fully-convolutional neural networks. In this case, for example, TensorFlow* models contain
-1 values in the
shape attribute of the
Placeholder operation. Inference Engine does not support input layers with undefined size, so if the input shapes are not defined in the model, the Model Optimizer fails to convert the model. The solution is to provide the input shape(s) using the
--input_shape command line parameter for all input(s) of the model or provide the batch size using the
-b command line parameter if the model contains just one input with undefined batch size only. In the latter case, the
Placeholder shape for the TensorFlow* model looks like this
[-1, 224, 224, 3].
When to Reverse Input Channels
Input data for your application can be of RGB or BRG color input order. For example, Inference Engine samples load input images in the BGR channels order. However, the model may be trained on images loaded with the opposite order (for example, most TensorFlow* models are trained with images in RGB order). In this case, inference results using the Inference Engine samples may be incorrect. The solution is to provide
--reverse_input_channels command line parameter. Taking this parameter, the Model Optimizer performs first convolution or other channel dependent operation weights modification so these operations output will be like the image is passed with RGB channels order.
When to Specify
--static_shape Command Line Parameter
--static_shape command line parameter is specified the Model Optimizer evaluates shapes of all operations in the model (shape propagation) for a fixed input(s) shape(s). During the shape propagation the Model Optimizer evaluates operations Shape and removes them from the computation graph. With that approach, the initial model which can consume inputs of different shapes may be converted to IR working with the input of one fixed shape only. For example, consider the case when some blob is reshaped from 4D of a shape [N, C, H, W] to a shape [N, C, H * W]. During the model conversion the Model Optimize calculates output shape as a constant 1D blob with values [N, C, H * W]. So if the input shape changes to some other value [N,C,H1,W1] (it is possible scenario for a fully convolutional model) then the reshape layer becomes invalid. Resulting Intermediate Representation will not be resizable with the help of Inference Engine.
Examples of CLI Commands
Launch the Model Optimizer for the Caffe bvlc_alexnet model with debug log level:
python3 mo.py --input_model bvlc_alexnet.caffemodel --log_level DEBUG
Launch the Model Optimizer for the Caffe bvlc_alexnet model with the output IR called
result.* in the specified
python3 mo.py --input_model bvlc_alexnet.caffemodel --model_name result --output_dir /../../models/
Launch the Model Optimizer for the Caffe bvlc_alexnet model with one input with scale values:
python3 mo.py --input_model bvlc_alexnet.caffemodel --scale_values [59,59,59]
Launch the Model Optimizer for the Caffe bvlc_alexnet model with multiple inputs with scale values:
python3 mo.py --input_model bvlc_alexnet.caffemodel --input data,rois --scale_values [59,59,59],[5,5,5]
Launch the Model Optimizer for the Caffe bvlc_alexnet model with multiple inputs with scale and mean values specified for the particular nodes:
python3 mo.py --input_model bvlc_alexnet.caffemodel --input data,rois --mean_values data[59,59,59] --scale_values rois[5,5,5]
Launch the Model Optimizer for the Caffe bvlc_alexnet model with specified input layer, overridden input shape, scale 5, batch 8 and specified name of an output operation:
python3 mo.py --input_model bvlc_alexnet.caffemodel --input "data[1 3 224 224]" --output pool5 -s 5 -b 8
Launch the Model Optimizer for the Caffe bvlc_alexnet model with disabled fusing for linear operations to Convolution and grouped convolutions:
python3 mo.py --input_model bvlc_alexnet.caffemodel --disable_fusing --disable_gfusing
Launch the Model Optimizer for the Caffe bvlc_alexnet model with reversed input channels order between RGB and BGR, specified mean values to be used for the input image per channel and specified data type for input tensor values:
python3 mo.py --input_model bvlc_alexnet.caffemodel --reverse_input_channels --mean_values [255,255,255] --data_type FP16
Launch the Model Optimizer for the Caffe bvlc_alexnet model with extensions listed in specified directories, specified mean_images binaryproto. file For more information about extensions, please refer to this page.
python3 mo.py --input_model bvlc_alexnet.caffemodel --extensions /home/,/some/other/path/ --mean_file /path/to/binaryproto
Launch the Model Optimizer for TensorFlow* FaceNet* model with a placeholder freezing value. It replaces the placeholder with a constant layer that contains the passed value. For more information about FaceNet conversion, please refer to this page
python3 mo.py --input_model FaceNet.pb --input "phase_train->False"
Launch the Model Optimizer for any model with a placeholder freezing tensor of values. It replaces the placeholder with a constant layer that contains the passed values.
Tensor here is represented in square brackets with each value separated from another by a whitespace. If data type is set in the model, this tensor will be reshaped to a placeholder shape and casted to placeholder data type. Otherwise, it will be casted to data type passed to
--data_type parameter (by default, it is FP32).
python3 mo.py --input_model FaceNet.pb --input "placeholder_layer_name->[0.1 1.2 2.3]"