Save and load ONNX models

Exporting to onnx

void save_net_to_onnx_file(Net *net, string path, int seq_len = 0)

Saves a model with the onnx format in the file path provided.

Parameters

  • net – Net to be saved

  • path – Path to the file where the net in ONNX format will be saved

  • seq_len – In the case of exporting a recurrent model, set the sequence length of the model input to the provided value. By default is 0, which means that the sequence length will be generic. But in some cases, the export function will need a fixed value to export the model. We recommend using this argument only when the function asks for it

Returns

(void)

Example:

save_net_to_onnx_file(net, "my_model.onnx");

Importing onnx files

Net *import_net_from_onnx_file(string path, int mem = 0, LOG_LEVEL log_level = LOG_LEVEL::INFO)

Imports ONNX Net from file.

Parameters

  • path – Path to the file where the net in ONNX format is saved

  • mem – (default 0)

  • log_level – Available: LOG_LEVEL::{TRACE, DEBUG, INFO, WARN, ERROR, NO_LOGS}. (default LOG_LEVEL::INFO)

Returns

Net

Example:

Net* net = import_net_from_onnx_file("my_model.onnx");
Net *import_net_from_onnx_file(string path, vector<int> input_shape, int mem = 0, LOG_LEVEL log_level = LOG_LEVEL::INFO)

Imports ONNX Net from file and changes its input shape. Note that this function is for models with only one input layer, in other case it will fail.

Parameters

  • path – Path to the file where the net in ONNX format is saved

  • input_shape – Shape of the input data (without batch dimension)

  • mem – (default 0)

  • log_level – Available: LOG_LEVEL::{TRACE, DEBUG, INFO, WARN, ERROR, NO_LOGS}. (default LOG_LEVEL::INFO)

Returns

Net

Example:

Net* net = import_net_from_onnx_file("my_model.onnx", {3, 32, 32});

Simplifying onnx models

Not all onnx models can be loaded by the EDDL since in order to read the model correctly, all its layers and arguments must be supported by our library.

The best way to know if a model is supported by our current version is to try to load it. If import step produces an error, you try to “standardize” or “simplifying” using onnx_simplifier_.

Installation

This is only needed is you are not using the conda environment for the EDDL, as we ship ONNX simplifier with it.

pip3 install -U pip && pip3 install onnx-simplifier

Example #1: Simplifying a “ResNet18” (one input)

# Download onnx model from: https://github.com/onnx/models/tree/master/vision/classification/resnet

# Simplify
python3 -m onnxsim resnet18-v1-7.onnx resnet18-v1-7_simplify.onnx

Example #2: Simplifying a “MobileNet” (input’s shape required)

# Download onnx model from: https://github.com/onnx/models/tree/master/vision/classification/mobilenet

# Simplify
python3 -m onnxsim mobilenetv2-7.onnx mobilenetv2-7_simplified.onnx --input-shape 1,3,224,224

Example #3: Simplifying a “TinyYOLOv3” (two inputs, one dynamic)

# Download onnx model from: https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/tiny-yolov3

# Simplify
python3 -m onnxsim tiny-yolov3-11.onnx tiny-yolov3-11_simplified.onnx --dynamic-input-shape --input-shape input_1:1,3,416,416 image_shape:1,2