Obtain ONNX models

Convert from PyTorch

ONNX conversion is natively supported in PyTorch with the torch.onnx.export function. An example of a pre-trained PyTorch model conversion to ONNX is provided in tools/pytorch_to_onnx.py:

import torch
from MobileNetV2 import mobilenet_v2

dummy_input = torch.randn(10, 3, 224, 224)
model = mobilenet_v2(pretrained=True)

input_names = [ "input" ]
output_names = [ "output" ]

torch.onnx.export(model, dummy_input, "mobilenet_v2_pytorch.onnx", verbose=True, input_names=input_names, output_names=output_names)

Convert from TF/Keras

ONNX conversion is not natively supported by TF/Keras. Instead, a third-party tool must be used, like keras2onnx or tf2onnx. Currently, the tf2onnx is the most active and most maintained solution.

The tf2onnx tool can be used in command line, by providing a TensorFlow frozen graph (.pb).


Make sure to use the option --inputs-as-nchw on the model input(s) because N2D2 expects NCHW inputs, but the default format in TF/Keras is NHWC. Otherwise you would typically get an error like:

Error: Unexpected size for ONNX input "conv2d_77_input": got 3 224 224  , but StimuliProvider provides 224 224 3

The format of the exported ONNX graph from TF/Keras will depend on the execution platform (CPU or GPU). The default format is NHWC on CPU and NCHW on GPU. ONNX mandates the NCHW format for the operators, so exporting an ONNX model on CPU can result in the insertion of many Transpose operations in the graph before and after other operators.

tgz=$(basename $url)

if [ ! -r $tgz ]; then
    wget  -q  $url
    tar zxvf $tgz
python3 -m tf2onnx.convert --input $tfmodel --output $onnxmodel \
    --opset 10 --verbose \
    --inputs-as-nchw input:0 \
    --inputs input:0 \
    --outputs MobilenetV1/Predictions/Reshape_1:0

Example conversion scripts are provided for the Mobilenet families: tools/mobilenet_v1_to_onnx.sh, tools/mobilenet_v2_to_onnx.sh and tools/mobilenet_v3_to_onnx.sh.

Download pre-trained models

Many already trained ONNX models are freely available and ready to use in the ONNX Model Zoo: https://github.com/onnx/models/blob/master/README.md