onnx-tool 0.2.8

A tool for ONNX model:Shape inference, MACs(FLOPs) counting for each layer, Extract subgraph from ONNX model, or do inplace op fusion, and any operation you can image with ONNX.

onnx-tool

A tool for ONNX model:

• Shape inference.
• MACs(FLOPs) counting for each layer.
• Extract subgraph from ONNX model, or do inplace op fusion. ...
  and any operation you can image with ONNX.

New: Pytorch models are also valid if torch.onnx.export succeeded.

---

Shape inference

how to use: data/Profile.md.
pytorch usage: data/PytorchUsage.md.

---

MACs counting for each layer (FLOPs=2*MACs)

Float MultipleAdd Count, Memory Usage(in bytes), Parameters(elements number)

how to use: data/Profile.md.
pytorch usage: data/PytorchUsage.md.

---

Extract subgraph from ONNX model

how to use: data/Subgraph.md.

---

Inplace op fusion

how to use: data/Subgraph.md.

---

Add any hidden tensors to model's outputs

how to use: data/Profile.md.

---

Tensor operations

• Export weight tensors to files
• Simplify tensor and node names, convert name from a long string to a short string
• Remove unused tensors, models like vgg19-7.onnx set its static weight tensors as its input tensors
• Set custom input and output tensors' name and dimension, change model from fixed input to dynamic input
  how to use: data/Tensors.md.

---

How to install

pip install onnx-tool

OR

pip install --upgrade git+https://github.com/ThanatosShinji/onnx-tool.git

python>=3.6

If pip install onnx-tool failed by onnx's installation, you may try pip install onnx==1.8.1 (a lower version like this) first.
Then pip install onnx-tool again.

---

Known Issues

• Loop op is not supported

---

Results of ONNX Model Zoo and SOTA models

Some models have dynamic input shapes. The MACs varies from input shapes. The input shapes used in these results are writen to data/public/config.py. These onnx models with all tensors' shape can be downloaded: baidu drive(code: p91k) google drive

Model Params(M) MACs(M) MobileNet v2-1.0-fp32 3.3 300 ResNet50_fp32 25 3868 SqueezeNet 1.0 1.23 351 VGG 19 143.66 19643 AlexNet 60.96 665 GoogleNet 6.99 1606 googlenet_age_adience 5.98 1605 LResNet100E-IR 65.22 12102 BERT-Squad 113.61 22767 BiDAF 18.08 9.87 EfficientNet-Lite4 12.96 1361 Emotion FERPlus 12.95 877 Mask R-CNN R-50-FPN-fp32 46.77 92077

Model Params(M) MACs(M) rvm_mobilenetv3_fp32.onnx 3.73 4289 yolov4 64.33 33019 ConvNeXt-L 229.79 34872 edgenext_small 5.58 1357 SSD 19.98 216598 RealESRGAN_x4plus.pth 16.69 73551 ShuffleNet-v2-fp32 2.29 146 GPT-2 137.02 1103 T5-encoder 109.62 686 T5-decoder-with-lm-head 162.62 1113 RoBERTa-BASE 124.64 688 Faster R-CNN R-50-FPN-fp32 44.10 46018 FCN ResNet-50 35.29 37056