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:

• The speedup of shape inference is 100x in v0.5.0 release. Release detail
• Better support of Tensorflow-converted models in v0.4.0 release.
• Sparse Models are initially supported in v0.3.1 release. view Sparse Model
• Quantized models are initially supported in v0.3.0 release.
• 4 onnx models of Stable Diffusion are supported in v0.2.14 release. view results
  ...

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

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

how to use: data/Subgraph.md.

how to use: data/Subgraph.md.

how to use: data/Profile.md.

• 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.

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.

• Loop op is not supported

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