model-explorer-onnx 0.3.7

Adapter for ai-edge-model-explorer to support ONNX models

Model Explorer ONNX Adapter

ONNX Adapter for google-ai-edge/model-explorer

Installation

pip install --upgrade model-explorer-onnx

Usage

model-explorer --extensions=model_explorer_onnx

# Or as a shortcut
onnxvis

# Supply model path
onnxvis model.onnx

[!NOTE] Model Explorer only supports WSL on Windows.

Read more on the Model Explorer User Guide.

Notes on representation

Graph input/output/initializers in ONNX are values (edges), not nodes. A node is displayed here for visualization. Graph inputs that are initialized by initializers are displayed as InitializedInput, and are displayed closer to nodes that use them.

Color Themes

Get node color themes here

Visualizing PyTorch ONNX exporter (dynamo=True) accuracy results

[!NOTE] verify_onnx_program requires PyTorch 2.7 or newer

import torch
from torch.onnx.verification import verify_onnx_program

from model_explorer_onnx.torch_utils import save_node_data_from_verification_info

# Export the and save model
onnx_program = torch.onnx.export(model, args, dynamo=True)
onnx_program.save("model.onnx")

verification_infos = verify_onnx_program(onnx_program, compare_intermediates=True)

# Produce node data for Model Explorer for visualization
save_node_data_from_verification_info(
    verification_infos, onnx_program.model, model_name="model"
)

You can then use Model Explorer to visualize the results by loading the generated node data files:

onnxvis model.onnx --node_data_paths=model_max_abs_diff.json,model_max_rel_diff.json

Screenshots