onnx2tf

A tool for converting ONNX files to LiteRT/TFLite/TensorFlow, PyTorch native code (nn.Module), TorchScript (.pt), state_dict (.pt), Exported Program (.pt2), and Dynamo ONNX. It also supports direct conversion from LiteRT to PyTorch.

You should use LiteRT Torch rather than onnx2tf. https://github.com/google-ai-edge/litert-torch and https://github.com/google-ai-edge/ai-edge-quantizer

tf_converter supported layers

• https://github.com/onnx/onnx/blob/main/docs/Operators.md
• :heavy_check_mark:: Supported　:white_check_mark:: Partial support　Help wanted: Pull Request are welcome

See the list of supported layers

flatbuffer_direct execution path

flatbuffer_direct is now the default backend. It is faster and has a higher success rate than tf_converter for the supported direct path. The simplest conversion command now outputs only a LiteRT model by default, but if you add --flatbuffer_direct_output_saved_model, it will also output a saved_model. Unlike the legacy tf_converter path, this SavedModel is built from the LiteRT-side ModelIR.

[!IMPORTANT] flatbuffer_direct is the current default backend. Use --tflite_backend tf_converter only when you explicitly need the legacy TensorFlow Lite Converter compatibility path.

With the default flatbuffer_direct backend, onnx2tf uses a direct fast path for both ONNX input and -it/--input_tflite_file_path input:

1. ONNX graph preprocessing (tflite_builder.preprocess) and direct lowering (lower_onnx_to_ir)
2. Direct FlatBuffer export (*_float32.tflite, *_float16.tflite, and optional quantized variants)
3. Optional direct reports/evaluation (*_op_coverage_report.json, tensor correspondence, ONNX/TFLite check)

In this fast path, the per-node TensorFlow conversion (op.make_node() over all ONNX nodes) is skipped. This removes the long debug traces such as:

• INFO: <index> / <total>
• INFO: onnx_op_type: ...
• INFO: tf_op_type: ...

Measured example (same model, float32 TFLite write stage):

• tf_converter: ~24.947s
• flatbuffer_direct: ~0.239s
• flatbuffer_direct was approximately 107x faster than tf_converter in this case.

Actual speedup depends on model structure, enabled options, and runtime environment.

Direct export can also generate TF-side artifacts without falling back to tf_converter:

• --output_h5
• --output_keras_v3
• --output_tfv1_pb
• --flatbuffer_direct_output_pytorch

These artifacts are generated from an internal SavedModel bridge built from float32 ModelIR. If direct export fails, conversion stops with an explicit error.

-inimc / -onimc also stay on the direct path in flatbuffer_direct. For ONNX input and -it input, these options crop the imported/lowered ModelIR at the specified boundary tensor names instead of splitting the ONNX graph.

-dgc, -ebu, and -eru also stay on the direct path in flatbuffer_direct. For ONNX input they are applied during lowering or as post-lowering ModelIR rewrites. For -it input they are applied to imported ModelIR before SavedModel bridge, split planning, or rewritten TFLite export. If the requested rewrite cannot be applied safely, conversion stops with an explicit error.

-me also stays on the direct path in flatbuffer_direct. For ONNX MeanVarianceNormalization, direct lowering uses primitive builtin ops and applies mvn_epsilon to the internal variance + epsilon term without falling back to tf_converter.

--disable_model_save also stays on the direct path. In flatbuffer_direct, it means the conversion can still run internal validation and temporary staging, but no final artifacts are left in the requested output directory.

Invalid combinations are rejected explicitly:

• --disable_model_save with --output_h5, --output_keras_v3, or --output_tfv1_pb
• --enable_auto_split_model with --output_h5, --output_keras_v3, or --output_tfv1_pb

SavedModel direct export from flatbuffer_direct ModelIR is available with --flatbuffer_direct_output_saved_model. PyTorch package direct export is available with --flatbuffer_direct_output_pytorch. These options have the following constraints:

• --tflite_backend flatbuffer_direct is required for both
• --flatbuffer_direct_output_saved_model cannot be combined with --disable_model_save
• CUSTOM ops are rejected with an explicit error

INT8 ONNX	INT8 TFLite(LiteRT)

• e.g. LiteRT only output bash onnx2tf \ -i iat_llie_180x320.onnx \ -tb flatbuffer_direct
• e.g. Generate additional saved_model from LiteRT after LiteRT output bash onnx2tf \ -i iat_llie_180x320.onnx \ -tb flatbuffer_direct \ -fdosm
• e.g. Generate saved_model directly from an existing LiteRT (.tflite) file bash onnx2tf \ -it iat_llie_180x320_float32.tflite \ -tb flatbuffer_direct

• e.g. Generate .h5 directly from an existing LiteRT (.tflite) file without tf_converter fallback bash onnx2tf \ -it iat_llie_180x320_float32.tflite \ -tb flatbuffer_direct \ -oh5

• e.g. Generate a PyTorch package directly from an existing LiteRT (.tflite) file bash onnx2tf \ -it iat_llie_180x320_float32.tflite \ -o tmp_iat_llie_180x320_from_tflite \ -tb flatbuffer_direct \ -fdopt

• e.g. Compare the input LiteRT model and the generated PyTorch package with the same seeded inputs ```bash onnx2tf \ -it iat_llie_180x320_float32.tflite \ -o tmp_iat_llie_180x320_from_tflite \ -tb flatbuf