onnx2tf 0.0.3

Unique tool to convert ONNX files (NCHW) to TensorFlow format (NHWC). The purpose of this tool is to solve the massive Transpose extrapolation problem in onnx-tensorflow (onnx-tf).

[WIP] onnx2tf

Self-Created Tools to convert ONNX files (NCHW) to TensorFlow format (NHWC). The purpose of this tool is to solve the massive Transpose extrapolation problem in onnx-tensorflow (onnx-tf).

Key concept

• onnx-tensorflow is a very useful tool, but the performance of the generated TensorFlow models is significantly degraded due to the extrapolation of a large number of Transpose OPs before and after each OP during the format conversion from NCHW to NHWC. Therefore, I will make this tool myself as a derivative tool of onnx-tensorflow without extrapolating Transpose.
• Not only does it handle conversions of 4-dimensional inputs, such as NCHW to NHWC, but also the number of input dimensions in 3, 5, or even more dimensions. For example, NCDHW to NDHWC, etc. However, since 1-D, 2-D, 3-D and 6-D input may produce patterns that are mechanically difficult to convert, it should be possible to give parameters to externally modify the tool's behavior.
• Immediately following a Reshape OP with dimensional compression and dimensional decompression, there is a 95% probability that the model transformation operation will be disrupted and errors will occur. For example, patterns such as [1,200,200,5] -> [1,200,-1] or [10,20,30,40,50] -> [10,2,10,30,10,4,50].
• Support conversion to TensorFlow saved model and TFLite (Float32/Float16).
• Does not support quantization to INT8. For quantization, use the official TensorFlow converter to convert from saved_model to your own.
• Files exceeding the Protocol Buffers file size limit of 2GB are not supported. Therefore, the external format is not supported at the initial stage of tool creation.
• If there are ONNX OPs that are not supported by TensorFlow, use simple-onnx-processing-tools to replace them with harmless OPs in advance and then use this tool to convert them. In other words, you can convert any model with your efforts.
• BatchNormalization supports only inference mode.
• Only for opset=11 or higher

Demo

Sample Usage

$ pip install -U onnx2tf

$ wget https://github.com/PINTO0309/onnx2tf/releases/download/0.0.2/resnet18-v1-7.onnx
$ onnx2tf -i resnet18-v1-7.onnx -o saved_model

CLI Parameter

$ onnx2tf -h

usage: onnx2tf
[-h]
-i INPUT_ONNX_FILE_PATH
[-o OUTPUT_FOLDER_PATH]
[-k KEEP_NCW_OR_NCHW_OR_NCDHW_INPUT_NAMES [KEEP_NCW_OR_NCHW_OR_NCDHW_INPUT_NAMES ...]]
[-rari64 | -rarf32]
[-rasin]
[-racos]
[-n]

optional arguments:
  -h, --help
    show this help message and exit

  -i INPUT_ONNX_FILE_PATH, --input_onnx_file_path INPUT_ONNX_FILE_PATH
    Input onnx file path.

  -o OUTPUT_FOLDER_PATH, --output_folder_path OUTPUT_FOLDER_PATH
    Output folder path. Default: "saved_model"

  -k KEEP_NCW_OR_NCHW_OR_NCDHW_INPUT_NAMES [KEEP_NCW_OR_NCHW_OR_NCDHW_INPUT_NAMES ...], --keep_ncw_or_nchw_or_ncdhw_input_names KEEP_NCW_OR_NCHW_OR_NCDHW_INPUT_NAMES [KEEP_NCW_OR_NCHW_OR_NCDHW_INPUT_NAMES ...]
    Holds the NCW or NCHW or NCDHW of the input shape for the specified INPUT OP names.
    If a nonexistent INPUT OP name is specified, it is ignored. Valid only for 3D, 4D and 5D input tensors.
    e.g. --keep_ncw_or_nchw_or_ncdhw_input_names "input0" "input1" "input2"

  -rari64, --replace_argmax_to_reducemax_and_indicies_is_int64
    Replace ArgMax with a ReduceMax. The returned indicies are int64.
    Only one of replace_argmax_to_reducemax_and_indicies_is_int64
    and replace_argmax_to_reducemax_and_indicies_is_float32 can be specified.

  -rarf32, --replace_argmax_to_reducemax_and_indicies_is_float32
    Replace ArgMax with a ReduceMax. The returned indicies are float32.
    Only one of replace_argmax_to_reducemax_and_indicies_is_int64
    and replace_argmax_to_reducemax_and_indicies_is_float32 can be specified.

  -rasin, --replace_asin_to_pseudo_asin
    Replace Asin with a pseudo Asin.

  -racos, --replace_acos_to_pseudo_acos
    Replace Acos with a pseudo Acos.

  -n, --non_verbose
    Do not show all information logs. Only error logs are displayed.

In-script Usage

>>> from onnx2tf import convert
>>> help(convert)

Help on function convert in module onnx2tf.onnx2tf:

convert(
  input_onnx_file_path: Union[str, NoneType] = '',
  onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None,
  output_folder_path: Union[str, NoneType] = 'saved_model',
  keep_ncw_or_nchw_or_ncdhw_input_names: Union[List[str], NoneType] = None,
  replace_argmax_to_reducemax_and_indicies_is_int64: Union[bool, NoneType] = False,
  replace_argmax_to_reducemax_and_indicies_is_float32: Union[bool, NoneType] = False,
  replace_asin_to_pseudo_asin: Union[bool, NoneType] = False,
  replace_acos_to_pseudo_acos: Union[bool, NoneType] = False,
  non_verbose: Union[bool, NoneType] = False
) -> keras.engine.training.Model

    Convert ONNX to TensorFlow models.
    
    Parameters
    ----------
    input_onnx_file_path: Optional[str]
        Input onnx file path.
        Either input_onnx_file_path or onnx_graph must be specified.
    
    onnx_graph: Optional[onnx.ModelProto]
        onnx.ModelProto.
        Either input_onnx_file_path or onnx_graph must be specified.
        onnx_graph If specified, ignore input_onnx_file_path and process onnx_graph.
    
    output_folder_path: Optional[str]
        Output tensorflow model folder path.
        Default: "saved_model"

    keep_ncw_or_nchw_or_ncdhw_input_names: Optional[List[str]]
        Holds the NCW or NCHW or NCDHW of the input shape for the specified INPUT OP names.
        If a nonexistent INPUT OP name is specified, it is ignored.
        Valid only for 3D, 4D and 5D input tensors.
        e.g. 
        --keep_ncw_or_nchw_or_ncdhw_input_names=['input0', 'input1', 'input2']
    
    replace_argmax_to_reducemax_and_indicies_is_int64: Optional[bool]
        Replace ArgMax with a ReduceMax. The returned indicies are int64.
        Only one of replace_argmax_to_reducemax_and_indicies_is_int64 and 
        replace_argmax_to_reducemax_and_indicies_is_float32 can be specified.
        Default: False
    
    replace_argmax_to_reducemax_and_indicies_is_float32: Optional[bool]
        Replace ArgMax with a ReduceMax. The returned indicies are float32.
        Only one of replace_argmax_to_reducemax_and_indicies_is_int64 and 
        replace_argmax_to_reducemax_and_indicies_is_float32 can be specified.
        Default: False
    
    replace_asin_to_pseudo_asin: Optional[bool]
        Replace Asin with a pseudo Asin.
    
    replace_acos_to_pseudo_acos: Optional[bool]
        Replace Acos with a pseudo Acos.
    
    non_verbose: Optional[bool]
        Do not show all information logs. Only error logs are displayed.
        Only one of replace_argmax_to_reducemax_and_indicies_is_int64 and 
        replace_argmax_to_reducemax_and_indicies_is_float32 can be specified.
        Default: False
    
    Returns
    ----------
    model: tf.keras.Model
        Model