1.26.3

• MatMul
  Fix incorrect tensor expansion in MatMul operation

1. Content and background

The MatMul operation was incorrectly handling 1-dimensional tensors by expanding
the wrong input tensor. When handling a 1D input tensor (shape [256]), it was
erroneously expanding input_tensor_2 (shape [256, 254]) instead of input_tensor_1,
leading to incorrect shape transformations.

2. Summary of corrections

Changed:

input_tensor_1 = tf.expand_dims(input_tensor_2, axis=0)

to

input_tensor_1 = tf.expand_dims(input_tensor_1, axis=0)

This ensures the correct tensor is expanded when handling 1D inputs.

3. Before/After

Before:

Input1 shape: [256] -> incorrectly became [1,256,254]
Input2 shape: [256,254] remained unchanged

After:

Input1 shape: [256] -> correctly becomes [1,256]
Input2 shape: [256,254] remains unchanged

What's Changed

• renamed replace_GRU.json to allow cloning to Windows by @kwikwag in #718
• Bugfix in MatMul.py by @oesi333 in #725

New Contributors

• @kwikwag made their first contribution in #718
• @oesi333 made their first contribution in #725

Full Changelog: 1.26.2...1.26.3

1.26.2

• Supports multi-batch quantization of image input.

onnx2tf \
-i batch_size_2.onnx \
-oiqt \
-cind images test.npy [[[[0.485,0.456,0.406]]]] [[[[0.229,0.224,0.225]]]]

• Problem of quantization with batch_size 2 #714

What's Changed

• Multi batch quant by @PINTO0309 in #715

Full Changelog: 1.26.1...1.26.2

1.26.1

• Added Float32 as an option for input and output types after quantization.

  -iqd {int8,uint8,float32}, --input_quant_dtype {int8,uint8,float32}
    Input dtypes when doing Full INT8 Quantization.
    "int8"(default) or "uint8" or "float32"
  
  -oqd {int8,uint8,float32}, --output_quant_dtype {int8,uint8,float32}
    Output dtypes when doing Full INT8 Quantization.
    "int8"(default) or "uint8" or "float32"

What's Changed

• Comment fix. input_quant_dtype, output_quant_dtype by @PINTO0309 in #706
• Update replace_slice.json reference by @emmanuel-ferdman in #708
• Fixed mistake of #710 by @marcoschepis in #711
• Added Float32 option by @PINTO0309 in #712

New Contributors

• @emmanuel-ferdman made their first contribution in #708

Full Changelog: 1.26.0...1.26.1

1.26.0

• API changed

  • The input and output quantization types can now be specified with separate, different parameters for input and output.
  • Abolition
    • input_output_quant_dtype
  • Addition
    • input_quant_dtype
    • output_quant_dtype

• Conv

  • Supports parameter substitution for post-processing of Conv.
  • replace_conv.json

    {
      "format_version": 1,
      "operations": [
        {
          "op_name": "wa/conv/Conv",
          "param_target": "outputs",
          "param_name": "output",
          "post_process_transpose_perm": [0,3,1,2]
        }
      ]
    }

  • test

    onnx2tf -i model_conv.onnx -kat input -prf replace_conv.json
    

    

• Mul

  • Improved conversion stability of Mul.
    

• Error by broadcasting in tf.math.Multiply Operation #698

• Add transposition to the tflite model #700

• README corrections due to API changes (I'll get serious from tomorrow) #702

What's Changed

• Input and output quantization chosen separately by @marcoschepis in #701
• Updated README.md by @marcoschepis in #703
• Supports parameter substitution for post-processing of Conv by @PINTO0309 in #704
• Improved conversion stability of Mul by @PINTO0309 in #705

New Contributors

• @marcoschepis made their first contribution in #701

Full Changelog: 1.25.15...1.26.0

1.25.15

• Mul, Add
  • Fixed to force switch between X and Y when X: np.ndarray, Y: Tensor

    onnx2tf -i 1005_s0_nonar_text_decoder.onnx -cotof
    

    
• Error by broadcasting in tf.math.Multiply Operation #698

What's Changed

• Fixed to force switch between X and Y when X: np.ndarray, Y: Tensor by @PINTO0309 in #699

Full Changelog: 1.25.14...1.25.15

1.25.14

• ArgMin
  • Dealing with garbage-like broken structures in ONNX (ArgMin) #695
• [YOLOv7] None in graph_node_input.shape #694

What's Changed

• Dealing with garbage-like broken structures in ONNX (ArgMin) #695 by @PINTO0309 in #696

Full Changelog: 1.25.13...1.25.14

1.25.13

• ArgMax, Faltten
  • Dealing with garbage-like broken structures in ONNX.
    
    
• [YOLOv7] None in graph_node_input.shape #694

What's Changed

• Dealing with garbage-like broken structures in ONNX by @PINTO0309 in #695

Full Changelog: 1.25.12...1.25.13

1.25.12

• Flatten
  • Improved handling when axis attribute is not defined and the batch size of the first dimension is undefined.
  • ONNX
    

    wget https://github.com/PINTO0309/onnx2tf/releases/download/0.0.2/resnet18-v1-7.onnx
    onnx2tf -i resnet18-v1-7.onnx
    
    ls -lh saved_model/
    
    assets
    fingerprint.pb
    resnet18-v1-7_float16.tflite
    resnet18-v1-7_float32.tflite
    saved_model.pb
    variables
    
    TF_CPP_MIN_LOG_LEVEL=3 \
    saved_model_cli show \
    --dir saved_model \
    --signature_def serving_default \
    --tag_set serve
    
    The given SavedModel SignatureDef contains the following input(s):
      inputs['data'] tensor_info:
          dtype: DT_FLOAT
          shape: (-1, 224, 224, 3)
          name: serving_default_data:0
    The given SavedModel SignatureDef contains the following output(s):
      outputs['output_0'] tensor_info:
          dtype: DT_FLOAT
          shape: (-1, 1000)
          name: PartitionedCall:0
    Method name is: tensorflow/serving/predict

  • TFLite
    

What's Changed

• Improved handling when axis attribute is not defined and the batch size of the first dimension is undefined. by @PINTO0309 in #692

Full Changelog: 1.25.11...1.25.12

1.25.11

• BatchNormalization
  • Improved the conversion stability of BatchNormalization.

What's Changed

• Improved the conversion stability of BatchNormalization. by @PINTO0309 in #690

Full Changelog: 1.25.10...1.25.11

1.25.10

• Flatten
  • Addressed the issue of missing conversions when multi-dimensional flattening is performed and the batch size of the first dimension is an undefined dimension.

    ONNX	TFLite

• [EfficientNetV2_m] Output size is constant not variable #688

What's Changed

• Addressed the issue of missing conversions when multi-dimensional flattening is performed and the batch size of the first dimension is an undefined dimension. by @PINTO0309 in #689

Full Changelog: 1.25.9...1.25.10