Tools to convert ONNX files (NCHW) to TensorFlow format (NHWC)

• https://github.com/tensorflow/tensorflow/issues/56537
• https://github.com/PINTO0309/onnx2tf/issues/93

• Added option to run TFLite ModelAnalyzer

  -cgdc, --check_gpu_delegate_compatibility
    Run TFLite ModelAnalyzer on the generated Float16 tflite model
    to check if the model can be supported by GPU Delegate.
  

  e.g. 6D Transpose to xxxD Transpose

  === TFLite ModelAnalyzer ===
  
  Your TFLite model has '1' subgraph(s). In the subgraph description below,
  T# represents the Tensor numbers. For example, in Subgraph#0, the RESHAPE op takes
  tensor #0 and tensor #6 as input and produces tensor #7 as output.
  
  Subgraph#0 main(T#0) -> [T#17]
    Op#0 RESHAPE(T#0, T#6[2, 8, 8, 3, 2, ...]) -> [T#7]
    Op#1 SPLIT(T#5[0], T#7) -> [T#8, T#9]
    Op#2 RESHAPE(T#8, T#1[8, 8, 3, 2, 2]) -> [T#10]
    Op#3 TRANSPOSE(T#10, T#4[0, 3, 1, 4, 2]) -> [T#11]
    Op#4 RESHAPE(T#11, T#2[1, 8, 2, 8, 2, ...]) -> [T#12]
    Op#5 RESHAPE(T#9, T#1[8, 8, 3, 2, 2]) -> [T#13]
    Op#6 TRANSPOSE(T#13, T#4[0, 3, 1, 4, 2]) -> [T#14]
    Op#7 RESHAPE(T#14, T#2[1, 8, 2, 8, 2, ...]) -> [T#15]
    Op#8 CONCATENATION(T#12, T#15) -> [T#16]
    Op#9 RESHAPE(T#16, T#3[2, 16, 16, 3]) -> [T#17]
  
  Tensors of Subgraph#0
    T#0(inputs_0) shape:[2, 8, 8, 12], type:FLOAT32
    T#1(model/tf.compat.v1.squeeze_2/Squeeze) shape:[5], type:INT32 RO 20 bytes, data:[8, 8, 3, 2, 2]
    T#2(model/tf.expand_dims_1/ExpandDims) shape:[6], type:INT32 RO 24 bytes, data:[1, 8, 2, 8, 2, ...]
    T#3(model/tf.reshape_1/Reshape/shape) shape:[4], type:INT32 RO 16 bytes, data:[2, 16, 16, 3]
    T#4(model/tf.compat.v1.transpose/transpose/perm) shape:[5], type:INT32 RO 20 bytes, data:[0, 3, 1, 4, 2]
    T#5(model/tf.concat/concat/axis) shape:[], type:INT32 RO 4 bytes, data:[0]
    T#6(model/tf.reshape/Reshape/shape) shape:[6], type:INT32 RO 24 bytes, data:[2, 8, 8, 3, 2, ...]
    T#7(model/tf.reshape/Reshape) shape:[2, 8, 8, 3, 2, 2], type:FLOAT32
    T#8(model/tf.split/split) shape:[1, 8, 8, 3, 2, 2], type:FLOAT32
    T#9(model/tf.split/split1) shape:[1, 8, 8, 3, 2, 2], type:FLOAT32
    T#10(model/tf.compat.v1.squeeze_1/Squeeze) shape:[8, 8, 3, 2, 2], type:FLOAT32
    T#11(model/tf.compat.v1.transpose/transpose) shape:[8, 2, 8, 2, 3], type:FLOAT32
    T#12(model/tf.expand_dims/ExpandDims) shape:[1, 8, 2, 8, 2, 3], type:FLOAT32
    T#13(model/tf.compat.v1.squeeze_2/Squeeze1) shape:[8, 8, 3, 2, 2], type:FLOAT32
    T#14(model/tf.compat.v1.transpose_1/transpose) shape:[8, 2, 8, 2, 3], type:FLOAT32
    T#15(model/tf.expand_dims_1/ExpandDims1) shape:[1, 8, 2, 8, 2, 3], type:FLOAT32
    T#16(model/tf.concat/concat) shape:[2, 8, 2, 8, 2, 3], type:FLOAT32
    T#17(Identity) shape:[2, 16, 16, 3], type:FLOAT32
  
  Your model looks compatibile with GPU delegate with TFLite runtime version 2.10.0.
  But it doesn't guarantee that your model works well with GPU delegate.
  There could be some runtime incompatibililty happen.
  ---------------------------------------------------------------
                Model size:       2988 bytes
      Non-data buffer size:       2757 bytes (92.27 %)
    Total data buffer size:        231 bytes (07.73 %)
      (Zero value buffers):          4 bytes (00.13 %)
  
  * Buffers of TFLite model are mostly used for constant tensors.
    And zero value buffers are buffers filled with zeros.
    Non-data buffers area are used to store operators, subgraphs and etc.
    You can find more details from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs
  

• TFLite ModelAnalyzer (Check if the model can be supported by GPU Delegate, 6D Transpose to xxxD Transpose) by @PINTO0309 in https://github.com/PINTO0309/onnx2tf/pull/100

Full Changelog: https://github.com/PINTO0309/onnx2tf/compare/1.3.19...1.4.0