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opencv/modules
CNOCycle 7713c84465 Merge pull request #25297 from CNOCycle:tflite/transpose
Support Transpose op in TFlite #25297

**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1168

The purpose of this PR is to introduce support for the Transpose op in TFlite format and to add a shape comparison between the output tensors and the references. In some occasional cases, the shape of the output tensor is `[1,4,1,1]`, while the shape of the reference tensor is `[1,4]`. Consequently, the norm check incorrectly reports that the test has passed, as the residual is zero.

Below is a Python script for generating testing data. The generated data can be integrated into the repo `opencv_extra`.

```python
import numpy as np
import tensorflow as tf

PREFIX_TFL = '/path/to/opencv_extra/testdata/dnn/tflite/'

def generator(input_tensor, model, saved_name):

    # convert keras model to .tflite format
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    #converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.optimizations = [None]
    tflite_model = converter.convert()
    with open(f'{PREFIX_TFL}/{saved_name}.tflite', 'wb') as f:
        f.write(tflite_model)

    # save the input tensor to .npy
    if input_tensor.ndim == 4:
        opencv_tensor = np.transpose(input_tensor, (0,3,1,2))
    else:
        opencv_tensor = input_tensor
    opencv_tensor = np.copy(opencv_tensor, order='C').astype(np.float32)
    np.save(f'{PREFIX_TFL}/{saved_name}_inp.npy', opencv_tensor)

    # generate output tenosr and save it to .npy
    mat_out = model(input_tensor).numpy()
    mat_out = np.copy(mat_out, order='C').astype(np.float32)
    if mat_out.ndim == 4:
        mat_out = np.transpose(mat_out, (0,3,1,2))
    interpreter = tf.lite.Interpreter(model_content=tflite_model)
    out_name = interpreter.get_output_details()[0]['name']
    np.save(f'{PREFIX_TFL}/{saved_name}_out_{out_name}.npy', mat_out)

def build_transpose():

    model_name = "keras_permute"
    mat_in = np.array([[[1,2,3], [4,5,6]]], dtype=np.float32)

    model = tf.keras.Sequential()
    model.add(tf.keras.Input(shape=(2,3)))
    model.add(tf.keras.layers.Permute((2,1)))
    model.summary()

    generator(mat_in, model, model_name)

if __name__ == '__main__':
    build_transpose()
```

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
- [X] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [X] The feature is well documented and sample code can be built with the project CMake
2024-05-15 20:07:25 +03:00
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