Unable to convert .onnx to tflite

[Roshnee]

❔Question

I have tensorflow 2.3.1 installed.

I was able to convert the pytorch model into an onnx file using, python models/export.py --weights yolov5s.pt --img 640 --batch 1

i also was able to convert .onnx into a tensorflow model using the following code, `import onnx from onnx_tf.backend import prepare import tensorflow as tf

onnx_model = onnx.load('yolov5s.onnx') tf_rep = prepare(onnx_model) tf_rep.export_graph("yolov5.pb") `

This yolov5.pb directory consists of the saved_model.pb file and 2 other folders: variables (2 files) and assets (empty folder)

I couldnt further convert it to a tflite model. I used the following code, `import tensorflow as tf

saved_model_dir = 'yolov5.pb'

Convert the model.

converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) tflite_model = converter.convert()

Save the TF Lite model.

with tf.io.gfile.GFile('model.tflite', 'wb') as f: f.write(tflite_model)`

This causes a strange error:

I also used:

_tflite_convert --saved_model_dir=yolov5 --outputfile=yolo.tflite

which gives me the same error.

Additional context

UPDATE:

looks like the error is because the conversion from onnx to tensorflow model is the saved model and not the frozen tf file. By finding the signature of the saved model using saved_model_cli show --dir yolov5_trial2 --all

gives me weird signature def for input and output arrays

signature_def['__saved_model_init_op']: The given SavedModel SignatureDef contains the following input(s): The given SavedModel SignatureDef contains the following output(s): outputs['__saved_model_init_op'] tensor_info: dtype: DT_INVALID shape: unknown_rank name: NoOp Method name is:

signature_def['serving_default']: The given SavedModel SignatureDef contains the following input(s): inputs['images'] tensor_info: dtype: DT_FLOAT shape: (1, 3, 640, 640) name: serving_default_images:0 The given SavedModel SignatureDef contains the following output(s): outputs['output_0'] tensor_info: dtype: DT_FLOAT shape: (1, 25200, 85) name: StatefulPartitionedCall:0 outputs['output_1'] tensor_info: dtype: DT_FLOAT shape: (1, 3, 80, 80, 85) name: StatefulPartitionedCall:1 outputs['output_2'] tensor_info: dtype: DT_FLOAT shape: (1, 3, 40, 40, 85) name: StatefulPartitionedCall:2 outputs['output_3'] tensor_info: dtype: DT_FLOAT shape: (1, 3, 20, 20, 85) name: StatefulPartitionedCall:3 Method name is: tensorflow/serving/predict

Defined Functions: Function Name: 'call' Named Argument #1 images

Function Name: 'gen_tensor_dict'

Please help.

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