TFLite model outputs

[Sezen ceyhan]

Dear All,

I trained some models on tflite type and trying to use them  on my scripts. I have some confusion on the outputs of the model. Basically, what I need is some specific ones such as locator, input etc but each model provides different outputs and I can not decide which one is right or how could I standardise these outputs. Doews anybody know how to reach this information? I have tried tflite model maker and tensorflow models for the aim.

[Blaine Rister]

Hi Sezen,

You could try using signature runners to assign meaningful input / output names to the model, feeding it a {name: value} dict instead of a list of tensors. I have only been able to get this to work by wrapping my Keras model in a tf.Module, which contains some tf.function with the desired IO signature. I have some LSTM example code here: https://github.com/femtosense/external_imdb_lstm_sample

Regards,
Blaine

[Murat Kocaman]

Hi, thank you for your reply,

I have tried it and changed the name of the layer but in my opinion because I defined a random layer i couldn’t do what I desired. It was providing “dense” layer but I renamed it as locator. How could I check this? Is it possible defining a layer with desired properties(bounding box locators for my train)

Best regards,

Sezen

On 1 Mar 2023, at 00:12, Blaine Rister <blaine...@femtosense.ai> wrote:

Hi Sezen,

[Blaine Rister]

I may have misinterpreted your question. Are you referring to the model's numerical output, or the metadata names attached to that output?

Regards,
Blaine

[Murat Kocaman]

Model’s numerical outputs.

On 7 Mar 2023, at 22:01, Blaine Rister <blaine...@femtosense.ai> wrote:

I may have misinterpreted your question. Are you referring to the model's numerical output, or the metadata names attached to that output?

[Blaine Rister]

Have you verified that your Tensorflow model gives the desired outputs? TFLite usually approximates Tensorflow reasonably well, but it works better for some models than for others.

Regards,
Blaine

[Murat Kocaman]

No. The output I desired is like below:

[{‘name’: ‘serving_default_input_2:0’, ‘index’: 0, ‘shape’: array([ 1, 384, 384, 3]), ‘shape_signature’: array([ -1, 384, 384, 3]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]
[{‘name’: ‘StatefulPartitionedCall:1’, ‘index’: 245, ‘shape’: array([ 1, 16]), ‘shape_signature’: array([-1, 16]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}, {‘name’: ‘StatefulPartitionedCall:0’, ‘index’: 238, ‘shape’: array([ 1, 100]), ‘shape_signature’: array([ -1, 100]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]
{‘serving_default’: {‘inputs’: [‘input_2’], ‘outputs’: [‘classifier’, ‘locator’]}}

but the output I got is:

[{‘name’: ‘serving_default_images:0’, ‘index’: 0, ‘shape’: array([ 1, 320, 320, 3]), ‘shape_signature’: array([ 1, 320, 320, 3]), ‘dtype’: <class ‘numpy.uint8’>, ‘quantization’: (0.0078125, 127), ‘quantization_parameters’: {‘scales’: array([0.0078125], dtype=float32), ‘zero_points’: array([127]), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]
[{‘name’: ‘StatefulPartitionedCall:1’, ‘index’: 600, ‘shape’: array([ 1, 25]), ‘shape_signature’: array([ 1, 25]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}, {‘name’: ‘StatefulPartitionedCall:3’, ‘index’: 598, ‘shape’: array([ 1, 25, 4]), ‘shape_signature’: array([ 1, 25, 4]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}, {‘name’: ‘StatefulPartitionedCall:0’, ‘index’: 601, ‘shape’: array([1]), ‘shape_signature’: array([1]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}, {‘name’: ‘StatefulPartitionedCall:2’, ‘index’: 599, ‘shape’: array([ 1, 25]), ‘shape_signature’: array([ 1, 25]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]
{‘serving_default’: {‘inputs’: [‘images’], ‘outputs’: [‘output_0’, ‘output_1’, ‘output_2’, ‘output_3’]}}

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Blaine Rister <blaine...@femtosense.ai>, 29 Mar 2023 Çar, 03:37 tarihinde şunu yazdı: