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TFOpLambda not supported in INT8 Quantization Aware Training (Mobilenetv3)

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
35/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Stale
Tech stack
python, tensorflow
Domain
machine-learning

Research direction

Start by running the provided Python reproduction with tf_keras MobileNetV3Small and tfmot.quantization.keras.quantize_model. Trace how the TFOpLambda created by the hard-swish activation reaches quantization and where the list-versus-dtype error occurs. Done means the model can be quantized successfully without this error, while preserving the reported reproduction case.

Written by the indexing model from the issue text.

Description

bug

Describe the bug

I cannot quantize Mobilenetv3 from keras2 because the hard-swish activation fuction is implemented as a TFOpLambda.

System information

tensorflow version: 2.17
tf_keras version: 2.17
tensorflow_model_optimization version: 0.8.0

TensorFlow Model Optimization version installed from pip

Python version: Python 3.9.19

Describe the expected behavior

Quantization aware training can be applied to keras.applications.MobileNetV3Small using tfmot.quantization.keras.quantize_model

Describe the current behavior

When some layer is a TFOpLambda the following error raises:

AttributeError: Exception encountered when calling layer "tf.operators.add" (type TFOpLambda).

'list' object has no attribute 'dtype'

Call arguments received by layer "tf.operators.add" (type TFOpLambda):
• x=['tf.Tensor(shape=(None, 112, 112, 16), dtype=float32)']
• y=3.0
• name=None

Code to reproduce the issue

import os
os.environ["TF_USE_LEGACY_KERAS"] = "1"

import tf_keras as keras

model = keras.applications.MobileNetV3Small(
        input_shape=tuple([224,224,3]),
        alpha=1.0,
        minimalistic=False,
        include_top=True,
        weights="imagenet",
        input_tensor=None,
        classes=1000,
        pooling=None,
        dropout_rate=0.2,
        classifier_activation="softmax",
        include_preprocessing=True,
    )


import tensorflow_model_optimization as tfmot

quantize_model = tfmot.quantization.keras.quantize_model

# q_aware stands for for quantization aware.
q_aware_model = quantize_model(model)
Dominant language
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