DevHeads IoT Integration Server•3mo ago

Fixing INT8 Quantization Error for Depthwise Conv2D Layers

Hey everyone, Thanks for the previous suggestions on tackling the inference timeout issue in my vibration anomaly detection project. I implemented quantization to optimize the model, but now I'm encountering a new error: Error Message:

Quantization Error: Unsupported Layer Type in INT8 Conversion - Layer 5 (Depthwise Conv2D)

Quantization Error: Unsupported Layer Type in INT8 Conversion - Layer 5 (Depthwise Conv2D)

It seems like the quantization process is failing specifically at Layer 5, which uses a Depthwise Conv2D operation. What’s the best approach to handle layers that aren’t compatible with INT8 quantization? Should I consider retraining with a different architecture, or is there a workaround to manually adjust these layers? Thanks in advance for your help!

Solution:

Instead of fully quantizing the model to INT8, you can use mixed precision quantization. This approach leaves unsupported layers like Depthwise Conv2D in float32 FP32 while quantizing the rest of the model to INT8 For TensorFlow Lite, you can specify dynamic range quantization for unsupported layers. See how you can adjust your conversion script: ``` converter = tf.lite.TFLiteConverter.from_keras_model(model)...

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Solution

Marvee Amasi•3mo ago

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8,  # INT8 quantized ops
                                       tf.lite.OpsSet.TFLITE_BUILTINS]  # FP32 fallback for unsupported layers
converter.inference_input_type = tf.int8  # Input quantized as int8
converter.inference_output_type = tf.int8  # Output quantized as int8
tflite_model = converter.convert()

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8,  # INT8 quantized ops
                                       tf.lite.OpsSet.TFLITE_BUILTINS]  # FP32 fallback for unsupported layers
converter.inference_input_type = tf.int8  # Input quantized as int8
converter.inference_output_type = tf.int8  # Output quantized as int8
tflite_model = converter.convert()

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Fixing INT8 Quantization Error for Depthwise Conv2D Layers