Tensorflow Quantization Aware Training

Question

I want to quantize a DenseNet model. I am using Tensorflow 2.4.

import tensorflow_model_optimization as tfmot
model = tf.keras.applications.DenseNet121(include_top=True,weights=None,input_tensor=None,input_shape=None,pooling=None,classes=1000) 
quantize_model = tfmot.quantization.keras.quantize_model
model = quantize_model(model)

But I got the following message:

RuntimeError: Layer conv2_block1_0_bn:<class 'tensorflow.python.keras.layers.normalization_v2.BatchNormalization'> is not supported. You can quantize this layer by passing a tfmot.quantization.keras.QuantizeConfig instance to the quantize_annotate_layer API.

Is there a way how I can do this. I can not change the keras code.

score 0 · Accepted Answer · answered Jan 28 '21 at 19:39

In your case you need to quantize the layer BatchNormalization seperately.

If you see the below example code snippet from this Quantization TF Guide, DefaultDenseQuantizeConfig is used to handle this problem. Hope this guide helpy you solve this.

quantize_annotate_layer = tfmot.quantization.keras.quantize_annotate_layer
quantize_annotate_model = tfmot.quantization.keras.quantize_annotate_model
quantize_scope = tfmot.quantization.keras.quantize_scope

class CustomLayer(tf.keras.layers.Dense):
  pass

model = quantize_annotate_model(tf.keras.Sequential([
   quantize_annotate_layer(CustomLayer(20, input_shape=(20,)), DefaultDenseQuantizeConfig()),
   tf.keras.layers.Flatten()
]))

# `quantize_apply` requires mentioning `DefaultDenseQuantizeConfig` with `quantize_scope`
# as well as the custom Keras layer.
with quantize_scope(
  {'DefaultDenseQuantizeConfig': DefaultDenseQuantizeConfig,
   'CustomLayer': CustomLayer}):
  # Use `quantize_apply` to actually make the model quantization aware.
  quant_aware_model = tfmot.quantization.keras.quantize_apply(model)

quant_aware_model.summary()

Tensorflow Quantization Aware Training

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