A error when using tfmot.quantization.keras.quantize_model to quantize keras model
@lenscloth is already working on this.
Since Jan 11, 2023.
Assessment
This issue has not been assessed yet.
Description
Describe the bug
When quantizing the keras model after pruning, an error is reported as follows.
RuntimeError: Layer conv1d:<class 'tensorflow.python.keras.layers.convolutional.Conv1D'> is not supported. You can quantize this layer by passing a tfmot.quantization.keras.QuantizeConfig instance to the quantize_annotate_layer API.
Dose fmot.quantization.keras.quantize_model not support 1DCNN?
System information
TensorFlow version (installed from source or binary): tf=2.4.1
TensorFlow Model Optimization version (installed from source or binary): tensorflow_model_optimization==0.5.0
Python version: python==3.6.9
Code to reproduce the issue
LOGGER.info(f"starting")
prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude
pruning_params = {'pruning_schedule':
tfmot.sparsity.keras.ConstantSparsity(target_sparsity=0.30,begin_step=0,end_step=-1)}
LOGGER.info(f"pruning")
self._model = prune_low_magnitude(self._model, **pruning_params)
LOGGER.info(f"compiling")
self._model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=["accuracy"])
LOGGER.info(f"fitting")
history=self._model.fit(x=data, epochs=8, verbose=1, shuffle=True, callbacks=callbacks)
LOGGER.info(f"striping")
self._model = tfmot.sparsity.keras.strip_pruning(self._model)
LOGGER.info(f"quantizing")
quantize_model = tfmot.quantization.keras.quantize_model
self._model = quantize_model(self._model)
LOGGER.info(f"compiling")
self._model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=["accuracy"])
LOGGER.info(f"fitting")
history = self._model.fit(x=data, epochs=2, verbose=1, shuffle=True, callbacks=callbacks)
tf. keras model
import tensorflow as tf
inputs = tf.keras.Input(shape=(58,))
x=tf.keras.layers.Reshape((58,1))(inputs)
x = tf.keras.layers.Conv1D(8, 1,strides=1,padding='same')(x)
x=tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)
x1 = tf.keras.layers.Conv1D(16, 1,strides=1,padding='same',dilation_rate=3)(x)
x2 = tf.keras.layers.Conv1D(16, 3,strides=1,padding='same',dilation_rate=3)(x)
x1=tf.keras.layers.BatchNormalization()(x1)
x2=tf.keras.layers.BatchNormalization()(x2)
x1 = tf.keras.layers.Activation('relu')(x1)
x2 = tf.keras.layers.Activation('relu')(x2)
x=tf.keras.layers.Concatenate(-1)([x1,x2])
x = tf.keras.layers.Conv1D(16, 1,strides=1,padding='same')(x)
x=tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)
x1 = tf.keras.layers.Conv1D(32, 1,strides=1,padding='same',dilation_rate=3)(x)
x2 = tf.keras.layers.Conv1D(32, 3,strides=1,padding='same',dilation_rate=3)(x)
x1=tf.keras.layers.BatchNormalization()(x1)
x2=tf.keras.layers.BatchNormalization()(x2)
x1 = tf.keras.layers.Activation('relu')(x1)
x2 = tf.keras.layers.Activation('relu')(x2)
x=tf.keras.layers.Concatenate(-1)([x1,x2])
x = tf.keras.layers.Conv1D(32, 1,strides=1,padding='same')(x)
x=tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)
x1 = tf.keras.layers.Conv1D(64, 1,strides=1,padding='same',dilation_rate=3)(x)
x2 = tf.keras.layers.Conv1D(64, 3,strides=1,padding='same',dilation_rate=3)(x)
x1=tf.keras.layers.BatchNormalization()(x1)
x2=tf.keras.layers.BatchNormalization()(x2)
x1 = tf.keras.layers.Activation('relu')(x1)
x2 = tf.keras.layers.Activation('relu')(x2)
x=tf.keras.layers.Concatenate(-1)([x1,x2])
x=tf.keras.layers.Dropout(0.5)(x)
x = tf.keras.layers.Conv1D(128, 4,strides=1,padding='same')(x)
x=tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)
x_shortcut=x
x = tf.keras.layers.Conv1D(128, 1,strides=1,padding='same')(x)
x=tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)
x=tf.keras.layers.SeparableConv1D(128,2,strides=1,padding='same')(x)
x=tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)
se_x = x
se_x=tf.keras.layers.GlobalAveragePooling1D()(se_x)
se_x =tf.keras.layers.Dense(128)(se_x)
se_x= tf.keras.layers.Activation('relu')(se_x)
se_x= tf.keras.layers.Multiply()([x,se_x])
x = tf.keras.layers.add([x,se_x])
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.add([x,x_shortcut])
x = tf.keras.layers.Activation('relu')(x)
x=tf.keras.layers.GlobalAveragePooling1D()(x)
x=tf.keras.layers.Dropout(0.5)(x)
x =tf.keras.layers.Dense(7)(x)
pred = tf.keras.layers.Activation('softmax')(x)
model = tf.keras.Model(inputs=inputs, outputs=pred)
model.summary()
print(model.to_json())
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