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A error when using tfmot.quantization.keras.quantize_model to quantize keras model

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#1,031 2 comentarios 0 reacciones 1 asignado Ver en GitHub

@lenscloth ya está trabajando en esto.

Desde el 11/1/2023.

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Descripción

bug

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())

Lenguaje dominante
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