» Code examples / Computer Vision / Knowledge Distillation

Knowledge Distillation

Author: Kenneth Borup
Date created: 2020/09/01
Last modified: 2020/09/01
Description: Implementation of classical Knowledge Distillation.

View in Colab GitHub source

Introduction to Knowledge Distillation

Knowledge Distillation is a procedure for model compression, in which a small (student) model is trained to match a large pre-trained (teacher) model. Knowledge is transferred from the teacher model to the student by minimizing a loss function, aimed at matching softened teacher logits as well as ground-truth labels.

The logits are softened by applying a "temperature" scaling function in the softmax, effectively smoothing out the probability distribution and revealing inter-class relationships learned by the teacher.



import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np

Construct Distiller() class

The custom Distiller() class, overrides the Model methods train_step, test_step, and compile(). In order to use the distiller, we need:

  • A trained teacher model
  • A student model to train
  • A student loss function on the difference between student predictions and ground-truth
  • A distillation loss function, along with a temperature, on the difference between the soft student predictions and the soft teacher labels
  • An alpha factor to weight the student and distillation loss
  • An optimizer for the student and (optional) metrics to evaluate performance

In the train_step method, we perform a forward pass of both the teacher and student, calculate the loss with weighting of the student_loss and distillation_loss by alpha and 1 - alpha, respectively, and perform the backward pass. Note: only the student weights are updated, and therefore we only calculate the gradients for the student weights.

In the test_step method, we evaluate the student model on the provided dataset.

class Distiller(keras.Model):
    def __init__(self, student, teacher):
        self.teacher = teacher
        self.student = student

    def compile(
        """ Configure the distiller.

            optimizer: Keras optimizer for the student weights
            metrics: Keras metrics for evaluation
            student_loss_fn: Loss function of difference between student
                predictions and ground-truth
            distillation_loss_fn: Loss function of difference between soft
                student predictions and soft teacher predictions
            alpha: weight to student_loss_fn and 1-alpha to distillation_loss_fn
            temperature: Temperature for softening probability distributions.
                Larger temperature gives softer distributions.
        super().compile(optimizer=optimizer, metrics=metrics)
        self.student_loss_fn = student_loss_fn
        self.distillation_loss_fn = distillation_loss_fn
        self.alpha = alpha
        self.temperature = temperature

    def train_step(self, data):
        # Unpack data
        x, y = data

        # Forward pass of teacher
        teacher_predictions = self.teacher(x, training=False)

        with tf.GradientTape() as tape:
            # Forward pass of student
            student_predictions = self.student(x, training=True)

            # Compute losses
            student_loss = self.student_loss_fn(y, student_predictions)

            # Compute scaled distillation loss from https://arxiv.org/abs/1503.02531
            # The magnitudes of the gradients produced by the soft targets scale
            # as 1/T^2, multiply them by T^2 when using both hard and soft targets.
            distillation_loss = (
                    tf.nn.softmax(teacher_predictions / self.temperature, axis=1),
                    tf.nn.softmax(student_predictions / self.temperature, axis=1),
                * self.temperature**2

            loss = self.alpha * student_loss + (1 - self.alpha) * distillation_loss

        # Compute gradients
        trainable_vars = self.student.trainable_variables
        gradients = tape.gradient(loss, trainable_vars)

        # Update weights
        self.optimizer.apply_gradients(zip(gradients, trainable_vars))

        # Update the metrics configured in `compile()`.
        self.compiled_metrics.update_state(y, student_predictions)

        # Return a dict of performance
        results = {m.name: m.result() for m in self.metrics}
            {"student_loss": student_loss, "distillation_loss": distillation_loss}
        return results

    def test_step(self, data):
        # Unpack the data
        x, y = data

        # Compute predictions
        y_prediction = self.student(x, training=False)

        # Calculate the loss
        student_loss = self.student_loss_fn(y, y_prediction)

        # Update the metrics.
        self.compiled_metrics.update_state(y, y_prediction)

        # Return a dict of performance
        results = {m.name: m.result() for m in self.metrics}
        results.update({"student_loss": student_loss})
        return results

Create student and teacher models

Initialy, we create a teacher model and a smaller student model. Both models are convolutional neural networks and created using Sequential(), but could be any Keras model.

# Create the teacher
teacher = keras.Sequential(
        keras.Input(shape=(28, 28, 1)),
        layers.Conv2D(256, (3, 3), strides=(2, 2), padding="same"),
        layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding="same"),
        layers.Conv2D(512, (3, 3), strides=(2, 2), padding="same"),

# Create the student
student = keras.Sequential(
        keras.Input(shape=(28, 28, 1)),
        layers.Conv2D(16, (3, 3), strides=(2, 2), padding="same"),
        layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding="same"),
        layers.Conv2D(32, (3, 3), strides=(2, 2), padding="same"),

# Clone student for later comparison
student_scratch = keras.models.clone_model(student)

Prepare the dataset

The dataset used for training the teacher and distilling the teacher is MNIST, and the procedure would be equivalent for any other dataset, e.g. CIFAR-10, with a suitable choice of models. Both the student and teacher are trained on the training set and evaluated on the test set.

# Prepare the train and test dataset.
batch_size = 64
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Normalize data
x_train = x_train.astype("float32") / 255.0
x_train = np.reshape(x_train, (-1, 28, 28, 1))

x_test = x_test.astype("float32") / 255.0
x_test = np.reshape(x_test, (-1, 28, 28, 1))

Train the teacher

In knowledge distillation we assume that the teacher is trained and fixed. Thus, we start by training the teacher model on the training set in the usual way.

# Train teacher as usual

# Train and evaluate teacher on data.
teacher.fit(x_train, y_train, epochs=5)
teacher.evaluate(x_test, y_test)
Epoch 1/5
1875/1875 [==============================] - 248s 132ms/step - loss: 0.2438 - sparse_categorical_accuracy: 0.9220
Epoch 2/5
1875/1875 [==============================] - 263s 140ms/step - loss: 0.0881 - sparse_categorical_accuracy: 0.9738
Epoch 3/5
1875/1875 [==============================] - 245s 131ms/step - loss: 0.0650 - sparse_categorical_accuracy: 0.9811
Epoch 5/5
 363/1875 [====>.........................] - ETA: 3:18 - loss: 0.0555 - sparse_categorical_accuracy: 0.9839

Distill teacher to student

We have already trained the teacher model, and we only need to initialize a Distiller(student, teacher) instance, compile() it with the desired losses, hyperparameters and optimizer, and distill the teacher to the student.

# Initialize and compile distiller
distiller = Distiller(student=student, teacher=teacher)

# Distill teacher to student
distiller.fit(x_train, y_train, epochs=3)

# Evaluate student on test dataset
distiller.evaluate(x_test, y_test)
Epoch 1/3
1875/1875 [==============================] - 242s 129ms/step - sparse_categorical_accuracy: 0.9761 - student_loss: 0.1526 - distillation_loss: 0.0226
Epoch 2/3
1875/1875 [==============================] - 281s 150ms/step - sparse_categorical_accuracy: 0.9863 - student_loss: 0.1384 - distillation_loss: 0.0185
Epoch 3/3
 399/1875 [=====>........................] - ETA: 3:27 - sparse_categorical_accuracy: 0.9896 - student_loss: 0.1300 - distillation_loss: 0.0182

Train student from scratch for comparison

We can also train an equivalent student model from scratch without the teacher, in order to evaluate the performance gain obtained by knowledge distillation.

# Train student as doen usually

# Train and evaluate student trained from scratch.
student_scratch.fit(x_train, y_train, epochs=3)
student_scratch.evaluate(x_test, y_test)
Epoch 1/3
1875/1875 [==============================] - 4s 2ms/step - loss: 0.4731 - sparse_categorical_accuracy: 0.8550
Epoch 2/3
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0966 - sparse_categorical_accuracy: 0.9710
Epoch 3/3
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0750 - sparse_categorical_accuracy: 0.9773
313/313 [==============================] - 0s 963us/step - loss: 0.0691 - sparse_categorical_accuracy: 0.9778

[0.06905383616685867, 0.9778000116348267]

If the teacher is trained for 5 full epochs and the student is distilled on this teacher for 3 full epochs, you should in this example experience a performance boost compared to training the same student model from scratch, and even compared to the teacher itself. You should expect the teacher to have accuracy around 97.6%, the student trained from scratch should be around 97.6%, and the distilled student should be around 98.1%. Remove or try out different seeds to use different weight initializations.