text stringlengths 0 4.99k |
|---|
get_resnetv2().count_params() |
23773798 |
Compared to the teacher model, this model has 358 Million fewer parameters. |
Distillation utility |
We will reuse some code from this example on knowledge distillation. |
class Distiller(tf.keras.Model): |
def __init__(self, student, teacher): |
super(Distiller, self).__init__() |
self.student = student |
self.teacher = teacher |
self.loss_tracker = keras.metrics.Mean(name=\"distillation_loss\") |
@property |
def metrics(self): |
metrics = super().metrics |
metrics.append(self.loss_tracker) |
return metrics |
def compile( |
self, optimizer, metrics, distillation_loss_fn, temperature=TEMPERATURE, |
): |
super(Distiller, self).compile(optimizer=optimizer, metrics=metrics) |
self.distillation_loss_fn = distillation_loss_fn |
self.temperature = temperature |
def train_step(self, data): |
# Unpack data |
x, _ = 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 loss |
distillation_loss = self.distillation_loss_fn( |
tf.nn.softmax(teacher_predictions / self.temperature, axis=1), |
tf.nn.softmax(student_predictions / self.temperature, axis=1), |
) |
# Compute gradients |
trainable_vars = self.student.trainable_variables |
gradients = tape.gradient(distillation_loss, trainable_vars) |
# Update weights |
self.optimizer.apply_gradients(zip(gradients, trainable_vars)) |
# Report progress |
self.loss_tracker.update_state(distillation_loss) |
return {\"distillation_loss\": self.loss_tracker.result()} |
def test_step(self, data): |
# Unpack data |
x, y = data |
# Forward passes |
teacher_predictions = self.teacher(x, training=False) |
student_predictions = self.student(x, training=False) |
# Calculate the loss |
distillation_loss = self.distillation_loss_fn( |
tf.nn.softmax(teacher_predictions / self.temperature, axis=1), |
tf.nn.softmax(student_predictions / self.temperature, axis=1), |
) |
# Report progress |
self.loss_tracker.update_state(distillation_loss) |
self.compiled_metrics.update_state(y, student_predictions) |
results = {m.name: m.result() for m in self.metrics} |
return results |
Learning rate schedule |
A warmup cosine learning rate schedule is used in the paper. This schedule is also typical for many pre-training methods especially for computer vision. |
# Some code is taken from: |
# https://www.kaggle.com/ashusma/training-rfcx-tensorflow-tpu-effnet-b2. |
class WarmUpCosine(keras.optimizers.schedules.LearningRateSchedule): |
def __init__( |
self, learning_rate_base, total_steps, warmup_learning_rate, warmup_steps |
): |
super(WarmUpCosine, self).__init__() |
self.learning_rate_base = learning_rate_base |
self.total_steps = total_steps |
self.warmup_learning_rate = warmup_learning_rate |
self.warmup_steps = warmup_steps |
self.pi = tf.constant(np.pi) |
def __call__(self, step): |
if self.total_steps < self.warmup_steps: |
raise ValueError(\"Total_steps must be larger or equal to warmup_steps.\") |
cos_annealed_lr = tf.cos( |
self.pi |
* (tf.cast(step, tf.float32) - self.warmup_steps) |
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