An implementation of ArcFace for TensorFlow

Introduction

Implemented ArcFace for TensorFlow 2.x as a combination of custom layers and custom loss functions.

background

Among various methods of deep distance learning, ArcFace, which can be configured simply by adding it to the output layer of the classification problem, can be said to be a simple and clear method. (Reference: Modern deep metric learning method: SphereFace, CosFace, ArcFace) Realized with TensorFlow 2.x (Keras) because the connection layer of a normal neural network requires only the input value and weight to the layer in the calculation of the output value, whereas the calculation of ArcFace also requires the correct label. To do this, you need to devise something. As a precedent example of realizing ArcFace with Keras, there is "[Keras] MobileNetV2 + I tried to classify PET bottles using ArcFace!". In this case, ArcFace is implemented as a 2-input custom layer, and the correct label is input by bypassing the correct label from the generator of the training data set. The author also tried it with reference to the above, but when I wanted to replace ArcFace with the normal classification, I found it complicated that the network structure changed, so I considered implementing it by another method.

Implementation method

The rough calculation procedure of ArcFace is shown below.

1. L2 regularization of input X and weight W
2. Calculate cos (θ) = X · W (inner product) for labels other than the correct answer
3. Calculate cos (θ + m) for the correct label
4. Calculate Softmax

Here, cos (θ + m) = cos (θ) ・ cos (m) -sin (θ) ・ sin (m) (addition theorem), and sin (θ) = √ (1-cos (θ) ^ 2 ), So in practice, cos (θ) should be calculated including the correct answer label in step 2.

2'. Calculate cos (θ) = X · W (inner product) for all labels

This eliminates the need for correct labels up to step 2'. And can be implemented as a custom layer. In addition, since step 3 and subsequent steps do not require input or weights, they can be implemented in the loss function. However, it is necessary to calculate Softmax when calculating Accuracy.

An implementation example is shown below.

arcface.py

import TensorFlow as tf

#First half of ArcFace
class ArcFaceLayer0(tf.keras.layers.Layer) :
    def __init__(self, num_outputs, kernel_regularizer = None, **kargs) :
        super(ArcFaceLayer0, self).__init__(**kargs)
        self.num_outputs = num_outputs
        self.kernel_regularizer = kernel_regularizer

    def build(self, input_shape) :
        weight_shape = (input_shape[-1] , self.num_outputs)
        self.kernel = self.add_weight(
            name='kernel',
            shape = weight_shape,
            initializer = tf.keras.initializers.TruncatedNormal(),
            regularizer = self.kernel_regularizer,
            trainable = True
            )
        super(ArcFaceLayer0, self).build(input_shape)

    def call(self, input) :
        n_input = tf.math.l2_normalize(input, axis = 1)               #L2 regularization of input
        n_kernel = tf.math.l2_normalize(self.kernel, axis = 0)        #L2 regularization of weights
        return tf.matmul(n_input, n_kernel)      # W.Inner product of Tx

#ArcFace implemented on the loss function side
class ArcFaceLoss(tf.keras.losses.Loss) :
    # m:margin
    # s:magnification
    # loss_func:Original loss function tf.keras.losses.CategoricalCrossentropy(from_logits = True)Such
    def __init__(self, loss_func, m = 0.5, s = 30, name = "arcface_loss", **kwargs) :
        self.loss_func = loss_func
        self.margin = m
        self.s = s
        self.enable = True
        super(ArcFaceLoss, self).__init__(name = name, **kwargs)

    def call(self, y_true, y_pred):
        # y_pred is cos(θ)
        #Sin for the addition theorem(θ)To calculate
        sine = tf.keras.backend.sqrt(1.0 - tf.keras.backend.square(y_pred))
        phi = y_pred * self.cos_m - sine * self.sin_m       # cos(θ+m)Addition theorem
        phi = tf.where(y_pred > 0, phi, y_pred)             #As it is when facing the day after tomorrow

        #Correct answer class:cos(θ+m)Other classes:cosθ 
        logits = (y_true * phi) + ((1.0 - y_true) * y_pred)

        #Call the original loss function
        return self.loss_func(y_true, logits * self.s)

#Evaluation function for ArcFace
class ArcFaceAccuracy(tf.keras.metrics.Mean) :
    def __init__(self, metrics_func, s = 30, name = "arcface_accuracy", dtype = None) :
        self.metrics_func = metrics_func
        self.s = s
        super(ArcFaceAccuracy, self).__init__(name, dtype)

    def update_state(self, y_true, y_pred, sample_weight = None) :
        output = tf.nn.softmax(y_pred * self.s)
        matches = self.metrics_func(y_true, output)

        return super(ArcFaceAccuracy, self).update_state(matches, sample_weight = sample_weight)