Rewiring Arcface to make it simpler

bob/learn/tensorflow/models/arcface.py

+import tensorflow as tf
+from .embedding_validation import EmbeddingValidation
+from bob.learn.tensorflow.metrics.embedding_accuracy import accuracy_from_embeddings
+import math
+
+
+class ArcFaceModel(EmbeddingValidation):
+    def train_step(self, data):
+        X, y = data
+
+        with tf.GradientTape() as tape:
+
+            logits, _ = self((X, y), training=True)
+            loss = self.compiled_loss(
+                y, logits, sample_weight=None, regularization_losses=self.losses
+            )
+        self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
+        self.compiled_metrics.update_state(y, logits, sample_weight=None)
+
+        self.train_loss(loss)
+        return {m.name: m.result() for m in self.metrics + [self.train_loss]}
+
+    def test_step(self, data):
+        """
+        Test Step
+        """
+
+        images, labels = data
+
+        # No worries, labels not used in validation
+        _, embeddings = self((images, labels), training=False)
+        self.validation_acc(accuracy_from_embeddings(labels, embeddings))
+        return {m.name: m.result() for m in [self.validation_acc]}
+
+
+class ArcFaceLayer(tf.keras.layers.Layer):
+    """
+    Implements the ArcFace from equation (3) of `ArcFace: Additive Angular Margin Loss for Deep Face Recognition <https://arxiv.org/abs/1801.07698>`_
+
+    Defined as:
+
+    :math:`s(cos(\\theta_i) + m`
+
+    Parameters
+    ----------
+
+      n_classes: int
+        Number of classes
+
+      m: float
+         Margin  
+
+      s: int
+         Scale  
+    """
+
+    def __init__(self, n_classes=10, s=30, m=0.5):
+        super(ArcFaceLayer, self).__init__(name="arc_face_logits")
+        self.n_classes = n_classes
+        self.s = s
+        self.m = m
+
+    def build(self, input_shape):
+        super(ArcFaceLayer, self).build(input_shape[0])
+        shape = [input_shape[-1], self.n_classes]
+
+        self.W = self.add_variable("W", shape=shape)
+
+        self.cos_m = tf.identity(math.cos(self.m), name="cos_m")
+        self.sin_m = tf.identity(math.sin(self.m), name="sin_m")
+        self.th = tf.identity(math.cos(math.pi - self.m), name="th")
+        self.mm = tf.identity(math.sin(math.pi - self.m) * self.m)
+
+    def call(self, X, y, training=None):
+
+        # normalize feature
+        X = tf.nn.l2_normalize(X, axis=1)
+        W = tf.nn.l2_normalize(self.W, axis=0)
+
+        # cos between X and W
+        cos_yi = tf.matmul(X, W)
+
+        # sin_yi = tf.math.sqrt(1-cos_yi**2)
+        sin_yi = tf.clip_by_value(tf.math.sqrt(1 - cos_yi ** 2), 0, 1)
+
+        # cos(x+m) = cos(x)*cos(m) - sin(x)*sin(m)
+        cos_yi_m = cos_yi * self.cos_m - sin_yi * self.sin_m
+
+        cos_yi_m = tf.where(cos_yi > self.th, cos_yi_m, cos_yi - self.mm)
+
+        # Preparing the hot-output
+        one_hot = tf.one_hot(
+            tf.cast(y, tf.int32), depth=self.n_classes, name="one_hot_mask"
+        )
+
+        logits = (one_hot * cos_yi_m) + ((1.0 - one_hot) * cos_yi)
+        logits = self.s * logits
+
+        return logits
+
+
+class ArcFaceLayer3Penalties(tf.keras.layers.Layer):
+    """
+    Implements the ArcFace loss from equation (4) of `ArcFace: Additive Angular Margin Loss for Deep Face Recognition <https://arxiv.org/abs/1801.07698>`_
+    
+    Defined as:
+    
+      :math:`s(cos(m_1\\theta_i + m_2) -m_3`
+    """
+
+    def __init__(self, n_classes=10, s=30, m1=0.5, m2=0.5, m3=0.5):
+        super(ArcFaceLayer3Penalties, self).__init__(name="arc_face_logits")
+        self.n_classes = n_classes
+        self.s = s
+        self.m1 = m1
+        self.m2 = m2
+        self.m3 = m3
+
+    def build(self, input_shape):
+        super(ArcFaceLayer3Penalties, self).build(input_shape[0])
+        shape = [input_shape[-1], self.n_classes]
+
+        self.W = self.add_variable("W", shape=shape)
+
+    def call(self, X, y, training=None):
+
+        # normalize feature
+        X = tf.nn.l2_normalize(X, axis=1)
+        W = tf.nn.l2_normalize(self.W, axis=0)
+
+        # cos between X and W
+        cos_yi = tf.matmul(X, W)
+
+        # Getting the angle
+        theta = tf.math.acos(cos_yi)
+
+        cos_yi_m = tf.math.cos(self.m1 * theta + self.m2) - self.m3
+
+        # logits = self.s*cos_theta_m
+
+        # Preparing the hot-output
+        one_hot = tf.one_hot(
+            tf.cast(y, tf.int32), depth=self.n_classes, name="one_hot_mask"
+        )
+
+        logits = (one_hot * cos_yi_m) + ((1.0 - one_hot) * cos_yi)
+
+        logits = self.s * logits
+
+        return logits
+