Commit 92c9880e authored by Amir MOHAMMADI's avatar Amir MOHAMMADI

Merge branch 'resnet101' into 'master'

Updated ARCFACE

See merge request !96
parents 4f80284c d255f06e
Pipeline #51357 passed with stages
in 5 minutes and 58 seconds
......@@ -17,9 +17,18 @@ class ArcFaceModel(EmbeddingValidation):
loss = self.compiled_loss(
y, logits, sample_weight=None, regularization_losses=self.losses
)
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
reg_loss = tf.reduce_sum(self.losses)
total_loss = loss + reg_loss
trainable_vars = self.trainable_variables
self.optimizer.minimize(total_loss, trainable_vars, tape=tape)
self.compiled_metrics.update_state(y, logits, sample_weight=None)
tf.summary.scalar("arc_face_loss", data=loss, step=self._train_counter)
tf.summary.scalar("total_loss", data=total_loss, step=self._train_counter)
self.train_loss(loss)
return {m.name: m.result() for m in self.metrics + [self.train_loss]}
......@@ -55,12 +64,16 @@ class ArcFaceLayer(tf.keras.layers.Layer):
s: int
Scale
arc: bool
If `True`, uses arcface loss. If `False`, it's a regular dense layer
"""
def __init__(self, n_classes=10, s=30, m=0.5):
def __init__(self, n_classes=10, s=30, m=0.5, arc=True):
super(ArcFaceLayer, self).__init__(name="arc_face_logits")
self.n_classes = n_classes
self.s = s
self.arc = arc
self.m = m
def build(self, input_shape):
......@@ -75,29 +88,31 @@ class ArcFaceLayer(tf.keras.layers.Layer):
self.mm = tf.identity(math.sin(math.pi - self.m) * self.m)
def call(self, X, y, training=None):
if self.arc:
# normalize feature
X = tf.nn.l2_normalize(X, axis=1)
W = tf.nn.l2_normalize(self.W, axis=0)
# 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)
# 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)
# 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(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)
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"
)
# 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
logits = (one_hot * cos_yi_m) + ((1.0 - one_hot) * cos_yi)
logits = self.s * logits
else:
logits = tf.matmul(X, self.W)
return logits
......@@ -136,6 +151,9 @@ class ArcFaceLayer3Penalties(tf.keras.layers.Layer):
# Getting the angle
theta = tf.math.acos(cos_yi)
theta = tf.clip_by_value(
theta, -1.0 + tf.keras.backend.epsilon(), 1 - tf.keras.backend.epsilon()
)
cos_yi_m = tf.math.cos(self.m1 * theta + self.m2) - self.m3
......@@ -146,8 +164,9 @@ class ArcFaceLayer3Penalties(tf.keras.layers.Layer):
tf.cast(y, tf.int32), depth=self.n_classes, name="one_hot_mask"
)
one_hot = tf.cast(one_hot, cos_yi_m.dtype)
logits = (one_hot * cos_yi_m) + ((1.0 - one_hot) * cos_yi)
logits = self.s * logits
return logits
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