diff --git a/bob/bio/face/tensorflow/__init__.py b/bob/bio/face/tensorflow/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/bob/bio/face/tensorflow/preprocessing.py b/bob/bio/face/tensorflow/preprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf87921c171140bb2f364bae7715da744fc4c8b5
--- /dev/null
+++ b/bob/bio/face/tensorflow/preprocessing.py
@@ -0,0 +1,112 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""
+Tensor pre-processing for somr face recognition CNNs
+"""
+
+import os
+from functools import partial
+from multiprocessing import cpu_count
+import tensorflow as tf
+from tensorflow.keras import layers
+
+# STANDARD FEATURES FROM OUR TF-RECORDS
+FEATURES = {
+ "data": tf.io.FixedLenFeature([], tf.string),
+ "label": tf.io.FixedLenFeature([], tf.int64),
+ "key": tf.io.FixedLenFeature([], tf.string),
+}
+
+
+def decode_tfrecords(x, data_shape, data_type=tf.uint8):
+ features = tf.io.parse_single_example(x, FEATURES)
+ image = tf.io.decode_raw(features["data"], data_type)
+ image = tf.reshape(image, data_shape)
+ features["data"] = image
+ return features
+
+
+def get_preprocessor(output_shape):
+ """
+
+ """
+ preprocessor = tf.keras.Sequential(
+ [
+ # rotate before cropping
+ # 5 random degree rotation
+ layers.experimental.preprocessing.RandomRotation(5 / 360),
+ layers.experimental.preprocessing.RandomCrop(
+ height=output_shape[0], width=output_shape[1]
+ ),
+ layers.experimental.preprocessing.RandomFlip("horizontal"),
+ # FIXED_STANDARDIZATION from https://github.com/davidsandberg/facenet
+ # [-0.99609375, 0.99609375]
+ layers.experimental.preprocessing.Rescaling(
+ scale=1 / 128, offset=-127.5 / 128
+ ),
+ ]
+ )
+ return preprocessor
+
+
+def preprocess(preprocessor, features, augment=False):
+ image = features["data"]
+ label = features["label"]
+ image = preprocessor(image, training=augment)
+ return image, label
+
+
+def prepare_dataset(
+ tf_record_paths,
+ batch_size,
+ epochs,
+ data_shape,
+ output_shape,
+ shuffle=False,
+ augment=False,
+ autotune=tf.data.experimental.AUTOTUNE,
+ n_cpus=cpu_count(),
+ shuffle_buffer=int(2e4),
+):
+ """
+ Create batches from a list of TF-Records
+
+ Parameters
+ ----------
+
+ tf_record_paths: list
+ List of paths of the TF-Records
+
+ batch_size: int
+
+ epochs: int
+
+ shuffle: bool
+
+ augment: bool
+
+ autotune: int
+
+ n_cpus: int
+
+ shuffle_buffer: int
+ """
+
+ ds = tf.data.Dataset.list_files(tf_record_paths, shuffle=shuffle)
+ ds = tf.data.TFRecordDataset(ds, num_parallel_reads=n_cpus)
+ if shuffle:
+ # ignore order and read files as soon as they come in
+ ignore_order = tf.data.Options()
+ ignore_order.experimental_deterministic = False
+ ds = ds.with_options(ignore_order)
+ ds = ds.map(partial(decode_tfrecords, data_shape=data_shape)).prefetch(buffer_size=autotune)
+ if shuffle:
+ ds = ds.shuffle(shuffle_buffer).repeat(epochs)
+ preprocessor = get_preprocessor(output_shape)
+ ds = ds.batch(batch_size).map(
+ partial(preprocess, preprocessor, augment=augment), num_parallel_calls=autotune,
+ )
+
+ # Use buffered prefecting on all datasets
+ return ds.prefetch(buffer_size=autotune)
diff --git a/cnn_training/._centerloss_mixed_precision.py b/cnn_training/._centerloss_mixed_precision.py
new file mode 100644
index 0000000000000000000000000000000000000000..881da051d61cfeb4e02036b9ea9c9df85b2fefdf
Binary files /dev/null and b/cnn_training/._centerloss_mixed_precision.py differ
diff --git a/cnn_training/arcface.py b/cnn_training/arcface.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb59350947ca4cfc8a0f8bc7eb171d779f7b536b
--- /dev/null
+++ b/cnn_training/arcface.py
@@ -0,0 +1,409 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""
+Trains some face recognition baselines using ARC based models
+
+# ARCFACE PARAMETERS from eq.4
+# FROM https://github.com/deepinsight/insightface/blob/master/recognition/ArcFace/sample_config.py#L153
+ M1 = 1.0
+ M2 = 0.3
+ M3 = 0.2
+
+
+# ARCFACE PARAMETERS from eq.3
+M = 0.5 # ArcFace Margin #CHECK SECTION 3.1
+SCALE = 64.0 # Scale
+# ORIGINAL = False # Original implementation
+
+
+The config file has the following format to train an ARCFACE model:
+
+```yml
+# VGG2 params
+batch-size: 90
+face-size: 182
+face-output-size: 160
+n-classes: 87662
+
+
+## Backbone
+backbone: 'mobilenet-v2'
+head: 'arcface'
+s: 10
+bottleneck: 512
+m: 0.5
+
+# Training parameters
+#solver: "rmsprop"
+solver: "sgd"
+lr: 0.1
+dropout-rate: 0.5
+epochs: 310
+lerning-rate-schedule: 'cosine-decay-restarts'
+
+
+
+train-tf-record-path: "/path/*.tfrecord"
+validation-tf-record-path: "/path/lfw_pairs.tfrecord"
+```
+
+
+
+
+Usage:
+ arcface.py <config-yaml> <checkpoint_path>
+ arcface.py -h | --help
+
+Options:
+ -h --help Show this screen.
+ arcface.py arcface -h | help
+
+"""
+
+import os
+from functools import partial
+
+import pkg_resources
+import tensorflow as tf
+from bob.learn.tensorflow.models.inception_resnet_v2 import InceptionResNetV2
+from bob.learn.tensorflow.metrics import predict_using_tensors
+from tensorflow.keras import layers
+from bob.learn.tensorflow.callbacks import add_backup_callback
+from bob.learn.tensorflow.metrics.embedding_accuracy import accuracy_from_embeddings
+from bob.extension import rc
+from bob.bio.face.tensorflow.preprocessing import prepare_dataset
+import yaml
+
+from bob.learn.tensorflow.layers import (
+ add_bottleneck,
+ add_top,
+ SphereFaceLayer,
+ ModifiedSoftMaxLayer,
+)
+
+from bob.learn.tensorflow.models import (
+ EmbeddingValidation,
+ ArcFaceLayer,
+ ArcFaceModel,
+ ArcFaceLayer3Penalties,
+)
+
+
+##############################
+# CNN Backbones
+# Add your NN backbone here
+##############################
+BACKBONES = dict()
+BACKBONES["inception-resnet-v2"] = InceptionResNetV2
+BACKBONES["efficientnet-B0"] = tf.keras.applications.EfficientNetB0
+BACKBONES["resnet50"] = tf.keras.applications.ResNet50
+BACKBONES["mobilenet-v2"] = tf.keras.applications.MobileNetV2
+
+##############################
+# SOLVER SPECIFICATIONS
+##############################
+
+SOLVERS = dict()
+# Parameters taken from https://github.com/davidsandberg/facenet/blob/master/src/facenet.py#L181
+# Fixing the start learning rate
+learning_rate = 0.1
+SOLVERS["rmsprop"] = partial(
+ tf.keras.optimizers.RMSprop,
+ learning_rate=learning_rate,
+ rho=0.9,
+ momentum=0.9,
+ epsilon=1.0,
+)
+SOLVERS["adam"] = partial(tf.keras.optimizers.Adam, learning_rate=learning_rate)
+SOLVERS["adagrad"] = partial(tf.keras.optimizers.Adagrad, learning_rate=learning_rate)
+SOLVERS["sgd"] = partial(
+ tf.keras.optimizers.SGD, learning_rate=learning_rate, momentum=0.9, nesterov=True
+)
+
+
+################################
+# DATA SPECIFICATION
+###############################
+DATA_SHAPES = dict()
+
+# Inputs with 182x182 are cropped to 160x160
+DATA_SHAPES[182] = 160
+DATA_SHAPES[112] = 98
+DATA_SHAPES[126] = 112
+
+
+# SHAPES EXPECTED FROM THE DATASET USING THIS BACKBONE
+# DATA_SHAPE = (182, 182, 3) # size of faces
+DATA_TYPE = tf.uint8
+# OUTPUT_SHAPE = (160, 160)
+
+AUTOTUNE = tf.data.experimental.AUTOTUNE
+
+# HERE WE VALIDATE WITH LFW RUNNING A
+# INFORMATION ABOUT THE VALIDATION SET
+# VALIDATION_TF_RECORD_PATHS = rc["bob.bio.face.cnn.lfw_tfrecord_path"]
+
+# there are 2812 samples in the validation set
+VALIDATION_SAMPLES = 2812
+VALIDATION_BATCH_SIZE = 38
+
+
+def create_model(
+ n_classes, model_spec, backbone, bottleneck, dropout_rate, input_shape
+):
+
+ if backbone == "inception-resnet-v2":
+ pre_model = BACKBONES[backbone](
+ include_top=False, bottleneck=False, input_shape=input_shape,
+ )
+ else:
+ pre_model = BACKBONES[backbone](
+ include_top=False, input_shape=input_shape, weights=None,
+ )
+
+ # Adding the bottleneck
+ pre_model = add_bottleneck(
+ pre_model, bottleneck_size=bottleneck, dropout_rate=dropout_rate
+ )
+ pre_model = add_top(pre_model, n_classes=n_classes)
+
+ float32_layer = layers.Activation("linear", dtype="float32")
+
+ embeddings = tf.nn.l2_normalize(
+ pre_model.get_layer("embeddings/BatchNorm").output, axis=1
+ )
+
+ logits_premodel = float32_layer(pre_model.get_layer("logits").output)
+
+ # Wrapping the embedding validation
+ pre_model = EmbeddingValidation(
+ pre_model.input, outputs=[logits_premodel, embeddings], name=pre_model.name
+ )
+
+ ################################
+ ## Creating the specific models
+ if "arcface" in model_spec:
+ labels = tf.keras.layers.Input([], name="label")
+ logits_arcface = ArcFaceLayer(
+ n_classes, s=model_spec["arcface"]["s"], m=model_spec["arcface"]["m"]
+ )(embeddings, labels)
+ arc_model = ArcFaceModel(
+ inputs=(pre_model.input, labels), outputs=[logits_arcface, embeddings]
+ )
+ elif "arcface-3p" in model_spec:
+ labels = tf.keras.layers.Input([], name="label")
+ logits_arcface = ArcFaceLayer3Penalties(
+ n_classes,
+ s=model_spec["arcface-3p"]["s"],
+ m1=model_spec["arcface-3p"]["m1"],
+ m2=model_spec["arcface-3p"]["m2"],
+ m3=model_spec["arcface-3p"]["m3"],
+ )(embeddings, labels)
+ arc_model = ArcFaceModel(
+ inputs=(pre_model.input, labels), outputs=[logits_arcface, embeddings]
+ )
+ elif "sphereface" in model_spec:
+ logits_arcface = SphereFaceLayer(n_classes, m=model_spec["sphereface"]["m"],)(
+ embeddings
+ )
+ arc_model = EmbeddingValidation(
+ pre_model.input, outputs=[logits_arcface, embeddings]
+ )
+
+ elif "modified-softmax" in model_spec:
+ logits_modified_softmax = ModifiedSoftMaxLayer(n_classes)(embeddings)
+ arc_model = EmbeddingValidation(
+ pre_model.input, outputs=[logits_modified_softmax, embeddings]
+ )
+
+ return pre_model, arc_model
+
+
+def build_and_compile_models(
+ n_classes, optimizer, model_spec, backbone, bottleneck, dropout_rate, input_shape
+):
+ pre_model, arc_model = create_model(
+ n_classes, model_spec, backbone, bottleneck, dropout_rate, input_shape
+ )
+
+ cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(
+ from_logits=True, name="cross_entropy"
+ )
+
+ pre_model.compile(optimizer=optimizer, loss=cross_entropy, metrics=["accuracy"])
+
+ arc_model.compile(optimizer=optimizer, loss=cross_entropy, metrics=["accuracy"])
+
+ return pre_model, arc_model
+
+
+def train_and_evaluate(
+ tf_record_paths,
+ checkpoint_path,
+ n_classes,
+ batch_size,
+ epochs,
+ model_spec,
+ backbone,
+ optimizer,
+ bottleneck,
+ dropout_rate,
+ face_size,
+ validation_path,
+ lerning_rate_schedule,
+):
+
+ # number of training steps to do before validating a model. This also defines an epoch
+ # for keras which is not really true. We want to evaluate every 180000 (90 * 2000)
+ # samples
+ # STEPS_PER_EPOCH = 180000 // batch_size
+ # KERAS_EPOCH_MULTIPLIER = 6
+ STEPS_PER_EPOCH = 2000
+
+ DATA_SHAPE = (face_size, face_size, 3)
+ OUTPUT_SHAPE = (DATA_SHAPES[face_size], DATA_SHAPES[face_size])
+
+ if validation_path is None:
+ validation_path = rc["bob.bio.face.cnn.lfw_tfrecord_path"]
+ if validation_path is None:
+ raise ValueError(
+ "No validation set was set. Please, do `bob config set bob.bio.face.cnn.lfw_tfrecord_path [PATH]`"
+ )
+
+ train_ds = prepare_dataset(
+ tf_record_paths,
+ batch_size,
+ epochs,
+ data_shape=DATA_SHAPE,
+ output_shape=OUTPUT_SHAPE,
+ shuffle=True,
+ augment=True,
+ )
+
+ val_ds = prepare_dataset(
+ validation_path,
+ data_shape=DATA_SHAPE,
+ output_shape=OUTPUT_SHAPE,
+ epochs=epochs,
+ batch_size=VALIDATION_BATCH_SIZE,
+ shuffle=False,
+ augment=False,
+ )
+ val_metric_name = "val_accuracy"
+
+ pre_model, arc_model = build_and_compile_models(
+ n_classes,
+ optimizer,
+ model_spec,
+ backbone,
+ bottleneck=bottleneck,
+ dropout_rate=dropout_rate,
+ input_shape=OUTPUT_SHAPE + (3,),
+ )
+
+ def scheduler(epoch, lr):
+ # 200 epochs at 0.1, 10 at 0.01 and 5 0.001
+ # The epoch number here is Keras's which is different from actual epoch number
+ # epoch = epoch // KERAS_EPOCH_MULTIPLIER
+
+ # Tracking in the tensorboard
+ tf.summary.scalar("learning rate", data=lr, step=epoch)
+
+ if epoch in range(200):
+ return 1 * lr
+ else:
+ return lr * tf.math.exp(-0.01)
+
+ if lerning_rate_schedule == "cosine-decay-restarts":
+ decay_steps = 50
+ lr_decayed_fn = tf.keras.callbacks.LearningRateScheduler(
+ tf.keras.experimental.CosineDecayRestarts(
+ 0.1, decay_steps, t_mul=2.0, m_mul=0.8, alpha=0.1
+ ),
+ verbose=1,
+ )
+
+ else:
+ lr_decayed_fn = tf.keras.callbacks.LearningRateScheduler(scheduler, verbose=1)
+
+ callbacks = {
+ "latest": tf.keras.callbacks.ModelCheckpoint(
+ f"{checkpoint_path}/latest", verbose=1
+ ),
+ "tensorboard": tf.keras.callbacks.TensorBoard(
+ log_dir=f"{checkpoint_path}/logs", update_freq=15, profile_batch=0
+ ),
+ "lr": lr_decayed_fn,
+ "nan": tf.keras.callbacks.TerminateOnNaN(),
+ }
+
+ callbacks = add_backup_callback(callbacks, backup_dir=f"{checkpoint_path}/backup")
+ # STEPS_PER_EPOCH
+ pre_model.fit(
+ train_ds,
+ epochs=2,
+ validation_data=val_ds,
+ steps_per_epoch=STEPS_PER_EPOCH,
+ validation_steps=VALIDATION_SAMPLES // VALIDATION_BATCH_SIZE,
+ callbacks=callbacks,
+ verbose=2,
+ )
+
+ # STEPS_PER_EPOCH
+ # epochs=epochs * KERAS_EPOCH_MULTIPLIER,
+ arc_model.fit(
+ train_ds,
+ validation_data=val_ds,
+ epochs=epochs,
+ steps_per_epoch=STEPS_PER_EPOCH,
+ validation_steps=VALIDATION_SAMPLES // VALIDATION_BATCH_SIZE,
+ callbacks=callbacks,
+ verbose=2,
+ )
+
+
+from docopt import docopt
+
+if __name__ == "__main__":
+ args = docopt(__doc__)
+
+ config = yaml.full_load(open(args["<config-yaml>"]))
+
+ model_spec = dict()
+ if config["head"] == "arcface":
+ model_spec["arcface"] = dict()
+ model_spec["arcface"]["m"] = float(config["m"])
+ model_spec["arcface"]["s"] = int(config["s"])
+
+ if config["head"] == "arcface-3p":
+ model_spec["arcface-3p"] = dict()
+ model_spec["arcface-3p"]["m1"] = float(config["m1"])
+ model_spec["arcface-3p"]["m2"] = float(config["m2"])
+ model_spec["arcface-3p"]["m3"] = float(config["m3"])
+ model_spec["arcface-3p"]["s"] = int(config["s"])
+
+ if config["head"] == "sphereface":
+ model_spec["sphereface"] = dict()
+ model_spec["sphereface"]["m"] = float(config["m"])
+
+ if config["head"] == "modified-softmax":
+ # There's no hyper parameter here
+ model_spec["modified-softmax"] = dict()
+
+ train_and_evaluate(
+ config["train-tf-record-path"],
+ args["<checkpoint_path>"],
+ int(config["n-classes"]),
+ int(config["batch-size"]),
+ int(config["epochs"]),
+ model_spec,
+ config["backbone"],
+ optimizer=SOLVERS[config["solver"]](learning_rate=float(config["lr"])),
+ bottleneck=int(config["bottleneck"]),
+ dropout_rate=float(config["dropout-rate"]),
+ face_size=int(config["face-size"]),
+ validation_path=config["validation-tf-record-path"],
+ lerning_rate_schedule=config["lerning-rate-schedule"],
+ )
+
diff --git a/cnn_training/centerloss.py b/cnn_training/centerloss.py
new file mode 100644
index 0000000000000000000000000000000000000000..bac26eb57220579cae2b4895009558b2fa9139cd
--- /dev/null
+++ b/cnn_training/centerloss.py
@@ -0,0 +1,251 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""
+Trains a face recognition CNN using the strategy from the paper
+
+"A Discriminative Feature Learning Approach
+for Deep Face Recognition" https://ydwen.github.io/papers/WenECCV16.pdf
+
+The default backbone is the InceptionResnetv2
+
+Do `./bin/python centerloss.py --help` for more information
+
+"""
+
+import os
+from functools import partial
+import click
+import pkg_resources
+import tensorflow as tf
+from bob.learn.tensorflow.losses import CenterLoss, CenterLossLayer
+from bob.learn.tensorflow.models.inception_resnet_v2 import InceptionResNetV2
+from bob.learn.tensorflow.metrics import predict_using_tensors
+from tensorflow.keras import layers
+from bob.learn.tensorflow.callbacks import add_backup_callback
+from bob.learn.tensorflow.metrics.embedding_accuracy import accuracy_from_embeddings
+from bob.extension import rc
+from bob.bio.face.tensorflow.preprocessing import prepare_dataset
+
+# CNN Backbone
+# Change your NN backbone here
+BACKBONE = InceptionResNetV2
+
+# SHAPES EXPECTED FROM THE DATASET USING THIS BACKBONE
+DATA_SHAPE = (182, 182, 3) # size of faces
+DATA_TYPE = tf.uint8
+OUTPUT_SHAPE = (160, 160)
+
+AUTOTUNE = tf.data.experimental.AUTOTUNE
+
+# HERE WE VALIDATE WITH LFW RUNNING A
+# INFORMATION ABOUT THE VALIDATION SET
+VALIDATION_TF_RECORD_PATHS = rc["bob.bio.face.cnn.lfw_tfrecord_path"]
+
+# there are 2812 samples in the validation set
+VALIDATION_SAMPLES = 2812
+VALIDATION_BATCH_SIZE = 38
+
+# WEIGHTS BEWTWEEN the two losses
+LOSS_WEIGHTS = {"cross_entropy": 1.0, "center_loss": 0.01}
+
+
+class CenterLossModel(tf.keras.Model):
+ def compile(
+ self,
+ cross_entropy,
+ center_loss,
+ loss_weights,
+ train_loss,
+ train_cross_entropy,
+ train_center_loss,
+ test_acc,
+ **kwargs,
+ ):
+ super().compile(**kwargs)
+ self.cross_entropy = cross_entropy
+ self.center_loss = center_loss
+ self.loss_weights = loss_weights
+ self.train_loss = train_loss
+ self.train_cross_entropy = train_cross_entropy
+ self.train_center_loss = train_center_loss
+ self.test_acc = test_acc
+
+ def train_step(self, data):
+ images, labels = data
+ with tf.GradientTape() as tape:
+ logits, prelogits = self(images, training=True)
+ loss_cross = self.cross_entropy(labels, logits)
+ loss_center = self.center_loss(labels, prelogits)
+ loss = (
+ loss_cross * self.loss_weights[self.cross_entropy.name]
+ + loss_center * self.loss_weights[self.center_loss.name]
+ )
+ trainable_vars = self.trainable_variables
+ gradients = tape.gradient(loss, trainable_vars)
+ self.optimizer.apply_gradients(zip(gradients, trainable_vars))
+
+ self.train_loss(loss)
+ self.train_cross_entropy(loss_cross)
+ self.train_center_loss(loss_center)
+ return {
+ m.name: m.result()
+ for m in [self.train_loss, self.train_cross_entropy, self.train_center_loss]
+ }
+
+ def test_step(self, data):
+ images, labels = data
+ logits, prelogits = self(images, training=False)
+ self.test_acc(accuracy_from_embeddings(labels, prelogits))
+ return {m.name: m.result() for m in [self.test_acc]}
+
+
+def create_model(n_classes):
+
+ model = BACKBONE(
+ include_top=True,
+ classes=n_classes,
+ bottleneck=True,
+ input_shape=OUTPUT_SHAPE + (3,),
+ )
+
+ prelogits = model.get_layer("Bottleneck/BatchNorm").output
+ prelogits = CenterLossLayer(
+ n_classes=n_classes, n_features=prelogits.shape[-1], name="centers"
+ )(prelogits)
+
+ logits = model.get_layer("logits").output
+ model = CenterLossModel(
+ inputs=model.input, outputs=[logits, prelogits], name=model.name
+ )
+ return model
+
+
+def build_and_compile_model(n_classes, learning_rate):
+ model = create_model(n_classes)
+
+ cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(
+ from_logits=True, name="cross_entropy"
+ )
+ center_loss = CenterLoss(
+ centers_layer=model.get_layer("centers"), alpha=0.9, name="center_loss",
+ )
+
+ optimizer = tf.keras.optimizers.RMSprop(
+ learning_rate=learning_rate, rho=0.9, momentum=0.9, epsilon=1.0
+ )
+
+ train_loss = tf.keras.metrics.Mean(name="loss")
+ train_cross_entropy = tf.keras.metrics.Mean(name="cross_entropy")
+ train_center_loss = tf.keras.metrics.Mean(name="center_loss")
+
+ test_acc = tf.keras.metrics.Mean(name="accuracy")
+
+ model.compile(
+ optimizer=optimizer,
+ cross_entropy=cross_entropy,
+ center_loss=center_loss,
+ loss_weights=LOSS_WEIGHTS,
+ train_loss=train_loss,
+ train_cross_entropy=train_cross_entropy,
+ train_center_loss=train_center_loss,
+ test_acc=test_acc,
+ )
+ return model
+
+
+@click.command()
+@click.argument("tf-record-paths")
+@click.argument("checkpoint-path")
+@click.option(
+ "-n",
+ "--n-classes",
+ default=87662,
+ help="Number of classes in the classification problem. Default to `87662`, which is the number of identities in our pruned MSCeleb",
+)
+@click.option(
+ "-b",
+ "--batch-size",
+ default=90,
+ help="Batch size. Be aware that we are using single precision. Batch size should be high.",
+)
+@click.option(
+ "-e", "--epochs", default=35, help="Number of epochs",
+)
+def train_and_evaluate(tf_record_paths, checkpoint_path, n_classes, batch_size, epochs):
+ # number of training steps to do before validating a model. This also defines an epoch
+ # for keras which is not really true. We want to evaluate every 180000 (90 * 2000)
+ # samples
+ STEPS_PER_EPOCH = 180000 // batch_size
+ learning_rate = 0.1
+ KERAS_EPOCH_MULTIPLIER = 6
+ train_ds = prepare_dataset(
+ tf_record_paths,
+ batch_size,
+ epochs,
+ data_shape=DATA_SHAPE,
+ output_shape=OUTPUT_SHAPE,
+ shuffle=True,
+ augment=True,
+ )
+
+ if VALIDATION_TF_RECORD_PATHS is None:
+ raise ValueError(
+ "No validation set was set. Please, do `bob config set bob.bio.face.cnn.lfw_tfrecord_path [PATH]`"
+ )
+
+ val_ds = prepare_dataset(
+ VALIDATION_TF_RECORD_PATHS,
+ data_shape=DATA_SHAPE,
+ output_shape=OUTPUT_SHAPE,
+ epochs=epochs,
+ batch_size=VALIDATION_BATCH_SIZE,
+ shuffle=False,
+ augment=False,
+ )
+ val_metric_name = "val_accuracy"
+
+ model = build_and_compile_model(n_classes, learning_rate)
+
+ def scheduler(epoch, lr):
+ # 20 epochs at 0.1, 10 at 0.01 and 5 0.001
+ # The epoch number here is Keras's which is different from actual epoch number
+ epoch = epoch // KERAS_EPOCH_MULTIPLIER
+ if epoch in range(20):
+ return 0.1
+ elif epoch in range(20, 30):
+ return 0.01
+ else:
+ return 0.001
+
+ callbacks = {
+ "latest": tf.keras.callbacks.ModelCheckpoint(
+ f"{checkpoint_path}/latest", verbose=1
+ ),
+ "best": tf.keras.callbacks.ModelCheckpoint(
+ f"{checkpoint_path}/best",
+ monitor=val_metric_name,
+ save_best_only=True,
+ mode="max",
+ verbose=1,
+ ),
+ "tensorboard": tf.keras.callbacks.TensorBoard(
+ log_dir=f"{checkpoint_path}/logs", update_freq=15, profile_batch=0
+ ),
+ "lr": tf.keras.callbacks.LearningRateScheduler(scheduler, verbose=1),
+ "nan": tf.keras.callbacks.TerminateOnNaN(),
+ }
+ callbacks = add_backup_callback(callbacks, backup_dir=f"{checkpoint_path}/backup")
+ model.fit(
+ train_ds,
+ validation_data=val_ds,
+ epochs=epochs * KERAS_EPOCH_MULTIPLIER,
+ steps_per_epoch=STEPS_PER_EPOCH,
+ validation_steps=VALIDATION_SAMPLES // VALIDATION_BATCH_SIZE,
+ callbacks=callbacks,
+ verbose=2,
+ )
+
+
+if __name__ == "__main__":
+ train_and_evaluate()
diff --git a/cnn_training/centerloss_mixed_precision.py b/cnn_training/centerloss_mixed_precision.py
new file mode 100644
index 0000000000000000000000000000000000000000..e14069f50536e6a53e4f67a5b792807f840a3c02
--- /dev/null
+++ b/cnn_training/centerloss_mixed_precision.py
@@ -0,0 +1,279 @@
+w #!/usr/bin/env python
+# coding: utf-8
+
+"""
+Trains a face recognition CNN using the strategy from the paper
+
+"A Discriminative Feature Learning Approach
+for Deep Face Recognition" https://ydwen.github.io/papers/WenECCV16.pdf
+
+#########
+# THIS ONE USES FLOAT16 TO COMPUTE THE GRADIENTS
+# CHECKE HERE FOR MORE INFO: # https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/experimental/Policy
+########
+
+The default backbone is the InceptionResnetv2
+
+Do `./bin/python centerloss_mixed_precision.py --help` for more information
+
+"""
+
+import os
+from functools import partial
+import click
+import pkg_resources
+import tensorflow as tf
+from bob.learn.tensorflow.losses import CenterLoss, CenterLossLayer
+from bob.learn.tensorflow.models.inception_resnet_v2 import InceptionResNetV2
+from bob.learn.tensorflow.metrics import predict_using_tensors
+from tensorflow.keras import layers
+from tensorflow.keras.mixed_precision import experimental as mixed_precision
+from bob.learn.tensorflow.callbacks import add_backup_callback
+from bob.learn.tensorflow.metrics.embedding_accuracy import accuracy_from_embeddings
+from bob.extension import rc
+from bob.bio.face.tensorflow.preprocessing import prepare_dataset
+
+# Setting mixed precision policy
+# https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/experimental/Policy
+policy = mixed_precision.Policy("mixed_float16")
+mixed_precision.set_policy(policy)
+
+# CNN Backbone
+# Change your NN backbone here
+BACKBONE = InceptionResNetV2
+
+# SHAPES EXPECTED FROM THE DATASET USING THIS BACKBONE
+DATA_SHAPE = (182, 182, 3) # size of faces
+DATA_TYPE = tf.uint8
+OUTPUT_SHAPE = (160, 160)
+
+AUTOTUNE = tf.data.experimental.AUTOTUNE
+
+# HERE WE VALIDATE WITH LFW RUNNING A
+# INFORMATION ABOUT THE VALIDATION SET
+VALIDATION_TF_RECORD_PATHS = rc["bob.bio.face.cnn.lfw_tfrecord_path"]
+
+# there are 2812 samples in the validation set
+VALIDATION_SAMPLES = 2812
+VALIDATION_BATCH_SIZE = 38
+
+# WEIGHTS BEWTWEEN the two losses
+LOSS_WEIGHTS = {"cross_entropy": 1.0, "center_loss": 0.01}
+
+
+class CenterLossModel(tf.keras.Model):
+ def compile(
+ self,
+ cross_entropy,
+ center_loss,
+ loss_weights,
+ train_loss,
+ train_cross_entropy,
+ train_center_loss,
+ test_acc,
+ global_batch_size,
+ **kwargs,
+ ):
+ super().compile(**kwargs)
+ self.cross_entropy = cross_entropy
+ self.center_loss = center_loss
+ self.loss_weights = loss_weights
+ self.train_loss = train_loss
+ self.train_cross_entropy = train_cross_entropy
+ self.train_center_loss = train_center_loss
+ self.test_acc = test_acc
+ self.global_batch_size = global_batch_size
+
+ def train_step(self, data):
+ images, labels = data
+ with tf.GradientTape() as tape:
+ logits, prelogits = self(images, training=True)
+ loss_cross = self.cross_entropy(labels, logits)
+ loss_center = self.center_loss(labels, prelogits)
+ loss = (
+ loss_cross * self.loss_weights[self.cross_entropy.name]
+ + loss_center * self.loss_weights[self.center_loss.name]
+ )
+ unscaled_loss = tf.nn.compute_average_loss(
+ loss, global_batch_size=self.global_batch_size
+ )
+ loss = self.optimizer.get_scaled_loss(unscaled_loss)
+
+ trainable_vars = self.trainable_variables
+ gradients = tape.gradient(loss, trainable_vars)
+ gradients = self.optimizer.get_unscaled_gradients(gradients)
+ self.optimizer.apply_gradients(zip(gradients, trainable_vars))
+
+ self.train_loss(unscaled_loss)
+ self.train_cross_entropy(loss_cross)
+ self.train_center_loss(loss_center)
+ return {
+ m.name: m.result()
+ for m in [self.train_loss, self.train_cross_entropy, self.train_center_loss]
+ }
+
+ def test_step(self, data):
+ images, labels = data
+ logits, prelogits = self(images, training=False)
+ self.test_acc(accuracy_from_embeddings(labels, prelogits))
+ return {m.name: m.result() for m in [self.test_acc]}
+
+
+def create_model(n_classes):
+
+ model = BACKBONE(
+ include_top=True,
+ classes=n_classes,
+ bottleneck=True,
+ input_shape=OUTPUT_SHAPE + (3,),
+ kernel_regularizer=tf.keras.regularizers.L2(5e-5),
+ )
+ float32_layer = layers.Activation("linear", dtype="float32")
+
+ prelogits = model.get_layer("Bottleneck/BatchNorm").output
+ prelogits = CenterLossLayer(
+ n_classes=n_classes, n_features=prelogits.shape[-1], name="centers"
+ )(prelogits)
+ prelogits = float32_layer(prelogits)
+ logits = float32_layer(model.get_layer("logits").output)
+ model = CenterLossModel(
+ inputs=model.input, outputs=[logits, prelogits], name=model.name
+ )
+ return model
+
+
+def build_and_compile_model(n_classes, learning_rate, global_batch_size):
+ model = create_model(n_classes)
+
+ cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(
+ from_logits=True, name="cross_entropy", reduction=tf.keras.losses.Reduction.NONE
+ )
+ center_loss = CenterLoss(
+ centers_layer=model.get_layer("centers"),
+ alpha=0.9,
+ name="center_loss",
+ reduction=tf.keras.losses.Reduction.NONE,
+ )
+
+ optimizer = tf.keras.optimizers.RMSprop(
+ learning_rate=learning_rate, rho=0.9, momentum=0.9, epsilon=1.0
+ )
+ optimizer = mixed_precision.LossScaleOptimizer(optimizer, loss_scale="dynamic")
+
+ train_loss = tf.keras.metrics.Mean(name="loss")
+ train_cross_entropy = tf.keras.metrics.Mean(name="cross_entropy")
+ train_center_loss = tf.keras.metrics.Mean(name="center_loss")
+
+ test_acc = tf.keras.metrics.Mean(name="accuracy")
+
+ model.compile(
+ optimizer=optimizer,
+ cross_entropy=cross_entropy,
+ center_loss=center_loss,
+ loss_weights=LOSS_WEIGHTS,
+ train_loss=train_loss,
+ train_cross_entropy=train_cross_entropy,
+ train_center_loss=train_center_loss,
+ test_acc=test_acc,
+ global_batch_size=global_batch_size,
+ )
+ return model
+
+
+@click.command()
+@click.argument("tf-record-paths")
+@click.argument("checkpoint-path")
+@click.option(
+ "-n",
+ "--n-classes",
+ default=87662,
+ help="Number of classes in the classification problem. Default to `87662`, which is the number of identities in our pruned MSCeleb",
+)
+@click.option(
+ "-b",
+ "--batch-size",
+ default=90 * 2,
+ help="Batch size. Be aware that we are using single precision. Batch size should be high.",
+)
+@click.option(
+ "-e", "--epochs", default=35, help="Number of epochs",
+)
+def train_and_evaluate(tf_record_paths, checkpoint_path, n_classes, batch_size, epochs):
+ # number of training steps to do before validating a model. This also defines an epoch
+ # for keras which is not really true. We want to evaluate every 180000 (90 * 2000)
+ # samples
+ STEPS_PER_EPOCH = 180000 // batch_size
+ learning_rate = 0.1
+ KERAS_EPOCH_MULTIPLIER = 6
+ train_ds = prepare_dataset(
+ tf_record_paths,
+ batch_size,
+ epochs,
+ data_shape=DATA_SHAPE,
+ output_shape=OUTPUT_SHAPE,
+ shuffle=True,
+ augment=True,
+ )
+
+ if VALIDATION_TF_RECORD_PATHS is None:
+ raise ValueError(
+ "No validation set was set. Please, do `bob config set bob.bio.face.cnn.lfw_tfrecord_path [PATH]`"
+ )
+
+ val_ds = prepare_dataset(
+ VALIDATION_TF_RECORD_PATHS,
+ data_shape=DATA_SHAPE,
+ output_shape=OUTPUT_SHAPE,
+ epochs=epochs,
+ batch_size=VALIDATION_BATCH_SIZE,
+ shuffle=False,
+ augment=False,
+ )
+ val_metric_name = "val_accuracy"
+
+ model = build_and_compile_model(
+ n_classes, learning_rate, global_batch_size=batch_size
+ )
+
+ def scheduler(epoch, lr):
+ # 20 epochs at 0.1, 10 at 0.01 and 5 0.001
+ # The epoch number here is Keras's which is different from actual epoch number
+ epoch = epoch // KERAS_EPOCH_MULTIPLIER
+ if epoch in range(20):
+ return 0.1
+ elif epoch in range(20, 30):
+ return 0.01
+ else:
+ return 0.001
+
+ callbacks = {
+ "latest": tf.keras.callbacks.ModelCheckpoint(
+ f"{checkpoint_path}/latest", verbose=1
+ ),
+ "best": tf.keras.callbacks.ModelCheckpoint(
+ f"{checkpoint_path}/best",
+ monitor=val_metric_name,
+ save_best_only=True,
+ mode="max",
+ verbose=1,
+ ),
+ "tensorboard": tf.keras.callbacks.TensorBoard(
+ log_dir=f"{checkpoint_path}/logs", update_freq=15, profile_batch="10,50"
+ ),
+ "lr": tf.keras.callbacks.LearningRateScheduler(scheduler, verbose=1),
+ "nan": tf.keras.callbacks.TerminateOnNaN(),
+ }
+ callbacks = add_backup_callback(callbacks, backup_dir=f"{checkpoint_path}/backup")
+ model.fit(
+ train_ds,
+ validation_data=val_ds,
+ epochs=epochs * KERAS_EPOCH_MULTIPLIER,
+ steps_per_epoch=STEPS_PER_EPOCH,
+ validation_steps=VALIDATION_SAMPLES // VALIDATION_BATCH_SIZE,
+ callbacks=callbacks,
+ verbose=2,
+ )
+
+
+if __name__ == "__main__":
+ train_and_evaluate()