Keras insightface

License

This is the keras implementation of deepinsight/insightface, and is released under the MIT License. There is no limitation for both academic and commercial usage.

The training data containing the annotation (and the models trained with these data) are available for non-commercial research purposes only.

Table of Contents

• Current accuracy
• Usage
  • Environment
  • Beforehand Data Prepare
  • Project Structure
  • Basic Training
  • Other Basic Functions and Parameters
  • Learning rate
  • Mixed precision float16
  • Optimizers
  • Multi GPU train using horovod or distribute strategy
• Sub Center ArcFace
• Knowledge distillation
• Evaluating on IJB datasets
• TFLite model inference time test on ARM64
• Related Projects
• Citing

Current accuracy

• Some comparing on EfficientNetV2_b0 with activation / data augmentation / loss function / others
• Model structures may change due to changing default behavior of building models.
• IJBB and IJBC are scored at TAR@FAR=1e-4
• Links in Model backbone are h5 models in Google drive. Links in Training are training details.
• The r18 / r34 / r50 / r100 on glint360k are models loaded weights from official publication.
• r50 magface and r100 magface are ported from Github IrvingMeng/MagFace.
• r100 4m adaface and r100 12m adaface are ported from Github mk-minchul/AdaFace.
• Please note WebFace4M / WebFace12M pretrained models cannot be used for any commercial purposes: WebFace.

Usage

Environment

• Currently using Tensorflow 2.9.1 with cuda==11.2 cudnn==8.1
  • Install cuda-toolkit
  • Install cudnn
• python and tensorflow version

  # $ ipython
  # Python 3.8.5 (default, Sep  4 2020, 07:30:14)
  >>> tf.__version__
  # '2.9.1'
  
  >>> import tensorflow_addons as tfa
  >>> tfa.__version__
  Out[3]: '0.17.0'

  Or tf-nightly

  conda create -n tf-nightly python==3.8.5
  conda activate tf-nightly
  pip install tf-nightly tfa-nightly glob2 pandas tqdm scikit-image scikit-learn ipython
  # Not required
  pip install pip-search icecream opencv-python cupy-cuda112 tensorflow-datasets tabulate mxnet-cu112 torch

• Default import for ipython

  import os
  import sys
  import pandas as pd
  import numpy as np
  import tensorflow as tf
  from tensorflow import keras
  
  gpus = tf.config.experimental.list_physical_devices("GPU")
  for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)

• All from scratch #71 is an explanation of the basic implementation line by line from scratch, depending only on basic packages like tensorflow / numpy.

Beforehand Data Prepare

• Training Data in this project is downloaded from Insightface Dataset Zoo
• Evaluating data is LFW CFP-FP AgeDB-30 bin files included in MS1M-ArcFace dataset
• Any other data is also available just in the right format
• prepare_data.py script, Extract data from mxnet record format to folders.

  # Convert `/datasets/faces_emore` to `/datasets/faces_emore_112x112_folders`
  CUDA_VISIBLE_DEVICES='-1' ./prepare_data.py -D /datasets/faces_emore
  # Convert evaluating bin files
  CUDA_VISIBLE_DEVICES='-1' ./prepare_data.py -D /datasets/faces_emore -T lfw.bin cfp_fp.bin agedb_30.bin

  Executing again will skip dataset conversion.
• Training dataset Required is a folder including person folders, each person folder including multi face images. Format like

  .               # dataset folder
  ├── 0           # person folder
  │   ├── 100.jpg # face image
  │   ├── 101.jpg # face image
  │   └── 102.jpg # face image
  ├── 1           # person folder
  │   ├── 111.jpg
  │   ├── 112.jpg
  │   └── 113.jpg
  ├── 10
  │   ├── 707.jpg
  │   ├── 708.jpg
  │   └── 709.jpg

• Evaluating bin files include jpeg image data pairs, and a label indicating if it's a same person, so there are double images than labels

  #    bins   | issame_list
  img_1 img_2 | True
  img_3 img_4 | True
  img_5 img_6 | False
  img_7 img_8 | False

  Image data in bin files like CFP-FP AgeDB-30 is not compatible with tf.image.decode_jpeg, we need to reformat it, which is done by -T parameter.

  ''' Throw error if not reformated yet '''
  ValueError: Can't convert non-rectangular Python sequence to Tensor.

• Custom dataset if in format like the required training dataset, means a dataset folder containing person folders, and person folder containing face images. May run

  # For dataset folder name `/dataset/Foo`
  CUDA_VISIBLE_DEVICES='0' ./face_detector.py /dataset/Foo

  to detect and align face images. Target saving directory will be /dataset/Foo_aligned_112_112. Then this one can be used as data_path for train.Train.
• Cache file {dataset_name}_shuffle.npz is saved in first time training. Remove it if dataset content changed.

Project Structure

• Basic Modules
  • backbones basic model implementation of mobilefacenet / mobilenetv3 / efficientnet / botnet / ghostnet. Most of them are copied from keras.applications source code and modified. Other backbones like ResNet101V2 is loaded from keras.applications in train.buildin_models.
  • data.py loads image data as tf.dataset for training. Triplet dataset is different from others.
  • evals.py contains evaluating callback using bin files.
  • losses.py contains softmax / arcface / centerloss / triplet loss functions.
  • myCallbacks.py contains my other callbacks, like saving model / learning rate adjusting / save history.
  • models.py contains model build related functions, like buildin_models / add_l2_regularizer_2_model / replace_ReLU_with_PReLU.
  • train.py contains a Train class. It uses a scheduler to connect different loss / optimizer / epochs. The basic function is simply basic_model --> build dataset --> add output layer --> add callbacks --> compile --> fit.
• Other Modules
  • augment.py including implementation of RandAug and AutoAug.
  • IJB_evals.py evaluates model accuracy using insightface/evaluation/IJB/ datasets.
  • data_distiller.py create dataset for Knowledge distillation.
  • data_drop_top_k.py create dataset after trained with Sub Center ArcFace method.
  • eval_folder.py Run model evaluation on any custom dataset folder, which is in the same format with Training dataset.
  • face_detector.py contains face detectors. Currently 2 added, pure Keras one YoloV5FaceDetector, and ONNX one SCRFD.
  • plot.py contains a history plot function.
  • video_test.py can be used to test model using video camera.

Basic Training

• Training example train.Train is mostly functioned as a scheduler.

  from tensorflow import keras
  import losses, train, models
  import tensorflow_addons as tfa
  
  # basic_model = models.buildin_models("ResNet101V2", dropout=0.4, emb_shape=512, output_layer="E")
  basic_model = models.buildin_models("MobileNet", dropout=0, emb_shape=256, output_layer="GDC")
  data_path = '/datasets/faces_emore_112x112_folders'
  eval_paths = ['/datasets/faces_emore/lfw.bin', '/datasets/faces_emore/cfp_fp.bin', '/datasets/faces_emore/agedb_30.bin']
  
  tt = train.Train(data_path, save_path='keras_mobilenet_emore.h5', eval_paths=eval_paths,
                  basic_model=basic_model, batch_size=512, random_status=0,
                  lr_base=0.1, lr_decay=0.5, lr_decay_steps=16, lr_min=1e-5)
  optimizer = tfa.optimizers.SGDW(learning_rate=0.1, momentum=0.9, weight_decay=5e-5)
  sch = [
    {"loss": losses.ArcfaceLoss(scale=16), "epoch": 5, "optimizer": optimizer},
    {"loss": losses.ArcfaceLoss(scale=32), "epoch": 5},
    {"loss": losses.ArcfaceLoss(scale=64), "epoch": 40},
    # {"loss": losses.ArcfaceLoss(), "epoch": 20, "triplet": 64, "alpha": 0.35},
  ]
  tt.train(sch, 0)

  May use tt.train_single_scheduler controlling the behavior more detail.

• Model basically containing two parts:

  • Basic model is layers from input to embedding.
  • Model is Basic model + bottleneck layer, like softmax / arcface layer. For triplet training, Model == Basic model. For combined loss training, it may have multiple outputs.

• Saving strategy

  • Model will save the latest one on every epoch end to local path ./checkpoints, name is specified by train.Train save_path.
  • basic_model will be saved monitoring on the last eval_paths evaluating bin item, and save the best only.

• train.Train model parameters including basic_model / model. Combine them to initialize model from different sources. Sometimes may need custom_objects to load model.

  basic_model	model	Used for
  model structure	None	Scratch train
  basic model .h5 file	None	Continue training from a saved basic model
  None for 'embedding' layer or layer index of basic model output	model .h5 file	Continue training from last saved model
  None for 'embedding' layer or layer index of basic model output	model structure	Continue training from a modified model
  None	None	Reload model from "checkpoints/{save_path}"

• Scheduler is a list of dicts, each containing a training plan

  • epoch indicates how many epochs will be trained. Required.
  • loss indicates the loss function. If not provided, will try to use the previous one if model.built is True.
  • optimizer is the optimizer used in this plan, None indicates using the last one.
  • bottleneckOnly True / False, True will set basic_model.trainable = False, train the output layer only.
  • centerloss float value, if set a non zero value, attach a CenterLoss to logits_loss, and the value means loss_weight.
  • triplet float value, if set a non zero value, attach a BatchHardTripletLoss to logits_loss, and the value means loss_weight.
  • alpha float value, default to 0.35. Alpha value for BatchHardTripletLoss if attached.
  • lossTopK indicates the top K value for Sub Center ArcFace method.
  • distill indicates the loss_weight for distiller_loss using Knowledge distillation, default 7.
  • type softmax / arcface / triplet / center, but mostly this could be guessed from loss.

  # Scheduler examples
  sch = [
      {"loss": losses.scale_softmax, "optimizer": "adam", "epoch": 2},
      {"loss": keras.losses.CategoricalCrossentropy(label_smoothing=0.1), "centerloss": 0.01, "epoch": 2},
      {"loss": losses.ArcfaceLoss(scale=32.0, label_smoothing=0.1), "optimizer": keras.optimizers.SGD(0.1, momentum=0.9), "epoch": 2},
      {"loss": losses.BatchAllTripletLoss(0.3), "epoch": 2},
      {"loss": losses.BatchHardTripletLoss(0.25), "epoch": 2},
      {"loss": losses.CenterLoss(num_classes=85742, emb_shape=256), "epoch": 2},
      {"loss": losses.CurricularFaceLoss(), "epoch": 2},
  ]

  Some more complicated combinations are also supported.

  # `softmax` + `centerloss`, `"centerloss": 0.1` means loss_weight
  sch = [{"loss": keras.losses.CategoricalCrossentropy(label_smoothing=0.1), "centerloss": 0.1, "epoch": 2}]
  # `softmax` / `arcface` + `triplet`, `"triplet": 64` means loss_weight
  sch = [{"loss": keras.losses.ArcfaceLoss(scale=64), "triplet": 64, "alpha": 0.3, "epoch": 2}]
  # `triplet` + `centerloss`
  sch = [{"loss": losses.BatchHardTripletLoss(0.25), "centerloss": 0.01, "epoch": 2}]
  sch = [{"loss": losses.CenterLoss(num_classes=85742, emb_shape=256), "triplet": 10, "alpha": 0.25, "epoch": 2}]
  # `softmax` / `arcface` + `triplet` + `centerloss`
  sch = [{"loss": losses.ArcfaceLoss(), "centerloss": 1, "triplet": 32, "alpha": 0.2, "epoch": 2}]

• Restore training from break point

  from tensorflow import keras
  import losses, train
  data_path = '/datasets/faces_emore_112x112_folders'
  eval_paths = ['/datasets/faces_emore/lfw.bin', '/datasets/faces_emore/cfp_fp.bin', '/datasets/faces_emore/agedb_30.bin']
  tt = train.Train(data_path, 'keras_mobilenet_emore.h5', eval_paths, model='./checkpoints/keras_mobilenet_emore.h5',
                  batch_size=512, random_status=0, lr_base=0.1, lr_decay=0.5, lr_decay_steps=16, lr_min=1e-5)
  
  sch = [
    # {"loss": losses.ArcfaceLoss(scale=16), "epoch": 5, "optimizer": optimizer},
    # {"loss": losses.ArcfaceLoss(scale=32), "epoch": 5},
    {"loss": losses.ArcfaceLoss(scale=64), "epoch": 35},
    # {"loss": losses.ArcfaceLoss(), "epoch": 20, "triplet": 64, "alpha": 0.35},
  ]
  tt.train(sch, initial_epoch=15)

• Evaluation

  import evals
  basic_model = keras.models.load_model('checkpoints/keras_mobilefacenet_256_basic_agedb_30_epoch_39_0.942500.h5', compile=False)
  ee = evals.eval_callback(basic_model, '/datasets/faces_emore/lfw.bin')
  ee.on_epoch_end(0)
  # >>>> lfw evaluation max accuracy: 0.993167, thresh: 0.316535, previous max accuracy: 0.000000, PCA accuray = 0.993167 ± 0.003905
  # >>>> Improved = 0.993167

  For training process, default evaluating strategy is on_epoch_end. Setting an eval_freq greater than 1 in train.Train will also add an on_batch_end evaluation.

  # Change evaluating strategy to `on_epoch_end`, as long as `on_batch_end` for every `1000` batch.
  tt = train.Train(data_path, 'keras_mobilefacenet_256.h5', eval_paths, basic_model=basic_model, eval_freq=1000)

Other Basic Functions and Parameters

• train.Train output_weight_decay controls L2 regularizer value added to output_layer.
  • 0 for None.
  • (0, 1) for specific value, actual added value will also divided by 2.
  • >= 1 will be value multiplied by L2 regularizer value in basic_model if added.
• train.Train random_status controls data augmentation weights.
  • -1 will disable all augmentation.
  • 0 will apply random_flip_left_right only.
  • 1 will also apply random_brightness.
  • 2 will also apply random_contrast and random_saturation.
  • 3 will also apply random_crop.
  • >= 100 will apply RandAugment with magnitude = 5 * random_status / 100, so random_status=100 means using RandAugment with magnitude=5.
• train.Train random_cutout_mask_area set ratio of randomly cutout image bottom 2/5 area, regarding as ignoring mask area.
• train.Train partial_fc_split set a int number like 2 / 4, will build model and dataset with total classes split in partial_fc_split parts. Works also on a single GPU. Currently only ArcFace loss family like ArcFace / AirFaceLoss / CosFaceLoss / MagFaceLoss supports. Still under testing.
• models.buildin_models is mainly for adding output feature layer GDC / E or others to a backbone model. The first parameter stem_model can be:
  • String like MobileNet / r50 / ResNet50 or other names printed by models.print_buildin_models().
  • Self built keras.models.Model instance. Like keras.applications.MobileNet(input_shape=(112, 112, 3), include_top=False).
• models.add_l2_regularizer_2_model will add l2_regularizer to dense / convolution layers, or set apply_to_batch_normal=True also to PReLU / BatchNormalization layers. The actual added l2 value is divided by 2.

  # Will add keras.regularizers.L2(5e-4) to `dense` / `convolution` layers.
  basic_model = models.add_l2_regularizer_2_model(basic_model, 1e-3, apply_to_batch_normal=False)

• Gently stop is a callback to stop training gently. Input an n and <Enter> anytime during training, will set training stop on that epoch ends.
• My history
  • This is a callback collecting training loss, accuracy and evaluating accuracy.
  • On every epoch end, backup to the path save_path defined in train.Train with suffix _hist.json.
  • Reload when initializing, if the backup <save_path>_hist.json file exists.
  • The saved _hist.json can be used for plotting using plot.py.
• eval_folder.py is used for test evaluating accuracy on custom test dataset:

  CUDA_VISIBLE_DEVICES='0' ./eval_folder.py -d {DATA_PATH} -m {BASIC_MODEL.h5}

  Or create own test bin file which can be used in train.Train eval_paths:

  CUDA_VISIBLE_DEVICES='0' ./eval_folder.py -d {DATA_PATH} -m {BASIC_MODEL.h5} -B {BIN_FILE.bin}

Learning rate

• train.Train parameters lr_base / lr_decay / lr_decay_steps / lr_warmup_steps set different decay strategies and their parameters.

• tt.lr_scheduler can also be used to set learning rate scheduler directly.

  tt = train.Train(...)
  import myCallbacks
  tt.lr_scheduler = myCallbacks.CosineLrSchedulerEpoch(lr_base=1e-3, first_restart_step=16, warmup_steps=3)

• lr_decay_steps controls different decay types.

  • Default is Exponential decay with lr_base=0.001, lr_decay=0.05.
  • For CosineLrScheduler, steps_per_epoch is set after dataset been inited.
  • For CosineLrScheduler, default value of cooldown_steps=1, means will train 1 epoch using lr_min before each restart.

  lr_decay_steps	decay type	mean of lr_decay_steps	mean of lr_decay
  <= 1	Exponential decay		decay_rate
  > 1	Cosine decay, will multiply with steps_per_epoch	first_restart_step, epoch	m_mul
  list	Constant decay	lr_decay_steps	decay_rate

  # lr_decay_steps == 0, Exponential
  tt = train.Train(..., lr_base=0.001, lr_decay=0.05, ...)
  # 1 < lr_decay_steps, Cosine decay, first_restart_step = lr_decay_steps * steps_per_epoch
  # restart on epoch [16 * 1 + 1, 16 * 3 + 2, 16 * 7 + 3] == [17, 50, 115]
  tt = train.Train(..., lr_base=0.001, lr_decay=0.5, lr_decay_steps=16, lr_min=1e-7, ...)
  # 1 < lr_decay_steps, lr_min == lr_base * lr_decay, Cosine decay, no restart
  tt = train.Train(..., lr_base=0.001, lr_decay=1e-4, lr_decay_steps=24, lr_min=1e-7, ...)
  # lr_decay_steps is a list, Constant
  tt = train.Train(..., lr_base=0.1, lr_decay=0.1, lr_decay_steps=[3, 5, 7, 16, 20, 24], ...)

• Example learning rates

  from myCallbacks import exp_scheduler, CosineLrScheduler, constant_scheduler
  epochs = np.arange(60)
  plt.figure(figsize=(14, 6))
  plt.plot(epochs, [exp_scheduler(ii, 0.001, 0.1, warmup_steps=10) for ii in epochs], label="lr=0.001, decay=0.1")
  plt.plot(epochs, [exp_scheduler(ii, 0.001, 0.05, warmup_steps=10) for ii in epochs], label="lr=0.001, decay=0.05")
  plt.plot(epochs, [constant_scheduler(ii, 0.001, [10, 20, 30, 40], 0.1) for ii in epochs], label="Constant, lr=0.001, decay_steps=[10, 20, 30, 40], decay_rate=0.1")
  
  steps_per_epoch = 100
  batchs = np.arange(60 * steps_per_epoch)
  aa = CosineLrScheduler(0.001, first_restart_step=50, lr_min=1e-6, warmup_steps=0, m_mul=1e-3, steps_per_epoch=steps_per_epoch)
  lrs = []
  for ii in epochs:
      aa.on_epoch_begin(ii)
      lrs.extend([aa.on_train_batch_begin(jj) for jj in range(steps_per_epoch)])
  plt.plot(batchs / steps_per_epoch, lrs, label="Cosine, first_restart_step=50, min=1e-6, m_mul=1e-3")
  
  bb = CosineLrScheduler(0.001, first_restart_step=16, lr_min=1e-7, warmup_steps=1, m_mul=0.4, steps_per_epoch=steps_per_epoch)
  lrs = []
  for ii in epochs:
      bb.on_epoch_begin(ii)
      lrs.extend([bb.on_train_batch_begin(jj) for jj in range(steps_per_epoch)])
  plt.plot(batchs / steps_per_epoch, lrs, label="Cosine restart, first_restart_step=16, min=1e-7, warmup=1, m_mul=0.4")
  
  plt.xlim(0, 60)
  plt.legend()
  plt.grid(True)
  plt.tight_layout()

  

Mixed precision float16

• Tensorflow Guide - Mixed precision
• Enable Mixed precision at the beginning of all functional code by

  keras.mixed_precision.set_global_policy("mixed_float16")

• In most training case, it will have a ~2x speedup and less GPU memory consumption.

Optimizers

• SGDW / AdamW tensorflow_addons AdamW.

  # !pip install tensorflow-addons
  !pip install tfa-nightly
  
  import tensorflow_addons as tfa
  optimizer = tfa.optimizers.SGDW(learning_rate=0.1, weight_decay=5e-4, momentum=0.9)
  optimizer = tfa.optimizers.AdamW(learning_rate=0.001, weight_decay=5e-5)

  weight_decay and learning_rate should share the same decay strategy. A callback OptimizerWeightDecay will set weight_decay according to learning_rate.

  opt = tfa.optimizers.AdamW(weight_decay=5e-5)
  sch = [{"loss": keras.losses.CategoricalCrossentropy(label_smoothing=0.1), "centerloss": True, "epoch": 60, "optimizer": opt}]

  • The different behavior of mx.optimizer.SGD weight_decay / tfa.optimizers.SGDW weight_decay / L2_regulalizer is explained here the discussion.
  • PDF DECOUPLED WEIGHT DECAY REGULARIZATION
  • Train test of SGDW on cifar10
• RAdam / Lookahead / Ranger optimizer tensorflow_addons RectifiedAdam.

  # Rectified Adam,a.k.a. RAdam, [ON THE VARIANCE OF THE ADAPTIVE LEARNING RATE AND BEYOND](https://arxiv.org/pdf/1908.03265.pdf)
  optimizer = tfa.optimizers.RectifiedAdam()
  # SGD with Lookahead [Lookahead Optimizer: k steps forward, 1 step back](https://arxiv.org/pdf/1907.08610.pdf)
  optmizer = tfa.optimizers.Lookahead(keras.optimizers.SGD(0.1))
  # Ranger [Gradient Centralization: A New Optimization Technique for Deep Neural Networks](https://arxiv.org/pdf/2004.01461.pdf)
  optmizer = tfa.optimizers.Lookahead(tfa.optimizers.RectifiedAdam())

Multi GPU train using horovod or distribute strategy

• Horovod usage is still under test. Tensorflow multi GPU training using distribute strategies vs Horovod
• Add an overall tf.distribute.MirroredStrategy().scope() with block. This is just working in my case... The batch_size will be multiplied by count of GPUs.

  with tf.distribute.MirroredStrategy().scope():
      basic_model = ...
      tt = train.Train(..., batch_size=1024, ...) # With 2 GPUs, `batch_size` will be 2048
      sch = [...]
      tt.train(sch, 0)

• Using build-in loss functions like keras.losses.CategoricalCrossentropy should specify the reduction parameter.

  sch = [{"loss": keras.losses.CategoricalCrossentropy(label_smoothing=0.1, reduction=tf.keras.losses.Reduction.NONE), "epoch": 25}]

Sub Center ArcFace

• Original MXNet Subcenter ArcFace

• PDF Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces

• This is still under test, Multi GPU is NOT tested

• As far as I can see

  • Sub Center ArcFace works like cleaning the dataset.
  • In lossTopK=3 case, it will train 3 sub classes in each label, and each sub class is a center.
  • Then choose a domain center, and remove those are too far away from this center.
  • So it's better train a large model to clean the dataset, and then train other models on the cleaned dataset.

• Train Original MXNet version

  cd ~/workspace/insightface/recognition/SubCenter-ArcFace
  cp sample_config.py config.py
  sed -i 's/config.ckpt_embedding = True/config.ckpt_embedding = False/' config.py
  CUDA_VISIBLE_DEVICES='1' python train_parall.py --network r50 --per-batch-size 512
  # Iter[20] Batch [8540], accuracy 0.80078125, loss 1.311261, lfw 0.99817, cfp_fp 0.97557, agedb_30 0.98167
  
  CUDA_VISIBLE_DEVICES='1' python drop.py --data /datasets/faces_emore --model models/r50-arcface-emore/model,1 --threshold 75 --k 3 --output /datasets/faces_emore_topk3_1
  # header0 label [5822654. 5908396.] (5822653, 4)
  # total: 5800493
  
  sed -i 's/config.ckpt_embedding = False/config.ckpt_embedding = True/' config.py
  sed -i 's/config.loss_K = 3/config.loss_K = 1/' config.py
  sed -i 's#/datasets/faces_emore#/datasets/faces_emore_topk3_1#' config.py
  ls -1 /datasets/faces_emore/*.bin | xargs -I '{}' ln -s {} /datasets/faces_emore_topk3_1/
  CUDA_VISIBLE_DEVICES='1' python train_parall.py --network r50 --per-batch-size 512
  # 5800493
  # Iter[20] Batch [5400], accuracy 0.8222656, loss 1.469272, lfw 0.99833, cfp_fp 0.97986, agedb_30 0.98050

• Keras version train mobilenet on CASIA test

  import tensorflow_addons as tfa
  import train, losses, models
  
  data_basic_path = '/datasets/faces_casia'
  data_path = data_basic_path + '_112x112_folders'
  eval_paths = [os.path.join(data_basic_path, ii) for ii in ['lfw.bin', 'cfp_fp.bin', 'agedb_30.bin']]
  
  """ First, Train with `lossTopK = 3` """
  basic_model = models.buildin_models("mobilenet", dropout=0, emb_shape=256, output_layer='E')
  tt = train.Train(data_path, save_path='TT_mobilenet_topk_bs256.h5', eval_paths=eval_paths,
      basic_model=basic_model, model=None, lr_base=0.1, lr_decay=0.1, lr_decay_steps=[20, 30],
      batch_size=256, random_status=0, output_wd_multiply=1)
  
  optimizer = tfa.optimizers.SGDW(learning_rate=0.1, weight_decay=5e-4, momentum=0.9)
  sch = [
      {"loss": losses.ArcfaceLoss(scale=16), "epoch": 5, "optimizer": optimizer, "lossTopK": 3},
      {"loss": losses.ArcfaceLoss(scale=32), "epoch": 5, "lossTopK": 3},
      {"loss": losses.ArcfaceLoss(scale=64), "epoch": 40, "lossTopK": 3},
  ]
  tt.train(sch, 0)
  
  """ Then drop non-dominant subcenters and high-confident noisy data, which is `>75 degrees` """
  import data_drop_top_k
  # data_drop_top_k.data_drop_top_k('./checkpoints/TT_mobilenet_topk_bs256.h5', '/datasets/faces_casia_112x112_folders/', limit=20)
  new_data_path = data_drop_top_k.data_drop_top_k(tt.model, tt.data_path)
  
  """ Train with the new dataset again, this time `lossTopK = 1` """
  tt.reset_dataset(new_data_path)
  optimizer = tfa.optimizers.SGDW(learning_rate=0.1, weight_decay=5e-4, momentum=0.9)
  sch = [
      {"loss": losses.ArcfaceLoss(scale=16), "epoch": 5, "optimizer": optimizer},
      {"loss": losses.ArcfaceLoss(scale=32), "epoch": 5},
      {"loss": losses.ArcfaceLoss(scale=64), "epoch": 40},
  ]
  tt.train(sch, 0)

• data_drop_top_k.py can also be used as a script. -M and -D are required.

  $ CUDA_VISIBLE_DEVICES='-1' ./data_drop_top_k.py -h
  # usage: data_drop_top_k.py [-h] -M MODEL_FILE -D DATA_PATH [-d DEST_FILE]
  #                           [-t DEG_THRESH] [-L LIMIT]
  #
  # optional arguments:
  #   -h, --help            show this help message and exit
  #   -M MODEL_FILE, --model_file MODEL_FILE
  #                         Saved model file path, NOT basic_model (default: None)
  #   -D DATA_PATH, --data_path DATA_PATH
  #                         Original dataset path (default: None)
  #   -d DEST_FILE, --dest_file DEST_FILE
  #                         Dest file path to save the processed dataset npz
  #                         (default: None)
  #   -t DEG_THRESH, --deg_thresh DEG_THRESH
  #                         Thresh value in degree, [0, 180] (default: 75)
  #   -L LIMIT, --limit LIMIT
  #                         Test parameter, limit converting only the first [NUM]
  #                         ones (default: 0)

  $ CUDA_VISIBLE_DEVICES='-1' ./data_drop_top_k.py -M checkpoints/TT_mobilenet_topk_bs256.h5 -D /datasets/faces_casia_112x112_folders/ -L 20

• [Discussions] SubCenter_training_Mobilenet_on_CASIA

  Scenario	Max lfw	Max cfp_fp	Max agedb_30
  Baseline, topk 1	0.9822	0.8694	0.8695
  TopK 3	0.9838	0.9044	0.8743
  TopK 3->1	0.9838	0.8960	0.8768
  TopK 3->1, bottleneckOnly, initial_epoch=0	0.9878	0.8920	0.8857
  TopK 3->1, bottleneckOnly, initial_epoch=40	0.9835	0.9030	0.8763

Knowledge distillation

• PDF Improving Face Recognition from Hard Samples via Distribution Distillation Loss

• PDF VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition

• data_distiller.py works to extract embedding data from images and save locally. MODEL_FILE can be Keras h5 / pytorch jit pth / MXNet model.

  • --save_npz Default saving format is .tfrecord, which needs less memory while training.
  • -D xxx.npz Convert xxx.npz to xxx.tfrecord.
  • --use_fp16 Save embedding data in float16 format, which needs half less disk space than default float32.

  $ CUDA_VISIBLE_DEVICES='-1' ./data_distiller.py -h
  # usage: data_distiller.py [-h] -D DATA_PATH [-M MODEL_FILE] [-d DEST_FILE]
  #                          [-b BATCH_SIZE] [-L LIMIT] [--use_fp16] [--save_npz]
  #
  # optional arguments:
  #   -h, --help            show this help message and exit
  #   -D DATA_PATH, --data_path DATA_PATH
  #                         Data path, or npz file converting to tfrecord
  #                         (default: None)
  #   -M MODEL_FILE, --model_file MODEL_FILE
  #                         Model file, keras h5 / pytorch pth / mxnet (default:
  #                         None)
  #   -d DEST_FILE, --dest_file DEST_FILE
  #                         Dest file path to save the processed dataset (default:
  #                         None)
  #   -b BATCH_SIZE, --batch_size BATCH_SIZE
  #                         Batch size (default: 256)
  #   -L LIMIT, --limit LIMIT
  #                         Test parameter, limit converting only the first [NUM]
  #                         (default: -1)
  #   --use_fp16            Save using float16 (default: False)
  #   --save_npz            Save as npz file, default is tfrecord (default: False)

  $ CUDA_VISIBLE_DEVICES='0' ./data_distiller.py -M subcenter-arcface-logs/r100-arcface-msfdrop75/model,0 -D /datasets/faces_casia_112x112_folders/ -b 32 --use_fp16
  # >>>> Output: faces_casia_112x112_folders_shuffle_label_embs_normed_512.npz

• Then this dataset can be used to train a new model.

  • Just specify data_path as the new dataset path. If key embeddings is in, then it will be a distiller train.
  • A new loss distiller_loss_cosine will be added to match this embeddings data, default loss_weights = [1, 7]. Parameter distill in scheduler set this loss weight.
  • Distill loss can be used along or combined with softmax / arcface / centerloss / triplet.
  • The emb_shape can be differ from teacher, in this case, a dense layer distill_emb_map_layer will be added between basic_model embedding layer output and teacher embedding data.

  import train, losses, models
  import tensorflow_addons as tfa
  
  data_basic_path = '/datasets/faces_casia'
  data_path = 'faces_casia_112x112_folders_shuffle_label_embs_512_fp16.tfrecord'
  eval_paths = [os.parh.join(data_basic_path, ii) for ii in ['lfw.bin', 'cfp_fp.bin', 'agedb_30.bin']]
  
  basic_model = models.buildin_models("mobilenet", dropout=0.4, emb_shape=512, output_layer='E')
  tt = train.Train(data_path, save_path='TT_mobilenet_distill_bs400.h5', eval_paths=eval_paths,
      basic_model=basic_model, model=None, lr_base=0.1, lr_decay=0.1, lr_decay_steps=[20, 30],
      batch_size=400, random_status=0)
  
  optimizer = tfa.optimizers.SGDW(learning_rate=0.1, weight_decay=5e-4, momentum=0.9)
  sch = [
      {"loss": losses.ArcfaceLoss(scale=16), "epoch": 5, "optimizer": optimizer, "distill": 128},
      {"loss": losses.ArcfaceLoss(scale=32), "epoch": 5, "distill": 128},
      {"loss": losses.ArcfaceLoss(scale=64), "epoch": 40, "distill": 128},
  ]
  tt.train(sch, 0)

• Knowledge distillation result of training Mobilenet on CASIA

  Teacher	emb_shape	Dropout	Optimizer	Distill	Max lfw	Max cfp_fp	Max agedb_30
  None	512	0	SGDW	0	0.9838	0.8730	0.8697
  None	512	0.4	SGDW	0	0.9837	0.8491	0.8745
  r100	512	0	SGDW	7	0.9900	0.9111	0.9068
  r100	512	0.4	SGDW	7	0.9905	0.9170	0.9112
  r100	512	0.4	SGDW	128	0.9955	0.9376	0.9465
  r100	512	0.4	AdamW	128	0.9920	0.9346	0.9387
  r100	512	0.4	AdamW	128	0.9920	0.9346	0.9387
  r100	256	0	SGDW	128	0.9937	0.9337	0.9427
  r100	256	0.4	SGDW	128	0.9942	0.9369	0.9448

• Knowledge distillation using Mobilenet on MS1M dataset

  Teacher	emb_shape	Dropout	Optimizer	Distill	Max lfw	Max cfp_fp	Max agedb_30
  r100	512	0.4	SGDW	128	0.997	0.964	0.972833

Evaluating on IJB datasets

• IJB_evals.py evaluates model accuracy using insightface/evaluation/IJB/ datasets.
• In case placing IJB dataset /media/SD/IJB_release, basic usage will be:

  # Test mxnet model, default scenario N0D1F1
  CUDA_VISIBLE_DEVICES='1' python IJB_evals.py -m '/media/SD/IJB_release/pretrained_models/MS1MV2-ResNet100-Arcface/model,0' -d /media/SD/IJB_release -L
  
  # Test keras h5 model, default scenario N0D1F1
  CUDA_VISIBLE_DEVICES='1' python IJB_evals.py -m 'checkpoints/basic_model.h5' -d /media/SD/IJB_release -L
  
  # `-B` to run all 8 tests N{0,1}D{0,1}F{0,1}
  CUDA_VISIBLE_DEVICES='1' python IJB_evals.py -m 'checkpoints/basic_model.h5' -d /media/SD/IJB_release -B -L
  
  # `-N` to run 1N test
  CUDA_VISIBLE_DEVICES='1' python IJB_evals.py -m 'checkpoints/basic_model.h5' -d /media/SD/IJB_release -N -L
  
  # `-E` to save embeddings data
  CUDA_VISIBLE_DEVICES='1' python IJB_evals.py -m 'checkpoints/basic_model.h5' -d /media/SD/IJB_release -E
  # Then can be restored for other tests, add `-E` to save again
  python IJB_evals.py -R IJB_result/MS1MV2-ResNet100-Arcface_IJBB.npz -d /media/SD/IJB_release -B
  
  # Plot result only, this needs the `label` data, which can be saved using `-L` parameter.
  # Or should provide the label txt file.
  python IJB_evals.py --plot_only /media/SD/IJB_release/IJBB/result/*100*.npy /media/SD/IJB_release/IJBB/meta/ijbb_template_pair_label.txt

• See -h for detail usage.

  python IJB_evals.py -h

TFLite model inference time test on ARM64

• Test using TFLite Model Benchmark Tool

• Platform

  • CPU: Qualcomm Technologies, Inc SDM630
  • System: Android
  • Inference: TFLite

• mobilenet_v2 comparing orignal / dynamic / float16 / uint8 conversion of TFLite model. Using header GDC + emb_shape=512 + pointwise_conv=False.

  mobilenet_v2	Size (MB)	threads=1 (ms)	threads=4 (ms)
  orignal	11.576	52.224	18.102
  orignal xnn	11.576	29.116	8.744
  dynamic	3.36376	38.497	20.008
  dynamic xnn	3.36376	37.433	19.234
  float16	5.8267	53.986	19.191
  float16 xnn	5.8267	29.862	8.661
  uint8	3.59032	27.247	10.783

• mobilenet_v2 comparing different headers using float16 conversion + xnn + threads=4

  emb_shape	output_layer	pointwise_conv	PReLU	Size (MB)	Time (ms)
  256	GDC	False	False	5.17011	8.214
  512	GDC	False	False	5.82598	8.436
  256	GDC	True	False	6.06384	9.129
  512	GDC	True	False	6.32542	9.357
  256	E	True	False	9.98053	10.669
  256	E	False	False	14.9618	11.502
  512	E	True	False	14.174	11.958
  512	E	False	False	25.4481	15.063
  512	GDC	False	True	5.85275	10.481

• Backbones comparing using float16 conversion + xnn + threads=4, header GDC + emb_shape=512 + pointwise_conv=False

  Model	Size (MB)	Time (ms)
  mobilenet_v3_small	2.80058	4.211
  mobilenet_v3_large	6.95015	10.025
  ghostnet strides=2	8.06546	11.125
  mobilenet	7.4905	11.836
  se_mobilefacenet	1.88518	18.713
  mobilefacenet	1.84267	20.443
  EB0	9.40449	22.054
  EB1	14.4268	31.881
  ghostnet strides=1	8.16576	46.142
  mobilenet_m1	7.02651	52.648

Related Projects

• Triplet Loss and Online Triplet Mining in TensorFlow
• TensorFlow Addons Losses: TripletSemiHardLoss
• TensorFlow Addons Layers: WeightNormalization
• Github deepinsight/insightface
• Github cavalleria/cavaface.pytorch
• Github titu1994/keras-squeeze-excite-network
• Github qubvel/EfficientNet
• Github QiaoranC/tf_ResNeSt_RegNet_model
• Partial FC: Training 10 Million Identities on a Single Machine
• Github IrvingMeng/MagFace

Citing

• BibTeX

  @misc{leondgarse,
    author = {Leondgarse},
    title = {Keras Insightface},
    year = {2022},
    publisher = {GitHub},
    journal = {GitHub repository},
    doi = {10.5281/zenodo.6506949},
    howpublished = {\url{https://github.com/leondgarse/Keras_insightface}}
  }

• Latest DOI: