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.

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.

Model backbone	Training	lfw	cfp_fp	agedb_30	IJBB	IJBC
Resnet34	CASIA, E40	0.994667	0.949143	0.9495
Mobilenet emb256	Emore, E110	0.996000	0.951714	0.959333	0.887147	0.911745
Mobilenet_distill swish	MS1MV3, E50	0.997333	0.969	0.975333	0.91889	0.940328
Ghostnet strides 2	MS1MV3, E49	0.997167	0.959429	0.969333	0.912463	0.934499
Ghostnet strides 1	MS1MV3, E50	0.997833	0.978286	0.980000	0.931159	0.94943
EfficientNetV2 B0	MS1MV3, E67	0.997833	0.976571	0.977333	0.940701	0.955259
Botnet50 relu GDC	MS1MV3, E52	0.9985	0.980286	0.979667	0.940019	0.95577
r50 swish	MS1MV3, E50	0.998333	0.989571	0.984333	0.950828	0.964463
se_r50 swish SD	MS1MV3, E50	0.9985	0.989429	0.9845	0.954333	0.966252
Renet101V2 swish	MS1MV3, E50	0.9985	0.989143	0.9845	0.952483	0.966406
r18 PReLU	Glint360k converted	0.997500	0.977143	0.976500	0.936806	0.9533
r34 PReLU	Glint360k converted	0.998167	0.987000	0.982833	0.951801	0.9656
r50 PReLU	Glint360k converted	0.998333	0.991	0.9835	0.957157	0.970292
r100 PReLU	Glint360k converted	0.9985	0.992286	0.985167	0.962512	0.974689

Usage

Environment

Nvidia CUDA and cudnn Tensorflow 2.5.0 now using 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)
In [1]: tf.__version__
Out[1]: '2.6.0'

In [2]: import tensorflow_addons as tfa
In [3]: tfa.__version__
Out[3]: '0.14.0-dev'

Or tf-nightly

# conda create -n tf-nightly python==3.7.6
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

In [1]: tf.__version__
Out[1]: '2.7.0-dev202108115'

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)

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.
```

Training scripts

Basic Scripts
- 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 Scripts
- IJB_evals.py evaluates model accuracy using insightface/evaluation/IJB/ datasets.
- data_drop_top_k.py create dataset after trained with Sub Center ArcFace method.
- data_distiller.py create dataset for Knowledge distillation.
- eval_folder.py Run model evaluation on any custom dataset folder, which is in the same format with Training dataset.
- plot.py contains a history plot function.
- video_test.py can be used to test model using video camera.
Model contains two part
- 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.

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)

Using tt.train_single_scheduler can control the behavior more detail.

models.print_buildin_models is used to print build-in model names in models.py, which can be used in models.buildin_models.
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)
```

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}"

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.

Scheduler is a list of dicts, each contains 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}]

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.

''' Continue training from last saved file '''
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_II.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, 15) # 15 is the initial_epoch

If reload a centerloss trained model, please keep save_path same as previous, as centerloss needs to reload saved xxx_centers.npy by save_path name.

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.

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

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)

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.

Learning rate

train.Train parameters lr_base / lr_decay / lr_decay_steps set different decay strategies and their parameters.
tt.lr_scheduler can also be used to set learning rate scheduler directly.

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 keep_as_min=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, < 500	Cosine decay, will multiply with steps_per_epoch	first_restart_step, epoch	m_mul
>= 500	Cosine decay, specific batch number	first_restart_step, batch	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 < 500, 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 < 500, 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, ...)
# 500 <= lr_decay_steps, Cosine decay, first_restart_step = lr_decay_steps
# restart on batch [16000 + steps_per_epoch, 48000 + 2 * steps_per_epoch, 112000 + 3 * steps_per_epoch]
tt = train.Train(..., lr_base=0.001, lr_decay=0.5, lr_decay_steps=16 * 1000, 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) for ii in epochs], label="lr=0.001, decay=0.1")
plt.plot(epochs, [exp_scheduler(ii, 0.001, 0.05) 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=0, m_mul=1e-3, steps_per_epoch=steps_per_epoch)
aa.build()
plt.plot(batchs / steps_per_epoch, [aa.on_train_batch_begin(ii) for ii in batchs], label="Cosine, first_restart_step=50, min=1e-6, m_mul=1e-3")
aa_60 = np.float(aa.on_train_batch_begin(60 * steps_per_epoch))
plt.text(60, aa_60, "[Cosine]\n({}, {:.2e})".format(60, aa_60), va="bottom", ha="right")

bb = CosineLrScheduler(0.001, first_restart_step=16, lr_min=1e-7, warmup=1, m_mul=0.4, steps_per_epoch=steps_per_epoch)
bb.build()
plt.plot(batchs / steps_per_epoch, [bb.on_train_batch_begin(ii) for ii in batchs], label="Cosine restart, first_restart_step=16, min=1e-7, warmup=1, m_mul=0.4")
plt.scatter(18, 0.0004, c='r')
plt.text(18, 0.0004, (18, 0.0004))

cc = CosineLrScheduler(0.001, first_restart_step=13 * 100, lr_min=1e-7, warmup=4, m_mul=0.5, steps_per_epoch=steps_per_epoch)
cc.build()
plt.plot(batchs / steps_per_epoch, [cc.on_train_batch_begin(ii) for ii in batchs], label="Cosine restart, on batch, first_restart_step=1300, min=1e-7, warmup=4, m_mul=0.5")
plt.scatter(18, 0.0005, c='r')
plt.text(18, 0.0005, (18, 0.0005))

plt.xlim(0, 60)
plt.legend()
# plt.grid()
plt.tight_layout()

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}]

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

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
```

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Keras insightface

License

Table of Contents

Current accuracy

Usage

Environment

Beforehand Data Prepare

Training scripts

Mixed precision float16

Learning rate

Optimizers

Multi GPU train using horovod or distribute strategy

TFLite model inference time test on ARM64

Sub Center ArcFace

Knowledge distillation

Evaluating on IJB datasets

Related Projects

About

Releases

Packages

Languages

Name		Name	Last commit message	Last commit date
Latest commit History 239 Commits
backbones		backbones
checkpoints		checkpoints
IJB_evals.py		IJB_evals.py
LICENSE		LICENSE
README.md		README.md
augment.py		augment.py
data.py		data.py
data_distiller.py		data_distiller.py
data_drop_top_k.py		data_drop_top_k.py
eval_folder.py		eval_folder.py
evals.py		evals.py
losses.py		losses.py
models.py		models.py
myCallbacks.py		myCallbacks.py
plot.py		plot.py
prepare_data.py		prepare_data.py
train.py		train.py
video_test.py		video_test.py

License

macsunmood/Keras_insightface

Folders and files

Latest commit

History

Repository files navigation

Keras insightface

License

Table of Contents

Current accuracy

Usage

Environment

Beforehand Data Prepare

Training scripts

Mixed precision float16

Learning rate

Optimizers

Multi GPU train using horovod or distribute strategy

TFLite model inference time test on ARM64

Sub Center ArcFace

Knowledge distillation

Evaluating on IJB datasets

Related Projects

About

Resources

License

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages