Face Attribute Description
Model Architecture
Dataset
Environment Requirements
Script Description
- Script and Sample Code
- Running Example
Model Description
- Performance
ModelZoo Homepage

Face Attribute Description

This is a Face Attributes Recognition network based on Resnet18, with support for training and evaluation on 910.

ResNet (residual neural network) was proposed by Kaiming He and other four Chinese of Microsoft Research Institute. Through the use of ResNet unit, it successfully trained 152 layers of neural network, and won the championship in ilsvrc2015. The error rate on top 5 was 3.57%, and the parameter quantity was lower than vggnet, so the effect was very outstanding. Traditional convolution network or full connection network will have more or less information loss. At the same time, it will lead to the disappearance or explosion of gradient, which leads to the failure of deep network training. ResNet solves this problem to a certain extent. By passing the input information to the output, the integrity of the information is protected. The whole network only needs to learn the part of the difference between input and output, which simplifies the learning objectives and difficulties.The structure of ResNet can accelerate the training of neural network very quickly, and the accuracy of the model is also greatly improved. At the same time, ResNet is very popular, even can be directly used in the concept net network.

Paper: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition"

Model Architecture

Face Attribute uses a modified-Resnet18 network for performing feature extraction.

Dataset

This network can recognize the age/gender/mask from a human face. The default rule is:

age:
    0: 0~2 years
    1: 3~9 years
    2: 10~19 years
    3: 20~29 years
    4: 30~39 years
    5: 40~49 years
    6: 50~59 years
    7: 60~69 years
    8: 70+ years

gender:
    0: male
    1: female

mask:
    0: wearing mask
    1: without mask

We use about 91K face images as training dataset and 11K as evaluating dataset in this example(e.g. FairFace and RWMFD)

step 1: Download FairFace and RWMFD dataset

step 2: Preprocess dataset

2.1. Download the FairFace and RWMFD datasets respectively, and put the downloaded zip file in the directory where the description file is located.
2.2. Unzip package RWMFD in place, get folder 'Real-World-Masked-Face-Dataset-master' after unzipping, enter this folder, unzip package 'RMFD_part_2', and get folder 'RWMFD_part_2_pro'.
2.3. Unzip the fairface package in place to get the 'train' and 'val' folders
2.4. Convert the dataset to txt file，execute the following scripts in sequence:
        python src/far_process_fairface.py
        python src/far_process_RWMF.py
        python src/far_merge_RWMF_fairface.py
2.5. Convert the dataset to mindrecord:
        python src/data_to_mindrecord_train.py
        python src/data_to_mindrecord_eval.py

You can also use your own datasets or other open source datasets or other open source datasets

step 1: The dataset should be saved in a txt file, which contain the following contents:
```
[PATH_TO_IMAGE]/1.jpg [LABEL_AGE] [LABEL_GENDER] [LABEL_MASK]
[PATH_TO_IMAGE]/2.jpg [LABEL_AGE] [LABEL_GENDER] [LABEL_MASK]
[PATH_TO_IMAGE]/3.jpg [LABEL_AGE] [LABEL_GENDER] [LABEL_MASK]
...
```
The value range of [LABEL_AGE] is [-1, 0, 1, 2, 3, 4, 5, 6, 7, 8], -1 means the label should be ignored.

The value range of [LABEL_GENDER] is [-1, 0, 1], -1 means the label should be ignored.

The value range of [LABEL_MASK] is [-1, 0, 1], -1 means the label should be ignored.
step 2: Convert the dataset to mindrecord:
```
python src/data_to_mindrecord_train.py
```
or
```
python src/data_to_mindrecord_eval.py
```
If your dataset is too big to convert at a time, you can add data to an existed mindrecord in turn:
```
python src/data_to_mindrecord_train_append.py
```

Environment Requirements

Hardware(GPU)
- Prepare hardware environment with processor.
Framework
- MindSpore
For more information, please check the resources below:
- MindSpore tutorials
- MindSpore Python API

Script Description

Script and Sample Code

The entire code structure is as following:

.
└─ Face Attribute
  ├─ README.md
  ├── model_utils
  │   ├──__init__.py              // module init file
  │   ├──config.py                // Parse arguments
  │   ├──device_adapter.py        // Device adapter for ModelArts
  │   ├──local_adapter.py         // Local adapter
  │   ├──moxing_adapter.py        // Moxing adapter for ModelArts
  ├─ scripts
    ├─ run_standalone_train.sh              # launch standalone training(1p) in 
    ├─ run_distribute_train.sh              # launch distributed training(8p) in 
    ├─ run_standalone_train_gpu.sh          # launch standalone training(1p) in GPU
    ├─ run_distribute_train_gpu.sh          # launch distributed training(8p) in GPU
    ├─ run_eval.sh                          # launch evaluating in 
    ├─ run_eval_gpu.sh                      # launch evaluating in gpu
    └─ run_export.sh                        # launch exporting air/mindir model
    ├─ run_infer_310.sh                     # shell script for 310 inference
  ├─ src
    ├─ FaceAttribute
      ├─ cross_entropy.py                   # cross entroy loss
      ├─ custom_net.py                      # network unit
      ├─ loss_factory.py                    # loss function
      ├─ head_factory.py                    # network head
      ├─ resnet18.py                        # network backbone
      ├─ head_factory_softmax.py            # network head with softmax
      └─ resnet18_softmax.py                # network backbone with softmax
    ├─ dataset_eval.py                      # dataset loading and preprocessing for evaluating
    ├─ dataset_train.py                     # dataset loading and preprocessing for training
    ├─ log.py                               # log function
    ├─ lrsche_factory.py                    # generate learning rate
    ├─ data_to_mindrecord_train.py          # convert dataset to mindrecord for training
    ├─ data_to_mindrecord_train_append.py   # add dataset to an existed mindrecord for training
    └─ data_to_mindrecord_eval.py           # convert dataset to mindrecord for evaluating
  ├─ default_config.yaml                    # Configurations
  ├─ postprocess.py                         # postprocess scripts
  ├─ preprocess.py                          # preprocess scripts
  ├─ train.py                               # training scripts
  ├─ eval.py                                # evaluation scripts
  └─ export.py                              # export air/mindir model

Running Example

Train

Stand alone mode

cd ./scripts
sh run_standalone_train.sh [MINDRECORD_FILE] [USE_DEVICE_ID]

or (fine-tune)

cd ./scripts
sh run_standalone_train.sh [MINDRECORD_FILE] [USE_DEVICE_ID] [PRETRAINED_BACKBONE]

for example:

cd ./scripts
sh run_standalone_train.sh /home/train.mindrecord 0 /home/a.ckpt

cd ./scripts
sh run_standalone_train_gpu.sh [MINDRECORD_FILE] [CUDA_VISIBLE_DEVICES]

or (fine-tune)

cd ./scripts
sh run_standalone_train_gpu.sh [MINDRECORD_FILE] [CUDA_VISIBLE_DEVICES] [PRETRAINED_BACKBONE]

for example:

cd ./scripts
sh run_standalone_train_gpu.sh /home/train.mindrecord 0 /home/a.ckpt

Distribute mode (recommended)

cd ./scripts
sh run_distribute_train.sh [MINDRECORD_FILE] [RANK_TABLE]

or (fine-tune)

cd ./scripts
sh run_distribute_train.sh [MINDRECORD_FILE] [RANK_TABLE] [PRETRAINED_BACKBONE]

for example:

cd ./scripts
sh run_distribute_train_gpu.sh [DEVICE_NUM] [CUDA_VISIBLE_DEVICES(0,1,2,3,4,5,6,7)] [MINDRECORD_FILE]

or (fine-tune)

cd ./scripts
sh run_distribute_train_gpu.sh [DEVICE_NUM] [CUDA_VISIBLE_DEVICES(0,1,2,3,4,5,6,7)] [MINDRECORD_FILE] [PRETRAINED_BACKBONE]

for example:

cd ./scripts
sh run_distribute_train_gpu.sh 8 0,1,2,3,4,5,6,7 /home/train.mindrecord ./rank_table_8p.json /home/a.ckpt

You will get the loss value of each step as following in "./output/[TIME]/[TIME].log" or "./scripts/device0/train.log":

epoch[0], iter[0], loss:4.489518, 12.92 imgs/sec
epoch[0], iter[10], loss:3.619693, 13792.76 imgs/sec
epoch[0], iter[20], loss:3.580932, 13817.78 imgs/sec
epoch[0], iter[30], loss:3.574254, 7834.65 imgs/sec
epoch[0], iter[40], loss:3.557742, 7884.87 imgs/sec

...
epoch[69], iter[6120], loss:1.225308, 9561.00 imgs/sec
epoch[69], iter[6130], loss:1.209557, 8913.28 imgs/sec
epoch[69], iter[6140], loss:1.158641, 9755.81 imgs/sec
epoch[69], iter[6150], loss:1.167064, 9300.77 imgs/sec

ModelArts (If you want to run in modelarts, please check the official documentation of modelarts, and you can start training as follows)

# Train 8p on ModelArts
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on default_config.yaml file.
#          Set "mindrecord_path='/cache/data/face_attribute_dataset/train/data_train.mindrecord'" on default_config.yaml file.
#          (option) Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file if load pretrain.
#          (option) Set "pretrained='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file if load pretrain.
#          Set other parameters on default_config.yaml file you need.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add "mindrecord_path=/cache/data/face_attribute_dataset/train/data_train.mindrecord" on the website UI interface.
#          (option) Add "checkpoint_url=s3://dir_to_trained_ckpt/" on the website UI interface if load pretrain.
#          (option) Add "pretrained=/cache/checkpoint_path/model.ckpt" on the website UI interface if load pretrain.
#          Add other parameters on the website UI interface.
# (2) (option) Upload or copy your pretrained model to S3 bucket if load pretrain.
# (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
# (4) Set the code directory to "/path/FaceAttribute" on the website UI interface.
# (5) Set the startup file to "train.py" on the website UI interface.
# (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (7) Create your job.
#
# Train 1p on ModelArts
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on default_config.yaml file.
#          Set "world_size=1" on default_config.yaml file.
#          Set "mindrecord_path='/cache/data/face_attribute_dataset/train/data_train.mindrecord'" on default_config.yaml file.
#          (option) Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file if load pretrain.
#          (option) Set "pretrained='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file if load pretrain.
#          Set other parameters on default_config.yaml file you need.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add "world_size=1" on the website UI interface.
#          Add "mindrecord_path=/cache/data/face_attribute_dataset/train/data_train.mindrecord" on the website UI interface.
#          (option) Add "checkpoint_url=s3://dir_to_trained_ckpt/" on the website UI interface if load pretrain.
#          (option) Add "pretrained=/cache/checkpoint_path/model.ckpt" on the website UI interface if load pretrain.
#          Add other parameters on the website UI interface.
# (2) (option) Upload or copy your pretrained model to S3 bucket if load pretrain.
# (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
# (4) Set the code directory to "/path/FaceAttribute" on the website UI interface.
# (5) Set the startup file to "train.py" on the website UI interface.
# (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (7) Create your job.
#
# Eval 1p on ModelArts
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on default_config.yaml file.
#          Set "mindrecord_path='/cache/data/face_attribute_dataset/train/data_train.mindrecord'" on default_config.yaml file.
#          Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file.
#          Set "model_path='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
#          Set other parameters on default_config.yaml file you need.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add "mindrecord_path=/cache/data/face_attribute_dataset/train/data_train.mindrecord" on the website UI interface.
#          Add "checkpoint_url=s3://dir_to_trained_ckpt/" on the website UI interface.
#          Add "model_path=/cache/checkpoint_path/model.ckpt" on the website UI interface.
#          Add other parameters on the website UI interface.
# (2) Upload or copy your trained model to S3 bucket.
# (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
# (4) Set the code directory to "/path/FaceAttribute" on the website UI interface.
# (5) Set the startup file to "eval.py" on the website UI interface.
# (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (7) Create your job.
#
# Export 1p on ModelArts
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on default_config.yaml file.
#          Set "file_name='faceattri'" on default_config.yaml file.
#          Set "file_format='MINDIR'" on default_config.yaml file.
#          Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file.
#          Set "ckpt_file='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
#          Set other parameters on default_config.yaml file you need.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add "file_name=faceattri" on the website UI interface.
#          Add "file_format=MINDIR" on the website UI interface.
#          Add "checkpoint_url=s3://dir_to_trained_ckpt/" on the website UI interface.
#          Add "ckpt_file=/cache/checkpoint_path/model.ckpt" on the website UI interface.
#          Add other parameters on the website UI interface.
# (2) Upload or copy your trained model to S3 bucket.
# (3) Set the code directory to "/path/FaceAttribute" on the website UI interface.
# (4) Set the startup file to "export.py" on the website UI interface.
# (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (6) Create your job.

Evaluation

cd ./scripts
sh run_eval_gpu.sh [MINDRECORD_FILE] [CUDA_VISIBLE_DEVICES] [PRETRAINED_BACKBONE]

for example:

cd ./scripts
sh run_eval.sh /home/eval.mindrecord 0 /home/a.ckpt

You will get the result as following in "./scripts/device0/eval.log" or txt file in [PRETRAINED_BACKBONE]'s folder:

age accuracy:  0.45773233522001094
gen accuracy:  0.8950155194449516
mask accuracy:  0.992539346357495
gen precision:  0.8869598765432098
gen recall:  0.8907400232468036
gen f1:  0.88884593079451
mask precision:  1.0
mask recall:  0.998539346357495
mask f1:  0.9992691394116572

Convert model

If you want to infer the network on 310, you should convert the model to AIR/MINDIR:

cd ./scripts
sh run_export.sh [BATCH_SIZE] [USE_DEVICE_ID] [PRETRAINED_BACKBONE]

Inference Process

Export MindIR

python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] --device_target [device_target]

The ckpt_file parameter is required, file_format should be in ["AIR", "MINDIR"] ckpt_path ckpt file path

Infer on 310

Before performing inference, the mindir file must be exported by export.py script. We only provide an example of inference using MINDIR model. Current batch_Size for imagenet2012 dataset can only be set to 1.

# 310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]

MINDIR_PATH specifies path of used "MINDIR" OR "AIR" model.
DATASET_PATH specifies path of cifar10 datasets
DEVICE_ID is optional, default value is 0.

Result

Inference result is saved in current path, you can find result like this in acc.log file.

'age accuracy': 0.4937
'gen accuracy': 0.9093
'mask accuracy': 0.9903

Model Description

Performance

Training Performance

Parameters	Face Attribute
Model Version	V1
Resource	Tesla V100-PICE-32G
uploaded Date	07/19/2021 (month/day/year)
MindSpore Version	1.3.0
Dataset	91K images
Training Parameters	epoch=70, batch_size=128, momentum=0.9, lr=0.001
Optimizer	Momentum
Loss Function	Softmax Cross Entropy
outputs	probability
Speed	1pc: 115~~125 ms/step; 8pcs: 150~~200 ms/step
Total time	1pc: 1.5 hours; 8pcs: 0.4 hours
Checkpoint for Fine tuning	88M (.ckpt file)

Evaluation Performance

Parameters	Face Attribute
Model Version	V1
Resource	Tesla V100-PICE-32G
Uploaded Date	07/19/2021 (month/day/year)
MindSpore Version	1.3.0
Dataset	11K images
batch_size	1
outputs	accuracy
Accuracy(8pcs)	age:49.0%
	gender:90.8%
	mask:99.3%
Model for inference	88M (.ckpt file)

ModelZoo Homepage

Please check the official homepage.

kingcong / gpu_FaceAttribute

Contents

Face Attribute Description

Model Architecture

Dataset

Environment Requirements

Script Description

Script and Sample Code

Running Example

Train

Evaluation

Convert model

Inference Process

Export MindIR

Infer on 310

Result

Model Description

Performance

Training Performance

Evaluation Performance

ModelZoo Homepage

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