Huawei_Technology
/
mindspore
Not watched
1
0
Fork 0
Code
Releases 13 Wiki evaluate Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain HPC
You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.
22144 Commits
11 Branches
1.3 GB
177 Download
 
 
 
 
 
 
Tree: 2f64dbc28a
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from '2f64dbc28a'
${ noResults }
mindspore/model_zoo/research/cv/FaceRecognitionForTracking
mindspore-ci-bot 0aa5ff7879 !15279 Adding GPU and CPU mode to FaceRecognitionForTracking 4 years ago
..
scripts adding GPU mode and CPU mode 4 years ago
src adding GPU mode and CPU mode 4 years ago
README.md adding GPU mode and CPU mode 4 years ago
eval.py adding GPU mode and CPU mode 4 years ago
export.py adding GPU mode and CPU mode 4 years ago
train.py !15279 Adding GPU and CPU mode to FaceRecognitionForTracking 4 years ago

README.md

Contents

Face Recognition For Tracking Description

This is a face recognition for tracking network based on Resnet, with support for training and evaluation on Ascend910, GPU and CPU.

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 Recognition For Tracking uses a Resnet network for performing feature extraction.

Dataset

We use about 10K face images as training dataset and 2K as evaluating dataset in this example, and you can also use your own datasets or open source datasets (e.g. Labeled Faces in the Wild)
The directory structure is as follows:

.
└─ dataset
  ├─ train dataset
    ├─ ID1
      ├─ ID1_0001.jpg
      ├─ ID1_0002.jpg
      ...
    ├─ ID2
      ...
    ├─ ID3
      ...
    ...
  ├─ test dataset
    ├─ ID1
      ├─ ID1_0001.jpg
      ├─ ID1_0002.jpg
      ...
    ├─ ID2
      ...
    ├─ ID3
      ...
    ...

Environment Requirements

Script Description

Script and Sample Code

The entire code structure is as following:

.
└─ Face Recognition For Tracking
  ├─ README.md
  ├─ scripts
    ├─ run_standalone_train.sh              # launch standalone training(1p) in ascend
    ├─ run_distribute_train.sh              # launch distributed training(8p) in ascend
    ├─ run_eval.sh                          # launch evaluating in ascend
    ├─ run_export.sh                        # launch exporting air/mindir model
    ├─ run_standalone_train_gpu.sh          # launch standalone training(1p) in gpu
    ├─ run_distribute_train_gpu.sh          # launch distributed training(8p) in gpu
    ├─ run_eval_gpu.sh                      # launch evaluating in gpu
    ├─ run_export_gpu.sh                    # launch exporting mindir model in gpu
    ├─ run_train_cpu.sh                     # launch standalone training in cpu
    ├─ run_eval_cpu.sh                      # launch evaluating in cpu
    └─ run_export_cpu.sh                    # launch exporting mindir model in cpu
  ├─ src
    ├─ config.py                            # parameter configuration
    ├─ dataset.py                           # dataset loading and preprocessing for training
    ├─ reid.py                              # network backbone
    ├─ log.py                               # log function
    ├─ loss.py                              # loss function
    ├─ lr_generator.py                      # generate learning rate
    └─ me_init.py                           # network initialization
  ├─ train.py                               # training scripts
  ├─ eval.py                                # evaluation scripts
  └─ export.py                              # export air/mindir model

Running Example

Train

  • Stand alone mode

    Ascend:

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

    GPU:

cd ./scripts
sh run_standalone_train_gpu.sh [DATA_DIR]

    CPU:

cd ./scripts
sh run_train_cpu.sh [DATA_DIR]

    or (fine-tune)

    Ascend:

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

    GPU:

cd ./scripts
sh run_standalone_train.sh [DATA_DIR] [PRETRAINED_BACKBONE]

    CPU:

cd ./scripts
sh run_train.sh [DATA_DIR] [PRETRAINED_BACKBONE]

    for example, on Ascend:

    cd ./scripts
sh run_standalone_train.sh /home/train_dataset 0 /home/a.ckpt

  • Distribute mode (recommended)

    Ascend:

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

    GPU:

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

    or (fine-tune)

    Ascend:

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

    GPU:

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

    for example:

    cd ./scripts
sh run_distribute_train.sh /home/train_dataset ./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[10], loss:43.314265, 8574.83 imgs/sec, lr=0.800000011920929
epoch[0], iter[20], loss:45.121095, 8915.66 imgs/sec, lr=0.800000011920929
epoch[0], iter[30], loss:42.342847, 9162.85 imgs/sec, lr=0.800000011920929
epoch[0], iter[40], loss:39.456583, 9178.83 imgs/sec, lr=0.800000011920929

...
epoch[179], iter[14900], loss:1.651353, 13001.25 imgs/sec, lr=0.02500000037252903
epoch[179], iter[14910], loss:1.532123, 12669.85 imgs/sec, lr=0.02500000037252903
epoch[179], iter[14920], loss:1.760322, 13457.81 imgs/sec, lr=0.02500000037252903
epoch[179], iter[14930], loss:1.694281, 13417.38 imgs/sec, lr=0.02500000037252903

Evaluation

Ascend:

cd ./scripts
sh run_eval.sh [EVAL_DIR] [USE_DEVICE_ID] [PRETRAINED_BACKBONE]

GPU:

cd ./scripts
sh run_eval_gpu.sh [EVAL_DIR] [PRETRAINED_BACKBONE]

CPU:

cd ./scripts
sh run_eval_cpu.sh [EVAL_DIR] [PRETRAINED_BACKBONE]

for example, on Ascend:

cd ./scripts
sh run_eval.sh /home/test_dataset 0 /home/a.ckpt

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

0.5: 0.9273788254649683@0.020893691253149882
0.3: 0.8393850978779193@0.07438552515516506
0.1: 0.6220871197028316@0.1523084478903911
0.01: 0.2683641598437038@0.26217882879427634
0.001: 0.11060269148211463@0.34509718987101223
0.0001: 0.05381678898728808@0.4187797093636618
1e-05: 0.035770748447963394@0.5053771466191392

Convert model

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

Ascend:

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

Or if you would like to convert your model to MINDIR file on GPU or CPU:

GPU:

cd ./scripts
sh run_export_gpu.sh [PRETRAINED_BACKBONE] [BATCH_SIZE] [FILE_NAME](optional)

CPU:

cd ./scripts
sh run_export_cpu.sh [PRETRAINED_BACKBONE] [BATCH_SIZE] [FILE_NAME](optional)

Model Description

Performance

Training Performance

Parameters Ascend GPU CPU
Model Version V1 V1 V1
Resource Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G; OS Euler2.8 Tesla V100-PCIE Intel(R) Xeon(R) CPU E5-2690 v4
uploaded Date 09/30/2020 (month/day/year) 04/17/2021 (month/day/year) 04/17/2021 (month/day/year)
MindSpore Version 1.0.0 1.2.0 1.2.0
Dataset 10K images 10K images 10K images
Training Parameters epoch=180, batch_size=16, momentum=0.9 epoch=40, batch_size=128(1p); 16(8p), momentum=0.9 epoch=40, batch_size=128, momentum=0.9
Optimizer SGD SGD SGD
Loss Function Softmax Cross Entropy Softmax Cross Entropy Softmax Cross Entropy
outputs probability probability probability
Speed 1pc: 8-10 ms/step; 8pcs: 9-11 ms/step 1pc: 30 ms/step; 8pcs: 20 ms/step 1pc: 2.5 s/step
Total time 1pc: 1 hour; 8pcs: 0.1 hours 1pc: 2 minutes; 8pcs: 1.5 minutes 1pc: 2 hours
Checkpoint for Fine tuning 17M (.ckpt file) 17M (.ckpt file) 17M (.ckpt file)

Evaluation Performance

Parameters Ascend GPU CPU
Model Version V1 V1 V1
Resource Ascend 910; OS Euler2.8 Tesla V100-PCIE Intel(R) Xeon(R) CPU E5-2690 v4
Uploaded Date 09/30/2020 (month/day/year) 04/17/2021 (month/day/year) 04/17/2021 (month/day/year)
MindSpore Version 1.0.0 1.2.0 1.2.0
Dataset 2K images 2K images 2K images
batch_size 128 128 128
outputs recall recall recall
Recall 0.62(FAR=0.1) 0.62(FAR=0.1) 0.62(FAR=0.1)
Model for inference 17M (.ckpt file) 17M (.ckpt file) 17M (.ckpt file)

ModelZoo Homepage

Please check the official homepage.

Delete task

Are you sure you want to delete this task? Once this task is deleted, it cannot be recovered.

cancel
confirm

MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.

C++ Python Text Unity3D Asset C other