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EvanBay
1baeadecb7
|
2 years ago | |
|---|---|---|
| ascend310_infer | 2 years ago | |
| scripts | 2 years ago | |
| src | 2 years ago | |
| README.md | 2 years ago | |
| README_CN.md | 2 years ago | |
| eval.py | 2 years ago | |
| export.py | 2 years ago | |
| postprocess.py | 2 years ago | |
| preprocess.py | 2 years ago | |
| requirment.txt | 2 years ago | |
| sphereface_config.yaml | 2 years ago | |
| train.py | 2 years ago | |
README.md
Contents
- Contents
- Sphereface Description
- Model Architecture
- Dataset
- Environment Requirements
- Quick Start
- Script Description
- Model Description
- Description of Random Situation
- ModelZoo Homepage
Sphereface Description
Spherenet is an innovation in the field of face recognition using improved softamax proposed in 2017. He proposed angular softmax loss to improve the original softmax loss. On the basis of large margin softmax loss, two constraints of ||W||=1 and b=0 are added, so that the prediction result depends only on W and x. The angle between. His application scenario is in an open set environment. The paper also mainly solves this task, so that in a specific measurement space, the maximum intra-class distance is less than the minimum inter-class distance of different classes as much as possible. The paper Make the feature more discriminable.
Paper: Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, Le Song."SphereFace: Deep Hypersphere Embedding for Face Recognition."Proceedings of the IEEE conference on computer vision and pattern recognition.2017.
Model Architecture
A variety of network architectures are given in the Sphereface paper. This code only implements its 20-layer network architecture. It uses multiple 3x3 convolutions with steps of 1 and 2, and uses the residual structure and PReLu to perform Feature extraction, and image classification in the last fully connected layer, through the training of the last layer, the weight can be used to distinguish the features of the image.
Dataset
Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
Dataset used: CASIA-WebFace
- Dataset size：4G，494,414 250*250 colorful images in 10575 classes
- Train：3.7G，454,000 images
- Test：0.3G，40,000 images
- Data format：RGB images
- Note：Data will be processed in src/dataset.py
Dataset used: LFW
- Dataset size: 180M, 13,233 colorful images in 5749 classes
- Train: 162M, 11,910 images
- Test: 18M, 1,323 images
- Data format: RGB images.
- Note: Data will be processed in src/eval.py
Environment Requirements
- Hardware（Ascend）
- Prepare hardware environment with Ascend and GPU processor.
- Framework
- For more information, please check the resources below：
Quick Start
After installing MindSpore via the official website, you can start training and evaluation as follows:
-
running on Ascend
# Add data set path, take training CASIA-WebFace as an example train_data_dir: "../casia_landmark.txt" train_img_dir : "../CASIA-WebFace/" # Add checkpoint path parameters before inference ckpt_files: "../CKPTFILE"# run training example python train.py > train.log 2>&1 & # run distributed training example bash scripts/run_distribute_train.sh # run evaluation example python eval.py > eval.log 2>&1 & OR bash run_eval_standalone_Ascend.sh [DEVICE_ID] [CKPT_FILES] # example: bash run_train_Ascend.sh 0 "/data/sphereface/6000-9923.ckpt" -
running on GPU
# run training example python train.py > train.log 2>&1 & # run distributed training example bash scripts/run_distribute_train_GPU.sh 8 0,1,2,3,4,5,6,7 # run evaluation example python eval.py > eval.log 2>&1 & OR bash run_eval_standalone_GPU.sh [DEVICE_ID] [CKPT_FILES] # example: bash run_eval_standalone_GPU.sh 0 "/data/sphereface/6000-9923.ckpt" For distributed training, a hccl configuration file with JSON format needs to be created in advance. Please follow the instructions in the link below: <https://gitee.com/mindspore/models/tree/master/utils/hccl_tools>.
We use CASIA-WebFace dataset by default. Your can also pass $dataset_type to the scripts so that select different datasets. For more details, please refer the specify script.
-
ModelArts (If you want to run in modelarts, please check the official documentation of modelarts, and you can start training as follows)
-
Train sphereface 1p on ModelArts
# (1) Add "config_path='/path_to_code/sphereface_config.yaml'" on the website UI interface. # (2) Perform a or b. # a. Set "enable_modelarts=True" on sphereface_config.yaml file. # Set "data_url='/user_name/CAISA/'" on sphereface_config.yaml file. # Set "train_url='/user_name/Sphereface/output/'" on sphereface_config.yaml file. # Set other parameters on sphereface_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "train_url='/user_name/Sphereface/output/'" on the website UI interface. # Add "data_url=‘/user_name/CAISA/’" on the website UI interface. # Add other parameters on the website UI interface. # (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/sphereface" 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.
-
Script Description
Script and Sample Code
├── model_zoo
├── README.md // All model related instructions
├── sphereface
├── README.md // Sphereface related instructions
├── ascend310_infer // Implement 310 inference source code
├── scripts
│ ├──run_distribute_train_GPU.sh // Distributed to GPU training shell script
│ ├──run_eval_standalone_GPU.sh // Shell script evaluated by GPU
│ ├──run_train_standalone_GPU.sh // Shell script for single-card GPU training
│ ├──run_distribute_train.sh // Distributed to Ascend training shell script
│ ├──run_eval_standalone_Ascend.sh // Shell script evaluated by Ascend
│ ├──run_train_standalone_Ascend.sh // Shell script for single-card Ascend training
├── src
│ ├──datasets // Data set processing
│ │ ├──classfication.py // Data classfication set processing
│ │ ├──sampler.py // Data shuffle
│ ├──losses // Loss function
│ │ ├──crossentropy.py // ASoftMax loss function
│ ├──network // Net structure
│ │ ├──spherenet.py // Sphereface 20 layer net structure
├── train.py // Train script
├── eval.py // Eval script
├── export.py // Export the checkpoint file to air/mindir
├── sphereface_config.yaml // Config file
Script Parameters
Parameters for both training and evaluation can be set in config.py
-
config for Sphereface, CASIA-WebFace dataset and LFW dataset.
device_target: 'Ascend' #target device net: "sphereface20a" #net's name dataset: "CASIA-WebFace" #dataset's name is_distributed: 1 #whether distributed rank: 0 #class group_size: 1 #data group size label_smooth: 1 #whether label smooth smooth_factor: 0.15 #label_smooth factore train_data_dir: "/data/sphereface/casia_landmark.txt" #train data txt file train_img_dir : "/data/sphereface/CASIA-WebFace/" #train data file train_pretrained: "" #pretrained ckpt file(optional) image_size: "112, 96" #crop image size num_classes: 10574 #train target class nums lr: 0.15 #learing rate lr_scheduler: "cosine_annealing" #learning rate adjustment model eta_min: 0 #cos eta T_max: 20 #cos half-cycle max_epoch: 20 #train cycle per_batch_size: 32 #batch size warmup_epochs: 0 #warmup epoch weight_decay: 0.0005 #weight_decay factor momentum: 0.9 #momentum factor ckpt_interval: 10000 #ckpt save needs iter save_ckpt_path: "./" #ckpt save route is_save_on_master: 1 #whether save the ckpt ckpt_files: "/data/sphereface/Sphereface/scripts/device0/" #eval ckpt route datatxt_src: '/data/sphereface/lfw_landmark.txt' #eval data txt route pairtxt_src: '/data/sphereface/pairs.txt' #eval data pair txt route datasrc: '/data/sphereface/lfw/' #eval data route device_id: 7 #device id batch_size: 32 #eval batch size
For more configuration details, please refer the script config.py.
Training Process
Training
-
running on Ascend
bash scripts/run_train_standalone_Ascend.sh 3The python command above will run in the background, you can view the results through the file
train.log.After training, you'll get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
# grep "loss is " train.log 2021-10-25 09:21:16,137:INFO:epoch[0], iter[1774], loss:8.9384, mean_fps:0.00 imgs/sec 2021-10-25 09:44:13,485:INFO:epoch[1], iter[3549], loss:8.737117, mean_fps:329.93 imgs/sec 2021-10-25 10:07:25,687:INFO:epoch[2], iter[5324], loss:8.615589, mean_fps:326.40 imgs/sec ...The model checkpoint will be saved in the current directory.
-
running on GPU
bash scripts/run_train_standalone_GPU.sh 3The python command above will run in the background, you can view the results through the file
train.log.After training, you'll get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
2021-11-12 12:47:04,004:INFO:epoch[0], iter[1774], loss:8.9929695, mean_fps:0.00 imgs/sec 2021-11-12 13:07:43,106:INFO:epoch[1], iter[3549], loss:8.491371, mean_fps:366.72 imgs/sec 2021-11-12 13:28:21,060:INFO:epoch[2], iter[5324], loss:8.061005, mean_fps:367.06 imgs/sec 2021-11-12 13:48:55,650:INFO:epoch[3], iter[7099], loss:7.3936157, mean_fps:368.06 imgs/sec ...
Distributed Training
-
running on Ascend
bash scripts/run_distribute_train.shThe above shell script will run distribute training in the background. You can view the results through the file
train_parallel[X]/log. The loss value will be achieved as follows:/scripts/device0/output.log: 2021-10-26 17:43:54,981:INFO:epoch[0], iter[1774], loss:8.913538, mean_fps:0.00 imgs/sec 2021-10-26 17:47:19,239:INFO:epoch[1], iter[3549], loss:8.307044, mean_fps:2224.67 imgs/sec 2021-10-26 17:50:43,860:INFO:epoch[2], iter[5324], loss:8.096157, mean_fps:2220.70 imgs/sec ... /scripts/device1/output.log 2021-10-26 17:43:53,738:INFO:epoch[0], iter[1774], loss:9.072665, mean_fps:0.00 imgs/sec 2021-10-26 17:47:18,135:INFO:epoch[1], iter[3549], loss:8.373915, mean_fps:2223.15 imgs/sec 2021-10-26 17:50:42,717:INFO:epoch[2], iter[5324], loss:8.244397, mean_fps:2221.12 imgs/sec ... -
running on Ascend
bash scripts/run_distribute_train_GPU.sh 8 0,1,2,3,4,5,6,7The above shell script will run distribute training in the background. You can view the results through the file
train_parallel[X]/log. The loss value will be achieved as follows:2021-11-12 08:04:58,607:INFO:epoch[0], iter[1774], loss:9.040621, mean_fps:0.00 imgs/sec 2021-11-12 08:04:58,607:INFO:epoch[0], iter[1774], loss:8.997707, mean_fps:0.00 imgs/sec 2021-11-12 08:04:58,608:INFO:epoch[0], iter[1774], loss:8.859007, mean_fps:0.00 imgs/sec 2021-11-12 08:04:59,476:INFO:epoch[0], iter[1774], loss:8.927963, mean_fps:0.00 imgs/sec 2021-11-12 08:16:03,775:INFO:epoch[1], iter[3549], loss:8.358135, mean_fps:683.14 imgs/sec 2021-11-12 08:16:03,776:INFO:epoch[1], iter[3549], loss:8.495537, mean_fps:683.14 imgs/sec 2021-11-12 08:16:03,776:INFO:epoch[1], iter[3549], loss:8.600464, mean_fps:683.14 imgs/sec 2021-11-12 08:16:03,776:INFO:epoch[1], iter[3549], loss:8.598774, mean_fps:683.14 imgs/sec 2021-11-12 08:16:03,776:INFO:epoch[1], iter[3549], loss:8.281206, mean_fps:683.14 imgs/sec ...
Evaluation Process
Evaluation
-
evaluation on LFW dataset when running on Ascend
Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/train10-125_390.ckpt".
python eval.py > eval.log 2>&1 & OR bash run_eval_standalone.sh 0 "/data/sphereface/6000 9923.ckpt"The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
# grep "accuracy:" eval.log LFWACC=0.9923 std=0.005 thd=0.3050Note that for evaluation after distributed training, please set the checkpoint_path to be the last saved checkpoint file such as "/data/sphereface/scripts/device0/0-20_1775.ckpt". The accuracy of the test dataset will be as follows:
# grep "accuracy:" eval.log LFWACC=0.9928 std=0.0045 thd=0.2940 -
evaluation on LFW dataset when running on GPU
Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/train10-125_390.ckpt".
python eval.py > eval.log 2>&1 & OR bash run_eval_standalone_GPU.sh 0 "/data/sphereface/6000 9923.ckpt"The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
# grep "accuracy:" eval.log LFWACC=0.9913 std=0.004 thd=0.2840Note that for evaluation after distributed training, please set the checkpoint_path to be the last saved checkpoint file such as "/data/sphereface/scripts/device0/0-20_1775.ckpt". The accuracy of the test dataset will be as follows:
# grep "accuracy:" eval.log LFWACC=0.9918 std=0.0045 thd=0.2800
Export Process
export
python export.py --file_format [EXPORT_FORMAT]
EXPORT_FORMAT should be in ["AIR", "MINDIR"]
Inference Process
Inference
Before performing inference, we need to export the model first. Air model can only be exported in Ascend 910 environment, mindir can be exported in any environment.
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATASET_NAME] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_ID]
-NOTE:Ascend310 inference use sphereface dataset .
Inference result is saved in shell
The accuracy of evaluating DenseNet121 on the test dataset of ImageNet will be as follows:
now the idx is %d 9
LFWACC=0.9922 std=0.0044 thd=0.2800
Model Description
Performance
Evaluation Performance
Sphereface on CASIA-WebFace
| Parameters | Ascend | GPU |
|---|---|---|
| Model Version | Spherenet20a | Sphereface20a |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 | V100*8; CPU 2.6GHZ 56Cores; Memory 377G; OS Euler2.8 |
| uploaded Date | 10/28/2021 (month/day/year) | 11/13/2021 |
| MindSpore Version | 1.3.0 | 1.5.0 |
| Dataset | CASIA-WebFace | CASIA-WebFace |
| Training Parameters | epoch=20, steps=1775, batch_size = 256, lr=0.15 | epoch=20, steps=1775, batch_size = 256, lr=0.15 |
| Optimizer | Momentum | Momentum |
| Loss Function | ASoftmax Cross Entropy | ASoftmax Cross Entropy |
| outputs | probability | probability |
| Loss | 3.1677 | 3.2163 |
| Speed | 1pc: 795 ms/step; 8pcs: 931 ms/step | 1pc: 694 ms/step; 8pcs: 373 ms/step |
| Total time | 1pc: 469.85 mins; 8pcs: 68.79 mins | 1pc: 412.39 mins; 8pcs: 220.69 mins |
| Parameters (M) | 13.0 | 13.0 |
| Checkpoint for Fine tuning | 214.58M (.ckpt file) | 214.58M (.ckpt file) |
| Scripts | sphereface script | sphereface script |
Inference Performance
Sphereface on LFW
| Parameters | Ascend | GPU |
|---|---|---|
| Model Version | Sphereface20a | Sphereface20a |
| Resource | Ascend 910; OS Euler2.8 | V100; OS Euler2.8 |
| Uploaded Date | 10/28/2021 (month/day/year) | 11/13/2021 |
| MindSpore Version | 1.3.0 | 1.5.0 |
| Dataset | LFW, 13,323 images | LFW, 13,323 images |
| batch_size | 60 | 60 |
| outputs | probability | probability |
| Accuracy | 1pc: 99.23%; 8pcs: 99.28% | 1pc: 99.13%; 8pcs: 99.18% |
How to use
Inference
If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this Link. Following the steps below, this is a simple example:
-
Running on Ascend and GPU
# Set context config.image_size = list(map(int,config.image_size.split(','))) context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, save_graphs=False) # Load unseen dataset for inference ImgOut = getImg(datatxt_src,pairtxt_src,datasrc,network) # Define model network = spherenet(config.num_classes,True) # Load pre-trained model param_dict = load_checkpoint(model) param_dict_new = {} for key, values in param_dict.items(): if key.startswith('moments.'): continue elif key.startswith('network.'): param_dict_new[key[8:]] = values else: param_dict_new[key] = values load_param_into_net(network, param_dict_new) # Make predictions on the unseen dataset for idx, (train, test) in enumerate(folds): best_thresh = find_best_threshold(thresholds, predicts[train]) accuracy.append(eval_acc(best_thresh, predicts[test])) thd.append(best_thresh) print('LFWACC={:.4f} std={:.4f} thd={:.4f}'.format(np.mean(accuracy), np.std(accuracy), np.mean(thd)))
Continue Training on the Pretrained Model
-
running on Ascend and GPU
# Load dataset de_dataset = classification_dataset_imagenet(data_dir, image_size=[112,96], per_batch_size=config.per_batch_size, max_epoch=config.max_epoch, rank=0, group_size=config.group_size, input_mode="txt", root=images_dir, shuffle=True) # Define model network = sphere20a(config.num_classes,feature=False) # Continue training if set pre_trained to be True if os.path.isfile(config.train_pretrained): param_dict = load_checkpoint(config.train_pretrained) param_dict_new = {} for key, values in param_dict.items(): if key.startswith('moments.'): continue elif key.startswith('network.'): param_dict_new[key[8:]] = values else: param_dict_new[key] = values load_param_into_net(network, param_dict_new) config.logger.info('load model %s success', str(config.train_predtrained)) lr_schedule = lr_scheduler.get_lr() opt = Momentum(params=get_param_groups(network), learning_rate=Tensor(lr_schedule), momentum=config.momentum, weight_decay=config.weight_decay, loss_scale=config.loss_scale) criterion = AngleLoss(classnum=config.num_classes,smooth_factor=config.smooth_factor) model = Model(train_net, optimizer=opt, metrics=None, loss_scale_manager=loss_scale_manager, amp_level="O0") # Set callbacks ckpt_max_num = config.max_epoch * config.steps_per_epoch // config.ckpt_interval ckpt_config = CheckpointConfig(save_checkpoint_steps=config.ckpt_interval, keep_checkpoint_max=ckpt_max_num) ckpt_cb = ModelCheckpoint(config=ckpt_config, directory=config.save_ckpt_path, prefix='%s' % config.rank) callbacks.append(ckpt_cb) # Start training model.train(config.max_epoch, de_dataset, callbacks=callbacks,dataset_sink_mode=False)
Transfer Learning
To be added.
Description of Random Situation
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
ModelZoo Homepage
Please check the official homepage.
Spherenet是2017年提出的使用改进的softamax在人脸识别领域进行的一项创新。他提出了angular softmax loss用来改进原来的softmax loss，在large margin softmax loss的基础上增加了||W||=1以及b=0的两个限制条件，使得预测结果仅仅取决于W和x之间的角度。他的应用场景是在开集环境下的，该论文也主要解决了这一任务，使得在特定的测度空间下，能够尽可能的满足最大类内距离小于不同类的最小类间距离，该论文使得特征更加具有可判别性。
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