diff --git a/.gitignore b/.gitignore index 7bbc71c..4f61b8c 100644 --- a/.gitignore +++ b/.gitignore @@ -99,3 +99,5 @@ ENV/ # mypy .mypy_cache/ + +.DS_Store diff --git a/README.md b/README.md index 89751e1..8930133 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,10 @@ # InsightFace: 2D and 3D Face Analysis Project +
+ +
+ By [Jia Guo](mailto:guojia@gmail.com?subject=[GitHub]%20InsightFace%20Project) and [Jiankang Deng](https://jiankangdeng.github.io/) ## Top News @@ -21,244 +25,62 @@ The training data containing the annotation (and the models trained with these d ## Introduction -InsightFace is an open source 2D&3D deep face analysis toolbox, mainly based on MXNet and PyTorch. +[InsightFace](https://insightface.ai) is an open source 2D&3D deep face analysis toolbox, mainly based on PyTorch and MXNet. Please check our [website](https://insightface.ai) for detail. -The master branch works with **MXNet 1.2 to 1.6**, **PyTorch 1.6+**, with **Python 3.x**. +The master branch works with **PyTorch 1.6+** and/or **MXNet=1.6-1.8**, with **Python 3.x**. + +InsightFace efficiently implements a rich variety of state of the art algorithms of face recognition, face detection and face alignment, which optimized for both training and deployment. - ## ArcFace Video Demo +### ArcFace Video Demo [![ArcFace Demo](https://github.com/deepinsight/insightface/blob/master/resources/facerecognitionfromvideo.PNG)](https://www.youtube.com/watch?v=y-D1tReryGA&t=81s) Please click the image to watch the Youtube video. For Bilibili users, click [here](https://www.bilibili.com/video/av38041494?from=search&seid=11501833604850032313). -## Recent Update - -**`2021-06-05`**: We launch a [Masked Face Recognition Challenge & Workshop](https://github.com/deepinsight/insightface/tree/master/challenges/iccv21-mfr) on ICCV 2021. - -**`2021-05-15`**: We released an efficient high accuracy face detection approach called [SCRFD](https://github.com/deepinsight/insightface/tree/master/detection/scrfd). - -**`2021-04-18`**: We achieved Rank-4th on NIST-FRVT 1:1, see [leaderboard](https://pages.nist.gov/frvt/html/frvt11.html). - -**`2021-03-13`**: We have released our official ArcFace PyTorch implementation, see [here](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch). - -**`2021-03-09`**: [Tips](https://github.com/deepinsight/insightface/issues/1426) for training large-scale face recognition model, such as millions of IDs(classes). - -**`2021-02-21`**: We provide a simple face mask renderer [here](https://github.com/deepinsight/insightface/tree/master/recognition/tools) which can be used as a data augmentation tool while training face recognition models. - -**`2021-01-20`**: [OneFlow](https://github.com/Oneflow-Inc/oneflow) based implementation of ArcFace and Partial-FC, [here](https://github.com/deepinsight/insightface/tree/master/recognition/oneflow_face). - -**`2020-10-13`**: A new training method and one large training set(360K IDs) were released [here](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc) by DeepGlint. - -**`2020-10-09`**: We opened a large scale recognition test benchmark [IFRT](https://github.com/deepinsight/insightface/tree/master/challenges/IFRT) - -**`2020-08-01`**: We released lightweight facial landmark models with fast coordinate regression(106 points). See detail [here](https://github.com/deepinsight/insightface/tree/master/alignment/coordinateReg). - -**`2020-04-27`**: InsightFace pretrained models and MS1M-Arcface are now specified as the only external training dataset, for iQIYI iCartoonFace challenge, see detail [here](http://challenge.ai.iqiyi.com/detail?raceId=5def71b4e9fcf68aef76a75e). - -**`2020.02.21`**: Instant discussion group created on QQ with group-id: 711302608. For English developers, see install tutorial [here](https://github.com/deepinsight/insightface/issues/1069). - -**`2020.02.16`**: RetinaFace now can detect faces with mask, for anti-CoVID19, see detail [here](https://github.com/deepinsight/insightface/tree/master/detection/RetinaFaceAntiCov) - -**`2019.08.10`**: We achieved 2nd place at [WIDER Face Detection Challenge 2019](http://wider-challenge.org/2019.html). - -**`2019.05.30`**: [Presentation at cvmart](https://pan.baidu.com/s/1v9fFHBJ8Q9Kl9Z6GwhbY6A) - -**`2019.04.30`**: Our Face detector ([RetinaFace](https://github.com/deepinsight/insightface/tree/master/detection/RetinaFace)) obtains state-of-the-art results on [the WiderFace dataset](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html). - -**`2019.04.14`**: We will launch a [Light-weight Face Recognition challenge/workshop](https://github.com/deepinsight/insightface/tree/master/challenges/iccv19-lfr) on ICCV 2019. - -**`2019.04.04`**: Arcface achieved state-of-the-art performance (7/109) on the NIST Face Recognition Vendor Test (FRVT) (1:1 verification) -[report](https://www.nist.gov/sites/default/files/documents/2019/04/04/frvt_report_2019_04_04.pdf) (name: Imperial-000 and Imperial-001). Our solution is based on [MS1MV2+DeepGlintAsian, ResNet100, ArcFace loss]. - -**`2019.02.08`**: Please check [https://github.com/deepinsight/insightface/tree/master/recognition/ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition/ArcFace) for our parallel training code which can easily and efficiently support one million identities on a single machine (8* 1080ti). - -**`2018.12.13`**: Inference acceleration [TVM-Benchmark](https://github.com/deepinsight/insightface/wiki/TVM-Benchmark). - -**`2018.10.28`**: Light-weight attribute model [Gender-Age](https://github.com/deepinsight/insightface/tree/master/gender-age). About 1MB, 10ms on single CPU core. Gender accuracy 96% on validation set and 4.1 age MAE. - -**`2018.10.16`**: We achieved state-of-the-art performance on [Trillionpairs](http://trillionpairs.deepglint.com/results) (name: nttstar) and [IQIYI_VID](http://challenge.ai.iqiyi.com/detail?raceId=5afc36639689443e8f815f9e) (name: WitcheR). -## Contents -[Deep Face Recognition](#deep-face-recognition) -- [Introduction](#introduction) -- [Training Data](#training-data) -- [Train](#train) -- [Pretrained Models](#pretrained-models) -- [Verification Results On Combined Margin](#verification-results-on-combined-margin) -- [Test on MegaFace](#test-on-megaface) -- [512-D Feature Embedding](#512-d-feature-embedding) -- [Third-party Re-implementation](#third-party-re-implementation) +## Projects -[Face Detection](#face-detection) -- [RetinaFace](#retinaface) -- [RetinaFaceAntiCov](#retinafaceanticov) +The [page](https://insightface.ai/projects) on InsightFace website also describes all supported projects in InsightFace. -[Face Alignment](#face-alignment) -- [DenseUNet](#denseunet) -- [CoordinateReg](#coordinatereg) +You may also interested in some [challenges](https://insightface.ai/challenges) hold by InsightFace. -[Citation](#citation) -[Contact](#contact) - -## Deep Face Recognition +## Face Recognition ### Introduction In this module, we provide training data, network settings and loss designs for deep face recognition. -The training data includes, but not limited to the cleaned MS1M, VGG2 and CASIA-Webface datasets, which were already packed in MXNet binary format. -The network backbones include ResNet, MobilefaceNet, MobileNet, InceptionResNet_v2, DenseNet, etc.. -The loss functions include Softmax, SphereFace, CosineFace, ArcFace, Sub-Center ArcFace and Triplet (Euclidean/Angular) Loss. -You can check the detail page of our work [ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition/ArcFace)(which accepted in CVPR-2019) and [SubCenter-ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition/SubCenter-ArcFace)(which accepted in ECCV-2020). +The supported methods are as follows: -![margin penalty for target logit](https://github.com/deepinsight/insightface/raw/master/resources/arcface.png) +- [x] [ArcFace_mxnet (CVPR'2019)](recognition/arcface_mxnet) +- [x] [ArcFace_torch (CVPR'2019)](recognition/arcface_torch) +- [x] [SubCenter ArcFace (ECCV'2020)](recognition/subcenter_arcface) +- [x] [PartialFC_mxnet (Arxiv'2020)](recognition/partial_fc) +- [x] [PartialFC_torch (Arxiv'2020)](recognition/arcface_torch) +- [x] [VPL (CVPR'2021)](recognition/vpl) +- [x] [OneFlow_face](recognition/oneflow_face) -Our method, ArcFace, was initially described in an [arXiv technical report](https://arxiv.org/abs/1801.07698). By using this module, you can simply achieve LFW 99.83%+ and Megaface 98%+ by a single model. This module can help researcher/engineer to develop deep face recognition algorithms quickly by only two steps: download the binary dataset and run the training script. - -### Training Data - -All face images are aligned by ficial five landmarks and cropped to 112x112: - -Please check [Dataset-Zoo](https://github.com/deepinsight/insightface/wiki/Dataset-Zoo) for detail information and dataset downloading. +Commonly used network backbones are included in most of the methods, such as IResNet, MobilefaceNet, MobileNet, InceptionResNet_v2, DenseNet, etc.. -* Please check *recognition/tools/face2rec2.py* on how to build a binary face dataset. You can either choose *MTCNN* or *RetinaFace* to align the faces. +### Datasets -### Train +The training data includes, but not limited to the cleaned MS1M, VGG2 and CASIA-Webface datasets, which were already packed in MXNet binary format. Please [dataset](recognition/_dataset_) page for detail. -1. Install `MXNet` with GPU support (Python 3.X). - -``` -pip install mxnet-cu101 # which should match your installed cuda version -``` - -2. Clone the InsightFace repository. We call the directory insightface as *`INSIGHTFACE_ROOT`*. - -``` -git clone --recursive https://github.com/deepinsight/insightface.git -``` - -3. Download the training set (`MS1M-Arcface`) and place it in *`$INSIGHTFACE_ROOT/recognition/datasets/`*. Each training dataset includes at least following 6 files: - -```Shell - faces_emore/ - train.idx - train.rec - property - lfw.bin - cfp_fp.bin - agedb_30.bin -``` - -The first three files are the training dataset while the last three files are verification sets. - -4. Train deep face recognition models. -In this part, we assume you are in the directory *`$INSIGHTFACE_ROOT/recognition/ArcFace/`*. - -Place and edit config file: -```Shell -cp sample_config.py config.py -vim config.py # edit dataset path etc.. -``` - -We give some examples below. Our experiments were conducted on the Tesla P40 GPU. - -(1). Train ArcFace with LResNet100E-IR. - -```Shell -CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network r100 --loss arcface --dataset emore -``` -It will output verification results of *LFW*, *CFP-FP* and *AgeDB-30* every 2000 batches. You can check all options in *config.py*. -This model can achieve *LFW 99.83+* and *MegaFace 98.3%+*. - -(2). Train CosineFace with LResNet50E-IR. - -```Shell -CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network r50 --loss cosface --dataset emore -``` - -(3). Train Softmax with LMobileNet-GAP. - -```Shell -CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network m1 --loss softmax --dataset emore -``` - -(4). Fine-turn the above Softmax model with Triplet loss. - -```Shell -CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network m1 --loss triplet --lr 0.005 --pretrained ./models/m1-softmax-emore,1 -``` - -(5). Training in model parallel acceleration. - -```Shell -CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_parall.py --network r100 --loss arcface --dataset emore -``` - -5. Verification results. - -*LResNet100E-IR* network trained on *MS1M-Arcface* dataset with ArcFace loss: - -| Method | LFW(%) | CFP-FP(%) | AgeDB-30(%) | -| ------- | ------ | --------- | ----------- | -| Ours | 99.80+ | 98.0+ | 98.20+ | +### Evaluation +We provide standard IJB and Megaface evaluation pipelines in [evaluation](recognition/_evaluation_) ### Pretrained Models -You can use `$INSIGHTFACE_ROOT/recognition/arcface_torch/eval/verification.py` to test all the pre-trained models. - **Please check [Model-Zoo](https://github.com/deepinsight/insightface/wiki/Model-Zoo) for more pretrained models.** - - -### Verification Results on Combined Margin - -A combined margin method was proposed as a function of target logits value and original `θ`: - -``` -COM(θ) = cos(m_1*θ+m_2) - m_3 -``` - -For training with `m1=1.0, m2=0.3, m3=0.2`, run following command: -``` -CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network r100 --loss combined --dataset emore -``` - -Results by using ``MS1M-IBUG(MS1M-V1)`` - -| Method | m1 | m2 | m3 | LFW | CFP-FP | AgeDB-30 | -| ---------------- | ---- | ---- | ---- | ----- | ------ | -------- | -| W&F Norm Softmax | 1 | 0 | 0 | 99.28 | 88.50 | 95.13 | -| SphereFace | 1.5 | 0 | 0 | 99.76 | 94.17 | 97.30 | -| CosineFace | 1 | 0 | 0.35 | 99.80 | 94.4 | 97.91 | -| ArcFace | 1 | 0.5 | 0 | 99.83 | 94.04 | 98.08 | -| Combined Margin | 1.2 | 0.4 | 0 | 99.80 | 94.08 | 98.05 | -| Combined Margin | 1.1 | 0 | 0.35 | 99.81 | 94.50 | 98.08 | -| Combined Margin | 1 | 0.3 | 0.2 | 99.83 | 94.51 | 98.13 | -| Combined Margin | 0.9 | 0.4 | 0.15 | 99.83 | 94.20 | 98.16 | - -### Test on MegaFace - -Please check *`$INSIGHTFACE_ROOT/evaluation/megaface/`* to evaluate the model accuracy on Megaface. All aligned images were already provided. - - -### 512-D Feature Embedding - -In this part, we assume you are in the directory *`$INSIGHTFACE_ROOT/deploy/`*. The input face image should be generally centre cropped. We use *RNet+ONet* of *MTCNN* to further align the image before sending it to the feature embedding network. - -1. Prepare a pre-trained model. -2. Put the model under *`$INSIGHTFACE_ROOT/models/`*. For example, *`$INSIGHTFACE_ROOT/models/model-r100-ii`*. -3. Run the test script *`$INSIGHTFACE_ROOT/deploy/test.py`*. - -For single cropped face image(112x112), total inference time is only 17ms on our testing server(Intel E5-2660 @ 2.00GHz, Tesla M40, *LResNet34E-IR*). - -### Third-party Re-implementation +### Third-party Re-implementation of ArcFace - TensorFlow: [InsightFace_TF](https://github.com/auroua/InsightFace_TF) - TensorFlow: [tf-insightface](https://github.com/AIInAi/tf-insightface) @@ -272,39 +94,43 @@ For single cropped face image(112x112), total inference time is only 17ms on our ## Face Detection -### RetinaFace +### Introduction - RetinaFace is a practical single-stage [SOTA](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html) face detector which is initially introduced in [arXiv technical report](https://arxiv.org/abs/1905.00641) and then accepted by [CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_RetinaFace_Single-Shot_Multi-Level_Face_Localisation_in_the_Wild_CVPR_2020_paper.html). We provide training code, training dataset, pretrained models and evaluation scripts. - - ![demoimg1](https://github.com/deepinsight/insightface/blob/master/resources/11513D05.jpg) - - Please check [RetinaFace](https://github.com/deepinsight/insightface/tree/master/detection/RetinaFace) for detail. - -### RetinaFaceAntiCov - - RetinaFaceAntiCov is an experimental module to identify face boxes with masks. Please check [RetinaFaceAntiCov](https://github.com/deepinsight/insightface/tree/master/detection/RetinaFaceAntiCov) for detail. - - ![demoimg1](https://github.com/deepinsight/insightface/blob/master/resources/cov_test.jpg) +
+ +
+ +In this module, we provide training data with annotation, network settings and loss designs for face detection training, evaluation and inference. + +The supported methods are as follows: + +- [x] [RetinaFace (CVPR'2020)](detection/retinaface) +- [x] [SCRFD (Arxiv'2021)](detection/scrfd) + +[RetinaFace](detection/retinaface) is a practical single-stage face detector which is accepted by [CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_RetinaFace_Single-Shot_Multi-Level_Face_Localisation_in_the_Wild_CVPR_2020_paper.html). We provide training code, training dataset, pretrained models and evaluation scripts. + +[SCRFD](detection/scrfd) is an efficient high accuracy face detection approach which is initialy described in [Arxiv](https://arxiv.org/abs/2105.04714). We provide an easy-to-use pipeline to train high efficiency face detectors with NAS supporting. ## Face Alignment -### DenseUNet +### Introduction - Please check the [Menpo](https://github.com/jiankangdeng/MenpoBenchmark) Benchmark and our [Dense U-Net](https://github.com/deepinsight/insightface/tree/master/alignment/heatmapReg) for detail. We also provide other network settings such as classic hourglass. You can find all of training code, training dataset and evaluation scripts there. - -### CoordinateReg - - On the other hand, in contrast to heatmap based approaches, we provide some lightweight facial landmark models with fast coordinate regression. The input of these models is loose cropped face image while the output is the direct landmark coordinates. See detail at [alignment-coordinateReg](https://github.com/deepinsight/insightface/tree/master/alignment/coordinateReg). Now only pretrained models available. - -
- imagevis +
+
+In this module, we provide datasets and training/inference pipelines for face alignment. -
- videovis -
+Supported methods: + +- [x] [SDUNets (BMVC'2018)](alignment/heatmap) +- [x] [SimpleRegression](alignment/coordinate_reg) + + +[SDUNets](alignment/heatmap) is a heatmap based method which accepted on [BMVC](http://bmvc2018.org/contents/papers/0051.pdf). + +[SimpleRegression](alignment/coordinate_reg) provides very lightweight facial landmark models with fast coordinate regression. The input of these models is loose cropped face image while the output is the direct landmark coordinates. ## Citation @@ -312,11 +138,34 @@ For single cropped face image(112x112), total inference time is only 17ms on our If you find *InsightFace* useful in your research, please consider to cite the following related papers: ``` -@inproceedings{deng2019retinaface, -title={RetinaFace: Single-stage Dense Face Localisation in the Wild}, -author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos}, -booktitle={arxiv}, -year={2019} + +@article{guo2021sample, + title={Sample and Computation Redistribution for Efficient Face Detection}, + author={Guo, Jia and Deng, Jiankang and Lattas, Alexandros and Zafeiriou, Stefanos}, + journal={arXiv preprint arXiv:2105.04714}, + year={2021} +} + +@inproceedings{an2020partical_fc, + title={Partial FC: Training 10 Million Identities on a Single Machine}, + author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and + Zhang, Debing and Fu Ying}, + booktitle={Arxiv 2010.05222}, + year={2020} +} + +@inproceedings{deng2020subcenter, + title={Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces}, + author={Deng, Jiankang and Guo, Jia and Liu, Tongliang and Gong, Mingming and Zafeiriou, Stefanos}, + booktitle={Proceedings of the IEEE Conference on European Conference on Computer Vision}, + year={2020} +} + +@inproceedings{Deng2020CVPR, +title = {RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild}, +author = {Deng, Jiankang and Guo, Jia and Ververas, Evangelos and Kotsia, Irene and Zafeiriou, Stefanos}, +booktitle = {CVPR}, +year = {2020} } @inproceedings{guo2018stacked, diff --git a/alignment/README.md b/alignment/README.md index 9656d13..7b3706b 100644 --- a/alignment/README.md +++ b/alignment/README.md @@ -1,4 +1,42 @@ -You can now find heatmap based approaches under ``heatmapReg`` directory. +## Face Alignment + + +
+ +
+ + +## Introduction + +These are the face alignment methods of [InsightFace](https://insightface.ai) + + +
+ +
+ + +### Datasets + + Please refer to [datasets](_datasets_) page for the details of face alignment datasets used for training and evaluation. + +### Evaluation + + Please refer to [evaluation](_evaluation_) page for the details of face alignment evaluation. + + +## Methods + + +Supported methods: + +- [x] [SDUNets (BMVC'2018)](heatmap) +- [x] [SimpleRegression](coordinate_reg) + + + +## Contributing + +We appreciate all contributions to improve the face alignment model zoo of InsightFace. -You can now find coordinate regression approaches under ``coordinateReg`` directory. diff --git a/alignment/coordinateReg/README.md b/alignment/coordinate_reg/README.md similarity index 100% rename from alignment/coordinateReg/README.md rename to alignment/coordinate_reg/README.md diff --git a/alignment/coordinateReg/image_infer.py b/alignment/coordinate_reg/image_infer.py similarity index 100% rename from alignment/coordinateReg/image_infer.py rename to alignment/coordinate_reg/image_infer.py diff --git a/alignment/heatmapReg/README.md b/alignment/heatmap/README.md similarity index 100% rename from alignment/heatmapReg/README.md rename to alignment/heatmap/README.md diff --git a/alignment/heatmapReg/data.py b/alignment/heatmap/data.py similarity index 100% rename from alignment/heatmapReg/data.py rename to alignment/heatmap/data.py diff --git a/alignment/heatmapReg/img_helper.py b/alignment/heatmap/img_helper.py similarity index 100% rename from alignment/heatmapReg/img_helper.py rename to alignment/heatmap/img_helper.py diff --git a/alignment/heatmapReg/metric.py b/alignment/heatmap/metric.py similarity index 100% rename from alignment/heatmapReg/metric.py rename to alignment/heatmap/metric.py diff --git a/alignment/heatmapReg/optimizer.py b/alignment/heatmap/optimizer.py similarity index 100% rename from alignment/heatmapReg/optimizer.py rename to alignment/heatmap/optimizer.py diff --git a/alignment/heatmapReg/sample_config.py b/alignment/heatmap/sample_config.py similarity index 100% rename from alignment/heatmapReg/sample_config.py rename to alignment/heatmap/sample_config.py diff --git a/alignment/heatmapReg/symbol/sym_heatmap.py b/alignment/heatmap/symbol/sym_heatmap.py similarity index 100% rename from alignment/heatmapReg/symbol/sym_heatmap.py rename to alignment/heatmap/symbol/sym_heatmap.py diff --git a/alignment/heatmapReg/test.py b/alignment/heatmap/test.py similarity index 100% rename from alignment/heatmapReg/test.py rename to alignment/heatmap/test.py diff --git a/alignment/heatmapReg/test_rec_nme.py b/alignment/heatmap/test_rec_nme.py similarity index 100% rename from alignment/heatmapReg/test_rec_nme.py rename to alignment/heatmap/test_rec_nme.py diff --git a/alignment/heatmapReg/train.py b/alignment/heatmap/train.py similarity index 100% rename from alignment/heatmapReg/train.py rename to alignment/heatmap/train.py diff --git a/attribute/README.md b/attribute/README.md new file mode 100644 index 0000000..1a8379c --- /dev/null +++ b/attribute/README.md @@ -0,0 +1,41 @@ +## Face Alignment + + +
+ +
+ + +## Introduction + +These are the face attribute methods of [InsightFace](https://insightface.ai) + + +
+ +
+ + +### Datasets + + Please refer to [datasets](_datasets_) page for the details of face attribute datasets used for training and evaluation. + +### Evaluation + + Please refer to [evaluation](_evaluation_) page for the details of face attribute evaluation. + + +## Methods + + +Supported methods: + +- [x] [Gender_Age](gender_age) + + + +## Contributing + +We appreciate all contributions to improve the face attribute model zoo of InsightFace. + + diff --git a/deploy/test.py b/attribute/gender_age/test.py similarity index 53% rename from deploy/test.py rename to attribute/gender_age/test.py index f5ecdf2..a92b216 100644 --- a/deploy/test.py +++ b/attribute/gender_age/test.py @@ -4,25 +4,21 @@ import sys import numpy as np import insightface from insightface.app import FaceAnalysis +from insightface.data import get_image as ins_get_image -assert insightface.__version__>='0.2' -parser = argparse.ArgumentParser(description='insightface test') +parser = argparse.ArgumentParser(description='insightface gender-age test') # general parser.add_argument('--ctx', default=0, type=int, help='ctx id, <0 means using cpu') args = parser.parse_args() -app = FaceAnalysis(name='antelope') +app = FaceAnalysis(allowed_modules=['detection', 'genderage']) app.prepare(ctx_id=args.ctx, det_size=(640,640)) -img = cv2.imread('../sample-images/t1.jpg') +img = ins_get_image('t1') faces = app.get(img) assert len(faces)==6 -rimg = app.draw_on(img, faces) -cv2.imwrite("./t1_output.jpg", rimg) -print(len(faces)) for face in faces: print(face.bbox) - print(face.kps) - print(face.embedding.shape) + print(face.sex, face.age) diff --git a/challenges/README.md b/challenges/README.md new file mode 100644 index 0000000..b2422cc --- /dev/null +++ b/challenges/README.md @@ -0,0 +1,31 @@ +## Challenges + + +
+ +
+ + +## Introduction + +These are challenges hold by [InsightFace](https://insightface.ai) + + +
+ +
+ + + +## List + + +Supported methods: + +- [LFR19 (ICCVW'2019)](iccv19-lfr) +- [MFR21 (ICCVW'2021)](iccv21-mfr) +- [IFRT](ifrt) + + + + diff --git a/challenges/iccv19-lfr/README.md b/challenges/iccv19-lfr/README.md index f9ae0e0..12aa535 100644 --- a/challenges/iccv19-lfr/README.md +++ b/challenges/iccv19-lfr/README.md @@ -31,7 +31,7 @@ insightface.challenge@gmail.com *For Chinese:* -![wechat](https://github.com/deepinsight/insightface/blob/master/resources/lfr19_wechat1.jpg) +![wechat](https://insightface.ai/assets/img/github/lfr19_wechat1.jpg) *For English:* diff --git a/deploy/README.md b/deploy/README.md deleted file mode 100644 index e65643d..0000000 --- a/deploy/README.md +++ /dev/null @@ -1,8 +0,0 @@ -InsightFace deployment README ---- - -For insightface pip-package <= 0.1.5, we use MXNet as inference backend, please download all models from [onedrive](https://1drv.ms/u/s!AswpsDO2toNKrUy0VktHTWgIQ0bn?e=UEF7C4), and put them all under `~/.insightface/models/` directory. - -Starting from insightface>=0.2, we use onnxruntime as inference backend, please download our **antelope** model release from [onedrive](https://1drv.ms/u/s!AswpsDO2toNKrU0ydGgDkrHPdJ3m?e=iVgZox), and put it under `~/.insightface/models/`, so there're onnx models at `~/.insightface/models/antelope/*.onnx`. - -The **antelope** model release contains `ResNet100@Glint360K recognition model` and `SCRFD-10GF detection model`. Please check `test.py` for detail. diff --git a/deploy/convert_onnx.py b/deploy/convert_onnx.py deleted file mode 100644 index 3ed583d..0000000 --- a/deploy/convert_onnx.py +++ /dev/null @@ -1,40 +0,0 @@ -import sys -import os -import argparse -import onnx -import mxnet as mx - -print('mxnet version:', mx.__version__) -print('onnx version:', onnx.__version__) -#make sure to install onnx-1.2.1 -#pip uninstall onnx -#pip install onnx==1.2.1 -assert onnx.__version__ == '1.2.1' -import numpy as np -from mxnet.contrib import onnx as onnx_mxnet - -parser = argparse.ArgumentParser( - description='convert insightface models to onnx') -# general -parser.add_argument('--prefix', - default='./r100-arcface/model', - help='prefix to load model.') -parser.add_argument('--epoch', - default=0, - type=int, - help='epoch number to load model.') -parser.add_argument('--input-shape', default='3,112,112', help='input shape.') -parser.add_argument('--output-onnx', - default='./r100.onnx', - help='path to write onnx model.') -args = parser.parse_args() -input_shape = (1, ) + tuple([int(x) for x in args.input_shape.split(',')]) -print('input-shape:', input_shape) - -sym_file = "%s-symbol.json" % args.prefix -params_file = "%s-%04d.params" % (args.prefix, args.epoch) -assert os.path.exists(sym_file) -assert os.path.exists(params_file) -converted_model_path = onnx_mxnet.export_model(sym_file, params_file, - [input_shape], np.float32, - args.output_onnx) diff --git a/deploy/face_model.py b/deploy/face_model.py deleted file mode 100644 index 2e4a361..0000000 --- a/deploy/face_model.py +++ /dev/null @@ -1,67 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import sys -import os -import argparse -import numpy as np -import mxnet as mx -import cv2 -import insightface -from insightface.utils import face_align - - -def do_flip(data): - for idx in range(data.shape[0]): - data[idx, :, :] = np.fliplr(data[idx, :, :]) - - -def get_model(ctx, image_size, prefix, epoch, layer): - print('loading', prefix, epoch) - sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) - all_layers = sym.get_internals() - sym = all_layers[layer + '_output'] - model = mx.mod.Module(symbol=sym, context=ctx, label_names=None) - #model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))]) - model.bind(data_shapes=[('data', (1, 3, image_size[0], image_size[1]))]) - model.set_params(arg_params, aux_params) - return model - - -class FaceModel: - def __init__(self, ctx_id, model_prefix, model_epoch, use_large_detector=False): - if use_large_detector: - self.detector = insightface.model_zoo.get_model('retinaface_r50_v1') - else: - self.detector = insightface.model_zoo.get_model('retinaface_mnet025_v2') - self.detector.prepare(ctx_id=ctx_id) - if ctx_id>=0: - ctx = mx.gpu(ctx_id) - else: - ctx = mx.cpu() - image_size = (112,112) - self.model = get_model(ctx, image_size, model_prefix, model_epoch, 'fc1') - self.image_size = image_size - - def get_input(self, face_img): - bbox, pts5 = self.detector.detect(face_img, threshold=0.8) - if bbox.shape[0]==0: - return None - bbox = bbox[0, 0:4] - pts5 = pts5[0, :] - nimg = face_align.norm_crop(face_img, pts5) - return nimg - - def get_feature(self, aligned): - a = cv2.cvtColor(aligned, cv2.COLOR_BGR2RGB) - a = np.transpose(a, (2, 0, 1)) - input_blob = np.expand_dims(a, axis=0) - data = mx.nd.array(input_blob) - db = mx.io.DataBatch(data=(data, )) - self.model.forward(db, is_train=False) - emb = self.model.get_outputs()[0].asnumpy()[0] - norm = np.sqrt(np.sum(emb*emb)+0.00001) - emb /= norm - return emb - diff --git a/deploy/model_slim.py b/deploy/model_slim.py deleted file mode 100644 index 421b0cd..0000000 --- a/deploy/model_slim.py +++ /dev/null @@ -1,32 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import sys -import os -import argparse -import numpy as np -import mxnet as mx - -parser = argparse.ArgumentParser(description='face model slim') -# general -parser.add_argument('--model', - default='../models/model-r34-amf/model,60', - help='path to load model.') -args = parser.parse_args() - -_vec = args.model.split(',') -assert len(_vec) == 2 -prefix = _vec[0] -epoch = int(_vec[1]) -print('loading', prefix, epoch) -sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) -all_layers = sym.get_internals() -sym = all_layers['fc1_output'] -dellist = [] -for k, v in arg_params.iteritems(): - if k.startswith('fc7'): - dellist.append(k) -for d in dellist: - del arg_params[d] -mx.model.save_checkpoint(prefix + "s", 0, sym, arg_params, aux_params) diff --git a/deploy/mtcnn-model/det1-0001.params b/deploy/mtcnn-model/det1-0001.params deleted file mode 100644 index e4b04aa..0000000 Binary files a/deploy/mtcnn-model/det1-0001.params and /dev/null differ diff --git a/deploy/mtcnn-model/det1-symbol.json b/deploy/mtcnn-model/det1-symbol.json deleted file mode 100644 index bd9b772..0000000 --- a/deploy/mtcnn-model/det1-symbol.json +++ /dev/null @@ -1,266 +0,0 @@ -{ - "nodes": [ - { - "op": "null", - "param": {}, - "name": "data", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv1_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv1_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "Convolution", - "param": { - "cudnn_off": "False", - "cudnn_tune": "off", - "dilate": "(1,1)", - "kernel": "(3,3)", - "no_bias": "False", - "num_filter": "10", - "num_group": "1", - "pad": "(0,0)", - "stride": "(1,1)", - "workspace": "1024" - }, - "name": "conv1", - "inputs": [[0, 0], [1, 0], [2, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "prelu1_gamma", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "LeakyReLU", - "param": { - "act_type": "prelu", - "lower_bound": "0.125", - "slope": "0.25", - "upper_bound": "0.334" - }, - "name": "prelu1", - "inputs": [[3, 0], [4, 0]], - "backward_source_id": -1 - }, - { - "op": "Pooling", - "param": { - "global_pool": "False", - "kernel": "(2,2)", - "pad": "(0,0)", - "pool_type": "max", - "pooling_convention": "full", - "stride": "(2,2)" - }, - "name": "pool1", - "inputs": [[5, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv2_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv2_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "Convolution", - "param": { - "cudnn_off": "False", - "cudnn_tune": "off", - "dilate": "(1,1)", - "kernel": "(3,3)", - "no_bias": "False", - "num_filter": "16", - "num_group": "1", - "pad": "(0,0)", - "stride": "(1,1)", - "workspace": "1024" - }, - "name": "conv2", - "inputs": [[6, 0], [7, 0], [8, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "prelu2_gamma", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "LeakyReLU", - "param": { - "act_type": "prelu", - "lower_bound": "0.125", - "slope": "0.25", - "upper_bound": "0.334" - }, - "name": "prelu2", - "inputs": [[9, 0], [10, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv3_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv3_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "Convolution", - "param": { - "cudnn_off": "False", - "cudnn_tune": "off", - "dilate": "(1,1)", - "kernel": "(3,3)", - "no_bias": "False", - "num_filter": "32", - "num_group": "1", - "pad": "(0,0)", - "stride": "(1,1)", - "workspace": "1024" - }, - "name": "conv3", - "inputs": [[11, 0], [12, 0], [13, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "prelu3_gamma", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "LeakyReLU", - "param": { - "act_type": "prelu", - "lower_bound": "0.125", - "slope": "0.25", - "upper_bound": "0.334" - }, - "name": "prelu3", - "inputs": [[14, 0], [15, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv4_2_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv4_2_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "Convolution", - "param": { - "cudnn_off": "False", - "cudnn_tune": "off", - "dilate": "(1,1)", - "kernel": "(1,1)", - "no_bias": "False", - "num_filter": "4", - "num_group": "1", - "pad": "(0,0)", - "stride": "(1,1)", - "workspace": "1024" - }, - "name": "conv4_2", - "inputs": [[16, 0], [17, 0], [18, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv4_1_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv4_1_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "Convolution", - "param": { - "cudnn_off": "False", - "cudnn_tune": "off", - "dilate": "(1,1)", - "kernel": "(1,1)", - "no_bias": "False", - "num_filter": "2", - "num_group": "1", - "pad": "(0,0)", - "stride": "(1,1)", - "workspace": "1024" - }, - "name": "conv4_1", - "inputs": [[16, 0], [20, 0], [21, 0]], - "backward_source_id": -1 - }, - { - "op": "SoftmaxActivation", - "param": {"mode": "channel"}, - "name": "prob1", - "inputs": [[22, 0]], - "backward_source_id": -1 - } - ], - "arg_nodes": [ - 0, - 1, - 2, - 4, - 7, - 8, - 10, - 12, - 13, - 15, - 17, - 18, - 20, - 21 - ], - "heads": [[19, 0], [23, 0]] -} \ No newline at end of file diff --git a/deploy/mtcnn-model/det1.caffemodel b/deploy/mtcnn-model/det1.caffemodel deleted file mode 100644 index 79e93b4..0000000 Binary files a/deploy/mtcnn-model/det1.caffemodel and /dev/null differ diff --git a/deploy/mtcnn-model/det1.prototxt b/deploy/mtcnn-model/det1.prototxt deleted file mode 100644 index c5c1657..0000000 --- a/deploy/mtcnn-model/det1.prototxt +++ /dev/null @@ -1,177 +0,0 @@ -name: "PNet" -input: "data" -input_dim: 1 -input_dim: 3 -input_dim: 12 -input_dim: 12 - -layer { - name: "conv1" - type: "Convolution" - bottom: "data" - top: "conv1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 10 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "PReLU1" - type: "PReLU" - bottom: "conv1" - top: "conv1" -} -layer { - name: "pool1" - type: "Pooling" - bottom: "conv1" - top: "pool1" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} - -layer { - name: "conv2" - type: "Convolution" - bottom: "pool1" - top: "conv2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 16 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "PReLU2" - type: "PReLU" - bottom: "conv2" - top: "conv2" -} - -layer { - name: "conv3" - type: "Convolution" - bottom: "conv2" - top: "conv3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 32 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "PReLU3" - type: "PReLU" - bottom: "conv3" - top: "conv3" -} - - -layer { - name: "conv4-1" - type: "Convolution" - bottom: "conv3" - top: "conv4-1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 2 - kernel_size: 1 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -layer { - name: "conv4-2" - type: "Convolution" - bottom: "conv3" - top: "conv4-2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 4 - kernel_size: 1 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prob1" - type: "Softmax" - bottom: "conv4-1" - top: "prob1" -} diff --git a/deploy/mtcnn-model/det2-0001.params b/deploy/mtcnn-model/det2-0001.params deleted file mode 100644 index a14a478..0000000 Binary files a/deploy/mtcnn-model/det2-0001.params and /dev/null differ diff --git a/deploy/mtcnn-model/det2-symbol.json b/deploy/mtcnn-model/det2-symbol.json deleted file mode 100644 index a13246a..0000000 --- a/deploy/mtcnn-model/det2-symbol.json +++ /dev/null @@ -1,324 +0,0 @@ -{ - "nodes": [ - { - "op": "null", - "param": {}, - "name": "data", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv1_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv1_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "Convolution", - "param": { - "cudnn_off": "False", - "cudnn_tune": "off", - "dilate": "(1,1)", - "kernel": "(3,3)", - "no_bias": "False", - "num_filter": "28", - "num_group": "1", - "pad": "(0,0)", - "stride": "(1,1)", - "workspace": "1024" - }, - "name": "conv1", - "inputs": [[0, 0], [1, 0], [2, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "prelu1_gamma", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "LeakyReLU", - "param": { - "act_type": "prelu", - "lower_bound": "0.125", - "slope": "0.25", - "upper_bound": "0.334" - }, - "name": "prelu1", - "inputs": [[3, 0], [4, 0]], - "backward_source_id": -1 - }, - { - "op": "Pooling", - "param": { - "global_pool": "False", - "kernel": "(3,3)", - "pad": "(0,0)", - "pool_type": "max", - "pooling_convention": "full", - "stride": "(2,2)" - }, - "name": "pool1", - "inputs": [[5, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv2_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv2_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "Convolution", - "param": { - "cudnn_off": "False", - "cudnn_tune": "off", - "dilate": "(1,1)", - "kernel": "(3,3)", - "no_bias": "False", - "num_filter": "48", - "num_group": "1", - "pad": "(0,0)", - "stride": "(1,1)", - "workspace": "1024" - }, - "name": "conv2", - "inputs": [[6, 0], [7, 0], [8, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "prelu2_gamma", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "LeakyReLU", - "param": { - "act_type": "prelu", - "lower_bound": "0.125", - "slope": "0.25", - "upper_bound": "0.334" - }, - "name": "prelu2", - "inputs": [[9, 0], [10, 0]], - "backward_source_id": -1 - }, - { - "op": "Pooling", - "param": { - "global_pool": "False", - "kernel": "(3,3)", - "pad": "(0,0)", - "pool_type": "max", - "pooling_convention": "full", - "stride": "(2,2)" - }, - "name": "pool2", - "inputs": [[11, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv3_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv3_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "Convolution", - "param": { - "cudnn_off": "False", - "cudnn_tune": "off", - "dilate": "(1,1)", - "kernel": "(2,2)", - "no_bias": "False", - "num_filter": "64", - "num_group": "1", - "pad": "(0,0)", - "stride": "(1,1)", - "workspace": "1024" - }, - "name": "conv3", - "inputs": [[12, 0], [13, 0], [14, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "prelu3_gamma", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "LeakyReLU", - "param": { - "act_type": "prelu", - "lower_bound": "0.125", - "slope": "0.25", - "upper_bound": "0.334" - }, - "name": "prelu3", - "inputs": [[15, 0], [16, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv4_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv4_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "FullyConnected", - "param": { - "no_bias": "False", - "num_hidden": "128" - }, - "name": "conv4", - "inputs": [[17, 0], [18, 0], [19, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "prelu4_gamma", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "LeakyReLU", - "param": { - "act_type": "prelu", - "lower_bound": "0.125", - "slope": "0.25", - "upper_bound": "0.334" - }, - "name": "prelu4", - "inputs": [[20, 0], [21, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv5_2_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv5_2_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "FullyConnected", - "param": { - "no_bias": "False", - "num_hidden": "4" - }, - "name": "conv5_2", - "inputs": [[22, 0], [23, 0], [24, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv5_1_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv5_1_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "FullyConnected", - "param": { - "no_bias": "False", - "num_hidden": "2" - }, - "name": "conv5_1", - "inputs": [[22, 0], [26, 0], [27, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "prob1_label", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "SoftmaxOutput", - "param": { - "grad_scale": "1", - "ignore_label": "-1", - "multi_output": "False", - "normalization": "null", - "use_ignore": "False" - }, - "name": "prob1", - "inputs": [[28, 0], [29, 0]], - "backward_source_id": -1 - } - ], - "arg_nodes": [ - 0, - 1, - 2, - 4, - 7, - 8, - 10, - 13, - 14, - 16, - 18, - 19, - 21, - 23, - 24, - 26, - 27, - 29 - ], - "heads": [[25, 0], [30, 0]] -} \ No newline at end of file diff --git a/deploy/mtcnn-model/det2.caffemodel b/deploy/mtcnn-model/det2.caffemodel deleted file mode 100644 index a5a540c..0000000 Binary files a/deploy/mtcnn-model/det2.caffemodel and /dev/null differ diff --git a/deploy/mtcnn-model/det2.prototxt b/deploy/mtcnn-model/det2.prototxt deleted file mode 100644 index 51093e6..0000000 --- a/deploy/mtcnn-model/det2.prototxt +++ /dev/null @@ -1,228 +0,0 @@ -name: "RNet" -input: "data" -input_dim: 1 -input_dim: 3 -input_dim: 24 -input_dim: 24 - - -########################## -###################### -layer { - name: "conv1" - type: "Convolution" - bottom: "data" - top: "conv1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu1" - type: "PReLU" - bottom: "conv1" - top: "conv1" - propagate_down: true -} -layer { - name: "pool1" - type: "Pooling" - bottom: "conv1" - top: "pool1" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2" - type: "Convolution" - bottom: "pool1" - top: "conv2" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu2" - type: "PReLU" - bottom: "conv2" - top: "conv2" - propagate_down: true -} -layer { - name: "pool2" - type: "Pooling" - bottom: "conv2" - top: "pool2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} -#################################### - -################################## -layer { - name: "conv3" - type: "Convolution" - bottom: "pool2" - top: "conv3" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu3" - type: "PReLU" - bottom: "conv3" - top: "conv3" - propagate_down: true -} -############################### - -############################### - -layer { - name: "conv4" - type: "InnerProduct" - bottom: "conv3" - top: "conv4" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - inner_product_param { - num_output: 128 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu4" - type: "PReLU" - bottom: "conv4" - top: "conv4" -} - -layer { - name: "conv5-1" - type: "InnerProduct" - bottom: "conv4" - top: "conv5-1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - inner_product_param { - num_output: 2 - #kernel_size: 1 - #stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv5-2" - type: "InnerProduct" - bottom: "conv4" - top: "conv5-2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 4 - #kernel_size: 1 - #stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prob1" - type: "Softmax" - bottom: "conv5-1" - top: "prob1" -} \ No newline at end of file diff --git a/deploy/mtcnn-model/det3-0001.params b/deploy/mtcnn-model/det3-0001.params deleted file mode 100644 index cae898b..0000000 Binary files a/deploy/mtcnn-model/det3-0001.params and /dev/null differ diff --git a/deploy/mtcnn-model/det3-symbol.json b/deploy/mtcnn-model/det3-symbol.json deleted file mode 100644 index 00061ed..0000000 --- a/deploy/mtcnn-model/det3-symbol.json +++ /dev/null @@ -1,418 +0,0 @@ -{ - 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name: "conv1" - type: "Convolution" - bottom: "data" - top: "conv1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 32 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu1" - type: "PReLU" - bottom: "conv1" - top: "conv1" -} -layer { - name: "pool1" - type: "Pooling" - bottom: "conv1" - top: "pool1" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} -layer { - name: "conv2" - type: "Convolution" - bottom: "pool1" - top: "conv2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -layer { - name: "prelu2" - type: "PReLU" - bottom: "conv2" - top: "conv2" -} -layer { - name: "pool2" - type: "Pooling" - bottom: "conv2" - top: "pool2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv3" - type: "Convolution" - bottom: "pool2" - top: "conv3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu3" - type: "PReLU" - bottom: "conv3" - top: "conv3" -} -layer { - name: "pool3" - type: "Pooling" - bottom: "conv3" - top: "pool3" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv4" - type: "Convolution" - bottom: "pool3" - top: "conv4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 128 - kernel_size: 2 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu4" - type: "PReLU" - bottom: "conv4" - top: "conv4" -} - - -layer { - name: "conv5" - type: "InnerProduct" - bottom: "conv4" - top: "conv5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - #kernel_size: 3 - num_output: 256 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -layer { - name: "drop5" - type: "Dropout" - bottom: "conv5" - top: "conv5" - dropout_param { - dropout_ratio: 0.25 - } -} -layer { - name: "prelu5" - type: "PReLU" - bottom: "conv5" - top: "conv5" -} - - -layer { - name: "conv6-1" - type: "InnerProduct" - bottom: "conv5" - top: "conv6-1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - #kernel_size: 1 - num_output: 2 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv6-2" - type: "InnerProduct" - bottom: "conv5" - top: "conv6-2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - #kernel_size: 1 - num_output: 4 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv6-3" - type: "InnerProduct" - bottom: "conv5" - top: "conv6-3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - #kernel_size: 1 - num_output: 10 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prob1" - type: "Softmax" - bottom: "conv6-1" - top: "prob1" -} diff --git a/deploy/mtcnn-model/det4-0001.params b/deploy/mtcnn-model/det4-0001.params deleted file mode 100644 index efca9a9..0000000 Binary files a/deploy/mtcnn-model/det4-0001.params and /dev/null differ diff --git a/deploy/mtcnn-model/det4-symbol.json b/deploy/mtcnn-model/det4-symbol.json deleted file mode 100644 index aa90e2a..0000000 --- a/deploy/mtcnn-model/det4-symbol.json +++ /dev/null @@ -1,1392 +0,0 @@ -{ - "nodes": [ - { - "op": "null", - "param": {}, - "name": "data", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "SliceChannel", - "param": { - "axis": "1", - "num_outputs": "5", - "squeeze_axis": "False" - }, - "name": "slice", - "inputs": [[0, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv1_1_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv1_1_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "Convolution", - "param": { - "cudnn_off": "False", - "cudnn_tune": "off", - "dilate": "(1,1)", - "kernel": "(3,3)", - "no_bias": "False", - "num_filter": "28", - "num_group": "1", - "pad": "(0,0)", - "stride": "(1,1)", - "workspace": "1024" - }, - "name": "conv1_1", - "inputs": [[1, 0], [2, 0], [3, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "prelu1_1_gamma", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "LeakyReLU", - 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}, - { - "op": "null", - "param": {}, - "name": "prelu4_5_gamma", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "LeakyReLU", - "param": { - "act_type": "prelu", - "lower_bound": "0.125", - "slope": "0.25", - "upper_bound": "0.334" - }, - "name": "prelu4_5", - "inputs": [[127, 0], [128, 0]], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "fc5_5_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "fc5_5_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "FullyConnected", - "param": { - "no_bias": "False", - "num_hidden": "2" - }, - "name": "fc5_5", - "inputs": [[129, 0], [130, 0], [131, 0]], - "backward_source_id": -1 - } - ], - "arg_nodes": [ - 0, - 2, - 3, - 5, - 8, - 9, - 11, - 14, - 15, - 17, - 19, - 20, - 22, - 25, - 26, - 28, - 31, - 32, - 34, - 36, - 37, - 39, - 42, - 43, - 45, - 48, - 49, - 51, - 53, - 54, - 56, - 59, - 60, - 62, - 65, - 66, - 68, - 70, - 71, - 73, - 76, - 77, - 79, - 82, - 83, - 85, - 88, - 89, - 91, - 93, - 94, - 96, - 98, - 99, - 101, - 102, - 104, - 106, - 107, - 109, - 110, - 112, - 114, - 115, - 117, - 118, - 120, - 122, - 123, - 125, - 126, - 128, - 130, - 131 - ], - "heads": [[100, 0], [108, 0], [116, 0], [124, 0], [132, 0]] -} \ No newline at end of file diff --git a/deploy/mtcnn-model/det4.caffemodel b/deploy/mtcnn-model/det4.caffemodel deleted file mode 100644 index 38353c4..0000000 Binary files a/deploy/mtcnn-model/det4.caffemodel and /dev/null differ diff --git a/deploy/mtcnn-model/det4.prototxt b/deploy/mtcnn-model/det4.prototxt deleted file mode 100644 index 4cdc329..0000000 --- a/deploy/mtcnn-model/det4.prototxt +++ /dev/null @@ -1,995 +0,0 @@ -name: "LNet" -input: "data" -input_dim: 1 -input_dim: 15 -input_dim: 24 -input_dim: 24 - -layer { - name: "slicer_data" - type: "Slice" - bottom: "data" - top: "data241" - top: "data242" - top: "data243" - top: "data244" - top: "data245" - slice_param { - axis: 1 - slice_point: 3 - slice_point: 6 - slice_point: 9 - slice_point: 12 - } -} -layer { - name: "conv1_1" - type: "Convolution" - bottom: "data241" - top: "conv1_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu1_1" - type: "PReLU" - bottom: "conv1_1" - top: "conv1_1" - -} -layer { - name: "pool1_1" - type: "Pooling" - bottom: "conv1_1" - top: "pool1_1" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2_1" - type: "Convolution" - bottom: "pool1_1" - top: "conv2_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu2_1" - type: "PReLU" - bottom: "conv2_1" - top: "conv2_1" -} -layer { - name: "pool2_1" - type: "Pooling" - bottom: "conv2_1" - top: "pool2_1" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } - -} -layer { - name: "conv3_1" - type: "Convolution" - bottom: "pool2_1" - top: "conv3_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu3_1" - type: "PReLU" - bottom: "conv3_1" - top: "conv3_1" -} -########################## -layer { - name: "conv1_2" - type: "Convolution" - bottom: "data242" - top: "conv1_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu1_2" - type: "PReLU" - bottom: "conv1_2" - top: "conv1_2" - -} -layer { - name: "pool1_2" - type: "Pooling" - bottom: "conv1_2" - top: "pool1_2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2_2" - type: "Convolution" - bottom: "pool1_2" - top: "conv2_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu2_2" - type: "PReLU" - bottom: "conv2_2" - top: "conv2_2" -} -layer { - name: "pool2_2" - type: "Pooling" - bottom: "conv2_2" - top: "pool2_2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } - -} -layer { - name: "conv3_2" - type: "Convolution" - bottom: "pool2_2" - top: "conv3_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu3_2" - type: "PReLU" - bottom: "conv3_2" - top: "conv3_2" -} -########################## -########################## -layer { - name: "conv1_3" - type: "Convolution" - bottom: "data243" - top: "conv1_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu1_3" - type: "PReLU" - bottom: "conv1_3" - top: "conv1_3" - -} -layer { - name: "pool1_3" - type: "Pooling" - bottom: "conv1_3" - top: "pool1_3" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2_3" - type: "Convolution" - bottom: "pool1_3" - top: "conv2_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu2_3" - type: "PReLU" - bottom: "conv2_3" - top: "conv2_3" -} -layer { - name: "pool2_3" - type: "Pooling" - bottom: "conv2_3" - top: "pool2_3" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } - -} -layer { - name: "conv3_3" - type: "Convolution" - bottom: "pool2_3" - top: "conv3_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu3_3" - type: "PReLU" - bottom: "conv3_3" - top: "conv3_3" -} -########################## -########################## -layer { - name: "conv1_4" - type: "Convolution" - bottom: "data244" - top: "conv1_4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu1_4" - type: "PReLU" - bottom: "conv1_4" - top: "conv1_4" - -} -layer { - name: "pool1_4" - type: "Pooling" - bottom: "conv1_4" - top: "pool1_4" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2_4" - type: "Convolution" - bottom: "pool1_4" - top: "conv2_4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu2_4" - type: "PReLU" - bottom: "conv2_4" - top: "conv2_4" -} -layer { - name: "pool2_4" - type: "Pooling" - bottom: "conv2_4" - top: "pool2_4" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } - -} -layer { - name: "conv3_4" - type: "Convolution" - bottom: "pool2_4" - top: "conv3_4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu3_4" - type: "PReLU" - bottom: "conv3_4" - top: "conv3_4" -} -########################## -########################## -layer { - name: "conv1_5" - type: "Convolution" - bottom: "data245" - top: "conv1_5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu1_5" - type: "PReLU" - bottom: "conv1_5" - top: "conv1_5" - -} -layer { - name: "pool1_5" - type: "Pooling" - bottom: "conv1_5" - top: "pool1_5" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2_5" - type: "Convolution" - bottom: "pool1_5" - top: "conv2_5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu2_5" - type: "PReLU" - bottom: "conv2_5" - top: "conv2_5" -} -layer { - name: "pool2_5" - type: "Pooling" - bottom: "conv2_5" - top: "pool2_5" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } - -} -layer { - name: "conv3_5" - type: "Convolution" - bottom: "pool2_5" - top: "conv3_5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu3_5" - type: "PReLU" - bottom: "conv3_5" - top: "conv3_5" -} -########################## -layer { - name: "concat" - bottom: "conv3_1" - bottom: "conv3_2" - bottom: "conv3_3" - bottom: "conv3_4" - bottom: "conv3_5" - top: "conv3" - type: "Concat" - concat_param { - axis: 1 - } -} -########################## -layer { - name: "fc4" - type: "InnerProduct" - bottom: "conv3" - top: "fc4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 256 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4" - type: "PReLU" - bottom: "fc4" - top: "fc4" -} -############################ -layer { - name: "fc4_1" - type: "InnerProduct" - bottom: "fc4" - top: "fc4_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 64 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4_1" - type: "PReLU" - bottom: "fc4_1" - top: "fc4_1" -} -layer { - name: "fc5_1" - type: "InnerProduct" - bottom: "fc4_1" - top: "fc5_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 2 - weight_filler { - type: "xavier" - #type: "constant" - #value: 0 - } - bias_filler { - type: "constant" - value: 0 - } - } -} - - -######################### -layer { - name: "fc4_2" - type: "InnerProduct" - bottom: "fc4" - top: "fc4_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 64 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4_2" - type: "PReLU" - bottom: "fc4_2" - top: "fc4_2" -} -layer { - name: "fc5_2" - type: "InnerProduct" - bottom: "fc4_2" - top: "fc5_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 2 - weight_filler { - type: "xavier" - #type: "constant" - #value: 0 - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -######################### -layer { - name: "fc4_3" - type: "InnerProduct" - bottom: "fc4" - top: "fc4_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 64 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4_3" - type: "PReLU" - bottom: "fc4_3" - top: "fc4_3" -} -layer { - name: "fc5_3" - type: "InnerProduct" - bottom: "fc4_3" - top: "fc5_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 2 - weight_filler { - type: "xavier" - #type: "constant" - #value: 0 - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -######################### -layer { - name: "fc4_4" - type: "InnerProduct" - bottom: "fc4" - top: "fc4_4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 64 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4_4" - type: "PReLU" - bottom: "fc4_4" - top: "fc4_4" -} -layer { - name: "fc5_4" - type: "InnerProduct" - bottom: "fc4_4" - top: "fc5_4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 2 - weight_filler { - type: "xavier" - #type: "constant" - #value: 0 - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -######################### -layer { - name: "fc4_5" - type: "InnerProduct" - bottom: "fc4" - top: "fc4_5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 64 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4_5" - type: "PReLU" - bottom: "fc4_5" - top: "fc4_5" -} -layer { - name: "fc5_5" - type: "InnerProduct" - bottom: "fc4_5" - top: "fc5_5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 2 - weight_filler { - type: "xavier" - #type: "constant" - #value: 0 - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -######################### - diff --git a/deploy/mtcnn_detector.py b/deploy/mtcnn_detector.py deleted file mode 100644 index 8fa22f2..0000000 --- a/deploy/mtcnn_detector.py +++ /dev/null @@ -1,864 +0,0 @@ -# coding: utf-8 -import os -import mxnet as mx -import numpy as np -import math -import cv2 -from multiprocessing import Pool -from itertools import repeat -try: - from itertools import izip -except ImportError: - izip = zip - -def nms(boxes, overlap_threshold, mode='Union'): - """ - non max suppression - - Parameters: - ---------- - box: numpy array n x 5 - input bbox array - overlap_threshold: float number - threshold of overlap - mode: float number - how to compute overlap ratio, 'Union' or 'Min' - Returns: - ------- - index array of the selected bbox - """ - # if there are no boxes, return an empty list - if len(boxes) == 0: - return [] - - # if the bounding boxes integers, convert them to floats - if boxes.dtype.kind == "i": - boxes = boxes.astype("float") - - # initialize the list of picked indexes - pick = [] - - # grab the coordinates of the bounding boxes - x1, y1, x2, y2, score = [boxes[:, i] for i in range(5)] - - area = (x2 - x1 + 1) * (y2 - y1 + 1) - idxs = np.argsort(score) - - # keep looping while some indexes still remain in the indexes list - while len(idxs) > 0: - # grab the last index in the indexes list and add the index value to the list of picked indexes - last = len(idxs) - 1 - i = idxs[last] - pick.append(i) - - xx1 = np.maximum(x1[i], x1[idxs[:last]]) - yy1 = np.maximum(y1[i], y1[idxs[:last]]) - xx2 = np.minimum(x2[i], x2[idxs[:last]]) - yy2 = np.minimum(y2[i], y2[idxs[:last]]) - - # compute the width and height of the bounding box - w = np.maximum(0, xx2 - xx1 + 1) - h = np.maximum(0, yy2 - yy1 + 1) - - inter = w * h - if mode == 'Min': - overlap = inter / np.minimum(area[i], area[idxs[:last]]) - else: - overlap = inter / (area[i] + area[idxs[:last]] - inter) - - # delete all indexes from the index list that have - idxs = np.delete( - idxs, - np.concatenate(([last], np.where(overlap > overlap_threshold)[0]))) - - return pick - - -def adjust_input(in_data): - """ - adjust the input from (h, w, c) to ( 1, c, h, w) for network input - - Parameters: - ---------- - in_data: numpy array of shape (h, w, c) - input data - Returns: - ------- - out_data: numpy array of shape (1, c, h, w) - reshaped array - """ - if in_data.dtype is not np.dtype('float32'): - out_data = in_data.astype(np.float32) - else: - out_data = in_data - - out_data = out_data.transpose((2, 0, 1)) - out_data = np.expand_dims(out_data, 0) - out_data = (out_data - 127.5) * 0.0078125 - return out_data - - -def generate_bbox(map, reg, scale, threshold): - """ - generate bbox from feature map - Parameters: - ---------- - map: numpy array , n x m x 1 - detect score for each position - reg: numpy array , n x m x 4 - bbox - scale: float number - scale of this detection - threshold: float number - detect threshold - Returns: - ------- - bbox array - """ - stride = 2 - cellsize = 12 - - t_index = np.where(map > threshold) - - # find nothing - if t_index[0].size == 0: - return np.array([]) - - dx1, dy1, dx2, dy2 = [reg[0, i, t_index[0], t_index[1]] for i in range(4)] - - reg = np.array([dx1, dy1, dx2, dy2]) - score = map[t_index[0], t_index[1]] - boundingbox = np.vstack([ - np.round((stride * t_index[1] + 1) / scale), - np.round((stride * t_index[0] + 1) / scale), - np.round((stride * t_index[1] + 1 + cellsize) / scale), - np.round((stride * t_index[0] + 1 + cellsize) / scale), score, reg - ]) - - return boundingbox.T - - -def detect_first_stage(img, net, scale, threshold): - """ - run PNet for first stage - - Parameters: - ---------- - img: numpy array, bgr order - input image - scale: float number - how much should the input image scale - net: PNet - worker - Returns: - ------- - total_boxes : bboxes - """ - height, width, _ = img.shape - hs = int(math.ceil(height * scale)) - ws = int(math.ceil(width * scale)) - - im_data = cv2.resize(img, (ws, hs)) - - # adjust for the network input - input_buf = adjust_input(im_data) - output = net.predict(input_buf) - boxes = generate_bbox(output[1][0, 1, :, :], output[0], scale, threshold) - - if boxes.size == 0: - return None - - # nms - pick = nms(boxes[:, 0:5], 0.5, mode='Union') - boxes = boxes[pick] - return boxes - - -def detect_first_stage_warpper(args): - return detect_first_stage(*args) - -class MtcnnDetector(object): - """ - Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks - see https://github.com/kpzhang93/MTCNN_face_detection_alignment - this is a mxnet version - """ - def __init__(self, - model_folder='.', - minsize=20, - threshold=[0.6, 0.7, 0.8], - factor=0.709, - num_worker=1, - accurate_landmark=False, - ctx=mx.cpu()): - """ - Initialize the detector - - Parameters: - ---------- - model_folder : string - path for the models - minsize : float number - minimal face to detect - threshold : float number - detect threshold for 3 stages - factor: float number - scale factor for image pyramid - num_worker: int number - number of processes we use for first stage - accurate_landmark: bool - use accurate landmark localization or not - - """ - self.num_worker = num_worker - self.accurate_landmark = accurate_landmark - - # load 4 models from folder - models = ['det1', 'det2', 'det3', 'det4'] - models = [os.path.join(model_folder, f) for f in models] - - self.PNets = [] - for i in range(num_worker): - workner_net = mx.model.FeedForward.load(models[0], 1, ctx=ctx) - self.PNets.append(workner_net) - - #self.Pool = Pool(num_worker) - - self.RNet = mx.model.FeedForward.load(models[1], 1, ctx=ctx) - self.ONet = mx.model.FeedForward.load(models[2], 1, ctx=ctx) - self.LNet = mx.model.FeedForward.load(models[3], 1, ctx=ctx) - - self.minsize = float(minsize) - self.factor = float(factor) - self.threshold = threshold - - def convert_to_square(self, bbox): - """ - convert bbox to square - - Parameters: - ---------- - bbox: numpy array , shape n x 5 - input bbox - - Returns: - ------- - square bbox - """ - square_bbox = bbox.copy() - - h = bbox[:, 3] - bbox[:, 1] + 1 - w = bbox[:, 2] - bbox[:, 0] + 1 - max_side = np.maximum(h, w) - square_bbox[:, 0] = bbox[:, 0] + w * 0.5 - max_side * 0.5 - square_bbox[:, 1] = bbox[:, 1] + h * 0.5 - max_side * 0.5 - square_bbox[:, 2] = square_bbox[:, 0] + max_side - 1 - square_bbox[:, 3] = square_bbox[:, 1] + max_side - 1 - return square_bbox - - def calibrate_box(self, bbox, reg): - """ - calibrate bboxes - - Parameters: - ---------- - bbox: numpy array, shape n x 5 - input bboxes - reg: numpy array, shape n x 4 - bboxex adjustment - - Returns: - ------- - bboxes after refinement - - """ - w = bbox[:, 2] - bbox[:, 0] + 1 - w = np.expand_dims(w, 1) - h = bbox[:, 3] - bbox[:, 1] + 1 - h = np.expand_dims(h, 1) - reg_m = np.hstack([w, h, w, h]) - aug = reg_m * reg - bbox[:, 0:4] = bbox[:, 0:4] + aug - return bbox - - def pad(self, bboxes, w, h): - """ - pad the the bboxes, alse restrict the size of it - - Parameters: - ---------- - bboxes: numpy array, n x 5 - input bboxes - w: float number - width of the input image - h: float number - height of the input image - Returns : - ------s - dy, dx : numpy array, n x 1 - start point of the bbox in target image - edy, edx : numpy array, n x 1 - end point of the bbox in target image - y, x : numpy array, n x 1 - start point of the bbox in original image - ex, ex : numpy array, n x 1 - end point of the bbox in original image - tmph, tmpw: numpy array, n x 1 - height and width of the bbox - - """ - tmpw, tmph = bboxes[:, 2] - bboxes[:, 0] + 1, bboxes[:, - 3] - bboxes[:, - 1] + 1 - num_box = bboxes.shape[0] - - dx, dy = np.zeros((num_box, )), np.zeros((num_box, )) - edx, edy = tmpw.copy() - 1, tmph.copy() - 1 - - x, y, ex, ey = bboxes[:, 0], bboxes[:, 1], bboxes[:, 2], bboxes[:, 3] - - tmp_index = np.where(ex > w - 1) - edx[tmp_index] = tmpw[tmp_index] + w - 2 - ex[tmp_index] - ex[tmp_index] = w - 1 - - tmp_index = np.where(ey > h - 1) - edy[tmp_index] = tmph[tmp_index] + h - 2 - ey[tmp_index] - ey[tmp_index] = h - 1 - - tmp_index = np.where(x < 0) - dx[tmp_index] = 0 - x[tmp_index] - x[tmp_index] = 0 - - tmp_index = np.where(y < 0) - dy[tmp_index] = 0 - y[tmp_index] - y[tmp_index] = 0 - - return_list = [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] - return_list = [item.astype(np.int32) for item in return_list] - - return return_list - - def slice_index(self, number): - """ - slice the index into (n,n,m), m < n - Parameters: - ---------- - number: int number - number - """ - def chunks(l, n): - """Yield successive n-sized chunks from l.""" - for i in range(0, len(l), n): - yield l[i:i + n] - - num_list = range(number) - return list(chunks(num_list, self.num_worker)) - - def detect_face_limited(self, img, det_type=2): - height, width, _ = img.shape - if det_type >= 2: - total_boxes = np.array( - [[0.0, 0.0, img.shape[1], img.shape[0], 0.9]], - dtype=np.float32) - num_box = total_boxes.shape[0] - - # pad the bbox - [dy, edy, dx, edx, y, ey, x, ex, tmpw, - tmph] = self.pad(total_boxes, width, height) - # (3, 24, 24) is the input shape for RNet - input_buf = np.zeros((num_box, 3, 24, 24), dtype=np.float32) - - for i in range(num_box): - tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8) - tmp[dy[i]:edy[i] + 1, - dx[i]:edx[i] + 1, :] = img[y[i]:ey[i] + 1, - x[i]:ex[i] + 1, :] - input_buf[i, :, :, :] = adjust_input(cv2.resize(tmp, (24, 24))) - - output = self.RNet.predict(input_buf) - - # filter the total_boxes with threshold - passed = np.where(output[1][:, 1] > self.threshold[1]) - total_boxes = total_boxes[passed] - - if total_boxes.size == 0: - return None - - total_boxes[:, 4] = output[1][passed, 1].reshape((-1, )) - reg = output[0][passed] - - # nms - pick = nms(total_boxes, 0.7, 'Union') - total_boxes = total_boxes[pick] - total_boxes = self.calibrate_box(total_boxes, reg[pick]) - total_boxes = self.convert_to_square(total_boxes) - total_boxes[:, 0:4] = np.round(total_boxes[:, 0:4]) - else: - total_boxes = np.array( - [[0.0, 0.0, img.shape[1], img.shape[0], 0.9]], - dtype=np.float32) - num_box = total_boxes.shape[0] - [dy, edy, dx, edx, y, ey, x, ex, tmpw, - tmph] = self.pad(total_boxes, width, height) - # (3, 48, 48) is the input shape for ONet - input_buf = np.zeros((num_box, 3, 48, 48), dtype=np.float32) - - for i in range(num_box): - tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.float32) - tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = img[y[i]:ey[i] + 1, - x[i]:ex[i] + 1, :] - input_buf[i, :, :, :] = adjust_input(cv2.resize(tmp, (48, 48))) - - output = self.ONet.predict(input_buf) - #print(output[2]) - - # filter the total_boxes with threshold - passed = np.where(output[2][:, 1] > self.threshold[2]) - total_boxes = total_boxes[passed] - - if total_boxes.size == 0: - return None - - total_boxes[:, 4] = output[2][passed, 1].reshape((-1, )) - reg = output[1][passed] - points = output[0][passed] - - # compute landmark points - bbw = total_boxes[:, 2] - total_boxes[:, 0] + 1 - bbh = total_boxes[:, 3] - total_boxes[:, 1] + 1 - points[:, 0:5] = np.expand_dims( - total_boxes[:, 0], 1) + np.expand_dims(bbw, 1) * points[:, 0:5] - points[:, 5:10] = np.expand_dims( - total_boxes[:, 1], 1) + np.expand_dims(bbh, 1) * points[:, 5:10] - - # nms - total_boxes = self.calibrate_box(total_boxes, reg) - pick = nms(total_boxes, 0.7, 'Min') - total_boxes = total_boxes[pick] - points = points[pick] - - if not self.accurate_landmark: - return total_boxes, points - - ############################################# - # extended stage - ############################################# - num_box = total_boxes.shape[0] - patchw = np.maximum(total_boxes[:, 2] - total_boxes[:, 0] + 1, - total_boxes[:, 3] - total_boxes[:, 1] + 1) - patchw = np.round(patchw * 0.25) - - # make it even - patchw[np.where(np.mod(patchw, 2) == 1)] += 1 - - input_buf = np.zeros((num_box, 15, 24, 24), dtype=np.float32) - for i in range(5): - x, y = points[:, i], points[:, i + 5] - x, y = np.round(x - 0.5 * patchw), np.round(y - 0.5 * patchw) - [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self.pad( - np.vstack([x, y, x + patchw - 1, y + patchw - 1]).T, width, - height) - for j in range(num_box): - tmpim = np.zeros((tmpw[j], tmpw[j], 3), dtype=np.float32) - tmpim[dy[j]:edy[j] + 1, - dx[j]:edx[j] + 1, :] = img[y[j]:ey[j] + 1, - x[j]:ex[j] + 1, :] - input_buf[j, i * 3:i * 3 + 3, :, :] = adjust_input( - cv2.resize(tmpim, (24, 24))) - - output = self.LNet.predict(input_buf) - - pointx = np.zeros((num_box, 5)) - pointy = np.zeros((num_box, 5)) - - for k in range(5): - # do not make a large movement - tmp_index = np.where(np.abs(output[k] - 0.5) > 0.35) - output[k][tmp_index[0]] = 0.5 - - pointx[:, k] = np.round(points[:, k] - - 0.5 * patchw) + output[k][:, 0] * patchw - pointy[:, k] = np.round(points[:, k + 5] - - 0.5 * patchw) + output[k][:, 1] * patchw - - points = np.hstack([pointx, pointy]) - points = points.astype(np.int32) - - return total_boxes, points - - def detect_face(self, img, det_type=0): - """ - detect face over img - Parameters: - ---------- - img: numpy array, bgr order of shape (1, 3, n, m) - input image - Retures: - ------- - bboxes: numpy array, n x 5 (x1,y2,x2,y2,score) - bboxes - points: numpy array, n x 10 (x1, x2 ... x5, y1, y2 ..y5) - landmarks - """ - - # check input - height, width, _ = img.shape - if det_type == 0: - MIN_DET_SIZE = 12 - - if img is None: - return None - - # only works for color image - if len(img.shape) != 3: - return None - - # detected boxes - total_boxes = [] - - minl = min(height, width) - - # get all the valid scales - scales = [] - m = MIN_DET_SIZE / self.minsize - minl *= m - factor_count = 0 - while minl > MIN_DET_SIZE: - scales.append(m * self.factor**factor_count) - minl *= self.factor - factor_count += 1 - - ############################################# - # first stage - ############################################# - #for scale in scales: - # return_boxes = self.detect_first_stage(img, scale, 0) - # if return_boxes is not None: - # total_boxes.append(return_boxes) - - sliced_index = self.slice_index(len(scales)) - total_boxes = [] - for batch in sliced_index: - #local_boxes = self.Pool.map( detect_first_stage_warpper, \ - # izip(repeat(img), self.PNets[:len(batch)], [scales[i] for i in batch], repeat(self.threshold[0])) ) - local_boxes = map( detect_first_stage_warpper, \ - izip(repeat(img), self.PNets[:len(batch)], [scales[i] for i in batch], repeat(self.threshold[0])) ) - total_boxes.extend(local_boxes) - - # remove the Nones - total_boxes = [i for i in total_boxes if i is not None] - - if len(total_boxes) == 0: - return None - - total_boxes = np.vstack(total_boxes) - - if total_boxes.size == 0: - return None - - # merge the detection from first stage - pick = nms(total_boxes[:, 0:5], 0.7, 'Union') - total_boxes = total_boxes[pick] - - bbw = total_boxes[:, 2] - total_boxes[:, 0] + 1 - bbh = total_boxes[:, 3] - total_boxes[:, 1] + 1 - - # refine the bboxes - total_boxes = np.vstack([ - total_boxes[:, 0] + total_boxes[:, 5] * bbw, - total_boxes[:, 1] + total_boxes[:, 6] * bbh, - total_boxes[:, 2] + total_boxes[:, 7] * bbw, - total_boxes[:, 3] + total_boxes[:, 8] * bbh, total_boxes[:, 4] - ]) - - total_boxes = total_boxes.T - total_boxes = self.convert_to_square(total_boxes) - total_boxes[:, 0:4] = np.round(total_boxes[:, 0:4]) - else: - total_boxes = np.array( - [[0.0, 0.0, img.shape[1], img.shape[0], 0.9]], - dtype=np.float32) - - ############################################# - # second stage - ############################################# - num_box = total_boxes.shape[0] - - # pad the bbox - [dy, edy, dx, edx, y, ey, x, ex, tmpw, - tmph] = self.pad(total_boxes, width, height) - # (3, 24, 24) is the input shape for RNet - input_buf = np.zeros((num_box, 3, 24, 24), dtype=np.float32) - - for i in range(num_box): - tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8) - tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = img[y[i]:ey[i] + 1, - x[i]:ex[i] + 1, :] - input_buf[i, :, :, :] = adjust_input(cv2.resize(tmp, (24, 24))) - - output = self.RNet.predict(input_buf) - - # filter the total_boxes with threshold - passed = np.where(output[1][:, 1] > self.threshold[1]) - total_boxes = total_boxes[passed] - - if total_boxes.size == 0: - return None - - total_boxes[:, 4] = output[1][passed, 1].reshape((-1, )) - reg = output[0][passed] - - # nms - pick = nms(total_boxes, 0.7, 'Union') - total_boxes = total_boxes[pick] - total_boxes = self.calibrate_box(total_boxes, reg[pick]) - total_boxes = self.convert_to_square(total_boxes) - total_boxes[:, 0:4] = np.round(total_boxes[:, 0:4]) - - ############################################# - # third stage - ############################################# - num_box = total_boxes.shape[0] - - # pad the bbox - [dy, edy, dx, edx, y, ey, x, ex, tmpw, - tmph] = self.pad(total_boxes, width, height) - # (3, 48, 48) is the input shape for ONet - input_buf = np.zeros((num_box, 3, 48, 48), dtype=np.float32) - - for i in range(num_box): - tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.float32) - tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = img[y[i]:ey[i] + 1, - x[i]:ex[i] + 1, :] - input_buf[i, :, :, :] = adjust_input(cv2.resize(tmp, (48, 48))) - - output = self.ONet.predict(input_buf) - - # filter the total_boxes with threshold - passed = np.where(output[2][:, 1] > self.threshold[2]) - total_boxes = total_boxes[passed] - - if total_boxes.size == 0: - return None - - total_boxes[:, 4] = output[2][passed, 1].reshape((-1, )) - reg = output[1][passed] - points = output[0][passed] - - # compute landmark points - bbw = total_boxes[:, 2] - total_boxes[:, 0] + 1 - bbh = total_boxes[:, 3] - total_boxes[:, 1] + 1 - points[:, 0:5] = np.expand_dims( - total_boxes[:, 0], 1) + np.expand_dims(bbw, 1) * points[:, 0:5] - points[:, 5:10] = np.expand_dims( - total_boxes[:, 1], 1) + np.expand_dims(bbh, 1) * points[:, 5:10] - - # nms - total_boxes = self.calibrate_box(total_boxes, reg) - pick = nms(total_boxes, 0.7, 'Min') - total_boxes = total_boxes[pick] - points = points[pick] - - if not self.accurate_landmark: - return total_boxes, points - - ############################################# - # extended stage - ############################################# - num_box = total_boxes.shape[0] - patchw = np.maximum(total_boxes[:, 2] - total_boxes[:, 0] + 1, - total_boxes[:, 3] - total_boxes[:, 1] + 1) - patchw = np.round(patchw * 0.25) - - # make it even - patchw[np.where(np.mod(patchw, 2) == 1)] += 1 - - input_buf = np.zeros((num_box, 15, 24, 24), dtype=np.float32) - for i in range(5): - x, y = points[:, i], points[:, i + 5] - x, y = np.round(x - 0.5 * patchw), np.round(y - 0.5 * patchw) - [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self.pad( - np.vstack([x, y, x + patchw - 1, y + patchw - 1]).T, width, - height) - for j in range(num_box): - tmpim = np.zeros((tmpw[j], tmpw[j], 3), dtype=np.float32) - tmpim[dy[j]:edy[j] + 1, - dx[j]:edx[j] + 1, :] = img[y[j]:ey[j] + 1, - x[j]:ex[j] + 1, :] - input_buf[j, i * 3:i * 3 + 3, :, :] = adjust_input( - cv2.resize(tmpim, (24, 24))) - - output = self.LNet.predict(input_buf) - - pointx = np.zeros((num_box, 5)) - pointy = np.zeros((num_box, 5)) - - for k in range(5): - # do not make a large movement - tmp_index = np.where(np.abs(output[k] - 0.5) > 0.35) - output[k][tmp_index[0]] = 0.5 - - pointx[:, k] = np.round(points[:, k] - - 0.5 * patchw) + output[k][:, 0] * patchw - pointy[:, k] = np.round(points[:, k + 5] - - 0.5 * patchw) + output[k][:, 1] * patchw - - points = np.hstack([pointx, pointy]) - points = points.astype(np.int32) - - return total_boxes, points - - def list2colmatrix(self, pts_list): - """ - convert list to column matrix - Parameters: - ---------- - pts_list: - input list - Retures: - ------- - colMat: - - """ - assert len(pts_list) > 0 - colMat = [] - for i in range(len(pts_list)): - colMat.append(pts_list[i][0]) - colMat.append(pts_list[i][1]) - colMat = np.matrix(colMat).transpose() - return colMat - - def find_tfrom_between_shapes(self, from_shape, to_shape): - """ - find transform between shapes - Parameters: - ---------- - from_shape: - to_shape: - Retures: - ------- - tran_m: - tran_b: - """ - assert from_shape.shape[0] == to_shape.shape[ - 0] and from_shape.shape[0] % 2 == 0 - - sigma_from = 0.0 - sigma_to = 0.0 - cov = np.matrix([[0.0, 0.0], [0.0, 0.0]]) - - # compute the mean and cov - from_shape_points = from_shape.reshape(from_shape.shape[0] / 2, 2) - to_shape_points = to_shape.reshape(to_shape.shape[0] / 2, 2) - mean_from = from_shape_points.mean(axis=0) - mean_to = to_shape_points.mean(axis=0) - - for i in range(from_shape_points.shape[0]): - temp_dis = np.linalg.norm(from_shape_points[i] - mean_from) - sigma_from += temp_dis * temp_dis - temp_dis = np.linalg.norm(to_shape_points[i] - mean_to) - sigma_to += temp_dis * temp_dis - cov += (to_shape_points[i].transpose() - - mean_to.transpose()) * (from_shape_points[i] - mean_from) - - sigma_from = sigma_from / to_shape_points.shape[0] - sigma_to = sigma_to / to_shape_points.shape[0] - cov = cov / to_shape_points.shape[0] - - # compute the affine matrix - s = np.matrix([[1.0, 0.0], [0.0, 1.0]]) - u, d, vt = np.linalg.svd(cov) - - if np.linalg.det(cov) < 0: - if d[1] < d[0]: - s[1, 1] = -1 - else: - s[0, 0] = -1 - r = u * s * vt - c = 1.0 - if sigma_from != 0: - c = 1.0 / sigma_from * np.trace(np.diag(d) * s) - - tran_b = mean_to.transpose() - c * r * mean_from.transpose() - tran_m = c * r - - return tran_m, tran_b - - def extract_image_chips(self, img, points, desired_size=256, padding=0): - """ - crop and align face - Parameters: - ---------- - img: numpy array, bgr order of shape (1, 3, n, m) - input image - points: numpy array, n x 10 (x1, x2 ... x5, y1, y2 ..y5) - desired_size: default 256 - padding: default 0 - Retures: - ------- - crop_imgs: list, n - cropped and aligned faces - """ - crop_imgs = [] - for p in points: - shape = [] - for k in range(len(p) / 2): - shape.append(p[k]) - shape.append(p[k + 5]) - - if padding > 0: - padding = padding - else: - padding = 0 - # average positions of face points - mean_face_shape_x = [ - 0.224152, 0.75610125, 0.490127, 0.254149, 0.726104 - ] - mean_face_shape_y = [ - 0.2119465, 0.2119465, 0.628106, 0.780233, 0.780233 - ] - - from_points = [] - to_points = [] - - for i in range(len(shape) / 2): - x = (padding + mean_face_shape_x[i]) / (2 * padding + - 1) * desired_size - y = (padding + mean_face_shape_y[i]) / (2 * padding + - 1) * desired_size - to_points.append([x, y]) - from_points.append([shape[2 * i], shape[2 * i + 1]]) - - # convert the points to Mat - from_mat = self.list2colmatrix(from_points) - to_mat = self.list2colmatrix(to_points) - - # compute the similar transfrom - tran_m, tran_b = self.find_tfrom_between_shapes(from_mat, to_mat) - - probe_vec = np.matrix([1.0, 0.0]).transpose() - probe_vec = tran_m * probe_vec - - scale = np.linalg.norm(probe_vec) - angle = 180.0 / math.pi * math.atan2(probe_vec[1, 0], probe_vec[0, - 0]) - - from_center = [(shape[0] + shape[2]) / 2.0, - (shape[1] + shape[3]) / 2.0] - to_center = [0, 0] - to_center[1] = desired_size * 0.4 - to_center[0] = desired_size * 0.5 - - ex = to_center[0] - from_center[0] - ey = to_center[1] - from_center[1] - - rot_mat = cv2.getRotationMatrix2D((from_center[0], from_center[1]), - -1 * angle, scale) - rot_mat[0][2] += ex - rot_mat[1][2] += ey - - chips = cv2.warpAffine(img, rot_mat, (desired_size, desired_size)) - crop_imgs.append(chips) - - return crop_imgs - diff --git a/detection/README.md b/detection/README.md new file mode 100644 index 0000000..229814e --- /dev/null +++ b/detection/README.md @@ -0,0 +1,42 @@ +## Face Detection + + +
+ +
+ + +## Introduction + +These are the face detection methods of [InsightFace](https://insightface.ai) + + +
+ +
+ + +### Datasets + + Please refer to [datasets](_datasets_) page for the details of face detection datasets used for training and evaluation. + +### Evaluation + + Please refer to [evaluation](_evaluation_) page for the details of face recognition evaluation. + + +## Methods + + +Supported methods: + +- [x] [RetinaFace (CVPR'2020)](retinaface) +- [x] [SCRFD (Arxiv'2021)](scrfd) + + + +## Contributing + +We appreciate all contributions to improve the face detection model zoo of InsightFace. + + diff --git a/detection/RetinaFace/README.md b/detection/RetinaFace/README.md index 1e1f07f..521d8d9 100644 --- a/detection/RetinaFace/README.md +++ b/detection/RetinaFace/README.md @@ -4,9 +4,9 @@ RetinaFace is a practical single-stage [SOTA](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html) face detector which is initially introduced in [arXiv technical report](https://arxiv.org/abs/1905.00641) and then accepted by [CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_RetinaFace_Single-Shot_Multi-Level_Face_Localisation_in_the_Wild_CVPR_2020_paper.html). -![demoimg1](https://github.com/deepinsight/insightface/blob/master/resources/11513D05.jpg) +![demoimg1](https://insightface.ai/assets/img/github/11513D05.jpg) -![demoimg2](https://github.com/deepinsight/insightface/blob/master/resources/widerfacevaltest.png) +![demoimg2](https://insightface.ai/assets/img/github/widerfacevaltest.png) ## Data diff --git a/detection/RetinaFaceAntiCov/README.md b/detection/retinaface_anticov/README.md similarity index 90% rename from detection/RetinaFaceAntiCov/README.md rename to detection/retinaface_anticov/README.md index ae6c222..97b3b24 100644 --- a/detection/RetinaFaceAntiCov/README.md +++ b/detection/retinaface_anticov/README.md @@ -4,7 +4,7 @@ RetinaFace-Anti-Cov is a customized one stage face detector to help people protect themselves from CovID-19. -![demoimg1](https://github.com/deepinsight/insightface/blob/master/resources/cov_test.jpg) +![demoimg1](https://insightface.ai/assets/img/github/cov_test.jpg) ## Testing diff --git a/detection/RetinaFaceAntiCov/rcnn/processing/__init__.py b/detection/retinaface_anticov/rcnn/processing/__init__.py similarity index 100% rename from detection/RetinaFaceAntiCov/rcnn/processing/__init__.py rename to detection/retinaface_anticov/rcnn/processing/__init__.py diff --git a/detection/RetinaFaceAntiCov/rcnn/processing/assign_levels.py b/detection/retinaface_anticov/rcnn/processing/assign_levels.py similarity index 100% rename from detection/RetinaFaceAntiCov/rcnn/processing/assign_levels.py rename to detection/retinaface_anticov/rcnn/processing/assign_levels.py diff --git a/detection/RetinaFaceAntiCov/rcnn/processing/bbox_regression.py b/detection/retinaface_anticov/rcnn/processing/bbox_regression.py similarity index 100% rename from detection/RetinaFaceAntiCov/rcnn/processing/bbox_regression.py rename to detection/retinaface_anticov/rcnn/processing/bbox_regression.py diff --git a/detection/RetinaFaceAntiCov/rcnn/processing/bbox_transform.py b/detection/retinaface_anticov/rcnn/processing/bbox_transform.py similarity index 100% rename from detection/RetinaFaceAntiCov/rcnn/processing/bbox_transform.py rename to detection/retinaface_anticov/rcnn/processing/bbox_transform.py diff --git a/detection/RetinaFaceAntiCov/rcnn/processing/bbox_transform.py.orig b/detection/retinaface_anticov/rcnn/processing/bbox_transform.py.orig similarity index 100% rename from detection/RetinaFaceAntiCov/rcnn/processing/bbox_transform.py.orig rename to detection/retinaface_anticov/rcnn/processing/bbox_transform.py.orig diff --git a/detection/RetinaFaceAntiCov/rcnn/processing/generate_anchor.py b/detection/retinaface_anticov/rcnn/processing/generate_anchor.py similarity index 100% rename from detection/RetinaFaceAntiCov/rcnn/processing/generate_anchor.py rename to detection/retinaface_anticov/rcnn/processing/generate_anchor.py diff --git a/detection/RetinaFaceAntiCov/rcnn/processing/nms.py b/detection/retinaface_anticov/rcnn/processing/nms.py similarity index 100% rename from detection/RetinaFaceAntiCov/rcnn/processing/nms.py rename to detection/retinaface_anticov/rcnn/processing/nms.py diff --git a/detection/RetinaFaceAntiCov/retinaface_cov.py b/detection/retinaface_anticov/retinaface_cov.py similarity index 100% rename from detection/RetinaFaceAntiCov/retinaface_cov.py rename to detection/retinaface_anticov/retinaface_cov.py diff --git a/detection/RetinaFaceAntiCov/test.py b/detection/retinaface_anticov/test.py similarity index 100% rename from detection/RetinaFaceAntiCov/test.py rename to detection/retinaface_anticov/test.py diff --git a/evaluation/IJB/IJBB_Evaluation_MS1MV2.ipynb b/evaluation/IJB/IJBB_Evaluation_MS1MV2.ipynb deleted file mode 100644 index c18234e..0000000 --- a/evaluation/IJB/IJBB_Evaluation_MS1MV2.ipynb +++ /dev/null @@ -1,520 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jd4615/miniconda3/envs/insightface/lib/python2.7/site-packages/sklearn/utils/fixes.py:313: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n", - " _nan_object_mask = _nan_object_array != _nan_object_array\n" - ] - } - ], - "source": [ - "import os\n", - "import numpy as np\n", - "import cPickle\n", - "from sklearn.metrics import roc_curve, auc\n", - "import matplotlib.pyplot as plt\n", - "import timeit\n", - "import sklearn\n", - "import cv2\n", - "import sys\n", - "import glob\n", - "sys.path.append('./recognition')\n", - "from embedding import Embedding\n", - "from menpo.visualize import print_progress\n", - "from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap\n", - "from prettytable import PrettyTable\n", - "from pathlib import Path\n", - "import warnings \n", - "warnings.filterwarnings(\"ignore\") " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def read_template_media_list(path):\n", - " ijb_meta = np.loadtxt(path, dtype=str)\n", - " templates = ijb_meta[:,1].astype(np.int)\n", - " medias = ijb_meta[:,2].astype(np.int)\n", - " return templates, medias" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def read_template_pair_list(path):\n", - " pairs = np.loadtxt(path, dtype=str)\n", - " t1 = pairs[:,0].astype(np.int)\n", - " t2 = pairs[:,1].astype(np.int)\n", - " label = pairs[:,2].astype(np.int)\n", - " return t1, t2, label" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def read_image_feature(path):\n", - " with open(path, 'rb') as fid:\n", - " img_feats = cPickle.load(fid)\n", - " return img_feats" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def get_image_feature(img_path, img_list_path, model_path, gpu_id):\n", - " img_list = open(img_list_path)\n", - " embedding = Embedding(model_path, 0, gpu_id)\n", - " files = img_list.readlines()\n", - " img_feats = []\n", - " faceness_scores = []\n", - " for img_index, each_line in enumerate(print_progress(files)):\n", - " name_lmk_score = each_line.strip().split(' ')\n", - " img_name = os.path.join(img_path, name_lmk_score[0])\n", - " img = cv2.imread(img_name)\n", - " lmk = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32)\n", - " lmk = lmk.reshape( (5,2) )\n", - " img_feats.append(embedding.get(img,lmk))\n", - " faceness_scores.append(name_lmk_score[-1])\n", - " img_feats = np.array(img_feats).astype(np.float32)\n", - " faceness_scores = np.array(faceness_scores).astype(np.float32)\n", - " return img_feats, faceness_scores" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def image2template_feature(img_feats = None, templates = None, medias = None):\n", - " # ==========================================================\n", - " # 1. face image feature l2 normalization. img_feats:[number_image x feats_dim]\n", - " # 2. compute media feature.\n", - " # 3. compute template feature.\n", - " # ========================================================== \n", - " unique_templates = np.unique(templates)\n", - " template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))\n", - "\n", - " for count_template, uqt in enumerate(unique_templates):\n", - " (ind_t,) = np.where(templates == uqt)\n", - " face_norm_feats = img_feats[ind_t]\n", - " face_medias = medias[ind_t]\n", - " unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True)\n", - " media_norm_feats = []\n", - " for u,ct in zip(unique_medias, unique_media_counts):\n", - " (ind_m,) = np.where(face_medias == u)\n", - " if ct == 1:\n", - " media_norm_feats += [face_norm_feats[ind_m]]\n", - " else: # image features from the same video will be aggregated into one feature\n", - " media_norm_feats += [np.mean(face_norm_feats[ind_m], 0, keepdims=True)]\n", - " media_norm_feats = np.array(media_norm_feats)\n", - " # media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True))\n", - " template_feats[count_template] = np.sum(media_norm_feats, 0)\n", - " if count_template % 2000 == 0: \n", - " print('Finish Calculating {} template features.'.format(count_template))\n", - " template_norm_feats = template_feats / np.sqrt(np.sum(template_feats ** 2, -1, keepdims=True))\n", - " return template_norm_feats, unique_templates" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def verification(template_norm_feats = None, unique_templates = None, p1 = None, p2 = None):\n", - " # ==========================================================\n", - " # Compute set-to-set Similarity Score.\n", - " # ==========================================================\n", - " template2id = np.zeros((max(unique_templates)+1,1),dtype=int)\n", - " for count_template, uqt in enumerate(unique_templates):\n", - " template2id[uqt] = count_template\n", - " \n", - " score = np.zeros((len(p1),)) # save cosine distance between pairs \n", - "\n", - " total_pairs = np.array(range(len(p1)))\n", - " batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation\n", - " sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)]\n", - " total_sublists = len(sublists)\n", - " for c, s in enumerate(sublists):\n", - " feat1 = template_norm_feats[template2id[p1[s]]]\n", - " feat2 = template_norm_feats[template2id[p2[s]]]\n", - " similarity_score = np.sum(feat1 * feat2, -1)\n", - " score[s] = similarity_score.flatten()\n", - " if c % 10 == 0:\n", - " print('Finish {}/{} pairs.'.format(c, total_sublists))\n", - " return score" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def read_score(path):\n", - " with open(path, 'rb') as fid:\n", - " img_feats = cPickle.load(fid)\n", - " return img_feats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step1: Load Meta Data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 0.83 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load image and template relationships for template feature embedding\n", - "# tid --> template id, mid --> media id \n", - "# format:\n", - "# image_name tid mid\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "templates, medias = read_template_media_list(os.path.join('IJBB/meta', 'ijbb_face_tid_mid.txt'))\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 31.88 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load template pairs for template-to-template verification\n", - "# tid : template id, label : 1/0\n", - "# format:\n", - "# tid_1 tid_2 label\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "p1, p2, label = read_template_pair_list(os.path.join('IJBB/meta', 'ijbb_template_pair_label.txt'))\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 2: Get Image Features" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('loading', './pretrained_models/MS1MV2-ResNet100-Arcface/model', 0)\n", - "[====================] 100% (227630/227630) - done. \n", - "Time: 3279.69 s. \n", - "Feature Shape: (227630 , 1024) .\n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load image features \n", - "# format:\n", - "# img_feats: [image_num x feats_dim] (227630, 512)\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "#img_feats = read_image_feature('./MS1MV2/IJBB_MS1MV2_r100_arcface.pkl')\n", - "img_path = './IJBB/loose_crop'\n", - "img_list_path = './IJBB/meta/ijbb_name_5pts_score.txt'\n", - "model_path = './pretrained_models/MS1MV2-ResNet100-Arcface/model'\n", - "gpu_id = 1\n", - "img_feats, faceness_scores = get_image_feature(img_path, img_list_path, model_path, gpu_id)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))\n", - "print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step3: Get Template Features" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finish Calculating 0 template features.\n", - "Finish Calculating 2000 template features.\n", - "Finish Calculating 4000 template features.\n", - "Finish Calculating 6000 template features.\n", - "Finish Calculating 8000 template features.\n", - "Finish Calculating 10000 template features.\n", - "Finish Calculating 12000 template features.\n", - "Time: 3.65 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# compute template features from image features.\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "# ========================================================== \n", - "# Norm feature before aggregation into template feature?\n", - "# Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face).\n", - "# ========================================================== \n", - "# 1. FaceScore （Feature Norm）\n", - "# 2. FaceScore （Detector）\n", - "\n", - "use_norm_score = True # if True, TestMode(N1) \n", - "use_detector_score = True # if True, TestMode(D1)\n", - "use_flip_test = True # if True, TestMode(F2)\n", - "\n", - "if use_flip_test:\n", - " # concat --- F1\n", - " # img_input_feats = img_feats \n", - " # add --- F2\n", - " img_input_feats = img_feats[:,0:img_feats.shape[1]/2] + img_feats[:,img_feats.shape[1]/2:]\n", - "else:\n", - " img_input_feats = img_feats[:,0:img_feats.shape[1]/2]\n", - " \n", - "if use_norm_score:\n", - " img_input_feats = img_input_feats\n", - "else:\n", - " # normalise features to remove norm information\n", - " img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True)) \n", - " \n", - "if use_detector_score:\n", - " img_input_feats = img_input_feats * np.matlib.repmat(faceness_scores[:,np.newaxis], 1, img_input_feats.shape[1])\n", - "else:\n", - " img_input_feats = img_input_feats\n", - "\n", - "template_norm_feats, unique_templates = image2template_feature(img_input_feats, templates, medias)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 4: Get Template Similarity Scores" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finish 0/81 pairs.\n", - "Finish 10/81 pairs.\n", - "Finish 20/81 pairs.\n", - "Finish 30/81 pairs.\n", - "Finish 40/81 pairs.\n", - "Finish 50/81 pairs.\n", - "Finish 60/81 pairs.\n", - "Finish 70/81 pairs.\n", - "Finish 80/81 pairs.\n", - "Time: 77.30 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# compute verification scores between template pairs.\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "score = verification(template_norm_feats, unique_templates, p1, p2)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "score_save_name = './IJBB/result/MS1MV2-ResNet100-ArcFace-TestMode(N1D1F2).npy'\n", - "np.save(score_save_name, score)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 5: Get ROC Curves and TPR@FPR Table" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "score_save_path = './IJBB/result'\n", - "files = glob.glob(score_save_path + '/MS1MV2*.npy') \n", - "methods = []\n", - "scores = []\n", - "for file in files:\n", - " methods.append(Path(file).stem)\n", - " scores.append(np.load(file)) \n", - "methods = np.array(methods)\n", - "scores = dict(zip(methods,scores))\n", - "colours = dict(zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\n", - "#x_labels = [1/(10**x) for x in np.linspace(6, 0, 6)]\n", - "x_labels = [10**-6, 10**-5, 10**-4,10**-3, 10**-2, 10**-1]\n", - "tpr_fpr_table = PrettyTable(['Methods'] + map(str, x_labels))\n", - "fig = plt.figure()\n", - "for method in methods:\n", - " fpr, tpr, _ = roc_curve(label, scores[method])\n", - " roc_auc = auc(fpr, tpr)\n", - " fpr = np.flipud(fpr)\n", - " tpr = np.flipud(tpr) # select largest tpr at same fpr\n", - " plt.plot(fpr, tpr, color=colours[method], lw=1, label=('[%s (AUC = %0.4f %%)]' % (method.split('-')[-1], roc_auc*100)))\n", - " tpr_fpr_row = []\n", - " tpr_fpr_row.append(method)\n", - " for fpr_iter in np.arange(len(x_labels)):\n", - " _, min_index = min(list(zip(abs(fpr-x_labels[fpr_iter]), range(len(fpr)))))\n", - " tpr_fpr_row.append('%.4f' % tpr[min_index])\n", - " tpr_fpr_table.add_row(tpr_fpr_row)\n", - "plt.xlim([10**-6, 0.1])\n", - "plt.ylim([0.3, 1.0])\n", - "plt.grid(linestyle='--', linewidth=1)\n", - "plt.xticks(x_labels) \n", - "plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True)) \n", - "plt.xscale('log')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('ROC on IJB-B')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "#fig.savefig('IJB-B.pdf')" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+-------------------------------------------+--------+--------+--------+--------+--------+--------+\n", - "| Methods | 1e-06 | 1e-05 | 0.0001 | 0.001 | 0.01 | 0.1 |\n", - "+-------------------------------------------+--------+--------+--------+--------+--------+--------+\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N1D1F1) | 0.4091 | 0.9081 | 0.9477 | 0.9636 | 0.9755 | 0.9863 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N0D1F2) | 0.4089 | 0.8995 | 0.9463 | 0.9642 | 0.9761 | 0.9867 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N1D1F2) | 0.4281 | 0.9082 | 0.9490 | 0.9647 | 0.9767 | 0.9866 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N1D0F0) | 0.3900 | 0.9042 | 0.9467 | 0.9620 | 0.9761 | 0.9860 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N0D0F0) | 0.3828 | 0.8933 | 0.9425 | 0.9615 | 0.9751 | 0.9856 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N1D1F0) | 0.3930 | 0.9039 | 0.9476 | 0.9630 | 0.9758 | 0.9861 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N0D1F0) | 0.3892 | 0.8984 | 0.9456 | 0.9626 | 0.9753 | 0.9861 |\n", - "+-------------------------------------------+--------+--------+--------+--------+--------+--------+\n" - ] - } - ], - "source": [ - "print(tpr_fpr_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# setting N1D1F2 is the best" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/evaluation/IJB/IJBB_Evaluation_VGG2.ipynb b/evaluation/IJB/IJBB_Evaluation_VGG2.ipynb deleted file mode 100644 index 0ed5fa7..0000000 --- a/evaluation/IJB/IJBB_Evaluation_VGG2.ipynb +++ /dev/null @@ -1,535 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jd4615/miniconda3/envs/insightface/lib/python2.7/site-packages/sklearn/utils/fixes.py:313: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n", - " _nan_object_mask = _nan_object_array != _nan_object_array\n" - ] - } - ], - "source": [ - "import os\n", - "import numpy as np\n", - "import cPickle\n", - "from sklearn.metrics import roc_curve, auc\n", - "import matplotlib.pyplot as plt\n", - "import timeit\n", - "import sklearn\n", - "import cv2\n", - "import sys\n", - "import glob\n", - "sys.path.append('./recognition')\n", - "from embedding import Embedding\n", - "from menpo.visualize import print_progress\n", - "from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap\n", - "from prettytable import PrettyTable\n", - "from pathlib import Path\n", - "import warnings \n", - "warnings.filterwarnings(\"ignore\") " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def read_template_media_list(path):\n", - " ijb_meta = np.loadtxt(path, dtype=str)\n", - " templates = ijb_meta[:,1].astype(np.int)\n", - " medias = ijb_meta[:,2].astype(np.int)\n", - " return templates, medias" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def read_template_pair_list(path):\n", - " pairs = np.loadtxt(path, dtype=str)\n", - " t1 = pairs[:,0].astype(np.int)\n", - " t2 = pairs[:,1].astype(np.int)\n", - " label = pairs[:,2].astype(np.int)\n", - " return t1, t2, label" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def read_image_feature(path):\n", - " with open(path, 'rb') as fid:\n", - " img_feats = cPickle.load(fid)\n", - " return img_feats" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def get_image_feature(img_path, img_list_path, model_path, gpu_id):\n", - " img_list = open(img_list_path)\n", - " embedding = Embedding(model_path, 0, gpu_id)\n", - " files = img_list.readlines()\n", - " img_feats = []\n", - " faceness_scores = []\n", - " for img_index, each_line in enumerate(print_progress(files)):\n", - " name_lmk_score = each_line.strip().split(' ')\n", - " img_name = os.path.join(img_path, name_lmk_score[0])\n", - " img = cv2.imread(img_name)\n", - " lmk = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32)\n", - " lmk = lmk.reshape( (5,2) )\n", - " img_feats.append(embedding.get(img,lmk))\n", - " faceness_scores.append(name_lmk_score[-1])\n", - " img_feats = np.array(img_feats).astype(np.float32)\n", - " faceness_scores = np.array(faceness_scores).astype(np.float32)\n", - " return img_feats, faceness_scores" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def image2template_feature(img_feats = None, templates = None, medias = None):\n", - " # ==========================================================\n", - " # 1. face image feature l2 normalization. img_feats:[number_image x feats_dim]\n", - " # 2. compute media feature.\n", - " # 3. compute template feature.\n", - " # ========================================================== \n", - " unique_templates = np.unique(templates)\n", - " template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))\n", - "\n", - " for count_template, uqt in enumerate(unique_templates):\n", - " (ind_t,) = np.where(templates == uqt)\n", - " face_norm_feats = img_feats[ind_t]\n", - " face_medias = medias[ind_t]\n", - " unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True)\n", - " media_norm_feats = []\n", - " for u,ct in zip(unique_medias, unique_media_counts):\n", - " (ind_m,) = np.where(face_medias == u)\n", - " if ct == 1:\n", - " media_norm_feats += [face_norm_feats[ind_m]]\n", - " else: # image features from the same video will be aggregated into one feature\n", - " media_norm_feats += [np.mean(face_norm_feats[ind_m], 0, keepdims=True)]\n", - " media_norm_feats = np.array(media_norm_feats)\n", - " # media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True))\n", - " template_feats[count_template] = np.sum(media_norm_feats, 0)\n", - " if count_template % 2000 == 0: \n", - " print('Finish Calculating {} template features.'.format(count_template))\n", - " template_norm_feats = template_feats / np.sqrt(np.sum(template_feats ** 2, -1, keepdims=True))\n", - " return template_norm_feats, unique_templates" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def verification(template_norm_feats = None, unique_templates = None, p1 = None, p2 = None):\n", - " # ==========================================================\n", - " # Compute set-to-set Similarity Score.\n", - " # ==========================================================\n", - " template2id = np.zeros((max(unique_templates)+1,1),dtype=int)\n", - " for count_template, uqt in enumerate(unique_templates):\n", - " template2id[uqt] = count_template\n", - " \n", - " score = np.zeros((len(p1),)) # save cosine distance between pairs \n", - "\n", - " total_pairs = np.array(range(len(p1)))\n", - " batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation\n", - " sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)]\n", - " total_sublists = len(sublists)\n", - " for c, s in enumerate(sublists):\n", - " feat1 = template_norm_feats[template2id[p1[s]]]\n", - " feat2 = template_norm_feats[template2id[p2[s]]]\n", - " similarity_score = np.sum(feat1 * feat2, -1)\n", - " score[s] = similarity_score.flatten()\n", - " if c % 10 == 0:\n", - " print('Finish {}/{} pairs.'.format(c, total_sublists))\n", - " return score" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def read_score(path):\n", - " with open(path, 'rb') as fid:\n", - " img_feats = cPickle.load(fid)\n", - " return img_feats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step1: Load Meta Data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 0.83 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load image and template relationships for template feature embedding\n", - "# tid --> template id, mid --> media id \n", - "# format:\n", - "# image_name tid mid\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "templates, medias = read_template_media_list(os.path.join('IJBB/meta', 'ijbb_face_tid_mid.txt'))\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 31.75 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load template pairs for template-to-template verification\n", - "# tid : template id, label : 1/0\n", - "# format:\n", - "# tid_1 tid_2 label\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "p1, p2, label = read_template_pair_list(os.path.join('IJBB/meta', 'ijbb_template_pair_label.txt'))\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 2: Get Image Features" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('loading', './pretrained_models/VGG2-ResNet50-Arcface/model', 0)\n", - "[====================] 100% (227630/227630) - done. \n", - "Time: 2386.28 s. \n", - "Feature Shape: (227630 , 1024) .\n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load image features \n", - "# format:\n", - "# img_feats: [image_num x feats_dim] (227630, 512)\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "#img_feats = read_image_feature('./MS1MV2/IJBB_MS1MV2_r100_arcface.pkl')\n", - "img_path = './IJBB/loose_crop'\n", - "img_list_path = './IJBB/meta/ijbb_name_5pts_score.txt'\n", - "model_path = './pretrained_models/VGG2-ResNet50-Arcface/model'\n", - "gpu_id = 0\n", - "img_feats, faceness_scores = get_image_feature(img_path, img_list_path, model_path, gpu_id)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))\n", - "print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step3: Get Template Features" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finish Calculating 0 template features.\n", - "Finish Calculating 2000 template features.\n", - "Finish Calculating 4000 template features.\n", - "Finish Calculating 6000 template features.\n", - "Finish Calculating 8000 template features.\n", - "Finish Calculating 10000 template features.\n", - "Finish Calculating 12000 template features.\n", - "Time: 3.41 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# compute template features from image features.\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "# ========================================================== \n", - "# Norm feature before aggregation into template feature?\n", - "# Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face).\n", - "# ========================================================== \n", - "# 1. FaceScore （Feature Norm）\n", - "# 2. FaceScore （Detector）\n", - "\n", - "use_norm_score = False # if True, TestMode(N1) \n", - "use_detector_score = True # if True, TestMode(D1)\n", - "use_flip_test = True # if True, TestMode(F1)\n", - "\n", - "if use_flip_test:\n", - " # concat --- F1\n", - " #img_input_feats = img_feats \n", - " # add --- F2\n", - " img_input_feats = img_feats[:,0:img_feats.shape[1]/2] + img_feats[:,img_feats.shape[1]/2:]\n", - "else:\n", - " img_input_feats = img_feats[:,0:img_feats.shape[1]/2]\n", - " \n", - "if use_norm_score:\n", - " img_input_feats = img_input_feats\n", - "else:\n", - " # normalise features to remove norm information\n", - " img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True)) \n", - " \n", - "if use_detector_score:\n", - " img_input_feats = img_input_feats * np.matlib.repmat(faceness_scores[:,np.newaxis], 1, img_input_feats.shape[1])\n", - "else:\n", - " img_input_feats = img_input_feats\n", - "\n", - "template_norm_feats, unique_templates = image2template_feature(img_input_feats, templates, medias)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 4: Get Template Similarity Scores" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finish 0/81 pairs.\n", - "Finish 10/81 pairs.\n", - "Finish 20/81 pairs.\n", - "Finish 30/81 pairs.\n", - "Finish 40/81 pairs.\n", - "Finish 50/81 pairs.\n", - "Finish 60/81 pairs.\n", - "Finish 70/81 pairs.\n", - "Finish 80/81 pairs.\n", - "Time: 38.38 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# compute verification scores between template pairs.\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "score = verification(template_norm_feats, unique_templates, p1, p2)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "score_save_name = './IJBB/result/VGG2-ResNet50-ArcFace-TestMode(N0D1F2).npy'\n", - "np.save(score_save_name, score)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 5: Get ROC Curves and TPR@FPR Table" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "score_save_path = './IJBB/result'\n", - "files = glob.glob(score_save_path + '/VGG2*.npy') \n", - "methods = []\n", - "scores = []\n", - "for file in files:\n", - " methods.append(Path(file).stem)\n", - " scores.append(np.load(file)) \n", - "methods = np.array(methods)\n", - "scores = dict(zip(methods,scores))\n", - "colours = dict(zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\n", - "#x_labels = [1/(10**x) for x in np.linspace(6, 0, 6)]\n", - "x_labels = [10**-6, 10**-5, 10**-4,10**-3, 10**-2, 10**-1]\n", - "tpr_fpr_table = PrettyTable(['Methods'] + map(str, x_labels))\n", - "fig = plt.figure()\n", - "for method in methods:\n", - " fpr, tpr, _ = roc_curve(label, scores[method])\n", - " roc_auc = auc(fpr, tpr)\n", - " fpr = np.flipud(fpr)\n", - " tpr = np.flipud(tpr) # select largest tpr at same fpr\n", - " plt.plot(fpr, tpr, color=colours[method], lw=1, label=('[%s (AUC = %0.4f %%)]' % (method.split('-')[-1], roc_auc*100)))\n", - " tpr_fpr_row = []\n", - " tpr_fpr_row.append(method)\n", - " for fpr_iter in np.arange(len(x_labels)):\n", - " _, min_index = min(list(zip(abs(fpr-x_labels[fpr_iter]), range(len(fpr)))))\n", - " tpr_fpr_row.append('%.4f' % tpr[min_index])\n", - " tpr_fpr_table.add_row(tpr_fpr_row)\n", - "plt.xlim([10**-6, 0.1])\n", - "plt.ylim([0.3, 1.0])\n", - "plt.grid(linestyle='--', linewidth=1)\n", - "plt.xticks(x_labels) \n", - "plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True)) \n", - "plt.xscale('log')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('ROC on IJB-B')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "#fig.savefig('IJB-B.pdf')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+----------------------------------------+--------+--------+--------+--------+--------+--------+\n", - "| Methods | 1e-06 | 1e-05 | 0.0001 | 0.001 | 0.01 | 0.1 |\n", - "+----------------------------------------+--------+--------+--------+--------+--------+--------+\n", - "| VGG2-ResNet50-ArcFace-TestMode(N1D1F2) | 0.4044 | 0.8145 | 0.9056 | 0.9497 | 0.9779 | 0.9922 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N1D0F0) | 0.4035 | 0.8038 | 0.8976 | 0.9437 | 0.9755 | 0.9914 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N1D1F1) | 0.3940 | 0.8124 | 0.9028 | 0.9479 | 0.9770 | 0.9919 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N0D0F0) | 0.3893 | 0.8050 | 0.8990 | 0.9448 | 0.9759 | 0.9918 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N1D1F0) | 0.4098 | 0.8123 | 0.9022 | 0.9463 | 0.9766 | 0.9918 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N0D1F0) | 0.3949 | 0.8130 | 0.9036 | 0.9471 | 0.9767 | 0.9919 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N0D1F2) | 0.4011 | 0.8210 | 0.9069 | 0.9500 | 0.9779 | 0.9924 |\n", - "+----------------------------------------+--------+--------+--------+--------+--------+--------+\n" - ] - } - ], - "source": [ - "print(tpr_fpr_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# setting N0D1F2 is the best" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test Setting Conclusions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (1) add is better than concat for the flip test (N1D1F2 v.s. N1D1F1)\n", - "#### (2) detection score contains some faceness information to decrease weights of noise samples within the template (N0D1F0 v.s. N0D0F0)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/evaluation/IJB/IJBC_Evaluation_MS1MV2.ipynb b/evaluation/IJB/IJBC_Evaluation_MS1MV2.ipynb deleted file mode 100644 index e364be8..0000000 --- a/evaluation/IJB/IJBC_Evaluation_MS1MV2.ipynb +++ /dev/null @@ -1,532 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jd4615/miniconda3/envs/insightface/lib/python2.7/site-packages/sklearn/utils/fixes.py:313: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n", - " _nan_object_mask = _nan_object_array != _nan_object_array\n" - ] - } - ], - "source": [ - "import os\n", - "import numpy as np\n", - "import cPickle\n", - "from sklearn.metrics import roc_curve, auc\n", - "import matplotlib.pyplot as plt\n", - "import timeit\n", - "import sklearn\n", - "import cv2\n", - "import sys\n", - "import glob\n", - "sys.path.append('./recognition')\n", - "from embedding import Embedding\n", - "from menpo.visualize import print_progress\n", - "from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap\n", - "from prettytable import PrettyTable\n", - "from pathlib import Path\n", - "import warnings \n", - "warnings.filterwarnings(\"ignore\") " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def read_template_media_list(path):\n", - " ijb_meta = np.loadtxt(path, dtype=str)\n", - " templates = ijb_meta[:,1].astype(np.int)\n", - " medias = ijb_meta[:,2].astype(np.int)\n", - " return templates, medias" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def read_template_pair_list(path):\n", - " pairs = np.loadtxt(path, dtype=str)\n", - " t1 = pairs[:,0].astype(np.int)\n", - " t2 = pairs[:,1].astype(np.int)\n", - " label = pairs[:,2].astype(np.int)\n", - " return t1, t2, label" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def read_image_feature(path):\n", - " with open(path, 'rb') as fid:\n", - " img_feats = cPickle.load(fid)\n", - " return img_feats" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def get_image_feature(img_path, img_list_path, model_path, gpu_id):\n", - " img_list = open(img_list_path)\n", - " embedding = Embedding(model_path, 0, gpu_id)\n", - " files = img_list.readlines()\n", - " img_feats = []\n", - " faceness_scores = []\n", - " for img_index, each_line in enumerate(print_progress(files)):\n", - " name_lmk_score = each_line.strip().split(' ')\n", - " img_name = os.path.join(img_path, name_lmk_score[0])\n", - " img = cv2.imread(img_name)\n", - " lmk = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32)\n", - " lmk = lmk.reshape( (5,2) )\n", - " img_feats.append(embedding.get(img,lmk))\n", - " faceness_scores.append(name_lmk_score[-1])\n", - " img_feats = np.array(img_feats).astype(np.float32)\n", - " faceness_scores = np.array(faceness_scores).astype(np.float32)\n", - " return img_feats, faceness_scores" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def image2template_feature(img_feats = None, templates = None, medias = None):\n", - " # ==========================================================\n", - " # 1. face image feature l2 normalization. img_feats:[number_image x feats_dim]\n", - " # 2. compute media feature.\n", - " # 3. compute template feature.\n", - " # ========================================================== \n", - " unique_templates = np.unique(templates)\n", - " template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))\n", - "\n", - " for count_template, uqt in enumerate(unique_templates):\n", - " (ind_t,) = np.where(templates == uqt)\n", - " face_norm_feats = img_feats[ind_t]\n", - " face_medias = medias[ind_t]\n", - " unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True)\n", - " media_norm_feats = []\n", - " for u,ct in zip(unique_medias, unique_media_counts):\n", - " (ind_m,) = np.where(face_medias == u)\n", - " if ct == 1:\n", - " media_norm_feats += [face_norm_feats[ind_m]]\n", - " else: # image features from the same video will be aggregated into one feature\n", - " media_norm_feats += [np.mean(face_norm_feats[ind_m], 0, keepdims=True)]\n", - " media_norm_feats = np.array(media_norm_feats)\n", - " # media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True))\n", - " template_feats[count_template] = np.sum(media_norm_feats, 0)\n", - " if count_template % 2000 == 0: \n", - " print('Finish Calculating {} template features.'.format(count_template))\n", - " template_norm_feats = template_feats / np.sqrt(np.sum(template_feats ** 2, -1, keepdims=True))\n", - " return template_norm_feats, unique_templates" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def verification(template_norm_feats = None, unique_templates = None, p1 = None, p2 = None):\n", - " # ==========================================================\n", - " # Compute set-to-set Similarity Score.\n", - " # ==========================================================\n", - " template2id = np.zeros((max(unique_templates)+1,1),dtype=int)\n", - " for count_template, uqt in enumerate(unique_templates):\n", - " template2id[uqt] = count_template\n", - " \n", - " score = np.zeros((len(p1),)) # save cosine distance between pairs \n", - "\n", - " total_pairs = np.array(range(len(p1)))\n", - " batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation\n", - " sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)]\n", - " total_sublists = len(sublists)\n", - " for c, s in enumerate(sublists):\n", - " feat1 = template_norm_feats[template2id[p1[s]]]\n", - " feat2 = template_norm_feats[template2id[p2[s]]]\n", - " similarity_score = np.sum(feat1 * feat2, -1)\n", - " score[s] = similarity_score.flatten()\n", - " if c % 10 == 0:\n", - " print('Finish {}/{} pairs.'.format(c, total_sublists))\n", - " return score" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def read_score(path):\n", - " with open(path, 'rb') as fid:\n", - " img_feats = cPickle.load(fid)\n", - " return img_feats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step1: Load Meta Data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 1.73 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load image and template relationships for template feature embedding\n", - "# tid --> template id, mid --> media id \n", - "# format:\n", - "# image_name tid mid\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "templates, medias = read_template_media_list(os.path.join('IJBC/meta', 'ijbc_face_tid_mid.txt'))\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 63.98 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load template pairs for template-to-template verification\n", - "# tid : template id, label : 1/0\n", - "# format:\n", - "# tid_1 tid_2 label\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "p1, p2, label = read_template_pair_list(os.path.join('IJBC/meta', 'ijbc_template_pair_label.txt'))\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 2: Get Image Features" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('loading', './pretrained_models/MS1MV2-ResNet100-Arcface/model', 0)\n", - "[====================] 100% (469375/469375) - done. \n", - "Time: 6806.24 s. \n", - "Feature Shape: (469375 , 1024) .\n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load image features \n", - "# format:\n", - "# img_feats: [image_num x feats_dim] (227630, 512)\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "#img_feats = read_image_feature('./MS1MV2/IJBB_MS1MV2_r100_arcface.pkl')\n", - "img_path = './IJBC/loose_crop'\n", - "img_list_path = './IJBC/meta/ijbc_name_5pts_score.txt'\n", - "model_path = './pretrained_models/MS1MV2-ResNet100-Arcface/model'\n", - "gpu_id = 1\n", - "img_feats, faceness_scores = get_image_feature(img_path, img_list_path, model_path, gpu_id)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))\n", - "print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step3: Get Template Features" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finish Calculating 0 template features.\n", - "Finish Calculating 2000 template features.\n", - "Finish Calculating 4000 template features.\n", - "Finish Calculating 6000 template features.\n", - "Finish Calculating 8000 template features.\n", - "Finish Calculating 10000 template features.\n", - "Finish Calculating 12000 template features.\n", - "Finish Calculating 14000 template features.\n", - "Finish Calculating 16000 template features.\n", - "Finish Calculating 18000 template features.\n", - "Finish Calculating 20000 template features.\n", - "Finish Calculating 22000 template features.\n", - "Time: 7.85 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# compute template features from image features.\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "# ========================================================== \n", - "# Norm feature before aggregation into template feature?\n", - "# Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face).\n", - "# ========================================================== \n", - "# 1. FaceScore （Feature Norm）\n", - "# 2. FaceScore （Detector）\n", - "\n", - "use_norm_score = False # if Ture, TestMode(N1) \n", - "use_detector_score = False # if Ture, TestMode(D1)\n", - "use_flip_test = False # if Ture, TestMode(F1)\n", - "\n", - "if use_flip_test:\n", - " # concat --- F1\n", - " #img_input_feats = img_feats \n", - " # add --- F2\n", - " img_input_feats = img_feats[:,0:img_feats.shape[1]/2] + img_feats[:,img_feats.shape[1]/2:]\n", - "else:\n", - " img_input_feats = img_feats[:,0:img_feats.shape[1]/2]\n", - " \n", - "if use_norm_score:\n", - " img_input_feats = img_input_feats\n", - "else:\n", - " # normalise features to remove norm information\n", - " img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True)) \n", - " \n", - "if use_detector_score:\n", - " img_input_feats = img_input_feats * np.matlib.repmat(faceness_scores[:,np.newaxis], 1, img_input_feats.shape[1])\n", - "else:\n", - " img_input_feats = img_input_feats\n", - "\n", - "template_norm_feats, unique_templates = image2template_feature(img_input_feats, templates, medias)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 4: Get Template Similarity Scores" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finish 0/157 pairs.\n", - "Finish 10/157 pairs.\n", - "Finish 20/157 pairs.\n", - "Finish 30/157 pairs.\n", - "Finish 40/157 pairs.\n", - "Finish 50/157 pairs.\n", - "Finish 60/157 pairs.\n", - "Finish 70/157 pairs.\n", - "Finish 80/157 pairs.\n", - "Finish 90/157 pairs.\n", - "Finish 100/157 pairs.\n", - "Finish 110/157 pairs.\n", - "Finish 120/157 pairs.\n", - "Finish 130/157 pairs.\n", - "Finish 140/157 pairs.\n", - "Finish 150/157 pairs.\n", - "Time: 67.17 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# compute verification scores between template pairs.\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "score = verification(template_norm_feats, unique_templates, p1, p2)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "score_save_name = './IJBC/result/MS1MV2-ResNet100-ArcFace-TestMode(N0D0F0).npy'\n", - "np.save(score_save_name, score)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 5: Get ROC Curves and TPR@FPR Table" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "score_save_path = './IJBC/result'\n", - "files = glob.glob(score_save_path + '/MS1MV2*.npy') \n", - "methods = []\n", - "scores = []\n", - "for file in files:\n", - " methods.append(Path(file).stem)\n", - " scores.append(np.load(file)) \n", - "methods = np.array(methods)\n", - "scores = dict(zip(methods,scores))\n", - "colours = dict(zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\n", - "#x_labels = [1/(10**x) for x in np.linspace(6, 0, 6)]\n", - "x_labels = [10**-6, 10**-5, 10**-4,10**-3, 10**-2, 10**-1]\n", - "tpr_fpr_table = PrettyTable(['Methods'] + map(str, x_labels))\n", - "fig = plt.figure()\n", - "for method in methods:\n", - " fpr, tpr, _ = roc_curve(label, scores[method])\n", - " roc_auc = auc(fpr, tpr)\n", - " fpr = np.flipud(fpr)\n", - " tpr = np.flipud(tpr) # select largest tpr at same fpr\n", - " plt.plot(fpr, tpr, color=colours[method], lw=1, label=('[%s (AUC = %0.4f %%)]' % (method.split('-')[-1], roc_auc*100)))\n", - " tpr_fpr_row = []\n", - " tpr_fpr_row.append(method)\n", - " for fpr_iter in np.arange(len(x_labels)):\n", - " _, min_index = min(list(zip(abs(fpr-x_labels[fpr_iter]), range(len(fpr)))))\n", - " tpr_fpr_row.append('%.4f' % tpr[min_index])\n", - " tpr_fpr_table.add_row(tpr_fpr_row)\n", - "plt.xlim([10**-6, 0.1])\n", - "plt.ylim([0.3, 1.0])\n", - "plt.grid(linestyle='--', linewidth=1)\n", - "plt.xticks(x_labels) \n", - "plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True)) \n", - "plt.xscale('log')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('ROC on IJB-C')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "#fig.savefig('IJB-B.pdf')" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+-------------------------------------------+--------+--------+--------+--------+--------+--------+\n", - "| Methods | 1e-06 | 1e-05 | 0.0001 | 0.001 | 0.01 | 0.1 |\n", - "+-------------------------------------------+--------+--------+--------+--------+--------+--------+\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N1D1F1) | 0.8997 | 0.9434 | 0.9618 | 0.9744 | 0.9832 | 0.9907 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N0D1F2) | 0.8829 | 0.9400 | 0.9607 | 0.9746 | 0.9833 | 0.9910 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N1D1F2) | 0.8985 | 0.9447 | 0.9628 | 0.9753 | 0.9836 | 0.9908 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N1D0F0) | 0.8906 | 0.9394 | 0.9603 | 0.9731 | 0.9829 | 0.9904 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N0D0F0) | 0.8625 | 0.9315 | 0.9565 | 0.9720 | 0.9818 | 0.9901 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N1D1F0) | 0.8943 | 0.9413 | 0.9610 | 0.9735 | 0.9829 | 0.9905 |\n", - "| MS1MV2-ResNet100-ArcFace-TestMode(N0D1F0) | 0.8795 | 0.9387 | 0.9591 | 0.9731 | 0.9824 | 0.9904 |\n", - "+-------------------------------------------+--------+--------+--------+--------+--------+--------+\n" - ] - } - ], - "source": [ - "print(tpr_fpr_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# setting N1D1F2 is the best" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/evaluation/IJB/IJBC_Evaluation_VGG2.ipynb b/evaluation/IJB/IJBC_Evaluation_VGG2.ipynb deleted file mode 100644 index 8f32634..0000000 --- a/evaluation/IJB/IJBC_Evaluation_VGG2.ipynb +++ /dev/null @@ -1,532 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jd4615/miniconda3/envs/insightface/lib/python2.7/site-packages/sklearn/utils/fixes.py:313: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n", - " _nan_object_mask = _nan_object_array != _nan_object_array\n" - ] - } - ], - "source": [ - "import os\n", - "import numpy as np\n", - "import cPickle\n", - "from sklearn.metrics import roc_curve, auc\n", - "import matplotlib.pyplot as plt\n", - "import timeit\n", - "import sklearn\n", - "import cv2\n", - "import sys\n", - "import glob\n", - "sys.path.append('./recognition')\n", - "from embedding import Embedding\n", - "from menpo.visualize import print_progress\n", - "from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap\n", - "from prettytable import PrettyTable\n", - "from pathlib import Path\n", - "import warnings \n", - "warnings.filterwarnings(\"ignore\") " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def read_template_media_list(path):\n", - " ijb_meta = np.loadtxt(path, dtype=str)\n", - " templates = ijb_meta[:,1].astype(np.int)\n", - " medias = ijb_meta[:,2].astype(np.int)\n", - " return templates, medias" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def read_template_pair_list(path):\n", - " pairs = np.loadtxt(path, dtype=str)\n", - " t1 = pairs[:,0].astype(np.int)\n", - " t2 = pairs[:,1].astype(np.int)\n", - " label = pairs[:,2].astype(np.int)\n", - " return t1, t2, label" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def read_image_feature(path):\n", - " with open(path, 'rb') as fid:\n", - " img_feats = cPickle.load(fid)\n", - " return img_feats" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def get_image_feature(img_path, img_list_path, model_path, gpu_id):\n", - " img_list = open(img_list_path)\n", - " embedding = Embedding(model_path, 0, gpu_id)\n", - " files = img_list.readlines()\n", - " img_feats = []\n", - " faceness_scores = []\n", - " for img_index, each_line in enumerate(print_progress(files)):\n", - " name_lmk_score = each_line.strip().split(' ')\n", - " img_name = os.path.join(img_path, name_lmk_score[0])\n", - " img = cv2.imread(img_name)\n", - " lmk = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32)\n", - " lmk = lmk.reshape( (5,2) )\n", - " img_feats.append(embedding.get(img,lmk))\n", - " faceness_scores.append(name_lmk_score[-1])\n", - " img_feats = np.array(img_feats).astype(np.float32)\n", - " faceness_scores = np.array(faceness_scores).astype(np.float32)\n", - " return img_feats, faceness_scores" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def image2template_feature(img_feats = None, templates = None, medias = None):\n", - " # ==========================================================\n", - " # 1. face image feature l2 normalization. img_feats:[number_image x feats_dim]\n", - " # 2. compute media feature.\n", - " # 3. compute template feature.\n", - " # ========================================================== \n", - " unique_templates = np.unique(templates)\n", - " template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))\n", - "\n", - " for count_template, uqt in enumerate(unique_templates):\n", - " (ind_t,) = np.where(templates == uqt)\n", - " face_norm_feats = img_feats[ind_t]\n", - " face_medias = medias[ind_t]\n", - " unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True)\n", - " media_norm_feats = []\n", - " for u,ct in zip(unique_medias, unique_media_counts):\n", - " (ind_m,) = np.where(face_medias == u)\n", - " if ct == 1:\n", - " media_norm_feats += [face_norm_feats[ind_m]]\n", - " else: # image features from the same video will be aggregated into one feature\n", - " media_norm_feats += [np.mean(face_norm_feats[ind_m], 0, keepdims=True)]\n", - " media_norm_feats = np.array(media_norm_feats)\n", - " # media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True))\n", - " template_feats[count_template] = np.sum(media_norm_feats, 0)\n", - " if count_template % 2000 == 0: \n", - " print('Finish Calculating {} template features.'.format(count_template))\n", - " template_norm_feats = template_feats / np.sqrt(np.sum(template_feats ** 2, -1, keepdims=True))\n", - " return template_norm_feats, unique_templates" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def verification(template_norm_feats = None, unique_templates = None, p1 = None, p2 = None):\n", - " # ==========================================================\n", - " # Compute set-to-set Similarity Score.\n", - " # ==========================================================\n", - " template2id = np.zeros((max(unique_templates)+1,1),dtype=int)\n", - " for count_template, uqt in enumerate(unique_templates):\n", - " template2id[uqt] = count_template\n", - " \n", - " score = np.zeros((len(p1),)) # save cosine distance between pairs \n", - "\n", - " total_pairs = np.array(range(len(p1)))\n", - " batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation\n", - " sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)]\n", - " total_sublists = len(sublists)\n", - " for c, s in enumerate(sublists):\n", - " feat1 = template_norm_feats[template2id[p1[s]]]\n", - " feat2 = template_norm_feats[template2id[p2[s]]]\n", - " similarity_score = np.sum(feat1 * feat2, -1)\n", - " score[s] = similarity_score.flatten()\n", - " if c % 10 == 0:\n", - " print('Finish {}/{} pairs.'.format(c, total_sublists))\n", - " return score" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def read_score(path):\n", - " with open(path, 'rb') as fid:\n", - " img_feats = cPickle.load(fid)\n", - " return img_feats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step1: Load Meta Data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 1.76 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load image and template relationships for template feature embedding\n", - "# tid --> template id, mid --> media id \n", - "# format:\n", - "# image_name tid mid\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "templates, medias = read_template_media_list(os.path.join('IJBC/meta', 'ijbc_face_tid_mid.txt'))\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 63.31 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load template pairs for template-to-template verification\n", - "# tid : template id, label : 1/0\n", - "# format:\n", - "# tid_1 tid_2 label\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "p1, p2, label = read_template_pair_list(os.path.join('IJBC/meta', 'ijbc_template_pair_label.txt'))\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 2: Get Image Features" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('loading', './pretrained_models/VGG2-ResNet50-Arcface/model', 0)\n", - "[====================] 100% (469375/469375) - done. \n", - "Time: 5087.25 s. \n", - "Feature Shape: (469375 , 1024) .\n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# load image features \n", - "# format:\n", - "# img_feats: [image_num x feats_dim] (227630, 512)\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "#img_feats = read_image_feature('./MS1MV2/IJBB_MS1MV2_r100_arcface.pkl')\n", - "img_path = './IJBC/loose_crop'\n", - "img_list_path = './IJBC/meta/ijbc_name_5pts_score.txt'\n", - "model_path = './pretrained_models/VGG2-ResNet50-Arcface/model'\n", - "gpu_id = 0\n", - "img_feats, faceness_scores = get_image_feature(img_path, img_list_path, model_path, gpu_id)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))\n", - "print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step3: Get Template Features" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finish Calculating 0 template features.\n", - "Finish Calculating 2000 template features.\n", - "Finish Calculating 4000 template features.\n", - "Finish Calculating 6000 template features.\n", - "Finish Calculating 8000 template features.\n", - "Finish Calculating 10000 template features.\n", - "Finish Calculating 12000 template features.\n", - "Finish Calculating 14000 template features.\n", - "Finish Calculating 16000 template features.\n", - "Finish Calculating 18000 template features.\n", - "Finish Calculating 20000 template features.\n", - "Finish Calculating 22000 template features.\n", - "Time: 9.98 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# compute template features from image features.\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "# ========================================================== \n", - "# Norm feature before aggregation into template feature?\n", - "# Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face).\n", - "# ========================================================== \n", - "# 1. FaceScore （Feature Norm）\n", - "# 2. FaceScore （Detector）\n", - "\n", - "use_norm_score = True # if True, TestMode(N1) \n", - "use_detector_score = True # if True, TestMode(D1)\n", - "use_flip_test = True # if True, TestMode(F1)\n", - "\n", - "if use_flip_test:\n", - " # concat --- F1\n", - " img_input_feats = img_feats \n", - " # add --- F2\n", - " # img_input_feats = img_feats[:,0:img_feats.shape[1]/2] + img_feats[:,img_feats.shape[1]/2:]\n", - "else:\n", - " img_input_feats = img_feats[:,0:img_feats.shape[1]/2]\n", - " \n", - "if use_norm_score:\n", - " img_input_feats = img_input_feats\n", - "else:\n", - " # normalise features to remove norm information\n", - " img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True)) \n", - " \n", - "if use_detector_score:\n", - " img_input_feats = img_input_feats * np.matlib.repmat(faceness_scores[:,np.newaxis], 1, img_input_feats.shape[1])\n", - "else:\n", - " img_input_feats = img_input_feats\n", - "\n", - "template_norm_feats, unique_templates = image2template_feature(img_input_feats, templates, medias)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 4: Get Template Similarity Scores" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finish 0/157 pairs.\n", - "Finish 10/157 pairs.\n", - "Finish 20/157 pairs.\n", - "Finish 30/157 pairs.\n", - "Finish 40/157 pairs.\n", - "Finish 50/157 pairs.\n", - "Finish 60/157 pairs.\n", - "Finish 70/157 pairs.\n", - "Finish 80/157 pairs.\n", - "Finish 90/157 pairs.\n", - "Finish 100/157 pairs.\n", - "Finish 110/157 pairs.\n", - "Finish 120/157 pairs.\n", - "Finish 130/157 pairs.\n", - "Finish 140/157 pairs.\n", - "Finish 150/157 pairs.\n", - "Time: 146.08 s. \n" - ] - } - ], - "source": [ - "# =============================================================\n", - "# compute verification scores between template pairs.\n", - "# =============================================================\n", - "start = timeit.default_timer()\n", - "score = verification(template_norm_feats, unique_templates, p1, p2)\n", - "stop = timeit.default_timer()\n", - "print('Time: %.2f s. ' % (stop - start))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "score_save_name = './IJBC/result/VGG2-ResNet50-ArcFace-TestMode(N1D1F1).npy'\n", - "np.save(score_save_name, score)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 5: Get ROC Curves and TPR@FPR Table" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "score_save_path = './IJBC/result'\n", - "files = glob.glob(score_save_path + '/VGG2*.npy') \n", - "methods = []\n", - "scores = []\n", - "for file in files:\n", - " methods.append(Path(file).stem)\n", - " scores.append(np.load(file)) \n", - "methods = np.array(methods)\n", - "scores = dict(zip(methods,scores))\n", - "colours = dict(zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\n", - "#x_labels = [1/(10**x) for x in np.linspace(6, 0, 6)]\n", - "x_labels = [10**-6, 10**-5, 10**-4,10**-3, 10**-2, 10**-1]\n", - "tpr_fpr_table = PrettyTable(['Methods'] + map(str, x_labels))\n", - "fig = plt.figure()\n", - "for method in methods:\n", - " fpr, tpr, _ = roc_curve(label, scores[method])\n", - " roc_auc = auc(fpr, tpr)\n", - " fpr = np.flipud(fpr)\n", - " tpr = np.flipud(tpr) # select largest tpr at same fpr\n", - " plt.plot(fpr, tpr, color=colours[method], lw=1, label=('[%s (AUC = %0.4f %%)]' % (method.split('-')[-1], roc_auc*100)))\n", - " tpr_fpr_row = []\n", - " tpr_fpr_row.append(method)\n", - " for fpr_iter in np.arange(len(x_labels)):\n", - " _, min_index = min(list(zip(abs(fpr-x_labels[fpr_iter]), range(len(fpr)))))\n", - " tpr_fpr_row.append('%.4f' % tpr[min_index])\n", - " tpr_fpr_table.add_row(tpr_fpr_row)\n", - "plt.xlim([10**-6, 0.1])\n", - "plt.ylim([0.3, 1.0])\n", - "plt.grid(linestyle='--', linewidth=1)\n", - "plt.xticks(x_labels) \n", - "plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True)) \n", - "plt.xscale('log')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('ROC on IJB-C')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "#fig.savefig('IJB-C.pdf')" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+----------------------------------------+--------+--------+--------+--------+--------+--------+\n", - "| Methods | 1e-06 | 1e-05 | 0.0001 | 0.001 | 0.01 | 0.1 |\n", - "+----------------------------------------+--------+--------+--------+--------+--------+--------+\n", - "| VGG2-ResNet50-ArcFace-TestMode(N1D1F2) | 0.7444 | 0.8751 | 0.9279 | 0.9635 | 0.9841 | 0.9939 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N1D0F0) | 0.6863 | 0.8554 | 0.9199 | 0.9586 | 0.9820 | 0.9934 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N1D1F1) | 0.7586 | 0.8717 | 0.9253 | 0.9620 | 0.9836 | 0.9937 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N0D0F0) | 0.7081 | 0.8612 | 0.9214 | 0.9595 | 0.9823 | 0.9934 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N1D1F0) | 0.7470 | 0.8675 | 0.9245 | 0.9610 | 0.9830 | 0.9935 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N0D1F0) | 0.7637 | 0.8733 | 0.9258 | 0.9617 | 0.9831 | 0.9936 |\n", - "| VGG2-ResNet50-ArcFace-TestMode(N0D1F2) | 0.7668 | 0.8796 | 0.9289 | 0.9636 | 0.9840 | 0.9941 |\n", - "+----------------------------------------+--------+--------+--------+--------+--------+--------+\n" - ] - } - ], - "source": [ - "print(tpr_fpr_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# setting N0D1F2 is the best" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/evaluation/IJB/readme.txt b/evaluation/IJB/readme.txt deleted file mode 100644 index f6027e1..0000000 --- a/evaluation/IJB/readme.txt +++ /dev/null @@ -1,19 +0,0 @@ -To reproduce the figures and tables in the notebook, please download everything (model, code, data and meta info) from here: -[Dropbox] https://www.dropbox.com/s/33a6haw7v79e5qe/IJB_release.tar?dl=0 -or -[Baidu Cloud] https://pan.baidu.com/s/1oer0p4_mcOrs4cfdeWfbFg - -Please apply for the IJB-B and IJB-C by yourself and strictly follow their distribution licenses. - -Aknowledgement -Great thanks for Weidi Xie's instruction [2,3,4,5] to evaluate ArcFace [1] on IJB-B[6] and IJB-C[7]. - -[1] Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition[J]. arXiv:1801.07698, 2018. -[2] https://github.com/ox-vgg/vgg_face2. -[3] Qiong Cao, Li Shen, Weidi Xie, Omkar M Parkhi, Andrew Zisserman. VGGFace2: A dataset for recognising faces across pose and age. FG, 2018. -[4] Weidi Xie, Andrew Zisserman. Multicolumn Networks for Face Recognition. BMVC 2018. -[5] Weidi Xie, Li Shen, Andrew Zisserman. Comparator Networks. ECCV, 2018. -[6] Whitelam, Cameron, Emma Taborsky, Austin Blanton, Brianna Maze, Jocelyn C. Adams, Tim Miller, Nathan D. Kalka et al. IARPA Janus Benchmark-B Face Dataset. CVPR Workshops, 2017. -[7] Maze, Brianna, Jocelyn Adams, James A. Duncan, Nathan Kalka, Tim Miller, Charles Otto, Anil K. Jain et al. IARPA Janus Benchmark–C: Face Dataset and Protocol. ICB, 2018. - - diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000..a414efb --- /dev/null +++ b/examples/README.md @@ -0,0 +1,4 @@ +InsightFace Example +--- + +Before running the examples, please install insightface package via `pip install -U insightface` diff --git a/examples/demo_analysis.py b/examples/demo_analysis.py new file mode 100644 index 0000000..28045da --- /dev/null +++ b/examples/demo_analysis.py @@ -0,0 +1,34 @@ +import argparse +import cv2 +import sys +import numpy as np +import insightface +from insightface.app import FaceAnalysis +from insightface.data import get_image as ins_get_image + +assert insightface.__version__>='0.3' + +parser = argparse.ArgumentParser(description='insightface app test') +# general +parser.add_argument('--ctx', default=0, type=int, help='ctx id, <0 means using cpu') +parser.add_argument('--det-size', default=640, type=int, help='detection size') +args = parser.parse_args() + +app = FaceAnalysis() +app.prepare(ctx_id=args.ctx, det_size=(args.det_size,args.det_size)) + +img = ins_get_image('t1') +faces = app.get(img) +assert len(faces)==6 +rimg = app.draw_on(img, faces) +cv2.imwrite("./t1_output.jpg", rimg) + +# then print all-to-all face similarity +feats = [] +for face in faces: + feats.append(face.normed_embedding) +feats = np.array(feats, dtype=np.float32) +sims = np.dot(feats, feats.T) +print(sims) + + diff --git a/examples/mask_renderer.py b/examples/mask_renderer.py new file mode 100644 index 0000000..5b04742 --- /dev/null +++ b/examples/mask_renderer.py @@ -0,0 +1,22 @@ +import os, sys, datetime +import numpy as np +import os.path as osp +import cv2 +import insightface +from insightface.app import MaskRenderer +from insightface.data import get_image as ins_get_image + + +if __name__ == "__main__": + #make sure that you have download correct insightface model pack. + #make sure that BFM.mat and BFM_UV.mat have been generated + tool = MaskRenderer() + tool.prepare(ctx_id=0, det_size=(128,128)) + image = ins_get_image('Tom_Hanks_54745') + mask_image = "mask_blue" + params = tool.build_params(image) + mask_out = tool.render_mask(image, mask_image, params) + + cv2.imwrite('output_mask.jpg', mask_out) + + diff --git a/deploy/mxnet_to_ort.py b/examples/mxnet_to_onnx.py similarity index 56% rename from deploy/mxnet_to_ort.py rename to examples/mxnet_to_onnx.py index 16f9f2f..5a9c73a 100644 --- a/deploy/mxnet_to_ort.py +++ b/examples/mxnet_to_onnx.py @@ -2,10 +2,13 @@ import sys import os import argparse import onnx +import json import mxnet as mx from onnx import helper from onnx import TensorProto from onnx import numpy_helper +import onnxruntime +import cv2 print('mxnet version:', mx.__version__) print('onnx version:', onnx.__version__) @@ -23,12 +26,15 @@ def create_map(graph_member_list): return member_map -parser = argparse.ArgumentParser(description='convert arcface models to onnx') +parser = argparse.ArgumentParser(description='convert mxnet model to onnx') # general parser.add_argument('params', default='./r100a/model-0000.params', help='mxnet params to load.') parser.add_argument('output', default='./r100a.onnx', help='path to write onnx model.') parser.add_argument('--eps', default=1.0e-8, type=float, help='eps for weights.') parser.add_argument('--input-shape', default='3,112,112', help='input shape.') +parser.add_argument('--check', action='store_true') +parser.add_argument('--input-mean', default=0.0, type=float, help='input mean for checking.') +parser.add_argument('--input-std', default=1.0, type=float, help='input std for checking.') args = parser.parse_args() input_shape = (1,) + tuple( [int(x) for x in args.input_shape.split(',')] ) @@ -41,6 +47,29 @@ assert os.path.exists(sym_file) assert os.path.exists(params_file) sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) + +nodes = json.loads(sym.tojson())['nodes'] +bn_fixgamma_list = [] +for nodeid, node in enumerate(nodes): + if node['op'] == 'BatchNorm': + attr = node['attrs'] + fix_gamma = False + if attr is not None and 'fix_gamma' in attr: + if str(attr['fix_gamma']).lower()=='true': + fix_gamma = True + if fix_gamma: + bn_fixgamma_list.append(node['name']) + #print(node, fix_gamma) + +print('fixgamma list:', bn_fixgamma_list) +layer = None +#layer = 'conv_2_dw_relu' #for debug + +if layer is not None: + all_layers = sym.get_internals() + sym = all_layers[layer + '_output'] + + eps = args.eps arg = {} @@ -50,24 +79,27 @@ ac = 0 for k in arg_params: v = arg_params[k] nv = v.asnumpy() - #print(k, nv.dtype) nv = nv.astype(np.float32) + #print(k, nv.shape) + if k.endswith('_gamma'): + bnname = k[:-6] + if bnname in bn_fixgamma_list: + nv[:] = 1.0 ac += nv.size invalid += np.count_nonzero(np.abs(nv)= len(self.seq): - raise StopIteration - idx = self.seq[self.cur] - self.cur += 1 - s = self.imgrec.read_idx(idx) - header, img = recordio.unpack(s) - label = header.label - return label, img, None, None - - def brightness_aug(self, src, x): - alpha = 1.0 + random.uniform(-x, x) - src *= alpha - return src - - def contrast_aug(self, src, x): - alpha = 1.0 + random.uniform(-x, x) - coef = nd.array([[[0.299, 0.587, 0.114]]]) - gray = src * coef - gray = (3.0 * (1.0 - alpha) / gray.size) * nd.sum(gray) - src *= alpha - src += gray - return src - - def saturation_aug(self, src, x): - alpha = 1.0 + random.uniform(-x, x) - coef = nd.array([[[0.299, 0.587, 0.114]]]) - gray = src * coef - gray = nd.sum(gray, axis=2, keepdims=True) - gray *= (1.0 - alpha) - src *= alpha - src += gray - return src - - def color_aug(self, img, x): - #augs = [self.brightness_aug, self.contrast_aug, self.saturation_aug] - #random.shuffle(augs) - #for aug in augs: - # #print(img.shape) - # img = aug(img, x) - # #print(img.shape) - #return img - return self.CJA(img) - - def mirror_aug(self, img): - _rd = random.randint(0, 1) - if _rd == 1: - for c in range(img.shape[2]): - img[:, :, c] = np.fliplr(img[:, :, c]) - return img - - def compress_aug(self, img): - buf = BytesIO() - img = Image.fromarray(img.asnumpy(), 'RGB') - q = random.randint(2, 20) - img.save(buf, format='JPEG', quality=q) - buf = buf.getvalue() - img = Image.open(BytesIO(buf)) - return nd.array(np.asarray(img, 'float32')) - - def next(self): - if not self.is_init: - self.reset() - self.is_init = True - """Returns the next batch of data.""" - #print('in next', self.cur, self.labelcur) - self.nbatch += 1 - batch_size = self.batch_size - c, h, w = self.data_shape - batch_data = nd.empty((batch_size, c, h, w)) - if self.provide_label is not None: - batch_label = nd.empty(self.provide_label[0][1]) - i = 0 - try: - while i < batch_size: - #print('XXXX', i) - label, s, bbox, landmark = self.next_sample() - gender = int(label[0]) - age = int(label[1]) - assert age >= 0 - #assert gender==0 or gender==1 - plabel = np.zeros(shape=(101, ), dtype=np.float32) - plabel[0] = gender - if age == 0: - age = 1 - if age > 100: - age = 100 - plabel[1:age + 1] = 1 - label = plabel - _data = self.imdecode(s) - if _data.shape[0] != self.data_shape[1]: - _data = mx.image.resize_short(_data, self.data_shape[1]) - if self.rand_mirror: - _rd = random.randint(0, 1) - if _rd == 1: - _data = mx.ndarray.flip(data=_data, axis=1) - if self.color_jittering > 0: - if self.color_jittering > 1: - _rd = random.randint(0, 1) - if _rd == 1: - _data = self.compress_aug(_data) - #print('do color aug') - _data = _data.astype('float32', copy=False) - #print(_data.__class__) - _data = self.color_aug(_data, 0.125) - if self.nd_mean is not None: - _data = _data.astype('float32', copy=False) - _data -= self.nd_mean - _data *= 0.0078125 - if self.cutoff > 0: - _rd = random.randint(0, 1) - if _rd == 1: - #print('do cutoff aug', self.cutoff) - centerh = random.randint(0, _data.shape[0] - 1) - centerw = random.randint(0, _data.shape[1] - 1) - half = self.cutoff // 2 - starth = max(0, centerh - half) - endh = min(_data.shape[0], centerh + half) - startw = max(0, centerw - half) - endw = min(_data.shape[1], centerw + half) - #print(starth, endh, startw, endw, _data.shape) - _data[starth:endh, startw:endw, :] = 128 - data = [_data] - for datum in data: - assert i < batch_size, 'Batch size must be multiples of augmenter output length' - #print(datum.shape) - batch_data[i][:] = self.postprocess_data(datum) - batch_label[i][:] = label - i += 1 - except StopIteration: - if i < batch_size: - raise StopIteration - - return io.DataBatch([batch_data], [batch_label], batch_size - i) - - def check_data_shape(self, data_shape): - """Checks if the input data shape is valid""" - if not len(data_shape) == 3: - raise ValueError( - 'data_shape should have length 3, with dimensions CxHxW') - if not data_shape[0] == 3: - raise ValueError( - 'This iterator expects inputs to have 3 channels.') - - def check_valid_image(self, data): - """Checks if the input data is valid""" - if len(data[0].shape) == 0: - raise RuntimeError('Data shape is wrong') - - def imdecode(self, s): - """Decodes a string or byte string to an NDArray. - See mx.img.imdecode for more details.""" - img = mx.image.imdecode(s) #mx.ndarray - return img - - def read_image(self, fname): - """Reads an input image `fname` and returns the decoded raw bytes. - - Example usage: - ---------- - >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes. - """ - with open(os.path.join(self.path_root, fname), 'rb') as fin: - img = fin.read() - return img - - def augmentation_transform(self, data): - """Transforms input data with specified augmentation.""" - for aug in self.auglist: - data = [ret for src in data for ret in aug(src)] - return data - - def postprocess_data(self, datum): - """Final postprocessing step before image is loaded into the batch.""" - return nd.transpose(datum, axes=(2, 0, 1)) - - -class FaceImageIterList(io.DataIter): - def __init__(self, iter_list): - assert len(iter_list) > 0 - self.provide_data = iter_list[0].provide_data - self.provide_label = iter_list[0].provide_label - self.iter_list = iter_list - self.cur_iter = None - - def reset(self): - self.cur_iter.reset() - - def next(self): - self.cur_iter = random.choice(self.iter_list) - while True: - try: - ret = self.cur_iter.next() - except StopIteration: - self.cur_iter.reset() - continue - return ret diff --git a/gender-age/face_model.py b/gender-age/face_model.py deleted file mode 100644 index 1ce54bb..0000000 --- a/gender-age/face_model.py +++ /dev/null @@ -1,109 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from scipy import misc -import sys -import os -import argparse -#import tensorflow as tf -import numpy as np -import mxnet as mx -import random -import cv2 -import sklearn -from sklearn.decomposition import PCA -from time import sleep -from easydict import EasyDict as edict -from mtcnn_detector import MtcnnDetector -sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src', 'common')) -import face_image -import face_preprocess - - -def do_flip(data): - for idx in range(data.shape[0]): - data[idx, :, :] = np.fliplr(data[idx, :, :]) - - -def get_model(ctx, image_size, model_str, layer): - _vec = model_str.split(',') - assert len(_vec) == 2 - prefix = _vec[0] - epoch = int(_vec[1]) - print('loading', prefix, epoch) - sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) - all_layers = sym.get_internals() - sym = all_layers[layer + '_output'] - model = mx.mod.Module(symbol=sym, context=ctx, label_names=None) - #model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))]) - model.bind(data_shapes=[('data', (1, 3, image_size[0], image_size[1]))]) - model.set_params(arg_params, aux_params) - return model - - -class FaceModel: - def __init__(self, args): - self.args = args - if args.gpu >= 0: - ctx = mx.gpu(args.gpu) - else: - ctx = mx.cpu() - _vec = args.image_size.split(',') - assert len(_vec) == 2 - image_size = (int(_vec[0]), int(_vec[1])) - self.model = None - if len(args.model) > 0: - self.model = get_model(ctx, image_size, args.model, 'fc1') - - self.det_minsize = 50 - self.det_threshold = [0.6, 0.7, 0.8] - #self.det_factor = 0.9 - self.image_size = image_size - mtcnn_path = os.path.join(os.path.dirname(__file__), 'mtcnn-model') - if args.det == 0: - detector = MtcnnDetector(model_folder=mtcnn_path, - ctx=ctx, - num_worker=1, - accurate_landmark=True, - threshold=self.det_threshold) - else: - detector = MtcnnDetector(model_folder=mtcnn_path, - ctx=ctx, - num_worker=1, - accurate_landmark=True, - threshold=[0.0, 0.0, 0.2]) - self.detector = detector - - def get_input(self, face_img): - ret = self.detector.detect_face(face_img, det_type=self.args.det) - if ret is None: - return None - bbox, points = ret - if bbox.shape[0] == 0: - return None - bbox = bbox[0, 0:4] - points = points[0, :].reshape((2, 5)).T - #print(bbox) - #print(points) - nimg = face_preprocess.preprocess(face_img, - bbox, - points, - image_size='112,112') - nimg = cv2.cvtColor(nimg, cv2.COLOR_BGR2RGB) - aligned = np.transpose(nimg, (2, 0, 1)) - input_blob = np.expand_dims(aligned, axis=0) - data = mx.nd.array(input_blob) - db = mx.io.DataBatch(data=(data, )) - return db - - def get_ga(self, data): - self.model.forward(data, is_train=False) - ret = self.model.get_outputs()[0].asnumpy() - g = ret[:, 0:2].flatten() - gender = np.argmax(g) - a = ret[:, 2:202].reshape((100, 2)) - a = np.argmax(a, axis=1) - age = int(sum(a)) - - return gender, age diff --git a/gender-age/helper.py b/gender-age/helper.py deleted file mode 100644 index 38f2c9c..0000000 --- a/gender-age/helper.py +++ /dev/null @@ -1,172 +0,0 @@ -# coding: utf-8 -# YuanYang -import math -import cv2 -import numpy as np - - -def nms(boxes, overlap_threshold, mode='Union'): - """ - non max suppression - - Parameters: - ---------- - box: numpy array n x 5 - input bbox array - overlap_threshold: float number - threshold of overlap - mode: float number - how to compute overlap ratio, 'Union' or 'Min' - Returns: - ------- - index array of the selected bbox - """ - # if there are no boxes, return an empty list - if len(boxes) == 0: - return [] - - # if the bounding boxes integers, convert them to floats - if boxes.dtype.kind == "i": - boxes = boxes.astype("float") - - # initialize the list of picked indexes - pick = [] - - # grab the coordinates of the bounding boxes - x1, y1, x2, y2, score = [boxes[:, i] for i in range(5)] - - area = (x2 - x1 + 1) * (y2 - y1 + 1) - idxs = np.argsort(score) - - # keep looping while some indexes still remain in the indexes list - while len(idxs) > 0: - # grab the last index in the indexes list and add the index value to the list of picked indexes - last = len(idxs) - 1 - i = idxs[last] - pick.append(i) - - xx1 = np.maximum(x1[i], x1[idxs[:last]]) - yy1 = np.maximum(y1[i], y1[idxs[:last]]) - xx2 = np.minimum(x2[i], x2[idxs[:last]]) - yy2 = np.minimum(y2[i], y2[idxs[:last]]) - - # compute the width and height of the bounding box - w = np.maximum(0, xx2 - xx1 + 1) - h = np.maximum(0, yy2 - yy1 + 1) - - inter = w * h - if mode == 'Min': - overlap = inter / np.minimum(area[i], area[idxs[:last]]) - else: - overlap = inter / (area[i] + area[idxs[:last]] - inter) - - # delete all indexes from the index list that have - idxs = np.delete( - idxs, - np.concatenate(([last], np.where(overlap > overlap_threshold)[0]))) - - return pick - - -def adjust_input(in_data): - """ - adjust the input from (h, w, c) to ( 1, c, h, w) for network input - - Parameters: - ---------- - in_data: numpy array of shape (h, w, c) - input data - Returns: - ------- - out_data: numpy array of shape (1, c, h, w) - reshaped array - """ - if in_data.dtype is not np.dtype('float32'): - out_data = in_data.astype(np.float32) - else: - out_data = in_data - - out_data = out_data.transpose((2, 0, 1)) - out_data = np.expand_dims(out_data, 0) - out_data = (out_data - 127.5) * 0.0078125 - return out_data - - -def generate_bbox(map, reg, scale, threshold): - """ - generate bbox from feature map - Parameters: - ---------- - map: numpy array , n x m x 1 - detect score for each position - reg: numpy array , n x m x 4 - bbox - scale: float number - scale of this detection - threshold: float number - detect threshold - Returns: - ------- - bbox array - """ - stride = 2 - cellsize = 12 - - t_index = np.where(map > threshold) - - # find nothing - if t_index[0].size == 0: - return np.array([]) - - dx1, dy1, dx2, dy2 = [reg[0, i, t_index[0], t_index[1]] for i in range(4)] - - reg = np.array([dx1, dy1, dx2, dy2]) - score = map[t_index[0], t_index[1]] - boundingbox = np.vstack([ - np.round((stride * t_index[1] + 1) / scale), - np.round((stride * t_index[0] + 1) / scale), - np.round((stride * t_index[1] + 1 + cellsize) / scale), - np.round((stride * t_index[0] + 1 + cellsize) / scale), score, reg - ]) - - return boundingbox.T - - -def detect_first_stage(img, net, scale, threshold): - """ - run PNet for first stage - - Parameters: - ---------- - img: numpy array, bgr order - input image - scale: float number - how much should the input image scale - net: PNet - worker - Returns: - ------- - total_boxes : bboxes - """ - height, width, _ = img.shape - hs = int(math.ceil(height * scale)) - ws = int(math.ceil(width * scale)) - - im_data = cv2.resize(img, (ws, hs)) - - # adjust for the network input - input_buf = adjust_input(im_data) - output = net.predict(input_buf) - boxes = generate_bbox(output[1][0, 1, :, :], output[0], scale, threshold) - - if boxes.size == 0: - return None - - # nms - pick = nms(boxes[:, 0:5], 0.5, mode='Union') - boxes = boxes[pick] - return boxes - - -def detect_first_stage_warpper(args): - return detect_first_stage(*args) diff --git a/gender-age/model/model-0000.params b/gender-age/model/model-0000.params deleted file mode 100644 index 35118ad..0000000 Binary files a/gender-age/model/model-0000.params and /dev/null differ diff --git a/gender-age/model/model-symbol.json b/gender-age/model/model-symbol.json deleted file mode 100644 index cea9abc..0000000 --- a/gender-age/model/model-symbol.json +++ /dev/null @@ -1,2399 +0,0 @@ -{ - 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bottom: "data" - top: "conv1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 10 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "PReLU1" - type: "PReLU" - bottom: "conv1" - top: "conv1" -} -layer { - name: "pool1" - type: "Pooling" - bottom: "conv1" - top: "pool1" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} - -layer { - name: "conv2" - type: "Convolution" - bottom: "pool1" - top: "conv2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 0 - } - convolution_param { - num_output: 16 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "PReLU2" - type: "PReLU" - bottom: "conv2" - top: "conv2" -} - -layer { - name: "conv3" - type: "Convolution" - bottom: "conv2" - top: "conv3" - param { - 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type: "Softmax" - bottom: "conv4-1" - top: "prob1" -} diff --git a/gender-age/mtcnn-model/det2-0001.params b/gender-age/mtcnn-model/det2-0001.params deleted file mode 100644 index a14a478..0000000 Binary files a/gender-age/mtcnn-model/det2-0001.params and /dev/null differ diff --git a/gender-age/mtcnn-model/det2-symbol.json b/gender-age/mtcnn-model/det2-symbol.json deleted file mode 100644 index a13246a..0000000 --- a/gender-age/mtcnn-model/det2-symbol.json +++ /dev/null @@ -1,324 +0,0 @@ -{ - "nodes": [ - { - "op": "null", - "param": {}, - "name": "data", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv1_weight", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "null", - "param": {}, - "name": "conv1_bias", - "inputs": [], - "backward_source_id": -1 - }, - { - "op": "Convolution", - "param": { - "cudnn_off": "False", - "cudnn_tune": "off", - "dilate": "(1,1)", - "kernel": "(3,3)", - "no_bias": "False", - "num_filter": "28", - 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- -########################## -###################### -layer { - name: "conv1" - type: "Convolution" - bottom: "data" - top: "conv1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu1" - type: "PReLU" - bottom: "conv1" - top: "conv1" - propagate_down: true -} -layer { - name: "pool1" - type: "Pooling" - bottom: "conv1" - top: "pool1" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2" - type: "Convolution" - bottom: "pool1" - top: "conv2" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu2" - type: "PReLU" - bottom: "conv2" - top: "conv2" - propagate_down: true -} -layer { - name: "pool2" - type: "Pooling" - bottom: "conv2" - top: "pool2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} -#################################### - -################################## -layer { - name: "conv3" - type: "Convolution" - bottom: "pool2" - top: "conv3" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu3" - type: "PReLU" - bottom: "conv3" - top: "conv3" - propagate_down: true -} -############################### - -############################### - -layer { - name: "conv4" - type: "InnerProduct" - bottom: "conv3" - top: "conv4" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - inner_product_param { - num_output: 128 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu4" - type: "PReLU" - bottom: "conv4" - top: "conv4" -} - -layer { - name: "conv5-1" - type: "InnerProduct" - bottom: "conv4" - top: "conv5-1" - param { - lr_mult: 0 - decay_mult: 0 - } - param { - lr_mult: 0 - decay_mult: 0 - } - inner_product_param { - num_output: 2 - #kernel_size: 1 - #stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv5-2" - type: "InnerProduct" - bottom: "conv4" - top: "conv5-2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 4 - #kernel_size: 1 - #stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prob1" - type: "Softmax" - bottom: "conv5-1" - top: "prob1" -} \ No newline at end of file diff --git a/gender-age/mtcnn-model/det3-0001.params b/gender-age/mtcnn-model/det3-0001.params deleted file mode 100644 index cae898b..0000000 Binary files a/gender-age/mtcnn-model/det3-0001.params and /dev/null differ diff --git a/gender-age/mtcnn-model/det3-symbol.json b/gender-age/mtcnn-model/det3-symbol.json deleted file mode 100644 index 00061ed..0000000 --- a/gender-age/mtcnn-model/det3-symbol.json +++ /dev/null @@ -1,418 +0,0 @@ -{ - 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name: "conv1" - type: "Convolution" - bottom: "data" - top: "conv1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 32 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu1" - type: "PReLU" - bottom: "conv1" - top: "conv1" -} -layer { - name: "pool1" - type: "Pooling" - bottom: "conv1" - top: "pool1" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} -layer { - name: "conv2" - type: "Convolution" - bottom: "pool1" - top: "conv2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -layer { - name: "prelu2" - type: "PReLU" - bottom: "conv2" - top: "conv2" -} -layer { - name: "pool2" - type: "Pooling" - bottom: "conv2" - top: "pool2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv3" - type: "Convolution" - bottom: "pool2" - top: "conv3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 3 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu3" - type: "PReLU" - bottom: "conv3" - top: "conv3" -} -layer { - name: "pool3" - type: "Pooling" - bottom: "conv3" - top: "pool3" - pooling_param { - pool: MAX - kernel_size: 2 - stride: 2 - } -} -layer { - name: "conv4" - type: "Convolution" - bottom: "pool3" - top: "conv4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 128 - kernel_size: 2 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prelu4" - type: "PReLU" - bottom: "conv4" - top: "conv4" -} - - -layer { - name: "conv5" - type: "InnerProduct" - bottom: "conv4" - top: "conv5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - #kernel_size: 3 - num_output: 256 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -layer { - name: "drop5" - type: "Dropout" - bottom: "conv5" - top: "conv5" - dropout_param { - dropout_ratio: 0.25 - } -} -layer { - name: "prelu5" - type: "PReLU" - bottom: "conv5" - top: "conv5" -} - - -layer { - name: "conv6-1" - type: "InnerProduct" - bottom: "conv5" - top: "conv6-1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - #kernel_size: 1 - num_output: 2 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv6-2" - type: "InnerProduct" - bottom: "conv5" - top: "conv6-2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - #kernel_size: 1 - num_output: 4 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "conv6-3" - type: "InnerProduct" - bottom: "conv5" - top: "conv6-3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - #kernel_size: 1 - num_output: 10 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } -} -layer { - name: "prob1" - type: "Softmax" - bottom: "conv6-1" - top: "prob1" -} diff --git a/gender-age/mtcnn-model/det4-0001.params b/gender-age/mtcnn-model/det4-0001.params deleted file mode 100644 index efca9a9..0000000 Binary files a/gender-age/mtcnn-model/det4-0001.params and /dev/null differ diff --git a/gender-age/mtcnn-model/det4-symbol.json b/gender-age/mtcnn-model/det4-symbol.json deleted file mode 100644 index aa90e2a..0000000 --- a/gender-age/mtcnn-model/det4-symbol.json +++ /dev/null @@ -1,1392 +0,0 @@ -{ - 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77, - 79, - 82, - 83, - 85, - 88, - 89, - 91, - 93, - 94, - 96, - 98, - 99, - 101, - 102, - 104, - 106, - 107, - 109, - 110, - 112, - 114, - 115, - 117, - 118, - 120, - 122, - 123, - 125, - 126, - 128, - 130, - 131 - ], - "heads": [[100, 0], [108, 0], [116, 0], [124, 0], [132, 0]] -} \ No newline at end of file diff --git a/gender-age/mtcnn-model/det4.caffemodel b/gender-age/mtcnn-model/det4.caffemodel deleted file mode 100644 index 38353c4..0000000 Binary files a/gender-age/mtcnn-model/det4.caffemodel and /dev/null differ diff --git a/gender-age/mtcnn-model/det4.prototxt b/gender-age/mtcnn-model/det4.prototxt deleted file mode 100644 index 4cdc329..0000000 --- a/gender-age/mtcnn-model/det4.prototxt +++ /dev/null @@ -1,995 +0,0 @@ -name: "LNet" -input: "data" -input_dim: 1 -input_dim: 15 -input_dim: 24 -input_dim: 24 - -layer { - name: "slicer_data" - type: "Slice" - bottom: "data" - top: "data241" - top: "data242" - top: "data243" - top: "data244" - top: "data245" - slice_param { - axis: 1 - slice_point: 3 - slice_point: 6 - slice_point: 9 - slice_point: 12 - } -} -layer { - name: "conv1_1" - type: "Convolution" - bottom: "data241" - top: "conv1_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu1_1" - type: "PReLU" - bottom: "conv1_1" - top: "conv1_1" - -} -layer { - name: "pool1_1" - type: "Pooling" - bottom: "conv1_1" - top: "pool1_1" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2_1" - type: "Convolution" - bottom: "pool1_1" - top: "conv2_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu2_1" - type: "PReLU" - bottom: "conv2_1" - top: "conv2_1" -} -layer { - name: "pool2_1" - type: "Pooling" - bottom: "conv2_1" - top: "pool2_1" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } - -} -layer { - name: "conv3_1" - type: "Convolution" - bottom: "pool2_1" - top: "conv3_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu3_1" - type: "PReLU" - bottom: "conv3_1" - top: "conv3_1" -} -########################## -layer { - name: "conv1_2" - type: "Convolution" - bottom: "data242" - top: "conv1_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu1_2" - type: "PReLU" - bottom: "conv1_2" - top: "conv1_2" - -} -layer { - name: "pool1_2" - type: "Pooling" - bottom: "conv1_2" - top: "pool1_2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2_2" - type: "Convolution" - bottom: "pool1_2" - top: "conv2_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu2_2" - type: "PReLU" - bottom: "conv2_2" - top: "conv2_2" -} -layer { - name: "pool2_2" - type: "Pooling" - bottom: "conv2_2" - top: "pool2_2" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } - -} -layer { - name: "conv3_2" - type: "Convolution" - bottom: "pool2_2" - top: "conv3_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu3_2" - type: "PReLU" - bottom: "conv3_2" - top: "conv3_2" -} -########################## -########################## -layer { - name: "conv1_3" - type: "Convolution" - bottom: "data243" - top: "conv1_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu1_3" - type: "PReLU" - bottom: "conv1_3" - top: "conv1_3" - -} -layer { - name: "pool1_3" - type: "Pooling" - bottom: "conv1_3" - top: "pool1_3" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2_3" - type: "Convolution" - bottom: "pool1_3" - top: "conv2_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu2_3" - type: "PReLU" - bottom: "conv2_3" - top: "conv2_3" -} -layer { - name: "pool2_3" - type: "Pooling" - bottom: "conv2_3" - top: "pool2_3" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } - -} -layer { - name: "conv3_3" - type: "Convolution" - bottom: "pool2_3" - top: "conv3_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu3_3" - type: "PReLU" - bottom: "conv3_3" - top: "conv3_3" -} -########################## -########################## -layer { - name: "conv1_4" - type: "Convolution" - bottom: "data244" - top: "conv1_4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu1_4" - type: "PReLU" - bottom: "conv1_4" - top: "conv1_4" - -} -layer { - name: "pool1_4" - type: "Pooling" - bottom: "conv1_4" - top: "pool1_4" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2_4" - type: "Convolution" - bottom: "pool1_4" - top: "conv2_4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu2_4" - type: "PReLU" - bottom: "conv2_4" - top: "conv2_4" -} -layer { - name: "pool2_4" - type: "Pooling" - bottom: "conv2_4" - top: "pool2_4" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } - -} -layer { - name: "conv3_4" - type: "Convolution" - bottom: "pool2_4" - top: "conv3_4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu3_4" - type: "PReLU" - bottom: "conv3_4" - top: "conv3_4" -} -########################## -########################## -layer { - name: "conv1_5" - type: "Convolution" - bottom: "data245" - top: "conv1_5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 28 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu1_5" - type: "PReLU" - bottom: "conv1_5" - top: "conv1_5" - -} -layer { - name: "pool1_5" - type: "Pooling" - bottom: "conv1_5" - top: "pool1_5" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } -} - -layer { - name: "conv2_5" - type: "Convolution" - bottom: "pool1_5" - top: "conv2_5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 48 - kernel_size: 3 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu2_5" - type: "PReLU" - bottom: "conv2_5" - top: "conv2_5" -} -layer { - name: "pool2_5" - type: "Pooling" - bottom: "conv2_5" - top: "pool2_5" - pooling_param { - pool: MAX - kernel_size: 3 - stride: 2 - } - -} -layer { - name: "conv3_5" - type: "Convolution" - bottom: "pool2_5" - top: "conv3_5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - convolution_param { - num_output: 64 - kernel_size: 2 - stride: 1 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu3_5" - type: "PReLU" - bottom: "conv3_5" - top: "conv3_5" -} -########################## -layer { - name: "concat" - bottom: "conv3_1" - bottom: "conv3_2" - bottom: "conv3_3" - bottom: "conv3_4" - bottom: "conv3_5" - top: "conv3" - type: "Concat" - concat_param { - axis: 1 - } -} -########################## -layer { - name: "fc4" - type: "InnerProduct" - bottom: "conv3" - top: "fc4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 256 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4" - type: "PReLU" - bottom: "fc4" - top: "fc4" -} -############################ -layer { - name: "fc4_1" - type: "InnerProduct" - bottom: "fc4" - top: "fc4_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 64 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4_1" - type: "PReLU" - bottom: "fc4_1" - top: "fc4_1" -} -layer { - name: "fc5_1" - type: "InnerProduct" - bottom: "fc4_1" - top: "fc5_1" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 2 - weight_filler { - type: "xavier" - #type: "constant" - #value: 0 - } - bias_filler { - type: "constant" - value: 0 - } - } -} - - -######################### -layer { - name: "fc4_2" - type: "InnerProduct" - bottom: "fc4" - top: "fc4_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 64 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4_2" - type: "PReLU" - bottom: "fc4_2" - top: "fc4_2" -} -layer { - name: "fc5_2" - type: "InnerProduct" - bottom: "fc4_2" - top: "fc5_2" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 2 - weight_filler { - type: "xavier" - #type: "constant" - #value: 0 - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -######################### -layer { - name: "fc4_3" - type: "InnerProduct" - bottom: "fc4" - top: "fc4_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 64 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4_3" - type: "PReLU" - bottom: "fc4_3" - top: "fc4_3" -} -layer { - name: "fc5_3" - type: "InnerProduct" - bottom: "fc4_3" - top: "fc5_3" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 2 - weight_filler { - type: "xavier" - #type: "constant" - #value: 0 - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -######################### -layer { - name: "fc4_4" - type: "InnerProduct" - bottom: "fc4" - top: "fc4_4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 64 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4_4" - type: "PReLU" - bottom: "fc4_4" - top: "fc4_4" -} -layer { - name: "fc5_4" - type: "InnerProduct" - bottom: "fc4_4" - top: "fc5_4" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 2 - weight_filler { - type: "xavier" - #type: "constant" - #value: 0 - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -######################### -layer { - name: "fc4_5" - type: "InnerProduct" - bottom: "fc4" - top: "fc4_5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 64 - weight_filler { - type: "xavier" - } - bias_filler { - type: "constant" - value: 0 - } - } - -} -layer { - name: "prelu4_5" - type: "PReLU" - bottom: "fc4_5" - top: "fc4_5" -} -layer { - name: "fc5_5" - type: "InnerProduct" - bottom: "fc4_5" - top: "fc5_5" - param { - lr_mult: 1 - decay_mult: 1 - } - param { - lr_mult: 2 - decay_mult: 1 - } - inner_product_param { - num_output: 2 - weight_filler { - type: "xavier" - #type: "constant" - #value: 0 - } - bias_filler { - type: "constant" - value: 0 - } - } -} - -######################### - diff --git a/gender-age/mtcnn_detector.py b/gender-age/mtcnn_detector.py deleted file mode 100644 index 1ce4146..0000000 --- a/gender-age/mtcnn_detector.py +++ /dev/null @@ -1,696 +0,0 @@ -# coding: utf-8 -import os -import mxnet as mx -import numpy as np -import math -import cv2 -from multiprocessing import Pool -from itertools import repeat -from itertools import izip -from helper import nms, adjust_input, generate_bbox, detect_first_stage_warpper - - -class MtcnnDetector(object): - """ - Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks - see https://github.com/kpzhang93/MTCNN_face_detection_alignment - this is a mxnet version - """ - def __init__(self, - model_folder='.', - minsize=20, - threshold=[0.6, 0.7, 0.8], - factor=0.709, - num_worker=1, - accurate_landmark=False, - ctx=mx.cpu()): - """ - Initialize the detector - - Parameters: - ---------- - model_folder : string - path for the models - minsize : float number - minimal face to detect - threshold : float number - detect threshold for 3 stages - factor: float number - scale factor for image pyramid - num_worker: int number - number of processes we use for first stage - accurate_landmark: bool - use accurate landmark localization or not - - """ - self.num_worker = num_worker - self.accurate_landmark = accurate_landmark - - # load 4 models from folder - models = ['det1', 'det2', 'det3', 'det4'] - models = [os.path.join(model_folder, f) for f in models] - - self.PNets = [] - for i in range(num_worker): - workner_net = mx.model.FeedForward.load(models[0], 1, ctx=ctx) - self.PNets.append(workner_net) - - #self.Pool = Pool(num_worker) - - self.RNet = mx.model.FeedForward.load(models[1], 1, ctx=ctx) - self.ONet = mx.model.FeedForward.load(models[2], 1, ctx=ctx) - self.LNet = mx.model.FeedForward.load(models[3], 1, ctx=ctx) - - self.minsize = float(minsize) - self.factor = float(factor) - self.threshold = threshold - - def convert_to_square(self, bbox): - """ - convert bbox to square - - Parameters: - ---------- - bbox: numpy array , shape n x 5 - input bbox - - Returns: - ------- - square bbox - """ - square_bbox = bbox.copy() - - h = bbox[:, 3] - bbox[:, 1] + 1 - w = bbox[:, 2] - bbox[:, 0] + 1 - max_side = np.maximum(h, w) - square_bbox[:, 0] = bbox[:, 0] + w * 0.5 - max_side * 0.5 - square_bbox[:, 1] = bbox[:, 1] + h * 0.5 - max_side * 0.5 - square_bbox[:, 2] = square_bbox[:, 0] + max_side - 1 - square_bbox[:, 3] = square_bbox[:, 1] + max_side - 1 - return square_bbox - - def calibrate_box(self, bbox, reg): - """ - calibrate bboxes - - Parameters: - ---------- - bbox: numpy array, shape n x 5 - input bboxes - reg: numpy array, shape n x 4 - bboxex adjustment - - Returns: - ------- - bboxes after refinement - - """ - w = bbox[:, 2] - bbox[:, 0] + 1 - w = np.expand_dims(w, 1) - h = bbox[:, 3] - bbox[:, 1] + 1 - h = np.expand_dims(h, 1) - reg_m = np.hstack([w, h, w, h]) - aug = reg_m * reg - bbox[:, 0:4] = bbox[:, 0:4] + aug - return bbox - - def pad(self, bboxes, w, h): - """ - pad the the bboxes, alse restrict the size of it - - Parameters: - ---------- - bboxes: numpy array, n x 5 - input bboxes - w: float number - width of the input image - h: float number - height of the input image - Returns : - ------s - dy, dx : numpy array, n x 1 - start point of the bbox in target image - edy, edx : numpy array, n x 1 - end point of the bbox in target image - y, x : numpy array, n x 1 - start point of the bbox in original image - ex, ex : numpy array, n x 1 - end point of the bbox in original image - tmph, tmpw: numpy array, n x 1 - height and width of the bbox - - """ - tmpw, tmph = bboxes[:, 2] - bboxes[:, 0] + 1, bboxes[:, - 3] - bboxes[:, - 1] + 1 - num_box = bboxes.shape[0] - - dx, dy = np.zeros((num_box, )), np.zeros((num_box, )) - edx, edy = tmpw.copy() - 1, tmph.copy() - 1 - - x, y, ex, ey = bboxes[:, 0], bboxes[:, 1], bboxes[:, 2], bboxes[:, 3] - - tmp_index = np.where(ex > w - 1) - edx[tmp_index] = tmpw[tmp_index] + w - 2 - ex[tmp_index] - ex[tmp_index] = w - 1 - - tmp_index = np.where(ey > h - 1) - edy[tmp_index] = tmph[tmp_index] + h - 2 - ey[tmp_index] - ey[tmp_index] = h - 1 - - tmp_index = np.where(x < 0) - dx[tmp_index] = 0 - x[tmp_index] - x[tmp_index] = 0 - - tmp_index = np.where(y < 0) - dy[tmp_index] = 0 - y[tmp_index] - y[tmp_index] = 0 - - return_list = [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] - return_list = [item.astype(np.int32) for item in return_list] - - return return_list - - def slice_index(self, number): - """ - slice the index into (n,n,m), m < n - Parameters: - ---------- - number: int number - number - """ - def chunks(l, n): - """Yield successive n-sized chunks from l.""" - for i in range(0, len(l), n): - yield l[i:i + n] - - num_list = range(number) - return list(chunks(num_list, self.num_worker)) - - def detect_face_limited(self, img, det_type=2): - height, width, _ = img.shape - if det_type >= 2: - total_boxes = np.array( - [[0.0, 0.0, img.shape[1], img.shape[0], 0.9]], - dtype=np.float32) - num_box = total_boxes.shape[0] - - # pad the bbox - [dy, edy, dx, edx, y, ey, x, ex, tmpw, - tmph] = self.pad(total_boxes, width, height) - # (3, 24, 24) is the input shape for RNet - input_buf = np.zeros((num_box, 3, 24, 24), dtype=np.float32) - - for i in range(num_box): - tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8) - tmp[dy[i]:edy[i] + 1, - dx[i]:edx[i] + 1, :] = img[y[i]:ey[i] + 1, - x[i]:ex[i] + 1, :] - input_buf[i, :, :, :] = adjust_input(cv2.resize(tmp, (24, 24))) - - output = self.RNet.predict(input_buf) - - # filter the total_boxes with threshold - passed = np.where(output[1][:, 1] > self.threshold[1]) - total_boxes = total_boxes[passed] - - if total_boxes.size == 0: - return None - - total_boxes[:, 4] = output[1][passed, 1].reshape((-1, )) - reg = output[0][passed] - - # nms - pick = nms(total_boxes, 0.7, 'Union') - total_boxes = total_boxes[pick] - total_boxes = self.calibrate_box(total_boxes, reg[pick]) - total_boxes = self.convert_to_square(total_boxes) - total_boxes[:, 0:4] = np.round(total_boxes[:, 0:4]) - else: - total_boxes = np.array( - [[0.0, 0.0, img.shape[1], img.shape[0], 0.9]], - dtype=np.float32) - num_box = total_boxes.shape[0] - [dy, edy, dx, edx, y, ey, x, ex, tmpw, - tmph] = self.pad(total_boxes, width, height) - # (3, 48, 48) is the input shape for ONet - input_buf = np.zeros((num_box, 3, 48, 48), dtype=np.float32) - - for i in range(num_box): - tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.float32) - tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = img[y[i]:ey[i] + 1, - x[i]:ex[i] + 1, :] - input_buf[i, :, :, :] = adjust_input(cv2.resize(tmp, (48, 48))) - - output = self.ONet.predict(input_buf) - #print(output[2]) - - # filter the total_boxes with threshold - passed = np.where(output[2][:, 1] > self.threshold[2]) - total_boxes = total_boxes[passed] - - if total_boxes.size == 0: - return None - - total_boxes[:, 4] = output[2][passed, 1].reshape((-1, )) - reg = output[1][passed] - points = output[0][passed] - - # compute landmark points - bbw = total_boxes[:, 2] - total_boxes[:, 0] + 1 - bbh = total_boxes[:, 3] - total_boxes[:, 1] + 1 - points[:, 0:5] = np.expand_dims( - total_boxes[:, 0], 1) + np.expand_dims(bbw, 1) * points[:, 0:5] - points[:, 5:10] = np.expand_dims( - total_boxes[:, 1], 1) + np.expand_dims(bbh, 1) * points[:, 5:10] - - # nms - total_boxes = self.calibrate_box(total_boxes, reg) - pick = nms(total_boxes, 0.7, 'Min') - total_boxes = total_boxes[pick] - points = points[pick] - - if not self.accurate_landmark: - return total_boxes, points - - ############################################# - # extended stage - ############################################# - num_box = total_boxes.shape[0] - patchw = np.maximum(total_boxes[:, 2] - total_boxes[:, 0] + 1, - total_boxes[:, 3] - total_boxes[:, 1] + 1) - patchw = np.round(patchw * 0.25) - - # make it even - patchw[np.where(np.mod(patchw, 2) == 1)] += 1 - - input_buf = np.zeros((num_box, 15, 24, 24), dtype=np.float32) - for i in range(5): - x, y = points[:, i], points[:, i + 5] - x, y = np.round(x - 0.5 * patchw), np.round(y - 0.5 * patchw) - [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self.pad( - np.vstack([x, y, x + patchw - 1, y + patchw - 1]).T, width, - height) - for j in range(num_box): - tmpim = np.zeros((tmpw[j], tmpw[j], 3), dtype=np.float32) - tmpim[dy[j]:edy[j] + 1, - dx[j]:edx[j] + 1, :] = img[y[j]:ey[j] + 1, - x[j]:ex[j] + 1, :] - input_buf[j, i * 3:i * 3 + 3, :, :] = adjust_input( - cv2.resize(tmpim, (24, 24))) - - output = self.LNet.predict(input_buf) - - pointx = np.zeros((num_box, 5)) - pointy = np.zeros((num_box, 5)) - - for k in range(5): - # do not make a large movement - tmp_index = np.where(np.abs(output[k] - 0.5) > 0.35) - output[k][tmp_index[0]] = 0.5 - - pointx[:, k] = np.round(points[:, k] - - 0.5 * patchw) + output[k][:, 0] * patchw - pointy[:, k] = np.round(points[:, k + 5] - - 0.5 * patchw) + output[k][:, 1] * patchw - - points = np.hstack([pointx, pointy]) - points = points.astype(np.int32) - - return total_boxes, points - - def detect_face(self, img, det_type=0): - """ - detect face over img - Parameters: - ---------- - img: numpy array, bgr order of shape (1, 3, n, m) - input image - Retures: - ------- - bboxes: numpy array, n x 5 (x1,y2,x2,y2,score) - bboxes - points: numpy array, n x 10 (x1, x2 ... x5, y1, y2 ..y5) - landmarks - """ - - # check input - height, width, _ = img.shape - if det_type == 0: - MIN_DET_SIZE = 12 - - if img is None: - return None - - # only works for color image - if len(img.shape) != 3: - return None - - # detected boxes - total_boxes = [] - - minl = min(height, width) - - # get all the valid scales - scales = [] - m = MIN_DET_SIZE / self.minsize - minl *= m - factor_count = 0 - while minl > MIN_DET_SIZE: - scales.append(m * self.factor**factor_count) - minl *= self.factor - factor_count += 1 - - ############################################# - # first stage - ############################################# - #for scale in scales: - # return_boxes = self.detect_first_stage(img, scale, 0) - # if return_boxes is not None: - # total_boxes.append(return_boxes) - - sliced_index = self.slice_index(len(scales)) - total_boxes = [] - for batch in sliced_index: - #local_boxes = self.Pool.map( detect_first_stage_warpper, \ - # izip(repeat(img), self.PNets[:len(batch)], [scales[i] for i in batch], repeat(self.threshold[0])) ) - local_boxes = map( detect_first_stage_warpper, \ - izip(repeat(img), self.PNets[:len(batch)], [scales[i] for i in batch], repeat(self.threshold[0])) ) - total_boxes.extend(local_boxes) - - # remove the Nones - total_boxes = [i for i in total_boxes if i is not None] - - if len(total_boxes) == 0: - return None - - total_boxes = np.vstack(total_boxes) - - if total_boxes.size == 0: - return None - - # merge the detection from first stage - pick = nms(total_boxes[:, 0:5], 0.7, 'Union') - total_boxes = total_boxes[pick] - - bbw = total_boxes[:, 2] - total_boxes[:, 0] + 1 - bbh = total_boxes[:, 3] - total_boxes[:, 1] + 1 - - # refine the bboxes - total_boxes = np.vstack([ - total_boxes[:, 0] + total_boxes[:, 5] * bbw, - total_boxes[:, 1] + total_boxes[:, 6] * bbh, - total_boxes[:, 2] + total_boxes[:, 7] * bbw, - total_boxes[:, 3] + total_boxes[:, 8] * bbh, total_boxes[:, 4] - ]) - - total_boxes = total_boxes.T - total_boxes = self.convert_to_square(total_boxes) - total_boxes[:, 0:4] = np.round(total_boxes[:, 0:4]) - else: - total_boxes = np.array( - [[0.0, 0.0, img.shape[1], img.shape[0], 0.9]], - dtype=np.float32) - - ############################################# - # second stage - ############################################# - num_box = total_boxes.shape[0] - - # pad the bbox - [dy, edy, dx, edx, y, ey, x, ex, tmpw, - tmph] = self.pad(total_boxes, width, height) - # (3, 24, 24) is the input shape for RNet - input_buf = np.zeros((num_box, 3, 24, 24), dtype=np.float32) - - for i in range(num_box): - tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8) - tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = img[y[i]:ey[i] + 1, - x[i]:ex[i] + 1, :] - input_buf[i, :, :, :] = adjust_input(cv2.resize(tmp, (24, 24))) - - output = self.RNet.predict(input_buf) - - # filter the total_boxes with threshold - passed = np.where(output[1][:, 1] > self.threshold[1]) - total_boxes = total_boxes[passed] - - if total_boxes.size == 0: - return None - - total_boxes[:, 4] = output[1][passed, 1].reshape((-1, )) - reg = output[0][passed] - - # nms - pick = nms(total_boxes, 0.7, 'Union') - total_boxes = total_boxes[pick] - total_boxes = self.calibrate_box(total_boxes, reg[pick]) - total_boxes = self.convert_to_square(total_boxes) - total_boxes[:, 0:4] = np.round(total_boxes[:, 0:4]) - - ############################################# - # third stage - ############################################# - num_box = total_boxes.shape[0] - - # pad the bbox - [dy, edy, dx, edx, y, ey, x, ex, tmpw, - tmph] = self.pad(total_boxes, width, height) - # (3, 48, 48) is the input shape for ONet - input_buf = np.zeros((num_box, 3, 48, 48), dtype=np.float32) - - for i in range(num_box): - tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.float32) - tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = img[y[i]:ey[i] + 1, - x[i]:ex[i] + 1, :] - input_buf[i, :, :, :] = adjust_input(cv2.resize(tmp, (48, 48))) - - output = self.ONet.predict(input_buf) - - # filter the total_boxes with threshold - passed = np.where(output[2][:, 1] > self.threshold[2]) - total_boxes = total_boxes[passed] - - if total_boxes.size == 0: - return None - - total_boxes[:, 4] = output[2][passed, 1].reshape((-1, )) - reg = output[1][passed] - points = output[0][passed] - - # compute landmark points - bbw = total_boxes[:, 2] - total_boxes[:, 0] + 1 - bbh = total_boxes[:, 3] - total_boxes[:, 1] + 1 - points[:, 0:5] = np.expand_dims( - total_boxes[:, 0], 1) + np.expand_dims(bbw, 1) * points[:, 0:5] - points[:, 5:10] = np.expand_dims( - total_boxes[:, 1], 1) + np.expand_dims(bbh, 1) * points[:, 5:10] - - # nms - total_boxes = self.calibrate_box(total_boxes, reg) - pick = nms(total_boxes, 0.7, 'Min') - total_boxes = total_boxes[pick] - points = points[pick] - - if not self.accurate_landmark: - return total_boxes, points - - ############################################# - # extended stage - ############################################# - num_box = total_boxes.shape[0] - patchw = np.maximum(total_boxes[:, 2] - total_boxes[:, 0] + 1, - total_boxes[:, 3] - total_boxes[:, 1] + 1) - patchw = np.round(patchw * 0.25) - - # make it even - patchw[np.where(np.mod(patchw, 2) == 1)] += 1 - - input_buf = np.zeros((num_box, 15, 24, 24), dtype=np.float32) - for i in range(5): - x, y = points[:, i], points[:, i + 5] - x, y = np.round(x - 0.5 * patchw), np.round(y - 0.5 * patchw) - [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self.pad( - np.vstack([x, y, x + patchw - 1, y + patchw - 1]).T, width, - height) - for j in range(num_box): - tmpim = np.zeros((tmpw[j], tmpw[j], 3), dtype=np.float32) - tmpim[dy[j]:edy[j] + 1, - dx[j]:edx[j] + 1, :] = img[y[j]:ey[j] + 1, - x[j]:ex[j] + 1, :] - input_buf[j, i * 3:i * 3 + 3, :, :] = adjust_input( - cv2.resize(tmpim, (24, 24))) - - output = self.LNet.predict(input_buf) - - pointx = np.zeros((num_box, 5)) - pointy = np.zeros((num_box, 5)) - - for k in range(5): - # do not make a large movement - tmp_index = np.where(np.abs(output[k] - 0.5) > 0.35) - output[k][tmp_index[0]] = 0.5 - - pointx[:, k] = np.round(points[:, k] - - 0.5 * patchw) + output[k][:, 0] * patchw - pointy[:, k] = np.round(points[:, k + 5] - - 0.5 * patchw) + output[k][:, 1] * patchw - - points = np.hstack([pointx, pointy]) - points = points.astype(np.int32) - - return total_boxes, points - - def list2colmatrix(self, pts_list): - """ - convert list to column matrix - Parameters: - ---------- - pts_list: - input list - Retures: - ------- - colMat: - - """ - assert len(pts_list) > 0 - colMat = [] - for i in range(len(pts_list)): - colMat.append(pts_list[i][0]) - colMat.append(pts_list[i][1]) - colMat = np.matrix(colMat).transpose() - return colMat - - def find_tfrom_between_shapes(self, from_shape, to_shape): - """ - find transform between shapes - Parameters: - ---------- - from_shape: - to_shape: - Retures: - ------- - tran_m: - tran_b: - """ - assert from_shape.shape[0] == to_shape.shape[ - 0] and from_shape.shape[0] % 2 == 0 - - sigma_from = 0.0 - sigma_to = 0.0 - cov = np.matrix([[0.0, 0.0], [0.0, 0.0]]) - - # compute the mean and cov - from_shape_points = from_shape.reshape(from_shape.shape[0] / 2, 2) - to_shape_points = to_shape.reshape(to_shape.shape[0] / 2, 2) - mean_from = from_shape_points.mean(axis=0) - mean_to = to_shape_points.mean(axis=0) - - for i in range(from_shape_points.shape[0]): - temp_dis = np.linalg.norm(from_shape_points[i] - mean_from) - sigma_from += temp_dis * temp_dis - temp_dis = np.linalg.norm(to_shape_points[i] - mean_to) - sigma_to += temp_dis * temp_dis - cov += (to_shape_points[i].transpose() - - mean_to.transpose()) * (from_shape_points[i] - mean_from) - - sigma_from = sigma_from / to_shape_points.shape[0] - sigma_to = sigma_to / to_shape_points.shape[0] - cov = cov / to_shape_points.shape[0] - - # compute the affine matrix - s = np.matrix([[1.0, 0.0], [0.0, 1.0]]) - u, d, vt = np.linalg.svd(cov) - - if np.linalg.det(cov) < 0: - if d[1] < d[0]: - s[1, 1] = -1 - else: - s[0, 0] = -1 - r = u * s * vt - c = 1.0 - if sigma_from != 0: - c = 1.0 / sigma_from * np.trace(np.diag(d) * s) - - tran_b = mean_to.transpose() - c * r * mean_from.transpose() - tran_m = c * r - - return tran_m, tran_b - - def extract_image_chips(self, img, points, desired_size=256, padding=0): - """ - crop and align face - Parameters: - ---------- - img: numpy array, bgr order of shape (1, 3, n, m) - input image - points: numpy array, n x 10 (x1, x2 ... x5, y1, y2 ..y5) - desired_size: default 256 - padding: default 0 - Retures: - ------- - crop_imgs: list, n - cropped and aligned faces - """ - crop_imgs = [] - for p in points: - shape = [] - for k in range(len(p) / 2): - shape.append(p[k]) - shape.append(p[k + 5]) - - if padding > 0: - padding = padding - else: - padding = 0 - # average positions of face points - mean_face_shape_x = [ - 0.224152, 0.75610125, 0.490127, 0.254149, 0.726104 - ] - mean_face_shape_y = [ - 0.2119465, 0.2119465, 0.628106, 0.780233, 0.780233 - ] - - from_points = [] - to_points = [] - - for i in range(len(shape) / 2): - x = (padding + mean_face_shape_x[i]) / (2 * padding + - 1) * desired_size - y = (padding + mean_face_shape_y[i]) / (2 * padding + - 1) * desired_size - to_points.append([x, y]) - from_points.append([shape[2 * i], shape[2 * i + 1]]) - - # convert the points to Mat - from_mat = self.list2colmatrix(from_points) - to_mat = self.list2colmatrix(to_points) - - # compute the similar transfrom - tran_m, tran_b = self.find_tfrom_between_shapes(from_mat, to_mat) - - probe_vec = np.matrix([1.0, 0.0]).transpose() - probe_vec = tran_m * probe_vec - - scale = np.linalg.norm(probe_vec) - angle = 180.0 / math.pi * math.atan2(probe_vec[1, 0], probe_vec[0, - 0]) - - from_center = [(shape[0] + shape[2]) / 2.0, - (shape[1] + shape[3]) / 2.0] - to_center = [0, 0] - to_center[1] = desired_size * 0.4 - to_center[0] = desired_size * 0.5 - - ex = to_center[0] - from_center[0] - ey = to_center[1] - from_center[1] - - rot_mat = cv2.getRotationMatrix2D((from_center[0], from_center[1]), - -1 * angle, scale) - rot_mat[0][2] += ex - rot_mat[1][2] += ey - - chips = cv2.warpAffine(img, rot_mat, (desired_size, desired_size)) - crop_imgs.append(chips) - - return crop_imgs diff --git a/gender-age/test.py b/gender-age/test.py deleted file mode 100644 index 78fc3b0..0000000 --- a/gender-age/test.py +++ /dev/null @@ -1,39 +0,0 @@ -import face_model -import argparse -import cv2 -import sys -import numpy as np -import datetime - -parser = argparse.ArgumentParser(description='face model test') -# general -parser.add_argument('--image-size', default='112,112', help='') -parser.add_argument('--image', default='Tom_Hanks_54745.png', help='') -parser.add_argument('--model', - default='model/model,0', - help='path to load model.') -parser.add_argument('--gpu', default=0, type=int, help='gpu id') -parser.add_argument( - '--det', - default=0, - type=int, - help='mtcnn option, 1 means using R+O, 0 means detect from begining') -args = parser.parse_args() - -model = face_model.FaceModel(args) -#img = cv2.imread('Tom_Hanks_54745.png') -img = cv2.imread(args.image) -img = model.get_input(img) -#f1 = model.get_feature(img) -#print(f1[0:10]) -for _ in range(5): - gender, age = model.get_ga(img) -time_now = datetime.datetime.now() -count = 200 -for _ in range(count): - gender, age = model.get_ga(img) -time_now2 = datetime.datetime.now() -diff = time_now2 - time_now -print('time cost', diff.total_seconds() / count) -print('gender is', gender) -print('age is', age) diff --git a/gender-age/train.py b/gender-age/train.py deleted file mode 100644 index 4948d78..0000000 --- a/gender-age/train.py +++ /dev/null @@ -1,420 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import sys -import math -import random -import logging -import pickle -import numpy as np -import sklearn -from data import FaceImageIter -import mxnet as mx -from mxnet import ndarray as nd -import argparse -import mxnet.optimizer as optimizer -sys.path.append(os.path.join(os.path.dirname(__file__), 'common')) -#import face_image -import fresnet -import fmobilenet - -logger = logging.getLogger() -logger.setLevel(logging.INFO) - -AGE = 100 - -args = None - - -class AccMetric(mx.metric.EvalMetric): - def __init__(self): - self.axis = 1 - super(AccMetric, self).__init__('acc', - axis=self.axis, - output_names=None, - label_names=None) - self.losses = [] - self.count = 0 - - def update(self, labels, preds): - self.count += 1 - label = labels[0].asnumpy()[:, 0:1] - pred_label = preds[-1].asnumpy()[:, 0:2] - pred_label = np.argmax(pred_label, axis=self.axis) - pred_label = pred_label.astype('int32').flatten() - label = label.astype('int32').flatten() - assert label.shape == pred_label.shape - self.sum_metric += (pred_label.flat == label.flat).sum() - self.num_inst += len(pred_label.flat) - - -class LossValueMetric(mx.metric.EvalMetric): - def __init__(self): - self.axis = 1 - super(LossValueMetric, self).__init__('lossvalue', - axis=self.axis, - output_names=None, - label_names=None) - self.losses = [] - - def update(self, labels, preds): - loss = preds[-1].asnumpy()[0] - self.sum_metric += loss - self.num_inst += 1.0 - gt_label = preds[-2].asnumpy() - #print(gt_label) - - -class MAEMetric(mx.metric.EvalMetric): - def __init__(self): - self.axis = 1 - super(MAEMetric, self).__init__('MAE', - axis=self.axis, - output_names=None, - label_names=None) - self.losses = [] - self.count = 0 - - def update(self, labels, preds): - self.count += 1 - label = labels[0].asnumpy() - label_age = np.count_nonzero(label[:, 1:], axis=1) - pred_age = np.zeros(label_age.shape, dtype=np.int) - #pred_age = np.zeros( label_age.shape, dtype=np.float32) - pred = preds[-1].asnumpy() - for i in range(AGE): - _pred = pred[:, 2 + i * 2:4 + i * 2] - _pred = np.argmax(_pred, axis=1) - #pred = pred[:,1] - pred_age += _pred - #pred_age = pred_age.astype(np.int) - mae = np.mean(np.abs(label_age - pred_age)) - self.sum_metric += mae - self.num_inst += 1.0 - - -class CUMMetric(mx.metric.EvalMetric): - def __init__(self, n=5): - self.axis = 1 - self.n = n - super(CUMMetric, self).__init__('CUM_%d' % n, - axis=self.axis, - output_names=None, - label_names=None) - self.losses = [] - self.count = 0 - - def update(self, labels, preds): - self.count += 1 - label = labels[0].asnumpy() - label_age = np.count_nonzero(label[:, 1:], axis=1) - pred_age = np.zeros(label_age.shape, dtype=np.int) - pred = preds[-1].asnumpy() - for i in range(AGE): - _pred = pred[:, 2 + i * 2:4 + i * 2] - _pred = np.argmax(_pred, axis=1) - #pred = pred[:,1] - pred_age += _pred - diff = np.abs(label_age - pred_age) - cum = np.sum((diff < self.n)) - self.sum_metric += cum - self.num_inst += len(label_age) - - -def parse_args(): - parser = argparse.ArgumentParser(description='Train face network') - # general - parser.add_argument('--data-dir', - default='', - help='training set directory') - parser.add_argument('--prefix', - default='../model/model', - help='directory to save model.') - parser.add_argument('--pretrained', - default='', - help='pretrained model to load') - parser.add_argument( - '--ckpt', - type=int, - default=1, - help= - 'checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save' - ) - parser.add_argument('--loss-type', type=int, default=4, help='loss type') - parser.add_argument( - '--verbose', - type=int, - default=2000, - help='do verification testing and model saving every verbose batches') - parser.add_argument('--max-steps', - type=int, - default=0, - help='max training batches') - parser.add_argument('--end-epoch', - type=int, - default=100000, - help='training epoch size.') - parser.add_argument('--network', default='r50', help='specify network') - parser.add_argument('--image-size', - default='112,112', - help='specify input image height and width') - parser.add_argument('--version-input', - type=int, - default=1, - help='network input config') - parser.add_argument('--version-output', - type=str, - default='GAP', - help='network embedding output config') - parser.add_argument('--version-act', - type=str, - default='prelu', - help='network activation config') - parser.add_argument('--multiplier', type=float, default=1.0, help='') - parser.add_argument('--lr', - type=float, - default=0.1, - help='start learning rate') - parser.add_argument('--lr-steps', - type=str, - default='', - help='steps of lr changing') - parser.add_argument('--wd', - type=float, - default=0.0005, - help='weight decay') - parser.add_argument('--bn-mom', type=float, default=0.9, help='bn mom') - parser.add_argument('--mom', type=float, default=0.9, help='momentum') - parser.add_argument('--per-batch-size', - type=int, - default=128, - help='batch size in each context') - parser.add_argument('--rand-mirror', - type=int, - default=1, - help='if do random mirror in training') - parser.add_argument('--cutoff', type=int, default=0, help='cut off aug') - parser.add_argument('--color', - type=int, - default=0, - help='color jittering aug') - parser.add_argument('--ce-loss', - default=False, - action='store_true', - help='if output ce loss') - args = parser.parse_args() - return args - - -def get_symbol(args, arg_params, aux_params): - data_shape = (args.image_channel, args.image_h, args.image_w) - image_shape = ",".join([str(x) for x in data_shape]) - margin_symbols = [] - if args.network[0] == 'm': - fc1 = fmobilenet.get_symbol(AGE * 2 + 2, - multiplier=args.multiplier, - version_input=args.version_input, - version_output=args.version_output) - else: - fc1 = fresnet.get_symbol(AGE * 2 + 2, - args.num_layers, - version_input=args.version_input, - version_output=args.version_output) - label = mx.symbol.Variable('softmax_label') - gender_label = mx.symbol.slice_axis(data=label, axis=1, begin=0, end=1) - gender_label = mx.symbol.reshape(gender_label, - shape=(args.per_batch_size, )) - gender_fc1 = mx.symbol.slice_axis(data=fc1, axis=1, begin=0, end=2) - #gender_fc7 = mx.sym.FullyConnected(data=gender_fc1, num_hidden=2, name='gender_fc7') - gender_softmax = mx.symbol.SoftmaxOutput(data=gender_fc1, - label=gender_label, - name='gender_softmax', - normalization='valid', - use_ignore=True, - ignore_label=9999) - outs = [gender_softmax] - for i in range(AGE): - age_label = mx.symbol.slice_axis(data=label, - axis=1, - begin=i + 1, - end=i + 2) - age_label = mx.symbol.reshape(age_label, shape=(args.per_batch_size, )) - age_fc1 = mx.symbol.slice_axis(data=fc1, - axis=1, - begin=2 + i * 2, - end=4 + i * 2) - #age_fc7 = mx.sym.FullyConnected(data=age_fc1, num_hidden=2, name='age_fc7_%i'%i) - age_softmax = mx.symbol.SoftmaxOutput(data=age_fc1, - label=age_label, - name='age_softmax_%d' % i, - normalization='valid', - grad_scale=1) - outs.append(age_softmax) - outs.append(mx.sym.BlockGrad(fc1)) - - out = mx.symbol.Group(outs) - return (out, arg_params, aux_params) - - -def train_net(args): - ctx = [] - cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip() - if len(cvd) > 0: - for i in range(len(cvd.split(','))): - ctx.append(mx.gpu(i)) - if len(ctx) == 0: - ctx = [mx.cpu()] - print('use cpu') - else: - print('gpu num:', len(ctx)) - prefix = args.prefix - prefix_dir = os.path.dirname(prefix) - if not os.path.exists(prefix_dir): - os.makedirs(prefix_dir) - end_epoch = args.end_epoch - args.ctx_num = len(ctx) - args.num_layers = int(args.network[1:]) - print('num_layers', args.num_layers) - if args.per_batch_size == 0: - args.per_batch_size = 128 - args.batch_size = args.per_batch_size * args.ctx_num - args.rescale_threshold = 0 - args.image_channel = 3 - - data_dir_list = args.data_dir.split(',') - assert len(data_dir_list) == 1 - data_dir = data_dir_list[0] - path_imgrec = None - path_imglist = None - image_size = [int(x) for x in args.image_size.split(',')] - assert len(image_size) == 2 - assert image_size[0] == image_size[1] - args.image_h = image_size[0] - args.image_w = image_size[1] - print('image_size', image_size) - path_imgrec = os.path.join(data_dir, "train.rec") - path_imgrec_val = os.path.join(data_dir, "val.rec") - - print('Called with argument:', args) - data_shape = (args.image_channel, image_size[0], image_size[1]) - mean = None - - begin_epoch = 0 - base_lr = args.lr - base_wd = args.wd - base_mom = args.mom - if len(args.pretrained) == 0: - arg_params = None - aux_params = None - sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params) - else: - vec = args.pretrained.split(',') - print('loading', vec) - _, arg_params, aux_params = mx.model.load_checkpoint( - vec[0], int(vec[1])) - sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params) - - #label_name = 'softmax_label' - #label_shape = (args.batch_size,) - model = mx.mod.Module( - context=ctx, - symbol=sym, - ) - val_dataiter = None - - train_dataiter = FaceImageIter( - batch_size=args.batch_size, - data_shape=data_shape, - path_imgrec=path_imgrec, - shuffle=True, - rand_mirror=args.rand_mirror, - mean=mean, - cutoff=args.cutoff, - color_jittering=args.color, - ) - val_dataiter = FaceImageIter( - batch_size=args.batch_size, - data_shape=data_shape, - path_imgrec=path_imgrec_val, - shuffle=False, - rand_mirror=False, - mean=mean, - ) - - metric = mx.metric.CompositeEvalMetric( - [AccMetric(), MAEMetric(), CUMMetric()]) - - if args.network[0] == 'r' or args.network[0] == 'y': - initializer = mx.init.Xavier(rnd_type='gaussian', - factor_type="out", - magnitude=2) #resnet style - elif args.network[0] == 'i' or args.network[0] == 'x': - initializer = mx.init.Xavier(rnd_type='gaussian', - factor_type="in", - magnitude=2) #inception - else: - initializer = mx.init.Xavier(rnd_type='uniform', - factor_type="in", - magnitude=2) - _rescale = 1.0 / args.ctx_num - opt = optimizer.SGD(learning_rate=base_lr, - momentum=base_mom, - wd=base_wd, - rescale_grad=_rescale) - #opt = optimizer.Nadam(learning_rate=base_lr, wd=base_wd, rescale_grad=_rescale) - som = 20 - _cb = mx.callback.Speedometer(args.batch_size, som) - lr_steps = [int(x) for x in args.lr_steps.split(',')] - - global_step = [0] - - def _batch_callback(param): - _cb(param) - global_step[0] += 1 - mbatch = global_step[0] - for _lr in lr_steps: - if mbatch == _lr: - opt.lr *= 0.1 - print('lr change to', opt.lr) - break - if mbatch % 1000 == 0: - print('lr-batch-epoch:', opt.lr, param.nbatch, param.epoch) - if mbatch == lr_steps[-1]: - arg, aux = model.get_params() - all_layers = model.symbol.get_internals() - _sym = all_layers['fc1_output'] - mx.model.save_checkpoint(args.prefix, 0, _sym, arg, aux) - sys.exit(0) - - epoch_cb = None - train_dataiter = mx.io.PrefetchingIter(train_dataiter) - print('start fitting') - - model.fit( - train_dataiter, - begin_epoch=begin_epoch, - num_epoch=end_epoch, - eval_data=val_dataiter, - eval_metric=metric, - kvstore='device', - optimizer=opt, - #optimizer_params = optimizer_params, - initializer=initializer, - arg_params=arg_params, - aux_params=aux_params, - allow_missing=True, - batch_end_callback=_batch_callback, - epoch_end_callback=epoch_cb) - - -def main(): - #time.sleep(3600*6.5) - global args - args = parse_args() - train_net(args) - - -if __name__ == '__main__': - main() diff --git a/python-package/README.md b/python-package/README.md index 10777df..df6eced 100644 --- a/python-package/README.md +++ b/python-package/README.md @@ -1,4 +1,4 @@ -## Python package of insightface README +## Python package For insightface pip-package <= 0.1.5, we use MXNet as inference backend, please download all models from [onedrive](https://1drv.ms/u/s!AswpsDO2toNKrUy0VktHTWgIQ0bn?e=UEF7C4), and put them all under `~/.insightface/models/` directory. diff --git a/deploy/Tom_Hanks_54745.png b/python-package/insightface/data/images/Tom_Hanks_54745.png similarity index 100% rename from deploy/Tom_Hanks_54745.png rename to python-package/insightface/data/images/Tom_Hanks_54745.png diff --git a/sample-images/t1.jpg b/python-package/insightface/data/images/t1.jpg similarity index 100% rename from sample-images/t1.jpg rename to python-package/insightface/data/images/t1.jpg diff --git a/python-package/insightface/model_zoo/arcface_onnx.py b/python-package/insightface/model_zoo/arcface_onnx.py index 02a6757..9c7de62 100644 --- a/python-package/insightface/model_zoo/arcface_onnx.py +++ b/python-package/insightface/model_zoo/arcface_onnx.py @@ -82,4 +82,14 @@ class ArcFaceONNX: sim = np.dot(feat1, feat2) / (norm(feat1) * norm(feat2)) return sim + def forward(self, imgs): + if not isinstance(imgs, list): + imgs = [imgs] + input_size = self.input_size + + blob = cv2.dnn.blobFromImages(imgs, 1.0 / self.input_std, input_size, + (self.input_mean, self.input_mean, self.input_mean), swapRB=True) + net_out = self.session.run(self.output_names, {self.input_name: blob})[0] + return net_out + diff --git a/recognition/README.md b/recognition/README.md index 5cda5a3..e494947 100644 --- a/recognition/README.md +++ b/recognition/README.md @@ -1,24 +1,46 @@ -## Angular Margin Loss for Deep Face Recognition +## Face Recognition -### Citation -If you find this project useful in your research, please consider to cite the following related papers: +
+ +
-``` -@inproceedings{deng2019arcface, - title={Arcface: Additive angular margin loss for deep face recognition}, - author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos}, - booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, - pages={4690--4699}, - year={2019} -} +## Introduction + +These are the face recognition methods of [InsightFace](https://insightface.ai) + + +
+ +
+ + +### Datasets + + Please refer to [datasets](_datasets_) page for the details of face recognition datasets used for training and evaluation. + +### Evaluation + + Please refer to [evaluation](_evaluation_) page for the details of face recognition evaluation. + + +## Methods + + +Supported methods: + +- [x] [ArcFace_mxnet (CVPR'2019)](arcface_mxnet) +- [x] [ArcFace_torch (CVPR'2019)](arcface_torch) +- [x] [SubCenter ArcFace (ECCV'2020)](subcenter_arcface) +- [x] [PartialFC_mxnet (Arxiv'2020)](partial_fc) +- [x] [PartialFC_torch (Arxiv'2020)](arcface_torch) +- [x] [VPL (CVPR'2021)](vpl) +- [x] [OneFlow_face](oneflow_face) + + +## Contributing + +We appreciate all contributions to improve the face recognition model zoo of InsightFace. -@inproceedings{deng2020subcenter, - title={Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces}, - author={Deng, Jiankang and Guo, Jia and Liu, Tongliang and Gong, Mingming and Zafeiriou, Stefanos}, - booktitle={Proceedings of the IEEE Conference on European Conference on Computer Vision}, - year={2020} -} -``` diff --git a/recognition/_datasets_/README.md b/recognition/_datasets_/README.md new file mode 100644 index 0000000..5cda5a3 --- /dev/null +++ b/recognition/_datasets_/README.md @@ -0,0 +1,24 @@ +## Angular Margin Loss for Deep Face Recognition + +### Citation + +If you find this project useful in your research, please consider to cite the following related papers: + +``` + +@inproceedings{deng2019arcface, + title={Arcface: Additive angular margin loss for deep face recognition}, + author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + pages={4690--4699}, + year={2019} +} + +@inproceedings{deng2020subcenter, + title={Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces}, + author={Deng, Jiankang and Guo, Jia and Liu, Tongliang and Gong, Mingming and Zafeiriou, Stefanos}, + booktitle={Proceedings of the IEEE Conference on European Conference on Computer Vision}, + year={2020} +} + +``` diff --git a/evaluation/IJB/README.md b/recognition/_evaluation_/ijb/README.md similarity index 100% rename from evaluation/IJB/README.md rename to recognition/_evaluation_/ijb/README.md diff --git a/evaluation/IJB/example.sh b/recognition/_evaluation_/ijb/example.sh similarity index 59% rename from evaluation/IJB/example.sh rename to recognition/_evaluation_/ijb/example.sh index 9a386c2..63f8269 100755 --- a/evaluation/IJB/example.sh +++ b/recognition/_evaluation_/ijb/example.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash -python -u IJB_11.py --model-prefix ./pretrained_models/r100-arcface/model --model-epoch 1 --gpu 0 --target IJBC --job arcface > ijbc_11.log 2>&1 & +python -u ijb_11.py --model-prefix ./pretrained_models/r100-arcface/model --model-epoch 1 --gpu 0 --target IJBC --job arcface > ijbc_11.log 2>&1 & -python -u IJB_1N.py --model-prefix ./pretrained_models/r100-arcface/model --model-epoch 1 --gpu 0 --target IJBB --job arcface > ijbb_1n.log 2>&1 & +python -u ijb_1n.py --model-prefix ./pretrained_models/r100-arcface/model --model-epoch 1 --gpu 0 --target IJBB --job arcface > ijbb_1n.log 2>&1 & diff --git a/evaluation/IJB/IJB_11.py b/recognition/_evaluation_/ijb/ijb_11.py similarity index 100% rename from evaluation/IJB/IJB_11.py rename to recognition/_evaluation_/ijb/ijb_11.py diff --git a/evaluation/IJB/IJB_1N.py b/recognition/_evaluation_/ijb/ijb_1n.py similarity index 100% rename from evaluation/IJB/IJB_1N.py rename to recognition/_evaluation_/ijb/ijb_1n.py diff --git a/evaluation/IJB/IJB_evals.py b/recognition/_evaluation_/ijb/ijb_evals.py similarity index 100% rename from evaluation/IJB/IJB_evals.py rename to recognition/_evaluation_/ijb/ijb_evals.py diff --git a/recognition/_evaluation_/ijb/ijb_onnx.py b/recognition/_evaluation_/ijb/ijb_onnx.py new file mode 100644 index 0000000..eb2edbe --- /dev/null +++ b/recognition/_evaluation_/ijb/ijb_onnx.py @@ -0,0 +1,267 @@ +import argparse +import os +import pickle +import timeit + +import cv2 +import mxnet as mx +import numpy as np +import pandas as pd +import prettytable +import skimage.transform +from sklearn.metrics import roc_curve +from sklearn.preprocessing import normalize +import insightface +from insightface.model_zoo import ArcFaceONNX + + +SRC = np.array( + [ + [30.2946, 51.6963], + [65.5318, 51.5014], + [48.0252, 71.7366], + [33.5493, 92.3655], + [62.7299, 92.2041]] + , dtype=np.float32) +SRC[:, 0] += 8.0 + + +class AlignedDataSet(mx.gluon.data.Dataset): + def __init__(self, root, lines, align=True): + self.lines = lines + self.root = root + self.align = align + + def __len__(self): + return len(self.lines) + + def __getitem__(self, idx): + each_line = self.lines[idx] + name_lmk_score = each_line.strip().split(' ') + name = os.path.join(self.root, name_lmk_score[0]) + img = cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB) + landmark5 = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32).reshape((5, 2)) + st = skimage.transform.SimilarityTransform() + st.estimate(landmark5, SRC) + img = cv2.warpAffine(img, st.params[0:2, :], (112, 112), borderValue=0.0) + img_1 = np.expand_dims(img, 0) + img_2 = np.expand_dims(np.fliplr(img), 0) + output = np.concatenate((img_1, img_2), axis=0).astype(np.float32) + output = np.transpose(output, (0, 3, 1, 2)) + output = mx.nd.array(output) + return output + + +def extract(model_file, dataset): + model = ArcFaceONNX(model_file=model_file) + model.check() + feat_mat = np.zeros(shape=(len(dataset), 2 * model.feat_dim)) + + def batchify_fn(data): + return mx.nd.concat(*data, dim=0) + + data_loader = mx.gluon.data.DataLoader( + dataset, 128, last_batch='keep', num_workers=4, + thread_pool=True, prefetch=16, batchify_fn=batchify_fn) + num_iter = 0 + for batch in data_loader: + batch = batch.asnumpy() + feat = model.forward(batch) + feat = np.reshape(feat, (-1, model.feat_dim * 2)) + feat_mat[128 * num_iter: 128 * num_iter + feat.shape[0], :] = feat + num_iter += 1 + if num_iter % 50 == 0: + print(num_iter) + return feat_mat + + +def read_template_media_list(path): + ijb_meta = pd.read_csv(path, sep=' ', header=None).values + templates = ijb_meta[:, 1].astype(np.int) + medias = ijb_meta[:, 2].astype(np.int) + return templates, medias + + +def read_template_pair_list(path): + pairs = pd.read_csv(path, sep=' ', header=None).values + t1 = pairs[:, 0].astype(np.int) + t2 = pairs[:, 1].astype(np.int) + label = pairs[:, 2].astype(np.int) + return t1, t2, label + + +def read_image_feature(path): + with open(path, 'rb') as fid: + img_feats = pickle.load(fid) + return img_feats + + +def image2template_feature(img_feats=None, + templates=None, + medias=None): + unique_templates = np.unique(templates) + template_feats = np.zeros((len(unique_templates), img_feats.shape[1])) + for count_template, uqt in enumerate(unique_templates): + (ind_t,) = np.where(templates == uqt) + face_norm_feats = img_feats[ind_t] + face_medias = medias[ind_t] + unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True) + media_norm_feats = [] + for u, ct in zip(unique_medias, unique_media_counts): + (ind_m,) = np.where(face_medias == u) + if ct == 1: + media_norm_feats += [face_norm_feats[ind_m]] + else: # image features from the same video will be aggregated into one feature + media_norm_feats += [np.mean(face_norm_feats[ind_m], axis=0, keepdims=True), ] + media_norm_feats = np.array(media_norm_feats) + template_feats[count_template] = np.sum(media_norm_feats, axis=0) + if count_template % 2000 == 0: + print('Finish Calculating {} template features.'.format( + count_template)) + template_norm_feats = normalize(template_feats) + return template_norm_feats, unique_templates + + +def verification(template_norm_feats=None, + unique_templates=None, + p1=None, + p2=None): + template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int) + for count_template, uqt in enumerate(unique_templates): + template2id[uqt] = count_template + score = np.zeros((len(p1),)) + total_pairs = np.array(range(len(p1))) + batchsize = 100000 + sublists = [total_pairs[i: i + batchsize] for i in range(0, len(p1), batchsize)] + total_sublists = len(sublists) + for c, s in enumerate(sublists): + feat1 = template_norm_feats[template2id[p1[s]]] + feat2 = template_norm_feats[template2id[p2[s]]] + similarity_score = np.sum(feat1 * feat2, -1) + score[s] = similarity_score.flatten() + if c % 10 == 0: + print('Finish {}/{} pairs.'.format(c, total_sublists)) + return score + + +def verification2(template_norm_feats=None, + unique_templates=None, + p1=None, + p2=None): + template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int) + for count_template, uqt in enumerate(unique_templates): + template2id[uqt] = count_template + score = np.zeros((len(p1),)) # save cosine distance between pairs + total_pairs = np.array(range(len(p1))) + batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation + sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)] + total_sublists = len(sublists) + for c, s in enumerate(sublists): + feat1 = template_norm_feats[template2id[p1[s]]] + feat2 = template_norm_feats[template2id[p2[s]]] + similarity_score = np.sum(feat1 * feat2, -1) + score[s] = similarity_score.flatten() + if c % 10 == 0: + print('Finish {}/{} pairs.'.format(c, total_sublists)) + return score + + +def main(args): + use_norm_score = True # if Ture, TestMode(N1) + use_detector_score = True # if Ture, TestMode(D1) + use_flip_test = True # if Ture, TestMode(F1) + assert args.target == 'IJBC' or args.target == 'IJBB' + + start = timeit.default_timer() + templates, medias = read_template_media_list( + os.path.join('%s/meta' % args.image_path, '%s_face_tid_mid.txt' % args.target.lower())) + stop = timeit.default_timer() + print('Time: %.2f s. ' % (stop - start)) + + start = timeit.default_timer() + p1, p2, label = read_template_pair_list( + os.path.join('%s/meta' % args.image_path, + '%s_template_pair_label.txt' % args.target.lower())) + stop = timeit.default_timer() + print('Time: %.2f s. ' % (stop - start)) + + start = timeit.default_timer() + img_path = '%s/loose_crop' % args.image_path + img_list_path = '%s/meta/%s_name_5pts_score.txt' % (args.image_path, args.target.lower()) + img_list = open(img_list_path) + files = img_list.readlines() + dataset = AlignedDataSet(root=img_path, lines=files, align=True) + img_feats = extract(args.model_file, dataset) + + faceness_scores = [] + for each_line in files: + name_lmk_score = each_line.split() + faceness_scores.append(name_lmk_score[-1]) + faceness_scores = np.array(faceness_scores).astype(np.float32) + stop = timeit.default_timer() + print('Time: %.2f s. ' % (stop - start)) + print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1])) + start = timeit.default_timer() + + if use_flip_test: + img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] + img_feats[:, img_feats.shape[1] // 2:] + else: + img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] + + if use_norm_score: + img_input_feats = img_input_feats + else: + img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True)) + + if use_detector_score: + print(img_input_feats.shape, faceness_scores.shape) + img_input_feats = img_input_feats * faceness_scores[:, np.newaxis] + else: + img_input_feats = img_input_feats + + template_norm_feats, unique_templates = image2template_feature( + img_input_feats, templates, medias) + stop = timeit.default_timer() + print('Time: %.2f s. ' % (stop - start)) + + start = timeit.default_timer() + score = verification(template_norm_feats, unique_templates, p1, p2) + stop = timeit.default_timer() + print('Time: %.2f s. ' % (stop - start)) + save_path = os.path.join(args.result_dir, "{}_result".format(args.target)) + if not os.path.exists(save_path): + os.makedirs(save_path) + score_save_file = os.path.join(save_path, "{}.npy".format(args.model_file.split('/')[-1])) + np.save(score_save_file, score) + files = [score_save_file] + methods = [] + scores = [] + for file in files: + methods.append(os.path.basename(file)) + scores.append(np.load(file)) + methods = np.array(methods) + scores = dict(zip(methods, scores)) + x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1] + tpr_fpr_table = prettytable.PrettyTable(['Methods'] + [str(x) for x in x_labels]) + for method in methods: + fpr, tpr, _ = roc_curve(label, scores[method]) + fpr = np.flipud(fpr) + tpr = np.flipud(tpr) + tpr_fpr_row = [] + tpr_fpr_row.append("%s-%s" % (method, args.target)) + for fpr_iter in np.arange(len(x_labels)): + _, min_index = min( + list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr))))) + tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100)) + tpr_fpr_table.add_row(tpr_fpr_row) + print(tpr_fpr_table) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='do onnx ijb test') + # general + parser.add_argument('--model-file', default='', help='path to onnx model.') + parser.add_argument('--image-path', default='', type=str, help='') + parser.add_argument('--result-dir', default='.', type=str, help='') + parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB') + main(parser.parse_args()) diff --git a/evaluation/Megaface/README.md b/recognition/_evaluation_/megaface/README.md similarity index 100% rename from evaluation/Megaface/README.md rename to recognition/_evaluation_/megaface/README.md diff --git a/evaluation/Megaface/gen_megaface.py b/recognition/_evaluation_/megaface/gen_megaface.py similarity index 100% rename from evaluation/Megaface/gen_megaface.py rename to recognition/_evaluation_/megaface/gen_megaface.py diff --git a/evaluation/Megaface/remove_noises.py b/recognition/_evaluation_/megaface/remove_noises.py similarity index 100% rename from evaluation/Megaface/remove_noises.py rename to recognition/_evaluation_/megaface/remove_noises.py diff --git a/evaluation/Megaface/run.sh b/recognition/_evaluation_/megaface/run.sh similarity index 100% rename from evaluation/Megaface/run.sh rename to recognition/_evaluation_/megaface/run.sh diff --git a/recognition/tools/README.md b/recognition/_tools_/README.md similarity index 100% rename from recognition/tools/README.md rename to recognition/_tools_/README.md diff --git a/recognition/tools/cpp-align/FacePreprocess.h b/recognition/_tools_/cpp_align/face_align.h similarity index 100% rename from recognition/tools/cpp-align/FacePreprocess.h rename to recognition/_tools_/cpp_align/face_align.h diff --git a/recognition/tools/mask_renderer.py b/recognition/_tools_/mask_renderer.py similarity index 100% rename from recognition/tools/mask_renderer.py rename to recognition/_tools_/mask_renderer.py diff --git a/recognition/ArcFace/README.md b/recognition/arcface_mxnet/README.md similarity index 93% rename from recognition/ArcFace/README.md rename to recognition/arcface_mxnet/README.md index 0fc1555..3d463f7 100644 --- a/recognition/ArcFace/README.md +++ b/recognition/arcface_mxnet/README.md @@ -2,13 +2,13 @@ ### Memory Consumption and Training Speed -![Memoryspeed](https://github.com/deepinsight/insightface/blob/master/resources/memoryspeed.png) +![Memoryspeed](https://insightface.ai/assets/img/github/memoryspeed.png) Parallel acceleration on both feature x and centre W. Setting: ResNet 50, batch size 8 * 64, feature dimension 512, float point 32, GPU 8 * P40 (24GB). ### Illustration of Main Steps -![Memoryspeed](https://github.com/deepinsight/insightface/blob/master/resources/mainsteps.png) +![Memoryspeed](https://insightface.ai/assets/img/github/mainsteps.png) Parallel calculation by simple matrix partition. Setting: ResNet 50, batch size 8 * 64, feature dimension 512, float point 32, identity number 1 Million, GPU 8 * 1080ti (11GB). Communication cost: 1MB (feature x). Training speed: 800 samples/second. diff --git a/recognition/common/build_eval_pack.py b/recognition/arcface_mxnet/common/build_eval_pack.py similarity index 100% rename from recognition/common/build_eval_pack.py rename to recognition/arcface_mxnet/common/build_eval_pack.py diff --git a/recognition/common/face_align.py b/recognition/arcface_mxnet/common/face_align.py similarity index 100% rename from recognition/common/face_align.py rename to recognition/arcface_mxnet/common/face_align.py diff --git a/recognition/common/flops_counter.py b/recognition/arcface_mxnet/common/flops_counter.py similarity index 100% rename from recognition/common/flops_counter.py rename to recognition/arcface_mxnet/common/flops_counter.py diff --git a/recognition/common/rec2image.py b/recognition/arcface_mxnet/common/rec2image.py similarity index 100% rename from recognition/common/rec2image.py rename to recognition/arcface_mxnet/common/rec2image.py diff --git a/recognition/common/rec2shufrec.py b/recognition/arcface_mxnet/common/rec2shufrec.py similarity index 100% rename from recognition/common/rec2shufrec.py rename to recognition/arcface_mxnet/common/rec2shufrec.py diff --git a/recognition/common/rec_builder.py b/recognition/arcface_mxnet/common/rec_builder.py similarity index 100% rename from recognition/common/rec_builder.py rename to recognition/arcface_mxnet/common/rec_builder.py diff --git a/recognition/common/verification.py b/recognition/arcface_mxnet/common/verification.py similarity index 100% rename from recognition/common/verification.py rename to recognition/arcface_mxnet/common/verification.py diff --git a/recognition/ArcFace/image_iter.py b/recognition/arcface_mxnet/image_iter.py similarity index 100% rename from recognition/ArcFace/image_iter.py rename to recognition/arcface_mxnet/image_iter.py diff --git a/recognition/ArcFace/metric.py b/recognition/arcface_mxnet/metric.py similarity index 100% rename from recognition/ArcFace/metric.py rename to recognition/arcface_mxnet/metric.py diff --git a/recognition/ArcFace/parall_module_local_v1.py b/recognition/arcface_mxnet/parall_module_local_v1.py similarity index 100% rename from recognition/ArcFace/parall_module_local_v1.py rename to recognition/arcface_mxnet/parall_module_local_v1.py diff --git a/recognition/ArcFace/sample_config.py b/recognition/arcface_mxnet/sample_config.py similarity index 100% rename from recognition/ArcFace/sample_config.py rename to recognition/arcface_mxnet/sample_config.py diff --git a/recognition/symbol/fdensenet.py b/recognition/arcface_mxnet/symbol/fdensenet.py similarity index 100% rename from recognition/symbol/fdensenet.py rename to recognition/arcface_mxnet/symbol/fdensenet.py diff --git a/recognition/symbol/fmnasnet.py b/recognition/arcface_mxnet/symbol/fmnasnet.py similarity index 100% rename from recognition/symbol/fmnasnet.py rename to recognition/arcface_mxnet/symbol/fmnasnet.py diff --git a/recognition/symbol/fmobilefacenet.py b/recognition/arcface_mxnet/symbol/fmobilefacenet.py similarity index 100% rename from recognition/symbol/fmobilefacenet.py rename to recognition/arcface_mxnet/symbol/fmobilefacenet.py diff --git a/recognition/symbol/fmobilenet.py b/recognition/arcface_mxnet/symbol/fmobilenet.py similarity index 100% rename from recognition/symbol/fmobilenet.py rename to recognition/arcface_mxnet/symbol/fmobilenet.py diff --git a/recognition/symbol/fresnet.py b/recognition/arcface_mxnet/symbol/fresnet.py similarity index 100% rename from recognition/symbol/fresnet.py rename to recognition/arcface_mxnet/symbol/fresnet.py diff --git a/recognition/symbol/memonger.py b/recognition/arcface_mxnet/symbol/memonger.py similarity index 100% rename from recognition/symbol/memonger.py rename to recognition/arcface_mxnet/symbol/memonger.py diff --git a/recognition/symbol/memonger_v2.py b/recognition/arcface_mxnet/symbol/memonger_v2.py similarity index 100% rename from recognition/symbol/memonger_v2.py rename to recognition/arcface_mxnet/symbol/memonger_v2.py diff --git a/recognition/symbol/symbol_utils.py b/recognition/arcface_mxnet/symbol/symbol_utils.py similarity index 100% rename from recognition/symbol/symbol_utils.py rename to recognition/arcface_mxnet/symbol/symbol_utils.py diff --git a/recognition/symbol/vargfacenet.py b/recognition/arcface_mxnet/symbol/vargfacenet.py similarity index 100% rename from recognition/symbol/vargfacenet.py rename to recognition/arcface_mxnet/symbol/vargfacenet.py diff --git a/recognition/ArcFace/train.py b/recognition/arcface_mxnet/train.py similarity index 99% rename from recognition/ArcFace/train.py rename to recognition/arcface_mxnet/train.py index 8cae863..2f2ebad 100644 --- a/recognition/ArcFace/train.py +++ b/recognition/arcface_mxnet/train.py @@ -16,10 +16,10 @@ import argparse import mxnet.optimizer as optimizer from config import config, default, generate_config from metric import * -sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common')) +sys.path.append(os.path.join(os.path.dirname(__file__), 'common')) import flops_counter import verification -sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'symbol')) +sys.path.append(os.path.join(os.path.dirname(__file__), 'symbol')) import fresnet import fmobilefacenet import fmobilenet diff --git a/recognition/ArcFace/train_parall.py b/recognition/arcface_mxnet/train_parall.py similarity index 99% rename from recognition/ArcFace/train_parall.py rename to recognition/arcface_mxnet/train_parall.py index 1351baa..ce6b916 100644 --- a/recognition/ArcFace/train_parall.py +++ b/recognition/arcface_mxnet/train_parall.py @@ -20,11 +20,11 @@ import mxnet as mx from mxnet import ndarray as nd import argparse import mxnet.optimizer as optimizer -sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common')) +sys.path.append(os.path.join(os.path.dirname(__file__), 'common')) import flops_counter from config import config, default, generate_config import verification -sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'symbol')) +sys.path.append(os.path.join(os.path.dirname(__file__), 'symbol')) import fresnet import fmobilefacenet import fmobilenet diff --git a/recognition/ArcFace/triplet_image_iter.py b/recognition/arcface_mxnet/triplet_image_iter.py similarity index 100% rename from recognition/ArcFace/triplet_image_iter.py rename to recognition/arcface_mxnet/triplet_image_iter.py diff --git a/recognition/ArcFace/verification.py b/recognition/arcface_mxnet/verification.py similarity index 100% rename from recognition/ArcFace/verification.py rename to recognition/arcface_mxnet/verification.py diff --git a/recognition/SubCenter-ArcFace/README.md b/recognition/subcenter_arcface/README.md similarity index 89% rename from recognition/SubCenter-ArcFace/README.md rename to recognition/subcenter_arcface/README.md index 56a91b5..ed642fd 100644 --- a/recognition/SubCenter-ArcFace/README.md +++ b/recognition/subcenter_arcface/README.md @@ -5,14 +5,14 @@ We introduce one extra hyperparameter (subcenter number `loss_K`) to ArcFace to relax the intra-class compactness constraint. In our experiments, we find ``loss_K=3`` can achieve a good balance between accuracy and robustness. -![difference](https://github.com/deepinsight/insightface/blob/master/resources/subcenterarcfacediff.png) +![difference](https://insightface.ai/assets/img/github/subcenterarcfacediff.png) ### 2. Implementation The training process of Subcenter ArcFace is almost same as [ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition/ArcFace) The increased GPU memory consumption can be easily alleviated by our parallel framework. -![framework](https://github.com/deepinsight/insightface/blob/master/resources/subcenterarcfaceframework.png) +![framework](https://insightface.ai/assets/img/github/subcenterarcfaceframework.png) ### 3. Training Dataset diff --git a/recognition/subcenter_arcface/common/build_eval_pack.py b/recognition/subcenter_arcface/common/build_eval_pack.py new file mode 100644 index 0000000..23208ce --- /dev/null +++ b/recognition/subcenter_arcface/common/build_eval_pack.py @@ -0,0 +1,136 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +#import mxnet as mx +#from mxnet import ndarray as nd +import argparse +import cv2 +import pickle +import numpy as np +import sys +import os +sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'common')) +sys.path.append( + os.path.join(os.path.dirname(__file__), '..', '..', 'RetinaFace')) +import face_align +from retinaface import RetinaFace + + +def to_rgb(img): + w, h = img.shape + ret = np.empty((w, h, 3), dtype=np.uint8) + ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img + return ret + + +def IOU(Reframe, GTframe): + x1 = Reframe[0] + y1 = Reframe[1] + width1 = Reframe[2] - Reframe[0] + height1 = Reframe[3] - Reframe[1] + + x2 = GTframe[0] + y2 = GTframe[1] + width2 = GTframe[2] - GTframe[0] + height2 = GTframe[3] - GTframe[1] + + endx = max(x1 + width1, x2 + width2) + startx = min(x1, x2) + width = width1 + width2 - (endx - startx) + + endy = max(y1 + height1, y2 + height2) + starty = min(y1, y2) + height = height1 + height2 - (endy - starty) + + if width <= 0 or height <= 0: + ratio = 0 + else: + Area = width * height + Area1 = width1 * height1 + Area2 = width2 * height2 + ratio = Area * 1. / (Area1 + Area2 - Area) + return ratio + + +parser = argparse.ArgumentParser(description='Package eval images') +# general +parser.add_argument('--data-dir', default='', help='') +parser.add_argument('--image-size', type=int, default=112, help='') +parser.add_argument('--gpu', type=int, default=0, help='') +parser.add_argument('--det-prefix', type=str, default='./model/R50', help='') +parser.add_argument('--output', default='./', help='path to save.') +parser.add_argument('--align-mode', default='arcface', help='align mode.') +args = parser.parse_args() + +gpu_id = args.gpu + +detector = RetinaFace(args.det_prefix, 0, gpu_id, network='net3') +target_size = 400 +max_size = 800 + + +def get_norm_crop(image_path): + im = cv2.imread(image_path) + im_shape = im.shape + im_size_min = np.min(im_shape[0:2]) + im_size_max = np.max(im_shape[0:2]) + im_scale = float(target_size) / float(im_size_min) + # prevent bigger axis from being more than max_size: + if np.round(im_scale * im_size_max) > max_size: + im_scale = float(max_size) / float(im_size_max) + bbox, landmark = detector.detect(im, threshold=0.5, scales=[im_scale]) + #print(im.shape, bbox.shape, landmark.shape) + if bbox.shape[0] == 0: + bbox, landmark = detector.detect( + im, + threshold=0.05, + scales=[im_scale * 0.75, im_scale, im_scale * 2.0]) + print('refine', im.shape, bbox.shape, landmark.shape) + nrof_faces = bbox.shape[0] + if nrof_faces > 0: + det = bbox[:, 0:4] + img_size = np.asarray(im.shape)[0:2] + bindex = 0 + if nrof_faces > 1: + bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] - + det[:, 1]) + img_center = img_size / 2 + offsets = np.vstack([(det[:, 0] + det[:, 2]) / 2 - img_center[1], + (det[:, 1] + det[:, 3]) / 2 - img_center[0]]) + offset_dist_squared = np.sum(np.power(offsets, 2.0), 0) + bindex = np.argmax(bounding_box_size - offset_dist_squared * + 2.0) # some extra weight on the centering + #_bbox = bounding_boxes[bindex, 0:4] + _landmark = landmark[bindex] + warped = face_align.norm_crop(im, + landmark=_landmark, + image_size=args.image_size, + mode=args.align_mode) + return warped + else: + return None + + +bins = [] +issame_list = [] +pp = 0 +for line in open(os.path.join(args.data_dir, 'pairs_label.txt'), 'r'): + pp += 1 + if pp % 100 == 0: + print('processing', pp) + line = line.strip().split() + assert len(line) == 3 + path1 = os.path.join(args.data_dir, line[0]) + path2 = os.path.join(args.data_dir, line[1]) + im1 = get_norm_crop(path1) + im2 = get_norm_crop(path2) + issame = True + if line[2] == '0': + issame = False + issame_list.append(issame) + for im in [im1, im2]: + _, s = cv2.imencode('.jpg', im) + bins.append(s) + +with open(args.output, 'wb') as f: + pickle.dump((bins, issame_list), f, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/recognition/subcenter_arcface/common/face_align.py b/recognition/subcenter_arcface/common/face_align.py new file mode 100644 index 0000000..4f48a76 --- /dev/null +++ b/recognition/subcenter_arcface/common/face_align.py @@ -0,0 +1,71 @@ +import cv2 +import numpy as np +from skimage import transform as trans + +src1 = np.array([[51.642, 50.115], [57.617, 49.990], [35.740, 69.007], + [51.157, 89.050], [57.025, 89.702]], + dtype=np.float32) +#<--left +src2 = np.array([[45.031, 50.118], [65.568, 50.872], [39.677, 68.111], + [45.177, 86.190], [64.246, 86.758]], + dtype=np.float32) + +#---frontal +src3 = np.array([[39.730, 51.138], [72.270, 51.138], [56.000, 68.493], + [42.463, 87.010], [69.537, 87.010]], + dtype=np.float32) + +#-->right +src4 = np.array([[46.845, 50.872], [67.382, 50.118], [72.737, 68.111], + [48.167, 86.758], [67.236, 86.190]], + dtype=np.float32) + +#-->right profile +src5 = np.array([[54.796, 49.990], [60.771, 50.115], [76.673, 69.007], + [55.388, 89.702], [61.257, 89.050]], + dtype=np.float32) + +src = np.array([src1, src2, src3, src4, src5]) +src_map = {112: src, 224: src * 2} + +arcface_src = np.array( + [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], + [41.5493, 92.3655], [70.7299, 92.2041]], + dtype=np.float32) + +arcface_src = np.expand_dims(arcface_src, axis=0) + +# In[66]: + + +# lmk is prediction; src is template +def estimate_norm(lmk, image_size=112, mode='arcface'): + assert lmk.shape == (5, 2) + tform = trans.SimilarityTransform() + lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1) + min_M = [] + min_index = [] + min_error = float('inf') + if mode == 'arcface': + assert image_size == 112 + src = arcface_src + else: + src = src_map[image_size] + for i in np.arange(src.shape[0]): + tform.estimate(lmk, src[i]) + M = tform.params[0:2, :] + results = np.dot(M, lmk_tran.T) + results = results.T + error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1))) + # print(error) + if error < min_error: + min_error = error + min_M = M + min_index = i + return min_M, min_index + + +def norm_crop(img, landmark, image_size=112, mode='arcface'): + M, pose_index = estimate_norm(landmark, image_size, mode) + warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0) + return warped diff --git a/recognition/subcenter_arcface/common/flops_counter.py b/recognition/subcenter_arcface/common/flops_counter.py new file mode 100644 index 0000000..8094241 --- /dev/null +++ b/recognition/subcenter_arcface/common/flops_counter.py @@ -0,0 +1,120 @@ +''' +@author: insightface +''' + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import sys +import os +import json +import argparse +import numpy as np +import mxnet as mx + + +def is_no_bias(attr): + ret = False + if 'no_bias' in attr and (attr['no_bias'] == True + or attr['no_bias'] == 'True'): + ret = True + return ret + + +def count_fc_flops(input_filter, output_filter, attr): + #print(input_filter, output_filter ,attr) + ret = 2 * input_filter * output_filter + if is_no_bias(attr): + ret -= output_filter + return int(ret) + + +def count_conv_flops(input_shape, output_shape, attr): + kernel = attr['kernel'][1:-1].split(',') + kernel = [int(x) for x in kernel] + + #print('kernel', kernel) + if is_no_bias(attr): + ret = (2 * input_shape[1] * kernel[0] * kernel[1] - + 1) * output_shape[2] * output_shape[3] * output_shape[1] + else: + ret = 2 * input_shape[1] * kernel[0] * kernel[1] * output_shape[ + 2] * output_shape[3] * output_shape[1] + num_group = 1 + if 'num_group' in attr: + num_group = int(attr['num_group']) + ret /= num_group + return int(ret) + + +def count_flops(sym, **data_shapes): + all_layers = sym.get_internals() + #print(all_layers) + arg_shapes, out_shapes, aux_shapes = all_layers.infer_shape(**data_shapes) + out_shape_dict = dict(zip(all_layers.list_outputs(), out_shapes)) + + nodes = json.loads(sym.tojson())['nodes'] + nodeid_shape = {} + for nodeid, node in enumerate(nodes): + name = node['name'] + layer_name = name + "_output" + if layer_name in out_shape_dict: + nodeid_shape[nodeid] = out_shape_dict[layer_name] + #print(nodeid_shape) + FLOPs = 0 + for nodeid, node in enumerate(nodes): + flops = 0 + if node['op'] == 'Convolution': + output_shape = nodeid_shape[nodeid] + name = node['name'] + attr = node['attrs'] + input_nodeid = node['inputs'][0][0] + input_shape = nodeid_shape[input_nodeid] + flops = count_conv_flops(input_shape, output_shape, attr) + elif node['op'] == 'FullyConnected': + attr = node['attrs'] + output_shape = nodeid_shape[nodeid] + input_nodeid = node['inputs'][0][0] + input_shape = nodeid_shape[input_nodeid] + output_filter = output_shape[1] + input_filter = input_shape[1] * input_shape[2] * input_shape[3] + #assert len(input_shape)==4 and input_shape[2]==1 and input_shape[3]==1 + flops = count_fc_flops(input_filter, output_filter, attr) + #print(node, flops) + FLOPs += flops + + return FLOPs + + +def flops_str(FLOPs): + preset = [(1e12, 'T'), (1e9, 'G'), (1e6, 'M'), (1e3, 'K')] + + for p in preset: + if FLOPs // p[0] > 0: + N = FLOPs / p[0] + ret = "%.1f%s" % (N, p[1]) + return ret + ret = "%.1f" % (FLOPs) + return ret + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='flops counter') + # general + #parser.add_argument('--model', default='../models2/y2-arcface-retinat1/model,1', help='path to load model.') + #parser.add_argument('--model', default='../models2/r100fc-arcface-retinaa/model,1', help='path to load model.') + parser.add_argument('--model', + default='../models2/r50fc-arcface-emore/model,1', + help='path to load model.') + args = parser.parse_args() + _vec = args.model.split(',') + assert len(_vec) == 2 + prefix = _vec[0] + epoch = int(_vec[1]) + print('loading', prefix, epoch) + sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) + all_layers = sym.get_internals() + sym = all_layers['fc1_output'] + FLOPs = count_flops(sym, data=(1, 3, 112, 112)) + print('FLOPs:', FLOPs) diff --git a/recognition/subcenter_arcface/common/rec2image.py b/recognition/subcenter_arcface/common/rec2image.py new file mode 100644 index 0000000..21e5ec4 --- /dev/null +++ b/recognition/subcenter_arcface/common/rec2image.py @@ -0,0 +1,60 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import os +import sys +import mxnet as mx +from mxnet import ndarray as nd +import random +import argparse +import cv2 +import time +import sklearn +import numpy as np + + +def main(args): + include_datasets = args.include.split(',') + rec_list = [] + for ds in include_datasets: + path_imgrec = os.path.join(ds, 'train.rec') + path_imgidx = os.path.join(ds, 'train.idx') + imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') # pylint: disable=redefined-variable-type + rec_list.append(imgrec) + if not os.path.exists(args.output): + os.makedirs(args.output) + for ds_id in range(len(rec_list)): + id_list = [] + imgrec = rec_list[ds_id] + s = imgrec.read_idx(0) + header, _ = mx.recordio.unpack(s) + assert header.flag > 0 + print('header0 label', header.label) + header0 = (int(header.label[0]), int(header.label[1])) + seq_identity = range(int(header.label[0]), int(header.label[1])) + pp = 0 + for identity in seq_identity: + id_dir = os.path.join(args.output, "%d_%d" % (ds_id, identity)) + os.makedirs(id_dir) + pp += 1 + if pp % 10 == 0: + print('processing id', pp) + s = imgrec.read_idx(identity) + header, _ = mx.recordio.unpack(s) + imgid = 0 + for _idx in range(int(header.label[0]), int(header.label[1])): + s = imgrec.read_idx(_idx) + _header, _img = mx.recordio.unpack(s) + _img = mx.image.imdecode(_img).asnumpy()[:, :, ::-1] # to bgr + image_path = os.path.join(id_dir, "%d.jpg" % imgid) + cv2.imwrite(image_path, _img) + imgid += 1 + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='do dataset merge') + # general + parser.add_argument('--include', default='', type=str, help='') + parser.add_argument('--output', default='', type=str, help='') + args = parser.parse_args() + main(args) diff --git a/recognition/subcenter_arcface/common/rec2shufrec.py b/recognition/subcenter_arcface/common/rec2shufrec.py new file mode 100644 index 0000000..cf916b4 --- /dev/null +++ b/recognition/subcenter_arcface/common/rec2shufrec.py @@ -0,0 +1,72 @@ +import os +import os.path as osp +import sys +import datetime +import glob +import shutil +import numbers +import mxnet as mx +from mxnet import ndarray as nd +from mxnet import io +from mxnet import recordio +import random +import argparse +import cv2 +import time +import numpy as np + +def main(args): + ds = args.input + path_imgrec = osp.join(ds, 'train.rec') + path_imgidx = osp.join(ds, 'train.idx') + imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') # pylint: disable=redefined-variable-type + if not osp.exists(args.output): + os.makedirs(args.output) + writer = mx.recordio.MXRecordIO(osp.join(args.output, 'train.rec'), 'w') + s = imgrec.read_idx(0) + header, _ = recordio.unpack(s) + if header.flag > 0: + print('header0 label', header.label) + header0 = (int(header.label[0]), int(header.label[1])) + imgidx = list(range(1, int(header.label[0]))) + else: + imgidx = list(imgrec.keys) + random.shuffle(imgidx) + label_stat = None + print('total images:', len(imgidx)) + for i, idx in enumerate(imgidx): + if i%10000==0: + print('processing', i, idx) + s = imgrec.read_idx(idx) + header, img = mx.recordio.unpack(s) + label = header.label + if not isinstance(label, numbers.Number): + label = label[0] + if label_stat is None: + label_stat = [label, label] + else: + label_stat[0] = min(label, label_stat[0]) + label_stat[1] = max(label, label_stat[1]) + wheader = mx.recordio.IRHeader(0, label, i, 0) + ws = mx.recordio.pack(wheader, img) + writer.write(ws) + print('label_stat:', label_stat) + writer.close() + if args.copy_vers: + for binfile in glob.glob(osp.join(args.input, '*.bin')): + target_file = osp.join(args.output, binfile.split('/')[-1]) + shutil.copyfile(binfile, target_file) + with open(osp.join(args.output, 'property'), 'w') as f: + f.write("%d,112,112\n"%(int(label_stat[1])+1)) + f.write("%d\n"%len(imgidx)) + f.write("shuffled\n") + f.write("%s\n"%(datetime.datetime.now())) + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='convert rec to shuffled rec') + # general + parser.add_argument('--input', default='', type=str, help='') + parser.add_argument('--output', default='', type=str, help='') + parser.add_argument('--copy-vers', action='store_true', help='copy verification bins') + args = parser.parse_args() + main(args) diff --git a/recognition/subcenter_arcface/common/rec_builder.py b/recognition/subcenter_arcface/common/rec_builder.py new file mode 100644 index 0000000..1d51715 --- /dev/null +++ b/recognition/subcenter_arcface/common/rec_builder.py @@ -0,0 +1,109 @@ +import os +import sys +import mxnet as mx +from mxnet import ndarray as nd +import random +import argparse +import cv2 +import time +import sklearn +import numpy as np + + +class SeqRecBuilder(): + def __init__(self, path, image_size=(112, 112)): + self.path = path + self.image_size = image_size + self.last_label = -1 + self.widx = 0 + if not os.path.exists(path): + os.makedirs(path) + self.writer = mx.recordio.MXIndexedRecordIO( + os.path.join(path, 'train.idx'), os.path.join(path, 'train.rec'), + 'w') + self.label_stat = [-1, -1] + + def add(self, label, img, is_image=True): + #img should be BGR + #if self.sis: + # assert label>=self.last_label + idx = self.widx + self.widx += 1 + header = mx.recordio.IRHeader(0, label, idx, 0) + if is_image: + s = mx.recordio.pack_img(header, img, quality=95, img_fmt='.jpg') + else: + s = mx.recordio.pack(header, img) + self.writer.write_idx(idx, s) + if self.label_stat[0] < 0: + self.label_stat = [label, label] + else: + self.label_stat[0] = min(self.label_stat[0], label) + self.label_stat[1] = max(self.label_stat[1], label) + + def close(self): + with open(os.path.join(self.path, 'property'), 'w') as f: + f.write("%d,%d,%d\n" % (self.label_stat[1] + 1, self.image_size[0], + self.image_size[1])) + + +class RecBuilder(): + def __init__(self, path, image_size=(112, 112)): + self.path = path + self.image_size = image_size + self.last_label = -1 + self.widx = 1 + if not os.path.exists(path): + os.makedirs(path) + self.writer = mx.recordio.MXIndexedRecordIO( + os.path.join(path, 'train.idx'), os.path.join(path, 'train.rec'), + 'w') + self.label_stat = [-1, -1] + self.identities = [] + + def add(self, label, imgs): + #img should be BGR + assert label >= 0 + assert label > self.last_label + assert len(imgs) > 0 + idflag = [self.widx, -1] + for img in imgs: + idx = self.widx + self.widx += 1 + header = mx.recordio.IRHeader(0, label, idx, 0) + if isinstance(img, np.ndarray): + s = mx.recordio.pack_img(header, + img, + quality=95, + img_fmt='.jpg') + else: + s = mx.recordio.pack(header, img) + self.writer.write_idx(idx, s) + idflag[1] = self.widx + self.identities.append(idflag) + if self.label_stat[0] < 0: + self.label_stat = [label, label] + else: + self.label_stat[0] = min(self.label_stat[0], label) + self.label_stat[1] = max(self.label_stat[1], label) + self.last_label = label + + def close(self): + id_idx = self.widx + for id_flag in self.identities: + idx = self.widx + self.widx += 1 + _header = mx.recordio.IRHeader(0, id_flag, idx, 0) + s = mx.recordio.pack(_header, b'') + self.writer.write_idx(idx, s) + + print('id0:', (id_idx, self.widx)) + idx = 0 + _header = mx.recordio.IRHeader(0, (id_idx, self.widx), idx, 1) + s = mx.recordio.pack(_header, b'') + self.writer.write_idx(idx, s) + print('label stat:', self.label_stat) + + with open(os.path.join(self.path, 'property'), 'w') as f: + f.write("%d,%d,%d\n" % (self.label_stat[1] + 1, self.image_size[0], + self.image_size[1])) diff --git a/recognition/subcenter_arcface/common/verification.py b/recognition/subcenter_arcface/common/verification.py new file mode 100644 index 0000000..f46942a --- /dev/null +++ b/recognition/subcenter_arcface/common/verification.py @@ -0,0 +1,423 @@ +"""Helper for evaluation on the Labeled Faces in the Wild dataset +""" + +# MIT License +# +# Copyright (c) 2016 David Sandberg +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import argparse +import sys +import numpy as np +from scipy import misc +from sklearn.model_selection import KFold +from scipy import interpolate +import sklearn +import cv2 +import math +import datetime +import pickle +from sklearn.decomposition import PCA +import mxnet as mx +from mxnet import ndarray as nd + + +class LFold: + def __init__(self, n_splits=2, shuffle=False): + self.n_splits = n_splits + if self.n_splits > 1: + self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle) + + def split(self, indices): + if self.n_splits > 1: + return self.k_fold.split(indices) + else: + return [(indices, indices)] + + +def calculate_roc(thresholds, + embeddings1, + embeddings2, + actual_issame, + nrof_folds=10, + pca=0): + assert (embeddings1.shape[0] == embeddings2.shape[0]) + assert (embeddings1.shape[1] == embeddings2.shape[1]) + nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) + nrof_thresholds = len(thresholds) + k_fold = LFold(n_splits=nrof_folds, shuffle=False) + + tprs = np.zeros((nrof_folds, nrof_thresholds)) + fprs = np.zeros((nrof_folds, nrof_thresholds)) + accuracy = np.zeros((nrof_folds)) + indices = np.arange(nrof_pairs) + #print('pca', pca) + + if pca == 0: + diff = np.subtract(embeddings1, embeddings2) + dist = np.sum(np.square(diff), 1) + + for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): + #print('train_set', train_set) + #print('test_set', test_set) + if pca > 0: + print('doing pca on', fold_idx) + embed1_train = embeddings1[train_set] + embed2_train = embeddings2[train_set] + _embed_train = np.concatenate((embed1_train, embed2_train), axis=0) + #print(_embed_train.shape) + pca_model = PCA(n_components=pca) + pca_model.fit(_embed_train) + embed1 = pca_model.transform(embeddings1) + embed2 = pca_model.transform(embeddings2) + embed1 = sklearn.preprocessing.normalize(embed1) + embed2 = sklearn.preprocessing.normalize(embed2) + #print(embed1.shape, embed2.shape) + diff = np.subtract(embed1, embed2) + dist = np.sum(np.square(diff), 1) + + # Find the best threshold for the fold + acc_train = np.zeros((nrof_thresholds)) + for threshold_idx, threshold in enumerate(thresholds): + _, _, acc_train[threshold_idx] = calculate_accuracy( + threshold, dist[train_set], actual_issame[train_set]) + best_threshold_index = np.argmax(acc_train) + #print('threshold', thresholds[best_threshold_index]) + for threshold_idx, threshold in enumerate(thresholds): + tprs[fold_idx, + threshold_idx], fprs[fold_idx, + threshold_idx], _ = calculate_accuracy( + threshold, dist[test_set], + actual_issame[test_set]) + _, _, accuracy[fold_idx] = calculate_accuracy( + thresholds[best_threshold_index], dist[test_set], + actual_issame[test_set]) + + tpr = np.mean(tprs, 0) + fpr = np.mean(fprs, 0) + return tpr, fpr, accuracy + + +def calculate_accuracy(threshold, dist, actual_issame): + predict_issame = np.less(dist, threshold) + tp = np.sum(np.logical_and(predict_issame, actual_issame)) + fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) + tn = np.sum( + np.logical_and(np.logical_not(predict_issame), + np.logical_not(actual_issame))) + fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame)) + + tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn) + fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn) + acc = float(tp + tn) / dist.size + return tpr, fpr, acc + + +def calculate_val(thresholds, + embeddings1, + embeddings2, + actual_issame, + far_target, + nrof_folds=10): + assert (embeddings1.shape[0] == embeddings2.shape[0]) + assert (embeddings1.shape[1] == embeddings2.shape[1]) + nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) + nrof_thresholds = len(thresholds) + k_fold = LFold(n_splits=nrof_folds, shuffle=False) + + val = np.zeros(nrof_folds) + far = np.zeros(nrof_folds) + + diff = np.subtract(embeddings1, embeddings2) + dist = np.sum(np.square(diff), 1) + indices = np.arange(nrof_pairs) + + for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): + + # Find the threshold that gives FAR = far_target + far_train = np.zeros(nrof_thresholds) + for threshold_idx, threshold in enumerate(thresholds): + _, far_train[threshold_idx] = calculate_val_far( + threshold, dist[train_set], actual_issame[train_set]) + if np.max(far_train) >= far_target: + f = interpolate.interp1d(far_train, thresholds, kind='slinear') + threshold = f(far_target) + else: + threshold = 0.0 + + val[fold_idx], far[fold_idx] = calculate_val_far( + threshold, dist[test_set], actual_issame[test_set]) + + val_mean = np.mean(val) + far_mean = np.mean(far) + val_std = np.std(val) + return val_mean, val_std, far_mean + + +def calculate_val_far(threshold, dist, actual_issame): + predict_issame = np.less(dist, threshold) + true_accept = np.sum(np.logical_and(predict_issame, actual_issame)) + false_accept = np.sum( + np.logical_and(predict_issame, np.logical_not(actual_issame))) + n_same = np.sum(actual_issame) + n_diff = np.sum(np.logical_not(actual_issame)) + #print(true_accept, false_accept) + #print(n_same, n_diff) + val = float(true_accept) / float(n_same) + far = float(false_accept) / float(n_diff) + return val, far + + +def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0): + # Calculate evaluation metrics + thresholds = np.arange(0, 4, 0.01) + embeddings1 = embeddings[0::2] + embeddings2 = embeddings[1::2] + tpr, fpr, accuracy = calculate_roc(thresholds, + embeddings1, + embeddings2, + np.asarray(actual_issame), + nrof_folds=nrof_folds, + pca=pca) + thresholds = np.arange(0, 4, 0.001) + val, val_std, far = calculate_val(thresholds, + embeddings1, + embeddings2, + np.asarray(actual_issame), + 1e-3, + nrof_folds=nrof_folds) + return tpr, fpr, accuracy, val, val_std, far + + +def load_bin(path, image_size): + try: + with open(path, 'rb') as f: + bins, issame_list = pickle.load(f) #py2 + except UnicodeDecodeError as e: + with open(path, 'rb') as f: + bins, issame_list = pickle.load(f, encoding='bytes') #py3 + data_list = [] + for flip in [0, 1]: + data = nd.empty( + (len(issame_list) * 2, 3, image_size[0], image_size[1])) + data_list.append(data) + for i in range(len(issame_list) * 2): + _bin = bins[i] + img = mx.image.imdecode(_bin) + if img.shape[1] != image_size[0]: + img = mx.image.resize_short(img, image_size[0]) + img = nd.transpose(img, axes=(2, 0, 1)) + for flip in [0, 1]: + if flip == 1: + img = mx.ndarray.flip(data=img, axis=2) + data_list[flip][i][:] = img + if i % 1000 == 0: + print('loading bin', i) + print(data_list[0].shape) + return (data_list, issame_list) + + +def test(data_set, + mx_model, + batch_size, + nfolds=10, + data_extra=None, + label_shape=None): + print('testing verification..') + data_list = data_set[0] + issame_list = data_set[1] + model = mx_model + embeddings_list = [] + if data_extra is not None: + _data_extra = nd.array(data_extra) + time_consumed = 0.0 + if label_shape is None: + _label = nd.ones((batch_size, )) + else: + _label = nd.ones(label_shape) + for i in range(len(data_list)): + data = data_list[i] + embeddings = None + ba = 0 + while ba < data.shape[0]: + bb = min(ba + batch_size, data.shape[0]) + count = bb - ba + _data = nd.slice_axis(data, axis=0, begin=bb - batch_size, end=bb) + #print(_data.shape, _label.shape) + time0 = datetime.datetime.now() + if data_extra is None: + db = mx.io.DataBatch(data=(_data, ), label=(_label, )) + else: + db = mx.io.DataBatch(data=(_data, _data_extra), + label=(_label, )) + model.forward(db, is_train=False) + net_out = model.get_outputs() + #_arg, _aux = model.get_params() + #__arg = {} + #for k,v in _arg.iteritems(): + # __arg[k] = v.as_in_context(_ctx) + #_arg = __arg + #_arg["data"] = _data.as_in_context(_ctx) + #_arg["softmax_label"] = _label.as_in_context(_ctx) + #for k,v in _arg.iteritems(): + # print(k,v.context) + #exe = sym.bind(_ctx, _arg ,args_grad=None, grad_req="null", aux_states=_aux) + #exe.forward(is_train=False) + #net_out = exe.outputs + _embeddings = net_out[0].asnumpy() + time_now = datetime.datetime.now() + diff = time_now - time0 + time_consumed += diff.total_seconds() + #print(_embeddings.shape) + if embeddings is None: + embeddings = np.zeros((data.shape[0], _embeddings.shape[1])) + embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :] + ba = bb + embeddings_list.append(embeddings) + + _xnorm = 0.0 + _xnorm_cnt = 0 + for embed in embeddings_list: + for i in range(embed.shape[0]): + _em = embed[i] + _norm = np.linalg.norm(_em) + #print(_em.shape, _norm) + _xnorm += _norm + _xnorm_cnt += 1 + _xnorm /= _xnorm_cnt + + embeddings = embeddings_list[0].copy() + embeddings = sklearn.preprocessing.normalize(embeddings) + acc1 = 0.0 + std1 = 0.0 + #_, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10) + #acc1, std1 = np.mean(accuracy), np.std(accuracy) + + #print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far)) + #embeddings = np.concatenate(embeddings_list, axis=1) + embeddings = embeddings_list[0] + embeddings_list[1] + embeddings = sklearn.preprocessing.normalize(embeddings) + print(embeddings.shape) + print('infer time', time_consumed) + _, _, accuracy, val, val_std, far = evaluate(embeddings, + issame_list, + nrof_folds=nfolds) + acc2, std2 = np.mean(accuracy), np.std(accuracy) + return acc1, std1, acc2, std2, _xnorm, embeddings_list + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser(description='do verification') + # general + parser.add_argument('--data-dir', default='', help='') + parser.add_argument('--model', + default='../model/softmax,50', + help='path to load model.') + parser.add_argument('--target', + default='lfw,cfp_ff,cfp_fp,agedb_30', + help='test targets.') + parser.add_argument('--gpu', default=0, type=int, help='gpu id') + parser.add_argument('--batch-size', default=32, type=int, help='') + parser.add_argument('--max', default='', type=str, help='') + parser.add_argument('--mode', default=0, type=int, help='') + parser.add_argument('--nfolds', default=10, type=int, help='') + args = parser.parse_args() + #sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common')) + #import face_image + #prop = face_image.load_property(args.data_dir) + #image_size = prop.image_size + image_size = [112, 112] + print('image_size', image_size) + ctx = mx.gpu(args.gpu) + nets = [] + vec = args.model.split(',') + prefix = args.model.split(',')[0] + epochs = [] + if len(vec) == 1: + pdir = os.path.dirname(prefix) + for fname in os.listdir(pdir): + if not fname.endswith('.params'): + continue + _file = os.path.join(pdir, fname) + if _file.startswith(prefix): + epoch = int(fname.split('.')[0].split('-')[1]) + epochs.append(epoch) + epochs = sorted(epochs, reverse=True) + if len(args.max) > 0: + _max = [int(x) for x in args.max.split(',')] + assert len(_max) == 2 + if len(epochs) > _max[1]: + epochs = epochs[_max[0]:_max[1]] + + else: + epochs = [int(x) for x in vec[1].split('|')] + print('model number', len(epochs)) + time0 = datetime.datetime.now() + for epoch in epochs: + print('loading', prefix, epoch) + sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) + #arg_params, aux_params = ch_dev(arg_params, aux_params, ctx) + all_layers = sym.get_internals() + sym = all_layers['fc1_output'] + model = mx.mod.Module(symbol=sym, context=ctx, label_names=None) + #model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))]) + model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], + image_size[1]))]) + model.set_params(arg_params, aux_params) + nets.append(model) + time_now = datetime.datetime.now() + diff = time_now - time0 + print('model loading time', diff.total_seconds()) + + ver_list = [] + ver_name_list = [] + for name in args.target.split(','): + path = os.path.join(args.data_dir, name + ".bin") + if os.path.exists(path): + print('loading.. ', name) + data_set = load_bin(path, image_size) + ver_list.append(data_set) + ver_name_list.append(name) + + if args.mode == 0: + for i in range(len(ver_list)): + results = [] + for model in nets: + acc1, std1, acc2, std2, xnorm, embeddings_list = test( + ver_list[i], model, args.batch_size, args.nfolds) + print('[%s]XNorm: %f' % (ver_name_list[i], xnorm)) + print('[%s]Accuracy: %1.5f+-%1.5f' % + (ver_name_list[i], acc1, std1)) + print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % + (ver_name_list[i], acc2, std2)) + results.append(acc2) + print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results))) + elif args.mode == 1: + model = nets[0] + test_badcase(ver_list[0], model, args.batch_size, args.target) + else: + model = nets[0] + dumpR(ver_list[0], model, args.batch_size, args.target) diff --git a/recognition/SubCenter-ArcFace/drop.py b/recognition/subcenter_arcface/drop.py similarity index 100% rename from recognition/SubCenter-ArcFace/drop.py rename to recognition/subcenter_arcface/drop.py diff --git a/recognition/SubCenter-ArcFace/image_iter.py b/recognition/subcenter_arcface/image_iter.py similarity index 100% rename from recognition/SubCenter-ArcFace/image_iter.py rename to recognition/subcenter_arcface/image_iter.py diff --git a/recognition/SubCenter-ArcFace/parall_module_local_v1.py b/recognition/subcenter_arcface/parall_module_local_v1.py similarity index 100% rename from recognition/SubCenter-ArcFace/parall_module_local_v1.py rename to recognition/subcenter_arcface/parall_module_local_v1.py diff --git a/recognition/SubCenter-ArcFace/sample_config.py b/recognition/subcenter_arcface/sample_config.py similarity index 100% rename from recognition/SubCenter-ArcFace/sample_config.py rename to recognition/subcenter_arcface/sample_config.py diff --git a/recognition/subcenter_arcface/symbol/fdensenet.py b/recognition/subcenter_arcface/symbol/fdensenet.py new file mode 100644 index 0000000..b3d49ee --- /dev/null +++ b/recognition/subcenter_arcface/symbol/fdensenet.py @@ -0,0 +1,169 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# coding: utf-8 +# pylint: disable= arguments-differ +"""DenseNet, implemented in Gluon.""" + +import sys +import os +import mxnet as mx +import mxnet.ndarray as nd +import mxnet.gluon as gluon +import mxnet.gluon.nn as nn +import mxnet.autograd as ag +import symbol_utils +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from config import config + + +def Act(): + if config.net_act == 'prelu': + return nn.PReLU() + else: + return nn.Activation(config.net_act) + + +# Helpers +def _make_dense_block(num_layers, bn_size, growth_rate, dropout, stage_index): + out = nn.HybridSequential(prefix='stage%d_' % stage_index) + with out.name_scope(): + for _ in range(num_layers): + out.add(_make_dense_layer(growth_rate, bn_size, dropout)) + return out + + +def _make_dense_layer(growth_rate, bn_size, dropout): + new_features = nn.HybridSequential(prefix='') + new_features.add(nn.BatchNorm()) + #new_features.add(nn.Activation('relu')) + new_features.add(Act()) + new_features.add( + nn.Conv2D(bn_size * growth_rate, kernel_size=1, use_bias=False)) + new_features.add(nn.BatchNorm()) + #new_features.add(nn.Activation('relu')) + new_features.add(Act()) + new_features.add( + nn.Conv2D(growth_rate, kernel_size=3, padding=1, use_bias=False)) + if dropout: + new_features.add(nn.Dropout(dropout)) + + out = gluon.contrib.nn.HybridConcurrent(axis=1, prefix='') + out.add(gluon.contrib.nn.Identity()) + out.add(new_features) + + return out + + +def _make_transition(num_output_features): + out = nn.HybridSequential(prefix='') + out.add(nn.BatchNorm()) + #out.add(nn.Activation('relu')) + out.add(Act()) + out.add(nn.Conv2D(num_output_features, kernel_size=1, use_bias=False)) + out.add(nn.AvgPool2D(pool_size=2, strides=2)) + return out + + +# Net +class DenseNet(nn.HybridBlock): + r"""Densenet-BC model from the + `"Densely Connected Convolutional Networks" `_ paper. + + Parameters + ---------- + num_init_features : int + Number of filters to learn in the first convolution layer. + growth_rate : int + Number of filters to add each layer (`k` in the paper). + block_config : list of int + List of integers for numbers of layers in each pooling block. + bn_size : int, default 4 + Multiplicative factor for number of bottle neck layers. + (i.e. bn_size * k features in the bottleneck layer) + dropout : float, default 0 + Rate of dropout after each dense layer. + classes : int, default 1000 + Number of classification classes. + """ + def __init__(self, + num_init_features, + growth_rate, + block_config, + bn_size=4, + dropout=0, + classes=1000, + **kwargs): + + super(DenseNet, self).__init__(**kwargs) + with self.name_scope(): + self.features = nn.HybridSequential(prefix='') + self.features.add( + nn.Conv2D(num_init_features, + kernel_size=3, + strides=1, + padding=1, + use_bias=False)) + self.features.add(nn.BatchNorm()) + self.features.add(nn.Activation('relu')) + self.features.add(nn.MaxPool2D(pool_size=3, strides=2, padding=1)) + # Add dense blocks + num_features = num_init_features + for i, num_layers in enumerate(block_config): + self.features.add( + _make_dense_block(num_layers, bn_size, growth_rate, + dropout, i + 1)) + num_features = num_features + num_layers * growth_rate + if i != len(block_config) - 1: + self.features.add(_make_transition(num_features // 2)) + num_features = num_features // 2 + self.features.add(nn.BatchNorm()) + self.features.add(nn.Activation('relu')) + #self.features.add(nn.AvgPool2D(pool_size=7)) + #self.features.add(nn.Flatten()) + + #self.output = nn.Dense(classes) + + def hybrid_forward(self, F, x): + x = self.features(x) + #x = self.output(x) + return x + + +# Specification +densenet_spec = { + 121: (64, 32, [6, 12, 24, 16]), + 161: (96, 48, [6, 12, 36, 24]), + 169: (64, 32, [6, 12, 32, 32]), + 201: (64, 32, [6, 12, 48, 32]) +} + + +# Constructor +def get_symbol(): + num_layers = config.num_layers + num_init_features, growth_rate, block_config = densenet_spec[num_layers] + net = DenseNet(num_init_features, + growth_rate, + block_config, + dropout=config.densenet_dropout) + data = mx.sym.Variable(name='data') + data = data - 127.5 + data = data * 0.0078125 + body = net(data) + fc1 = symbol_utils.get_fc1(body, config.emb_size, config.net_output) + return fc1 diff --git a/recognition/subcenter_arcface/symbol/fmnasnet.py b/recognition/subcenter_arcface/symbol/fmnasnet.py new file mode 100644 index 0000000..118beb9 --- /dev/null +++ b/recognition/subcenter_arcface/symbol/fmnasnet.py @@ -0,0 +1,213 @@ +import sys +import os +import mxnet as mx +import mxnet.ndarray as nd +import mxnet.gluon as gluon +import mxnet.gluon.nn as nn +import mxnet.autograd as ag +import symbol_utils +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from config import config + + +def Act(): + if config.net_act == 'prelu': + return nn.PReLU() + else: + return nn.Activation(config.net_act) + + +def ConvBlock(channels, kernel_size, strides, **kwargs): + out = nn.HybridSequential(**kwargs) + with out.name_scope(): + out.add( + nn.Conv2D(channels, + kernel_size, + strides=strides, + padding=1, + use_bias=False), nn.BatchNorm(scale=True), + Act() + #nn.Activation('relu') + ) + return out + + +def Conv1x1(channels, is_linear=False, **kwargs): + out = nn.HybridSequential(**kwargs) + with out.name_scope(): + out.add(nn.Conv2D(channels, 1, padding=0, use_bias=False), + nn.BatchNorm(scale=True)) + if not is_linear: + #out.add(nn.Activation('relu')) + out.add(Act()) + return out + + +def DWise(channels, strides, kernel_size=3, **kwargs): + out = nn.HybridSequential(**kwargs) + with out.name_scope(): + out.add( + nn.Conv2D(channels, + kernel_size, + strides=strides, + padding=kernel_size // 2, + groups=channels, + use_bias=False), nn.BatchNorm(scale=True), + Act() + #nn.Activation('relu') + ) + return out + + +class SepCONV(nn.HybridBlock): + def __init__(self, + inp, + output, + kernel_size, + depth_multiplier=1, + with_bn=True, + **kwargs): + super(SepCONV, self).__init__(**kwargs) + with self.name_scope(): + self.net = nn.HybridSequential() + cn = int(inp * depth_multiplier) + + if output is None: + self.net.add( + nn.Conv2D(in_channels=inp, + channels=cn, + groups=inp, + kernel_size=kernel_size, + strides=(1, 1), + padding=kernel_size // 2, + use_bias=not with_bn)) + else: + self.net.add( + nn.Conv2D(in_channels=inp, + channels=cn, + groups=inp, + kernel_size=kernel_size, + strides=(1, 1), + padding=kernel_size // 2, + use_bias=False), + nn.BatchNorm(), + Act(), + #nn.Activation('relu'), + nn.Conv2D(in_channels=cn, + channels=output, + kernel_size=(1, 1), + strides=(1, 1), + use_bias=not with_bn)) + + self.with_bn = with_bn + self.act = Act() + #self.act = nn.Activation('relu') + if with_bn: + self.bn = nn.BatchNorm() + + def hybrid_forward(self, F, x): + x = self.net(x) + if self.with_bn: + x = self.bn(x) + if self.act is not None: + x = self.act(x) + return x + + +class ExpandedConv(nn.HybridBlock): + def __init__(self, + inp, + oup, + t, + strides, + kernel=3, + same_shape=True, + **kwargs): + super(ExpandedConv, self).__init__(**kwargs) + + self.same_shape = same_shape + self.strides = strides + with self.name_scope(): + self.bottleneck = nn.HybridSequential() + self.bottleneck.add( + Conv1x1(inp * t, prefix="expand_"), + DWise(inp * t, self.strides, kernel, prefix="dwise_"), + Conv1x1(oup, is_linear=True, prefix="linear_")) + + def hybrid_forward(self, F, x): + out = self.bottleneck(x) + if self.strides == 1 and self.same_shape: + out = F.elemwise_add(out, x) + return out + + +def ExpandedConvSequence(t, k, inp, oup, repeats, first_strides, **kwargs): + seq = nn.HybridSequential(**kwargs) + with seq.name_scope(): + seq.add(ExpandedConv(inp, oup, t, first_strides, k, same_shape=False)) + curr_inp = oup + for i in range(1, repeats): + seq.add(ExpandedConv(curr_inp, oup, t, 1)) + curr_inp = oup + return seq + + +class MNasNet(nn.HybridBlock): + def __init__(self, m=1.0, **kwargs): + super(MNasNet, self).__init__(**kwargs) + + self.first_oup = int(32 * m) + self.second_oup = int(16 * m) + #self.second_oup = int(32*m) + self.interverted_residual_setting = [ + # t, c, n, s, k + [3, int(24 * m), 3, 2, 3, "stage2_"], # -> 56x56 + [3, int(40 * m), 3, 2, 5, "stage3_"], # -> 28x28 + [6, int(80 * m), 3, 2, 5, "stage4_1_"], # -> 14x14 + [6, int(96 * m), 2, 1, 3, "stage4_2_"], # -> 14x14 + [6, int(192 * m), 4, 2, 5, "stage5_1_"], # -> 7x7 + [6, int(320 * m), 1, 1, 3, "stage5_2_"], # -> 7x7 + ] + self.last_channels = int(1024 * m) + + with self.name_scope(): + self.features = nn.HybridSequential() + self.features.add( + ConvBlock(self.first_oup, 3, 1, prefix="stage1_conv0_")) + self.features.add( + SepCONV(self.first_oup, + self.second_oup, + 3, + prefix="stage1_sepconv0_")) + inp = self.second_oup + for i, (t, c, n, s, k, + prefix) in enumerate(self.interverted_residual_setting): + oup = c + self.features.add( + ExpandedConvSequence(t, k, inp, oup, n, s, prefix=prefix)) + inp = oup + + self.features.add(Conv1x1(self.last_channels, prefix="stage5_3_")) + #self.features.add(nn.GlobalAvgPool2D()) + #self.features.add(nn.Flatten()) + #self.output = nn.Dense(num_classes) + def hybrid_forward(self, F, x): + x = self.features(x) + #x = self.output(x) + return x + + def num_output_channel(self): + return self.last_channels + + +def get_symbol(): + net = MNasNet(config.net_multiplier) + data = mx.sym.Variable(name='data') + data = data - 127.5 + data = data * 0.0078125 + body = net(data) + fc1 = symbol_utils.get_fc1(body, + config.emb_size, + config.net_output, + input_channel=net.num_output_channel()) + return fc1 diff --git a/recognition/subcenter_arcface/symbol/fmobilefacenet.py b/recognition/subcenter_arcface/symbol/fmobilefacenet.py new file mode 100644 index 0000000..f498264 --- /dev/null +++ b/recognition/subcenter_arcface/symbol/fmobilefacenet.py @@ -0,0 +1,224 @@ +import sys +import os +import mxnet as mx +import symbol_utils +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from config import config + + +def Act(data, act_type, name): + #ignore param act_type, set it in this function + if act_type == 'prelu': + body = mx.sym.LeakyReLU(data=data, act_type='prelu', name=name) + else: + body = mx.sym.Activation(data=data, act_type=act_type, name=name) + return body + + +def Conv(data, + num_filter=1, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + num_group=1, + name=None, + suffix=''): + conv = mx.sym.Convolution(data=data, + num_filter=num_filter, + kernel=kernel, + num_group=num_group, + stride=stride, + pad=pad, + no_bias=True, + name='%s%s_conv2d' % (name, suffix)) + bn = mx.sym.BatchNorm(data=conv, + name='%s%s_batchnorm' % (name, suffix), + fix_gamma=False, + momentum=config.bn_mom) + act = Act(data=bn, + act_type=config.net_act, + name='%s%s_relu' % (name, suffix)) + return act + + +def Linear(data, + num_filter=1, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + num_group=1, + name=None, + suffix=''): + conv = mx.sym.Convolution(data=data, + num_filter=num_filter, + kernel=kernel, + num_group=num_group, + stride=stride, + pad=pad, + no_bias=True, + name='%s%s_conv2d' % (name, suffix)) + bn = mx.sym.BatchNorm(data=conv, + name='%s%s_batchnorm' % (name, suffix), + fix_gamma=False, + momentum=config.bn_mom) + return bn + + +def ConvOnly(data, + num_filter=1, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + num_group=1, + name=None, + suffix=''): + conv = mx.sym.Convolution(data=data, + num_filter=num_filter, + kernel=kernel, + num_group=num_group, + stride=stride, + pad=pad, + no_bias=True, + name='%s%s_conv2d' % (name, suffix)) + return conv + + +def DResidual(data, + num_out=1, + kernel=(3, 3), + stride=(2, 2), + pad=(1, 1), + num_group=1, + name=None, + suffix=''): + conv = Conv(data=data, + num_filter=num_group, + kernel=(1, 1), + pad=(0, 0), + stride=(1, 1), + name='%s%s_conv_sep' % (name, suffix)) + conv_dw = Conv(data=conv, + num_filter=num_group, + num_group=num_group, + kernel=kernel, + pad=pad, + stride=stride, + name='%s%s_conv_dw' % (name, suffix)) + proj = Linear(data=conv_dw, + num_filter=num_out, + kernel=(1, 1), + pad=(0, 0), + stride=(1, 1), + name='%s%s_conv_proj' % (name, suffix)) + return proj + + +def Residual(data, + num_block=1, + num_out=1, + kernel=(3, 3), + stride=(1, 1), + pad=(1, 1), + num_group=1, + name=None, + suffix=''): + identity = data + for i in range(num_block): + shortcut = identity + conv = DResidual(data=identity, + num_out=num_out, + kernel=kernel, + stride=stride, + pad=pad, + num_group=num_group, + name='%s%s_block' % (name, suffix), + suffix='%d' % i) + identity = conv + shortcut + return identity + + +def get_symbol(): + num_classes = config.emb_size + print('in_network', config) + fc_type = config.net_output + data = mx.symbol.Variable(name="data") + data = data - 127.5 + data = data * 0.0078125 + blocks = config.net_blocks + conv_1 = Conv(data, + num_filter=64, + kernel=(3, 3), + pad=(1, 1), + stride=(2, 2), + name="conv_1") + if blocks[0] == 1: + conv_2_dw = Conv(conv_1, + num_group=64, + num_filter=64, + kernel=(3, 3), + pad=(1, 1), + stride=(1, 1), + name="conv_2_dw") + else: + conv_2_dw = Residual(conv_1, + num_block=blocks[0], + num_out=64, + kernel=(3, 3), + stride=(1, 1), + pad=(1, 1), + num_group=64, + name="res_2") + conv_23 = DResidual(conv_2_dw, + num_out=64, + kernel=(3, 3), + stride=(2, 2), + pad=(1, 1), + num_group=128, + name="dconv_23") + conv_3 = Residual(conv_23, + num_block=blocks[1], + num_out=64, + kernel=(3, 3), + stride=(1, 1), + pad=(1, 1), + num_group=128, + name="res_3") + conv_34 = DResidual(conv_3, + num_out=128, + kernel=(3, 3), + stride=(2, 2), + pad=(1, 1), + num_group=256, + name="dconv_34") + conv_4 = Residual(conv_34, + num_block=blocks[2], + num_out=128, + kernel=(3, 3), + stride=(1, 1), + pad=(1, 1), + num_group=256, + name="res_4") + conv_45 = DResidual(conv_4, + num_out=128, + kernel=(3, 3), + stride=(2, 2), + pad=(1, 1), + num_group=512, + name="dconv_45") + conv_5 = Residual(conv_45, + num_block=blocks[3], + num_out=128, + kernel=(3, 3), + stride=(1, 1), + pad=(1, 1), + num_group=256, + name="res_5") + conv_6_sep = Conv(conv_5, + num_filter=512, + kernel=(1, 1), + pad=(0, 0), + stride=(1, 1), + name="conv_6sep") + + fc1 = symbol_utils.get_fc1(conv_6_sep, num_classes, fc_type) + return fc1 diff --git a/gender-age/fmobilenet.py b/recognition/subcenter_arcface/symbol/fmobilenet.py similarity index 93% rename from gender-age/fmobilenet.py rename to recognition/subcenter_arcface/symbol/fmobilenet.py index 6bb1c0a..bdbf8a5 100644 --- a/gender-age/fmobilenet.py +++ b/recognition/subcenter_arcface/symbol/fmobilenet.py @@ -15,14 +15,20 @@ # specific language governing permissions and limitations # under the License. +import sys +import os import mxnet as mx import symbol_utils +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from config import config def Act(data, act_type, name): #ignore param act_type, set it in this function - #body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name) - body = mx.sym.Activation(data=data, act_type='relu', name=name) + if act_type == 'prelu': + body = mx.sym.LeakyReLU(data=data, act_type='prelu', name=name) + else: + body = mx.sym.Activation(data=data, act_type=act_type, name=name) return body @@ -45,7 +51,9 @@ def Conv(data, bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' % (name, suffix), fix_gamma=True) - act = Act(data=bn, act_type='relu', name='%s%s_relu' % (name, suffix)) + act = Act(data=bn, + act_type=config.net_act, + name='%s%s_relu' % (name, suffix)) return act @@ -68,20 +76,16 @@ def ConvOnly(data, return conv -def get_symbol(num_classes, **kwargs): +def get_symbol(): + num_classes = config.emb_size + bn_mom = config.bn_mom + workspace = config.workspace data = mx.symbol.Variable(name="data") # 224 data = data - 127.5 data = data * 0.0078125 - version_input = kwargs.get('version_input', 1) - assert version_input >= 0 - version_output = kwargs.get('version_output', 'E') - multiplier = kwargs.get('multiplier', 1.0) - fc_type = version_output - base_filter = int(32 * multiplier) - bf = base_filter - print(version_input, version_output, base_filter) - - if version_input == 0: + fc_type = config.net_output + bf = int(32 * config.net_multiplier) + if config.net_input == 0: conv_1 = Conv(data, num_filter=bf, kernel=(3, 3), diff --git a/gender-age/fresnet.py b/recognition/subcenter_arcface/symbol/fresnet.py similarity index 94% rename from gender-age/fresnet.py rename to recognition/subcenter_arcface/symbol/fresnet.py index 037a42b..7b17788 100644 --- a/gender-age/fresnet.py +++ b/recognition/subcenter_arcface/symbol/fresnet.py @@ -25,10 +25,15 @@ Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Identity Mappings in Deep Re from __future__ import absolute_import from __future__ import division from __future__ import print_function +import sys +import os import mxnet as mx import numpy as np import symbol_utils +import memonger import sklearn +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from config import config def Conv(**kwargs): @@ -965,9 +970,19 @@ def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck, bottle_neck, **kwargs) -def resnet(units, num_stages, filter_list, num_classes, bottle_neck, **kwargs): - bn_mom = kwargs.get('bn_mom', 0.9) - workspace = kwargs.get('workspace', 256) +def resnet(units, num_stages, filter_list, num_classes, bottle_neck): + bn_mom = config.bn_mom + workspace = config.workspace + kwargs = { + 'version_se': config.net_se, + 'version_input': config.net_input, + 'version_output': config.net_output, + 'version_unit': config.net_unit, + 'version_act': config.net_act, + 'bn_mom': bn_mom, + 'workspace': workspace, + 'memonger': config.memonger, + } """Return ResNet symbol of Parameters ---------- @@ -984,17 +999,21 @@ def resnet(units, num_stages, filter_list, num_classes, bottle_neck, **kwargs): workspace : int Workspace used in convolution operator """ - version_se = kwargs.get('version_se', 0) + version_se = kwargs.get('version_se', 1) version_input = kwargs.get('version_input', 1) assert version_input >= 0 - version_output = kwargs.get('version_output', 'GAP') + version_output = kwargs.get('version_output', 'E') fc_type = version_output version_unit = kwargs.get('version_unit', 3) act_type = kwargs.get('version_act', 'prelu') - print(version_se, version_input, version_output, version_unit, act_type) + memonger = kwargs.get('memonger', False) + print(version_se, version_input, version_output, version_unit, act_type, + memonger) num_unit = len(units) assert (num_unit == num_stages) data = mx.sym.Variable(name='data') + if config.fp16: + data = mx.sym.Cast(data=data, dtype=np.float16) if version_input == 0: #data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data') data = mx.sym.identity(data=data, name='id') @@ -1062,6 +1081,8 @@ def resnet(units, num_stages, filter_list, num_classes, bottle_neck, **kwargs): #else: # body = residual_unit(body, filter_list[i+1], (2, 2), False, # name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs) + if i==num_stages-1 and config.fp16: + body = mx.sym.Cast(data=body, dtype=np.float32) body = residual_unit(body, filter_list[i + 1], (2, 2), False, @@ -1076,16 +1097,34 @@ def resnet(units, num_stages, filter_list, num_classes, bottle_neck, **kwargs): bottle_neck=bottle_neck, **kwargs) + if bottle_neck: + body = Conv(data=body, + num_filter=512, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + no_bias=True, + name="convd", + workspace=workspace) + body = mx.sym.BatchNorm(data=body, + fix_gamma=False, + eps=2e-5, + momentum=bn_mom, + name='bnd') + body = Act(data=body, act_type=act_type, name='relud') + fc1 = symbol_utils.get_fc1(body, num_classes, fc_type) return fc1 -def get_symbol(num_classes, num_layers, **kwargs): +def get_symbol(): """ Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py Original author Wei Wu """ - if num_layers >= 101: + num_classes = config.emb_size + num_layers = config.num_layers + if num_layers >= 500: filter_list = [64, 256, 512, 1024, 2048] bottle_neck = True else: @@ -1104,10 +1143,22 @@ def get_symbol(num_classes, num_layers, **kwargs): units = [3, 6, 24, 3] elif num_layers == 90: units = [3, 8, 30, 3] + elif num_layers == 98: + units = [3, 4, 38, 3] + elif num_layers == 99: + units = [3, 8, 35, 3] elif num_layers == 100: units = [3, 13, 30, 3] + elif num_layers == 134: + units = [3, 10, 50, 3] + elif num_layers == 136: + units = [3, 13, 48, 3] + elif num_layers == 140: + units = [3, 15, 48, 3] elif num_layers == 124: units = [3, 13, 40, 5] + elif num_layers == 160: + units = [3, 24, 49, 3] elif num_layers == 101: units = [3, 4, 23, 3] elif num_layers == 152: @@ -1121,9 +1172,20 @@ def get_symbol(num_classes, num_layers, **kwargs): "no experiments done on num_layers {}, you can do it yourself". format(num_layers)) - return resnet(units=units, - num_stages=num_stages, - filter_list=filter_list, - num_classes=num_classes, - bottle_neck=bottle_neck, - **kwargs) + net = resnet(units=units, + num_stages=num_stages, + filter_list=filter_list, + num_classes=num_classes, + bottle_neck=bottle_neck) + + if config.memonger: + dshape = (config.per_batch_size, config.image_shape[2], + config.image_shape[0], config.image_shape[1]) + net_mem_planned = memonger.search_plan(net, data=dshape) + old_cost = memonger.get_cost(net, data=dshape) + new_cost = memonger.get_cost(net_mem_planned, data=dshape) + + print('Old feature map cost=%d MB' % old_cost) + print('New feature map cost=%d MB' % new_cost) + net = net_mem_planned + return net diff --git a/recognition/subcenter_arcface/symbol/memonger.py b/recognition/subcenter_arcface/symbol/memonger.py new file mode 100644 index 0000000..8ad610b --- /dev/null +++ b/recognition/subcenter_arcface/symbol/memonger.py @@ -0,0 +1,175 @@ +import mxnet as mx +import math + + +def prod(shape): + """Get product of the shape. + """ + ret = 1 + for s in shape: + ret *= s + return ret + + +def is_param(name): + """Quick script to check if name is a parameter. + """ + if name == 'data': + return False + if name.endswith('weight'): + return True + if name.endswith('bias'): + return True + if name.endswith('beta'): + return True + if name.endswith('gamma'): + return True + return False + + +def make_mirror_plan(sym, threshold, plan_info=None, **kwargs): + """Memory allocation planner with a given threshold. + + The user can pass in a network configuration, + a threshold that limits memory per block. + And input shape configurations. + + Parameters + ---------- + sym : symbol + Input configuration of symbols. + The user need to pre-mark the attribute "mirror_stage" on the nodes + that can be book-kept as stage + + The algorithm will decide whether to disbale mirror on the stage nodes. + + threshold: integer + A tuning parameter to tune the approximate size of each stage blocks + + plan_info: dict, optional + Used to hold plan information. + + **kwargs: + The arguments to infer shape. + + Returns + ------- + alloc_sym: symbol + A symbol with force mirror tagged on the nodes for better allocation. + """ + threshold = threshold << 20 + sym = sym.__copy__() + internals = sym.get_internals() + _, out_shapes, _ = internals.infer_shape(**kwargs) + shape_dict = list(zip(internals.list_outputs(), out_shapes)) + total_size = 0 + param_size = 0 + local_size = 0 + save_size = 0 + max_size = 0 + last_sb = None + last_local = 0 + period = 1 + last_stage = '' + stage_decision = '' + + for idx, item in enumerate(shape_dict): + sb = internals[idx] + name, shape = item + if is_param(name): + param_size += prod(shape) * 4 + continue + else: + total_size += prod(shape) * 4 + local_size += prod(shape) * 4 + sb._set_attr(force_mirroring='True') + + if sb.attr('mirror_stage') is not None: + stage = sb.attr('mirror_stage') + if stage == 'True' or stage != last_stage: + if local_size > threshold: + save_size += prod(shape) * 4 + max_size = max(max_size, local_size) + local_size = 0 + stage_decision = 'False' + sb._set_attr(force_mirroring=stage_decision) + else: + stage_decision = 'True' + pass + last_stage = stage + elif stage == last_stage and stage_decision == 'False': + save_size += prod(shape) * 4 + sb._set_attr(force_mirroring=stage_decision) + + if plan_info is not None: + plan_info['max_size'] = max_size + plan_info['save_size'] = save_size + return sym + + +def get_cost(sym, type_dict=None, **kwargs): + """Get the cost of the current symbolic plan by running bind on CPU. + + sym : Symbolic Variable + + """ + texec = sym.simple_bind(ctx=mx.gpu(), + grad_req='write', + type_dict=type_dict, + **kwargs) + return int(texec.debug_str().split('\n')[-3].split()[1]) + + +def search_plan(sym, ntrial=6, type_dict=None, **kwargs): + """Quickly heurestic search over possible plans to find good memory plan. + + Parameters + ---------- + sym : symbolic + Symbolic configurations + + ntrial: integer + Additional grid search steps + """ + history = [] + threshold = 0 + min_threshold = None + min_cost = None + nbegin = 3 + + for k in range(nbegin): + info = {} + sym = make_mirror_plan(sym, + threshold=threshold, + plan_info=info, + **kwargs) + cost = get_cost(sym, type_dict, **kwargs) + save_size = info['save_size'] >> 20 + local_size = info['max_size'] >> 20 + guess = int(math.sqrt(save_size * local_size / 2)) + if min_cost is None or min_cost > cost: + min_cost = cost + if min_threshold is None or local_size < min_threshold: + min_threshold = local_size + print("Search threshold=%d MB, cost=%d MB" % (threshold, cost)) + history.append((cost, threshold, sym)) + threshold = guess + + max_threshold = threshold * math.sqrt(2) + step = int((max_threshold - min_threshold) / ntrial) + threshold = min_threshold + step + if step > 0: + for k in range(ntrial): + sym = make_mirror_plan(sym, + threshold=threshold, + plan_info=info, + **kwargs) + cost = get_cost(sym, type_dict, **kwargs) + print("Search threshold=%d MB, cost=%d MB" % (threshold, cost)) + history.append((cost, threshold, sym)) + threshold += step + + history.sort(key=lambda x: x[0]) + cost, threshold, sym = history[0] + print('Find best plan with threshold=%d, cost=%d MB' % (threshold, cost)) + return sym diff --git a/recognition/subcenter_arcface/symbol/memonger_v2.py b/recognition/subcenter_arcface/symbol/memonger_v2.py new file mode 100644 index 0000000..92963de --- /dev/null +++ b/recognition/subcenter_arcface/symbol/memonger_v2.py @@ -0,0 +1,300 @@ +import mxnet as mx +import math + + +def prod(shape): + """Get product of the shape. + """ + ret = 1 + for s in shape: + ret *= s + return ret + + +def is_param(name): + """Quick script to check if name is a parameter. + """ + if name == 'data': + return False + if name.endswith('weight'): + return True + if name.endswith('bias'): + return True + if name.endswith('beta'): + return True + if name.endswith('gamma'): + return True + return False + + +def make_mirror_plan(sym, threshold, plan_info=None, **kwargs): + """Memory allocation planner with a given threshold. + + The user can pass in a network configuration, + a threshold that limits memory per block. + And input shape configurations. + + Parameters + ---------- + sym : symbol + Input configuration of symbols. + The user need to pre-mark the attribute "mirror_stage" on the nodes + that can be book-kept as stage + + The algorithm will decide whether to disbale mirror on the stage nodes. + + threshold: integer + A tuning parameter to tune the approximate size of each stage blocks + + plan_info: dict, optional + Used to hold plan information. + + **kwargs: + The arguments to infer shape. + + Returns + ------- + alloc_sym: symbol + A symbol with force mirror tagged on the nodes for better allocation. + """ + threshold = threshold << 20 + sym = sym.__copy__() + internals = sym.get_internals() + _, out_shapes, _ = internals.infer_shape(**kwargs) + shape_dict = list(zip(internals.list_outputs(), out_shapes)) + total_size = 0 + param_size = 0 + local_size = 0 + save_size = 0 + max_size = 0 + last_sb = None + last_local = 0 + period = 1 + last_stage = '' + stage_decision = '' + + for idx, item in enumerate(shape_dict): + sb = internals[idx] + name, shape = item + if is_param(name): + param_size += prod(shape) * 4 + continue + else: + total_size += prod(shape) * 4 + local_size += prod(shape) * 4 + sb._set_attr(force_mirroring='True') + + if sb.attr('mirror_stage') is not None: + stage = sb.attr('mirror_stage') + if stage == 'True' or stage != last_stage: + if local_size > threshold: + save_size += prod(shape) * 4 + max_size = max(max_size, local_size) + local_size = 0 + stage_decision = 'False' + sb._set_attr(force_mirroring=stage_decision) + else: + stage_decision = 'True' + pass + last_stage = stage + elif stage == last_stage and stage_decision == 'False': + save_size += prod(shape) * 4 + sb._set_attr(force_mirroring=stage_decision) + + if plan_info is not None: + plan_info['max_size'] = max_size + plan_info['save_size'] = save_size + return sym + + +def get_cost(sym, type_dict=None, **kwargs): + """Get the cost of the current symbolic plan by running bind on CPU. + + sym : Symbolic Variable + + """ + texec = sym.simple_bind(ctx=mx.gpu(), + grad_req='write', + type_dict=type_dict, + **kwargs) + return int(texec.debug_str().split('\n')[-3].split()[1]) + + +def search_plan(sym, ntrial=6, type_dict=None, **kwargs): + """Quickly heurestic search over possible plans to find good memory plan. + + Parameters + ---------- + sym : symbolic + Symbolic configurations + + ntrial: integer + Additional grid search steps + """ + history = [] + threshold = 0 + min_threshold = None + min_cost = None + nbegin = 3 + + for k in range(nbegin): + info = {} + sym = make_mirror_plan(sym, + threshold=threshold, + plan_info=info, + **kwargs) + cost = get_cost(sym, type_dict, **kwargs) + save_size = info['save_size'] >> 20 + local_size = info['max_size'] >> 20 + guess = int(math.sqrt(save_size * local_size / 2)) + if min_cost is None or min_cost > cost: + min_cost = cost + if min_threshold is None or local_size < min_threshold: + min_threshold = local_size + print("Search threshold=%d MB, cost=%d MB" % (threshold, cost)) + history.append((cost, threshold, sym)) + threshold = guess + + max_threshold = threshold * math.sqrt(2) + step = int((max_threshold - min_threshold) / ntrial) + threshold = min_threshold + step + if step > 0: + for k in range(ntrial): + sym = make_mirror_plan(sym, + threshold=threshold, + plan_info=info, + **kwargs) + cost = get_cost(sym, type_dict, **kwargs) + print("Search threshold=%d MB, cost=%d MB" % (threshold, cost)) + history.append((cost, threshold, sym)) + threshold += step + + history.sort(key=lambda x: x[0]) + cost, threshold, sym = history[0] + print('Find best plan with threshold=%d, cost=%d MB' % (threshold, cost)) + return sym + + +def make_mirror_plan_to_layer(sym, + layer_name, + threshold, + plan_info=None, + **kwargs): + """ + sym is the original symbal + layer_name is a name to which layer of the network should be set as mirror + threshhold is the approximate size of each mirror block + """ + threshold = threshold << 20 + sym = sym.__copy__() + internals = sym.get_internals() + _, out_shapes, _ = internals.infer_shape(**kwargs) + shape_dict = list(zip(internals.list_outputs(), out_shapes)) + total_size = 0 + param_size = 0 + local_size = 0 + save_size = 0 + max_size = 0 + last_stage = '' + stage_decision = '' + switch = True + + for idx, item in enumerate(shape_dict): + sb = internals[idx] + name, shape = item + #print(name, switch) + if is_param(name): + param_size += prod(shape) * 4 + continue + elif switch and not 'bn' in name: + total_size += prod(shape) * 4 + local_size += prod(shape) * 4 + sb._set_attr(force_mirroring='True') + print('set force_mirroring', name, total_size, local_size) + if layer_name != '' and layer_name in name: + switch = False + + if sb.attr('mirror_stage') is not None: + stage = sb.attr('mirror_stage') + #print(name, stage) + if stage == 'True' or stage != last_stage: + if local_size > threshold: + save_size += prod(shape) * 4 + max_size = max(max_size, local_size) + local_size = 0 + stage_decision = 'False' + sb._set_attr(force_mirroring=stage_decision) + else: + stage_decision = 'True' + pass + last_stage = stage + elif stage == last_stage and stage_decision == 'False': + save_size += prod(shape) * 4 + sb._set_attr(force_mirroring=stage_decision) + + if plan_info is not None: + plan_info['max_size'] = max_size + plan_info['save_size'] = save_size + return sym + + +def search_plan_to_layer(sym, + layer_name=None, + threshold=500, + ntrial=6, + type_dict=None, + **kwargs): + """Quickly heurestic search over possible plans to find good memory plan. + + Parameters + ---------- + sym : symbolic + Symbolic configurations + + ntrial: integer + Additional grid search steps + """ + history = [] + min_threshold = None + min_cost = None + nbegin = 10 + + for k in range(nbegin): + info = {} + sym = make_mirror_plan_to_layer(sym, + layer_name=layer_name, + threshold=threshold, + plan_info=info, + **kwargs) + cost = get_cost(sym, type_dict, **kwargs) + save_size = info['save_size'] >> 20 + local_size = info['max_size'] >> 20 + guess = 300 * (k + 1) + if min_cost is None or min_cost > cost: + min_cost = cost + if min_threshold is None or local_size < min_threshold: + min_threshold = local_size + print("Search threshold=%d MB, cost=%d MB" % (threshold, cost)) + history.append((cost, threshold, sym)) + threshold = guess + + max_threshold = threshold * math.sqrt(2) + step = int((max_threshold - min_threshold) / ntrial) + print(min_threshold, max_threshold, step) + threshold = min_threshold + step + if step > 0: + for k in range(ntrial): + sym = make_mirror_plan_to_layer(sym, + layer_name=layer_name, + threshold=threshold, + plan_info=info, + **kwargs) + cost = get_cost(sym, type_dict, **kwargs) + print("Search threshold=%d MB, cost=%d MB" % (threshold, cost)) + history.append((cost, threshold, sym)) + threshold += step + + history.sort(key=lambda x: x[0]) + cost, threshold, sym = history[0] + print('Find best plan with threshold=%d, cost=%d MB' % (threshold, cost)) + return sym diff --git a/gender-age/symbol_utils.py b/recognition/subcenter_arcface/symbol/symbol_utils.py similarity index 53% rename from gender-age/symbol_utils.py rename to recognition/subcenter_arcface/symbol/symbol_utils.py index 0a48697..3eb6f1c 100644 --- a/gender-age/symbol_utils.py +++ b/recognition/subcenter_arcface/symbol/symbol_utils.py @@ -1,4 +1,8 @@ +import sys +import os import mxnet as mx +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from config import config def Conv(**kwargs): @@ -12,11 +16,14 @@ def Conv(**kwargs): def Act(data, act_type, name): #ignore param act_type, set it in this function - body = mx.sym.LeakyReLU(data=data, act_type='prelu', name=name) + if act_type == 'prelu': + body = mx.sym.LeakyReLU(data=data, act_type='prelu', name=name) + else: + body = mx.sym.Activation(data=data, act_type=act_type, name=name) return body -bn_mom = 0.9 +bn_mom = config.bn_mom def Linear(data, @@ -42,8 +49,7 @@ def Linear(data, return bn -def get_fc1(last_conv, num_classes, fc_type): - bn_mom = 0.9 +def get_fc1(last_conv, num_classes, fc_type, input_channel=512): body = last_conv if fc_type == 'Z': body = mx.sym.BatchNorm(data=body, @@ -68,13 +74,67 @@ def get_fc1(last_conv, num_classes, fc_type): eps=2e-5, momentum=bn_mom, name='fc1') + elif fc_type == 'FC': + body = mx.sym.BatchNorm(data=body, + fix_gamma=False, + eps=2e-5, + momentum=bn_mom, + name='bn1') + fc1 = mx.sym.FullyConnected(data=body, + num_hidden=num_classes, + name='pre_fc1') + fc1 = mx.sym.BatchNorm(data=fc1, + fix_gamma=True, + eps=2e-5, + momentum=bn_mom, + name='fc1') + elif fc_type == 'SFC': + body = mx.sym.BatchNorm(data=body, + fix_gamma=False, + eps=2e-5, + momentum=bn_mom, + name='bn1') + body = Conv(data=body, + num_filter=input_channel, + kernel=(3, 3), + stride=(2, 2), + pad=(1, 1), + no_bias=True, + name="convf", + num_group=input_channel) + body = mx.sym.BatchNorm(data=body, + fix_gamma=False, + eps=2e-5, + momentum=bn_mom, + name='bnf') + body = Act(data=body, act_type=config.net_act, name='reluf') + body = Conv(data=body, + num_filter=input_channel, + kernel=(1, 1), + pad=(0, 0), + stride=(1, 1), + name="convf2") + body = mx.sym.BatchNorm(data=body, + fix_gamma=False, + eps=2e-5, + momentum=bn_mom, + name='bnf2') + body = Act(data=body, act_type=config.net_act, name='reluf2') + fc1 = mx.sym.FullyConnected(data=body, + num_hidden=num_classes, + name='pre_fc1') + fc1 = mx.sym.BatchNorm(data=fc1, + fix_gamma=True, + eps=2e-5, + momentum=bn_mom, + name='fc1') elif fc_type == 'GAP': bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1') - relu1 = Act(data=bn1, act_type='relu', name='relu1') + relu1 = Act(data=bn1, act_type=config.net_act, name='relu1') # Although kernel is not used here when global_pool=True, we should put one pool1 = mx.sym.Pooling(data=relu1, global_pool=True, @@ -105,7 +165,7 @@ def get_fc1(last_conv, num_classes, fc_type): eps=2e-5, momentum=0.9, name='convx_bn') - body = Act(data=body, act_type='relu', name='convx_relu') + body = Act(data=body, act_type=config.net_act, name='convx_relu') filters_in = num_classes else: body = last_conv @@ -149,8 +209,8 @@ def get_fc1(last_conv, num_classes, fc_type): name='fc1') elif fc_type == "GDC": #mobilefacenet_v1 conv_6_dw = Linear(last_conv, - num_filter=512, - num_group=512, + num_filter=input_channel, + num_group=input_channel, kernel=(7, 7), pad=(0, 0), stride=(1, 1), @@ -209,46 +269,6 @@ def get_fc1(last_conv, num_classes, fc_type): eps=2e-5, momentum=bn_mom, name='fc1') - else: - bn1 = mx.sym.BatchNorm(data=body, - fix_gamma=False, - eps=2e-5, - momentum=bn_mom, - name='bn1') - relu1 = Act(data=bn1, act_type='relu', name='relu1') - # Although kernel is not used here when global_pool=True, we should put one - pool1 = mx.sym.Pooling(data=relu1, - global_pool=True, - kernel=(7, 7), - pool_type='avg', - name='pool1') - flat = mx.sym.Flatten(data=pool1) - if len(fc_type) > 1: - if fc_type[1] == 'X': - print('dropout mode') - flat = mx.symbol.Dropout(data=flat, p=0.2) - fc_type = fc_type[0] - if fc_type == 'A': - fc1 = flat - else: - #B-D - #B - fc1 = mx.sym.FullyConnected(data=flat, - num_hidden=num_classes, - name='pre_fc1') - if fc_type == 'C': - fc1 = mx.sym.BatchNorm(data=fc1, - fix_gamma=True, - eps=2e-5, - momentum=bn_mom, - name='fc1') - elif fc_type == 'D': - fc1 = mx.sym.BatchNorm(data=fc1, - fix_gamma=True, - eps=2e-5, - momentum=bn_mom, - name='fc1') - fc1 = Act(data=fc1, act_type='relu', name='fc1_relu') return fc1 @@ -293,7 +313,7 @@ def residual_unit_v3(data, num_filter, stride, dim_match, name, **kwargs): eps=2e-5, momentum=bn_mom, name=name + '_bn2') - act1 = Act(data=bn2, act_type='relu', name=name + '_relu1') + act1 = Act(data=bn2, act_type=config.net_act, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3, 3), @@ -328,9 +348,203 @@ def residual_unit_v3(data, num_filter, stride, dim_match, name, **kwargs): return bn3 + shortcut +def residual_unit_v1l(data, num_filter, stride, dim_match, name, bottle_neck): + """Return ResNet Unit symbol for building ResNet + Parameters + ---------- + data : str + Input data + num_filter : int + Number of output channels + bnf : int + Bottle neck channels factor with regard to num_filter + stride : tuple + Stride used in convolution + dim_match : Boolean + True means channel number between input and output is the same, otherwise means differ + name : str + Base name of the operators + workspace : int + Workspace used in convolution operator + """ + workspace = config.workspace + bn_mom = config.bn_mom + memonger = False + use_se = config.net_se + act_type = config.net_act + #print('in unit1') + if bottle_neck: + conv1 = Conv(data=data, + num_filter=int(num_filter * 0.25), + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + no_bias=True, + workspace=workspace, + name=name + '_conv1') + bn1 = mx.sym.BatchNorm(data=conv1, + fix_gamma=False, + eps=2e-5, + momentum=bn_mom, + name=name + '_bn1') + act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') + conv2 = Conv(data=act1, + num_filter=int(num_filter * 0.25), + kernel=(3, 3), + stride=(1, 1), + pad=(1, 1), + no_bias=True, + workspace=workspace, + name=name + '_conv2') + bn2 = mx.sym.BatchNorm(data=conv2, + fix_gamma=False, + eps=2e-5, + momentum=bn_mom, + name=name + '_bn2') + act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2') + conv3 = Conv(data=act2, + num_filter=num_filter, + kernel=(1, 1), + stride=stride, + pad=(0, 0), + no_bias=True, + workspace=workspace, + name=name + '_conv3') + bn3 = mx.sym.BatchNorm(data=conv3, + fix_gamma=False, + eps=2e-5, + momentum=bn_mom, + name=name + '_bn3') + + if use_se: + #se begin + body = mx.sym.Pooling(data=bn3, + global_pool=True, + kernel=(7, 7), + pool_type='avg', + name=name + '_se_pool1') + body = Conv(data=body, + num_filter=num_filter // 16, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + name=name + "_se_conv1", + workspace=workspace) + body = Act(data=body, act_type=act_type, name=name + '_se_relu1') + body = Conv(data=body, + num_filter=num_filter, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + name=name + "_se_conv2", + workspace=workspace) + body = mx.symbol.Activation(data=body, + act_type='sigmoid', + name=name + "_se_sigmoid") + bn3 = mx.symbol.broadcast_mul(bn3, body) + #se end + + if dim_match: + shortcut = data + else: + conv1sc = Conv(data=data, + num_filter=num_filter, + kernel=(1, 1), + stride=stride, + no_bias=True, + workspace=workspace, + name=name + '_conv1sc') + shortcut = mx.sym.BatchNorm(data=conv1sc, + fix_gamma=False, + eps=2e-5, + momentum=bn_mom, + name=name + '_sc') + if memonger: + shortcut._set_attr(mirror_stage='True') + return Act(data=bn3 + shortcut, + act_type=act_type, + name=name + '_relu3') + else: + conv1 = Conv(data=data, + num_filter=num_filter, + kernel=(3, 3), + stride=(1, 1), + pad=(1, 1), + no_bias=True, + workspace=workspace, + name=name + '_conv1') + bn1 = mx.sym.BatchNorm(data=conv1, + fix_gamma=False, + momentum=bn_mom, + eps=2e-5, + name=name + '_bn1') + act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') + conv2 = Conv(data=act1, + num_filter=num_filter, + kernel=(3, 3), + stride=stride, + pad=(1, 1), + no_bias=True, + workspace=workspace, + name=name + '_conv2') + bn2 = mx.sym.BatchNorm(data=conv2, + fix_gamma=False, + momentum=bn_mom, + eps=2e-5, + name=name + '_bn2') + if use_se: + #se begin + body = mx.sym.Pooling(data=bn2, + global_pool=True, + kernel=(7, 7), + pool_type='avg', + name=name + '_se_pool1') + body = Conv(data=body, + num_filter=num_filter // 16, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + name=name + "_se_conv1", + workspace=workspace) + body = Act(data=body, act_type=act_type, name=name + '_se_relu1') + body = Conv(data=body, + num_filter=num_filter, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + name=name + "_se_conv2", + workspace=workspace) + body = mx.symbol.Activation(data=body, + act_type='sigmoid', + name=name + "_se_sigmoid") + bn2 = mx.symbol.broadcast_mul(bn2, body) + #se end + + if dim_match: + shortcut = data + else: + conv1sc = Conv(data=data, + num_filter=num_filter, + kernel=(1, 1), + stride=stride, + no_bias=True, + workspace=workspace, + name=name + '_conv1sc') + shortcut = mx.sym.BatchNorm(data=conv1sc, + fix_gamma=False, + momentum=bn_mom, + eps=2e-5, + name=name + '_sc') + if memonger: + shortcut._set_attr(mirror_stage='True') + return Act(data=bn2 + shortcut, + act_type=act_type, + name=name + '_relu3') + + def get_head(data, version_input, num_filter): - bn_mom = 0.9 - workspace = 256 + bn_mom = config.bn_mom + workspace = config.workspace kwargs = {'bn_mom': bn_mom, 'workspace': workspace} data = data - 127.5 data = data * 0.0078125 @@ -349,7 +563,7 @@ def get_head(data, version_input, num_filter): eps=2e-5, momentum=bn_mom, name='bn0') - body = Act(data=body, act_type='relu', name='relu0') + body = Act(data=body, act_type=config.net_act, name='relu0') body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2, 2), @@ -371,10 +585,11 @@ def get_head(data, version_input, num_filter): eps=2e-5, momentum=bn_mom, name='bn0') - body = Act(data=body, act_type='relu', name='relu0') - body = residual_unit_v3(body, - _num_filter, (2, 2), - False, - name='head', - **kwargs) + body = Act(data=body, act_type=config.net_act, name='relu0') + #body = residual_unit_v3(body, _num_filter, (2, 2), False, name='head', **kwargs) + body = residual_unit_v1l(body, + _num_filter, (2, 2), + False, + name='head', + bottle_neck=False) return body diff --git a/recognition/subcenter_arcface/symbol/vargfacenet.py b/recognition/subcenter_arcface/symbol/vargfacenet.py new file mode 100644 index 0000000..434e8be --- /dev/null +++ b/recognition/subcenter_arcface/symbol/vargfacenet.py @@ -0,0 +1,578 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +''' +Author: Horizon Robotics Inc. +The company is committed to be the global leader of edge AI platform. +The model implemented in this scripts runs ~200fps on the Sunrise 2. +Sunrise 2 is the second generation of an embedded AI chip designed by Horizon Robotics, +targeting to empower AIoT devices by AI. + +Implemented the following paper: +Mengjia Yan, Mengao Zhao, Zining Xu, Qian Zhang, Guoli Wang, Zhizhong Su. "VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition" (https://arxiv.org/abs/1910.04985) + +''' + +import os +import sys + +import mxnet as mx +import symbol_utils + +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from config import config + + +def Act(data, act_type, name): + if act_type == 'prelu': + body = mx.sym.LeakyReLU(data=data, act_type='prelu', name=name) + else: + body = mx.symbol.Activation(data=data, act_type=act_type, name=name) + return body + + +def get_setting_params(**kwargs): + # bn_params + bn_mom = kwargs.get('bn_mom', 0.9) + bn_eps = kwargs.get('bn_eps', 2e-5) + fix_gamma = kwargs.get('fix_gamma', False) + use_global_stats = kwargs.get('use_global_stats', False) + # net_setting param + workspace = kwargs.get('workspace', 512) + act_type = kwargs.get('act_type', 'prelu') + use_se = kwargs.get('use_se', True) + se_ratio = kwargs.get('se_ratio', 4) + group_base = kwargs.get('group_base', 8) + + setting_params = {} + setting_params['bn_mom'] = bn_mom + setting_params['bn_eps'] = bn_eps + setting_params['fix_gamma'] = fix_gamma + setting_params['use_global_stats'] = use_global_stats + setting_params['workspace'] = workspace + setting_params['act_type'] = act_type + setting_params['use_se'] = use_se + setting_params['se_ratio'] = se_ratio + setting_params['group_base'] = group_base + + return setting_params + + +def se_block(data, num_filter, setting_params, name): + se_ratio = setting_params['se_ratio'] + act_type = setting_params['act_type'] + + pool1 = mx.sym.Pooling(data=data, + global_pool=True, + pool_type='avg', + name=name + '_se_pool1') + conv1 = mx.sym.Convolution(data=pool1, + num_filter=num_filter // se_ratio, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + name=name + "_se_conv1") + act1 = Act(data=conv1, act_type=act_type, name=name + '_se_act1') + + conv2 = mx.sym.Convolution(data=act1, + num_filter=num_filter, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + name=name + "_se_conv2") + act2 = mx.symbol.Activation(data=conv2, + act_type='sigmoid', + name=name + "_se_sigmoid") + out_data = mx.symbol.broadcast_mul(data, act2) + return out_data + + +def separable_conv2d(data, + in_channels, + out_channels, + kernel, + pad, + setting_params, + stride=(1, 1), + factor=1, + bias=False, + bn_dw_out=True, + act_dw_out=True, + bn_pw_out=True, + act_pw_out=True, + dilate=1, + name=None): + bn_mom = setting_params['bn_mom'] + bn_eps = setting_params['bn_eps'] + fix_gamma = setting_params['fix_gamma'] + use_global_stats = setting_params['use_global_stats'] + workspace = setting_params['workspace'] + group_base = setting_params['group_base'] + act_type = setting_params['act_type'] + assert in_channels % group_base == 0 + + # depthwise + dw_out = mx.sym.Convolution(data=data, + num_filter=int(in_channels * factor), + kernel=kernel, + pad=pad, + stride=stride, + no_bias=False if bias else True, + num_group=int(in_channels / group_base), + dilate=(dilate, dilate), + workspace=workspace, + name=name + '_conv2d_depthwise') + if bn_dw_out: + dw_out = mx.sym.BatchNorm(data=dw_out, + fix_gamma=fix_gamma, + eps=bn_eps, + momentum=bn_mom, + use_global_stats=use_global_stats, + name=name + '_conv2d_depthwise_bn') + if act_dw_out: + dw_out = Act(data=dw_out, + act_type=act_type, + name=name + '_conv2d_depthwise_act') + # pointwise + pw_out = mx.sym.Convolution(data=dw_out, + num_filter=out_channels, + kernel=(1, 1), + stride=(1, 1), + pad=(0, 0), + num_group=1, + no_bias=False if bias else True, + workspace=workspace, + name=name + '_conv2d_pointwise') + if bn_pw_out: + pw_out = mx.sym.BatchNorm(data=pw_out, + fix_gamma=fix_gamma, + eps=bn_eps, + momentum=bn_mom, + use_global_stats=use_global_stats, + name=name + '_conv2d_pointwise_bn') + if act_pw_out: + pw_out = Act(data=pw_out, + act_type=act_type, + name=name + '_conv2d_pointwise_act') + return pw_out + + +def vargnet_block(data, + n_out_ch1, + n_out_ch2, + n_out_ch3, + setting_params, + factor=2, + dim_match=True, + multiplier=1, + kernel=(3, 3), + stride=(1, 1), + dilate=1, + with_dilate=False, + name=None): + use_se = setting_params['use_se'] + act_type = setting_params['act_type'] + + out_channels_1 = int(n_out_ch1 * multiplier) + out_channels_2 = int(n_out_ch2 * multiplier) + out_channels_3 = int(n_out_ch3 * multiplier) + + pad = (((kernel[0] - 1) * dilate + 1) // 2, + ((kernel[1] - 1) * dilate + 1) // 2) + + if with_dilate: + stride = (1, 1) + if dim_match: + short_cut = data + else: + short_cut = separable_conv2d(data=data, + in_channels=out_channels_1, + out_channels=out_channels_3, + kernel=kernel, + pad=pad, + setting_params=setting_params, + stride=stride, + factor=factor, + bias=False, + act_pw_out=False, + dilate=dilate, + name=name + '_shortcut') + sep1_data = separable_conv2d(data=data, + in_channels=out_channels_1, + out_channels=out_channels_2, + kernel=kernel, + pad=pad, + setting_params=setting_params, + stride=stride, + factor=factor, + bias=False, + dilate=dilate, + name=name + '_sep1_data') + sep2_data = separable_conv2d(data=sep1_data, + in_channels=out_channels_2, + out_channels=out_channels_3, + kernel=kernel, + pad=pad, + setting_params=setting_params, + stride=(1, 1), + factor=factor, + bias=False, + dilate=dilate, + act_pw_out=False, + name=name + '_sep2_data') + + if use_se: + sep2_data = se_block(data=sep2_data, + num_filter=out_channels_3, + setting_params=setting_params, + name=name) + + out_data = sep2_data + short_cut + out_data = Act(data=out_data, + act_type=act_type, + name=name + '_out_data_act') + return out_data + + +def vargnet_branch_merge_block(data, + n_out_ch1, + n_out_ch2, + n_out_ch3, + setting_params, + factor=2, + dim_match=False, + multiplier=1, + kernel=(3, 3), + stride=(2, 2), + dilate=1, + with_dilate=False, + name=None): + act_type = setting_params['act_type'] + + out_channels_1 = int(n_out_ch1 * multiplier) + out_channels_2 = int(n_out_ch2 * multiplier) + out_channels_3 = int(n_out_ch3 * multiplier) + + pad = (((kernel[0] - 1) * dilate + 1) // 2, + ((kernel[1] - 1) * dilate + 1) // 2) + + if with_dilate: + stride = (1, 1) + if dim_match: + short_cut = data + else: + short_cut = separable_conv2d(data=data, + in_channels=out_channels_1, + out_channels=out_channels_3, + kernel=kernel, + pad=pad, + setting_params=setting_params, + stride=stride, + factor=factor, + bias=False, + act_pw_out=False, + dilate=dilate, + name=name + '_shortcut') + sep1_data_brach1 = separable_conv2d(data=data, + in_channels=out_channels_1, + out_channels=out_channels_2, + kernel=kernel, + pad=pad, + setting_params=setting_params, + stride=stride, + factor=factor, + bias=False, + dilate=dilate, + act_pw_out=False, + name=name + '_sep1_data_branch') + sep1_data_brach2 = separable_conv2d(data=data, + in_channels=out_channels_1, + out_channels=out_channels_2, + kernel=kernel, + pad=pad, + setting_params=setting_params, + stride=stride, + factor=factor, + bias=False, + dilate=dilate, + act_pw_out=False, + name=name + '_sep2_data_branch') + sep1_data = sep1_data_brach1 + sep1_data_brach2 + sep1_data = Act(data=sep1_data, + act_type=act_type, + name=name + '_sep1_data_act') + sep2_data = separable_conv2d(data=sep1_data, + in_channels=out_channels_2, + out_channels=out_channels_3, + kernel=kernel, + pad=pad, + setting_params=setting_params, + stride=(1, 1), + factor=factor, + bias=False, + dilate=dilate, + act_pw_out=False, + name=name + '_sep2_data') + out_data = sep2_data + short_cut + out_data = Act(data=out_data, + act_type=act_type, + name=name + '_out_data_act') + return out_data + + +def add_vargnet_conv_block(data, + stage, + units, + in_channels, + out_channels, + setting_params, + kernel=(3, 3), + stride=(2, 2), + multiplier=1, + factor=2, + dilate=1, + with_dilate=False, + name=None): + assert stage >= 2, 'stage is {}, stage must be set >=2'.format(stage) + data = vargnet_branch_merge_block(data=data, + n_out_ch1=in_channels, + n_out_ch2=out_channels, + n_out_ch3=out_channels, + setting_params=setting_params, + factor=factor, + dim_match=False, + multiplier=multiplier, + kernel=kernel, + stride=stride, + dilate=dilate, + with_dilate=with_dilate, + name=name + + '_stage_{}_unit_1'.format(stage)) + for i in range(units - 1): + data = vargnet_block(data=data, + n_out_ch1=out_channels, + n_out_ch2=out_channels, + n_out_ch3=out_channels, + setting_params=setting_params, + factor=factor, + dim_match=True, + multiplier=multiplier, + kernel=kernel, + stride=(1, 1), + dilate=dilate, + with_dilate=with_dilate, + name=name + + '_stage_{}_unit_{}'.format(stage, i + 2)) + return data + + +def add_head_block(data, + num_filter, + setting_params, + multiplier, + head_pooling=False, + kernel=(3, 3), + stride=(2, 2), + pad=(1, 1), + name=None): + bn_mom = setting_params['bn_mom'] + bn_eps = setting_params['bn_eps'] + fix_gamma = setting_params['fix_gamma'] + use_global_stats = setting_params['use_global_stats'] + workspace = setting_params['workspace'] + act_type = setting_params['act_type'] + channels = int(num_filter * multiplier) + + conv1 = mx.sym.Convolution(data=data, + num_filter=channels, + kernel=kernel, + pad=pad, + stride=stride, + no_bias=True, + num_group=1, + workspace=workspace, + name=name + '_conv1') + bn1 = mx.sym.BatchNorm(data=conv1, + fix_gamma=fix_gamma, + eps=bn_eps, + momentum=bn_mom, + use_global_stats=use_global_stats, + name=name + '_conv1_bn') + + act1 = Act(data=bn1, act_type=act_type, name=name + '_conv1_act') + + if head_pooling: + head_data = mx.symbol.Pooling(data=act1, + kernel=(3, 3), + stride=(2, 2), + pad=(1, 1), + pool_type='max', + name=name + '_max_pooling') + else: + head_data = vargnet_block(data=act1, + n_out_ch1=num_filter, + n_out_ch2=num_filter, + n_out_ch3=num_filter, + setting_params=setting_params, + factor=1, + dim_match=False, + multiplier=multiplier, + kernel=kernel, + stride=(2, 2), + dilate=1, + with_dilate=False, + name=name + '_head_pooling') + return head_data + + +def add_emb_block(data, + input_channels, + last_channels, + emb_size, + fc_type, + setting_params, + bias=False, + name=None): + bn_mom = setting_params['bn_mom'] + bn_eps = setting_params['bn_eps'] + fix_gamma = setting_params['fix_gamma'] + use_global_stats = setting_params['use_global_stats'] + workspace = setting_params['workspace'] + act_type = setting_params['act_type'] + group_base = setting_params['group_base'] + # last channels + if input_channels != last_channels: + data = mx.sym.Convolution(data=data, + num_filter=last_channels, + kernel=(1, 1), + pad=(0, 0), + stride=(1, 1), + no_bias=False if bias else True, + workspace=workspace, + name=name + '_convx') + data = mx.sym.BatchNorm(data=data, + fix_gamma=fix_gamma, + eps=bn_eps, + momentum=bn_mom, + use_global_stats=use_global_stats, + name=name + '_convx_bn') + data = Act(data=data, act_type=act_type, name=name + '_convx_act') + # depthwise + convx_depthwise = mx.sym.Convolution(data=data, + num_filter=last_channels, + num_group=int(last_channels / + group_base), + kernel=(7, 7), + pad=(0, 0), + stride=(1, 1), + no_bias=False if bias else True, + workspace=workspace, + name=name + '_convx_depthwise') + convx_depthwise = mx.sym.BatchNorm(data=convx_depthwise, + fix_gamma=fix_gamma, + eps=bn_eps, + momentum=bn_mom, + use_global_stats=use_global_stats, + name=name + '_convx_depthwise_bn') + # pointwise + convx_pointwise = mx.sym.Convolution(data=convx_depthwise, + num_filter=last_channels // 2, + kernel=(1, 1), + pad=(0, 0), + stride=(1, 1), + no_bias=False if bias else True, + workspace=workspace, + name=name + '_convx_pointwise') + convx_pointwise = mx.sym.BatchNorm(data=convx_pointwise, + fix_gamma=fix_gamma, + eps=bn_eps, + momentum=bn_mom, + use_global_stats=use_global_stats, + name=name + '_convx_pointwise_bn') + convx_pointwise = Act(data=convx_pointwise, + act_type=act_type, + name=name + '_convx_pointwise_act') + + fc1 = symbol_utils.get_fc1(convx_pointwise, emb_size, fc_type) + return fc1 + + +def get_symbol(): + multiplier = config.net_multiplier + emb_size = config.emb_size + fc_type = config.net_output + + kwargs = { + 'use_se': config.net_se, + 'act_type': config.net_act, + 'bn_mom': config.bn_mom, + 'workspace': config.workspace, + } + + setting_params = get_setting_params(**kwargs) + + factor = 2 + head_pooling = False + num_stage = 3 + stage_list = [2, 3, 4] + units = [3, 7, 4] + filter_list = [32, 64, 128, 256] + last_channels = 1024 + dilate_list = [1, 1, 1] + with_dilate_list = [False, False, False] + + data = mx.sym.Variable(name='data') + data = mx.sym.identity(data=data, name='id') + data = data - 127.5 + data = data * 0.0078125 + + body = add_head_block(data=data, + num_filter=filter_list[0], + setting_params=setting_params, + multiplier=multiplier, + head_pooling=head_pooling, + kernel=(3, 3), + stride=(1, 1), + pad=(1, 1), + name="vargface_head") + + for i in range(num_stage): + body = add_vargnet_conv_block(data=body, + stage=stage_list[i], + units=units[i], + in_channels=filter_list[i], + out_channels=filter_list[i + 1], + setting_params=setting_params, + kernel=(3, 3), + stride=(2, 2), + multiplier=multiplier, + factor=factor, + dilate=dilate_list[i], + with_dilate=with_dilate_list[i], + name="vargface") + emb_feat = add_emb_block(data=body, + input_channels=filter_list[3], + last_channels=last_channels, + emb_size=emb_size, + fc_type=fc_type, + setting_params=setting_params, + bias=False, + name='embed') + return emb_feat + + +if __name__ == '__main__': + get_symbol() diff --git a/recognition/SubCenter-ArcFace/train_parall.py b/recognition/subcenter_arcface/train_parall.py similarity index 99% rename from recognition/SubCenter-ArcFace/train_parall.py rename to recognition/subcenter_arcface/train_parall.py index d444f6f..72b1ff7 100644 --- a/recognition/SubCenter-ArcFace/train_parall.py +++ b/recognition/subcenter_arcface/train_parall.py @@ -16,8 +16,10 @@ from mxnet import ndarray as nd import argparse import mxnet.optimizer as optimizer from config import config, default, generate_config +sys.path.append(os.path.join(os.path.dirname(__file__), 'symbol')) +sys.path.append(os.path.join(os.path.dirname(__file__), 'common')) import verification -sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'symbol')) + import fresnet import fmobilefacenet import fmobilenet diff --git a/recognition/tools/face2rec2.py b/recognition/tools/face2rec2.py deleted file mode 100644 index a6af12c..0000000 --- a/recognition/tools/face2rec2.py +++ /dev/null @@ -1,320 +0,0 @@ -import os -import sys -import mxnet as mx -import random -import argparse -import cv2 -import time -import traceback -from easydict import EasyDict as edict -sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common')) -import face_align - -try: - import multiprocessing -except ImportError: - multiprocessing = None - - -def parse_lst_line(line): - vec = line.strip().split("\t") - assert len(vec) >= 3 - aligned = int(vec[0]) - image_path = vec[1] - label = int(vec[2]) - bbox = None - landmark = None - #print(vec) - if len(vec) > 3: - bbox = np.zeros((4, ), dtype=np.int32) - for i in xrange(3, 7): - bbox[i - 3] = int(vec[i]) - landmark = None - if len(vec) > 7: - _l = [] - for i in xrange(7, 17): - _l.append(float(vec[i])) - landmark = np.array(_l).reshape((2, 5)).T - #print(aligned) - return image_path, label, bbox, landmark, aligned - - -def read_list(path_in): - with open(path_in) as fin: - identities = [] - last = [-1, -1] - _id = 1 - while True: - line = fin.readline() - if not line: - break - item = edict() - item.flag = 0 - item.image_path, label, item.bbox, item.landmark, item.aligned = parse_lst_line( - line) - if not item.aligned and item.landmark is None: - #print('ignore line', line) - continue - item.id = _id - item.label = [label, item.aligned] - yield item - if label != last[0]: - if last[1] >= 0: - identities.append((last[1], _id)) - last[0] = label - last[1] = _id - _id += 1 - identities.append((last[1], _id)) - item = edict() - item.flag = 2 - item.id = 0 - item.label = [float(_id), float(_id + len(identities))] - yield item - for identity in identities: - item = edict() - item.flag = 2 - item.id = _id - _id += 1 - item.label = [float(identity[0]), float(identity[1])] - yield item - - -def image_encode(args, i, item, q_out): - oitem = [item.id] - #print('flag', item.flag) - if item.flag == 0: - fullpath = item.image_path - header = mx.recordio.IRHeader(item.flag, item.label, item.id, 0) - #print('write', item.flag, item.id, item.label) - if item.aligned: - with open(fullpath, 'rb') as fin: - img = fin.read() - s = mx.recordio.pack(header, img) - q_out.put((i, s, oitem)) - else: - img = cv2.imread(fullpath, args.color) - assert item.landmark is not None - img = face_align.norm_crop(img, item.landmark) - s = mx.recordio.pack_img(header, - img, - quality=args.quality, - img_fmt=args.encoding) - q_out.put((i, s, oitem)) - else: - header = mx.recordio.IRHeader(item.flag, item.label, item.id, 0) - #print('write', item.flag, item.id, item.label) - s = mx.recordio.pack(header, '') - q_out.put((i, s, oitem)) - - -def read_worker(args, q_in, q_out): - while True: - deq = q_in.get() - if deq is None: - break - i, item = deq - image_encode(args, i, item, q_out) - - -def write_worker(q_out, fname, working_dir): - pre_time = time.time() - count = 0 - fname = os.path.basename(fname) - fname_rec = os.path.splitext(fname)[0] + '.rec' - fname_idx = os.path.splitext(fname)[0] + '.idx' - record = mx.recordio.MXIndexedRecordIO( - os.path.join(working_dir, fname_idx), - os.path.join(working_dir, fname_rec), 'w') - buf = {} - more = True - while more: - deq = q_out.get() - if deq is not None: - i, s, item = deq - buf[i] = (s, item) - else: - more = False - while count in buf: - s, item = buf[count] - del buf[count] - if s is not None: - #print('write idx', item[0]) - record.write_idx(item[0], s) - - if count % 1000 == 0: - cur_time = time.time() - print('time:', cur_time - pre_time, ' count:', count) - pre_time = cur_time - count += 1 - - -def parse_args(): - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - description='Create an image list or \ - make a record database by reading from an image list') - parser.add_argument('prefix', - help='prefix of input/output lst and rec files.') - #parser.add_argument('root', help='path to folder containing images.') - - cgroup = parser.add_argument_group('Options for creating image lists') - cgroup.add_argument( - '--list', - type=bool, - default=False, - help= - 'If this is set im2rec will create image list(s) by traversing root folder\ - and output to .lst.\ - Otherwise im2rec will read .lst and create a database at .rec' - ) - cgroup.add_argument('--exts', - nargs='+', - default=['.jpeg', '.jpg'], - help='list of acceptable image extensions.') - cgroup.add_argument('--chunks', - type=int, - default=1, - help='number of chunks.') - cgroup.add_argument('--train-ratio', - type=float, - default=1.0, - help='Ratio of images to use for training.') - cgroup.add_argument('--test-ratio', - type=float, - default=0, - help='Ratio of images to use for testing.') - cgroup.add_argument( - '--recursive', - type=bool, - default=False, - help= - 'If true recursively walk through subdirs and assign an unique label\ - to images in each folder. Otherwise only include images in the root folder\ - and give them label 0.') - cgroup.add_argument('--shuffle', - type=bool, - default=True, - help='If this is set as True, \ - im2rec will randomize the image order in .lst') - - rgroup = parser.add_argument_group('Options for creating database') - rgroup.add_argument( - '--quality', - type=int, - default=95, - help= - 'JPEG quality for encoding, 1-100; or PNG compression for encoding, 1-9' - ) - rgroup.add_argument( - '--num-thread', - type=int, - default=1, - help= - 'number of thread to use for encoding. order of images will be different\ - from the input list if >1. the input list will be modified to match the\ - resulting order.') - rgroup.add_argument('--color', - type=int, - default=1, - choices=[-1, 0, 1], - help='specify the color mode of the loaded image.\ - 1: Loads a color image. Any transparency of image will be neglected. It is the default flag.\ - 0: Loads image in grayscale mode.\ - -1:Loads image as such including alpha channel.') - rgroup.add_argument('--encoding', - type=str, - default='.jpg', - choices=['.jpg', '.png'], - help='specify the encoding of the images.') - rgroup.add_argument( - '--pack-label', - type=bool, - default=False, - help='Whether to also pack multi dimensional label in the record file') - args = parser.parse_args() - args.prefix = os.path.abspath(args.prefix) - #args.root = os.path.abspath(args.root) - return args - - -if __name__ == '__main__': - args = parse_args() - if args.list: - pass - #make_list(args) - else: - if os.path.isdir(args.prefix): - working_dir = args.prefix - else: - working_dir = os.path.dirname(args.prefix) - image_size = (112, 112) - print('image_size', image_size) - args.image_h = image_size[0] - args.image_w = image_size[1] - files = [ - os.path.join(working_dir, fname) - for fname in os.listdir(working_dir) - if os.path.isfile(os.path.join(working_dir, fname)) - ] - count = 0 - for fname in files: - if fname.startswith(args.prefix) and fname.endswith('.lst'): - print('Creating .rec file from', fname, 'in', working_dir) - count += 1 - image_list = read_list(fname) - # -- write_record -- # - if args.num_thread > 1 and multiprocessing is not None: - q_in = [ - multiprocessing.Queue(1024) - for i in range(args.num_thread) - ] - q_out = multiprocessing.Queue(1024) - read_process = [multiprocessing.Process(target=read_worker, args=(args, q_in[i], q_out)) \ - for i in range(args.num_thread)] - for p in read_process: - p.start() - write_process = multiprocessing.Process( - target=write_worker, args=(q_out, fname, working_dir)) - write_process.start() - - for i, item in enumerate(image_list): - q_in[i % len(q_in)].put((i, item)) - for q in q_in: - q.put(None) - for p in read_process: - p.join() - - q_out.put(None) - write_process.join() - else: - print( - 'multiprocessing not available, fall back to single threaded encoding' - ) - try: - import Queue as queue - except ImportError: - import queue - q_out = queue.Queue() - fname = os.path.basename(fname) - fname_rec = os.path.splitext(fname)[0] + '.rec' - fname_idx = os.path.splitext(fname)[0] + '.idx' - record = mx.recordio.MXIndexedRecordIO( - os.path.join(working_dir, fname_idx), - os.path.join(working_dir, fname_rec), 'w') - cnt = 0 - pre_time = time.time() - for i, item in enumerate(image_list): - image_encode(args, i, item, q_out) - if q_out.empty(): - continue - _, s, item = q_out.get() - #header, _ = mx.recordio.unpack(s) - #print('write header label', header.label) - record.write_idx(item[0], s) - if cnt % 1000 == 0: - cur_time = time.time() - print('time:', cur_time - pre_time, ' count:', cnt) - pre_time = cur_time - cnt += 1 - if not count: - print('Did not find and list file with prefix %s' % args.prefix) diff --git a/resources/11513D05.jpg b/resources/11513D05.jpg deleted file mode 100644 index c38bb0d..0000000 Binary files a/resources/11513D05.jpg and /dev/null differ diff --git a/resources/arcface.png b/resources/arcface.png deleted file mode 100644 index fa43f9e..0000000 Binary files a/resources/arcface.png and /dev/null differ diff --git a/resources/cov_test.jpg b/resources/cov_test.jpg deleted file mode 100644 index 8d5bbe5..0000000 Binary files a/resources/cov_test.jpg and /dev/null differ diff --git a/resources/facerecognitionfromvideo.PNG b/resources/facerecognitionfromvideo.PNG deleted file mode 100644 index 96f7724..0000000 Binary files a/resources/facerecognitionfromvideo.PNG and /dev/null differ diff --git a/resources/lfr19_wechat1.jpg b/resources/lfr19_wechat1.jpg deleted file mode 100644 index 3ba0dad..0000000 Binary files a/resources/lfr19_wechat1.jpg and /dev/null differ diff --git a/resources/mainsteps.png b/resources/mainsteps.png deleted file mode 100644 index 1d3d1ff..0000000 Binary files a/resources/mainsteps.png and /dev/null differ diff --git a/resources/memoryspeed.png b/resources/memoryspeed.png deleted file mode 100644 index d4e1a44..0000000 Binary files a/resources/memoryspeed.png and /dev/null differ diff --git a/resources/mfrlogo.jpg b/resources/mfrlogo.jpg deleted file mode 100644 index 112896c..0000000 Binary files a/resources/mfrlogo.jpg and /dev/null differ diff --git a/resources/retina_R50_ex1.jpg b/resources/retina_R50_ex1.jpg deleted file mode 100644 index 603cda3..0000000 Binary files a/resources/retina_R50_ex1.jpg and /dev/null differ diff --git a/resources/retina_R50_ex2.jpg b/resources/retina_R50_ex2.jpg deleted file mode 100644 index ec2a606..0000000 Binary files a/resources/retina_R50_ex2.jpg and /dev/null differ diff --git a/resources/subcenterarcfacediff.png b/resources/subcenterarcfacediff.png deleted file mode 100644 index 541fa76..0000000 Binary files a/resources/subcenterarcfacediff.png and /dev/null differ diff --git a/resources/subcenterarcfaceframework.png b/resources/subcenterarcfaceframework.png deleted file mode 100644 index b7be824..0000000 Binary files a/resources/subcenterarcfaceframework.png and /dev/null differ diff --git a/resources/widerfacevaltest.png b/resources/widerfacevaltest.png deleted file mode 100644 index 65807de..0000000 Binary files a/resources/widerfacevaltest.png and /dev/null differ diff --git a/sample-images/t2.jpg b/sample-images/t2.jpg deleted file mode 100644 index dcca930..0000000 Binary files a/sample-images/t2.jpg and /dev/null differ