Signed-off-by: NYH <175484793@qq.com>

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,81 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="68">
+            <item index="0" class="java.lang.String" itemvalue="thop" />
+            <item index="1" class="java.lang.String" itemvalue="Cython" />
+            <item index="2" class="java.lang.String" itemvalue="pycocotools" />
+            <item index="3" class="java.lang.String" itemvalue="pandas" />
+            <item index="4" class="java.lang.String" itemvalue="tqdm" />
+            <item index="5" class="java.lang.String" itemvalue="scipy" />
+            <item index="6" class="java.lang.String" itemvalue="pillow" />
+            <item index="7" class="java.lang.String" itemvalue="numpy" />
+            <item index="8" class="java.lang.String" itemvalue="requests" />
+            <item index="9" class="java.lang.String" itemvalue="easydict" />
+            <item index="10" class="java.lang.String" itemvalue="imutil" />
+            <item index="11" class="java.lang.String" itemvalue="pkg-resources" />
+            <item index="12" class="java.lang.String" itemvalue="hyperpyyaml" />
+            <item index="13" class="java.lang.String" itemvalue="pytest" />
+            <item index="14" class="java.lang.String" itemvalue="yamllint" />
+            <item index="15" class="java.lang.String" itemvalue="sentencepiece" />
+            <item index="16" class="java.lang.String" itemvalue="black" />
+            <item index="17" class="java.lang.String" itemvalue="pre-commit" />
+            <item index="18" class="java.lang.String" itemvalue="pycodestyle" />
+            <item index="19" class="java.lang.String" itemvalue="torch" />
+            <item index="20" class="java.lang.String" itemvalue="torchaudio" />
+            <item index="21" class="java.lang.String" itemvalue="flake8" />
+            <item index="22" class="java.lang.String" itemvalue="huggingface_hub" />
+            <item index="23" class="java.lang.String" itemvalue="numba" />
+            <item index="24" class="java.lang.String" itemvalue="librosa" />
+            <item index="25" class="java.lang.String" itemvalue="visualdl" />
+            <item index="26" class="java.lang.String" itemvalue="pyaudio" />
+            <item index="27" class="java.lang.String" itemvalue="joblib" />
+            <item index="28" class="java.lang.String" itemvalue="packaging" />
+            <item index="29" class="java.lang.String" itemvalue="opencv-python" />
+            <item index="30" class="java.lang.String" itemvalue="pytorch-lightning" />
+            <item index="31" class="java.lang.String" itemvalue="scikit-learn" />
+            <item index="32" class="java.lang.String" itemvalue="speechbrain" />
+            <item index="33" class="java.lang.String" itemvalue="webrtcvad" />
+            <item index="34" class="java.lang.String" itemvalue="yacs" />
+            <item index="35" class="java.lang.String" itemvalue="prefetch_generator" />
+            <item index="36" class="java.lang.String" itemvalue="pathspec" />
+            <item index="37" class="java.lang.String" itemvalue="addict" />
+            <item index="38" class="java.lang.String" itemvalue="loguru" />
+            <item index="39" class="java.lang.String" itemvalue="glog" />
+            <item index="40" class="java.lang.String" itemvalue="mmcv" />
+            <item index="41" class="java.lang.String" itemvalue="Shapely" />
+            <item index="42" class="java.lang.String" itemvalue="yapf" />
+            <item index="43" class="java.lang.String" itemvalue="cityscapesscripts" />
+            <item index="44" class="java.lang.String" itemvalue="asynctest" />
+            <item index="45" class="java.lang.String" itemvalue="albumentations" />
+            <item index="46" class="java.lang.String" itemvalue="imagecorruptions" />
+            <item index="47" class="java.lang.String" itemvalue="pytest-runner" />
+            <item index="48" class="java.lang.String" itemvalue="pytest-cov" />
+            <item index="49" class="java.lang.String" itemvalue="isort" />
+            <item index="50" class="java.lang.String" itemvalue="kwarray" />
+            <item index="51" class="java.lang.String" itemvalue="xdoctest" />
+            <item index="52" class="java.lang.String" itemvalue="codecov" />
+            <item index="53" class="java.lang.String" itemvalue="ubelt" />
+            <item index="54" class="java.lang.String" itemvalue="Pillow" />
+            <item index="55" class="java.lang.String" itemvalue="terminaltables" />
+            <item index="56" class="java.lang.String" itemvalue="tensorflow" />
+            <item index="57" class="java.lang.String" itemvalue="tensorflow_gpu" />
+            <item index="58" class="java.lang.String" itemvalue="scikit_learn" />
+            <item index="59" class="java.lang.String" itemvalue="matplotlib" />
+            <item index="60" class="java.lang.String" itemvalue="opencv_contrib_python" />
+            <item index="61" class="java.lang.String" itemvalue="tensorboard" />
+            <item index="62" class="java.lang.String" itemvalue="seaborn" />
+            <item index="63" class="java.lang.String" itemvalue="h5py" />
+            <item index="64" class="java.lang.String" itemvalue="torchvision" />
+            <item index="65" class="java.lang.String" itemvalue="opencv_python" />
+            <item index="66" class="java.lang.String" itemvalue="pytorch" />
+            <item index="67" class="java.lang.String" itemvalue="opencv" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

LICENSE.MIT

@@ -0,0 +1,21 @@
+MIT License 
+
+Copyright (c) 2019
+
+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.

README.md

@@ -1,3 +1,129 @@
-# Retinaface_3point_Detection
+# RetinaFace in PyTorch
 
-二维码三点方向检测
+A [PyTorch](https://pytorch.org/) implementation of [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://arxiv.org/abs/1905.00641). Model size only 1.7M, when Retinaface use mobilenet0.25 as backbone net. We also provide resnet50 as backbone net to get better result. The official code in Mxnet can be found [here](https://github.com/deepinsight/insightface/tree/master/RetinaFace).
+
+## Mobile or Edge device deploy
+We also provide a set of Face Detector for edge device in [here](https://github.com/biubug6/Face-Detector-1MB-with-landmark) from python training to C++ inference.
+
+## WiderFace Val Performance in single scale When using Resnet50 as backbone net.
+| Style | easy | medium | hard |
+|:-|:-:|:-:|:-:|
+| Pytorch (same parameter with Mxnet) | 94.82 % | 93.84% | 89.60% |
+| Pytorch (original image scale) | 95.48% | 94.04% | 84.43% |
+| Mxnet | 94.86% | 93.87% | 88.33% |
+| Mxnet(original image scale) | 94.97% | 93.89% | 82.27% |
+
+## WiderFace Val Performance in single scale When using Mobilenet0.25 as backbone net.
+| Style | easy | medium | hard |
+|:-|:-:|:-:|:-:|
+| Pytorch (same parameter with Mxnet) | 88.67% | 87.09% | 80.99% |
+| Pytorch (original image scale) | 90.70% | 88.16% | 73.82% |
+| Mxnet | 88.72% | 86.97% | 79.19% |
+| Mxnet(original image scale) | 89.58% | 87.11% | 69.12% |
+<p align="center"><img src="curve/Widerface.jpg" width="640"\></p>
+
+## FDDB Performance.
+| FDDB(pytorch) | performance |
+|:-|:-:|
+| Mobilenet0.25 | 98.64% |
+| Resnet50 | 99.22% |
+<p align="center"><img src="curve/FDDB.png" width="640"\></p>
+
+### Contents
+- [Installation](#installation)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [TensorRT](#tensorrt)
+- [References](#references)
+
+## Installation
+##### Clone and install
+1. git clone https://github.com/biubug6/Pytorch_Retinaface.git
+
+2. Pytorch version 1.1.0+ and torchvision 0.3.0+ are needed.
+
+3. Codes are based on Python 3
+
+##### Data
+1. Download the [WIDERFACE](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html) dataset.
+
+2. Download annotations (face bounding boxes & five facial landmarks) from [baidu cloud](https://pan.baidu.com/s/1Laby0EctfuJGgGMgRRgykA) or [dropbox](https://www.dropbox.com/s/7j70r3eeepe4r2g/retinaface_gt_v1.1.zip?dl=0)
+
+3. Organise the dataset directory as follows:
+
+```Shell
+  ./data/widerface/
+    train/
+      images/
+      label.txt
+    val/
+      images/
+      wider_val.txt
+```
+ps: wider_val.txt only include val file names but not label information.
+
+##### Data1
+We also provide the organized dataset we used as in the above directory structure.
+
+Link: from [google cloud](https://drive.google.com/open?id=11UGV3nbVv1x9IC--_tK3Uxf7hA6rlbsS) or [baidu cloud](https://pan.baidu.com/s/1jIp9t30oYivrAvrgUgIoLQ) Password: ruck
+
+## Training
+We provide restnet50 and mobilenet0.25 as backbone network to train model.
+We trained Mobilenet0.25 on imagenet dataset and get 46.58%  in top 1. If you do not wish to train the model, we also provide trained model. Pretrain model  and trained model are put in [google cloud](https://drive.google.com/open?id=1oZRSG0ZegbVkVwUd8wUIQx8W7yfZ_ki1) and [baidu cloud](https://pan.baidu.com/s/12h97Fy1RYuqMMIV-RpzdPg) Password: fstq . The model could be put as follows:
+```Shell
+  ./weights/
+      mobilenet0.25_Final.pth
+      mobilenetV1X0.25_pretrain.tar
+      Resnet50_Final.pth
+```
+1. Before training, you can check network configuration (e.g. batch_size, min_sizes and steps etc..) in ``data/config.py and train.py``.
+
+2. Train the model using WIDER FACE:
+  ```Shell
+  CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --network resnet50 or
+  CUDA_VISIBLE_DEVICES=0 python train.py --network mobile0.25
+  ```
+
+
+## Evaluation
+### Evaluation widerface val
+1. Generate txt file
+```Shell
+python test_widerface.py --trained_model weight_file --network mobile0.25 or resnet50
+```
+2. Evaluate txt results. Demo come from [Here](https://github.com/wondervictor/WiderFace-Evaluation)
+```Shell
+cd ./widerface_evaluate
+python setup.py build_ext --inplace
+python evaluation.py
+```
+3. You can also use widerface official Matlab evaluate demo in [Here](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/WiderFace_Results.html)
+### Evaluation FDDB
+
+1. Download the images [FDDB](https://drive.google.com/open?id=17t4WULUDgZgiSy5kpCax4aooyPaz3GQH) to:
+```Shell
+./data/FDDB/images/
+```
+
+2. Evaluate the trained model using:
+```Shell
+python test_fddb.py --trained_model weight_file --network mobile0.25 or resnet50
+```
+
+3. Download [eval_tool](https://bitbucket.org/marcopede/face-eval) to evaluate the performance.
+
+<p align="center"><img src="curve/1.jpg" width="640"\></p>
+
+## TensorRT
+-[TensorRT](https://github.com/wang-xinyu/tensorrtx/tree/master/retinaface)
+
+## References
+- [FaceBoxes](https://github.com/zisianw/FaceBoxes.PyTorch)
+- [Retinaface (mxnet)](https://github.com/deepinsight/insightface/tree/master/RetinaFace)
+```
+@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}
+```

convert_to_onnx.py

@@ -0,0 +1,88 @@
+from __future__ import print_function
+import os
+import argparse
+import torch
+import torch.backends.cudnn as cudnn
+import numpy as np
+from data import cfg_mnet, cfg_re50
+from layers.functions.prior_box import PriorBox
+from utils.nms.py_cpu_nms import py_cpu_nms
+import cv2
+from models.retinaface import RetinaFace
+from utils.box_utils import decode, decode_landm
+from utils.timer import Timer
+
+
+parser = argparse.ArgumentParser(description='Test')
+parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth',
+                    type=str, help='Trained state_dict file path to open')
+parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
+parser.add_argument('--long_side', default=640, help='when origin_size is false, long_side is scaled size(320 or 640 for long side)')
+parser.add_argument('--cpu', action="store_true", default=True, help='Use cpu inference')
+
+args = parser.parse_args()
+
+
+def check_keys(model, pretrained_state_dict):
+    ckpt_keys = set(pretrained_state_dict.keys())
+    model_keys = set(model.state_dict().keys())
+    used_pretrained_keys = model_keys & ckpt_keys
+    unused_pretrained_keys = ckpt_keys - model_keys
+    missing_keys = model_keys - ckpt_keys
+    print('Missing keys:{}'.format(len(missing_keys)))
+    print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
+    print('Used keys:{}'.format(len(used_pretrained_keys)))
+    assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
+    return True
+
+
+def remove_prefix(state_dict, prefix):
+    ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
+    print('remove prefix \'{}\''.format(prefix))
+    f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
+    return {f(key): value for key, value in state_dict.items()}
+
+
+def load_model(model, pretrained_path, load_to_cpu):
+    print('Loading pretrained model from {}'.format(pretrained_path))
+    if load_to_cpu:
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
+    else:
+        device = torch.cuda.current_device()
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
+    if "state_dict" in pretrained_dict.keys():
+        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
+    else:
+        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
+    check_keys(model, pretrained_dict)
+    model.load_state_dict(pretrained_dict, strict=False)
+    return model
+
+
+if __name__ == '__main__':
+    torch.set_grad_enabled(False)
+    cfg = None
+    if args.network == "mobile0.25":
+        cfg = cfg_mnet
+    elif args.network == "resnet50":
+        cfg = cfg_re50
+    # net and model
+    net = RetinaFace(cfg=cfg, phase = 'test')
+    net = load_model(net, args.trained_model, args.cpu)
+    net.eval()
+    print('Finished loading model!')
+    print(net)
+    device = torch.device("cpu" if args.cpu else "cuda")
+    net = net.to(device)
+
+    # ------------------------ export -----------------------------
+    output_onnx = 'FaceDetector.onnx'
+    print("==> Exporting model to ONNX format at '{}'".format(output_onnx))
+    input_names = ["input0"]
+    output_names = ["output0"]
+    inputs = torch.randn(1, 3, args.long_side, args.long_side).to(device)
+
+    torch_out = torch.onnx._export(net, inputs, output_onnx, export_params=True, verbose=False,
+                                   input_names=input_names, output_names=output_names)
+
+