init

.gitignore

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+human_detection/output
+examples/results
+PoseFlow/__pycache__
+PoseFlow/*.npy
+PoseFlow/alpha-pose-results-test.json
+PoseFlow/alpha-pose-results-val.json
+PoseFlow/test-predict
+PoseFlow/val-predict
+ssd/examples
+
+*.npy
+*.so
+*.pyc
+.ipynb_checkpoints
+*/.ipynb_checkpoints/
+
+*.pth
+*.json
+*.h5
+*.zip
+

README.md

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+
+<div align="center">
+    <img src="doc/logo.jpg", width="400">
+</div>
+
+
+## News!
+
+This is the **beta pytorch** version of AlphaPose. Stable version will be ready in two days. Currently AlphaPose runs at about **5 fps**. Realtime version is coming very soon. Stay tuned!
+
+## AlphaPose
+[Alpha Pose](http://www.mvig.org/research/alphapose.html) is an accurate multi-person pose estimator, which is the **first open-source system that achieves 70+ mAP (72.3 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset.** 
+To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the **first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset.**
+
+
+
+
+## Installation
+1. Get the code.
+  ```Shell
+  git clone -b pytorch https://github.com/MVIG-SJTU/AlphaPose.git
+
+  ```
+
+2. Install [pytorch](https://github.com/pytorch/pytorch)
+  ```Shell
+  chmod +x install.sh
+  ./install.sh
+  ```
+
+1. Download the models manually: **fpnssd512_20_trained.pth**([Baidu pan](https://pan.baidu.com/s/10ZQfHAqvn8SdFnPnuEg0fg)), **pyra_4.pth** ([Baidu pan](https://pan.baidu.com/s/13jZRPT21zK5L-cqyfe0Qww)). Place them into `./models/ssd` and `./models/sppe` respectively.
+
+
+## Quick Start
+- **Demo**:  Run AlphaPose for all images in a folder and visualize the results with:
+```
+python demo_fast.py \
+--inputlist ./list-coco-minival500.txt \
+--imgpath ${img_directory} \
+--outputpath ./coco-minival
+```
+
+
+
+## Contributors
+AlphaPose is based on RMPE(ICCV'17), authored by [Hao-shu Fang](https://fang-haoshu.github.io/), Shuqin Xie, [Yu-Wing Tai](https://scholar.google.com/citations?user=nFhLmFkAAAAJ&hl=en) and [Cewu Lu](http://www.mvig.org/), [Cewu Lu](http://mvig.sjtu.edu.cn/) is the corresponding author. Currently, it is developed and maintained by [Hao-shu Fang](https://fang-haoshu.github.io/), [Jiefeng Li](http://jeff-leaf.site/), [Yuliang Xiu](http://xiuyuliang.cn/about/) and [Ruiheng Chang](https://crh19970307.github.io/). 
+
+The main contributors are listed in [doc/contributors.md](doc/contributors.md).
+
+## Citation
+Please cite these papers in your publications if it helps your research:
+
+    @inproceedings{fang2017rmpe,
+      title={{RMPE}: Regional Multi-person Pose Estimation},
+      author={Fang, Hao-Shu and Xie, Shuqin and Tai, Yu-Wing and Lu, Cewu},
+      booktitle={ICCV},
+      year={2017}
+    }
+
+    @ARTICLE{2018arXiv180200977X,
+      author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
+      title = {{Pose Flow}: Efficient Online Pose Tracking},
+      journal = {ArXiv e-prints},
+      eprint = {1802.00977},
+      year = {2018}
+    }
+
+
+
+## License
+AlphaPose is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, contact [Cewu Lu](http://www.mvig.org/)

SPPE/.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

SPPE/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+.vscode/
+*.pkl
+exp
+exp/*
+data
+data/*
+model
+model/*
+*/images
+*/images/*
+
+*.h5
+*.pth
+

SPPE/LICENSE

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+MIT License
+
+Copyright (c) 2018 Jeff-sjtu
+
+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.

SPPE/README.md

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+# pytorch-AlphaPose

SPPE/src/main_fast_inference.py

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+import torch
+import torch.nn as nn
+import torch.utils.data
+import torch.utils.data.distributed
+import torch.nn.functional as F
+import numpy as np
+from opt import opt
+import h5py
+from tqdm import tqdm
+from SPPE.src.utils.img import flip_v, shuffleLR
+from SPPE.src.utils.eval import getPrediction
+from SPPE.src.models.hgPRM import createModel_Inference
+
+import visdom
+import time
+
+
+import torch._utils
+try:
+    torch._utils._rebuild_tensor_v2
+except AttributeError:
+    def _rebuild_tensor_v2(storage, storage_offset, size, stride, requires_grad, backward_hooks):
+        tensor = torch._utils._rebuild_tensor(storage, storage_offset, size, stride)
+        tensor.requires_grad = requires_grad
+        tensor._backward_hooks = backward_hooks
+        return tensor
+    torch._utils._rebuild_tensor_v2 = _rebuild_tensor_v2
+
+
+torch.backends.cudnn.benchmark = True
+batch_size = 128 * 8
+
+
+def gaussian(size):
+    sigma = 1
+    x = np.arange(0, size, 1, float)
+    y = x[:, np.newaxis]
+    x0 = y0 = size // 2
+    sigma = size / 4.0
+    # The gaussian is not normalized, we want the center value to equal 1
+    g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
+    g = g[np.newaxis, :]
+    return g
+
+
+class InferenNet(nn.Module):
+    def __init__(self, kernel_size, dataset):
+        super(InferenNet, self).__init__()
+
+        model = createModel_Inference().cuda()
+        print('Loading Model from {}'.format('./models/sppe/pyra_4.pth'))
+        model.load_state_dict(torch.load('./models/sppe/pyra_4.pth'))
+        model.eval()
+        self.pyranet = model
+        self.gaussian = nn.Conv2d(17, 17, kernel_size=kernel_size,
+                                  stride=1, padding=2, groups=17, bias=False)
+
+        g = torch.from_numpy(gaussian(kernel_size)).clone()
+        g = torch.unsqueeze(g, 1)
+        g = g.repeat(17, 1, 1, 1)
+        assert g.shape == self.gaussian.weight.data.shape
+        self.gaussian.weight.data = g.float()
+
+        self.dataset = dataset
+
+    def forward(self, x):
+        out = self.pyranet(x)
+        out = out.narrow(1, 0, 17)
+
+        flip_out = self.pyranet(flip_v(x))
+        flip_out = flip_out.narrow(1, 0, 17)
+
+        flip_out = flip_v(shuffleLR(
+            flip_out, self.dataset))
+
+        out = (flip_out + out) / 2
+        out = self.gaussian(F.relu(out, inplace=True))
+
+        return out

SPPE/src/models/__init__.py

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+from . import *