├── .DS_Store
├── .idea
    ├── lstm_pm_pytorch.iml
    ├── misc.xml
    ├── modules.xml
    ├── vcs.xml
    └── workspace.xml
├── README.md
├── ckpt
    ├── .ipynb_checkpoints
    │   └── draw_loss-checkpoint.ipynb
    └── draw_loss.ipynb
├── data
    ├── __init__.py
    ├── handpose_data2.py
    ├── penn_data.py
    └── penn_data_pre.py
├── dataset
    ├── .DS_Store
    ├── train_data
    │   ├── .DS_Store
    │   └── 001L0
    │   │   ├── L0455.jpg
    │   │   ├── L0461.jpg
    │   │   ├── L0467.jpg
    │   │   ├── L0473.jpg
    │   │   ├── L0479.jpg
    │   │   ├── L0485.jpg
    │   │   ├── L0491.jpg
    │   │   ├── L0497.jpg
    │   │   ├── L0503.jpg
    │   │   ├── L0509.jpg
    │   │   ├── L0515.jpg
    │   │   ├── L0521.jpg
    │   │   ├── L0527.jpg
    │   │   ├── L0533.jpg
    │   │   ├── L0539.jpg
    │   │   ├── L0545.jpg
    │   │   ├── L0551.jpg
    │   │   ├── L0557.jpg
    │   │   ├── L0563.jpg
    │   │   ├── L0569.jpg
    │   │   ├── L0575.jpg
    │   │   ├── L0581.jpg
    │   │   ├── L0587.jpg
    │   │   ├── L0599.jpg
    │   │   ├── L0605.jpg
    │   │   ├── L0611.jpg
    │   │   ├── L0617.jpg
    │   │   ├── L0623.jpg
    │   │   ├── L0629.jpg
    │   │   ├── L0635.jpg
    │   │   ├── L0641.jpg
    │   │   ├── L0647.jpg
    │   │   ├── L0653.jpg
    │   │   ├── L0659.jpg
    │   │   ├── L0665.jpg
    │   │   ├── L0671.jpg
    │   │   └── L0677.jpg
    ├── train_full_data
    │   └── 001L0
    │   │   ├── L0455.jpg
    │   │   ├── L0456.jpg
    │   │   ├── L0457.jpg
    │   │   ├── L0458.jpg
    │   │   ├── L0459.jpg
    │   │   ├── L0460.jpg
    │   │   ├── L0461.jpg
    │   │   ├── L0462.jpg
    │   │   ├── L0463.jpg
    │   │   ├── L0464.jpg
    │   │   ├── L0465.jpg
    │   │   ├── L0466.jpg
    │   │   ├── L0467.jpg
    │   │   ├── L0468.jpg
    │   │   ├── L0469.jpg
    │   │   ├── L0470.jpg
    │   │   ├── L0471.jpg
    │   │   ├── L0472.jpg
    │   │   ├── L0473.jpg
    │   │   ├── L0474.jpg
    │   │   ├── L0475.jpg
    │   │   ├── L0476.jpg
    │   │   ├── L0477.jpg
    │   │   ├── L0478.jpg
    │   │   ├── L0479.jpg
    │   │   ├── L0480.jpg
    │   │   ├── L0481.jpg
    │   │   ├── L0482.jpg
    │   │   ├── L0483.jpg
    │   │   ├── L0484.jpg
    │   │   ├── L0485.jpg
    │   │   ├── L0486.jpg
    │   │   ├── L0487.jpg
    │   │   ├── L0488.jpg
    │   │   ├── L0489.jpg
    │   │   ├── L0490.jpg
    │   │   ├── L0491.jpg
    │   │   ├── L0492.jpg
    │   │   ├── L0493.jpg
    │   │   ├── L0494.jpg
    │   │   ├── L0495.jpg
    │   │   ├── L0496.jpg
    │   │   ├── L0497.jpg
    │   │   ├── L0498.jpg
    │   │   ├── L0499.jpg
    │   │   ├── L0500.jpg
    │   │   ├── L0501.jpg
    │   │   ├── L0502.jpg
    │   │   ├── L0503.jpg
    │   │   ├── L0504.jpg
    │   │   ├── L0505.jpg
    │   │   ├── L0506.jpg
    │   │   ├── L0507.jpg
    │   │   ├── L0508.jpg
    │   │   ├── L0509.jpg
    │   │   ├── L0510.jpg
    │   │   ├── L0511.jpg
    │   │   ├── L0512.jpg
    │   │   ├── L0513.jpg
    │   │   ├── L0514.jpg
    │   │   ├── L0515.jpg
    │   │   ├── L0516.jpg
    │   │   ├── L0517.jpg
    │   │   ├── L0518.jpg
    │   │   ├── L0519.jpg
    │   │   ├── L0520.jpg
    │   │   ├── L0521.jpg
    │   │   ├── L0522.jpg
    │   │   ├── L0523.jpg
    │   │   ├── L0524.jpg
    │   │   ├── L0525.jpg
    │   │   ├── L0526.jpg
    │   │   ├── L0527.jpg
    │   │   ├── L0528.jpg
    │   │   ├── L0529.jpg
    │   │   ├── L0530.jpg
    │   │   ├── L0531.jpg
    │   │   ├── L0532.jpg
    │   │   ├── L0533.jpg
    │   │   ├── L0534.jpg
    │   │   ├── L0535.jpg
    │   │   ├── L0536.jpg
    │   │   ├── L0537.jpg
    │   │   ├── L0538.jpg
    │   │   ├── L0539.jpg
    │   │   ├── L0540.jpg
    │   │   ├── L0541.jpg
    │   │   ├── L0542.jpg
    │   │   ├── L0543.jpg
    │   │   ├── L0544.jpg
    │   │   ├── L0545.jpg
    │   │   ├── L0546.jpg
    │   │   ├── L0547.jpg
    │   │   ├── L0548.jpg
    │   │   ├── L0549.jpg
    │   │   ├── L0550.jpg
    │   │   ├── L0551.jpg
    │   │   ├── L0552.jpg
    │   │   ├── L0553.jpg
    │   │   ├── L0554.jpg
    │   │   ├── L0555.jpg
    │   │   ├── L0556.jpg
    │   │   ├── L0557.jpg
    │   │   ├── L0558.jpg
    │   │   ├── L0559.jpg
    │   │   ├── L0560.jpg
    │   │   ├── L0561.jpg
    │   │   ├── L0562.jpg
    │   │   ├── L0563.jpg
    │   │   ├── L0564.jpg
    │   │   ├── L0565.jpg
    │   │   ├── L0566.jpg
    │   │   ├── L0567.jpg
    │   │   ├── L0568.jpg
    │   │   ├── L0569.jpg
    │   │   ├── L0570.jpg
    │   │   ├── L0571.jpg
    │   │   ├── L0572.jpg
    │   │   ├── L0573.jpg
    │   │   ├── L0574.jpg
    │   │   ├── L0575.jpg
    │   │   ├── L0576.jpg
    │   │   ├── L0577.jpg
    │   │   ├── L0578.jpg
    │   │   ├── L0579.jpg
    │   │   ├── L0580.jpg
    │   │   ├── L0581.jpg
    │   │   ├── L0582.jpg
    │   │   ├── L0583.jpg
    │   │   ├── L0584.jpg
    │   │   ├── L0585.jpg
    │   │   ├── L0586.jpg
    │   │   ├── L0587.jpg
    │   │   ├── L0588.jpg
    │   │   ├── L0589.jpg
    │   │   ├── L0590.jpg
    │   │   ├── L0591.jpg
    │   │   ├── L0592.jpg
    │   │   ├── L0593.jpg
    │   │   ├── L0594.jpg
    │   │   ├── L0595.jpg
    │   │   ├── L0596.jpg
    │   │   ├── L0597.jpg
    │   │   ├── L0598.jpg
    │   │   ├── L0599.jpg
    │   │   ├── L0600.jpg
    │   │   ├── L0601.jpg
    │   │   ├── L0602.jpg
    │   │   ├── L0603.jpg
    │   │   ├── L0604.jpg
    │   │   ├── L0605.jpg
    │   │   ├── L0606.jpg
    │   │   ├── L0607.jpg
    │   │   ├── L0608.jpg
    │   │   ├── L0609.jpg
    │   │   ├── L0610.jpg
    │   │   ├── L0611.jpg
    │   │   ├── L0612.jpg
    │   │   ├── L0613.jpg
    │   │   ├── L0614.jpg
    │   │   ├── L0615.jpg
    │   │   ├── L0616.jpg
    │   │   ├── L0617.jpg
    │   │   ├── L0618.jpg
    │   │   ├── L0619.jpg
    │   │   ├── L0620.jpg
    │   │   ├── L0621.jpg
    │   │   ├── L0622.jpg
    │   │   ├── L0623.jpg
    │   │   ├── L0624.jpg
    │   │   ├── L0625.jpg
    │   │   ├── L0626.jpg
    │   │   ├── L0627.jpg
    │   │   ├── L0628.jpg
    │   │   ├── L0629.jpg
    │   │   ├── L0630.jpg
    │   │   ├── L0631.jpg
    │   │   ├── L0632.jpg
    │   │   ├── L0633.jpg
    │   │   ├── L0634.jpg
    │   │   ├── L0635.jpg
    │   │   ├── L0636.jpg
    │   │   ├── L0637.jpg
    │   │   ├── L0638.jpg
    │   │   ├── L0639.jpg
    │   │   ├── L0640.jpg
    │   │   ├── L0641.jpg
    │   │   ├── L0642.jpg
    │   │   ├── L0643.jpg
    │   │   ├── L0644.jpg
    │   │   ├── L0645.jpg
    │   │   ├── L0646.jpg
    │   │   ├── L0647.jpg
    │   │   ├── L0648.jpg
    │   │   ├── L0649.jpg
    │   │   ├── L0650.jpg
    │   │   ├── L0651.jpg
    │   │   ├── L0652.jpg
    │   │   ├── L0653.jpg
    │   │   ├── L0654.jpg
    │   │   ├── L0655.jpg
    │   │   ├── L0656.jpg
    │   │   ├── L0657.jpg
    │   │   ├── L0658.jpg
    │   │   ├── L0659.jpg
    │   │   ├── L0660.jpg
    │   │   ├── L0661.jpg
    │   │   ├── L0662.jpg
    │   │   ├── L0663.jpg
    │   │   ├── L0664.jpg
    │   │   ├── L0665.jpg
    │   │   ├── L0666.jpg
    │   │   ├── L0667.jpg
    │   │   ├── L0668.jpg
    │   │   ├── L0669.jpg
    │   │   ├── L0670.jpg
    │   │   ├── L0671.jpg
    │   │   ├── L0672.jpg
    │   │   ├── L0673.jpg
    │   │   ├── L0674.jpg
    │   │   ├── L0675.jpg
    │   │   ├── L0676.jpg
    │   │   └── L0677.jpg
    └── train_label
    │   └── 001L0.json
├── lstm_pm_train.py
├── model
    ├── __init__.py
    └── lstm_pm.py
├── src
    ├── __init__.py
    ├── __init__.pyc
    ├── utils.py
    └── utils.pyc
└── test_lstm_pm.py


/.DS_Store:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/HowieMa/lstm_pm_pytorch/4e7af6d44a9d8b791d0ace368f1a950a562546eb/.DS_Store


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/.idea/lstm_pm_pytorch.iml:
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748 |   </component>
749 | </project>


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # lstm_pm_pytorch
 2 | implementation of LSTM Pose Machines with Pytorch
 3 | 
 4 | This is pytorch implement of [LSTM Pose Machines](https://arxiv.org/pdf/1712.06316.pdf)
 5 | 
 6 | Please note that this model is used for my own datasets. But the architecture and training are very close to the original model, please feel free to use it for your own project.
 7 | 
 8 | 
 9 | 
10 | ## Prerequisites
11 | * Python 2.7
12 | * scipy
13 | * sklearn
14 | * pillow
15 | * PyTorch 0.3
16 | * torchvision 0.1.9
17 | * pandas
18 | * numpy
19 | 
20 | 
21 | ## Train
22 | python lstm_pm_train.py
23 | 
24 | 
25 | ## Test
26 | python test_lstm_pm.py
27 | 
28 | 
29 | ## References
30 | [LSTM Pose Machines](https://arxiv.org/pdf/1712.06316.pdf)
31 | 
32 | [lawy623/LSTM_Pose_Machines](https://github.com/lawy623/LSTM_Pose_Machines)
33 | 
34 | 


--------------------------------------------------------------------------------
/ckpt/.ipynb_checkpoints/draw_loss-checkpoint.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 20,
 6 |    "metadata": {},
 7 |    "outputs": [],
 8 |    "source": [
 9 |     "import json\n",
10 |     "import os\n",
11 |     "import sys\n"
12 |    ]
13 |   },
14 |   {
15 |    "cell_type": "code",
16 |    "execution_count": 23,
17 |    "metadata": {},
18 |    "outputs": [],
19 |    "source": [
20 |     "losses = os.listdir(os.getcwd())"
21 |    ]
22 |   },
23 |   {
24 |    "cell_type": "code",
25 |    "execution_count": 26,
26 |    "metadata": {},
27 |    "outputs": [],
28 |    "source": [
29 |     "for l in losses:\n",
30 |     "    if l == '.ipynb_checkpoints':\n",
31 |     "        continue \n",
32 |     "    lossj = json.load(open(os.getcwd() + '/' + l))\n",
33 |     "    \n",
34 |     "    "
35 |    ]
36 |   },
37 |   {
38 |    "cell_type": "code",
39 |    "execution_count": null,
40 |    "metadata": {},
41 |    "outputs": [],
42 |    "source": []
43 |   },
44 |   {
45 |    "cell_type": "code",
46 |    "execution_count": null,
47 |    "metadata": {},
48 |    "outputs": [],
49 |    "source": []
50 |   }
51 |  ],
52 |  "metadata": {
53 |   "kernelspec": {
54 |    "display_name": "Python 2",
55 |    "language": "python",
56 |    "name": "python2"
57 |   },
58 |   "language_info": {
59 |    "codemirror_mode": {
60 |     "name": "ipython",
61 |     "version": 2
62 |    },
63 |    "file_extension": ".py",
64 |    "mimetype": "text/x-python",
65 |    "name": "python",
66 |    "nbconvert_exporter": "python",
67 |    "pygments_lexer": "ipython2",
68 |    "version": "2.7.15"
69 |   }
70 |  },
71 |  "nbformat": 4,
72 |  "nbformat_minor": 2
73 | }
74 | 


--------------------------------------------------------------------------------
/ckpt/draw_loss.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import json\n",
 10 |     "import os\n",
 11 |     "import matplotlib.pyplot as plt\n"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 4,
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "data": {
 21 |       "text/plain": [
 22 |        "[<matplotlib.lines.Line2D at 0x110d899d0>]"
 23 |       ]
 24 |      },
 25 |      "execution_count": 4,
 26 |      "metadata": {},
 27 |      "output_type": "execute_result"
 28 |     },
 29 |     {
 30 |      "data": {
 31 |       "image/png": 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\n",
 32 |       "text/plain": [
 33 |        "<Figure size 432x288 with 1 Axes>"
 34 |       ]
 35 |      },
 36 |      "metadata": {},
 37 |      "output_type": "display_data"
 38 |     }
 39 |    ],
 40 |    "source": [
 41 |     "aa=[]\n",
 42 |     "\n",
 43 |     "for epoch in range(0, 50):\n",
 44 |     "    losses = os.listdir('loss_epoch'+str(epoch))\n",
 45 |     "    losses.sort()\n",
 46 |     "\n",
 47 |     "    loss=[]\n",
 48 |     "    for l in losses:\n",
 49 |     "        if l == '.DS_Store':\n",
 50 |     "            continue \n",
 51 |     "\n",
 52 |     "        lossj = json.load(open('loss_epoch'+str(epoch) + '/' + l))\n",
 53 |     "\n",
 54 |     "        n = l.split('.')[0][1:]\n",
 55 |     "        a = lossj['total']\n",
 56 |     "        loss.append([int(n), a])\n",
 57 |     "\n",
 58 |     "    loss.sort()\n",
 59 |     "\n",
 60 |     "    for l in loss:\n",
 61 |     "        aa.append(l[1])\n",
 62 |     "\n",
 63 |     "plt.plot(aa)\n"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "code",
 68 |    "execution_count": 91,
 69 |    "metadata": {},
 70 |    "outputs": [],
 71 |    "source": []
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": null,
 76 |    "metadata": {},
 77 |    "outputs": [],
 78 |    "source": []
 79 |   },
 80 |   {
 81 |    "cell_type": "code",
 82 |    "execution_count": null,
 83 |    "metadata": {},
 84 |    "outputs": [],
 85 |    "source": []
 86 |   },
 87 |   {
 88 |    "cell_type": "code",
 89 |    "execution_count": null,
 90 |    "metadata": {},
 91 |    "outputs": [],
 92 |    "source": []
 93 |   },
 94 |   {
 95 |    "cell_type": "code",
 96 |    "execution_count": null,
 97 |    "metadata": {},
 98 |    "outputs": [],
 99 |    "source": []
100 |   }
101 |  ],
102 |  "metadata": {
103 |   "kernelspec": {
104 |    "display_name": "Python 2",
105 |    "language": "python",
106 |    "name": "python2"
107 |   },
108 |   "language_info": {
109 |    "codemirror_mode": {
110 |     "name": "ipython",
111 |     "version": 2
112 |    },
113 |    "file_extension": ".py",
114 |    "mimetype": "text/x-python",
115 |    "name": "python",
116 |    "nbconvert_exporter": "python",
117 |    "pygments_lexer": "ipython2",
118 |    "version": "2.7.15"
119 |   }
120 |  },
121 |  "nbformat": 4,
122 |  "nbformat_minor": 2
123 | }
124 | 


--------------------------------------------------------------------------------
/data/__init__.py:
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https://raw.githubusercontent.com/HowieMa/lstm_pm_pytorch/4e7af6d44a9d8b791d0ace368f1a950a562546eb/data/__init__.py


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/data/handpose_data2.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | import torchvision.transforms as transforms
  3 | import torch
  4 | from torch.utils.data import Dataset
  5 | import numpy as np
  6 | import json
  7 | from PIL import Image
  8 | 
  9 | 
 10 | class UCIHandPoseDataset(Dataset):
 11 | 
 12 |     def __init__(self, data_dir, label_dir, train, temporal=5, joints=21, transform=None, sigma=1):
 13 |         self.height = 368
 14 |         self.width = 368
 15 | 
 16 |         self.seqs = os.listdir(data_dir)  # 001L00, 001L01, L02,... 151L08, R01, R02, R03,...
 17 |         self.data_dir = data_dir
 18 |         self.label_dir = label_dir
 19 | 
 20 |         self.temporal = temporal
 21 |         self.transform = transform
 22 |         self.joints = joints  # 21 heat maps
 23 |         self.sigma = sigma  # gaussian center heat map sigma
 24 | 
 25 |         self.temporal_dir = []
 26 | 
 27 |         self.train = train
 28 |         if self.train is True:
 29 |             self.gen_temporal_dir(1)
 30 |         else:
 31 |             self.gen_temporal_dir(temporal)
 32 | 
 33 |     def gen_temporal_dir(self, step):
 34 |         """
 35 |         build temporal directory in order to guarantee get all images has equal chance to be trained
 36 |         for train dataset, make each image has the same possibility to be trained
 37 | 
 38 |         :param step: for training set, step = 1, for test set, step = temporal
 39 |         :return:
 40 |         """
 41 | 
 42 |         for seq in self.seqs:
 43 |             if seq == '.DS_Store':
 44 |                 continue
 45 |             image_path = os.path.join(self.data_dir, seq)  #
 46 |             imgs = os.listdir(image_path)  # [0005.jpg, 0011.jpg......]
 47 |             imgs.sort()
 48 | 
 49 |             img_num = len(imgs)
 50 |             if img_num < self.temporal:
 51 |                 continue  # ignore sequences whose length is less than temporal
 52 | 
 53 |             for i in range(0, img_num - self.temporal + 1, step):
 54 |                 tmp = []
 55 |                 for k in range(i, i + self.temporal):
 56 |                     tmp.append(os.path.join(image_path, imgs[k]))
 57 |                 self.temporal_dir.append(tmp)  #
 58 | 
 59 |         self.temporal_dir.sort()
 60 |         print 'total numbers of image sequence is ' + str(len(self.temporal_dir))
 61 | 
 62 |     def __len__(self):
 63 |         if self.train is True:
 64 |             length = len(self.temporal_dir)/self.temporal
 65 |         else:
 66 |             length = len(self.temporal_dir)
 67 |         return length
 68 | 
 69 |     def __getitem__(self, idx):
 70 |         """
 71 |         :param idx:
 72 |         :return:
 73 |         images          3D Tensor      (temporal * 3)   *   height(368)   *   weight(368)
 74 |         label_map       4D Tensor      temporal         *   joints        *   label_size(45)   *   label_size(45)
 75 |         center_map      3D Tensor      1                *   height(368)   *   weight(368)
 76 |         imgs            list of image directory
 77 |         """
 78 |         label_size = self.width / 8 - 1         # 45
 79 | 
 80 |         imgs = self.temporal_dir[idx]           # ['.../001L0/L0005.jpg', '.../001L0/L0011.jpg', ... ]
 81 |         imgs.sort()
 82 |         seq = imgs[0].split('/')[-2]            # 001L0
 83 |         label_path = os.path.join(self.label_dir, seq)
 84 |         labels = json.load(open(label_path + '.json'))
 85 | 
 86 |         # initialize
 87 |         images = torch.zeros(self.temporal * 3, self.width, self.height)
 88 |         label_maps = torch.zeros(self.temporal, self.joints, label_size, label_size)
 89 | 
 90 |         for i in range(self.temporal):          # get temporal images
 91 |             img = imgs[i]                       # '.../001L0/L0005.jpg'
 92 | 
 93 |             # get image
 94 |             im = Image.open(img)                # read image
 95 |             w, h, c = np.asarray(im).shape      # weight 256 * height 256 * 3
 96 |             ratio_x = self.width / float(w)
 97 |             ratio_y = self.height / float(h)    # 368 / 256 = 1.4375
 98 | 
 99 |             im = im.resize((self.width, self.height))                       # unit8      weight 368 * height 368 * 3
100 |             images[(i * 3):(i * 3 + 3), :, :] = transforms.ToTensor()(im)   # 3D Tensor  3 * height 368 * weight 368
101 |             # ToTensor function will normalize data
102 | 
103 |             # get label map
104 |             img_num = img.split('/')[-1][1:5]
105 | 
106 |             if img_num in labels: # for images without label, set label to zero
107 |                 label = labels[img_num]         # 0005  list       21 * 2
108 |                 lbl = self.genLabelMap(label, label_size=label_size, joints=self.joints, ratio_x=ratio_x, ratio_y=ratio_y)
109 |                 label_maps[i, :, :, :] = torch.from_numpy(lbl)
110 | 
111 |         # generate the Gaussian heat map
112 |         center_map = self.genCenterMap(x=self.width / 2.0, y=self.height / 2.0, sigma=21,
113 |                                        size_w=self.width, size_h=self.height)
114 |         center_map = torch.from_numpy(center_map)
115 |         center_map = center_map.unsqueeze_(0)
116 | 
117 |         return images.float(), label_maps.float(), center_map.float(), imgs
118 | 
119 |     def genCenterMap(self, x, y, sigma, size_w, size_h):
120 |         """
121 |         generate Gaussian heat map
122 |         :param x: center point
123 |         :param y: center point
124 |         :param sigma:
125 |         :param size_w: image width
126 |         :param size_h: image height
127 |         :return:            numpy           w * h
128 |         """
129 |         gridy, gridx = np.mgrid[0:size_h, 0:size_w]
130 |         D2 = (gridx - x) ** 2 + (gridy - y) ** 2
131 |         return np.exp(-D2 / 2.0 / sigma / sigma)  # numpy 2d
132 | 
133 |     def genLabelMap(self, label, label_size, joints, ratio_x, ratio_y):
134 |         """
135 |         generate label heat map
136 |         :param label:               list            21 * 2
137 |         :param label_size:          int             45
138 |         :param joints:              int             21
139 |         :param ratio_x:             float           1.4375
140 |         :param ratio_y:             float           1.4375
141 |         :return:  heatmap           numpy           joints * boxsize/stride * boxsize/stride
142 |         """
143 |         # initialize
144 |         label_maps = np.zeros((joints, label_size, label_size))
145 |         background = np.zeros((label_size, label_size))
146 | 
147 |         # each joint
148 |         for i in range(len(label)):
149 |             lbl = label[i]                      # [x, y]
150 |             x = lbl[0] * ratio_x / 8.0          # modify the label
151 |             y = lbl[1] * ratio_y / 8.0
152 |             heatmap = self.genCenterMap(y, x, sigma=self.sigma, size_w=label_size, size_h=label_size)  # numpy
153 |             background += heatmap               # numpy
154 |             label_maps[i, :, :] = np.transpose(heatmap)
155 | 
156 |         return label_maps  # numpy           label_size * label_size * joints
157 | 
158 | 
159 | # test case
160 | 
161 | if __name__ == '__main__':
162 |     temporal = 5
163 |     data_dir = '../dataset/frames/001'
164 |     label_dir = '../dataset/label/001'
165 | 
166 |     dataset = UCIHandPoseDataset(data_dir=data_dir, label_dir=label_dir, temporal=temporal,train=True)
167 | 
168 |     a = dataset.temporal_dir
169 |     images, label_maps,center_map =  dataset[2]
170 |     print images.shape  # (5*3) * 368 * 368
171 |     print label_maps.shape  # 5 21 45 45
172 | 
173 | 


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/data/penn_data.py:
--------------------------------------------------------------------------------
  1 | '''
  2 | only used for penn_action datasets
  3 | '''
  4 | 
  5 | import os
  6 | import cv2
  7 | import torch
  8 | import numpy as np
  9 | from torch.utils.data import Dataset
 10 | from torchvision import transforms
 11 | from PIL import Image
 12 | 
 13 | 
 14 | class Penn_Data(Dataset):
 15 |     def __init__(self, data_dir='Penn_Action/', train=True, transform=None):
 16 | 
 17 |         self.input_h = 368
 18 |         self.input_w = 368
 19 |         self.map_h = 45
 20 |         self.map_w = 45
 21 | 
 22 |         self.parts_num = 13
 23 |         self.seqTrain = 5
 24 | 
 25 |         self.gaussian_sigma = 21
 26 | 
 27 |         self.transform = transform
 28 | 
 29 |         self.train = train
 30 |         if self.train is True:
 31 |             self.data_dir = data_dir + 'train/'
 32 |         else:
 33 |             self.data_dir = data_dir + 'test/'
 34 | 
 35 |         self.frames_data = os.listdir(self.data_dir)
 36 | 
 37 |     def __len__(self):
 38 |         return len(self.frames_data)  # number of videos in train or test
 39 | 
 40 |     def __getitem__(self, idx):  # get a video sequence
 41 |         '''
 42 | 
 43 |         :param idx:
 44 |         :return:
 45 |             images:     Tensor    seqtrain * 3 * width * height
 46 |             label_map:  Tensor    46 * 46 * (class+1) * seqtrain
 47 |             center_map: Tensor    1 * 368 * 368
 48 |         '''
 49 |         frames = self.frames_data[idx]
 50 |         data = np.load(os.path.join(self.data_dir, frames)).item()
 51 | 
 52 |         images, label_map, center_map = self.transformation_penn(data, boxsize=self.input_w, parts_num=13,
 53 |                                                                train=self.train)
 54 | 
 55 | 
 56 |         center_map = center_map.unsqueeze_(0)
 57 | 
 58 |         return images, label_map, center_map
 59 | 
 60 |     def transformation_penn(self, data, boxsize=368, parts_num=13, train=True):
 61 |         '''
 62 |         :param data:
 63 |         :param boxsize:
 64 |         :param parts_num:
 65 |         :param seqTrain:
 66 |         :param train:
 67 |         :return:
 68 |         images tensor seq
 69 |         '''
 70 |         nframes = data['nframes']                           # 151
 71 |         framespath = data['framepath']
 72 |         dim = data['dimensions']                            # [360, 480]
 73 |         x = data['x']                                       # 151 * 13
 74 |         y = data['y']                                       # 151 * 13
 75 |         visibility = data['visibility']                     # 151 * 13
 76 | 
 77 |         start_index = np.random.randint(0, nframes - 1 - self.seqTrain + 1)  #
 78 | 
 79 |         images = torch.zeros(self.seqTrain, 3, dim[0], dim[1])  # tensor seqTrain * 3 * 368 * 368
 80 |         label = np.zeros((3, parts_num + 1, self.seqTrain))     # numpy 3
 81 |         bbox = np.zeros((self.seqTrain, 4))  # seqTrain * ()    # numpy
 82 | 
 83 |         for i in range(self.seqTrain):
 84 |             # read image
 85 |             img_path = os.path.join(framespath,'%06d' % (start_index + i + 1) + '.jpg')
 86 |             img = Image.open(img_path)  # Image
 87 |             images[i, :, :, :] = transforms.ToTensor()(img)  # store image
 88 | 
 89 |             # read label
 90 |             label[0, :-1, i] = x[start_index + i]
 91 |             label[1, :-1, i] = y[start_index + i]
 92 |             label[2, :-1, i] = visibility[start_index + i]  # 1 * 13
 93 |             bbox[i, :] = data['bbox'][start_index + 1]  #
 94 | 
 95 |         # adjust label----------
 96 |         if train is True:
 97 |             # create label for neck to keep consistence of model. But it will be ignored during testing.
 98 |             # We interpolate the pos by head(1) and shoulders(2 & 3).
 99 |             label[0, -1, :] = 0.5 * label[0, 0, :] + 0.25 * (label[0, 1, :] + label[0, 2, :])
100 |             label[1, -1, :] = 0.5 * label[1, 0, :] + 0.25 * (label[1, 1, :] + label[1, 2, :])
101 |             label[2, -1, :] = np.floor((label[2, 0, :] + label[2, 1, :] + label[2, 2, :]) / 3.0)
102 | 
103 |         # make the joints not in the figure vis=-1(Do not produce label)
104 |         for i in range(self.seqTrain):  # for each image
105 |             for part in range(0, parts_num + 1):  # for each part
106 |                 if self.isNotOnPlane(label[0, part, i], label[1, part, i], dim[1], dim[0]):
107 |                     label[2, part, i] = -1
108 | 
109 |         # build data set--------
110 |         label_map = self.genLabelMap(label, boxsize=368, stride=8, sigma=7)  # 46 * 46 * (13 + 1) * seq
111 | 
112 |         center_map = self.genCenterMap(size_w=boxsize, size_h=boxsize, sigma=21, x=boxsize / 2.0, y=boxsize / 2.0)
113 |         center_map = torch.from_numpy(center_map)
114 |         return images, label_map, center_map
115 | 
116 |     def genCenterMap(self, x, y, sigma, size_w, size_h):
117 |         '''
118 |         generate Gaussian heat map
119 |         :param x: center point
120 |         :param y: center point
121 |         :param sigma:
122 |         :param size_w:
123 |         :param size_h:
124 |         :return: numpy w * h
125 |         '''
126 |         gridy, gridx = np.mgrid[0:size_h, 0:size_w]
127 |         D2 = (gridx - x) ** 2 + (gridy - y) ** 2
128 |         return np.exp(-D2 / 2.0 / sigma / sigma)  # numpy 2d
129 | 
130 |     def isNotOnPlane(self, x, y, width, height):
131 |         notOn = x < 0.001 or y < 0.001 or x > width or y > height
132 |         return notOn
133 | 
134 |     def genLabelMap(self, label, boxsize, stride, sigma):
135 |         '''
136 |         generate label heat map for each part
137 |         :param label:       3 * parts_num * seqTrain
138 |         :param boxsize:     368
139 |         :param stride:      8
140 |         :param sigma:       7
141 |         :return:
142 |         seqtrain * (parts_num + 1 ) * label_size * label_size
143 |         5 * 14 * 46 * 46
144 |         '''
145 |         label_size = boxsize / stride               # 368 / 8 = 46
146 |         label_map = torch.zeros(self.seqTrain, self.parts_num + 1, label_size, label_size)
147 | 
148 |         #
149 |         for k in range(self.seqTrain):              # for each frame
150 |             for i in range(self.parts_num):         # for each parts
151 |                 if label[2, i, k] >= 0:             # if exists
152 |                     cx, cy = label[0, i, k], label[1, i, k]  # get the center
153 |                     heat_map = self.genCenterMap(x=cx, y=cy, sigma=sigma, size_w=label_size, size_h=label_size)
154 |                     # build heat map of this part
155 |                 else:                               # not exists
156 |                     heat_map = np.zeros((label_size, label_size))
157 |                 label_map[k, i, :, :] = torch.from_numpy(np.transpose(heat_map))  #
158 | 
159 |             # build background
160 |             background = np.ones((label_size, label_size))  #
161 |             for m in range(label_size):
162 |                 for n in range(label_size):
163 |                     maxV = max(label_map[k, :, m, n])
164 |                     background[m, n] = max(1 - maxV, 0)
165 |             label_map[k, self.parts_num, :, :] = torch.from_numpy(background)
166 | 
167 |         return label_map
168 | 
169 | transform1 = transforms.Compose([
170 |     transforms.ToTensor(), # range [0, 255] -> [0.0,1.0]
171 |     ]
172 | )
173 | 
174 | 
175 | # test case
176 | #data = Penn_Data(data_dir='Penn_Action/', transform=transform1)
177 | #images, label_map, center_map = data[1]


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/data/penn_data_pre.py:
--------------------------------------------------------------------------------
 1 | '''
 2 | rebuild dataset
 3 | split train and test dataset
 4 | 
 5 | '''
 6 | 
 7 | import os
 8 | import numpy as np
 9 | import scipy.io
10 | 
11 | frame_dir = 'Penn_Action/frames'
12 | label_dir = 'Penn_Action/labels'
13 | train_dir = 'Penn_Action/train'
14 | test_dir = 'Penn_Action/test'
15 | 
16 | nums = os.listdir(label_dir)
17 | 
18 | for idx, num in enumerate(nums, 1):
19 |     print idx
20 |     data = scipy.io.loadmat(os.path.join(label_dir, num))
21 |     num = num.split('.')[0]
22 |     
23 |     npy_Data = dict()
24 |     npy_Data['framepath'] = os.path.join(frame_dir, num)
25 |     npy_Data['dimensions'] = list(data['dimensions'][0][0:2])
26 |     npy_Data['pose'] = str(data['pose'][0])
27 |     npy_Data['nframes'] = data['nframes'][0][0]
28 |     npy_Data['action'] = str(data['action'][0])
29 |     npy_Data['x'] = data['x']
30 |     npy_Data['y'] = data['y']
31 |     npy_Data['bbox'] = data['bbox']
32 |     npy_Data['visibility'] = data['visibility']
33 |     npy_Data['seq'] = int(num)
34 | 
35 |     if data['train'][0][0] == -1:
36 |         save_dir = os.path.join(test_dir, num + '.npy')
37 |     else:
38 |         save_dir = os.path.join(train_dir, num + '.npy')
39 |     np.save(save_dir, npy_Data)
40 | 
41 | 
42 | 
43 | 
44 | 
45 | 
46 | 
47 | 
48 | 
49 | 
50 | 
51 | 
52 | 
53 | 
54 | 
55 | 


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121], [88, 104], [84, 89]], "0533": [[128, 193], [104, 183], [77, 167], [62, 148], [48, 131], [97, 133], [88, 104], [84, 85], [82, 66], [116, 129], [111, 93], [109, 70], [107, 49], [130, 127], [128, 95], [128, 74], [126, 55], [147, 129], [149, 104], [153, 87], [153, 74]], "0473": [[153, 190], [128, 187], [99, 174], [84, 154], [75, 136], [110, 135], [97, 109], [90, 92], [84, 74], [127, 126], [116, 93], [111, 73], [107, 55], [143, 123], [136, 93], [130, 73], [125, 57], [159, 125], [158, 100], [157, 85], [154, 71]], "0641": [[124, 205], [137, 185], [151, 164], [159, 143], [166, 123], [134, 132], [134, 103], [135, 82], [132, 64], [122, 133], [119, 98], [116, 74], [113, 54], [109, 137], [105, 105], [102, 83], [98, 64], [97, 146], [91, 120], [85, 104], [82, 90]], "0539": [[129, 193], [104, 184], [77, 168], [61, 149], [47, 132], [96, 134], [89, 104], [83, 85], [82, 67], [116, 130], [111, 93], [109, 70], [108, 50], [131, 129], [128, 95], [126, 74], [124, 54], [147, 130], [150, 104], [152, 89], [154, 74]], "0515": [[129, 196], [124, 189], [117, 179], [116, 156], [108, 144], [121, 138], [117, 110], [110, 92], [107, 74], [123, 131], [118, 100], [111, 77], [109, 57], [124, 130], [119, 98], [115, 78], [110, 59], [126, 131], [123, 101], [118, 86], [116, 73]], "0479": [[153, 190], [129, 182], [109, 163], [98, 142], [88, 126], [123, 131], [112, 104], [105, 86], [102, 69], [137, 126], [130, 92], [124, 72], [118, 54], [149, 126], [146, 96], [140, 75], [134, 58], [161, 129], [160, 106], [160, 90], [157, 76]], "0629": [[117, 207], [112, 193], [109, 178], [108, 163], [109, 145], [116, 140], [117, 111], [115, 86], [113, 66], [116, 141], [117, 104], [113, 80], [112, 57], [114, 144], [115, 109], [114, 85], [111, 65], [112, 149], [111, 124], [109, 109], [109, 93]], "0605": [[116, 214], [89, 200], [63, 178], [53, 156], [43, 139], [89, 147], [85, 117], [83, 96], [83, 76], [109, 144], [109, 107], [111, 83], [113, 63], [124, 145], [127, 112], [130, 90], [131, 71], [139, 150], [147, 125], [152, 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102], [68, 87]], "0599": [[114, 213], [87, 199], [62, 178], [50, 157], [40, 139], [87, 147], [83, 116], [80, 96], [80, 77], [105, 145], [105, 108], [107, 84], [108, 63], [121, 145], [123, 113], [126, 90], [127, 71], [136, 149], [144, 123], [148, 108], [151, 93]]}


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/lstm_pm_train.py:
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  1 | # https://github.com/HowieMa/lstm_pm_pytorch.git
  2 | import argparse
  3 | from model.lstm_pm import LSTM_PM
  4 | from data.handpose_data2 import UCIHandPoseDataset
  5 | from src.utils import *
  6 | 
  7 | import torch
  8 | import torch.optim as optim
  9 | import torch.nn as nn
 10 | 
 11 | from torch.optim.lr_scheduler import StepLR
 12 | from torch.autograd import Variable
 13 | from torch.utils.data import DataLoader
 14 | from torchvision import transforms
 15 | 
 16 | # multi-GPU
 17 | device_ids = [0, 1, 2, 3]
 18 | 
 19 | # hyper parameter
 20 | temporal = 5
 21 | train_data_dir = '/home/haoyum/UCIHand/train/train_data'
 22 | train_label_dir = '/home/haoyum/UCIHand/train/train_label'
 23 | 
 24 | # add parameter
 25 | parser = argparse.ArgumentParser(description='Pytorch LSTM_PM with Penn_Action')
 26 | parser.add_argument('--learning_rate', type=float, default=8e-6, help='learning rate')
 27 | parser.add_argument('--batch_size', default=4, type=int, help='batch size for training')
 28 | parser.add_argument('--epochs', default=50, type=int, help='number of epochs for training')
 29 | parser.add_argument('--begin_epoch', default=0, type=int, help='how many epochs the model has been trained')
 30 | parser.add_argument('--save_dir', default='ckpt', type=str, help='directory of checkpoint')
 31 | parser.add_argument('--cuda', default=1, type=int, help='if you use GPU, set cuda = 1,else set cuda = 0')
 32 | parser.add_argument('--temporal', default=4, type=int, help='how many temporals you want ')
 33 | args = parser.parse_args()
 34 | 
 35 | if not os.path.exists(args.save_dir):
 36 |     os.mkdir(args.save_dir)
 37 | 
 38 | transform = transforms.Compose([transforms.ToTensor()])
 39 | 
 40 | # Build dataset
 41 | train_data = UCIHandPoseDataset(data_dir=train_data_dir, label_dir=train_label_dir, temporal=temporal, train=True)
 42 | print 'Train dataset total number of images sequence is ----' + str(len(train_data))
 43 | 
 44 | # Data Loader
 45 | train_dataset = DataLoader(train_data, batch_size=args.batch_size, shuffle=True)
 46 | 
 47 | # Build model
 48 | net = LSTM_PM(T=temporal)
 49 | if args.cuda:
 50 |     net = net.cuda(device_ids[0])
 51 |     net = nn.DataParallel(net, device_ids=device_ids)  # multi-Gpu
 52 | 
 53 | 
 54 | def train():
 55 |     # initialize optimizer
 56 |     optimizer = optim.Adam(params=net.parameters(), lr=args.learning_rate, betas=(0.9, 0.999))
 57 | 
 58 |     # optimizer = optim.SGD(params=net.parameters(), lr=args.learning_rate, momentum=0.9, weight_decay=5e-4)
 59 |     # scheduler = StepLR(optimizer, step_size=40000, gamma=0.333)
 60 | 
 61 |     criterion = nn.MSELoss(size_average=True)                       # loss function MSE average
 62 | 
 63 |     net.train()
 64 |     for epoch in range(args.begin_epoch, args.epochs + 1):
 65 | 
 66 |         print 'epoch....................................' + str(epoch)
 67 |         for step, (images, label_map, center_map, imgs) in enumerate(train_dataset):
 68 | 
 69 |             images = Variable(images.cuda() if args.cuda else images)               # 4D Tensor
 70 |             # Batch_size  *  (temporal * 3)  *  width(368)  *  height(368)
 71 |             label_map = Variable(label_map.cuda() if args.cuda else label_map)      # 5D Tensor
 72 |             # Batch_size  *  Temporal        * (joints+1) *   45 * 45
 73 |             center_map = Variable(center_map.cuda() if args.cuda else center_map)   # 4D Tensor
 74 |             # Batch_size  *  1          * width(368) * height(368)
 75 | 
 76 |             optimizer.zero_grad()
 77 |             predict_heatmaps = net(images, center_map)  # get a list size: (temporal + 1 ) * 4D Tensor
 78 | 
 79 |             # ******************** calculate and save loss of each joints ********************
 80 |             total_loss = save_loss(predict_heatmaps, label_map, epoch, step, criterion, train=True, temporal=temporal)
 81 |             if step % 10 == 0:
 82 |                 print '--step .....' + str(step)
 83 |                 print '--loss ' + str(float(total_loss))
 84 | 
 85 |             # ******************** save training heat maps per 100 steps ********************
 86 |             if step % 100 == 0:
 87 |                 save_images(label_map, predict_heatmaps, step, epoch, imgs, train=True, temporal=temporal)
 88 | 
 89 |             # backward
 90 |             total_loss.backward()
 91 |             optimizer.step()
 92 |             # scheduler.step()
 93 | 
 94 |         #  ************************* save model per 10 epochs  *************************
 95 |         if epoch % 5 == 0:
 96 |             torch.save(net.state_dict(), os.path.join(args.save_dir, 'ucihand_lstm_pm{:d}.pth'.format(epoch)))
 97 | 
 98 |     print 'train done!'
 99 | 
100 | 
101 | if __name__ == '__main__':
102 |     train()
103 | 
104 | 
105 | 
106 | 
107 | 
108 | 
109 | 
110 | 
111 | 


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/model/__init__.py:
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https://raw.githubusercontent.com/HowieMa/lstm_pm_pytorch/4e7af6d44a9d8b791d0ace368f1a950a562546eb/model/__init__.py


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/model/lstm_pm.py:
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  1 | import torch.nn as nn
  2 | import torch.nn.functional as F
  3 | import torch
  4 | 
  5 | 
  6 | class LSTM_PM(nn.Module):
  7 | 
  8 |     def __init__(self, outclass=21, T=7):
  9 |         super(LSTM_PM, self).__init__()
 10 |         self.outclass = outclass
 11 |         self.T = T
 12 |         self.pool_center_lower = nn.AvgPool2d(kernel_size=9, stride=8)
 13 | 
 14 |         # conv_net1
 15 |         self.conv1_convnet1 = nn.Conv2d(3, 128, kernel_size=9, padding=4)  # 3 * 368 * 368
 16 |         self.pool1_convnet1 = nn.MaxPool2d(kernel_size=3, stride=2)
 17 |         self.conv2_convnet1 = nn.Conv2d(128, 128, kernel_size=9, padding=4)
 18 |         self.pool2_convnet1 = nn.MaxPool2d(kernel_size=3, stride=2)
 19 |         self.conv3_convnet1 = nn.Conv2d(128, 128, kernel_size=9, padding=4)
 20 |         self.pool3_convnet1 = nn.MaxPool2d(kernel_size=3, stride=2)
 21 |         self.conv4_convnet1 = nn.Conv2d(128, 32, kernel_size=5, padding=2)
 22 |         self.conv5_convnet1 = nn.Conv2d(32, 512, kernel_size=9, padding=4)
 23 |         self.conv6_convnet1 = nn.Conv2d(512, 512, kernel_size=1)
 24 |         self.conv7_convnet1 = nn.Conv2d(512, self.outclass, kernel_size=1)  # 512 * 45 * 45
 25 | 
 26 |         # conv_net2
 27 |         self.conv1_convnet2 = nn.Conv2d(3, 128, kernel_size=9, padding=4)  # 3 * 368 * 368
 28 |         self.pool1_convnet2 = nn.MaxPool2d(kernel_size=3, stride=2)
 29 |         self.conv2_convnet2 = nn.Conv2d(128, 128, kernel_size=9, padding=4)  # 128 * 184 * 184
 30 |         self.pool2_convnet2 = nn.MaxPool2d(kernel_size=3, stride=2)
 31 |         self.conv3_convnet2 = nn.Conv2d(128, 128, kernel_size=9, padding=4)  # 128 * 92 * 92
 32 |         self.pool3_convnet2 = nn.MaxPool2d(kernel_size=3, stride=2)
 33 |         self.conv4_convnet2 = nn.Conv2d(128, 32, kernel_size=5, padding=2)  # 32 * 45 * 45
 34 | 
 35 |         # conv_net3
 36 |         self.Mconv1_convnet3 = nn.Conv2d(48, 128, kernel_size=11, padding=5)
 37 |         self.Mconv2_convnet3 = nn.Conv2d(128, 128, kernel_size=11, padding=5)
 38 |         self.Mconv3_convnet3 = nn.Conv2d(128, 128, kernel_size=11, padding=5)
 39 |         self.Mconv4_convnet3 = nn.Conv2d(128, 128, kernel_size=1, padding=0)
 40 |         self.Mconv5_convnet3 = nn.Conv2d(128, self.outclass, kernel_size=1, padding=0)
 41 | 
 42 |         # lstm
 43 |         self.conv_ix_lstm = nn.Conv2d(32 + 1 + self.outclass, 48, kernel_size=3, padding=1, bias=True)
 44 |         self.conv_ih_lstm = nn.Conv2d(48, 48, kernel_size=3, padding=1, bias=False)
 45 | 
 46 |         self.conv_fx_lstm = nn.Conv2d(32 + 1 + self.outclass, 48, kernel_size=3, padding=1, bias=True)
 47 |         self.conv_fh_lstm = nn.Conv2d(48, 48, kernel_size=3, padding=1, bias=False)
 48 | 
 49 |         self.conv_ox_lstm = nn.Conv2d(32 + 1 + self.outclass, 48, kernel_size=3, padding=1, bias=True)
 50 |         self.conv_oh_lstm = nn.Conv2d(48, 48, kernel_size=3, padding=1, bias=False)
 51 | 
 52 |         self.conv_gx_lstm = nn.Conv2d(32 + 1 + self.outclass, 48, kernel_size=3, padding=1, bias=True)
 53 |         self.conv_gh_lstm = nn.Conv2d(48, 48, kernel_size=3, padding=1, bias=False)
 54 | 
 55 |         # initial lstm
 56 |         self.conv_gx_lstm0 = nn.Conv2d(32 + 1 + self.outclass, 48, kernel_size=3, padding=1)
 57 |         self.conv_ix_lstm0 = nn.Conv2d(32 + 1 + self.outclass, 48, kernel_size=3, padding=1)
 58 |         self.conv_ox_lstm0 = nn.Conv2d(32 + 1 + self.outclass, 48, kernel_size=3, padding=1)
 59 | 
 60 |     def convnet1(self, image):
 61 |         '''
 62 |         :param image: 3 * 368 * 368
 63 |         :return: initial_heatmap out_class * 45 * 45
 64 |         '''
 65 |         x = self.pool1_convnet1(F.relu(self.conv1_convnet1(image)))  # output 128 * 184 * 184
 66 |         x = self.pool2_convnet1(F.relu(self.conv2_convnet1(x)))  # output 128 * 92 * 92
 67 |         x = self.pool3_convnet1(F.relu(self.conv3_convnet1(x)))  # output 128 * 45 * 45
 68 |         x = F.relu(self.conv4_convnet1(x))  # output 32 * 45 * 45
 69 |         x = F.relu(self.conv5_convnet1(x))  # output 512 * 45 * 45
 70 |         x = F.relu(self.conv6_convnet1(x))  # output 512 * 45 * 45
 71 |         initial_heatmap = self.conv7_convnet1(x)  # output (class + 1) * 45 * 45
 72 |         return initial_heatmap
 73 | 
 74 |     def convnet2(self, image):
 75 |         '''
 76 |         :param image: 3 * 368 * 368
 77 |         :return: Fs(.) features 32 * 45 * 45
 78 |         '''
 79 |         x = self.pool1_convnet2(F.relu(self.conv1_convnet2(image)))  # output 128 * 184 * 184
 80 |         x = self.pool2_convnet2(F.relu(self.conv2_convnet2(x)))  # output 128 * 92 * 92
 81 |         x = self.pool3_convnet2(F.relu(self.conv3_convnet2(x)))  # output 128 * 45 * 45
 82 |         x = F.relu(self.conv4_convnet2(x))  # output 32 * 45 * 45
 83 |         return x  # output 32 * 45 * 45
 84 | 
 85 |     def convnet3(self, hide_t):
 86 |         """
 87 |         :param h_t: 48 * 45 * 45
 88 |         :return: heatmap   out_class * 45 * 45
 89 |         """
 90 |         x = F.relu(self.Mconv1_convnet3(hide_t))  # output 128 * 45 * 45
 91 |         x = F.relu(self.Mconv2_convnet3(x))  # output 128 * 45 * 45
 92 |         x = F.relu(self.Mconv3_convnet3(x))  # output 128 * 45 * 45
 93 |         x = F.relu(self.Mconv4_convnet3(x))  # output 128 * 45 * 45
 94 |         x = self.Mconv5_convnet3(x)  # output (class+1) * 45 * 45
 95 |         return x  # heatmap (class+1) * 45 * 45
 96 | 
 97 |     def lstm(self, heatmap, features, centermap, hide_t_1, cell_t_1):
 98 |         '''
 99 |         :param heatmap:     (class+1) * 45 * 45
100 |         :param features:    32 * 45 * 45
101 |         :param centermap:   1 * 45 * 45
102 |         :param hide_t_1:    48 * 45 * 45
103 |         :param cell_t_1:    48 * 45 * 45
104 |         :return:
105 |         hide_t:    48 * 45 * 45
106 |         cell_t:    48 * 45 * 45
107 |         '''
108 |         xt = torch.cat([heatmap, features, centermap], dim=1)  # (32+ class+1 +1 ) * 45 * 45
109 | 
110 |         gx = self.conv_gx_lstm(xt)  # output: 48 * 45 * 45
111 |         gh = self.conv_gh_lstm(hide_t_1)  # output: 48 * 45 * 45
112 |         g_sum = gx + gh
113 |         gt = F.tanh(g_sum)
114 | 
115 |         ox = self.conv_ox_lstm(xt)  # output: 48 * 45 * 45
116 |         oh = self.conv_oh_lstm(hide_t_1)  # output: 48 * 45 * 45
117 |         o_sum = ox + oh
118 |         ot = F.sigmoid(o_sum)
119 | 
120 |         ix = self.conv_ix_lstm(xt)  # output: 48 * 45 * 45
121 |         ih = self.conv_ih_lstm(hide_t_1)  # output: 48 * 45 * 45
122 |         i_sum = ix + ih
123 |         it = F.sigmoid(i_sum)
124 | 
125 |         fx = self.conv_fx_lstm(xt)  # output: 48 * 45 * 45
126 |         fh = self.conv_fh_lstm(hide_t_1)  # output: 48 * 45 * 45
127 |         f_sum = fx + fh
128 |         ft = F.sigmoid(f_sum)
129 | 
130 |         cell_t = ft * cell_t_1 + it * gt
131 |         hide_t = ot * F.tanh(cell_t)
132 | 
133 |         return cell_t, hide_t
134 | 
135 |     def lstm0(self, x):
136 |         gx = self.conv_gx_lstm0(x)
137 |         ix = self.conv_ix_lstm0(x)
138 |         ox = self.conv_ox_lstm0(x)
139 | 
140 |         gx = F.tanh(gx)
141 |         ix = F.sigmoid(ix)
142 |         ox = F.sigmoid(ox)
143 | 
144 |         cell1 = F.tanh(gx * ix)
145 |         hide_1 = ox * cell1
146 |         return cell1, hide_1
147 | 
148 |     def stage2(self, image, cmap, heatmap, cell_t_1, hide_t_1):
149 |         '''
150 |         :param image:               3 * 368 * 368
151 |         :param cmap: gaussian       1 * 368 * 368
152 |         :param heatmap:             out_class * 45 * 45
153 |         :param cell_t_1:            48 * 45 * 45
154 |         :param hide_t_1:            48 * 45 * 45
155 |         :return:
156 |         new_heatmap:                out_class * 45 * 45
157 |         cell_t:                     48 * 45 * 45
158 |         hide_t:                     48 * 45 * 45
159 |         '''
160 |         features = self.convnet2(image)
161 |         centermap = self.pool_center_lower(cmap)
162 |         cell_t, hide_t = self.lstm(heatmap, features, centermap, hide_t_1, cell_t_1)
163 |         new_heat_map = self.convnet3(hide_t)
164 |         return new_heat_map, cell_t, hide_t
165 | 
166 |     def stage1(self, image, cmap):
167 |         '''
168 |         :param image:                3 * 368 * 368
169 |         :param cmap:                 1 * 368 * 368
170 |         :return:
171 |         heatmap:                     out_class * 45 * 45
172 |         cell_t:                      48 * 45 * 45
173 |         hide_t:                      48 * 45 * 45
174 |         '''
175 |         initial_heatmap = self.convnet1(image)
176 |         features = self.convnet2(image)
177 |         centermap = self.pool_center_lower(cmap)
178 | 
179 |         x = torch.cat([initial_heatmap, features, centermap], dim=1)
180 |         cell1, hide1 = self.lstm0(x)
181 |         heatmap = self.convnet3(hide1)
182 |         return initial_heatmap, heatmap, cell1, hide1
183 | 
184 |     def forward(self, images, center_map):  
185 |         '''
186 | 
187 |         :param images:      Tensor      (T * 3) * w(368) * h(368)
188 |         :param center_map:  Tensor      1 * 368 * 368
189 |         :return:
190 |         heatmaps            list        (T + 1)* out_class * 45 * 45  includes the initial heatmap
191 |         '''  
192 |         image = images[:, 0:3, :, :]
193 |         
194 |         heat_maps = []
195 |         initial_heatmap, heatmap, cell, hide = self.stage1(image, center_map)  # initial heat map
196 | 
197 |         heat_maps.append(initial_heatmap)  # for initial loss
198 |         heat_maps.append(heatmap)
199 |         #
200 |         for i in range(1, self.T):
201 |             image = images[:, (3 * i):(3 * i + 3), :, :]
202 |             heatmap, cell, hide = self.stage2(image, center_map, heatmap, cell, hide)
203 |             heat_maps.append(heatmap)
204 |         return heat_maps
205 | 
206 | 
207 | # test case
208 | if __name__ == '__main__':
209 |     net = LSTM_PM(T=4)
210 |     a = torch.randn(2, 12, 368, 368)  # batch size = 2
211 |     c = torch.randn(2, 1, 368, 368)
212 |     maps = net(a, c)
213 |     for m in maps:
214 |        print m.shape
215 | 
216 | 


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/src/__init__.py:
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https://raw.githubusercontent.com/HowieMa/lstm_pm_pytorch/4e7af6d44a9d8b791d0ace368f1a950a562546eb/src/__init__.py


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/src/__init__.pyc:
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https://raw.githubusercontent.com/HowieMa/lstm_pm_pytorch/4e7af6d44a9d8b791d0ace368f1a950a562546eb/src/__init__.pyc


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/src/utils.py:
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  1 | import json
  2 | import numpy as np
  3 | import os
  4 | import scipy.misc
  5 | 
  6 | 
  7 | def loss_history_init(temporal=5):
  8 |     loss_history = {}
  9 |     for t in range(temporal):
 10 |         loss_history['temporal'+str(t)] = []
 11 |     loss_history['total'] = 0.0
 12 |     return loss_history
 13 | 
 14 | 
 15 | def save_loss(predict_heatmaps, label_map, epoch, step, criterion, train, temporal=5, save_dir='ckpt/'):
 16 |     loss_save = loss_history_init(temporal=temporal)
 17 | 
 18 |     predict = predict_heatmaps[0]
 19 |     target = label_map[:, 0, :, :, :]
 20 |     initial_loss = criterion(predict, target)  # loss initial
 21 |     total_loss = initial_loss
 22 | 
 23 |     for t in range(temporal):
 24 |         predict = predict_heatmaps[t + 1]
 25 |         target = label_map[:, t, :, :, :]
 26 |         tmp_loss = criterion(predict, target)  # loss in each stage
 27 |         total_loss += tmp_loss
 28 |         loss_save['temporal' + str(t)] = float('%.8f' % tmp_loss)
 29 | 
 30 |     total_loss = total_loss
 31 |     loss_save['total'] = float(total_loss)
 32 | 
 33 |     # save loss to file
 34 |     if train is True:
 35 |         if not os.path.exists(save_dir + 'loss_epoch' + str(epoch)):
 36 |             os.mkdir(save_dir + 'loss_epoch' + str(epoch))
 37 |         json.dump(loss_save, open(save_dir + 'loss_epoch' + str(epoch) + '/s' + str(step).zfill(4) + '.json', 'wb'))
 38 | 
 39 |     else:
 40 |         if not os.path.exists(save_dir + 'loss_test/'):
 41 |             os.mkdir(save_dir + 'loss_test/')
 42 |         json.dump(loss_save, open(save_dir + 'loss_test/' + str(step).zfill(4) + '.json', 'wb'))
 43 | 
 44 |     return total_loss
 45 | 
 46 | 
 47 | def save_images(label_map, predict_heatmaps, step, epoch, imgs, train, pck=1, temporal=5, save_dir='ckpt/'):
 48 |     """
 49 |     :param label_map:
 50 |     :param predict_heatmaps:    5D Tensor    Batch_size  *  Temporal * joints *   45 * 45
 51 |     :param step:
 52 |     :param temporal:
 53 |     :param epoch:
 54 |     :param train:
 55 |     :param imgs: list [(), (), ()] temporal * batch_size
 56 |     :return:
 57 |     """
 58 | 
 59 |     for b in range(label_map.shape[0]):                     # for each batch (person)
 60 |         output = np.ones((50 * 2, 50 * temporal))           # cd .. temporal save a single image
 61 |         seq = imgs[0][b].split('/')[-2]                     # sequence name 001L0
 62 |         img = ""
 63 |         for t in range(temporal):                           # for each temporal
 64 |             im = imgs[t][b].split('/')[-1][1:5]             # image name 0005
 65 |             img += '_' + im
 66 |             pre = np.zeros((45, 45))  #
 67 |             gth = np.zeros((45, 45))
 68 |             for i in range(21):                             # for each joint
 69 |                 pre += np.asarray(predict_heatmaps[t][b, i, :, :].data)  # 2D
 70 |                 gth += np.asarray(label_map[b, t, i, :, :].data)         # 2D
 71 | 
 72 |             output[0:45,  50 * t: 50 * t + 45] = gth
 73 |             output[50:95, 50 * t: 50 * t + 45] = pre
 74 | 
 75 |         if train is True:
 76 |             if not os.path.exists(save_dir + 'epoch'+str(epoch)):
 77 |                 os.mkdir(save_dir + 'epoch'+str(epoch))
 78 |             scipy.misc.imsave(save_dir + 'epoch'+str(epoch) + '/s'+str(step) + '_b' + str(b) + '_' + seq + img + '.jpg', output)
 79 |         else:
 80 | 
 81 |             if not os.path.exists(save_dir + 'test'):
 82 |                 os.mkdir(save_dir + 'test')
 83 |             scipy.misc.imsave(save_dir + 'test' + '/s' + str(step) + '_b' + str(b) + '_'
 84 |                               + seq + img + '_' + str(round(pck, 4)) + '.jpg', output)
 85 | 
 86 | 
 87 | def lstm_pm_evaluation(label_map, predict_heatmaps, sigma=0.04, temporal=5):
 88 |     pck_eval = []
 89 |     empty = np.zeros((21, 45, 45))                                      # 3D numpy 21 * 45 * 45
 90 |     for b in range(label_map.shape[0]):        # for each batch (person)
 91 |         for t in range(temporal):           # for each temporal
 92 |             target = np.asarray(label_map[b, t, :, :, :].data)          # 3D numpy 21 * 45 * 45
 93 |             predict = np.asarray(predict_heatmaps[t][b, :, :, :].data)  # 3D numpy 21 * 45 * 45
 94 |             if not np.equal(empty, target).all():
 95 |                 pck_eval.append(PCK(predict, target, sigma=sigma))
 96 | 
 97 |     return sum(pck_eval) / float(len(pck_eval))  #
 98 | 
 99 | 
100 | def PCK(predict, target, label_size=45, sigma=0.04):
101 |     """
102 |     calculate possibility of correct key point of one single image
103 |     if distance of ground truth and predict point is less than sigma, than  the value is 1, otherwise it is 0
104 |     :param predict:         3D numpy       21 * 45 * 45
105 |     :param target:          3D numpy       21 * 45 * 45
106 |     :param label_size:
107 |     :param sigma:
108 |     :return: 0/21, 1/21, ...
109 |     """
110 |     pck = 0
111 |     for i in range(predict.shape[0]):
112 |         pre_x, pre_y = np.where(predict[i, :, :] == np.max(predict[i, :, :]))
113 |         tar_x, tar_y = np.where(target[i, :, :] == np.max(target[i, :, :]))
114 | 
115 |         dis = np.sqrt((pre_x[0] - tar_x[0])**2 + (pre_y[0] - tar_y[0])**2)
116 |         if dis < sigma * label_size:
117 |             pck += 1
118 |     return pck / float(predict.shape[0])
119 | 
120 | 
121 | def draw_loss(epoch):
122 |     all_losses = os.listdir('ckpt/loss_epoch'+str(epoch))
123 |     losses = []
124 | 
125 |     for loss_j in all_losses:
126 |         loss = json.load('ckpt/loss_epoch'+str(epoch) + '/' +loss_j)
127 |         a = loss['total']
128 |         losses.append(a)
129 | 
130 | 
131 | def Tests_save_label_imgs(label_map, predict_heatmaps, step, imgs, temporal=13, save_dir='ckpt/'):
132 |     """
133 |     :param label_map:
134 |     :param predict_heatmaps:    5D Tensor    Batch_size  *  Temporal * joints *   45 * 45
135 |     :param step:
136 |     :param temporal:
137 |     :param epoch:
138 |     :param train:
139 |     :param imgs: list [(), (), ()] temporal * batch_size
140 |     :return:
141 |     """
142 | 
143 |     for b in range(label_map.shape[0]):  # for each batch (person)
144 |         output = np.ones((50 * 2, 50 * temporal))  # cd .. temporal save a single image
145 |         seq = imgs[0][b].split('/')[-2]  # sequence name 001L0
146 |         img = ""  # all image name in the same seq
147 |         label_dict = {}  # all image label in the same seq
148 |         pck_dict = {}
149 |         for t in range(temporal):  # for each temporal
150 |             labels_list = []  # 21 points label for one image [[], [], [], .. ,[]]
151 | 
152 |             im = imgs[t][b].split('/')[-1][1:5]  # image name 0005
153 |             img += '_' + im
154 |             pre = np.zeros((45, 45))  #
155 |             gth = np.zeros((45, 45))
156 | 
157 |             # ****************** get pck of one image ************************
158 |             target = np.asarray(label_map[b, t, :, :, :].data)  # 3D numpy 21 * 45 * 45
159 |             predict = np.asarray(predict_heatmaps[t][b, :, :, :].data)  # 3D numpy 21 * 45 * 45
160 |             empty = np.zeros((21, 45, 45))
161 | 
162 |             if not np.equal(empty, target).all():
163 |                 pck = PCK(predict, target, sigma=0.04)
164 |                 pck_dict[seq + '_' + im] = pck
165 | 
166 |             # ****************** save image and label of 21 joints ******************
167 |             for i in range(21):  # for each joint
168 |                 gth += np.asarray(label_map[b, t, i, :, :].data)  # 2D
169 |                 tmp_pre = np.asarray(predict_heatmaps[t][b, i, :, :].data)  # 2D
170 |                 pre += tmp_pre
171 | 
172 |                 #  get label of original image
173 |                 corr = np.where(tmp_pre == np.max(tmp_pre))
174 |                 x = corr[0][0] * (256.0 / 45.0)
175 |                 x = int(x)
176 |                 y = corr[1][0] * (256.0 / 45.0)
177 |                 y = int(y)
178 |                 labels_list.append([y, x])  # save img label
179 | 
180 |             output[0:45, 50 * t: 50 * t + 45] = gth  # save image
181 |             output[50:95, 50 * t: 50 * t + 45] = pre
182 | 
183 |             label_dict[im] = labels_list  # save label
184 | 
185 |         # calculate average PCK
186 |         # print pck_dict
187 |         avg_pck = sum(pck_dict.values()) / float(pck_dict.__len__())
188 |         print 'step ...%d ... PCK %f  ....' % (step, avg_pck)
189 | 
190 |         # ****************** save image ******************
191 |         if not os.path.exists(save_dir + 'test'):
192 |             os.mkdir(save_dir + 'test')
193 |         scipy.misc.imsave(save_dir + 'test' + '/s' + str(step) + '_'
194 |                           + seq + img + '_' + str(round(avg_pck, 4)) + '.jpg', output)
195 | 
196 |         # ****************** save label ******************
197 |         if not os.path.exists(os.path.join(save_dir, 'test_predict')):
198 |             os.mkdir(os.path.join(save_dir, 'test_predict'))
199 | 
200 |         save_dir_label = os.path.join(save_dir, 'test_predict') + '/' + seq
201 |         if not os.path.exists(save_dir_label):
202 |             os.mkdir(save_dir_label)
203 | 
204 |         json.dump(label_dict, open(save_dir_label + '/' + str(step) + '.json', 'w'), sort_keys=True, indent=4)
205 |         return pck_dict
206 | 
207 | 
208 | 
209 | from PIL import Image
210 | from PIL import ImageDraw
211 | 
212 | 
213 | 
214 | def draw_point(points, im):
215 |     """
216 |     draw key point on image
217 |     :param points: list 21 [ [x1,y1], ..., [x21,y21]  ]
218 |     :param im: PIL Image
219 |     :return:
220 |     """
221 |     i = 0
222 |     draw=ImageDraw.Draw(im)
223 | 
224 |     for point in points:
225 |         x = point[1]
226 |         y = point[0]
227 | 
228 |         if i==0:
229 |             rootx=x
230 |             rooty=y
231 |         if i==1 or i==5 or i==9 or i==13 or i==17:
232 |             prex=rootx
233 |             prey=rooty
234 | 
235 |         if i >0 and i<=4:
236 |             draw.line((prex,prey,x,y),'red')
237 |             draw.ellipse((x-3, y-3, x+3, y+3), 'red', 'black')
238 |         if i >4 and i<=8:
239 |             draw.line((prex,prey,x,y),'yellow')
240 |             draw.ellipse((x-3, y-3, x+3, y+3), 'yellow', 'black')
241 | 
242 |         if i >8 and i<=12:
243 |             draw.line((prex,prey,x,y),'green')
244 |             draw.ellipse((x-3, y-3, x+3, y+3), 'green', 'black')
245 |         if i >12 and i<=16:
246 |             draw.line((prex,prey,x,y),'blue')
247 |             draw.ellipse((x-3, y-3, x+3, y+3), 'blue', 'black')
248 |         if i >16 and i<=20:
249 |             draw.line((prex,prey,x,y),'purple')
250 |             draw.ellipse((x-3, y-3, x+3, y+3), 'purple', 'black')
251 | 
252 | 
253 |         prex=x
254 |         prey=y
255 |         i=i+1
256 |     return im
257 | 
258 | 
259 | 


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/src/utils.pyc:
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https://raw.githubusercontent.com/HowieMa/lstm_pm_pytorch/4e7af6d44a9d8b791d0ace368f1a950a562546eb/src/utils.pyc


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/test_lstm_pm.py:
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  1 | # test
  2 | from data.handpose_data2 import UCIHandPoseDataset
  3 | from model.lstm_pm import LSTM_PM
  4 | from src.utils import *
  5 | 
  6 | import argparse
  7 | import pandas as pd
  8 | import os
  9 | import torch
 10 | import torch.nn as nn
 11 | from torch.autograd import Variable
 12 | from collections import OrderedDict
 13 | from torch.utils.data import DataLoader
 14 | 
 15 | os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
 16 | 
 17 | # add parameter
 18 | parser = argparse.ArgumentParser(description='Pytorch LSTM_PM with Penn_Action')
 19 | parser.add_argument('--learning_rate', type=float, default=8e-6, help='learning rate')
 20 | parser.add_argument('--batch_size', default=1, type=int, help='batch size for training')
 21 | parser.add_argument('--save_dir', default='ckpt', type=str, help='directory of checkpoint')
 22 | parser.add_argument('--cuda', default=1, type=int, help='if you use GPU, set cuda = 1,else set cuda = 0')
 23 | parser.add_argument('--temporal', default=4, type=int, help='how many temporals you want ')
 24 | args = parser.parse_args()
 25 | 
 26 | # hyper parameter
 27 | temporal = 5
 28 | test_data_dir = '/mnt/data/haoyum/UCIHand/test/test_data'
 29 | test_label_dir = '/mnt/data/haoyum/UCIHand/test/test_label'
 30 | model_epo = [10, 15, 20, 25, 30, 35, 40, 45, 50]
 31 | 
 32 | # load data
 33 | test_data = UCIHandPoseDataset(data_dir=test_data_dir, label_dir=test_label_dir, temporal=temporal, train=False)
 34 | print 'Test  dataset total number of images sequence is ----' + str(len(test_data))
 35 | test_dataset = DataLoader(test_data, batch_size=args.batch_size, shuffle=False)
 36 | 
 37 | 
 38 | def load_model(model):
 39 |     # build model
 40 |     net = LSTM_PM(T=temporal)
 41 |     if torch.cuda.is_available():
 42 |         net = net.cuda()
 43 |         net = nn.DataParallel(net)  # multi-Gpu
 44 | 
 45 |     save_path = os.path.join('ckpt/ucihand_lstm_pm' + str(model)+'.pth')
 46 |     state_dict = torch.load(save_path)
 47 |     net.load_state_dict(state_dict)
 48 |     return net
 49 | 
 50 |     # ******************** transfer from multi-GPU model ********************
 51 |     # create new OrderedDict that does not contain `module.`
 52 |     # new_state_dict = OrderedDict()
 53 |     # for k, v in state_dict.items():
 54 |     #     namekey = k[7:]  # remove `module.`
 55 |     #     new_state_dict[namekey] = v
 56 |     # # load params
 57 |     # return net
 58 | 
 59 | # **************************************** test all images ****************************************
 60 | 
 61 | 
 62 | print '********* test data *********'
 63 | 
 64 | for model in model_epo:
 65 | 
 66 |     net = load_model(model)
 67 |     net.eval()
 68 | 
 69 |     sigma = 0.01
 70 |     results = []
 71 |     for i in range(5):
 72 | 
 73 |         result = []  # save sigma and pck
 74 |         result.append(sigma)
 75 |         pck_all = []
 76 |         for step, (images, label_map, center_map, imgs) in enumerate(test_dataset):
 77 | 
 78 |             images = Variable(images.cuda() if args.cuda else images)           # 4D Tensor
 79 |             # Batch_size  *  (temporal * 3)  *  width(368)  *  height(368)
 80 |             label_map = Variable(label_map.cuda() if args.cuda else label_map)  # 5D Tensor
 81 |             # Batch_size  *  Temporal        * joint *   45 * 45
 82 |             center_map = Variable(center_map.cuda() if args.cuda else center_map)  # 4D Tensor
 83 |             # Batch_size  *  1          * width(368) * height(368)
 84 | 
 85 |             predict_heatmaps = net(images, center_map)  # get a list size: temporal * 4D Tensor
 86 |             predict_heatmaps =  predict_heatmaps[1:]
 87 |             # calculate pck
 88 |             pck = lstm_pm_evaluation(label_map, predict_heatmaps, sigma=sigma, temporal=temporal)
 89 |             pck_all.append(pck)
 90 | 
 91 |             if step % 100 == 0:
 92 |                 print '--step ...' + str(step)
 93 |                 print '--pck.....' + str(pck)
 94 |                 save_images(label_map, predict_heatmaps, step, epoch=-1, imgs=imgs, train=False, temporal=temporal,pck=pck)
 95 | 
 96 | 
 97 |             if pck < 0.8:
 98 |                 save_images(label_map, predict_heatmaps, step, epoch=-1, imgs=imgs, train=False, temporal=temporal,pck=pck)
 99 | 
100 |         print 'sigma ==========> ' + str(sigma)
101 |         print '===PCK evaluation in test dataset is ' + str(sum(pck_all) / len(pck_all))
102 |         result.append(str(sum(pck_all) / len(pck_all)))
103 |         results.append(result)
104 | 
105 |         sigma += 0.01
106 | 
107 |     results = pd.DataFrame(results)
108 |     results.to_csv('ckpt/' + str(model) + 'test_pck.csv')
109 | 
110 | 
111 | 
112 | 
113 | 
114 | 
115 | 
116 | 
117 | 
118 | 
119 | 
120 | 
121 | 


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