Base3DModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>CCF2019-阿尔茨海默症的识别</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **赛题背景**\n",
    "\n",
    "阿尔茨海默症：俗称“老年痴呆症”，多发于老人（听说已经往年轻化发展的趋势）。随之年龄增长患病的概率也陡然增加。至今还**没有客观明确的诊断方法**（并不像高血压关注血压值，糖尿病关注血糖值），也没有特效药（对老年痴呆有治愈作用，让记忆恢复到正常状态，目前只有控制病情发展的药）。病症到了后期，直接导致生活不能自理，需要人24小时陪伴。各大药厂都投入巨资（全球投入几十亿美元），针对病例检测（如何找到一个可靠客观确认方法），治疗和康复进行研究探索，但是多数都失败告终（虽然也有上市的药品、方法但是效果都有限）。所以赛题有很强的现实意义和探索性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **医疗数据特点**\n",
    "\n",
    "医疗数据的比较难收集，主要是以下原因：\n",
    "\n",
    "个人隐私问题\n",
    "数据分散问题（大部分病并不是集中在特定地区，所以分布很随机）\n",
    "收集成本高\n",
    "数据标注、清洗困难\n",
    "所以 医疗数据大部分都是“小数据集”（一个实验过程，有1~2百多人就算不小了），这对于以“大数据”为基础的深度学习矛盾，也许随着科技的发展，国家对医疗数据的重视，通过较长时间累积应该会有更多“大”的医疗数据集出现。针对本次竞赛而言因为数据量不大，模型的泛化能力没有办法验证，结果很可能是 **过拟合 Test集**。所以本次**探索意义大于结果成绩**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **MRI（磁共振）**\n",
    "\n",
    "磁共振成像（MRI）是一种利用磁共振现象产生的信号来重建图像的成像技术。它只是相当于氢质子的振动，然后平静下来，去感受到图像中的振动的方式。优势：1.基于目前的研究，MRI对人体没有伤害；2.MRI可以获得脑和脊柱的立体图像，而CT图像则是组织的切片2D图像，使得医生很容易漏掉不明显的病变组织。劣势：1.对于肺部的诊断效果没有CT好，但是费用比CT高；2.对于胃肠道的病变检查效果不如内窥镜\n",
    "\n",
    "赛会提供给就是MRI数据，结构是：(300,1,79,95,79) 。其中**300代表采样个体**（应该是患者人数），**1代表 通道数**，**79代表MRI数据切片层数**（切片并不是连续，大约间隔5mm一层，也可以根据需要减小间隙提高3D成像效果），**95*79代表切片图像**（浮点型灰度图）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align=center><img src='image/切片示例.jpg' width=20% height=20% />切片图像(放大四倍)</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>↑ 以上的图片就是从赛会给的H5文件抓取的切片图像（编号0，第41层）</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **可视化分析**\n",
    "\n",
    "有了背景知识和数据，我们就可以进行EDA（数据探索分析），探索方法并不固定，但推荐对数据进行可视化。可视化可以帮助加深对数据的理解，例如数据是H5的存储，里面存储的是什么数据，很难直接理解："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import imageio\n",
    "from PIL import Image\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch import optim\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import help\n",
    "from Base3DModel import Base3DModel\n",
    "from MyDataSet import MyDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于样本标签，数字0代表阿尔茨海默症样本，数字1代表轻度认知障碍样本，数字2代表正常样本。对于csv格式的标签文件，字段id表示图像的序号，字段label表示图像对应标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300, 1, 79, 95, 79)\n",
      "(300, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label\n",
       "0   0      1\n",
       "1   1      0\n",
       "2   2      0\n",
       "3   3      1\n",
       "4   4      0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(help.features.shape)\n",
    "print(help.labels.shape)\n",
    "help.labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 1, 79, 95, 79)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "help.train['data'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f694406dda0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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7bqWTBcfyugF0nPX5559XNlZxbzxYcTAZG5UxbkAXwMsGBEC4+6QsNrZ0Msa1a9cuHDp0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\nKNlRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 215
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_sample = help.features[0,0]\n",
    "# 读取图片,灰度化，并转为数组\n",
    "img = Image.fromarray(one_sample[40]).convert('L')\n",
    "# Image.ANTIALIAS等比例缩放\n",
    "img.resize((79*4,95*4),Image.ANTIALIAS).save('image/temp.jpg')#放大四倍以便观察\n",
    "plt.imshow(img,cmap ='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frames = []\n",
    "for index in range(len(one_sample)):\n",
    "    img = Image.fromarray(one_sample[index]).convert('L')\n",
    "    img.resize((79*3, 95*3),Image.ANTIALIAS).save('image/temp{}.jpg'.format(index))\n",
    "    frames.append(imageio.imread('image/temp{}.jpg'.format(index)))\n",
    "imageio.mimsave('image/{0}.gif'.format('1'), frames, 'GIF', duration = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img id=\"img1\" src=\"image/1.gif\">数据集样本，间隔0.1S顺序播放切片图片，放大3倍</img></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JLk8iEAAAwAoy1pCQ5PgktyZ5Zmvt3DGfGwAAmIBx32706CSnCwgAALByjTsk/DTJdWM+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\nYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 388
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "order = help.labels['label'].value_counts().index\n",
    "sb.countplot(data=help.labels,x='label',order=order);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图可以看出大部分样本是轻度认知障碍，正常样本略多于阿尔茨海默症样本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**torch.nn.Conv3D**\n",
    "\n",
    "3D卷积, 输入的shape是(N,Cin,D,H,W)，输出shape (N,Cout,Dout,Hout,Wout)\n",
    "\n",
    "__N__ 就是batch_size,无话可说,\n",
    "\n",
    "__Cin__ 则对应着输入图像的通道数，如RGB\\BGR图像这一维度就是3；\n",
    "\n",
    "__D__ 则是深度，如果是对于视频序列使用的3d conv，那么这个D实际对应的就是要执行卷积的frame_size，\n",
    "\n",
    "__H,W__ 对应的就是输入图像的高和宽\n",
    "\n",
    "使用的时候如果不指定具体size，如:$\\color{red}{padding=2}$，则是每一维度都为size是2的padding，也就是$\\color{red}{padding=(2,2,2)}$；如果指定每一维的padding的话则有就是指定的size，如:$\\color{red}{padding=(3,2,1)}$，则是分别是在三个维度上有3,2,1三种不同的padding，另外$\\color{red}{kernel}$和$\\color{red}{stride}$也是如此\n",
    "\n",
    "<center><img id=\"img1\" width=\"300px\" height=\"300px\" src=\"image/3D卷积.webp\">3D卷积</img></center>\n",
    "\n",
    "图片中的 Time轴 = 切片，Latitutde = 图片的width轴， Longitude = 图片的height轴，本次医疗数据是灰度图，不存在色彩通道\n",
    "\n",
    "3D卷积，经过卷积层后，结果还是3D的数据，保留了“时空的特征”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(help.features,help.labels['label'].values,test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data load success\n"
     ]
    }
   ],
   "source": [
    "train_datasets = MyDataset(datas=X_train,labels=y_train,shape=3,input_D=help.input_D,input_H=help.input_H,input_W=help.input_W,phase='train')\n",
    "val_datasets = MyDataset(datas=X_val,labels=y_val,shape=3,input_D=help.input_D,input_H=help.input_H,input_W=help.input_W,phase='train')\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_datasets, batch_size=8, shuffle=True)\n",
    "val_loader = torch.utils.data.DataLoader(dataset=val_datasets, batch_size=4, shuffle=False)\n",
    "print(\"Data load success\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Base3DModel(\n",
       "  (conv): Sequential(\n",
       "    (conv1): Conv3d(1, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
       "    (conv2): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (conv3): ReLU(inplace)\n",
       "    (conv4): MaxPool3d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (conv5): Conv3d(32, 256, kernel_size=(2, 2, 2), stride=(1, 1, 1), dilation=(2, 2, 2))\n",
       "    (conv6): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (conv7): ReLU(inplace)\n",
       "    (conv8): MaxPool3d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (conv9): Conv3d(256, 512, kernel_size=(2, 2, 2), stride=(1, 1, 1), padding=(2, 2, 2), dilation=(2, 2, 2))\n",
       "    (conv10): InstanceNorm3d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (conv11): ReLU(inplace)\n",
       "    (conv12): MaxPool3d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (conv13): Conv3d(512, 512, kernel_size=(2, 2, 2), stride=(1, 1, 1), padding=(1, 1, 1), dilation=(2, 2, 2))\n",
       "    (conv14): InstanceNorm3d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (conv15): ReLU(inplace)\n",
       "    (conv16): MaxPool3d(kernel_size=5, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (fc): Sequential(\n",
       "    (fc1): Linear(in_features=18432, out_features=1024, bias=True)\n",
       "    (dp1): Dropout(p=0.3)\n",
       "    (fc2): Linear(in_features=1024, out_features=3, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basemodel_3d = Base3DModel(num_seg_classes=help.num_seg_classes,f=help.basemodel_3d_f)\n",
    "basemodel_3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs: 1/100... Trian Loss:774.188... Trian Accuracy:0.492... Trian F1 Score:0.442... Val Loss:502.072... Val Accuracy:0.183... Val F1 Score:0.103\n",
      "Epochs: 2/100... Trian Loss:137.239... Trian Accuracy:0.671... Trian F1 Score:0.590... Val Loss:86.366... Val Accuracy:0.817... Val F1 Score:0.577\n",
      "Epochs: 3/100... Trian Loss:158.815... Trian Accuracy:0.750... Trian F1 Score:0.674... Val Loss:235.260... Val Accuracy:0.817... Val F1 Score:0.577\n",
      "Epochs: 4/100... Trian Loss:168.346... Trian Accuracy:0.746... Trian F1 Score:0.665... Val Loss:108.645... Val Accuracy:0.750... Val F1 Score:0.532\n",
      "Epochs: 5/100... Trian Loss:60.506... Trian Accuracy:0.729... Trian F1 Score:0.645... Val Loss:10.252... Val Accuracy:0.817... Val F1 Score:0.577\n",
      "Epochs: 6/100... Trian Loss:24.469... Trian Accuracy:0.725... Trian F1 Score:0.641... Val Loss:9.112... Val Accuracy:0.750... Val F1 Score:0.532\n",
      "Epochs: 7/100... Trian Loss:9.567... Trian Accuracy:0.758... Trian F1 Score:0.685... Val Loss:0.431... Val Accuracy:0.917... Val F1 Score:0.854\n",
      "Epochs: 8/100... Trian Loss:5.852... Trian Accuracy:0.767... Trian F1 Score:0.692... Val Loss:8.622... Val Accuracy:0.817... Val F1 Score:0.577\n",
      "Epochs: 9/100... Trian Loss:10.301... Trian Accuracy:0.775... Trian F1 Score:0.704... Val Loss:46.830... Val Accuracy:0.750... Val F1 Score:0.532\n",
      "Epochs: 10/100... Trian Loss:18.485... Trian Accuracy:0.758... Trian F1 Score:0.686... Val Loss:1.255... Val Accuracy:0.883... Val F1 Score:0.818\n",
      "Epochs: 11/100... Trian Loss:4.906... Trian Accuracy:0.796... Trian F1 Score:0.727... Val Loss:13.657... Val Accuracy:0.750... Val F1 Score:0.532\n",
      "Epochs: 12/100... Trian Loss:9.753... Trian Accuracy:0.787... Trian F1 Score:0.724... Val Loss:1.039... Val Accuracy:0.883... Val F1 Score:0.818\n",
      "Epochs: 13/100... Trian Loss:3.259... Trian Accuracy:0.821... Trian F1 Score:0.764... Val Loss:0.150... Val Accuracy:0.933... Val F1 Score:0.897\n",
      "Epochs: 14/100... Trian Loss:1.859... Trian Accuracy:0.821... Trian F1 Score:0.765... Val Loss:2.294... Val Accuracy:0.750... Val F1 Score:0.532\n",
      "Epochs: 15/100... Trian Loss:1.682... Trian Accuracy:0.825... Trian F1 Score:0.772... Val Loss:0.272... Val Accuracy:0.950... Val F1 Score:0.925\n",
      "Epochs: 16/100... Trian Loss:0.626... Trian Accuracy:0.858... Trian F1 Score:0.814... Val Loss:0.193... Val Accuracy:0.900... Val F1 Score:0.819\n",
      "Epochs: 17/100... Trian Loss:0.585... Trian Accuracy:0.825... Trian F1 Score:0.771... Val Loss:0.108... Val Accuracy:0.967... Val F1 Score:0.946\n",
      "Epochs: 18/100... Trian Loss:0.346... Trian Accuracy:0.871... Trian F1 Score:0.828... Val Loss:0.194... Val Accuracy:0.900... Val F1 Score:0.845\n",
      "Epochs: 19/100... Trian Loss:0.830... Trian Accuracy:0.804... Trian F1 Score:0.746... Val Loss:1.573... Val Accuracy:0.817... Val F1 Score:0.577\n",
      "Epochs: 20/100... Trian Loss:1.038... Trian Accuracy:0.833... Trian F1 Score:0.781... Val Loss:1.539... Val Accuracy:0.750... Val F1 Score:0.532\n",
      "Epochs: 21/100... Trian Loss:1.083... Trian Accuracy:0.821... Trian F1 Score:0.766... Val Loss:0.230... Val Accuracy:0.933... Val F1 Score:0.887\n",
      "Epochs: 22/100... Trian Loss:0.672... Trian Accuracy:0.850... Trian F1 Score:0.802... Val Loss:0.094... Val Accuracy:0.933... Val F1 Score:0.895\n",
      "Epochs: 23/100... Trian Loss:0.701... Trian Accuracy:0.829... Trian F1 Score:0.777... Val Loss:0.701... Val Accuracy:0.750... Val F1 Score:0.532\n",
      "Epochs: 24/100... Trian Loss:0.423... Trian Accuracy:0.900... Trian F1 Score:0.868... Val Loss:0.133... Val Accuracy:0.950... Val F1 Score:0.923\n",
      "Epochs: 25/100... Trian Loss:0.414... Trian Accuracy:0.879... Trian F1 Score:0.842... Val Loss:0.091... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 26/100... Trian Loss:0.333... Trian Accuracy:0.858... Trian F1 Score:0.815... Val Loss:0.134... Val Accuracy:0.950... Val F1 Score:0.917\n",
      "Epochs: 27/100... Trian Loss:0.318... Trian Accuracy:0.883... Trian F1 Score:0.846... Val Loss:0.124... Val Accuracy:0.933... Val F1 Score:0.897\n",
      "Early stopping!\n",
      "Epochs: 28/100... Trian Loss:0.321... Trian Accuracy:0.858... Trian F1 Score:0.815... Val Loss:0.128... Val Accuracy:0.950... Val F1 Score:0.917\n",
      "Epochs: 29/100... Trian Loss:0.245... Trian Accuracy:0.892... Trian F1 Score:0.857... Val Loss:0.103... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 30/100... Trian Loss:0.254... Trian Accuracy:0.896... Trian F1 Score:0.863... Val Loss:0.119... Val Accuracy:0.933... Val F1 Score:0.895\n",
      "Epochs: 31/100... Trian Loss:0.265... Trian Accuracy:0.892... Trian F1 Score:0.858... Val Loss:0.109... Val Accuracy:0.967... Val F1 Score:0.946\n",
      "Epochs: 32/100... Trian Loss:0.251... Trian Accuracy:0.867... Trian F1 Score:0.823... Val Loss:0.105... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 33/100... Trian Loss:0.281... Trian Accuracy:0.867... Trian F1 Score:0.826... Val Loss:0.111... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 34/100... Trian Loss:0.271... Trian Accuracy:0.887... Trian F1 Score:0.853... Val Loss:0.117... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 35/100... Trian Loss:0.269... Trian Accuracy:0.879... Trian F1 Score:0.841... Val Loss:0.111... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 36/100... Trian Loss:0.238... Trian Accuracy:0.896... Trian F1 Score:0.862... Val Loss:0.168... Val Accuracy:0.917... Val F1 Score:0.872\n",
      "Epochs: 37/100... Trian Loss:0.247... Trian Accuracy:0.883... Trian F1 Score:0.848... Val Loss:0.162... Val Accuracy:0.933... Val F1 Score:0.897\n",
      "Epochs: 38/100... Trian Loss:0.244... Trian Accuracy:0.875... Trian F1 Score:0.837... Val Loss:0.115... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 39/100... Trian Loss:0.224... Trian Accuracy:0.912... Trian F1 Score:0.884... Val Loss:0.117... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 40/100... Trian Loss:0.222... Trian Accuracy:0.896... Trian F1 Score:0.864... Val Loss:0.112... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 41/100... Trian Loss:0.210... Trian Accuracy:0.900... Trian F1 Score:0.868... Val Loss:0.127... Val Accuracy:0.967... Val F1 Score:0.947\n",
      "Epochs: 42/100... Trian Loss:0.236... Trian Accuracy:0.904... Trian F1 Score:0.874... Val Loss:0.121... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 43/100... Trian Loss:0.200... Trian Accuracy:0.925... Trian F1 Score:0.901... Val Loss:0.123... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 44/100... Trian Loss:0.224... Trian Accuracy:0.892... Trian F1 Score:0.857... Val Loss:0.144... Val Accuracy:0.950... Val F1 Score:0.922\n",
      "Epochs: 45/100... Trian Loss:0.213... Trian Accuracy:0.904... Trian F1 Score:0.873... Val Loss:0.125... Val Accuracy:0.967... Val F1 Score:0.947\n",
      "Epochs: 46/100... Trian Loss:0.208... Trian Accuracy:0.912... Trian F1 Score:0.885... Val Loss:0.126... Val Accuracy:0.967... Val F1 Score:0.947\n",
      "Epochs: 47/100... Trian Loss:0.226... Trian Accuracy:0.900... Trian F1 Score:0.867... Val Loss:0.143... Val Accuracy:0.933... Val F1 Score:0.897\n",
      "Epochs: 48/100... Trian Loss:0.219... Trian Accuracy:0.887... Trian F1 Score:0.853... Val Loss:0.119... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 49/100... Trian Loss:0.215... Trian Accuracy:0.933... Trian F1 Score:0.912... Val Loss:0.144... Val Accuracy:0.933... Val F1 Score:0.897\n",
      "Epochs: 50/100... Trian Loss:0.240... Trian Accuracy:0.896... Trian F1 Score:0.864... Val Loss:0.113... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 51/100... Trian Loss:0.212... Trian Accuracy:0.904... Trian F1 Score:0.871... Val Loss:0.129... Val Accuracy:0.967... Val F1 Score:0.947\n",
      "Early stopping!\n",
      "Epochs: 52/100... Trian Loss:0.211... Trian Accuracy:0.912... Trian F1 Score:0.886... Val Loss:0.118... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 53/100... Trian Loss:0.226... Trian Accuracy:0.912... Trian F1 Score:0.885... Val Loss:0.126... Val Accuracy:0.967... Val F1 Score:0.947\n",
      "Epochs: 54/100... Trian Loss:0.204... Trian Accuracy:0.912... Trian F1 Score:0.884... Val Loss:0.124... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 55/100... Trian Loss:0.222... Trian Accuracy:0.908... Trian F1 Score:0.879... Val Loss:0.128... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 56/100... Trian Loss:0.189... Trian Accuracy:0.917... Trian F1 Score:0.890... Val Loss:0.117... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 57/100... Trian Loss:0.196... Trian Accuracy:0.912... Trian F1 Score:0.885... Val Loss:0.120... Val Accuracy:0.950... Val F1 Score:0.920\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs: 58/100... Trian Loss:0.204... Trian Accuracy:0.925... Trian F1 Score:0.901... Val Loss:0.135... Val Accuracy:0.967... Val F1 Score:0.947\n",
      "Epochs: 59/100... Trian Loss:0.184... Trian Accuracy:0.929... Trian F1 Score:0.907... Val Loss:0.120... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 60/100... Trian Loss:0.206... Trian Accuracy:0.912... Trian F1 Score:0.885... Val Loss:0.123... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 61/100... Trian Loss:0.190... Trian Accuracy:0.925... Trian F1 Score:0.902... Val Loss:0.123... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 62/100... Trian Loss:0.196... Trian Accuracy:0.917... Trian F1 Score:0.890... Val Loss:0.120... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 63/100... Trian Loss:0.209... Trian Accuracy:0.917... Trian F1 Score:0.890... Val Loss:0.126... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 64/100... Trian Loss:0.193... Trian Accuracy:0.925... Trian F1 Score:0.902... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 65/100... Trian Loss:0.211... Trian Accuracy:0.929... Trian F1 Score:0.906... Val Loss:0.121... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 66/100... Trian Loss:0.188... Trian Accuracy:0.929... Trian F1 Score:0.906... Val Loss:0.120... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 67/100... Trian Loss:0.207... Trian Accuracy:0.925... Trian F1 Score:0.901... Val Loss:0.119... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 68/100... Trian Loss:0.227... Trian Accuracy:0.904... Trian F1 Score:0.873... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 69/100... Trian Loss:0.192... Trian Accuracy:0.925... Trian F1 Score:0.901... Val Loss:0.124... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 70/100... Trian Loss:0.185... Trian Accuracy:0.933... Trian F1 Score:0.913... Val Loss:0.124... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 71/100... Trian Loss:0.195... Trian Accuracy:0.917... Trian F1 Score:0.891... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 72/100... Trian Loss:0.209... Trian Accuracy:0.912... Trian F1 Score:0.885... Val Loss:0.124... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 73/100... Trian Loss:0.200... Trian Accuracy:0.912... Trian F1 Score:0.885... Val Loss:0.123... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 74/100... Trian Loss:0.189... Trian Accuracy:0.921... Trian F1 Score:0.896... Val Loss:0.121... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 75/100... Trian Loss:0.202... Trian Accuracy:0.925... Trian F1 Score:0.901... Val Loss:0.118... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 76/100... Trian Loss:0.194... Trian Accuracy:0.929... Trian F1 Score:0.907... Val Loss:0.120... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 77/100... Trian Loss:0.199... Trian Accuracy:0.908... Trian F1 Score:0.879... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 78/100... Trian Loss:0.200... Trian Accuracy:0.917... Trian F1 Score:0.890... Val Loss:0.121... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 79/100... Trian Loss:0.198... Trian Accuracy:0.938... Trian F1 Score:0.918... Val Loss:0.120... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 80/100... Trian Loss:0.183... Trian Accuracy:0.929... Trian F1 Score:0.907... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 81/100... Trian Loss:0.202... Trian Accuracy:0.921... Trian F1 Score:0.895... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 82/100... Trian Loss:0.217... Trian Accuracy:0.921... Trian F1 Score:0.895... Val Loss:0.121... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 83/100... Trian Loss:0.176... Trian Accuracy:0.933... Trian F1 Score:0.913... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 84/100... Trian Loss:0.199... Trian Accuracy:0.912... Trian F1 Score:0.885... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 85/100... Trian Loss:0.195... Trian Accuracy:0.912... Trian F1 Score:0.885... Val Loss:0.121... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 86/100... Trian Loss:0.203... Trian Accuracy:0.917... Trian F1 Score:0.891... Val Loss:0.123... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 87/100... Trian Loss:0.206... Trian Accuracy:0.908... Trian F1 Score:0.879... Val Loss:0.121... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 88/100... Trian Loss:0.193... Trian Accuracy:0.908... Trian F1 Score:0.879... Val Loss:0.123... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 89/100... Trian Loss:0.196... Trian Accuracy:0.917... Trian F1 Score:0.890... Val Loss:0.121... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 90/100... Trian Loss:0.210... Trian Accuracy:0.908... Trian F1 Score:0.879... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 91/100... Trian Loss:0.187... Trian Accuracy:0.933... Trian F1 Score:0.912... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 92/100... Trian Loss:0.194... Trian Accuracy:0.921... Trian F1 Score:0.896... Val Loss:0.119... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 93/100... Trian Loss:0.187... Trian Accuracy:0.929... Trian F1 Score:0.906... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 94/100... Trian Loss:0.190... Trian Accuracy:0.917... Trian F1 Score:0.890... Val Loss:0.121... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 95/100... Trian Loss:0.204... Trian Accuracy:0.925... Trian F1 Score:0.901... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 96/100... Trian Loss:0.178... Trian Accuracy:0.938... Trian F1 Score:0.918... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 97/100... Trian Loss:0.183... Trian Accuracy:0.917... Trian F1 Score:0.891... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 98/100... Trian Loss:0.187... Trian Accuracy:0.912... Trian F1 Score:0.884... Val Loss:0.121... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 99/100... Trian Loss:0.193... Trian Accuracy:0.929... Trian F1 Score:0.906... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n",
      "Epochs: 100/100... Trian Loss:0.193... Trian Accuracy:0.925... Trian F1 Score:0.901... Val Loss:0.122... Val Accuracy:0.950... Val F1 Score:0.920\n"
     ]
    },
    {
     "data": {
      "image/png": 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lKSwSEVGBwDoeERExWEp0lBORMtBWxZdAW8JVgA4IvQaaPmSwMWZl6kpIRERE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\n2We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      "text/plain": [
       "<Figure size 1152x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 318,
       "width": 933
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time is 53m 55s\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "optimizer = optim.Adam(basemodel_3d.parameters(), lr=0.01)\n",
    "scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)\n",
    "help.train_data(basemodel_3d,train_loader,val_loader,epochs,optimizer,scheduler,help.criterion,help.basemodel_3d_checkpoint_path,help.device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loadmodel = help.load_checkpoint(help.basemodel_3d_checkpoint_path,'basemodel_3d',help.device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_datasets = MyDataset(datas=help.temp_data,shape=3,input_D=help.input_D,input_H=help.input_H,input_W=help.input_W,phase='test')\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 test data have been predicted\n",
      "----------------------------------------\n",
      "20 test data have been predicted\n",
      "----------------------------------------\n",
      "40 test data have been predicted\n",
      "----------------------------------------\n",
      "60 test data have been predicted\n",
      "----------------------------------------\n",
      "80 test data have been predicted\n",
      "----------------------------------------\n",
      "100 test data have been predicted\n",
      "----------------------------------------\n",
      "120 test data have been predicted\n",
      "----------------------------------------\n",
      "140 test data have been predicted\n",
      "----------------------------------------\n",
      "160 test data have been predicted\n",
      "----------------------------------------\n",
      "180 test data have been predicted\n",
      "----------------------------------------\n",
      "200 test data have been predicted\n",
      "----------------------------------------\n",
      "220 test data have been predicted\n",
      "----------------------------------------\n",
      "Test time is 0m 13s\n"
     ]
    }
   ],
   "source": [
    "help.all_predict(test_loader,loadmodel,help.device,help.result_3d_basemodel)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}