Agriculture Net.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "bjwqokS89dTt"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch \n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader as DL\n",
    "import torch.nn as nn\n",
    "import json\n",
    "from PIL import Image as Im\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "SDZJm7os9oXX",
    "outputId": "8879b18f-c2b6-45f7-88d2-98dca94ed358"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "Lmwg7A1m9j8m"
   },
   "outputs": [],
   "source": [
    "!cp \"/content/drive/My Drive/Agriculture_sat_view.zip\" \"Ag.zip\"\n",
    "!unzip \"Ag.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "HjQA_rZL9lsc"
   },
   "outputs": [],
   "source": [
    "!cp  \"/content/drive/My Drive/val_81.9.pth\" \"val_81.9.pth\"\n",
    "!cp  \"/content/drive/My Drive/val_list.npy\"  \"val_list.npy\"  \n",
    "##Instead Of Using Continuos Value Range For Validation ex [1400:] I created the validation set using and saved the list so to use same list(i.e same validation for all Training) using np.random.permutation(1593)[:180]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Own Image Augmentation For More Than 3 Channels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def applytransform(x,y,mode):\n",
    "  return x,y\n",
    "  if mode==\"val\" :\n",
    "    \n",
    "    return x,y\n",
    "     \n",
    "  \n",
    "  if random.random()<=0.5:\n",
    "    return x,y\n",
    "  else:\n",
    "    r=random.random()\n",
    "    if r<=0.5:\n",
    "      \n",
    "      for i in range(len(x)):x[i]=np.fliplr(x[i])\n",
    "      y=np.fliplr(y)\n",
    "    if r>=0.5:\n",
    "      for i in range(len(x)):x[i]=np.flipud(x[i])\n",
    "      y=np.flipud(y)\n",
    "    if random.random()>=0.5:\n",
    "      k=random.randint(0,3)\n",
    "      for i in range(len(x)):x[i]=np.rot90(x[i],k)\n",
    "      y=np.rot90(y,k)\n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "ecJRbUJe-B0C"
   },
   "outputs": [],
   "source": [
    "def combine_image(mask_path,file_name,mode,resize=None):\n",
    "  mask_file_path=mask_path+\"/\"+file_name\n",
    "  \n",
    "  mask=Im.open(mask_file_path)\n",
    "  file_path=mask_path.replace(\"target\",\"input\")\n",
    "  file_name=file_name.replace(\"cdl\",\"lc8\")\n",
    "  file_name=file_name.split(\".\")[0]\n",
    "  file_path=file_path+\"/\"+file_name\n",
    "  a=Im.open(file_path+\"_1.tif\")\n",
    "  a=np.array(a)/255\n",
    "  a=torch.from_numpy(a[None])\n",
    "  for i in range(1,7):\n",
    "    im=Im.open(file_path+\"_\"+str(i+1)+\".tif\")\n",
    "    b=np.array(im)/255\n",
    "    b=torch.from_numpy(b[None])\n",
    "    a=torch.cat((a,b),dim=0)\n",
    "  a=a.type('torch.FloatTensor')\n",
    "    \n",
    "  return applytransform(a,torch.from_numpy(np.array(mask))) \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "osHmTMCd-CLQ"
   },
   "outputs": [],
   "source": [
    "class AGNet(Dataset):\n",
    "    def __init__(self,root,mask_list,transforms=None):\n",
    "        self.root=root\n",
    "        self.mask_list=mask_list\n",
    "        self.transformation=transforms\n",
    "    def __len__(self):\n",
    "        return len(self.mask_list)\n",
    "    def __getitem__(self,i):\n",
    "      \n",
    "      #print(self.mask_list[i])\n",
    "      x,y=combine_image(self.root+\"/target\",self.mask_list[i],\"val\")\n",
    "      return x,y\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "RwQEAgCr-D3O"
   },
   "outputs": [],
   "source": [
    "path='Agriculture_sat_view'\n",
    "full_data=AGNet(path,os.listdir(path+\"/target\")[0:1593])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "99QBM60r-Vs8"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "KVVdxoDD-n8f"
   },
   "outputs": [],
   "source": [
    "def Conv(ni,nf,kernel_size=3,stride=1):\n",
    "  return (nn.Sequential(*[nn.Conv2d(ni,nf,stride=stride,kernel_size=kernel_size,padding=kernel_size//2),nn.BatchNorm2d(nf),nn.ReLU()]))\n",
    "\n",
    "\n",
    "\n",
    "class Scse(nn.Module):\n",
    "  def __init__(self,ni):\n",
    "    super().__init__()\n",
    "    self.cSE = nn.Sequential(nn.AdaptiveAvgPool2d(1),\n",
    "                             nn.Conv2d(ni,ni//2,1),\n",
    "                             nn.ReLU(inplace=True),\n",
    "                             nn.Conv2d(ni//2, ni,1),\n",
    "                             nn.Sigmoid()\n",
    "                               )\n",
    "    self.sSE = nn.Sequential(nn.Conv2d(ni,ni,1),\n",
    "                             nn.Sigmoid())\n",
    "  def forward(self,x):\n",
    "    \n",
    "    x=torch.addcmul(x * self.cSE(x), 1, x, self.sSE(x))\n",
    "    return x\n",
    "\n",
    "\n",
    "class DownBlock(nn.Module):\n",
    "  def __init__(self,ni,nf,scale_factor=2,down=None):\n",
    "    super().__init__()\n",
    "    self.conv1=Conv(ni,nf,stride=2)\n",
    "    self.conv2=Conv(nf,nf)\n",
    "    if down is not None:\n",
    "      \n",
    "      self.scse_encoder=Scse(nf)\n",
    "    else:self.scse_encoder=None\n",
    "  def forward(self,x):\n",
    "    inp=x\n",
    "    x=self.conv2(self.conv1(x))\n",
    "    x+=self.conv1(inp)\n",
    "    if self.scse_encoder is None:return x\n",
    "    return self.scse_encoder(x)\n",
    "\n",
    "\n",
    "\n",
    "class UpBlock(nn.Module):\n",
    "  def __init__(self,ni,nf,down=None):\n",
    "    super().__init__()\n",
    "    self.deconv1=nn.ConvTranspose2d(ni,nf,2,stride=2)\n",
    "    self.conv=Conv(2*nf,nf)\n",
    "    if down is not None:\n",
    "      self.scse_decoder=Scse(nf)\n",
    "    else:self.scse_decoder=None\n",
    "  def forward(self,down,cross):\n",
    "    x=self.deconv1(down)\n",
    "    x=torch.cat((x,cross),dim=1)\n",
    "    x=self.conv(x)\n",
    "    if self.scse_decoder is None:return x\n",
    "    return self.scse_decoder(x)\n",
    "\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "  def __init__(self):\n",
    "    super().__init__()\n",
    "    self.d1=nn.Conv2d(7,28,kernel_size=5,padding=2,stride=1)\n",
    "    self.d2=nn.Conv2d(28,98,kernel_size=3,padding=1,stride=1)\n",
    "    self.down1=DownBlock(7,128,down=12)##64*64*128\n",
    "    self.down2=DownBlock(128,256)##32*32*256\n",
    "    self.down3=DownBlock(256,512)##16*16*512\n",
    "    self.down4=DownBlock(512,1024)##8*8*1024\n",
    "    self.up1=UpBlock(1024,512)#16*16*512\n",
    "    self.up2=UpBlock(512,256,down=12)#32\n",
    "    self.up3=UpBlock(256,128)#64\n",
    "    self.fin=nn.ConvTranspose2d(128,256,2,stride=2)\n",
    "  def forward(self,down):\n",
    "    d1=self.down1(down)\n",
    "    d2=self.down2(d1)\n",
    "    d3=self.down3(d2)\n",
    "    d4=self.down4(d3)\n",
    "    d4=self.up1(d4,d3)\n",
    "    d5=self.up2(d4,d2)\n",
    "    d5=self.up3(d5,d1)\n",
    "    return self.fin(d5)\n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "bUXNo6qJ-3_0"
   },
   "outputs": [],
   "source": [
    "m=Net().cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "EaC-u5Z2-tgp"
   },
   "outputs": [],
   "source": [
    "m.load_state_dict(torch.load(\"val_81.9.pth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "DJV9l6Xc-2pQ",
    "outputId": "6733c0e4-d2a5-48ca-94e5-9aec0c195822"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The color of class 5 is:  (38, 112, 0)\n"
     ]
    }
   ],
   "source": [
    "colormap = [[\"#000000\",\"0\",\"0\"],\n",
    " [\"#ffd300\",\"1\",\"1\"],\n",
    " [\"#ff2626\",\"2\",\"2\"],\n",
    " [\"#00a8e2\",\"3\",\"3\"],\n",
    " [\"#ff9e0a\",\"4\",\"4\"],\n",
    " [\"#267000\",\"5\",\"5\"],\n",
    " [\"#ffff00\",\"6\",\"6\"],\n",
    " [\"#000000\",\"7\",\"7\"],\n",
    " [\"#000000\",\"8\",\"8\"],\n",
    " [\"#000000\",\"9\",\"9\"],\n",
    " [\"#70a500\",\"10\",\"10\"],\n",
    " [\"#00af49\",\"11\",\"11\"],\n",
    " [\"#dda50a\",\"12\",\"12\"],\n",
    " [\"#dda50a\",\"13\",\"13\"],\n",
    " [\"#7cd3ff\",\"14\",\"14\"],\n",
    " [\"#000000\",\"15\",\"15\"],\n",
    " [\"#000000\",\"16\",\"16\"],\n",
    " [\"#000000\",\"17\",\"17\"],\n",
    " [\"#000000\",\"18\",\"18\"],\n",
    " [\"#000000\",\"19\",\"19\"],\n",
    " [\"#000000\",\"20\",\"20\"],\n",
    " [\"#e2007c\",\"21\",\"21\"],\n",
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    " [\"#d8b56b\",\"23\",\"23\"],\n",
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    " [\"#d69ebc\",\"30\",\"30\"],\n",
    " [\"#d1ff00\",\"31\",\"31\"],\n",
    " [\"#7c99ff\",\"32\",\"32\"],\n",
    " [\"#d6d600\",\"33\",\"33\"],\n",
    " [\"#d1ff00\",\"34\",\"34\"],\n",
    " [\"#00af49\",\"35\",\"35\"],\n",
    " [\"#ffa5e2\",\"36\",\"36\"],\n",
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    " [\"#d69ebc\",\"39\",\"39\"],\n",
    " [\"#000000\",\"40\",\"40\"],\n",
    " [\"#a800e2\",\"41\",\"41\"],\n",
    " [\"#a50000\",\"42\",\"42\"],\n",
    " [\"#702600\",\"43\",\"43\"],\n",
    " [\"#00af49\",\"44\",\"44\"],\n",
    " [\"#af7cff\",\"45\",\"45\"],\n",
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    " [\"#ff6666\",\"47\",\"47\"],\n",
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    " [\"#000000\",\"180\",\"180\"],\n",
    " [\"#000000\",\"181\",\"181\"],\n",
    " [\"#000000\",\"182\",\"182\"],\n",
    " [\"#000000\",\"183\",\"183\"],\n",
    " [\"#000000\",\"184\",\"184\"],\n",
    " [\"#000000\",\"185\",\"185\"],\n",
    " [\"#000000\",\"186\",\"186\"],\n",
    " [\"#000000\",\"187\",\"187\"],\n",
    " [\"#000000\",\"188\",\"188\"],\n",
    " [\"#000000\",\"189\",\"189\"],\n",
    " [\"#7cafaf\",\"190\",\"190\"],\n",
    " [\"#000000\",\"191\",\"191\"],\n",
    " [\"#000000\",\"192\",\"192\"],\n",
    " [\"#000000\",\"193\",\"193\"],\n",
    " [\"#000000\",\"194\",\"194\"],\n",
    " [\"#7cafaf\",\"195\",\"195\"],\n",
    " [\"#000000\",\"196\",\"196\"],\n",
    " [\"#000000\",\"197\",\"197\"],\n",
    " [\"#000000\",\"198\",\"198\"],\n",
    " [\"#000000\",\"199\",\"199\"],\n",
    " [\"#000000\",\"200\",\"200\"],\n",
    " [\"#000000\",\"201\",\"201\"],\n",
    " [\"#000000\",\"202\",\"202\"],\n",
    " [\"#000000\",\"203\",\"203\"],\n",
    " [\"#00ff8c\",\"204\",\"204\"],\n",
    " [\"#d69ebc\",\"205\",\"205\"],\n",
    " [\"#ff6666\",\"206\",\"206\"],\n",
    " [\"#ff6666\",\"207\",\"207\"],\n",
    " [\"#ff6666\",\"208\",\"208\"],\n",
    " [\"#ff6666\",\"209\",\"209\"],\n",
    " [\"#ff8eaa\",\"210\",\"210\"],\n",
    " [\"#334933\",\"211\",\"211\"],\n",
    " [\"#e27026\",\"212\",\"212\"],\n",
    " [\"#ff6666\",\"213\",\"213\"],\n",
    " [\"#ff6666\",\"214\",\"214\"],\n",
    " [\"#000000\",\"215\",\"215\"],\n",
    " [\"#ff6666\",\"216\",\"216\"],\n",
    " [\"#af9970\",\"217\",\"217\"],\n",
    " [\"#ff8eaa\",\"218\",\"218\"],\n",
    " [\"#ff6666\",\"219\",\"219\"],\n",
    " [\"#ff8eaa\",\"220\",\"220\"],\n",
    " [\"#ff6666\",\"221\",\"221\"],\n",
    " [\"#ff6666\",\"222\",\"222\"],\n",
    " [\"#ff8eaa\",\"223\",\"223\"],\n",
    " [\"#00af49\",\"224\",\"224\"],\n",
    " [\"#ffd300\",\"225\",\"225\"],\n",
    " [\"#ffd300\",\"226\",\"226\"],\n",
    " [\"#ff6666\",\"227\",\"227\"],\n",
    " [\"#000000\",\"228\",\"228\"],\n",
    " [\"#ff6666\",\"229\",\"229\"],\n",
    " [\"#896054\",\"230\",\"230\"],\n",
    " [\"#ff6666\",\"231\",\"231\"],\n",
    " [\"#ff2626\",\"232\",\"232\"],\n",
    " [\"#e2007c\",\"233\",\"233\"],\n",
    " [\"#ff9e0a\",\"234\",\"234\"],\n",
    " [\"#ff9e0a\",\"235\",\"235\"],\n",
    " [\"#a57000\",\"236\",\"236\"],\n",
    " [\"#ffd300\",\"237\",\"237\"],\n",
    " [\"#a57000\",\"238\",\"238\"],\n",
    " [\"#267000\",\"239\",\"239\"],\n",
    " [\"#267000\",\"240\",\"240\"],\n",
    " [\"#ffd300\",\"241\",\"241\"],\n",
    " [\"#000099\",\"242\",\"242\"],\n",
    " [\"#ff6666\",\"243\",\"243\"],\n",
    " [\"#ff6666\",\"244\",\"244\"],\n",
    " [\"#ff6666\",\"245\",\"245\"],\n",
    " [\"#ff6666\",\"246\",\"246\"],\n",
    " [\"#ff6666\",\"247\",\"247\"],\n",
    " [\"#ff6666\",\"248\",\"248\"],\n",
    " [\"#ff6666\",\"249\",\"249\"],\n",
    " [\"#ff6666\",\"250\",\"250\"],\n",
    " [\"#ffd300\",\"251\",\"251\"],\n",
    " [\"#267000\",\"252\",\"252\"],\n",
    " [\"#a57000\",\"253\",\"253\"],\n",
    " [\"#267000\",\"254\",\"254\"],\n",
    " [\"#707000\",\"255\",\"255\"]]\n",
    "\n",
    "\n",
    "def hex_to_rgb(value):\n",
    " value = value.lstrip('#')\n",
    " lv = len(value)\n",
    " return tuple(int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3))\n",
    "\n",
    "\n",
    "def getCDLRGB(cdlvalue):\n",
    " # print(\"get cdl:\", cdlvalue)\n",
    " for x in range(len(colormap)):\n",
    "   if(int(colormap[x][1])==cdlvalue):\n",
    "    hex = colormap[x][0]\n",
    "    rgb = hex_to_rgb(hex)\n",
    "    return rgb\n",
    "\n",
    "print(\"The color of class 5 is: \", getCDLRGB(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "Q_gcQwFR_TB9"
   },
   "outputs": [],
   "source": [
    "label_colours=[np.array(getCDLRGB(i)) for i in range(0,256)]\n",
    "\n",
    "label_colours=np.array(label_colours)\n",
    "def view_annotated(tensor, plot=True):\n",
    "    temp = tensor.numpy()\n",
    "    r = temp.copy()\n",
    "    g = temp.copy()\n",
    "    b = temp.copy()\n",
    "    for l in range(0,256):\n",
    "        r[temp==l]=label_colours[l,0]\n",
    "        g[temp==l]=label_colours[l,1]\n",
    "        b[temp==l]=label_colours[l,2]\n",
    "\n",
    "    rgb = np.zeros((temp.shape[0], temp.shape[1], 3))\n",
    "    rgb[:,:,0] = (r/255.0)#[:,:,0]\n",
    "    rgb[:,:,1] = (g/255.0)#[:,:,1]\n",
    "    rgb[:,:,2] = (b/255.0)#[:,:,2]\n",
    "    if plot:\n",
    "        plt.imshow(rgb)\n",
    "        plt.show()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "OH4hmrwNAm95"
   },
   "outputs": [],
   "source": [
    "list_images=np.load(\"val_list.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "IE7dhaSt7J5F",
    "outputId": "8dc1d6c6-3026-42fc-b442-cff7c5bb9833"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Segmentation Masks Using The Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7gnnPVrB7SbM"
   },
   "source": [
    "***THe Ground Truth Image is The First One While The Second One is the Generated Image***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 683
    },
    "colab_type": "code",
    "id": "ynHJvPBg_ZDW",
    "outputId": "0dacf8bd-dbae-4a94-fb9a-de69c5d2b9c1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZjUmUiEhATCbRaLDyw5/aXQTbRCp9dn3Q5v88acHwiDwnuQ+TKBGRAFaiREQC2Jx3iVhu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\niASwEiUiEsBKlIhIwP8HlbSr3JvnkVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TKT2Z3UvVD0cFYmtr0b+f/nucks+ptI1gfPIyMYkSESngRZSISAGr8wEW03gUQO9+2QEA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\nZasAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x,y=full_data.__getitem__(list_images[0])\n",
    "y_pred=m.forward(x[None].cuda())\n",
    "y_pred_cat=torch.argmax(y_pred,dim=1)\n",
    "predicted=view_annotated(y_pred_cat.cpu()[0])\n",
    "original=view_annotated(y.cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 683
    },
    "colab_type": "code",
    "id": "NVXKPYW37OJ5",
    "outputId": "5fff08cd-e2d5-49b7-920c-c739fc0851fc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iREQSHNGc12pWiL28nb4wM4czxa4DVafsLsEwPR2fem+jidsHVmyoZ0WHkpWb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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yFlbaXYIpka47S2dbeb54lD9oW4uButSf9XCEcwKOFCfUYFYkPzOmTqIpKSno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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x,y=full_data.__getitem__(list_images[1])\n",
    "y_pred=m.forward(x[None].cuda())\n",
    "y_pred_cat=torch.argmax(y_pred,dim=1)\n",
    "predicted=view_annotated(y_pred_cat.cpu()[0])\n",
    "original=view_annotated(y.cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 683
    },
    "colab_type": "code",
    "id": "kQIPMq067fkk",
    "outputId": "6613d7fa-8098-42fd-8c5b-feb84eaf6210"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1n1rY8WH4Nf52DIrZPdAmf245d/V8rXVFndrn+ciCC4CUi++S/mroff+8fXjU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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IEhFJcEV1Xl1lJ2NOzeZEzlKPWJp8D0cs6empvvvV/8f/OWWNDDep+XgAQE73dB8tR3tW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\nIgn/H/DEUF4SQ/d4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x,y=full_data.__getitem__(list_images[2])\n",
    "y_pred=m.forward(x[None].cuda())\n",
    "y_pred_cat=torch.argmax(y_pred,dim=1)\n",
    "predicted=view_annotated(y_pred_cat.cpu()[0])\n",
    "original=view_annotated(y.cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "o-VKoiou-Vr5"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "AGNET_Visualization.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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"
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 },
 "nbformat": 4,
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}