55 AutoEncoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T02:52:39.937637Z",
     "start_time": "2020-04-22T02:52:39.087348Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from torch import nn,optim\n",
    "from torchvision import transforms,datasets\n",
    "import visdom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T02:52:39.954392Z",
     "start_time": "2020-04-22T02:52:39.944666Z"
    }
   },
   "outputs": [],
   "source": [
    "class AE(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(AE,self).__init__()\n",
    "        \n",
    "        # [b,784] => [b,20]\n",
    "        self.encoder=nn.Sequential(\n",
    "            nn.Linear(784,256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256,64),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(64,20),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        # [b,20] => [b,784]\n",
    "        self.decoder=nn.Sequential(\n",
    "            nn.Linear(20,64),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(64,256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256,784),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "        \n",
    "    def forward(self,x):\n",
    "        \"\"\"\n",
    "        param x: [b,1,28,28]\n",
    "        return x \n",
    "        \"\"\"\n",
    "        batchsz=x.size(0)\n",
    "        # flatten\n",
    "        x=x.view(batchsz,-1)\n",
    "        # encoder\n",
    "        x=self.encoder(x)\n",
    "        # decoder\n",
    "        x=self.decoder(x)\n",
    "        # reshape\n",
    "        x=x.view(batchsz,1,28,28)\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T02:52:39.979098Z",
     "start_time": "2020-04-22T02:52:39.960753Z"
    }
   },
   "outputs": [],
   "source": [
    "def main():\n",
    "    batch_size=32 \n",
    "    epochs=100\n",
    "    learning_rate=1e-3 \n",
    "    \n",
    "    train_db=datasets.MNIST('./data/mnist_data',train=True,download=True,\n",
    "                  transform=transforms.Compose([\n",
    "                      transforms.ToTensor(),\n",
    "                      transforms.Normalize((0.1307,),(0.3081,))\n",
    "                  ]))\n",
    "    train_loader=torch.utils.data.DataLoader(train_db,batch_size=batch_size,shuffle=True)\n",
    "\n",
    "    test_db=datasets.MNIST('./data/mnist_data',train=False,\n",
    "                  transform=transforms.Compose([\n",
    "                      transforms.ToTensor(),\n",
    "                      transforms.Normalize((0.1307,),(0.3081,))\n",
    "                  ]))\n",
    "    test_loader=torch.utils.data.DataLoader(test_db,batch_size=batch_size,shuffle=True)\n",
    "    \n",
    "    \n",
    "    x,_ =iter(train_loader).next()\n",
    "    print('x:',x.shape)\n",
    "    \n",
    "    device=torch.device('cuda')\n",
    "    model=AE().to(device)\n",
    "    criteon=nn.MSELoss()\n",
    "    optimizer=optim.Adam(model.parameters(),lr=learning_rate)\n",
    "    print(model)\n",
    "    \n",
    "    \n",
    "    viz=visdom.Visdom()\n",
    "    for epoch in range(epochs):\n",
    "        for batchidx,(x,_) in enumerate(train_loader):\n",
    "            # [b,1,28,28]\n",
    "            x=x.to(device)\n",
    "            \n",
    "            x_hat=model(x)   # logits\n",
    "            loss=criteon(x_hat,x)\n",
    "            \n",
    "            # backprop\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "        print('epoch={} loss={}'.format(epoch,loss.item()))\n",
    "        \n",
    "        x,_=iter(test_loader).next()\n",
    "        x=x.to(device)\n",
    "        with torch.no_grad():\n",
    "            x_hat=model(x)\n",
    "            \n",
    "        viz.images(x,nrow=8,win='x',opts=dict(title='x'))\n",
    "        viz.images(x_hat,nrow=8,win='x_hat',opts=dict(title='x_hat'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T03:12:54.807662Z",
     "start_time": "2020-04-22T02:52:39.982067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: torch.Size([32, 1, 28, 28])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting up a new session...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AE(\n",
      "  (encoder): Sequential(\n",
      "    (0): Linear(in_features=784, out_features=256, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=256, out_features=64, bias=True)\n",
      "    (3): ReLU()\n",
      "    (4): Linear(in_features=64, out_features=20, bias=True)\n",
      "    (5): ReLU()\n",
      "  )\n",
      "  (decoder): Sequential(\n",
      "    (0): Linear(in_features=20, out_features=64, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=64, out_features=256, bias=True)\n",
      "    (3): ReLU()\n",
      "    (4): Linear(in_features=256, out_features=784, bias=True)\n",
      "    (5): Sigmoid()\n",
      "  )\n",
      ")\n",
      "epoch=0 loss=0.5714783072471619\n",
      "epoch=1 loss=0.5603113770484924\n",
      "epoch=2 loss=0.5409227609634399\n",
      "epoch=3 loss=0.47055497765541077\n",
      "epoch=4 loss=0.527624249458313\n",
      "epoch=5 loss=0.5254924297332764\n",
      "epoch=6 loss=0.5143334865570068\n",
      "epoch=7 loss=0.5338783860206604\n",
      "epoch=8 loss=0.5495006442070007\n",
      "epoch=9 loss=0.5165415406227112\n",
      "epoch=10 loss=0.4805402159690857\n",
      "epoch=11 loss=0.47055667638778687\n",
      "epoch=12 loss=0.5304577946662903\n",
      "epoch=13 loss=0.5037247538566589\n",
      "epoch=14 loss=0.493367463350296\n",
      "epoch=15 loss=0.5206145644187927\n",
      "epoch=16 loss=0.515197217464447\n",
      "epoch=17 loss=0.5014196038246155\n",
      "epoch=18 loss=0.5417688488960266\n",
      "epoch=19 loss=0.5478198528289795\n",
      "epoch=20 loss=0.5448845028877258\n",
      "epoch=21 loss=0.5014660954475403\n",
      "epoch=22 loss=0.5140256285667419\n",
      "epoch=23 loss=0.5097986459732056\n",
      "epoch=24 loss=0.5321115255355835\n",
      "epoch=25 loss=0.5439805388450623\n",
      "epoch=26 loss=0.49594107270240784\n",
      "epoch=27 loss=0.4892566204071045\n",
      "epoch=28 loss=0.5415856242179871\n",
      "epoch=29 loss=0.4964054524898529\n",
      "epoch=30 loss=0.4703136682510376\n",
      "epoch=31 loss=0.5084589123725891\n",
      "epoch=32 loss=0.5290032029151917\n",
      "epoch=33 loss=0.4674481749534607\n",
      "epoch=34 loss=0.5510663986206055\n",
      "epoch=35 loss=0.4856927692890167\n",
      "epoch=36 loss=0.4925364553928375\n",
      "epoch=37 loss=0.4979722797870636\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-4be4dd58a851>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__name__\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'__main__'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-3-32a272d90dc9>\u001b[0m in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m     31\u001b[0m     \u001b[0mviz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvisdom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVisdom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mbatchidx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0m_\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m             \u001b[1;31m# [b,1,28,28]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    346\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    383\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    384\u001b[0m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 385\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    386\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    387\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\datasets\\mnist.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m     95\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 97\u001b[1;33m             \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     99\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget_transform\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\transforms\\transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m     68\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m             \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\transforms\\transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, tensor)\u001b[0m\n\u001b[0;32m    173\u001b[0m             \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mNormalized\u001b[0m \u001b[0mTensor\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m         \"\"\"\n\u001b[1;32m--> 175\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    177\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\transforms\\functional.py\u001b[0m in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 213\u001b[1;33m         \u001b[0mtensor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m     \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "if __name__=='__main__':\n",
    "    main()"
   ]
  }
 ],
 "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}