{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch \n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import torch.utils.data as data\n",
    "import numpy as np\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-1.2532 -1.1120  0.9717\n",
      "-2.3617  0.1516  1.1280\n",
      "-2.1599  0.0828 -1.4305\n",
      " 0.5265  0.5020 -2.1852\n",
      "-0.9197  0.1772 -1.1378\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# random normal\n",
    "x = torch.randn(5, 3)\n",
    "print (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# build a layer\n",
    "linear = nn.Linear(3, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      " 0.3884 -0.3335 -0.5146\n",
      "-0.3692  0.1977 -0.4081\n",
      "[torch.FloatTensor of size 2x3]\n",
      "\n",
      "Parameter containing:\n",
      "-0.4826\n",
      "-0.0038\n",
      "[torch.FloatTensor of size 2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Sess weight and bias\n",
    "print (linear.weight)\n",
    "print (linear.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable containing:\n",
      "-1.0986 -0.1575\n",
      "-2.0311  0.4378\n",
      "-0.6131  1.3938\n",
      " 0.6790  0.7929\n",
      "-0.3134  0.8351\n",
      "[torch.FloatTensor of size 5x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# forward propagate\n",
    "y = linear(Variable(x))\n",
    "print (y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert numpy array to torch tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# convert numpy array to tensor\n",
    "a = np.array([[1,2], [3,4]])\n",
    "b = torch.from_numpy(a)\n",
    "print (b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (1) Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Image Preprocessing \n",
    "transform = transforms.Compose([\n",
    "    transforms.Scale(40),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.RandomCrop(32),\n",
    "    transforms.ToTensor()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) Define Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "torch.Size([3, 32, 32])\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "# download and loading dataset f\n",
    "train_dataset = dsets.CIFAR10(root='./data/',\n",
    "                               train=True, \n",
    "                               transform=transform,\n",
    "                               download=True)\n",
    "\n",
    "image, label = train_dataset[0]\n",
    "print (image.size())\n",
    "print (label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3) Data Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data loader provides queue and thread in a very simple way\n",
    "train_loader = data.DataLoader(dataset=train_dataset,\n",
    "                               batch_size=100, \n",
    "                               shuffle=True,\n",
    "                               num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# iteration start then queue and thread start\n",
    "data_iter = iter(train_loader)\n",
    "\n",
    "# mini-batch images and labels\n",
    "images, labels = data_iter.next()\n",
    "\n",
    "for images, labels in train_loader:\n",
    "    # your training code will be written here\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (4) What about custom dataset not cifar10?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class CustomDataset(data.Dataset):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def __getitem__(self, index):\n",
    "        # You should build this function to return one data for given index\n",
    "        pass\n",
    "    def __len__(self):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object cannot be interpreted as an integer",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-a76c7b5c92c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m                 \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                 \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m                 num_workers=2)\n\u001b[0m",
      "\u001b[0;32m/home/yunjey/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dataset, batch_size, shuffle, sampler, num_workers, collate_fn, pin_memory)\u001b[0m\n\u001b[1;32m    250\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomSampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequentialSampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yunjey/anaconda3/lib/python3.5/site-packages/torch/utils/data/sampler.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data_source)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_source\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_source\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "custom_dataset = CustomDataset()\n",
    "data.DataLoader(dataset=custom_dataset,\n",
    "                batch_size=100, \n",
    "                shuffle=True,\n",
    "                num_workers=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Pretrained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth\" to /home/yunjey/.torch/models/resnet18-5c106cde.pth\n",
      "100%|██████████| 46827520/46827520 [07:48<00:00, 99907.53it/s] \n"
     ]
    }
   ],
   "source": [
    "# Download and load pretrained model\n",
    "resnet = torchvision.models.resnet18(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# delete top layer for finetuning\n",
    "sub_model = nn.Sequentialtial(*list(resnet.children()[:-1]))\n",
    "\n",
    "# for test\n",
    "images = Variable(torch.randn(10, 3, 256, 256))\n",
    "print (resnet(images).size())\n",
    "print (sub_model(images).size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save and Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save and load the trained model\n",
    "torch.save(sub_model, 'model.pkl')\n",
    "\n",
    "model = torch.load('model.pkl')"
   ]
  }
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