{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyTorch Intro\n",
    "PyTorch Official Tutorial: https://pytorch.org/tutorials/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import loader # a module (.py file) we created. Read for more information\n",
    "\n",
    "basic_transformer = transforms.Compose([transforms.ToTensor()])\n",
    "\n",
    "batch_size = 32\n",
    "\n",
    "trainloader, validloader = loader.get_data_loader(basic_transformer, basic_transformer, batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 3, 30, 30])\n",
      "torch.Size([32])\n",
      "tensor([3, 4, 2, 4, 3, 1, 3, 3, 4, 0, 0, 3, 0, 0, 0, 3, 0, 1, 3, 3, 3, 3, 2, 1,\n",
      "        1, 0, 2, 4, 0, 2, 2, 4])\n"
     ]
    },
    {
     "data": {
      "image/png": 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78dab3TFHtthfZdScIqnFml0tN5e3rUcAqCV2VdtNA/Y7p79930EzdtNNN7ljZp2zYHDv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\nq5tzWSxpY0oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x137641fdac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1376464f240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x137643585f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13764370a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [32, 3, 30, 30] = [batch size, channels, height, width]\n",
    "for x, y in trainloader:\n",
    "    print(x.shape)\n",
    "    print(y.shape)\n",
    "    print(y)\n",
    "    break\n",
    "\n",
    "# vis\n",
    "for i in range(4):\n",
    "    plt.imshow(np.transpose(x[i,:], (1,2,0))) # 30 x 30 x 3\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        \n",
    "        # define the layers\n",
    "        # kernel size = 3 means (3,3) kernel\n",
    "        # rgb -> 3 -> in channel\n",
    "        # number of feature maps = 16\n",
    "        # number of filters = 3 x 16\n",
    "        self.l1 = nn.Conv2d(kernel_size=3, in_channels=3, out_channels=16)\n",
    "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2) \n",
    "        # MaxPool2d, AvgPool2d. \n",
    "        # The first 2 = 2x2 kernel size, \n",
    "        # The second 2 means the stride=2\n",
    "        \n",
    "        self.l2 = nn.Conv2d(kernel_size=3, in_channels=16, out_channels=32)\n",
    "        \n",
    "        # FC layer\n",
    "        self.fc1 = nn.Linear(32 * 6 * 6, 5)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # define the data flow through the deep learning layers\n",
    "        x = self.pool(F.relu(self.l1(x))) # bs x 16 x 14 x 14\n",
    "        x = self.pool(F.relu(self.l2(x))) # bs x 32 x 6 x 6\n",
    "        # print(x.shape)\n",
    "        x = x.reshape(-1, 32*6*6) # [bs x 1152]# CRUCIAL: \n",
    "        # print(x.shape)\n",
    "        x = self.fc1(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 5])\n"
     ]
    }
   ],
   "source": [
    "m = CNN()\n",
    "pred = m(x)\n",
    "print(pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.1389, -0.1451, -0.0188,  0.1282,  0.0480],\n",
      "        [ 0.0837, -0.1823, -0.0440,  0.1306,  0.0502],\n",
      "        [ 0.0972, -0.1101, -0.0358,  0.0636, -0.0235],\n",
      "        [ 0.0963, -0.1282, -0.0590,  0.0743,  0.0378],\n",
      "        [ 0.0903, -0.1250,  0.0056,  0.0623,  0.0411],\n",
      "        [ 0.1539, -0.1484, -0.0230,  0.1110,  0.0798],\n",
      "        [ 0.1376, -0.1103, -0.0608,  0.1110,  0.0820],\n",
      "        [ 0.1142, -0.1438, -0.0356,  0.1158,  0.0724],\n",
      "        [ 0.1224, -0.1528, -0.0405,  0.0931,  0.0686],\n",
      "        [ 0.1317, -0.1428, -0.0551,  0.1094,  0.0748],\n",
      "        [ 0.0518, -0.0676, -0.0102,  0.0811, -0.0141],\n",
      "        [ 0.1016, -0.1283, -0.0497,  0.1051,  0.0614],\n",
      "        [ 0.0732, -0.1549, -0.0186,  0.1048,  0.0533],\n",
      "        [ 0.1063, -0.0692, -0.0035,  0.0940, -0.0044],\n",
      "        [ 0.1001, -0.1026, -0.0428,  0.1036,  0.0403],\n",
      "        [ 0.1442, -0.1446, -0.0355,  0.1190,  0.0485],\n",
      "        [ 0.0880, -0.1035, -0.0244,  0.0735,  0.0138],\n",
      "        [ 0.1369, -0.0970, -0.0386,  0.1251,  0.0291],\n",
      "        [ 0.0835, -0.1019, -0.0264,  0.0888,  0.0636],\n",
      "        [ 0.1267, -0.1036, -0.0540,  0.0873,  0.0554],\n",
      "        [ 0.1432, -0.1275, -0.0224,  0.1252,  0.0886],\n",
      "        [ 0.0985, -0.1454, -0.0625,  0.0864,  0.0945],\n",
      "        [ 0.1226, -0.1272, -0.0195,  0.0849,  0.0290],\n",
      "        [ 0.1117, -0.1112, -0.0201,  0.1037,  0.0056],\n",
      "        [ 0.1661, -0.1728, -0.0139,  0.1423,  0.0711],\n",
      "        [ 0.0902, -0.1262, -0.0275,  0.0879,  0.0407],\n",
      "        [ 0.1202, -0.1127, -0.0048,  0.1271,  0.0285],\n",
      "        [ 0.1135, -0.1468, -0.0286,  0.0741,  0.0678],\n",
      "        [ 0.1235, -0.1544,  0.0003,  0.1102,  0.0477],\n",
      "        [ 0.1101, -0.1680, -0.0343,  0.1219,  0.0501],\n",
      "        [ 0.1351, -0.1058, -0.0293,  0.1328,  0.0722],\n",
      "        [ 0.0958, -0.1386, -0.0311,  0.1073,  0.0655]],\n",
      "       grad_fn=<AddmmBackward>)\n"
     ]
    }
   ],
   "source": [
    "print(pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "num_epoches = 10\n",
    "import tqdm\n",
    "\n",
    "import torch.optim as optim\n",
    "\n",
    "\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "\n",
    "if USE_CUDA:\n",
    "    m = m.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 391/391 [00:21<00:00, 18.37it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 391/391 [00:07<00:00, 53.70it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 391/391 [00:08<00:00, 46.07it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 391/391 [00:08<00:00, 48.68it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 391/391 [00:08<00:00, 48.44it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 391/391 [00:07<00:00, 53.69it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 391/391 [00:06<00:00, 56.31it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 391/391 [00:06<00:00, 56.63it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 391/391 [00:07<00:00, 53.78it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 391/391 [00:07<00:00, 55.18it/s]\n"
     ]
    }
   ],
   "source": [
    "for epoch_id in range(num_epoches):\n",
    "    optimizer = optim.SGD(m.parameters(), lr=0.01 * 0.95 ** epoch_id)\n",
    "    for x, y in tqdm.tqdm(trainloader):\n",
    "        if USE_CUDA:\n",
    "            x, y = x.cuda(), y.cuda()\n",
    "        optimizer.zero_grad() # clear (reset) the gradient for the optimizer\n",
    "        pred = m(x)\n",
    "        loss = criterion(pred, y)\n",
    "        loss.backward() # calculating the gradient\n",
    "        optimizer.step() # backpropagation: optimize the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 79/79 [00:01<00:00, 66.07it/s]\n"
     ]
    }
   ],
   "source": [
    "all_gt = []\n",
    "all_pred = []\n",
    "\n",
    "for x, y in tqdm.tqdm(validloader):\n",
    "    if USE_CUDA:\n",
    "        x, y = x.cuda(), y.cuda()\n",
    "    all_gt += list(y.detach().cpu().numpy().reshape(-1))\n",
    "    pred = torch.argmax(m(x), dim=1)\n",
    "    all_pred += list(pred.detach().cpu().numpy().reshape(-1))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 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     ]
    }
   ],
   "source": [
    "print(all_gt)\n",
    "print(all_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 0.796\n"
     ]
    }
   ],
   "source": [
    "acc = np.sum(np.array(all_gt) == np.array(all_pred)) / len(all_gt)\n",
    "print(\"Accuracy is:\", acc)"
   ]
  }
 ],
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