{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9476fc52",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Image Classification (CIFAR-10) on Kaggle\n",
    "\n",
    "The web address of the competition is https://www.kaggle.com/c/cifar-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e5a3fb64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:26.260293Z",
     "iopub.status.busy": "2023-08-18T19:29:26.259545Z",
     "iopub.status.idle": "2023-08-18T19:29:29.089576Z",
     "shell.execute_reply": "2023-08-18T19:29:29.088675Z"
    },
    "origin_pos": 2,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "import math\n",
    "import os\n",
    "import shutil\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0241c1c6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We provide a small-scale sample of the dataset that\n",
    "contains the first 1000 training images and 5 random testing images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d41dcd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.095074Z",
     "iopub.status.busy": "2023-08-18T19:29:29.094404Z",
     "iopub.status.idle": "2023-08-18T19:29:29.393994Z",
     "shell.execute_reply": "2023-08-18T19:29:29.393137Z"
    },
    "origin_pos": 4,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading ../data/kaggle_cifar10_tiny.zip from http://d2l-data.s3-accelerate.amazonaws.com/kaggle_cifar10_tiny.zip...\n"
     ]
    }
   ],
   "source": [
    "d2l.DATA_HUB['cifar10_tiny'] = (d2l.DATA_URL + 'kaggle_cifar10_tiny.zip',\n",
    "                                '2068874e4b9a9f0fb07ebe0ad2b29754449ccacd')\n",
    "\n",
    "demo = True\n",
    "\n",
    "if demo:\n",
    "    data_dir = d2l.download_extract('cifar10_tiny')\n",
    "else:\n",
    "    data_dir = '../data/cifar-10/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "863e1e66",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Organizing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04bf8387",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.399003Z",
     "iopub.status.busy": "2023-08-18T19:29:29.398718Z",
     "iopub.status.idle": "2023-08-18T19:29:29.406335Z",
     "shell.execute_reply": "2023-08-18T19:29:29.405552Z"
    },
    "origin_pos": 6,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# training examples: 1000\n",
      "# classes: 10\n"
     ]
    }
   ],
   "source": [
    "def read_csv_labels(fname):\n",
    "    \"\"\"Read `fname` to return a filename to label dictionary.\"\"\"\n",
    "    with open(fname, 'r') as f:\n",
    "        lines = f.readlines()[1:]\n",
    "    tokens = [l.rstrip().split(',') for l in lines]\n",
    "    return dict(((name, label) for name, label in tokens))\n",
    "\n",
    "labels = read_csv_labels(os.path.join(data_dir, 'trainLabels.csv'))\n",
    "print('\n",
    "print('"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42d795c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Split the validation set out of the original training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ae3357e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.411145Z",
     "iopub.status.busy": "2023-08-18T19:29:29.410869Z",
     "iopub.status.idle": "2023-08-18T19:29:29.418258Z",
     "shell.execute_reply": "2023-08-18T19:29:29.417439Z"
    },
    "origin_pos": 8,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def copyfile(filename, target_dir):\n",
    "    \"\"\"Copy a file into a target directory.\"\"\"\n",
    "    os.makedirs(target_dir, exist_ok=True)\n",
    "    shutil.copy(filename, target_dir)\n",
    "\n",
    "def reorg_train_valid(data_dir, labels, valid_ratio):\n",
    "    \"\"\"Split the validation set out of the original training set.\"\"\"\n",
    "    n = collections.Counter(labels.values()).most_common()[-1][1]\n",
    "    n_valid_per_label = max(1, math.floor(n * valid_ratio))\n",
    "    label_count = {}\n",
    "    for train_file in os.listdir(os.path.join(data_dir, 'train')):\n",
    "        label = labels[train_file.split('.')[0]]\n",
    "        fname = os.path.join(data_dir, 'train', train_file)\n",
    "        copyfile(fname, os.path.join(data_dir, 'train_valid_test',\n",
    "                                     'train_valid', label))\n",
    "        if label not in label_count or label_count[label] < n_valid_per_label:\n",
    "            copyfile(fname, os.path.join(data_dir, 'train_valid_test',\n",
    "                                         'valid', label))\n",
    "            label_count[label] = label_count.get(label, 0) + 1\n",
    "        else:\n",
    "            copyfile(fname, os.path.join(data_dir, 'train_valid_test',\n",
    "                                         'train', label))\n",
    "    return n_valid_per_label"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab600f93",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Organizes the testing set for data loading during prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "890972a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.422565Z",
     "iopub.status.busy": "2023-08-18T19:29:29.422289Z",
     "iopub.status.idle": "2023-08-18T19:29:29.426856Z",
     "shell.execute_reply": "2023-08-18T19:29:29.426083Z"
    },
    "origin_pos": 10,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def reorg_test(data_dir):\n",
    "    \"\"\"Organize the testing set for data loading during prediction.\"\"\"\n",
    "    for test_file in os.listdir(os.path.join(data_dir, 'test')):\n",
    "        copyfile(os.path.join(data_dir, 'test', test_file),\n",
    "                 os.path.join(data_dir, 'train_valid_test', 'test',\n",
    "                              'unknown'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c5a3b9e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Invoke\n",
    "functions defined above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1daf58c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.439643Z",
     "iopub.status.busy": "2023-08-18T19:29:29.438882Z",
     "iopub.status.idle": "2023-08-18T19:29:29.700309Z",
     "shell.execute_reply": "2023-08-18T19:29:29.699321Z"
    },
    "origin_pos": 14,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def reorg_cifar10_data(data_dir, valid_ratio):\n",
    "    labels = read_csv_labels(os.path.join(data_dir, 'trainLabels.csv'))\n",
    "    reorg_train_valid(data_dir, labels, valid_ratio)\n",
    "    reorg_test(data_dir)\n",
    "\n",
    "batch_size = 32 if demo else 128\n",
    "valid_ratio = 0.1\n",
    "reorg_cifar10_data(data_dir, valid_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ef27b5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Image Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be0d5428",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.714890Z",
     "iopub.status.busy": "2023-08-18T19:29:29.714292Z",
     "iopub.status.idle": "2023-08-18T19:29:29.718602Z",
     "shell.execute_reply": "2023-08-18T19:29:29.717807Z"
    },
    "origin_pos": 20,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "transform_train = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.Resize(40),\n",
    "    torchvision.transforms.RandomResizedCrop(32, scale=(0.64, 1.0),\n",
    "                                                   ratio=(1.0, 1.0)),\n",
    "    torchvision.transforms.RandomHorizontalFlip(),\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize([0.4914, 0.4822, 0.4465],\n",
    "                                     [0.2023, 0.1994, 0.2010])])\n",
    "\n",
    "transform_test = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize([0.4914, 0.4822, 0.4465],\n",
    "                                     [0.2023, 0.1994, 0.2010])])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e743a95e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Read the organized dataset consisting of raw image files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "056ac33a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.722917Z",
     "iopub.status.busy": "2023-08-18T19:29:29.722506Z",
     "iopub.status.idle": "2023-08-18T19:29:29.733889Z",
     "shell.execute_reply": "2023-08-18T19:29:29.733119Z"
    },
    "origin_pos": 23,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "train_ds, train_valid_ds = [torchvision.datasets.ImageFolder(\n",
    "    os.path.join(data_dir, 'train_valid_test', folder),\n",
    "    transform=transform_train) for folder in ['train', 'train_valid']]\n",
    "\n",
    "valid_ds, test_ds = [torchvision.datasets.ImageFolder(\n",
    "    os.path.join(data_dir, 'train_valid_test', folder),\n",
    "    transform=transform_test) for folder in ['valid', 'test']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ab6608",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Specify all the image augmentation operations defined above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "06fa7207",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.738557Z",
     "iopub.status.busy": "2023-08-18T19:29:29.737952Z",
     "iopub.status.idle": "2023-08-18T19:29:29.743073Z",
     "shell.execute_reply": "2023-08-18T19:29:29.742323Z"
    },
    "origin_pos": 26,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "train_iter, train_valid_iter = [torch.utils.data.DataLoader(\n",
    "    dataset, batch_size, shuffle=True, drop_last=True)\n",
    "    for dataset in (train_ds, train_valid_ds)]\n",
    "\n",
    "valid_iter = torch.utils.data.DataLoader(valid_ds, batch_size, shuffle=False,\n",
    "                                         drop_last=True)\n",
    "\n",
    "test_iter = torch.utils.data.DataLoader(test_ds, batch_size, shuffle=False,\n",
    "                                        drop_last=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c95d19b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d527425d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.747678Z",
     "iopub.status.busy": "2023-08-18T19:29:29.747059Z",
     "iopub.status.idle": "2023-08-18T19:29:29.751129Z",
     "shell.execute_reply": "2023-08-18T19:29:29.750380Z"
    },
    "origin_pos": 35,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def get_net():\n",
    "    num_classes = 10\n",
    "    net = d2l.resnet18(num_classes, 3)\n",
    "    return net\n",
    "\n",
    "loss = nn.CrossEntropyLoss(reduction=\"none\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c66fef",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Training Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bde40789",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.755665Z",
     "iopub.status.busy": "2023-08-18T19:29:29.755131Z",
     "iopub.status.idle": "2023-08-18T19:29:29.764392Z",
     "shell.execute_reply": "2023-08-18T19:29:29.763621Z"
    },
    "origin_pos": 38,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,\n",
    "          lr_decay):\n",
    "    trainer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9,\n",
    "                              weight_decay=wd)\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay)\n",
    "    num_batches, timer = len(train_iter), d2l.Timer()\n",
    "    legend = ['train loss', 'train acc']\n",
    "    if valid_iter is not None:\n",
    "        legend.append('valid acc')\n",
    "    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],\n",
    "                            legend=legend)\n",
    "    net = nn.DataParallel(net, device_ids=devices).to(devices[0])\n",
    "    for epoch in range(num_epochs):\n",
    "        net.train()\n",
    "        metric = d2l.Accumulator(3)\n",
    "        for i, (features, labels) in enumerate(train_iter):\n",
    "            timer.start()\n",
    "            l, acc = d2l.train_batch_ch13(net, features, labels,\n",
    "                                          loss, trainer, devices)\n",
    "            metric.add(l, acc, labels.shape[0])\n",
    "            timer.stop()\n",
    "            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:\n",
    "                animator.add(epoch + (i + 1) / num_batches,\n",
    "                             (metric[0] / metric[2], metric[1] / metric[2],\n",
    "                              None))\n",
    "        if valid_iter is not None:\n",
    "            valid_acc = d2l.evaluate_accuracy_gpu(net, valid_iter)\n",
    "            animator.add(epoch + 1, (None, None, valid_acc))\n",
    "        scheduler.step()\n",
    "    measures = (f'train loss {metric[0] / metric[2]:.3f}, '\n",
    "                f'train acc {metric[1] / metric[2]:.3f}')\n",
    "    if valid_iter is not None:\n",
    "        measures += f', valid acc {valid_acc:.3f}'\n",
    "    print(measures + f'\\n{metric[2] * num_epochs / timer.sum():.1f}'\n",
    "          f' examples/sec on {str(devices)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c12f609a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Training and Validating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd4a55c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:29.768734Z",
     "iopub.status.busy": "2023-08-18T19:29:29.768227Z",
     "iopub.status.idle": "2023-08-18T19:30:37.496878Z",
     "shell.execute_reply": "2023-08-18T19:30:37.495860Z"
    },
    "origin_pos": 41,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss 0.654, train acc 0.789, valid acc 0.438\n",
      "958.1 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]\n"
     ]
    },
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   "source": [
    "devices, num_epochs, lr, wd = d2l.try_all_gpus(), 20, 2e-4, 5e-4\n",
    "lr_period, lr_decay, net = 4, 0.9, get_net()\n",
    "net(next(iter(train_iter))[0])\n",
    "train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,\n",
    "      lr_decay)"
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   "source": [
    "Classifying the Testing Set"
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     "shell.execute_reply": "2023-08-18T19:31:40.932837Z"
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    "origin_pos": 44,
    "tab": [
     "pytorch"
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     "name": "stdout",
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      "train loss 0.608, train acc 0.786\n",
      "1040.8 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]\n"
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   "source": [
    "net, preds = get_net(), []\n",
    "net(next(iter(train_valid_iter))[0])\n",
    "train(net, train_valid_iter, None, num_epochs, lr, wd, devices, lr_period,\n",
    "      lr_decay)\n",
    "\n",
    "for X, _ in test_iter:\n",
    "    y_hat = net(X.to(devices[0]))\n",
    "    preds.extend(y_hat.argmax(dim=1).type(torch.int32).cpu().numpy())\n",
    "sorted_ids = list(range(1, len(test_ds) + 1))\n",
    "sorted_ids.sort(key=lambda x: str(x))\n",
    "df = pd.DataFrame({'id': sorted_ids, 'label': preds})\n",
    "df['label'] = df['label'].apply(lambda x: train_valid_ds.classes[x])\n",
    "df.to_csv('submission.csv', index=False)"
   ]
  }
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