restnt18_on_cifar10.ipynb

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   },
   "source": [
    "Import of all relevant dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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   "outputs": [
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     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
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     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 539), started 0:45:59 ago. (Use '!kill 539' to kill it.)"
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       "    "
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     "text": [
      "Known TensorBoard instances:\n",
      "  - port 6006: logdir logs (started 0:45:59 ago; pid 539)\n"
     ]
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "#%tensorboard --logdir logs\n",
    "\n",
    "import torch \n",
    "import torch.nn as nn \n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "import torchvision.models as models  \n",
    "import torchvision.datasets as datasets\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "\n",
    "writer = SummaryWriter('logs/' + str(np.random.randint(0,100000)))\n",
    "writer.close()\n",
    "\n",
    "#show tensorboard \n",
    "from tensorboard import notebook\n",
    "%tensorboard --logdir logs\n",
    "notebook.list() # View open TensorBoard instances\n",
    "# notebook.display(port=6006, height=1000) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "odCm7xCyjK7G",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Load Resnet18 and necessary transformers for training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "z4RjR8kUjLKk",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#Resnet was pre-trained on imageNet with 1000 classes\n",
    "resnet18 = models.resnet18(pretrained=True)\n",
    "writer.add_graph(resnet18, torch.zeros([64, 3, 7, 7]))\n",
    "\n",
    "#transformation with augmentation and CIFAR10 normalization, for training and test set\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.RandomCrop(32, padding=8, padding_mode='reflect'),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TV9F87h_jcF7",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Load CIFAR 10 training and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VGHdW57rjkTM",
    "outputId": "f14a0427-6e9c-4408-8574-fb06ecdfabaa",
    "pycharm": {
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "#cifar10 training and test set with a dataloader\n",
    "trainset = datasets.CIFAR10(root='.', train=True, download=True, transform=transform_train)\n",
    "trainLoader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2, pin_memory=True, drop_last=True)\n",
    "\n",
    "testset = datasets.CIFAR10(root='.', train=False, download=True, transform=transform_test)\n",
    "classes = testset.classes\n",
    "testLoader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2, pin_memory=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s1nNoF7Sggat",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Plot some samples to get a sense of how they look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "wPnW3AaEDtFl",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from torchvision.utils import make_grid\n",
    "def show_batch(dl):\n",
    "  for images, label in dl:\n",
    "    fig, ax = plt.subplots(figsize=(10, 10))\n",
    "    image_grid = make_grid(images, 10)\n",
    "    plt.close()\n",
    "    writer.add_image('Data samples', image_grid)\n",
    "    break\n",
    "\n",
    "show_batch(trainLoader)\n",
    "show_batch(testLoader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fh-d9hl1gn7d",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Get working device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "ebNdxRb_gssX",
    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [],
   "source": [
    "def get_default_device():\n",
    "  return torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "\n",
    "def to_device(obj, device):\n",
    "  if isinstance(obj, (list, tuple)):\n",
    "    return [to_device(x, device) for x in obj]\n",
    "  \n",
    "  return obj.to(device, non_blocking=True)\n",
    "\n",
    "class DeviceDataLoader():\n",
    "  def __init__(self, dataloader, device):\n",
    "    self.dl = dataloader\n",
    "    self.device = device\n",
    "\n",
    "  def __iter__(self):\n",
    "    for cell in self.dl:\n",
    "      yield to_device(cell, self.device)\n",
    "  \n",
    "  def __len__(self):\n",
    "    return len(self.dl)\n",
    "  \n",
    "device = get_default_device()\n",
    "trainLoader = DeviceDataLoader(trainLoader, device)\n",
    "testLoader = DeviceDataLoader(testLoader, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CeVsuHj4jplQ",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Load the resnet18 model into a new model class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "nJ6lslK1zWzy"
   },
   "outputs": [],
   "source": [
    "class Model(nn.Module):\n",
    "    def __init__(self, _resnet18):\n",
    "        super(Model, self).__init__()\n",
    "        self.conv = _resnet18.conv1\n",
    "        self.first_block = _resnet18.layer1\n",
    "        self.second_block = _resnet18.layer2\n",
    "        self.third_block = _resnet18.layer3\n",
    "        self.fourth_block = _resnet18.layer4\n",
    "        self.max_pooling = _resnet18.maxpool\n",
    "        self.avg_pooling = _resnet18.avgpool\n",
    "        self.batchnorm = _resnet18.bn1\n",
    "        self.fc = nn.Linear(_resnet18.fc.in_features, 10)\n",
    "        self.dropout = nn.Dropout(p=0.3)\n",
    "        self.ReLU = nn.ReLU()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.softmax = nn.Softmax(dim=-1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.batchnorm(x)\n",
    "        x = self.ReLU(x)\n",
    "        x = self.max_pooling(x)\n",
    "        x = self.first_block(x)\n",
    "        x = self.second_block(x)\n",
    "        x = self.third_block(x)\n",
    "        x = self.fourth_block(x)\n",
    "        x = self.avg_pooling(x)\n",
    "        x = self.flatten(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc(x)\n",
    "        x = self.softmax(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "class ChooserModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(ChooserModel, self).__init__()\n",
    "        self.fc1 = nn.Linear(30, 10)\n",
    "        self.fc2 = nn.Linear(10, 10)\n",
    "        self.dropout = nn.Dropout(p=0.3)\n",
    "        self.ReLU = nn.ReLU()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.ReLU(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "uCJof0Yijw2C",
    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [],
   "source": [
    "# To increase certainty, I have made an ensemble of 3 models and added a \"chooser\"\n",
    "# network, simple MLP which takes the concatenated output of the ensemble and outputs \n",
    "# a final choice.\n",
    "\n",
    "for param_num, param in enumerate(resnet18.parameters()):\n",
    "  if param_num > 0.5 * len(list(resnet18.parameters())):\n",
    "    param.requires_grad = True\n",
    "  else:\n",
    "    param.requires_grad = False\n",
    "\n",
    "model_dict = {'model1': to_device(Model(resnet18), device), \n",
    "              'model2': to_device(Model(resnet18), device), \n",
    "              'model3': to_device(Model(resnet18), device),\n",
    "              'chooser': to_device(ChooserModel(), device)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DQepSFfVjyi4",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Initialize summary writer for log and define criterion and optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "uOfGjFb8j5gq",
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     "name": "#%%\n"
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   },
   "outputs": [],
   "source": [
    "epochs = 100\n",
    "max_lr = 1e-2\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "p = (set(model_dict['model1'].parameters()) |\n",
    "     set(model_dict['model2'].parameters()) |\n",
    "     set(model_dict['model3'].parameters()) |\n",
    "     set(model_dict['chooser'].parameters()))\n",
    "\n",
    "optimizer = optim.Adam(p, lr=max_lr, weight_decay=1e-5)\n",
    "scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr, epochs * len(trainLoader))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W9n1PXG1VvOJ",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Define accuracy metrics, train & test loops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "tCGlkD4oVtth"
   },
   "outputs": [],
   "source": [
    "def accuracy(predictions, labels):\n",
    "  predictions, class_id = torch.max(predictions, dim=1)\n",
    "  return torch.tensor(torch.sum(class_id == labels).item() / len(labels))\n",
    "\n",
    "def confusion_matrix(labels, predictions, epoch, categories, accuracy):\n",
    "    data = {'y_Actual': labels,\n",
    "            'y_Predicted': predictions}\n",
    "\n",
    "    df = pd.DataFrame(data, columns=['y_Actual', 'y_Predicted'])\n",
    "    confusion = pd.crosstab(df['y_Actual'], df['y_Predicted'],\n",
    "                            rownames=['Actual'], colnames=['Predicted'],\n",
    "                            margins=True)\n",
    "\n",
    "    for _key in confusion:\n",
    "        confusion[_key] = confusion[_key] / confusion[_key]['All']\n",
    "\n",
    "    heatmap = sn.heatmap(confusion.iloc[0:-1, 0:-1], annot=True,\n",
    "                         xticklabels=categories, yticklabels=categories,\n",
    "                         vmax=1, vmin=0)\n",
    "    plt.title(f'Epoch - {epoch + 1}, Test accuracy - {accuracy*100:.2f}%')\n",
    "    plt.tight_layout(pad=3.0)\n",
    "    plt.show()\n",
    "\n",
    "def train_loop(model_dict, _criterion, dataLoader, _epoch):\n",
    "  for key in model_dict:\n",
    "    model_dict[key].train()\n",
    "\n",
    "  batch_loss = []\n",
    "  print('   Training:')\n",
    "  for i, data in enumerate(tqdm(dataLoader)):      \n",
    "      inputs, labels = data\n",
    "      optimizer.zero_grad()\n",
    "\n",
    "      for ii, key in enumerate(model_dict):\n",
    "        if key != 'chooser':\n",
    "          if ii == 0:\n",
    "            outputs = model_dict[key](inputs)\n",
    "          else:\n",
    "            outputs = torch.cat([outputs, model_dict[key](inputs)], dim=1)\n",
    "      \n",
    "      outputs = model_dict['chooser'](outputs)\n",
    "\n",
    "      loss = _criterion(outputs, labels)\n",
    "      loss.backward()\n",
    "      optimizer.step()\n",
    "      scheduler.step()\n",
    "      batch_loss.append(loss)\n",
    "  \n",
    "  return torch.stack(batch_loss).mean()\n",
    "\n",
    "def eveluation_loop(model_dict, _criterion, dataLoader, _epoch):\n",
    "  for key in model_dict:\n",
    "    model_dict[key].eval()\n",
    "  \n",
    "  batch_loss = []\n",
    "  batch_accuracy = []\n",
    "  print('   Testing:')\n",
    "  for i, data in enumerate(tqdm(dataLoader)):     \n",
    "      inputs, labels = data\n",
    "      with torch.no_grad():\n",
    "        for ii, key in enumerate(model_dict):\n",
    "          if key != 'chooser':\n",
    "            if ii == 0:\n",
    "              outputs = model_dict[key](inputs)\n",
    "            else:\n",
    "              outputs = torch.cat([outputs, model_dict[key](inputs)], dim=1)\n",
    "        \n",
    "        outputs = model_dict['chooser'](outputs)\n",
    "\n",
    "        if i == 0:\n",
    "          all_labels = labels\n",
    "          all_outputs = outputs\n",
    "        else:\n",
    "          torch.cat([all_labels, labels])\n",
    "          torch.cat([all_outputs, outputs])\n",
    "\n",
    "        loss = _criterion(outputs, labels)\n",
    "\n",
    "        acc = accuracy(outputs, labels)\n",
    "        batch_accuracy.append(acc)\n",
    "        batch_loss.append(loss)\n",
    "  confusion_matrix(all_labels.tolist(), torch.argmax(all_outputs, dim=1).tolist(),\n",
    "                   _epoch, classes, torch.stack(batch_accuracy).mean() .item())\n",
    "  \n",
    "  return torch.stack(batch_loss).mean(), torch.stack(batch_accuracy).mean() \n"
   ]
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    {
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     "text": [
      "   Training:\n"
     ]
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     "text": [
      "   Testing:\n"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "epoch 1/100,    {'accuracy': 57.809996604919434, 'test_loss': 1.2897708415985107, 'train_loss': 1.6306102275848389}\n",
      "   Training:\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3517b0ad2aca4b77aecf6d249a3daebf",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": [
       "  0%|          | 0/1562 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {}
    }
   ],
   "source": [
    "results = []\n",
    "\n",
    "for epoch in tqdm(range(epochs)):\n",
    "  epoch_train_loss = train_loop(model_dict, criterion, trainLoader, epoch)\n",
    "  epoch_test_loss, epoch_accuracy = eveluation_loop(model_dict, criterion, testLoader, epoch)\n",
    "\n",
    "  result = {'accuracy': epoch_accuracy.item() * 100.,\n",
    "            'test_loss': epoch_test_loss.item(),\n",
    "            'train_loss': epoch_train_loss.item()}\n",
    "  results.append(result)\n",
    "  print(f'epoch {epoch + 1}/{epochs},    {result}')\n",
    "\n",
    "  writer.add_scalar('Loss\\Train', epoch_train_loss, epoch)\n",
    "  writer.add_scalar('Loss\\Test', epoch_test_loss, epoch)\n",
    "  writer.add_scalar('Accuracy', epoch_accuracy * 100., epoch)"
   ]
  }
 ],
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   "name": "AItransfer- Nadav.ipynb",
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