pytorch-lightning_ipynb/kfold/kfold-light-cnn-mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Sebastian Raschka\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.8.12\n",
      "IPython version      : 8.0.1\n",
      "\n",
      "torch            : 1.10.1\n",
      "pytorch_lightning: 1.5.10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p torch,pytorch_lightning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The little caveat is that it currently only supports PyTorch Lightning < 1.6.0. So, if you have 1.6.0 or newer, you'd need to downgrade:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install pytorch-lightning==1.5.10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Fold Cross-Validation Example -- Convolutional Neural Network on MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows how to apply k-fold cross-validation to deep neural networks using PyTorch Lightning and [`pl_cross`](https://github.com/SkafteNicki/pl_cross).\n",
    "\n",
    "This notebook is based on a basic PyTorch Lightning notebook at [./baseline-cnn-mnist.ipynb](baseline-cnn-mnist.ipynb). The changes are annotated and/or highlighted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Hyperparameters\n",
    "RANDOM_SEED = 1\n",
    "LEARNING_RATE = 0.001\n",
    "NUM_EPOCHS = 10\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "# Architecture\n",
    "NUM_CLASSES = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NEW: Install `pl_cross` Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncomment the following line if you don't have Git Python installed yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install gitpython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from git import Repo\n",
    "\n",
    "if not os.path.exists(\"pl_cross\"):\n",
    "    Repo.clone_from(\"https://github.com/SkafteNicki/pl_cross.git\", \"pl_cross\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncomment the following line to install the cloned repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !cd pl_cross; python setup.py installl; cd .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"mnist-pngs\"):\n",
    "    Repo.clone_from(\"https://github.com/rasbt/mnist-pngs\", \"mnist-pngs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "\n",
    "data_transforms = {\n",
    "    \"train\": transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(32),\n",
    "            transforms.RandomCrop((28, 28)),\n",
    "            transforms.ToTensor(),\n",
    "            # normalize images to [-1, 1] range\n",
    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "        ]\n",
    "    ),\n",
    "    \"test\": transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(32),\n",
    "            transforms.CenterCrop((28, 28)),\n",
    "            transforms.ToTensor(),\n",
    "            # normalize images to [-1, 1] range\n",
    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "        ]\n",
    "    ),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data.dataset import random_split\n",
    "from torchvision.datasets import ImageFolder\n",
    "\n",
    "train_dset = ImageFolder(root=\"mnist-pngs/train\", transform=data_transforms[\"train\"])\n",
    "\n",
    "train_dset, valid_dset = random_split(train_dset, lengths=[55000, 5000])\n",
    "\n",
    "test_dset = ImageFolder(root=\"mnist-pngs/test\", transform=data_transforms[\"test\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    dataset=train_dset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    drop_last=True,\n",
    "    num_workers=4,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "valid_loader = DataLoader(\n",
    "    dataset=valid_dset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    drop_last=False,\n",
    "    num_workers=4,\n",
    "    shuffle=False,\n",
    ")\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    dataset=test_dset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    drop_last=False,\n",
    "    num_workers=4,\n",
    "    shuffle=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import torchvision.utils as vutils\n",
    "\n",
    "real_batch = next(iter(train_loader))\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Training images\")\n",
    "plt.imshow(\n",
    "    np.transpose(\n",
    "        vutils.make_grid(real_batch[0][:64], padding=2, normalize=True), (1, 2, 0)\n",
    "    )\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model setup is exactly as it is before, except that the current k-fold code won't be using `validation_step`. We can still leave it in the `LightningModule` as it doesn't hurt either."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "class PyTorchCNN(torch.nn.Module):\n",
    "    def __init__(self, num_classes):\n",
    "        super().__init__()\n",
    "\n",
    "        self.num_classes = num_classes\n",
    "        self.features = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(\n",
    "                in_channels=3,\n",
    "                out_channels=8,\n",
    "                kernel_size=(3, 3),\n",
    "                stride=(1, 1),\n",
    "                padding=1,\n",
    "            ),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(\n",
    "                in_channels=8,\n",
    "                out_channels=16,\n",
    "                kernel_size=(3, 3),\n",
    "                stride=(1, 1),\n",
    "                padding=1,\n",
    "            ),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0),\n",
    "        )\n",
    "\n",
    "        self.classifier = torch.nn.Sequential(\n",
    "            torch.nn.Flatten(),\n",
    "            torch.nn.Linear(784, 128),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(128, num_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.features(x)\n",
    "        x = self.classifier(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytorch_lightning as pl\n",
    "import torchmetrics\n",
    "\n",
    "\n",
    "# LightningModule that receives a PyTorch model as input\n",
    "class LightningModel(pl.LightningModule):\n",
    "    def __init__(self, model, learning_rate):\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        # The inherited PyTorch module\n",
    "        self.model = model\n",
    "\n",
    "        # Save settings and hyperparameters to the log directory\n",
    "        # but skip the model parameters\n",
    "        self.save_hyperparameters(ignore=[\"model\"])\n",
    "\n",
    "        # Set up attributes for computing the accuracy\n",
    "        self.train_acc = torchmetrics.Accuracy()\n",
    "        self.valid_acc = torchmetrics.Accuracy()\n",
    "        self.test_acc = torchmetrics.Accuracy()\n",
    "\n",
    "    # Defining the forward method is only necessary\n",
    "    # if you want to use a Trainer's .predict() method (optional)\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    # A common forward step to compute the loss and labels\n",
    "    # this is used for training, validation, and testing below\n",
    "    def _shared_step(self, batch):\n",
    "        features, true_labels = batch\n",
    "        logits = self(features)\n",
    "        loss = torch.nn.functional.cross_entropy(logits, true_labels)\n",
    "        predicted_labels = torch.argmax(logits, dim=1)\n",
    "\n",
    "        return loss, true_labels, predicted_labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.log(\"train_loss\", loss)\n",
    "\n",
    "        # To account for Dropout behavior during evaluation\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            _, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.train_acc.update(predicted_labels, true_labels)\n",
    "        self.log(\"train_acc\", self.train_acc, on_epoch=True, on_step=False)\n",
    "        self.model.train()\n",
    "        return loss  # this is passed to the optimzer for training\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.log(\"valid_loss\", loss)\n",
    "        self.valid_acc(predicted_labels, true_labels)\n",
    "        self.log(\n",
    "            \"valid_acc\", self.valid_acc, on_epoch=True, on_step=False, prog_bar=True\n",
    "        )\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.test_acc(predicted_labels, true_labels)\n",
    "        self.log(\"test_acc\", self.test_acc, on_epoch=True, on_step=False)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
    "        return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NEW: Note that for logging, we need to import a modified version of the logger from the `pl_cross` library. Also, since the k-fold approach doesn't use `validation_step` we can't checkpoint based on teh validation set accuracy and have the `training_accuracy` instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OLD:\n",
    "# from pytorch_lightning.loggers import CSVLogger\n",
    "# NEW:\n",
    "from pl_cross.loggers import CSVLogger\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "\n",
    "torch.manual_seed(RANDOM_SEED)\n",
    "pytorch_model = PyTorchCNN(num_classes=NUM_CLASSES)\n",
    "\n",
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=LEARNING_RATE)\n",
    "\n",
    "\n",
    "# NEW: Kfold currently doesn't call model.validation_step()\n",
    "# so the valid_acc tracking does unfortunately not work, and\n",
    "# we have to change valid_acc to train_acc.\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(save_top_k=1, mode=\"max\", monitor=\"train_acc\"),  # save top 1 model\n",
    "    pl.callbacks.progress.TQDMProgressBar(refresh_rate=50),\n",
    "]\n",
    "\n",
    "logger = CSVLogger(save_dir=\"logs/\", name=\"my-kfold-model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NEW: Instead of using `pl.Trainer`, we use the `Trainer` from the `pl_cross` library, which lets us specify the number of folds as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name      | Type       | Params\n",
      "-----------------------------------------\n",
      "0 | model     | PyTorchCNN | 103 K \n",
      "1 | train_acc | Accuracy   | 0     \n",
      "2 | valid_acc | Accuracy   | 0     \n",
      "3 | test_acc  | Accuracy   | 0     \n",
      "-----------------------------------------\n",
      "103 K     Trainable params\n",
      "0         Non-trainable params\n",
      "103 K     Total params\n",
      "0.413     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py:489: UserWarning: One of given dataloaders is None and it will be skipped.\n",
      "  rank_zero_warn(\"One of given dataloaders is None and it will be skipped.\")\n",
      "Starting fold 1/5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37d6b65a74624395b7c7f411b2ff2a22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69a7132853284d84abdd36ab27edca28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Starting fold 2/5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04c49c19195f4c3b8bf5fbf08bc18470",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f604b42fcdd941f0a20183b5df7e21f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Starting fold 3/5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27ede6c5425e4bd598e382ddbb952690",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9daac84bb50a4fef9f8334d786b77d68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Starting fold 4/5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2658c92102654509a4f3ddb082f3dc58",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e063d1e7c1a041c1a9202ad8a492e500",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Starting fold 5/5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "604af2de09964cb784bac35185390338",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd418f4689ef4bcebe45f96082222631",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss_mean': tensor(0.0157), 'train_loss_std': tensor(0.0082), 'train_loss_raw': tensor([0.0139, 0.0143, 0.0181, 0.0046, 0.0274]), 'train_acc_mean': tensor(0.9892), 'train_acc_std': tensor(0.0006), 'train_acc_raw': tensor([0.9883, 0.9892, 0.9890, 0.9896, 0.9898]), 'test_acc_mean': tensor(0.9850), 'test_acc_std': tensor(0.0023), 'test_acc_raw': tensor([0.9818, 0.9836, 0.9868, 0.9875, 0.9855])}\n",
      "Training took 6.85 min in total.\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "# NEW:\n",
    "from pl_cross import Trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    num_folds=5,\n",
    "    shuffle=True,\n",
    "    # stratified=True,\n",
    "    max_epochs=NUM_EPOCHS,\n",
    "    callbacks=callbacks,\n",
    "    accelerator=\"auto\",  # Uses GPUs or TPUs if available\n",
    "    devices=\"auto\",  # Uses all available GPUs/TPUs if applicable\n",
    "    logger=logger,\n",
    "    log_every_n_steps=100,\n",
    ")\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# NEW\n",
    "kfold_results = trainer.cross_validate(\n",
    "    model=lightning_model,\n",
    "    train_dataloader=train_loader,\n",
    ")\n",
    "\n",
    "runtime = (time.time() - start_time) / 60\n",
    "print(f\"Training took {runtime:.2f} min in total.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NEW: We can now look at the different test-fold accuracies, which are returned by `trainer.cross_validate`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss_mean': tensor(0.0157), 'train_loss_std': tensor(0.0082), 'train_loss_raw': tensor([0.0139, 0.0143, 0.0181, 0.0046, 0.0274]), 'train_acc_mean': tensor(0.9892), 'train_acc_std': tensor(0.0006), 'train_acc_raw': tensor([0.9883, 0.9892, 0.9890, 0.9896, 0.9898]), 'test_acc_mean': tensor(0.9850), 'test_acc_std': tensor(0.0023), 'test_acc_raw': tensor([0.9818, 0.9836, 0.9868, 0.9875, 0.9855])}\n"
     ]
    }
   ],
   "source": [
    "print(kfold_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NEW: We could consider the cross-validation accuracy for model selection and once we are happy with the hyperparameter settings, we could use those to train a new model using the regular trainer approach as in [./baseline-cnn-mnist.ipynb](baseline-cnn-mnist.ipynb) (except that we use the best hyperparameters from k-fold cross-validation). \n",
    "\n",
    "Alternatively, we can create an ensemble model from the *k* models as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ensemble_model = trainer.create_ensemble(model=lightning_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trainer.test(model=ensemble_model, dataloaders=test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: for some reason, the `trainer.create_ensemble` is complaining about the checkpoint files, it is potentially a bug. Will update once it is fixed or I know more :)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy            : 1.22.2\n",
      "torchvision      : 0.11.2\n",
      "torch            : 1.10.1\n",
      "pytorch_lightning: 1.5.10\n",
      "torchmetrics     : 0.6.2\n",
      "matplotlib       : 3.3.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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