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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Свёрточные сети для классификации"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Type\n",
+    "\n",
+    "import torch\n",
+    "from torch import Tensor, nn\n",
+    "from torch.nn import functional as F"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Задание 1. Skip-connections (2 балла)\n",
+    "\n",
+    "Постройте архитектуру свёрточной сети, аналогичную архитектуре в примере ниже, но добавьте в неё skip-connections, то есть дополнительные рёбра в вычислительном графе, позволяющие пропускать градиент в более ранние слои напрямую, минуя очередной блок Conv2D + BatchNorm + ReLU:\n",
+    "\n",
+    "```python\n",
+    "def forward(self, x: Tensor) -> Tensor:\n",
+    "    x = x + self.block1(x)\n",
+    "    x = self.maxpool(x)\n",
+    "    x = x + self.block2(x)\n",
+    "    x = self.maxpool(x)\n",
+    "    ...\n",
+    "    x = x.adaptive_maxpool(x).flatten(1)\n",
+    "    logits = self.fc(x)\n",
+    "    return logits\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Наша верхнеуровневая архитектура будет выглядеть так:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MyResNet(nn.Module):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        block: Type[nn.Module],\n",
+    "        n_classes: int,\n",
+    "        hidden_channels: list[int] = [32, 64],\n",
+    "    ) -> None:\n",
+    "        super().__init__()\n",
+    "        # входной слой, принимающий изображение с 3-мя каналами\n",
+    "        self.in_conv = nn.Conv2d(3, hidden_channels[0], kernel_size=3, stride=1)\n",
+    "        self.relu = nn.ReLU(inplace=True)\n",
+    "\n",
+    "        # собираем свёрточные блоки, каждый задаётся кол-вом входных и выходных каналов\n",
+    "        blocks = []\n",
+    "        for c_in, c_out in zip(hidden_channels[:-1], hidden_channels[1:]):\n",
+    "            # добавляем очередной блок\n",
+    "            blocks.append(block(c_in, c_out))\n",
+    "            # добавляем Max pooling для уменьшения размерности\n",
+    "            blocks.append(nn.MaxPool2d(2, 2))\n",
+    "\n",
+    "        # собираем блоки в единый Sequential модуль для удобства\n",
+    "        self.features = nn.Sequential(*blocks)\n",
+    "        self.maxpool = nn.AdaptiveMaxPool2d(1)\n",
+    "\n",
+    "        # линейный слой для классификации\n",
+    "        self.fc = nn.Linear(hidden_channels[-1], n_classes)\n",
+    "\n",
+    "    def forward(self, x: Tensor) -> Tensor:\n",
+    "        h = self.features(self.relu(self.in_conv(x)))\n",
+    "        logits = self.fc(self.maxpool(h).flatten(1))\n",
+    "        return logits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Базовый блок, без residual connections, состоит из двух свёрток и нормализаций:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BasicBlock(nn.Module):\n",
+    "    def __init__(self, inplanes: int, planes: int) -> None:\n",
+    "        super().__init__()\n",
+    "        self.conv1 = nn.Conv2d(\n",
+    "            inplanes, planes, kernel_size=3, stride=1, padding=1, bias=False\n",
+    "        )\n",
+    "        self.bn1 = nn.BatchNorm2d(planes)\n",
+    "        self.relu = nn.ReLU(inplace=True)\n",
+    "        self.conv2 = nn.Conv2d(\n",
+    "            planes, planes, kernel_size=3, stride=1, padding=1, bias=False\n",
+    "        )\n",
+    "        self.bn2 = nn.BatchNorm2d(planes)\n",
+    "\n",
+    "    def forward(self, x: Tensor) -> Tensor:\n",
+    "        # first conv + bn + nonlinearity\n",
+    "        out = self.relu(self.bn1(self.conv1(x)))\n",
+    "        # second conv + bn\n",
+    "        out = self.bn2(self.conv2(out))\n",
+    "        # final nonlinearity\n",
+    "        out = self.relu(out)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Посмотрим на результат его применения к тензору:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 6, 32, 32])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "BasicBlock(4, 6).forward(torch.randn(3, 4, 32, 32)).shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Теперь нужно изменить этот блок, добавив в него skip-connection. Теперь в методе `forward` входной тензор `x` пойдёт по двум веткам:\n",
+    "1. как в базовом блоке, через наши всёртки и нормализации, до последней нелинейности\n",
+    "2. в обход свёрток и нормализаций\n",
+    "\n",
+    "В конце эти ветки нужно объединить через сумму. Тут есть проблема: в исходном тензоре `x` и обработанном нашим блоком `h(x)` отличается количество каналов (остальные размерности совпадают). То есть нам нужно сравнять количество каналов исходного тензора `inplanes` с количеством выходных каналов `outplanes`.\n",
+    "\n",
+    "Интуитивно, если рассматривать каждый пиксель входного тензора как вектор размера `inplanes`, в вектор размера `planes` его можно превратить домножением на матрицу размера `inplanes x planes`. Это можно сделать, создав свёрточный слой с размером кернела 1 - он и будет переводить наши пиксели в другую размерность.\n",
+    "\n",
+    "Не забудьте к сумме каналов применить нелинейность."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ResidualBlock(nn.Module):\n",
+    "    def __init__(self, inplanes: int, planes: int) -> None:\n",
+    "        super().__init__()\n",
+    "        self.conv1 = nn.Conv2d(\n",
+    "            inplanes, planes, kernel_size=3, stride=1, padding=1, bias=False\n",
+    "        )\n",
+    "        self.bn1 = nn.BatchNorm2d(planes)\n",
+    "        self.relu = nn.ReLU(inplace=True)\n",
+    "        self.conv2 = nn.Conv2d(\n",
+    "            planes, planes, kernel_size=3, stride=1, padding=1, bias=False\n",
+    "        )\n",
+    "        self.bn2 = nn.BatchNorm2d(planes)\n",
+    "\n",
+    "        # добавьте свёртку 1x1 для изменения кол-ва каналов входного тензора\n",
+    "        self.conv3 = nn.Conv2d(\n",
+    "            inplanes, planes, kernel_size=1, stride=1, bias=False\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x: Tensor) -> Tensor:\n",
+    "        # сохраним входной тензор на будущее\n",
+    "        identity = x\n",
+    "\n",
+    "        # ВАШ ХОД\n",
+    "\n",
+    "        # first conv + bn + nonlinearity\n",
+    "        out = self.relu(self.bn1(self.conv1(x)))\n",
+    "        # second conv + bn\n",
+    "        out = self.bn2(self.conv2(out))\n",
+    "        # final nonlinearity\n",
+    "        out = self.relu(out)\n",
+    "\n",
+    "        out = out + self.conv3(x)\n",
+    "\n",
+    "        out = self.relu(out)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверим размеры:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert ResidualBlock(4, 6).forward(torch.randn(3, 4, 32, 32)).shape == torch.Size(\n",
+    "    [3, 6, 32, 32]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверим, что модель выдаёт тензор ожидаемого размера:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 7])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MyResNet(ResidualBlock, 7, hidden_channels=[16, 32, 64, 128]).forward(\n",
+    "    torch.randn(3, 3, 32, 32)\n",
+    ").shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Теперь мы можем создавать модели разного размера, в том числе достаточно большие и глубокие, чтобы хорошо классифицировать изображения из датасета CIFAR-10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "151047"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(\n",
+    "    p.numel()\n",
+    "    for p in MyResNet(ResidualBlock, 7, hidden_channels=[16, 32, 64, 64]).parameters()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Задание 2. Обучение `MyResNet` с использованием Lightning (5 баллов)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ваша задача: добиться 80% точности на валидационной выборке с вашей реализацией `MyResNet`.\n",
+    "\n",
+    "После окончания обучения используйте метод `Trainer.validate` для вывода ваших метрик с удачного чекпоинта модели.\n",
+    "\n",
+    "NB: вызывайте `Trainer.validate` везде, где в задании требуется достичь какой-то точности\n",
+    "\n",
+    "\n",
+    "Советы:\n",
+    "- По умолчанию Lightning сохраняет только последний чекпоинт, так что вам может потребоваться `lightning.callbacks.ModelCheckpoint`, чтобы сохранять лучший чекпоинт в процессе обучения.\n",
+    "\n",
+    "- Чтобы добиться нужной точности, ваша модель должна быть достаточно глубокой, ориентируйтесь на 4-5 блоков.\n",
+    "\n",
+    "- Используйте tensorboard, чтобы следить за динамикой обучения. Если заметите переобучение — подключайте регуляризацию. Большая модель с регуляризацией обычно лучше маленькой модели без неё.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lightning as L\n",
+    "import torch\n",
+    "import torchmetrics\n",
+    "from torch import Tensor, nn\n",
+    "from torch.nn import functional as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Callable\n",
+    "\n",
+    "from lightning.pytorch.utilities.types import EVAL_DATALOADERS, TRAIN_DATALOADERS\n",
+    "from PIL.Image import Image\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import albumentations as A\n",
+    "from albumentations.pytorch import ToTensorV2\n",
+    "\n",
+    "\n",
+    "class Datamodule(L.LightningDataModule):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        batch_size: int,\n",
+    "        train_transform: Callable[[Image], Tensor] = transforms.ToTensor(),\n",
+    "        val_transform: Callable[[Image], Tensor] = transforms.ToTensor(),\n",
+    "        num_workers: int = 0,\n",
+    "    ):\n",
+    "        super().__init__()\n",
+    "        self.batch_size = batch_size\n",
+    "        self.train_transform = train_transform\n",
+    "        self.val_transform = val_transform\n",
+    "        self.num_workers = num_workers\n",
+    "\n",
+    "    def prepare_data(self) -> None:\n",
+    "        pass\n",
+    "\n",
+    "    def setup(self, stage: str) -> None:\n",
+    "        if stage == \"fit\":\n",
+    "            self.train_dataset = datasets.CIFAR10(\n",
+    "                \"data\",\n",
+    "                train=True,\n",
+    "                download=True,\n",
+    "                transform=self.train_transform,\n",
+    "            )\n",
+    "            self.val_dataset = datasets.CIFAR10(\n",
+    "                \"data\",\n",
+    "                train=False,\n",
+    "                download=True,\n",
+    "                transform=self.val_transform,\n",
+    "            )\n",
+    "        elif stage == \"validate\":\n",
+    "            self.val_dataset = datasets.CIFAR10(\n",
+    "                \"data\",\n",
+    "                train=False,\n",
+    "                download=True,\n",
+    "                transform=self.val_transform,\n",
+    "            )\n",
+    "        else:\n",
+    "            raise NotImplementedError\n",
+    "\n",
+    "    def train_dataloader(self) -> TRAIN_DATALOADERS:\n",
+    "        return DataLoader(\n",
+    "            self.train_dataset,\n",
+    "            batch_size=self.batch_size,\n",
+    "            shuffle=True,\n",
+    "            num_workers=self.num_workers,\n",
+    "        )\n",
+    "\n",
+    "    def val_dataloader(self) -> EVAL_DATALOADERS:\n",
+    "        return DataLoader(\n",
+    "            self.val_dataset,\n",
+    "            batch_size=self.batch_size,\n",
+    "            shuffle=False,\n",
+    "            num_workers=self.num_workers,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datamodule = Datamodule(batch_size=128, num_workers=0)\n",
+    "datamodule.setup(stage=\"fit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Any\n",
+    "from lightning.pytorch.utilities.types import STEP_OUTPUT\n",
+    "import torchmetrics.classification\n",
+    "\n",
+    "\n",
+    "class Lit(L.LightningModule):\n",
+    "    def __init__(self, model: nn.Module, learning_rate: float, milestones:[int] = []) -> None:\n",
+    "        super().__init__()\n",
+    "        self.save_hyperparameters()\n",
+    "        self.model = model\n",
+    "        self.learning_rate = learning_rate\n",
+    "        self.train_metric = torchmetrics.Accuracy(task=\"multiclass\", num_classes=10)\n",
+    "        self.val_metric = torchmetrics.Accuracy(task=\"multiclass\", num_classes=10)\n",
+    "        self.milestones = milestones\n",
+    "\n",
+    "    def training_step(\n",
+    "        self, batch: tuple[Tensor, Tensor], batch_idx: int\n",
+    "    ) -> STEP_OUTPUT:\n",
+    "        x, y = batch\n",
+    "        y_hat = self.model(x)\n",
+    "        loss = F.cross_entropy(y_hat, y)\n",
+    "        self.log(\"train_loss\", loss, on_epoch=True, on_step=False)\n",
+    "        self.train_metric.update(y_hat, y)\n",
+    "        self.log(\"train_acc\", self.train_metric.compute(), on_epoch=True, on_step=False)\n",
+    "\n",
+    "        return loss\n",
+    "\n",
+    "    def validation_step(\n",
+    "        self, batch: tuple[Tensor, Tensor], batch_idx: int\n",
+    "    ) -> STEP_OUTPUT | None:\n",
+    "        x, y = batch\n",
+    "        y_hat = self.model(x)\n",
+    "        loss = F.cross_entropy(y_hat, y)\n",
+    "        self.log(\"val_loss\", loss, on_epoch=True, on_step=False)\n",
+    "        self.val_metric.update(y_hat, y)\n",
+    "        self.log(\"val_acc\", self.val_metric.compute(), on_epoch=True, on_step=False)\n",
+    "\n",
+    "        return loss;\n",
+    "\n",
+    "    def configure_optimizers(self) -> dict[str, Any]:\n",
+    "        optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, self.model.parameters()), lr=self.learning_rate, momentum=0.9, weight_decay=1e-3)\n",
+    "        return {\n",
+    "            \"optimizer\": optimizer,\n",
+    "            \"lr_scheduler\": torch.optim.lr_scheduler.MultiStepLR(\n",
+    "                optimizer, milestones=self.milestones, gamma=0.1\n",
+    "            ),\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import cast\n",
+    "\n",
+    "from lightning.pytorch.callbacks import Callback\n",
+    "from lightning.pytorch.utilities.types import STEP_OUTPUT\n",
+    "from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "from lightning.pytorch.loggers import TensorBoardLogger"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "/home/mperikov/dl/dl-mcs/.venv/lib/python3.13/site-packages/lightning/pytorch/utilities/parsing.py:210: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "callbacks = [\n",
+    "    ModelCheckpoint(\n",
+    "        filename=\"best_vall_acc\",\n",
+    "        monitor=\"val_acc\",\n",
+    "        mode=\"max\",\n",
+    "        save_top_k=1,\n",
+    "        dirpath=\"checkpoints/\",\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "trainer = L.Trainer(\n",
+    "    accelerator=\"auto\",\n",
+    "    max_epochs=75,\n",
+    "    limit_train_batches=100,\n",
+    "    limit_val_batches=100,\n",
+    "    logger=TensorBoardLogger(save_dir=\".\"),\n",
+    "    callbacks=callbacks,\n",
+    ")\n",
+    "lit_module = Lit(\n",
+    "    model=MyResNet(ResidualBlock, 10, hidden_channels=[64, 128, 256, 256, 512]), learning_rate=0.01, milestones=[50]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "\n",
+      "  | Name         | Type               | Params | Mode \n",
+      "------------------------------------------------------------\n",
+      "0 | model        | MyResNet           | 6.1 M  | train\n",
+      "1 | train_metric | MulticlassAccuracy | 0      | train\n",
+      "2 | val_metric   | MulticlassAccuracy | 0      | train\n",
+      "------------------------------------------------------------\n",
+      "6.1 M     Trainable params\n",
+      "0         Non-trainable params\n",
+      "6.1 M     Total params\n",
+      "24.294    Total estimated model params size (MB)\n",
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+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "data": {
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+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ac9a806b7574e15b069d2f59248c4ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b073b1e00e3440a9b2a5650629d2ea6",
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+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5db5084a884f4f21979b526dedaed176",
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+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "def863cf7e8e4d44bd718a918170d585",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51815f9bc4924dfc9f4520cda7888759",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35618fad24794a5683619afed92a716b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47673602c4a84a0799e88f089e20dd13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08068b59503641db89e15b4d543bec77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9cc80864da4047dea3e9a65b780764fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "029e23e8b4b44564ac43ab9afcd905c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9c70bc733cf4d5da658222abf5d5299",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=75` reached.\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer.fit(\n",
+    "    model=lit_module,\n",
+    "    datamodule=datamodule,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Restoring states from the checkpoint path at checkpoints/best_vall_acc.ckpt\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "Loaded model weights from the checkpoint at checkpoints/best_vall_acc.ckpt\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd0c0aa28ab04dcf9cd64764120911e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "     Validate metric           DataLoader 0\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "         val_acc             0.814128577709198\n",
+      "        val_loss            0.5167098045349121\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'val_loss': 0.5167098045349121, 'val_acc': 0.814128577709198}]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_checkpoint_path = Path(\"checkpoints/best_vall_acc.ckpt\")\n",
+    "trainer.validate(\n",
+    "    model=lit_module,\n",
+    "    datamodule=datamodule,\n",
+    "    ckpt_path=best_checkpoint_path,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Задание 3. Добавление аугментаций (1 балл + 2 балла за точность на валидации более 85%)\n",
+    "\n",
+    "Добавьте к обучающему датасету аугментации - случайные трансформации входных данных. Для этого можно использовать `torchvision.transforms` и `albumentations`.\n",
+    "\n",
+    "С `torchvision.transforms` совсем просто: вам нужно будет при создании `Datamodule` из практики по `lightning` указать вместо\n",
+    "\n",
+    "```python\n",
+    "transform = transforms.ToTensor()\n",
+    "```\n",
+    "композицию трансформаций:\n",
+    "\n",
+    "```python\n",
+    "transform = transforms.Compose([\n",
+    "    transforms.RandomHorizontalFlip(),  # случайное зеркальное отражение\n",
+    "    ...\n",
+    "    transforms.ToTensor(),\n",
+    "])\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В пакете `albumentations` аугментаций значительно больше:\n",
+    "\n",
+    "![albumentations](https://albumentations.ai/_next/image/?url=%2Fassets%2Ftop_image.webp&w=1080&q=75)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aug_transform = transforms.Compose([\n",
+    "    transforms.RandomHorizontalFlip(),            # случайное горизонтальное отражение\n",
+    "    transforms.RandomRotation(15),                # случайная ротация до 15 градусов\n",
+    "    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1), # изменение оттенков цвета\n",
+    "    transforms.ToTensor(),\n",
+    "])\n",
+    "\n",
+    "datamodule = Datamodule(batch_size=128, num_workers=0, train_transform=aug_transform)\n",
+    "datamodule.setup(stage=\"fit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "HPU available: False, using: 0 HPUs\n"
+     ]
+    }
+   ],
+   "source": [
+    "callbacks = [\n",
+    "    ModelCheckpoint(\n",
+    "        filename=\"best_vall_acc_aug\",\n",
+    "        monitor=\"val_acc\",\n",
+    "        mode=\"max\",\n",
+    "        save_top_k=1,\n",
+    "        dirpath=\"checkpoints/\",\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "trainer = L.Trainer(\n",
+    "    accelerator=\"auto\",\n",
+    "    max_epochs=150,\n",
+    "    limit_train_batches=100,\n",
+    "    limit_val_batches=100,\n",
+    "    logger=TensorBoardLogger(save_dir=\".\"),\n",
+    "    callbacks=callbacks,\n",
+    ")\n",
+    "lit_module = Lit(\n",
+    "    model=MyResNet(ResidualBlock, 10, hidden_channels=[64, 128, 256, 256, 512]), learning_rate=0.01, milestones = [90, 140]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "\n",
+      "  | Name         | Type               | Params | Mode \n",
+      "------------------------------------------------------------\n",
+      "0 | model        | MyResNet           | 6.1 M  | train\n",
+      "1 | train_metric | MulticlassAccuracy | 0      | train\n",
+      "2 | val_metric   | MulticlassAccuracy | 0      | train\n",
+      "------------------------------------------------------------\n",
+      "6.1 M     Trainable params\n",
+      "0         Non-trainable params\n",
+      "6.1 M     Total params\n",
+      "24.294    Total estimated model params size (MB)\n",
+      "40        Modules in train mode\n",
+      "0         Modules in eval mode\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e60b3dcfc8c4d98bce79cf3e342d18f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |                                                                                | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c30125b2a0f4ce6af66e69616450acb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |                                                                                       | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f3a8199c2e8442f8d5a525a0090e7ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae6427f95c964ec9a65997dbd6866e41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17a970a1222847dabb40aac3373c8d7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c94c10a68e5046d691096c70eb7b2ab4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62eef2f5ac7147cb88f4e7dce1a445bc",
+       "version_major": 2,
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+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b3baaf4839144bdb06c463f5d33dd80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee723bd3259d449383efac8fe9664fad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "Validation: |                                                                                     | 0/? [00:00…"
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+       "Validation: |                                                                                     | 0/? [00:00…"
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+    {
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+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
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+    {
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+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
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+    },
+    {
+     "data": {
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+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "635c8641722643f4a6bbdbe6fef2ced1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc21542c562c4a6ca52cd13b93d9100c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=150` reached.\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer.fit(\n",
+    "    model=lit_module,\n",
+    "    datamodule=datamodule,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Restoring states from the checkpoint path at checkpoints/best_vall_acc_aug.ckpt\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "Loaded model weights from the checkpoint at checkpoints/best_vall_acc_aug.ckpt\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c90736fd7b8745c3822317216c6f191e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "     Validate metric           DataLoader 0\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "         val_acc            0.8628036379814148\n",
+      "        val_loss            0.27362456917762756\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'val_loss': 0.27362456917762756, 'val_acc': 0.8628036379814148}]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_checkpoint_path = Path(\"checkpoints/best_vall_acc_aug.ckpt\")\n",
+    "trainer.validate(\n",
+    "    model=lit_module,\n",
+    "    datamodule=datamodule,\n",
+    "    ckpt_path=best_checkpoint_path,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Задание 4. Использование предобученной модели (4 балла)\n",
+    "\n",
+    "Теперь мы научимся использовать модели, обученные на других задачах\n",
+    "\n",
+    "Ваша задача: добиться 90% точности на тестовой выборке CIFAR-10. Постарайтесь уложиться модель с ~5 млн параметров"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В `torchvision.models` есть много реализованных архитектур, размером которых можно удобно управлять. Например, ниже можно создать крошечную версию модели `MobileNetV2`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "46322"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from torchvision.models import MobileNetV2\n",
+    "\n",
+    "mobilenet = MobileNetV2(\n",
+    "    num_classes=10,\n",
+    "    width_mult=0.4,\n",
+    "    inverted_residual_setting=[\n",
+    "        # t, c, n, s\n",
+    "        [1, 16, 1, 1],\n",
+    "        [3, 24, 2, 2],\n",
+    "        [3, 32, 3, 2],\n",
+    "    ],\n",
+    "    dropout=0.2,\n",
+    ")\n",
+    "\n",
+    "sum([param.numel() for param in mobilenet.parameters()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Но кроме архитектуры модели, мы также можем скачать веса, полученные при обучении на каком-то датасете. Например, для нашей задачи можно использовать предобучение на самом известном датасете для классификации изображений - ImageNet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5288548"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from torchvision.models.efficientnet import EfficientNet_B0_Weights, efficientnet_b0\n",
+    "\n",
+    "# создаём EfficientNet с весами, полученными на ImageNet\n",
+    "weights = EfficientNet_B0_Weights.IMAGENET1K_V1\n",
+    "efficientnet = efficientnet_b0(weights=weights)\n",
+    "sum([param.numel() for param in efficientnet.parameters()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Указание 1.** С использованием модели в исходном виде есть проблема: в ImageNet 1000 классов, а у нас только 10. Поэтому в предобученной модели нужно будет полностью заменить последний линейный слой, который даёт распределение вероятностей классов. Это можно сделать уже в готовом объекте модели, переназначив атрибут.\n",
+    "\n",
+    "Подсказка: в `efficientnet_b0` линейный слой находится в атрибуте `classifier` \n",
+    "\n",
+    "\n",
+    "**Указание 2.** Все слои, кроме нескольких последних (может быть, только последнего) мы можем заморозить, то есть сделать значения параметров в них неизменными. Это позволит и сохранить способность модели выделять полезные низкоуровневые признаки (она научилась этому на ImageNet), и существенно ускорить дообучение.\n",
+    "\n",
+    "\n",
+    "Чтобы заморозить параметры, нужно всего лишь отключить для них расчёт градиентов. Вернитесь к первой практике, чтобы вспомнить, как это можно сделать. Нам подойдёт самый простой способ с `.requires_grad`.\n",
+    "\n",
+    "Подсказка: в `efficientnet_b0` свёрточные слои находятся в атрибуте `features` "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Указание 3.** Предобученные модели на ImageNet ожидают специальным образом трансформированные изображения:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Поэтому эти трансформации нужно будет передать в датамодуль (как мы делали с аугментациями)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ВАШ ХОД: Обучите модель и выведите результат метода validate на удачном чекпоинте"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ImageClassification(\n",
+       "    crop_size=[224]\n",
+       "    resize_size=[256]\n",
+       "    mean=[0.485, 0.456, 0.406]\n",
+       "    std=[0.229, 0.224, 0.225]\n",
+       "    interpolation=InterpolationMode.BICUBIC\n",
+       ")"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights.transforms()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "efficientnet.classifier = nn.Sequential(\n",
+    "    nn.Dropout(p=0.2, inplace=True),\n",
+    "    nn.Linear(1280, 256, bias=False),\n",
+    "    nn.BatchNorm1d(256),\n",
+    "    nn.ReLU(),\n",
+    "    nn.Linear(256, 128, bias=False),\n",
+    "    nn.BatchNorm1d(128),\n",
+    "    nn.ReLU(),\n",
+    "    nn.Linear(128, 10),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for param in efficientnet.features.parameters():\n",
+    "    param.requires_grad = False\n",
+    "for param in efficientnet.classifier.parameters():\n",
+    "    param.requires_grad = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datamodule = Datamodule(batch_size=128, num_workers=0, train_transform=weights.transforms(), val_transform=weights.transforms())\n",
+    "datamodule.setup(stage=\"fit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "HPU available: False, using: 0 HPUs\n"
+     ]
+    }
+   ],
+   "source": [
+    "callbacks = [\n",
+    "    ModelCheckpoint(\n",
+    "        filename=\"best_vall_acc_eff\",\n",
+    "        monitor=\"val_acc\",\n",
+    "        mode=\"max\",\n",
+    "        save_top_k=1,\n",
+    "        dirpath=\"checkpoints/\",\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "trainer = L.Trainer(\n",
+    "    accelerator=\"auto\",\n",
+    "    max_epochs=75,\n",
+    "    limit_train_batches=100,\n",
+    "    limit_val_batches=100,\n",
+    "    logger=TensorBoardLogger(save_dir=\".\"),\n",
+    "    callbacks=callbacks,\n",
+    ")\n",
+    "lit_module = Lit(\n",
+    "    model=efficientnet, learning_rate=0.01, milestones=[20]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "\n",
+      "  | Name         | Type               | Params | Mode \n",
+      "------------------------------------------------------------\n",
+      "0 | model        | EfficientNet       | 4.4 M  | train\n",
+      "1 | train_metric | MulticlassAccuracy | 0      | train\n",
+      "2 | val_metric   | MulticlassAccuracy | 0      | train\n",
+      "------------------------------------------------------------\n",
+      "362 K     Trainable params\n",
+      "4.0 M     Non-trainable params\n",
+      "4.4 M     Total params\n",
+      "17.481    Total estimated model params size (MB)\n",
+      "345       Modules in train mode\n",
+      "0         Modules in eval mode\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |                                                                                | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |                                                                                       | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
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+       "Validation: |                                                                                     | 0/? [00:00…"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=75` reached.\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer.fit(\n",
+    "    model=lit_module,\n",
+    "    datamodule=datamodule,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Restoring states from the checkpoint path at checkpoints/best_vall_acc_eff.ckpt\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "Loaded model weights from the checkpoint at checkpoints/best_vall_acc_eff.ckpt\n"
+     ]
+    },
+    {
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+      "text/plain": [
+       "Validation: |                                                                                     | 0/? [00:00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "     Validate metric           DataLoader 0\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "         val_acc            0.8380674123764038\n",
+      "        val_loss            0.4456300735473633\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'val_loss': 0.4456300735473633, 'val_acc': 0.8380674123764038}]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_checkpoint_path = Path(\"checkpoints/best_vall_acc_eff.ckpt\")\n",
+    "trainer.validate(\n",
+    "    model=lit_module,\n",
+    "    datamodule=datamodule,\n",
+    "    ckpt_path=best_checkpoint_path,\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
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