{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "JEFYtxdXQP1s"
},
"source": [
"# Введение в анализ данных\n",
"\n",
"\n",
"## Компьютерное зрение & генеративные модели\n"
]
},
{
"cell_type": "code",
"source": [
"!pip install -q torchinfo"
],
"metadata": {
"id": "O750g528ibDu"
},
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import time\n",
"import warnings\n",
"from collections import defaultdict\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"import torch\n",
"from torch import nn\n",
"import torchvision\n",
"from torchvision import transforms\n",
"from torchinfo import summary # не забудьте выполнить pip install torchinfo\n",
"\n",
"from IPython.display import clear_output\n",
"\n",
"sns.set(font_scale=1, style=\"darkgrid\", palette=\"Set2\")\n",
"warnings.simplefilter(\"ignore\")\n",
"\n",
"device = f\"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"print(device)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qfLlh46sxmBD",
"outputId": "d8602886-676b-49bd-f310-ce6d8a619366"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"cuda\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CKuaI37x8hjJ"
},
"source": [
"## 1. Классификация изображений"
]
},
{
"cell_type": "markdown",
"source": [
"На [прошлом семинаре](https://miptstats.github.io/courses/ad_fivt/nn_simple_examples.html) мы поняли, как использовать готовые модули из Pytorch и опробовали слои `nn.Linear`, `nn.ReLU` для создания полносвязной сети. В этом ноутбуке мы продолжим работать с Pytorch. Будем решать задачу классификации и сравним 2 подхода: полносвязная сеть (fully-connected NN или MLP) и сверточная сеть (CNN). Теорию можно посмотреть в [презентации](https://miptstats.github.io/courses/ad_fivt/lecture5_1.pdf).\n",
"\n",
"Чтобы все корректно отработало, этот ноутбук, как и в прошлый раз, нужно запускать в той среде, где есть графический процессор GPU. Бесплатно воспользоваться GPU можно в Google Colab и [Kaggle](https://www.kaggle.com/). Однако учтите, что на данный момент доступ к GPU ограничен работой в несколько часов в сутки. Для того, чтобы подключиться к GPU в Colab, зайдите в меню `Среда выполнения`, выберите опцию `Сменить среду выполнения`. В списке аппаратных ускорителей выберите GPU."
],
"metadata": {
"id": "AgKanCbjbT0N"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "CcJGk_pfaiLx"
},
"source": [
"### 1.1. Датасет CIFAR10\n",
"\n",
"Датасет состоит из 60 000 картинок размера 32х32х3 из 10 разных классов.\n",
"\n",
"50 000 — обучающая выборка, 10 000 — тестовая. Пример картинок из датасета:"
]
},
{
"cell_type": "markdown",
"source": [
""
],
"metadata": {
"id": "Hqi0HUt732v9"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "0PG-q-GYmBk4"
},
"source": [
"Загрузим его из коллекции датасетов библиотеки `torchvision` в RAM."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2022-11-29T01:58:56.474352Z",
"iopub.status.busy": "2022-11-29T01:58:56.473962Z",
"iopub.status.idle": "2022-11-29T01:59:15.441424Z",
"shell.execute_reply": "2022-11-29T01:59:15.439993Z",
"shell.execute_reply.started": "2022-11-29T01:58:56.474314Z"
},
"id": "pUylw7Ip3hG5",
"outputId": "fbc8da73-589b-4ba5-a0d2-76c14cef2563"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar/cifar-10-python.tar.gz\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 170498071/170498071 [00:03<00:00, 44469408.55it/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Extracting ./cifar/cifar-10-python.tar.gz to ./cifar\n",
"Files already downloaded and verified\n"
]
}
],
"source": [
"# Данные для обучения\n",
"train_dataset = torchvision.datasets.CIFAR10(\n",
" root=\"./cifar\", download=True, train=True, transform=transforms.ToTensor()\n",
")\n",
"\n",
"# Данные для тестирования\n",
"val_dataset = torchvision.datasets.CIFAR10(\n",
" root=\"./cifar\", download=True, train=False, transform=transforms.ToTensor()\n",
")\n",
"\n",
"# Классы объектов в датасете\n",
"classes = (\n",
" \"plane\",\n",
" \"car\",\n",
" \"bird\",\n",
" \"cat\",\n",
" \"deer\",\n",
" \"dog\",\n",
" \"frog\",\n",
" \"horse\",\n",
" \"ship\",\n",
" \"truck\",\n",
")"
]
},
{
"cell_type": "markdown",
"source": [
"Отметим, что датасеты из torchvision являются instance типа `torch.utils.data.Dataset`. Это позволяет использовать их с удобными инструментами из `torch.utils.data`."
],
"metadata": {
"id": "I5o9u8fLa7mr"
}
},
{
"cell_type": "code",
"source": [
"isinstance(train_dataset, torch.utils.data.Dataset)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8oWnnVcJ23-Q",
"outputId": "730593f9-f721-43fe-9247-60eed8f07923"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"source": [
"Проверим размеры датасетов."
],
"metadata": {
"id": "JsUyJpRJ0WG8"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2U-Pb8uzKUCU",
"outputId": "fd523b4d-b939-4903-8588-7414e6a57b32"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(50000, 10000)"
]
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"len(train_dataset), len(val_dataset)"
]
},
{
"cell_type": "markdown",
"source": [
"Визуализируем по картинке из train и val датасета."
],
"metadata": {
"id": "yYVNouE00YtN"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 217
},
"id": "TqAHbaVwL0tR",
"outputId": "79783b21-6286-44f3-f89e-2472d58f8abf"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Размер картинки:torch.Size([3, 32, 32])\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# берем 2 рандомных индекса\n",
"train_idx, val_idx = np.random.randint(0, 10000, 2)\n",
"print(f\"Размер картинки:{train_dataset[0][0].shape}\")\n",
"\n",
"# визуализируем\n",
"plt.figure(figsize=(4, 2))\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(train_dataset[train_idx][0].permute(1, 2, 0))\n",
"plt.title(f\"train[{train_idx}]: {classes[train_dataset[train_idx][1]]}\")\n",
"plt.axis(\"off\")\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.imshow(val_dataset[val_idx][0].permute(1, 2, 0))\n",
"plt.title(f\"val[{val_idx}]: {classes[val_dataset[val_idx][1]]}\")\n",
"plt.axis(\"off\");"
]
},
{
"cell_type": "markdown",
"source": [
"**Data Loader / Генератор батчей**\n",
"\n",
"Заметим, что в этот раз данных очень много, и прогонять весь датасет через модель за раз — плохая идея. Гораздо лучше тренировать модель постепенно, небольшими кусочками датасета (батчами). Нам нужен *генератор батчей*.\n",
"\n",
"Для этого на pytorch есть универсальный класс [`torch.utils.data.DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) — гибкий генератор батчей. Он поддерживает разные типы датасетов, перемешивание данных при семплировании, многопроцессорность.\n",
"\n",
"**Важные аргументы:**\n",
"\n",
"* `dataset` — объект типа [`torch.utils.data.Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset) или [`torch.utils.data.IterableDataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset), из которого будем семплировать.\n",
"* `batch_size` (int) — размер батча\n",
"* `shuffle` (bool) — перемешивать ли данные\n",
"* `num_workers` (int) — количество параллельных процессов. Ускоряет работу, даст предупреждение, если указать больше workers, чем допустимо на вашей системе. Установите значение -1, чтобы использовать максимально возможное количество.\n",
"\n",
"**Использование:**\n",
"```\n",
"for X_batch, y_batch in dataloader:\n",
" <...>\n",
"```\n",
"Здесь `X_batch`, `y_batch` — объекты размера `batch_size`. Цикл проходит по всему датасету в определенном порядке, пока не закончится объекты в датасете. То есть каждый объект будет возвращен ровно 1 раз.\n",
"\n",
"> Обучающую выборку принято передавать в перемешанном (`shuffle=True`) виде, это увеличивает обобщающую способность модели и уменьшает переобучение.\n",
"\n",
"> Тестовую выборку не перемешивают! Ведь обучение модели уже завершено, и порядок данных не влияет на результат.\n"
],
"metadata": {
"id": "30jlke600lOX"
}
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2022-11-29T01:59:15.447185Z",
"iopub.status.busy": "2022-11-29T01:59:15.446287Z",
"iopub.status.idle": "2022-11-29T01:59:15.460598Z",
"shell.execute_reply": "2022-11-29T01:59:15.459344Z",
"shell.execute_reply.started": "2022-11-29T01:59:15.447129Z"
},
"id": "Rmk1CD7C36e1"
},
"outputs": [],
"source": [
"batch_size = 64\n",
"\n",
"train_batch_gen = torch.utils.data.DataLoader(\n",
" train_dataset, batch_size=batch_size, shuffle=True\n",
")\n",
"val_batch_gen = torch.utils.data.DataLoader(\n",
" val_dataset, batch_size=batch_size, shuffle=False\n",
")"
]
},
{
"cell_type": "markdown",
"source": [
"### 1.2. Функции для обучения модели"
],
"metadata": {
"id": "0Ze5Zio6r_FS"
}
},
{
"cell_type": "markdown",
"source": [
"Стандартный цикл обучения на Pytorch:\n",
"\n",
"```\n",
"for i in range(num_epochs):\n",
" y_pred = model(x) # forward pass\n",
" loss = loss_function(y_pred, y) # вычисление лосса\n",
" loss.backward() # backward pass\n",
" optimizer.step() # шаг оптимизации\n",
" optimizer.zero_grad() # зануляем градиенты\n",
"```\n",
"\n",
"Такой цикл мы использовали в прошлый раз. В этот раз мы будем обучать несколько моделей, а также визуализировать историю. Для этого удобно обернуть все в функцию `train`. Она записывает лосс и метрики в `history`, измеряет время каждой эпохи, на каждой эпохе проводит два этапа:\n",
"1. Обучение модели — полный проход по `train` датасету, подсчет лосса и точности на каждой итерации. \n",
"2. Валидация модели — полный проход по `val` датасету, подсчет лосса и точности на каждой итерации.\n",
"\n",
"> Бывают слои, поведение которых отличается при обучении и тестировании. Переключать режим модели можно с помощью `model.train(True)` и `model.train(False)` (эквивалентно `model.eval()`). Такие слои появятся на следующих курсах, но приучиться менять режим стоит сразу.\n",
"\n",
"> Для ускорения работы и избежания ошибок в валидации принято использовать `with torch.no_grad():`. Эта обертка говорит Pytorch, что сейчас градиенты считать не надо."
],
"metadata": {
"id": "fCZo8RgZbHCL"
}
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2022-11-29T01:59:15.494539Z",
"iopub.status.busy": "2022-11-29T01:59:15.491044Z",
"iopub.status.idle": "2022-11-29T01:59:15.531239Z",
"shell.execute_reply": "2022-11-29T01:59:15.530128Z",
"shell.execute_reply.started": "2022-11-29T01:59:15.494474Z"
},
"id": "hW-bKkXc6Ipp"
},
"outputs": [],
"source": [
"def print_epoch(epoch, num_epochs, history, t):\n",
" \"\"\"\n",
" Функция для вывода информации про эпоху.\n",
" :param epoch: номер эпохи\n",
" :param num_epochs: общее количество эпох\n",
" :param history: (dict) accuracy и loss на обучении и валидации (\"история\" обучения)\n",
" :param t: время эпохи в секундах\n",
" \"\"\"\n",
" clear_output(wait=True)\n",
" print(\"Epoch {} of {} took {:.3f} s\".format(epoch + 1, num_epochs, t))\n",
" print(\" training loss: \\t{:.6f}\".format(history[\"loss\"][\"train\"][-1]))\n",
" print(\" validation loss: \\t{:.6f}\".format(history[\"loss\"][\"val\"][-1]))\n",
" print(\n",
" \" training accuracy: \\t\\t\\t{:.2f} %\".format(\n",
" history[\"acc\"][\"train\"][-1] * 100\n",
" )\n",
" )\n",
" print(\n",
" \" validation accuracy: \\t\\t\\t{:.2f} %\".format(\n",
" history[\"acc\"][\"val\"][-1] * 100\n",
" )\n",
" )\n",
"\n",
"\n",
"def update_history(history, loss, acc, num_batches, mode):\n",
" \"\"\"\n",
" Функция для сохранения лосса и точности в историю.\n",
" :param history: (dict) accuracy и loss на обучении и валидации (\"история\" обучения)\n",
" :param loss: сумма лосса за весь батч\n",
" :param acc: сумма точности за весь батч\n",
" :param num_batches: общее количество батчей\n",
" :param mode: train или val\n",
" \"\"\"\n",
" # Подсчитываем лоссы и сохраняем в \"историю\"\n",
" loss /= num_batches\n",
" acc /= num_batches\n",
" history[\"loss\"][mode].append(loss)\n",
" history[\"acc\"][mode].append(acc)\n",
"\n",
"\n",
"def get_batch_loss(\n",
" X_batch, y_batch, model, criterion, current_loss, current_acc\n",
"):\n",
" \"\"\"\n",
" Функция для подсчета лосса (без backward pass).\n",
" :param X_batch: батч картиок X\n",
" :param y_batch: батч меток y\n",
" :param model: модель для получения логитов\n",
" :param criterion: функция потерь\n",
" :param current_loss: текущий суммарный лосс за батч\n",
" :param current_acc: текущая суммарная точность за батч\n",
" :return: лосс на данном батче; current_loss; current_acc\n",
" \"\"\"\n",
"\n",
" # Обучаемся на батче (одна \"итерация\" обучения нейросети)\n",
" X_batch = X_batch.to(device)\n",
" y_batch = y_batch.to(device)\n",
"\n",
" # Логиты на выходе модели\n",
" logits = model(X_batch)\n",
"\n",
" # Подсчитываем лосс\n",
" loss = criterion(logits, y_batch.long().to(device))\n",
"\n",
" # Сохраняем лоссы и точность на трейне\n",
" current_loss += loss.detach().cpu().numpy()\n",
" y_pred = logits.max(1)[1].detach().cpu().numpy()\n",
" current_acc += np.mean(y_batch.cpu().numpy() == y_pred)\n",
" return loss, current_loss, current_acc\n",
"\n",
"\n",
"def train(\n",
" model, criterion, optimizer, train_batch_gen, val_batch_gen, num_epochs=40\n",
"):\n",
" \"\"\"\n",
" Функция для обучения модели и вывода лосса и метрики во время обучения.\n",
"\n",
" :param model: обучаемая модель\n",
" :param criterion: функция потерь\n",
" :param optimizer: метод оптимизации\n",
" :param train_batch_gen: генератор батчей для обучения\n",
" :param val_batch_gen: генератор батчей для валидации\n",
" :param num_epochs: количество эпох\n",
" :return: (dict) accuracy и loss на обучении и валидации (\"история\" обучения)\n",
" \"\"\"\n",
"\n",
" history = defaultdict(lambda: defaultdict(list))\n",
"\n",
" for epoch in range(num_epochs):\n",
" train_loss, val_loss = 0, 0\n",
" train_acc, val_acc = 0, 0\n",
" start_time = time.time()\n",
"\n",
" # ---------------------- ОБУЧЕНИЕ ----------------------#\n",
" model.train(True)\n",
" # На каждой \"эпохе\" делаем полный проход по данным\n",
" for X_batch, y_batch in train_batch_gen:\n",
" # Считаем лосс, обновляем train_loss, train_acc\n",
" loss, train_loss, train_acc = get_batch_loss(\n",
" X_batch, y_batch, model, criterion, train_loss, train_acc\n",
" )\n",
"\n",
" # Обратный проход\n",
" loss.backward()\n",
" # Шаг градиента\n",
" optimizer.step()\n",
" # Зануляем градиенты\n",
" optimizer.zero_grad()\n",
"\n",
" # Подсчитываем лоссы и сохраняем в \"историю\"\n",
" update_history(\n",
" history, train_loss, train_acc, len(train_batch_gen), \"train\"\n",
" )\n",
"\n",
" # ---------------------- ВАЛИДАЦИЯ ----------------------#\n",
" model.train(False)\n",
" # Контекстный менеджер, отключающий подсчет градиентов\n",
" with torch.no_grad():\n",
" # Полный проход по валидационному датасету\n",
" for X_batch, y_batch in val_batch_gen:\n",
" # Считаем лосс, обновляем val_loss, val_acc\n",
" _, val_loss, val_acc = get_batch_loss(\n",
" X_batch, y_batch, model, criterion, val_loss, val_acc\n",
" )\n",
"\n",
" # Подсчитываем лоссы и сохраняем в \"историю\"\n",
" update_history(history, val_loss, val_acc, len(val_batch_gen), \"val\")\n",
"\n",
" # Печатаем результаты после каждой эпохи\n",
" print_epoch(epoch, num_epochs, history, time.time() - start_time)\n",
"\n",
" return history"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2022-11-29T01:59:15.465012Z",
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"iopub.status.idle": "2022-11-29T01:59:15.485479Z",
"shell.execute_reply": "2022-11-29T01:59:15.483976Z",
"shell.execute_reply.started": "2022-11-29T01:59:15.464958Z"
},
"id": "iRTaDeAd8UWF"
},
"outputs": [],
"source": [
"def plot_histories(histories, names):\n",
" \"\"\"\n",
" Функция для визуализации лосса и метрики по нескольким историям.\n",
" :param history: (list) список историй моделей\n",
" :param names: (list) список названий моделей\n",
" \"\"\"\n",
" sns.set_style(\"darkgrid\")\n",
" colors = [\"darkblue\", \"lightcoral\", \"limegreen\", \"sandybrown\"]\n",
" fig, axs = plt.subplots(1, 2, figsize=(14, 5))\n",
"\n",
" epochs = np.min([len(h[\"loss\"][\"train\"]) for h in histories])\n",
" for i, (history, name) in enumerate(zip(histories, names)):\n",
" axs[0].set_title(\"Лосс\")\n",
" axs[0].plot(\n",
" history[\"loss\"][\"train\"][:epochs],\n",
" label=f\"{name}\",\n",
" lw=2,\n",
" c=colors[i],\n",
" )\n",
" axs[0].plot(\n",
" history[\"loss\"][\"val\"][:epochs], lw=1.5, c=colors[i], ls=\"--\"\n",
" )\n",
" axs[0].set_xlabel(\"Эпохи\")\n",
"\n",
" axs[1].set_title(\"Точность\")\n",
" axs[1].plot(\n",
" history[\"acc\"][\"train\"][:epochs], label=f\"{name}\", lw=2, c=colors[i]\n",
" )\n",
" axs[1].plot(\n",
" history[\"acc\"][\"val\"][:epochs], lw=1.5, c=colors[i], ls=\"--\"\n",
" )\n",
" axs[1].set_xlabel(\"Эпохи\")\n",
" axs[1].legend()\n",
"\n",
" dummy_lines = [\n",
" axs[0].plot([], [], c=\"black\", lw=2)[0],\n",
" axs[0].plot([], [], c=\"black\", lw=1.5, ls=\"--\")[0],\n",
" ]\n",
" for i in range(2):\n",
" legend = axs[i].legend(loc=3 - i)\n",
" axs[i].legend(dummy_lines, [\"train\", \"val\"], loc=4)\n",
" axs[i].add_artist(legend)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jIHYBn882FEI"
},
"source": [
"### 1.3. Multi-Layer Perceptron Baseline\n",
"\n",
"Начнем с простой нейросети-MLP из линейных слоев и функций активации, рассмотренной на прошлом семинаре по нейросетям:"
]
},
{
"cell_type": "code",
"source": [
"simple_mlp = nn.Sequential(\n",
" # расплющиваем картинку (C, H, W) в вектор (C * H * W, )\n",
" nn.Flatten(),\n",
" nn.Linear(in_features=3 * 32 * 32, out_features=192),\n",
" # без функции активации модель будет линейной и глупой :(\n",
" nn.ReLU(),\n",
" nn.Linear(in_features=192, out_features=64),\n",
" nn.ReLU(),\n",
" nn.Linear(in_features=64, out_features=32),\n",
" nn.ReLU(),\n",
" nn.Linear(\n",
" in_features=32, out_features=10\n",
" ), # логиты (logits) для 10 классов\n",
").to(device)"
],
"metadata": {
"id": "cegozawaqdRf"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Можно посмотреть количество параметров модели и размерность на промежуточных этапах с помощью `torchinfo.summary(model, input_size)`. Также это удобный способ проверить, что все размерности сходятся, иначе будет ошибка."
],
"metadata": {
"id": "F_lfXewgijDX"
}
},
{
"cell_type": "code",
"source": [
"summary(simple_mlp, input_size=(1, 3, 32, 32))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Keb3hxFJqmYj",
"outputId": "c62646f8-d3e5-4480-cd55-0ed313c33c64"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"==========================================================================================\n",
"Layer (type:depth-idx) Output Shape Param #\n",
"==========================================================================================\n",
"Sequential [1, 10] --\n",
"├─Flatten: 1-1 [1, 3072] --\n",
"├─Linear: 1-2 [1, 192] 590,016\n",
"├─ReLU: 1-3 [1, 192] --\n",
"├─Linear: 1-4 [1, 64] 12,352\n",
"├─ReLU: 1-5 [1, 64] --\n",
"├─Linear: 1-6 [1, 32] 2,080\n",
"├─ReLU: 1-7 [1, 32] --\n",
"├─Linear: 1-8 [1, 10] 330\n",
"==========================================================================================\n",
"Total params: 604,778\n",
"Trainable params: 604,778\n",
"Non-trainable params: 0\n",
"Total mult-adds (M): 0.60\n",
"==========================================================================================\n",
"Input size (MB): 0.01\n",
"Forward/backward pass size (MB): 0.00\n",
"Params size (MB): 2.42\n",
"Estimated Total Size (MB): 2.43\n",
"=========================================================================================="
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vm9cPk61TI-L"
},
"source": [
"Применим ее к нашим данным $-$ картинкам из CIFAR10:"
]
},
{
"cell_type": "code",
"source": [
"# Кросс-энтропия - общепринятый лосс для классификации\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.SGD(simple_mlp.parameters(), lr=0.01)\n",
"\n",
"history_mlp = train(\n",
" simple_mlp, criterion, optimizer, train_batch_gen, val_batch_gen\n",
")\n",
"# Сохраняем веса модели в файл\n",
"torch.save(simple_mlp.state_dict(), \"simple_mlp.pth\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CAMwJiWVrlql",
"outputId": "70f66d16-1137-44d1-adfe-4fd1dd6cd4b0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 40 of 40 took 9.270 s\n",
" training loss: \t1.199680\n",
" validation loss: \t1.552741\n",
" training accuracy: \t\t\t57.29 %\n",
" validation accuracy: \t\t\t46.89 %\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Визуализируем лосс и точность:"
],
"metadata": {
"id": "8cYcHSXci3_t"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 496
},
"execution": {
"iopub.execute_input": "2022-11-29T01:59:15.558513Z",
"iopub.status.busy": "2022-11-29T01:59:15.558166Z",
"iopub.status.idle": "2022-11-29T02:06:46.130611Z",
"shell.execute_reply": "2022-11-29T02:06:46.129582Z",
"shell.execute_reply.started": "2022-11-29T01:59:15.558480Z"
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"id": "alqDabUK3Luq",
"outputId": "9c6a00c4-764e-4e18-fe7b-7ee932bd5b09"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"plot_histories([history_mlp], [\"MLP\"])"
]
},
{
"cell_type": "markdown",
"source": [
"Как видим, модель MLP правильно предсказывает класс из возможных 10 примерно 50% случаев. Можно ли лучше?"
],
"metadata": {
"id": "B0YwGujsi-42"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "a1-rFSjJ2VrU"
},
"source": [
"\n",
"### 1.4. Свёрточная нейросеть\n",
"\n",
"**Свёрточная нейросеть (CNN)** — это многослойная нейросеть, имеющая в своей архитектуре свёрточные и pooling-слои.\n",
"\n",
"Простые свёрточные нейросети для классификации почти всегда строятся по следующему правилу:\n",
"\n",
"$INPUT \\to [[CONV -> RELU]^N \\to POOL?]^M \\to [FC -> RELU]^K \\to FC$, где \"?\" обозначает опциональные слои.\n"
]
},
{
"cell_type": "markdown",
"source": [
""
],
"metadata": {
"id": "Lilzs6m93pZA"
}
},
{
"cell_type": "markdown",
"source": [
"Поясним схему:\n",
"1. Вход — это batch картинок размера CxHxW.\n",
"\n",
"2. **Feature extractor** — последовательность сверток и pooling-слоев(convolution + pooling layers). Он распознает сложные паттерны и преобразует картинку в некоторый вектор, содержащий в себе всю информацию о найденных паттернах. Таким образом, мы значительно уменьшаем размерность картинки, оставляя только то, что нам интересно для классификации.\n",
"\n",
"> Лучше использовать несколько сверток с маленьким ядром, чем одну свертку с большим ядром, так как глубина увеличивает сложность паттернов, которые может распознать CNN.\n",
"\n",
"> Pooling — основной инструмент снижения размерности. Как следствие, он понижает вычислительную сложность, а также помогает бороться с переобучением.\n",
"\n",
"3. **Classifier** — принимает вектор-представление картинки после feature extractor-а и выдает 10 логитов, соответствующих каждому классу. Логиты - это вещественные числа. К ним можно применить `torch.softmax()`, чтобы получить вероятность."
],
"metadata": {
"id": "JE5tQOrr3mVt"
}
},
{
"cell_type": "markdown",
"source": [
"Создадим сверточную нейронную сеть. Нам понадобятся новые слои:\n",
"\n",
"[`torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0)`](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html)\n",
"\n",
"* `in_channels` (int) — количество каналов во входном изображении\n",
"\n",
"* `out_channels` (int) — количество каналов после применения свертки (кол-во фильтров, которые будут применены)\n",
"\n",
"* `kernel_size` (int, tuple) — размер сверточного ядра\n",
"\n",
"* `stride` (int, tuple) — шаг, с которым будет применена свертка.\n",
"\n",
"* `padding` (int, tuple) — добавление по краям изображения дополнительных пикселей.\n",
"\n",
"\n",
"[`nn.MaxPool2d(kernel_size, stride=None)`](https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html)\n",
"\n",
"* `kernel_size` (int, tuple) — размер окна, из которого брать максимум\n",
"\n",
"* `stride` (int, tuple) — шаг, с которым двигаем окно."
],
"metadata": {
"id": "W34UYfLmoYkV"
}
},
{
"cell_type": "code",
"source": [
"simple_cnn = nn.Sequential(\n",
" nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3),\n",
" nn.MaxPool2d(2),\n",
" nn.ReLU(),\n",
" nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),\n",
" nn.MaxPool2d(2),\n",
" nn.ReLU(),\n",
" nn.Flatten(),\n",
" nn.Linear(in_features=2304, out_features=256),\n",
" nn.ReLU(),\n",
" nn.Linear(in_features=256, out_features=10),\n",
").to(device)"
],
"metadata": {
"id": "Wm3BiOiKmz1o"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Подобрали архитектуру CNN так, чтобы количество параметров было примерно таким же, как в MLP. Иначе сравнение было бы нечестным: сеть с бóльшим количеством параметров имела бы преимущество."
],
"metadata": {
"id": "yR4zTyLWqM27"
}
},
{
"cell_type": "code",
"source": [
"summary(simple_cnn, input_size=(1, 3, 32, 32))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tCZKLIzRoJTx",
"outputId": "48ebfca9-926f-477d-8667-6c91e9c7d463"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"==========================================================================================\n",
"Layer (type:depth-idx) Output Shape Param #\n",
"==========================================================================================\n",
"Sequential [1, 10] --\n",
"├─Conv2d: 1-1 [1, 32, 30, 30] 896\n",
"├─MaxPool2d: 1-2 [1, 32, 15, 15] --\n",
"├─ReLU: 1-3 [1, 32, 15, 15] --\n",
"├─Conv2d: 1-4 [1, 64, 13, 13] 18,496\n",
"├─MaxPool2d: 1-5 [1, 64, 6, 6] --\n",
"├─ReLU: 1-6 [1, 64, 6, 6] --\n",
"├─Flatten: 1-7 [1, 2304] --\n",
"├─Linear: 1-8 [1, 256] 590,080\n",
"├─ReLU: 1-9 [1, 256] --\n",
"├─Linear: 1-10 [1, 10] 2,570\n",
"==========================================================================================\n",
"Total params: 612,042\n",
"Trainable params: 612,042\n",
"Non-trainable params: 0\n",
"Total mult-adds (M): 4.52\n",
"==========================================================================================\n",
"Input size (MB): 0.01\n",
"Forward/backward pass size (MB): 0.32\n",
"Params size (MB): 2.45\n",
"Estimated Total Size (MB): 2.78\n",
"=========================================================================================="
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "markdown",
"source": [
"Обучим CNN:"
],
"metadata": {
"id": "s4xRaTH6qZv2"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2022-11-29T02:06:46.314950Z",
"iopub.status.busy": "2022-11-29T02:06:46.313963Z",
"iopub.status.idle": "2022-11-29T02:15:25.832768Z",
"shell.execute_reply": "2022-11-29T02:15:25.831808Z",
"shell.execute_reply.started": "2022-11-29T02:06:46.314912Z"
},
"id": "EnEk2EiJVJog",
"outputId": "081dfa58-b51b-4b0d-86d0-ee04cfdc6217"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 40 of 40 took 10.382 s\n",
" training loss: \t0.449018\n",
" validation loss: \t1.384605\n",
" training accuracy: \t\t\t84.71 %\n",
" validation accuracy: \t\t\t61.51 %\n"
]
}
],
"source": [
"# Кросс-энтропия - общепринятый лосс для классификации\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.SGD(simple_cnn.parameters(), lr=0.01)\n",
"\n",
"history_cnn = train(\n",
" simple_cnn,\n",
" criterion,\n",
" optimizer,\n",
" train_batch_gen,\n",
" val_batch_gen,\n",
" num_epochs=40,\n",
")\n",
"# Сохраняем веса модели в файл\n",
"torch.save(simple_cnn.state_dict(), \"simple_cnn.pth\")"
]
},
{
"cell_type": "code",
"source": [
"plot_histories([history_mlp, history_cnn], [\"MLP\", \"CNN\"])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 496
},
"id": "C-tIcHs_bZ28",
"outputId": "68258ed0-6e6f-460f-e0e9-6cd75dd0e8bc"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aHmYIJY4asmT"
},
"source": [
"Мы смогли достичь лучшего качества на тесте, чем MLP. Обратим внимание на интересный эффект на графике справа: хотя точность продолжает расти на train датасете, она перестает улучшаться на тесте. Эта проблема называется *переобучением нейросети*, и говорит о том, что модель слишком сильно подстраивается под данные, которые видит при обучении. Больше всего переобучаются модели с большим количеством параметров. Существует множество способов борьбы с этим, подробнее — на 3 курсе."
]
},
{
"cell_type": "markdown",
"source": [
"### 1.5. Визуализация предсказаний"
],
"metadata": {
"id": "v9YUJUYHqf3Q"
}
},
{
"cell_type": "code",
"source": [
"# Если нужно загрузить модели из файла на CPU\n",
"simple_mlp.load_state_dict(\n",
" torch.load(\"simple_mlp.pth\", map_location=torch.device(device))\n",
")\n",
"simple_cnn.load_state_dict(\n",
" torch.load(\"simple_cnn.pth\", map_location=torch.device(device))\n",
")\n",
"simple_mlp.eval()\n",
"simple_cnn.eval();"
],
"metadata": {
"id": "MLZ1GIQkcNDD"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Наконец, посмотрим на сами предсказания!"
],
"metadata": {
"id": "m9d8qcDureyH"
}
},
{
"cell_type": "code",
"source": [
"for i in np.random.randint(0, len(val_dataset), 5):\n",
" with sns.axes_style(\"whitegrid\"):\n",
" plt.figure(figsize=(13, 3))\n",
"\n",
" # Достаем 1 рандомный объект из тестового датасета\n",
" image, label = val_dataset[i]\n",
"\n",
" plt.subplot(1, 3, 1)\n",
" plt.imshow(image.permute((1, 2, 0)))\n",
" plt.title(classes[label])\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
"\n",
" # Не забываем выключить градиенты на момент вычислений\n",
" with torch.no_grad():\n",
" logits_mlp = simple_mlp(image.to(device).unsqueeze(0))[0].cpu()\n",
" logits_cnn = simple_cnn(image.to(device).unsqueeze(0))[0].cpu()\n",
"\n",
" # Чтобы логиты перевести в вероятности применяем softmax по оси классов\n",
" prob_mlp = torch.softmax(logits_mlp, dim=0)\n",
" prob_cnn = torch.softmax(logits_cnn, dim=0)\n",
"\n",
" plt.subplot(1, 3, 2)\n",
" plt.barh(classes, prob_mlp)\n",
" plt.title(\"MLP\")\n",
"\n",
" plt.subplot(1, 3, 3)\n",
" plt.barh(classes, prob_cnn)\n",
" plt.title(\"CNN\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "ejsu_RMiAYNc",
"outputId": "6a5b1b8d-a1b0-42d0-d924-8c3358d15658"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
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}
]
},
{
"cell_type": "markdown",
"source": [
"**Вывод:** Обе модели не идеальны: на некоторых сложных картинках они обе ошибаются. Однако CNN обладает большей уверенностью при предсказании, в то время как MLP часто дает одинаковые вероятности нескольких объектам."
],
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"id": "GMNRoYNCt2mb"
}
}
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},
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"name": "python3"
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