{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import os\n", "from PIL import Image\n", "import torch\n", "from torch import nn, optim\n", "import torch.nn.functional as F\n", "from torch.utils.data import DataLoader, Dataset\n", "import albumentations as A\n", "from albumentations.pytorch import ToTensorV2 \n", "from tqdm import tqdm\n", "from torchvision import models\n", "from efficientnet_pytorch import EfficientNet\n", "from sklearn import metrics" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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