baseline_infer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gc\n",
    "import cv2\n",
    "import math\n",
    "import copy\n",
    "import time\n",
    "import random\n",
    "import glob\n",
    "from PIL import Image\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# For data manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Pytorch Imports\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.optim import lr_scheduler\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.cuda import amp\n",
    "import torchvision\n",
    "\n",
    "# Utils\n",
    "import joblib\n",
    "from tqdm import tqdm\n",
    "from collections import defaultdict\n",
    "\n",
    "# Sklearn Imports\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "# For Image Models\n",
    "import timm\n",
    "\n",
    "# Albumentations for augmentations\n",
    "import albumentations as A\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "\n",
    "# For colored terminal text\n",
    "from colorama import Fore, Back, Style\n",
    "b_ = Fore.BLUE\n",
    "sr_ = Style.RESET_ALL\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# For descriptive error messages\n",
    "#os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONFIG = {\n",
    "    \"seed\": 0,\n",
    "    \"img_size\": 2048,\n",
    "    \"model_name\": \"tf_efficientnet_b0_ns\",\n",
    "    \"num_classes\": 5,\n",
    "    \"valid_batch_size\": 4,\n",
    "    \"device\": torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"),\n",
    "}\n",
    "def set_seed(seed=0):\n",
    "    '''Sets the seed of the entire notebook so results are the same every time we run.\n",
    "    This is for REPRODUCIBILITY.'''\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    # When running on the CuDNN backend, two further options must be set\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    # Set a fixed value for the hash seed\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    \n",
    "set_seed(CONFIG['seed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROOT_DIR = '~/kaggle/input/UBC-OCEAN'\n",
    "TEST_DIR = '~/kaggle/input/UBC-OCEAN/test_thumbnails'\n",
    "\n",
    "LABEL_ENCODER_BIN = \"/kaggle/input/ubc-efficienetnetb0-fold1of10-2048pix-thumbnails/label_encoder.pkl\"\n",
    "BEST_WEIGHT = \"/kaggle/input/ubc-efficienetnetb0-fold1of10-2048pix-thumbnails/Recall0.9178_Acc0.9437_Loss0.1685_epoch9.bin\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_test_file_path(image_id):\n",
    "    return f\"{TEST_DIR}/{image_id}_thumbnail.png\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_id</th>\n",
       "      <th>image_width</th>\n",
       "      <th>image_height</th>\n",
       "      <th>file_path</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>28469</td>\n",
       "      <td>16987</td>\n",
       "      <td>~/kaggle/input/UBC-OCEAN/test_thumbnails/41_th...</td>\n",
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      "text/plain": [
       "   image_id  image_width  image_height  \\\n",
       "0        41        28469         16987   \n",
       "\n",
       "                                           file_path  label  \n",
       "0  ~/kaggle/input/UBC-OCEAN/test_thumbnails/41_th...      0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(f\"{ROOT_DIR}/test.csv\")\n",
    "df['file_path'] = df['image_id'].apply(get_test_file_path)\n",
    "df['label'] = 0 # dummy\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_id</th>\n",
       "      <th>label</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>HGSC</td>\n",
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      ],
      "text/plain": [
       "   image_id label\n",
       "0        41  HGSC"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub = pd.read_csv(f\"{ROOT_DIR}/sample_submission.csv\")\n",
    "df_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/kaggle/input/ubc-efficienetnetb0-fold1of10-2048pix-thumbnails/label_encoder.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/home/syo/kaggle/UBC-OCEAN/baseline.ipynb Cell 7\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bserver2/home/syo/kaggle/UBC-OCEAN/baseline.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m encoder \u001b[39m=\u001b[39m joblib\u001b[39m.\u001b[39;49mload( LABEL_ENCODER_BIN )\n",
      "File \u001b[0;32m~/opt/mambaforge/envs/kaggle/lib/python3.11/site-packages/joblib/numpy_pickle.py:650\u001b[0m, in \u001b[0;36mload\u001b[0;34m(filename, mmap_mode)\u001b[0m\n\u001b[1;32m    648\u001b[0m         obj \u001b[39m=\u001b[39m _unpickle(fobj)\n\u001b[1;32m    649\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 650\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(filename, \u001b[39m'\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m f:\n\u001b[1;32m    651\u001b[0m         \u001b[39mwith\u001b[39;00m _read_fileobject(f, filename, mmap_mode) \u001b[39mas\u001b[39;00m fobj:\n\u001b[1;32m    652\u001b[0m             \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(fobj, \u001b[39mstr\u001b[39m):\n\u001b[1;32m    653\u001b[0m                 \u001b[39m# if the returned file object is a string, this means we\u001b[39;00m\n\u001b[1;32m    654\u001b[0m                 \u001b[39m# try to load a pickle file generated with an version of\u001b[39;00m\n\u001b[1;32m    655\u001b[0m                 \u001b[39m# Joblib so we load it with joblib compatibility function.\u001b[39;00m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/kaggle/input/ubc-efficienetnetb0-fold1of10-2048pix-thumbnails/label_encoder.pkl'"
     ]
    }
   ],
   "source": [
    "encoder = joblib.load( LABEL_ENCODER_BIN )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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