experiment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mmenpo 0.8.1 has requirement matplotlib<2.0,>=1.4, but you'll have matplotlib 3.0.2 which is incompatible.\u001b[0m\n",
      "\u001b[31mmenpo 0.8.1 has requirement pillow<5.0,>=3.0, but you'll have pillow 5.4.1 which is incompatible.\u001b[0m\n",
      "\u001b[31mmenpo 0.8.1 has requirement scipy<1.0,>=0.16, but you'll have scipy 1.2.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mgoogle-cloud-bigquery 1.6.1 has requirement google-cloud-core<0.30dev,>=0.28.0, but you'll have google-cloud-core 1.0.3 which is incompatible.\u001b[0m\n",
      "\u001b[31mapache-beam 2.14.0 has requirement httplib2<=0.12.0,>=0.8, but you'll have httplib2 0.13.1 which is incompatible.\u001b[0m\n",
      "\u001b[31mapache-beam 2.14.0 has requirement oauth2client<4,>=2.0.1, but you'll have oauth2client 4.1.3 which is incompatible.\u001b[0m\n",
      "\u001b[33mYou are using pip version 10.0.1, however version 19.2.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install -q -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.utils.data as D\n",
    "from torch.optim.lr_scheduler import ExponentialLR\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from torchvision import models, transforms\n",
    "\n",
    "from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer\n",
    "from ignite.metrics import Loss, Accuracy\n",
    "from ignite.contrib.handlers.tqdm_logger import ProgressBar\n",
    "from ignite.handlers import  EarlyStopping, ModelCheckpoint\n",
    "\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from scripts.evaluate import eval_model \n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define dataset and model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "img_dir = '../input/rxrxairgb'\n",
    "path_data = '../input/rxrxaicsv'\n",
    "device = 'cuda'\n",
    "batch_size = 256\n",
    "torch.manual_seed(0)\n",
    "learning_rate = 0.1\n",
    "model_name = 'resnet18'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImagesDS(D.Dataset):\n",
    "    def __init__(self, df, img_dir=img_dir, mode='train', site=1):\n",
    "        self.records = df.to_records(index=False)\n",
    "        self.site = site\n",
    "        self.mode = mode\n",
    "        self.img_dir = img_dir\n",
    "        self.len = df.shape[0]\n",
    "        \n",
    "    @staticmethod\n",
    "    def _load_img_as_tensor(file_name):\n",
    "        with Image.open(file_name) as img:\n",
    "            return transforms.ToTensor()(img)\n",
    "\n",
    "    def _get_img_path(self, index):\n",
    "        experiment, well, plate = self.records[index].experiment, self.records[index].well, self.records[index].plate\n",
    "        return f'{self.img_dir}/{self.mode}/{experiment}_{plate}_{well}_s{self.site}.jpeg'\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        img = self._load_img_as_tensor(self._get_img_path(index))\n",
    "        if self.mode == 'train':\n",
    "            return img, int(self.records[index].sirna)\n",
    "        else:\n",
    "            return img, self.records[index].id_code\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframes for training, cross-validation, and testing\n",
    "df = pd.read_csv(path_data+'/train.csv')\n",
    "df_train, df_val = train_test_split(df, test_size = 0.1, random_state=42)\n",
    "df_test = pd.read_csv(path_data+'/test.csv')\n",
    "\n",
    "# pytorch training dataset & loader\n",
    "ds = ImagesDS(df_train, mode='train')\n",
    "loader = D.DataLoader(ds, batch_size=batch_size, shuffle=True, num_workers=4)\n",
    "\n",
    "# pytorch cross-validation dataset & loader\n",
    "ds_val = ImagesDS(df_val, mode='train')\n",
    "val_loader = D.DataLoader(ds_val, batch_size=batch_size, shuffle=True, num_workers=4)\n",
    "\n",
    "# pytorch test dataset & loader\n",
    "ds_test = ImagesDS(df_test, mode='test')\n",
    "tloader = D.DataLoader(ds_test, batch_size=batch_size, shuffle=False, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = 1108\n",
    "\n",
    "model = getattr(models, model_name)(pretrained=True)\n",
    "num_ftrs = model.fc.in_features\n",
    "model.fc = torch.nn.Linear(num_ftrs, classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = {\n",
    "    'loss': Loss(criterion),\n",
    "    'accuracy': Accuracy(),\n",
    "}\n",
    "\n",
    "trainer = create_supervised_trainer(model, optimizer, criterion, device=device)\n",
    "val_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "@trainer.on(Events.EPOCH_COMPLETED)\n",
    "def compute_and_display_val_metrics(engine):\n",
    "    epoch = engine.state.epoch\n",
    "    metrics = val_evaluator.run(val_loader).metrics\n",
    "    print(\"Validation Results - Epoch: {} | LR: {:.4f}  Average Loss: {:.4f} | Accuracy: {:.4f} \"\n",
    "          .format(engine.state.epoch, optimizer.param_groups[0]['lr'], metrics['loss'], metrics['accuracy']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_saved_model_path(epoch):\n",
    "    return f'models/Model_{model_name}_{epoch}.pth'\n",
    "\n",
    "best_acc = 0.\n",
    "best_epoch = 1\n",
    "best_epoch_file = ''\n",
    "\n",
    "@trainer.on(Events.EPOCH_COMPLETED)\n",
    "def save_best_epoch_only(engine):\n",
    "    epoch = engine.state.epoch\n",
    "\n",
    "    global best_acc\n",
    "    global best_epoch\n",
    "    global best_epoch_file\n",
    "    best_acc = 0. if epoch == 1 else best_acc\n",
    "    best_epoch = 1 if epoch == 1 else best_epoch\n",
    "    best_epoch_file = '' if epoch == 1 else best_epoch_file\n",
    "\n",
    "    metrics = val_evaluator.run(val_loader).metrics\n",
    "\n",
    "    if metrics['accuracy'] > best_acc:\n",
    "        prev_best_epoch_file = get_saved_model_path(best_epoch)\n",
    "        if os.path.exists(prev_best_epoch_file):\n",
    "            os.remove(prev_best_epoch_file)\n",
    "            \n",
    "        best_acc = metrics['accuracy']\n",
    "        best_epoch = epoch\n",
    "        best_epoch_file = get_saved_model_path(best_epoch)\n",
    "        print(f'\\nEpoch: {best_epoch} - New best accuracy! Accuracy: {best_acc}\\n\\n\\n')\n",
    "        torch.save(model.state_dict(), best_epoch_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_learning_rate(optimizer, epoch):\n",
    "    lr = learning_rate\n",
    "\n",
    "    if epoch > 30:\n",
    "        lr = learning_rate / 10.\n",
    "    if epoch > 60:\n",
    "        lr = learning_rate / 100.\n",
    "    if epoch > 90:\n",
    "        lr = learning_rate / 1000.\n",
    "\n",
    "    for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = lr\n",
    "\n",
    "@trainer.on(Events.EPOCH_COMPLETED)\n",
    "def update_lr_scheduler(engine):\n",
    "    adjust_learning_rate(optimizer, engine.state.epoch)\n",
    "    lr = float(optimizer.param_groups[0]['lr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training started\n"
     ]
    },
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     "text": [
      "Validation Results - Epoch: 1 | LR: 0.1000  Average Loss: 7.0691 | Accuracy: 0.0005 \n",
      "\n",
      "Epoch: 1 - New best accuracy! Accuracy: 0.000547645125958379\n"
     ]
    },
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     "execution_count": 13,
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   ],
   "source": [
    "print('Training started')\n",
    "trainer.run(loader, max_epochs=10)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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      "\n"
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   ],
   "source": [
    "eval_model(model, tloader, best_epoch_file, path_data)"
   ]
  }
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