Phase:03-LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0_UjJiNCyG5L"
   },
   "outputs": [],
   "source": [
    "from timm.utils import AverageMeter\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.model_zoo as model_zoo\n",
    "\n",
    "from torch_optimizer import *\n",
    "import ttach as tta\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import glob\n",
    "import os\n",
    "import copy\n",
    "import random\n",
    "import math\n",
    "import pandas as pd\n",
    "# import pandas_path\n",
    "import ast\n",
    "\n",
    "from tqdm import tqdm_notebook\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from collections import OrderedDict\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9hJLapKqyG5W"
   },
   "outputs": [],
   "source": [
    "class config:\n",
    "    BASE_LR = 1e-3\n",
    "    INPUT_FEATURES = 23\n",
    "    NUM_EPOCHS = 10\n",
    "    OPTIMIZER_NAME = \"Ranger\"\n",
    "    FILE_PREFIX = \"lstm_transformer_23_models\"\n",
    "    PREDS_FILE_PREFIX = 'transformer_class_regr_23models'\n",
    "    SEED = 43\n",
    "    BATCH_SIZE = 32\n",
    "    N = 64\n",
    "    WORKERS = 16\n",
    "    FOLD = [0,1,2,3,4]\n",
    "    DEBUG = False\n",
    "    MODE = 2 #{0: Train; 1: Val Logits; 2: Test Logits; 3: ALL}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NpW9aeHbyG5W"
   },
   "outputs": [],
   "source": [
    "def seed_everything(seed):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "seed_everything(config.SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TimeDatasetWIND(Dataset):\n",
    "\n",
    "    def __init__(self, df, df_train, N = config.N, D = config.INPUT_FEATURES, phase='train'):\n",
    "        self.data = df.reset_index(drop=True)\n",
    "        self.data_train = df_train.reset_index(drop=True)\n",
    "        self.label = df.wind_speed.values\n",
    "        self.N = N\n",
    "        self.D = D\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, index): # N, D\n",
    "        \n",
    "        images = []\n",
    "        labels = []\n",
    "        mask = []\n",
    "        final_images = torch.zeros(self.N, self.D)\n",
    "        final_labels = np.zeros((self.N, 1))\n",
    "        final_mask = torch.zeros((self.N))\n",
    "        images.append(torch.Tensor(ast.literal_eval(self.data.pred_list.iloc[index])).unsqueeze(0))\n",
    "        labels.append(torch.Tensor([self.label[index]]).unsqueeze(0))\n",
    "        mask.append(1)\n",
    "        embedding_num = int(self.data.image_id.iloc[index].split('_')[-1])\n",
    "        embedding_ocean_id = self.data.storm_id.iloc[index]\n",
    "        index_train = self.data_train[self.data_train.image_id==self.data.image_id.iloc[index]].index.values[0]\n",
    "        for i in range(1, self.N):\n",
    "            embedding_nump = int(self.data_train.image_id.iloc[index_train-i].split('_')[-1])\n",
    "            embedding_ocean_idp = self.data_train.storm_id.iloc[index_train-i]\n",
    "            if embedding_num-embedding_nump==i and embedding_ocean_id==embedding_ocean_idp:\n",
    "                images.append(torch.Tensor(ast.literal_eval(self.data_train.pred_list.iloc[index_train-i])).unsqueeze(0))\n",
    "                mask.append(1)\n",
    "                if self.label is not None:\n",
    "                    labels.append(torch.Tensor([self.data_train.wind_speed.iloc[index_train-i]]).unsqueeze(0))\n",
    "            else:\n",
    "                break\n",
    "\n",
    "        images = torch.cat(images, dim=0)\n",
    "        labels = torch.cat(labels, axis=0)\n",
    "        mask = torch.tensor(mask)\n",
    "        images = torch.flip(images, [0])\n",
    "        labels = torch.flip(labels, [0])\n",
    "        mask = torch.flip(mask, [0])\n",
    "        l = len(images)\n",
    "        final_images[:l] = images\n",
    "        final_labels[:l] = labels\n",
    "        final_mask[:l] = mask\n",
    "        if self.label is not None:\n",
    "            return final_images/10, final_mask, final_labels/10\n",
    "        else:\n",
    "            return final_images/10, final_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ufiN0LGiyG5Z"
   },
   "outputs": [],
   "source": [
    "class NeuralNet(nn.Module):\n",
    "    def __init__(self, embed_size=config.INPUT_FEATURES, LSTM_UNITS=128, DO=0.2):\n",
    "        super(NeuralNet, self).__init__()\n",
    "        self.lstm1 = nn.LSTM(embed_size, LSTM_UNITS, bidirectional=False, batch_first=True, dropout=DO)\n",
    "        self.lstm2 = nn.LSTM(LSTM_UNITS, LSTM_UNITS, bidirectional=False, batch_first=True, dropout=DO)\n",
    "\n",
    "        self.dropout = nn.Dropout(DO)\n",
    "        self.linear1 = nn.Linear(LSTM_UNITS, LSTM_UNITS)\n",
    "        self.linear2 = nn.Linear(LSTM_UNITS, LSTM_UNITS)\n",
    "        \n",
    "        # self.embedding = nn.Linear(embed_size, LSTM_UNITS)\n",
    "        self.linear_pe = nn.Linear(LSTM_UNITS, 1)\n",
    "\n",
    "    def forward(self, x, mask=None):\n",
    "        # x = self.embedding(x)\n",
    "        if mask is not None:\n",
    "            x = x*mask.unsqueeze(-1)\n",
    "        self.lstm1.flatten_parameters()\n",
    "        h_lstm1, _ = self.lstm1(x)\n",
    "        self.lstm2.flatten_parameters()\n",
    "        h_lstm2, _ = self.lstm2(h_lstm1)\n",
    "        \n",
    "        h_conc_linear1  = F.relu(self.linear1(h_lstm1))\n",
    "        h_conc_linear2  = F.relu(self.linear2(h_lstm2))\n",
    "        \n",
    "        hidden = h_lstm1 + h_lstm2 + h_conc_linear1 + h_conc_linear2\n",
    "\n",
    "        output = self.linear_pe(self.dropout(hidden))*mask.unsqueeze(-1)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kt9Zb3x7yG5a"
   },
   "outputs": [],
   "source": [
    "def run_epoch(model, loss_fn, optimizer, phase, scheduler=None):\n",
    "    running_loss = AverageMeter()\n",
    "    tk1 = tqdm_notebook(dataloaders[phase])\n",
    "    if phase == \"train\":\n",
    "        model.train()\n",
    "        for x_var, x_mask, y_var in tk1:\n",
    "            \n",
    "            x_var = x_var.to(device=device).float()\n",
    "            x_mask = x_mask.to(device=device).float()\n",
    "            y_var = y_var.to(device=device).float()\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            scores = model(x_var, x_mask)\n",
    "            loss = loss_fn(scores, y_var)\n",
    "                \n",
    "            running_loss.update(loss.item(), n=config.BATCH_SIZE*config.N)\n",
    "            tk1.set_postfix(loss=running_loss.avg)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            if scheduler is not None:\n",
    "                scheduler.step()\n",
    "\n",
    "            if config.DEBUG: break\n",
    "        return running_loss.avg\n",
    "    else:\n",
    "        model.eval()\n",
    "        y_true = np.array([])\n",
    "        y_pred = np.array([])\n",
    "        running_mse = AverageMeter()\n",
    "        with torch.no_grad():\n",
    "            for x_var, x_mask, y_var in tk1:\n",
    "                \n",
    "                bs = x_var.shape[0]\n",
    "                x_var = x_var.to(device=device).float()\n",
    "                x_mask = x_mask.to(device=device).float()\n",
    "                idx = len(x_mask[0])-np.argmax(x_mask.cpu().numpy()[:,::-1], axis=1)-1\n",
    "                y_var = y_var.to(device=device).float().squeeze()\n",
    "                \n",
    "                scores = model(x_var, x_mask).squeeze()\n",
    "                loss = loss_fn(scores, y_var)\n",
    "                \n",
    "                running_loss.update(loss.item(), n=config.BATCH_SIZE)\n",
    "                \n",
    "                y_var = y_var.squeeze().cpu().detach().numpy()\n",
    "                scores = scores.squeeze().cpu().detach().numpy()\n",
    "                y_var = y_var.T[idx].diagonal()\n",
    "                scores = scores.T[idx].diagonal()\n",
    "                \n",
    "                mse = np.sum((scores*10-y_var*10)**2)/len(scores)\n",
    "                running_mse.update(mse, n=len(scores))\n",
    "    \n",
    "                tk1.set_postfix(loss=running_loss.avg, rmse=math.sqrt(running_mse.avg))\n",
    "                if config.DEBUG: break\n",
    "        rmse = math.sqrt(running_mse.avg)\n",
    "        return running_loss.avg, rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir lstm_models lstm_logs"
   ]
  },
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    "scrolled": true
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   "outputs": [],
   "source": [
    "if config.MODE in [0,3]:\n",
    "    for fold in config.FOLD:\n",
    "        X = pd.read_csv(f'new_split_with_preds_{config.PREDS_FILE_PREFIX}.csv')\n",
    "        train = X[X['pct_of_storm']<=0.8].copy()\n",
    "        val = X[X['pct_of_storm']>0.8].copy()\n",
    "\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "        train_losses = []\n",
    "        valid_losses = []\n",
    "        valid_rmse = []\n",
    "\n",
    "        trainset = TimeDatasetWIND(train, train)\n",
    "        X = pd.concat([train, val]).sort_values(['image_id']).reset_index(drop=True)\n",
    "        validset = TimeDatasetWIND(val, X)\n",
    "        \n",
    "        train_loader = DataLoader(trainset, config.BATCH_SIZE, num_workers=config.WORKERS, shuffle=True, pin_memory=True, drop_last=True)\n",
    "        valid_loader = DataLoader(validset, config.BATCH_SIZE, num_workers=config.WORKERS, shuffle=False, pin_memory=True, drop_last=True)\n",
    "\n",
    "        dataloaders = {\n",
    "            \"train\" : train_loader,\n",
    "            \"valid\" : valid_loader,\n",
    "        }\n",
    "\n",
    "        model = NeuralNet()\n",
    "        model.to(device)\n",
    "        criterion = nn.MSELoss()\n",
    "#         optimizer = torch.optim.AdamW(model.parameters(), lr=config.BASE_LR, weight_decay = 0.1)\n",
    "        optimizer = globals()[config.OPTIMIZER_NAME](model.parameters(), lr=config.BASE_LR, weight_decay = 0.001)\n",
    "        best_rmse = 10000\n",
    "        for epoch in range(config.NUM_EPOCHS):\n",
    "            print('Starting epoch [%d / %d]' % (epoch + 1, config.NUM_EPOCHS))\n",
    "            train_loss = run_epoch(model, criterion, optimizer, \"train\")\n",
    "            valid_loss, rmse = run_epoch(model, criterion, optimizer, \"valid\")\n",
    "    #         scheduler.step()\n",
    "\n",
    "            if rmse<best_rmse:\n",
    "                print(\"**Saving model**\")\n",
    "                best_rmse=rmse\n",
    "                torch.save({\n",
    "                    \"epoch\": epoch + 1,\n",
    "                    \"state_dict\" : model.state_dict(),\n",
    "                    \"rmse\" : best_rmse,\n",
    "                    \"optim_dict\" : optimizer.state_dict(),\n",
    "                    \"config_class\" : config\n",
    "                }, f\"lstm_models/{config.FILE_PREFIX}_FOLD{fold}.pth\")\n",
    "\n",
    "            train_losses.append(train_loss)\n",
    "            valid_losses.append(valid_loss)\n",
    "            valid_rmse.append(rmse)\n",
    "            df_data=np.array([train_losses, valid_losses, valid_rmse]).T\n",
    "            df = pd.DataFrame(df_data, columns = ['train_losses','valid_losses','valid_rmse'])\n",
    "            df.to_csv(f'lstm_logs/{config.FILE_PREFIX}_FOLD{fold}.csv')\n",
    "            if config.DEBUG: break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation Logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir val_lstm_npys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if config.MODE in [1,3]:\n",
    "    for fold in config.FOLD:\n",
    "        X = pd.read_csv(f'new_split_with_preds_{config.PREDS_FILE_PREFIX}.csv')\n",
    "        train = X[X.fold!=fold]\n",
    "        for i in range(len(train)):\n",
    "            train.pred_list.iloc[i] = [train.wind_speed.iloc[i]]*config.INPUT_FEATURES\n",
    "        train.pred_list = train.pred_list.astype(str)\n",
    "        val = X[X.fold==fold]\n",
    "        X = pd.concat([train, val]).sort_values(['image_id']).reset_index(drop=True)\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        validset = TimeDatasetWIND(val, X,phase='test')\n",
    "        valid_loader = DataLoader(validset, config.BATCH_SIZE, num_workers=config.WORKERS, shuffle=False, pin_memory=True, drop_last=False)\n",
    "        model = NeuralNet()\n",
    "        model.to(device)\n",
    "        model.load_state_dict(torch.load(f'lstm_models/{config.FILE_PREFIX}_FOLD{fold}.pth','cpu')['state_dict'])\n",
    "        model.eval()\n",
    "        y_pred = []\n",
    "        tk1 = tqdm_notebook(valid_loader)\n",
    "        with torch.no_grad():\n",
    "            for x_var, x_mask, y_var in tk1:\n",
    "                x_var = x_var.to(device=device).float()\n",
    "                x_mask = x_mask.to(device=device).float()\n",
    "                idx = len(x_mask[0])-np.argmax(x_mask.cpu().numpy()[:,::-1], axis=1)-1\n",
    "                y_var = y_var.to(device=device).float().squeeze()\n",
    "                scores = model(x_var, x_mask).squeeze()\n",
    "\n",
    "                scores = scores.squeeze().cpu().detach().numpy()\n",
    "                scores = scores.T[idx].diagonal()\n",
    "                y_pred.extend(scores)\n",
    "        np.save(f'val_lstm_npys/lstm_val_preds_{config.FILE_PREFIX}_N={config.N}_FOLD{fold}.npy', y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir test_lstm_npys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_test_predictions(fold):\n",
    "    models_list = sorted(glob.glob(f'*transformer_class_test_npys/*{fold}.npy')) + sorted(glob.glob(f'*transformer_regr_test_npys/*transformer*{fold}.npy'))\n",
    "    pred_list = []\n",
    "    for i in models_list:\n",
    "        i = i.replace('val', 'test').replace('_test_preds_fold_', '_preds_fold_test_')\n",
    "        a = np.load(f'{i[:-5]}{fold}.npy')\n",
    "        if a.max()<20:\n",
    "            pred_list.append(a.reshape(-1)*10)\n",
    "        else:\n",
    "            pred_list.append(a.reshape(-1))\n",
    "    return np.array(pred_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if config.MODE in [2,3]:\n",
    "    for fold in config.FOLD:\n",
    "        X = pd.read_csv('sub.csv')\n",
    "        pred_list = get_test_predictions(fold)\n",
    "        X['storm_id'] = X.image_id.str[:3]\n",
    "        X['pred_list'] = pd.Series((pred_list.T).tolist()).values\n",
    "        X.to_csv('temp.csv',index=False)\n",
    "        X = pd.read_csv('temp.csv')\n",
    "        os.system('rm -rf temp.csv')\n",
    "        X_train = pd.read_csv(f'new_split_with_preds_{config.PREDS_FILE_PREFIX}.csv')\n",
    "        \n",
    "        for i in range(len(X_train)):\n",
    "            X_train.pred_list.iloc[i] = [X_train.wind_speed.iloc[i]]*config.INPUT_FEATURES\n",
    "        X_train.pred_list = X_train.pred_list.astype(str)\n",
    "        \n",
    "        X_full = pd.concat([X_train, X]).sort_values(['image_id']).reset_index(drop=True)\n",
    "\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        validset = TimeDatasetWIND(X, X_full,phase='test')\n",
    "        valid_loader = DataLoader(validset, config.BATCH_SIZE, num_workers=config.WORKERS, shuffle=False, pin_memory=True, drop_last=False)\n",
    "        model = NeuralNet()\n",
    "        model.to(device)\n",
    "        model.load_state_dict(torch.load(f'lstm_models/{config.FILE_PREFIX}_FOLD{fold}.pth','cpu')['state_dict'])\n",
    "        model.eval()\n",
    "        y_pred = []\n",
    "        tk1 = tqdm_notebook(valid_loader)\n",
    "        with torch.no_grad():\n",
    "            for x_var, x_mask, y_var in tk1:\n",
    "                x_var = x_var.to(device=device).float()\n",
    "                x_mask = x_mask.to(device=device).float()\n",
    "                idx = len(x_mask[0])-np.argmax(x_mask.cpu().numpy()[:,::-1], axis=1)-1\n",
    "                y_var = y_var.to(device=device).float().squeeze()\n",
    "                scores = model(x_var, x_mask).squeeze()\n",
    "\n",
    "                scores = scores.squeeze().cpu().detach().numpy()\n",
    "                scores = scores.T[idx].diagonal()\n",
    "                y_pred.extend(scores)\n",
    "        np.save(f'test_lstm_npys/lstm_test_preds_{config.FILE_PREFIX}_N={config.N}_FOLD{fold}.npy', y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing LSTM training Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_val_predictions(fold):\n",
    "    models_list = sorted(glob.glob(f'*transformer_class_val_npys/*{fold}.npy')) + sorted(glob.glob(f'*transformer_regr_val_npys/*transformer*{fold}.npy'))\n",
    "    print(len(models_list))\n",
    "    pred = np.zeros(len(np.load(models_list[0])))\n",
    "    pred_list = []\n",
    "    for i in models_list:\n",
    "        a = np.load(f'{i[:-5]}{fold}.npy')\n",
    "        if a.max()<20:\n",
    "            pred_list.append(a.reshape(-1)*10)\n",
    "        else:\n",
    "            pred_list.append(a.reshape(-1))\n",
    "    pred = (pred)/len(models_list)\n",
    "    return pred, np.array(pred_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "if config.MODE in [4]:\n",
    "    Y = pd.read_csv('new_split.csv')\n",
    "    Y['pred_list'] = None\n",
    "    for fold in [0,1,2,3,4]:\n",
    "        X = pd.read_csv('new_split.csv')\n",
    "        _, pred_list = get_val_predictions(fold)\n",
    "        Y['pred_list'][X.fold==fold] = pd.Series((pred_list.T).tolist()).values\n",
    "    Y.to_csv(f'new_split_with_preds_{config.PREDS_FILE_PREFIX}.csv',index = False)"
   ]
  }
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