Training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0cb081c8-e115-4a36-9771-d2b5d7b373fc",
   "metadata": {},
   "source": [
    "#### Training\n",
    "\n",
    "This notebook contains code to train the model for crypt segmentation in colon images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "30a69eeb-2c2c-4136-9a85-c6998e41826f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "531ec2a1-f87e-480e-b5fb-5861b39ccc96",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import torch.nn as nn\n",
    "import torch\n",
    "import segmentation_models_pytorch as sm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import skimage.io as io\n",
    "from PIL import Image\n",
    "import cv2\n",
    "from torch.utils.data import DataLoader,Dataset\n",
    "import matplotlib.pyplot as plt\n",
    "import tifffile\n",
    "from sklearn.model_selection import KFold\n",
    "import glob\n",
    "import torch_optimizer as t_optim\n",
    "import utils\n",
    "import cv2\n",
    "import torch.optim as optim\n",
    "from tqdm.notebook import tqdm\n",
    "import torchvision.transforms as transforms\n",
    "from torch.optim.lr_scheduler import OneCycleLR\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cff815-7abe-45e0-a255-0c7cf32142dc",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b19b1d8-3475-4772-8d31-286252b56e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Colon_Dataset(Dataset):\n",
    "    def __init__(self,data_csv_path:str=\"train_data.csv\",indexes:list= None,valid:bool=False,transform:transforms = None, target_transform:transforms=None,preprocessing=None):\n",
    "        self.data = pd.read_csv(data_csv_path)\n",
    "        self.indexed_data = self.data.iloc[indexes,:]\n",
    "        self.transform = transform\n",
    "        self.target_transform = target_transform\n",
    "        self.preprocessing = preprocessing\n",
    "        \n",
    "    def __getitem__(self,idx):\n",
    "        \n",
    "        image = tifffile.imread(self.indexed_data.iloc[idx,0])\n",
    "        mask = tifffile.imread(self.indexed_data.iloc[idx,1]).astype(float)\n",
    "        \n",
    "        \n",
    "        if self.transform:\n",
    "            augmentations = self.transform(image=image,mask=mask)\n",
    "            image,mask = augmentations['image'],augmentations['mask']\n",
    "            \n",
    "            \n",
    "        if self.preprocessing:\n",
    "            preprocessed = self.preprocessing(image=image,mask=mask)\n",
    "            image,mask = preprocessed['image'],preprocessed['mask']\n",
    "    \n",
    "        return image.float(),(mask[:,:,0]/255.0).type(torch.LongTensor)\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.indexed_data)        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ae7dfa-17dd-43bf-9e04-5df29df37081",
   "metadata": {},
   "source": [
    "### Config "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2733ca0c-7ded-4882-aa14-81edbadce209",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining configurations\n",
    "class Configuration:\n",
    "    MODEL_SAVEPATH = \"models/\"\n",
    "    ENCODER = \"efficientnet-b2\"\n",
    "    PRETRAINED_WEIGHTS = \"imagenet\"\n",
    "    BATCH_SIZE = 16\n",
    "    INPUT_CHANNELS = 3\n",
    "    INPUT_SHAPE = (512,512,3)\n",
    "    NFOLDS = 5\n",
    "    ACTIVATION = None\n",
    "    CLASSES = 2 #(crypts 1 background 0)\n",
    "    DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "    EPOCHS = 25\n",
    "    LOSS_CROSSENTROPY = nn.CrossEntropyLoss() \n",
    "    LOSS_DICE = utils.DiceScore(loss=True)#utils.DiceLoss()\n",
    "    DICE_COEF = utils.DiceScore(loss=False)#utils.DiceScore()\n",
    "    WEIGHT_DECAY = 1e-4\n",
    "    LEARNING_RATE = 1e-3\n",
    "    PREPROCESS = sm.encoders.get_preprocessing_fn(ENCODER,PRETRAINED_WEIGHTS)\n",
    "    ONECYCLELR = False\n",
    "    MODEL_NAME = 0\n",
    "    ARCHITECTURE = \"UNET\"\n",
    "    MAX_LR_FOR_ONECYCLELR = 1e-3\n",
    "       \n",
    "cfg = Configuration()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc5b4839-c315-4a83-8ddd-e3c4fc6ec2ea",
   "metadata": {},
   "source": [
    "Trainer class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b07e43d-fd4e-4936-8f70-a06c40d429ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Trainer:\n",
    "    def __init__(self,cfg:Configuration,train_data_loader:DataLoader,valid_data_loader:DataLoader)->None:\n",
    "        self.cfg = cfg\n",
    "        self.patience = 5\n",
    "        self.model = sm.Unet(encoder_name=self.cfg.ENCODER, \n",
    "                     encoder_weights=self.cfg.PRETRAINED_WEIGHTS, \n",
    "                     in_channels=self.cfg.INPUT_CHANNELS, \n",
    "                     classes=self.cfg.CLASSES)\n",
    "        self.loss_function = self.cfg.LOSS_CROSSENTROPY\n",
    "        self.lr = self.cfg.LEARNING_RATE\n",
    "        self.batch_size = self.cfg.BATCH_SIZE\n",
    "        self.train_dataloader = train_data_loader\n",
    "        self.valid_dataloader = valid_data_loader\n",
    "        self.device = self.cfg.DEVICE\n",
    "        self.epochs = self.cfg.EPOCHS\n",
    "        self.lr = 1e-3\n",
    "        self.track_best_valid = []\n",
    "        self.val_for_early_stopping = 9999999\n",
    "        if not os.path.isdir(self.cfg.MODEL_SAVEPATH):\n",
    "            os.makedirs(self.cfg.MODEL_SAVEPATH)\n",
    "        \n",
    "        \n",
    "            \n",
    "            \n",
    "        self.log = pd.DataFrame(columns=[\"model_name\",\"train_loss\",\"train_dice\",\"valid_loss\",\"valid_dice\"])\n",
    "        \n",
    "        \n",
    "        \n",
    "        self.optimizer = t_optim.Ranger(self.model.parameters(),weight_decay=self.cfg.WEIGHT_DECAY)\n",
    "        if self.cfg.ONECYCLELR:\n",
    "            self.optimizer = OneCycleLR(self.optimizer, max_lr=self.cfg.MAX_LR_FOR_ONECYCLELR, steps_per_epoch=len(self.train_dataloader), epochs=self.EPOCHS)\n",
    "        \n",
    "   \n",
    "    \n",
    "    def calculate_metrics(self,data_loader:DataLoader):\n",
    "        self.model.eval()\n",
    "        total_loss = 0\n",
    "        total_dice = 0 #batch wise dice loss\n",
    "        with torch.no_grad():\n",
    "            for data in tqdm(data_loader,total=len(data_loader)):\n",
    "                im = data[0].to(self.device)\n",
    "                mask = data[1].to(self.device)\n",
    "                out = self.model(im)\n",
    "                loss = self.loss_function(out.data,mask) #+ cfg.LOSS_DICE(out,mask)\n",
    "                total_loss+=loss.item()\n",
    "                total_dice+= self.cfg.DICE_COEF(out.data.to(\"cpu\"),mask.cpu())\n",
    "        return total_dice/len(data_loader),total_loss/len(data_loader)\n",
    "\n",
    "    \n",
    "    def earlystopping(self,val_loss):\n",
    "        \n",
    "        if val_loss < self.val_for_early_stopping:\n",
    "            self.val_for_early_stopping = val_loss\n",
    "            return True\n",
    "        else:\n",
    "            self.patience-=1\n",
    "            return False\n",
    " \n",
    "    def fit(self)->None:\n",
    "        print(\"started fitting the model\")\n",
    "        best_loss = 9999999\n",
    "        \n",
    "        for epoch in range(self.epochs):\n",
    "            self.model.train()\n",
    "            self.model.to(self.device)\n",
    "            \n",
    "            dice_score_ = 0\n",
    "            loss_ = 0\n",
    "            \n",
    "            for j,data in enumerate(tqdm(self.train_dataloader,total = len(self.train_dataloader))):\n",
    "                input_image_batch = data[0].to(self.device)\n",
    "                mask_batch = data[1].to(self.device)\n",
    "                self.optimizer.zero_grad()\n",
    "                output = self.model(input_image_batch)\n",
    "                loss = self.loss_function(output,mask_batch)#+cfg.LOSS_DICE(output,mask_batch.unsqueeze(1))\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                loss_+=loss.item()\n",
    "            \n",
    "                dice_score_+= self.cfg.DICE_COEF(output.data.to(\"cpu\"),mask_batch.to(\"cpu\"))\n",
    "\n",
    "            \n",
    "            dice_score_valid,loss_valid, = self.calculate_metrics(self.valid_dataloader)\n",
    "            train_dice = dice_score_/len(self.train_dataloader)\n",
    "            train_loss = loss_/len(self.train_dataloader)\n",
    "            print(f\"train dice score : {train_dice}, train loss {train_loss}\")\n",
    "            print(f\"valid dice score : {dice_score_valid}, valid loss {loss_valid}\")\n",
    "            \n",
    "            self.log.loc[epoch,:] = [f\"fold_{self.cfg.ENCODER}_{self.cfg.ENCODER}.pth\",f\"{train_loss}\",f\"{train_dice}\",f\"{loss_valid}\",f\"{dice_score_valid}\"]\n",
    "            self.log.to_csv(self.cfg.MODEL_SAVEPATH+f\"/fold_{self.cfg.MODEL_NAME}__{self.cfg.ENCODER}_{self.cfg.BATCH_SIZE}_CE_Valid_slicing_all.csv\",index=False)\n",
    "            \n",
    "            if self.patience >= 0 and self.earlystopping(loss_valid):\n",
    "                print(\"saving model\")\n",
    "                \n",
    "                torch.save(self.model.state_dict(),self.cfg.MODEL_SAVEPATH+f\"/fold_{self.cfg.MODEL_NAME}_{self.cfg.ENCODER}_{self.cfg.BATCH_SIZE}_CE_Valid_slicing_all.pth\")\n",
    "                self.patience= 5\n",
    "                \n",
    "            \n",
    "            if self.patience <= 0:\n",
    "                print(\"Training terminated, no improvement in valid loss\")\n",
    "                break\n",
    "                \n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "925d58f6-f602-4496-9b1e-f16e3ebcf22d",
   "metadata": {},
   "source": [
    "#### Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff2628e-508f-4dd1-8014-0d5b2763b118",
   "metadata": {},
   "source": [
    "##### Performing k fold cross validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "49fbfb93-36ac-4530-b7e1-159f38f2123a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_csv_path = \"Colonic_crypt_dataset/train.csv\"\n",
    "patches_csv_path = \"train_data.csv\"\n",
    "\n",
    "def kfold_training(train_data_csv_path:str,patches_csv_path:str,n_folds:int)->pd.DataFrame:\n",
    "    #get all the training data unique image ids\n",
    "    train_ids_from_csv = pd.read_csv(train_data_csv_path).iloc[0:-1,:]['id'].values\n",
    "    \n",
    "    nfold = KFold(n_folds, shuffle=True, random_state=0)\n",
    "    \n",
    "    patch_dataframe = pd.read_csv(patches_csv_path)\n",
    "    track_best_model = []\n",
    "    for i, (train_idx, val_idx) in enumerate(nfold.split(train_ids_from_csv)):\n",
    "        \n",
    "        train_ids = (patch_dataframe[patch_dataframe.Train_image_path.str.contains(\"|\".join(train_ids_from_csv[train_idx]))]).index\n",
    "        valid_ids = (patch_dataframe[patch_dataframe.Train_image_path.str.contains(\"|\".join(train_ids_from_csv[val_idx]))]).index\n",
    "        train_dataset = Colon_Dataset(\"train_data.csv\",indexes=train_ids,transform=utils.get_train_transforms(),preprocessing=utils.preprocessing_fucntion(cfg.PREPROCESS))\n",
    "        valid_dataset = Colon_Dataset(\"train_data.csv\",indexes=valid_ids,preprocessing=utils.preprocessing_fucntion(cfg.PREPROCESS))\n",
    "\n",
    "        train_dataloader = DataLoader(train_dataset,batch_size=cfg.BATCH_SIZE,shuffle=True)\n",
    "        valid_dataloader = DataLoader(valid_dataset,batch_size=cfg.BATCH_SIZE,shuffle=False)\n",
    "        cfg.MODEL_NAME = str(i) + \"_\"+cfg.ARCHITECTURE\n",
    "        trainer =Trainer(cfg,train_dataloader,valid_dataloader)\n",
    "        trainer.fit()\n",
    "        track_best_model.append([cfg.MODEL_NAME,trainer.val_for_early_stopping])\n",
    "        \n",
    "    pd.DataFrame(track_best_model,columns=[\"model name\",\"valid_loss\"]).to_csv(\"report_\"+cfg.MODEL_NAME+\".csv\",index=False)\n",
    "        \n",
    "    return track_best_model\n",
    "\n",
    "\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1cdbab23-c7ab-421e-a82b-f51dfb43beb8",
   "metadata": {},
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     "text": [
      "started fitting the model\n"
     ]
    },
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     "text": [
      "train dice score : 0.7793204188346863, train loss 0.36847229143406485\n",
      "valid dice score : 0.8037179708480835, valid loss 0.3701242655515671\n",
      "saving model\n"
     ]
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     "text": [
      "train dice score : 0.8323666453361511, train loss 0.27541542243450245\n",
      "valid dice score : 0.8157902956008911, valid loss 0.3339341804385185\n",
      "saving model\n"
     ]
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     "text": [
      "train dice score : 0.8674011826515198, train loss 0.20667448941063374\n",
      "valid dice score : 0.871990442276001, valid loss 0.19635743461549282\n",
      "saving model\n"
     ]
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     "text": [
      "train dice score : 0.9107339978218079, train loss 0.13198905152843354\n",
      "valid dice score : 0.8997569680213928, valid loss 0.15056656021624804\n",
      "saving model\n"
     ]
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     "text": [
      "train dice score : 0.9274612069129944, train loss 0.10924337970766615\n",
      "valid dice score : 0.914080023765564, valid loss 0.13697780389338732\n",
      "saving model\n"
     ]
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     "text": [
      "train dice score : 0.9372887015342712, train loss 0.09635114923436591\n",
      "valid dice score : 0.9264574646949768, valid loss 0.12529025366529822\n",
      "saving model\n"
     ]
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     "text": [
      "train dice score : 0.9480992555618286, train loss 0.08055320397970524\n",
      "valid dice score : 0.9313315749168396, valid loss 0.1256098411977291\n"
     ]
    },
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      "saving model\n"
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      "Training terminated, no improvement in valid loss\n",
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      "saving model\n"
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      "valid dice score : 0.9568755030632019, valid loss 0.11985030759345083\n",
      "Training terminated, no improvement in valid loss\n",
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      "saving model\n"
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      "Training terminated, no improvement in valid loss\n",
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      "valid dice score : 0.9455625414848328, valid loss 0.12468922813422978\n",
      "Training terminated, no improvement in valid loss\n",
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      "saving model\n"
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      "saving model\n"
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     "text": [
      "train dice score : 0.9454898238182068, train loss 0.08010670415898587\n",
      "valid dice score : 0.9374510645866394, valid loss 0.09411236131563783\n",
      "saving model\n"
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     "text": [
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     "text": [
      "train dice score : 0.9538592100143433, train loss 0.06763440980873209\n",
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     "text": [
      "train dice score : 0.9569834470748901, train loss 0.06677625090517897\n",
      "valid dice score : 0.9454435110092163, valid loss 0.09533842420205474\n"
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     "text": [
      "train dice score : 0.9620845317840576, train loss 0.05803274177014828\n",
      "valid dice score : 0.9442935585975647, valid loss 0.09872580133378506\n"
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     "text": [
      "train dice score : 0.9635301232337952, train loss 0.05889498046103944\n",
      "valid dice score : 0.9388368725776672, valid loss 0.10565839242190123\n",
      "Training terminated, no improvement in valid loss\n"
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    {
     "data": {
      "text/plain": [
       "[['0_UNET', 0.10941710323095322],\n",
       " ['1_UNET', 0.0695892146185917],\n",
       " ['2_UNET', 0.08970232548959114],\n",
       " ['3_UNET', 0.12134397961199284],\n",
       " ['4_UNET', 0.09411236131563783]]"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kfold_training(train_data_csv_path,patches_csv_path,n_folds = cfg.NFOLDS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89cb2b1a-d892-4d94-95e7-a2d2512b3e7b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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