Train_loop.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "994b046f-e5e7-48c9-9afd-a510f864b5e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on device cuda.\n"
     ]
    }
   ],
   "source": [
    "##############################\n",
    "#Training loop for NNs       #\n",
    "#Maintainer: Christopher Chan#\n",
    "#Version: 0.3.2              #\n",
    "#Date: 2022-05-29            #\n",
    "##############################\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import torch\n",
    "import pathlib\n",
    "import time\n",
    "import re\n",
    "import PIL\n",
    "import random\n",
    "#import ray\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch.nn as nn\n",
    "import segmentation_models_pytorch as smp\n",
    "#from ray import tune\n",
    "#from ray.tune.suggest.bohb import TuneBOHB\n",
    "#from ray.tune.schedulers import HyperBandForBOHB\n",
    "from scipy import ndimage\n",
    "from PIL import Image\n",
    "from torch import optim\n",
    "from tqdm import tqdm\n",
    "from torchvision import transforms, datasets\n",
    "from torchvision.utils import save_image\n",
    "from torch.utils.data import DataLoader, random_split, ChainDataset, ConcatDataset\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from Networks import Five_UNet, Four_UNet\n",
    "from dataloader import BuildingDataset\n",
    "%matplotlib inline\n",
    "\n",
    "device = (torch.device(\"cuda\") if torch.cuda.is_available()\n",
    "          else torch.device(\"cpu\"))\n",
    "\n",
    "print(f\"Training on device {device}.\")\n",
    "\n",
    "#td_KBYSamp = os.path.abspath(\"/home/chris/Dropbox/HOTOSM/SAMPLE/td_KBY\")\n",
    "#td_DZKSamp = os.path.abspath(\"/home/chris/Dropbox/HOTOSM/SAMPLE/td_DZK\")\n",
    "#td_DZKNSamp = os.path.abspath(\"/home/chris/Dropbox/HOTOSM/SAMPLE/td_DZKN\")\n",
    "\n",
    "td_KBY10 = os.path.abspath(\"/home/mnt/HOTOSM_data/Kakuma10cm/Kalobeyei/td_KBY\")\n",
    "td_DZK10 = os.path.abspath(\"/home/mnt/HOTOSM_data/Dzaleka10cm/td_DZK\")\n",
    "td_DZKN10 = os.path.abspath(\"/home/mnt/HOTOSM_data/Dzaleka_N10cm/td_DZKN\")\n",
    "\n",
    "td_KBY15 = os.path.abspath(\"/home/mnt/HOTOSM_data//Kakuma15cm/Kalobeyei/td_KBY\")\n",
    "td_DZK15 = os.path.abspath(\"/home/mnt/HOTOSM_data/Dzaleka15cm/td_DZK\")\n",
    "td_DZKN15 = os.path.abspath(\"/home/mnt/HOTOSM_data/Dzaleka_N15cm/td_DZKN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98591c8",
   "metadata": {},
   "source": [
    "## Change class label from 2 to 1 for binary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afff1f48",
   "metadata": {},
   "source": [
    "for root, dirs, filename in os.walk(os.path.join(td_DZK10, \"LBL\")):\n",
    "    for i in filename:\n",
    "        if i.endswith(\".png\"):\n",
    "            png = Image.open(root + \"/\" + i)\n",
    "            px = png.load()\n",
    "\n",
    "            for x in range(png.size[0]):\n",
    "                for y in range(png.size[1]):\n",
    "                    if px[x, y] == 2:\n",
    "                        px[x, y] = 1\n",
    "                        \n",
    "                png.save(root + \"/\" + i, \"PNG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba42bb2-e3e5-4b82-8757-2c591377fa0c",
   "metadata": {},
   "source": [
    "### Train Val Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f2cfbd46-14a4-46d0-955c-8bcb85c3d3f2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Splitting exists, skipping Train, Val, Test split.\n",
      "Total images and labels pair in DataLoader: 7079\n",
      "Concatenated TRAINING images and labels pair: 5719 :\n",
      "Concatenated VALIDATION images and labels pair: 1224 :\n",
      "Concatenated TESTING images and labels pair: 136 :\n",
      "torch.Size([3, 256, 256]) torch.Size([1, 256, 256])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Train Label')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Below is a set of relatively complex functions which:\n",
    "# Perform the train, val, test split at a rounded ratio of 62%, 27%, and 10% based on each sets of imagery\n",
    "# This will be followed by first pseudo changing the name of _LBL_ to _IMG_ to match the split imagery\n",
    "# Lastly, once the correct LBL files are matched, \n",
    "\n",
    "def tvt_split(td):\n",
    "    \n",
    "    img_ls = []\n",
    "    \n",
    "    for root, dirs, filename in os.walk(os.path.join(td, \"IMG\")):\n",
    "        for i in filename:\n",
    "            if i.endswith(\".png\"):\n",
    "                img_ls.append(root + \"/\" + i)\n",
    "        \n",
    "        img_ls = BuildingDataset(img_ls, _)\n",
    "        \n",
    "        train_IMG, val_IMG, test_IMG = random_split(img_ls.png_dir, [int(round(0.6 * len(img_ls.png_dir))),\n",
    "                                                                     int(round(0.3 * len(img_ls.png_dir))),\n",
    "                                                                     int(round(0.1 * len(img_ls.png_dir)))])\n",
    "        \n",
    "        return train_IMG, val_IMG, test_IMG\n",
    "\n",
    "data_path = os.path.abspath(\"/home/mnt/HOTOSM_data/TVT_15cm\")\n",
    "\n",
    "if os.path.exists(data_path) == True:\n",
    "\n",
    "    TrainIMG_ls = []\n",
    "    TrainLBL_ls = []\n",
    "    ValIMG_ls = []\n",
    "    ValLBL_ls = []\n",
    "    TestIMG_ls = []\n",
    "    TestLBL_ls = []\n",
    "\n",
    "    for root, dirs, filename in os.walk(os.path.join(data_path, \"Train\", \"IMG\")):\n",
    "        for f in filename:\n",
    "            if f.startswith(\"KBY15_IMG\"):\n",
    "                TrainIMG_ls.append(root + \"/\" + f)\n",
    "\n",
    "    for root, dirs, filename in os.walk(os.path.join(data_path, \"Val\", \"IMG\")):\n",
    "        for f in filename:\n",
    "            if f.startswith(\"KBY15_IMG\"):\n",
    "                ValIMG_ls.append(root + \"/\" + f)\n",
    "    \n",
    "    for root, dirs, filename in os.walk(os.path.join(data_path, \"Test\", \"IMG\")):\n",
    "        for f in filename:\n",
    "            if f.startswith(\"KBY15_IMG\"):\n",
    "                TestIMG_ls.append(root + \"/\" + f)\n",
    "\n",
    "    \n",
    "    for i in TrainIMG_ls:\n",
    "        i = re.sub(\"IMG\", \"LBL\", i, count = 2)\n",
    "        TrainLBL_ls.append(i)\n",
    "\n",
    "    for i in ValIMG_ls:\n",
    "        i = re.sub(\"IMG\", \"LBL\", i, count = 2)\n",
    "        ValLBL_ls.append(i)\n",
    "\n",
    "    for i in TestIMG_ls:\n",
    "        i = re.sub(\"IMG\", \"LBL\", i, count = 2)\n",
    "        TestLBL_ls.append(i)\n",
    "\n",
    "    print(\"Splitting exists, skipping Train, Val, Test split.\")\n",
    "\n",
    "else:\n",
    "\n",
    "    DZK_train, DZK_val, DZK_test = tvt_split(td_DZK10)\n",
    "    KBY_train, KBY_val, KBY_test = tvt_split(td_KBY10)\n",
    "    DZKN_train, DZKN_val, DZKN_test = tvt_split(td_DZKN10)\n",
    "\n",
    "    ##################\n",
    "    # TOO COMPLICATED#\n",
    "    ##################\n",
    "\n",
    "    #def match_LBL(td, imgs):\n",
    "    #    \n",
    "    #    lbl_ls = []\n",
    "    #    img_ls = []\n",
    "    #    match_ls = []\n",
    "    #    \n",
    "    #    imgs = list(imgs)\n",
    "    #    \n",
    "    #    for root, dirs, filename in os.walk(os.path.join(td, \"LBL\")):\n",
    "    #        for j in filename:\n",
    "    #            if j.endswith(\".png\"):\n",
    "    #                ps_name = j.rsplit(\"_LBL_\")[0] + \"_IMG_\" + j.rsplit(\"_LBL_\")[1] # Parse the string, PSEUDO-CHANGE _LBL_ to _IMG_\n",
    "    #                lbl_ls.append(ps_name)\n",
    "    #    \n",
    "    #    for k in imgs:\n",
    "    #        names = os.path.basename(k)\n",
    "    #        img_ls.append(names)\n",
    "    #        \n",
    "    #    def common(a, b):\n",
    "    #        a_set = set(a)\n",
    "    #        b_set = set(b)\n",
    "    #        if (a_set & b_set):\n",
    "    #            return (a_set & b_set)\n",
    "    #        else:\n",
    "    #            print(\"No common elements\")\n",
    "    #            \n",
    "    #            \n",
    "    #    match_ls = common(img_ls, lbl_ls)\n",
    "    #        \n",
    "    #    match_ls = [(root + \"/\" + n.replace(\"_IMG_\", \"_LBL_\")) for n in match_ls] # Change the _IMG_ back to _LBL_\n",
    "    #    \n",
    "    #    print(\"For the selected dataset of {0}, There are: {1} images, {2} labels, and {3} matching image/label pairs.\".format(os.path.basename(td), len(img_ls), len(lbl_ls), len(match_ls)))\n",
    "    #    \n",
    "    #    return match_ls\n",
    "\n",
    "    #########################################\n",
    "    # Assign matched LBL to new LBL datasets#\n",
    "    #########################################\n",
    "\n",
    "    #DZKLBL_Train = match_LBL(td_DZK, DZK_train)\n",
    "    #DZKLBL_Val = match_LBL(td_DZK, DZK_val)\n",
    "    #DZKLBL_Test = match_LBL(td_DZK, DZK_test)\n",
    "    #DZKNLBL_Train = match_LBL(td_DZKN, DZKN_train)\n",
    "    #DZKNLBL_Val = match_LBL(td_DZKN, DZKN_val)\n",
    "    #DZKNLBL_Test = match_LBL(td_DZKN, DZKN_test)\n",
    "    #KBYLBL_Train = match_LBL(td_KBY, KBY_train)\n",
    "    #KBYLBL_Val = match_LBL(td_KBY, KBY_val)\n",
    "    #KBYLBL_Test = match_LBL(td_KBY, KBY_test)\n",
    "\n",
    "    ############\n",
    "    # Try again#\n",
    "    ############\n",
    "\n",
    "    TrainLBL_ls = []\n",
    "    ValLBL_ls = []\n",
    "    TestLBL_ls = []\n",
    "    \n",
    "    TrainIMG_ls = list(DZK_train + KBY_train + DZKN_train)\n",
    "    ValIMG_ls = list(DZK_val + KBY_val + DZKN_val)\n",
    "    TestIMG_ls = list(DZK_test + KBY_test + DZKN_test)\n",
    "\n",
    "    for i in TrainIMG_ls:\n",
    "        i = re.sub(\"IMG\", \"LBL\", i, count = 2)\n",
    "        TrainLBL_ls.append(i)\n",
    "\n",
    "    for i in ValIMG_ls:\n",
    "        i = re.sub(\"IMG\", \"LBL\", i, count = 2)\n",
    "        ValLBL_ls.append(i)\n",
    "\n",
    "    for i in TestIMG_ls:\n",
    "        i = re.sub(\"IMG\", \"LBL\", i, count = 2)\n",
    "        TestLBL_ls.append(i)\n",
    "\n",
    "    import shutil\n",
    "    for i in TrainIMG_ls:\n",
    "        shutil.copy(i, r\"/home/mnt/HOTOSM_data/TVT_10cm/Train/IMG\")\n",
    "    for i in TrainLBL_ls:\n",
    "        shutil.copy(i, r\"/home/mnt/HOTOSM_data/TVT_10cm/Train/LBL\")\n",
    "    for i in ValIMG_ls:\n",
    "        shutil.copy(i, r\"/home/mnt/HOTOSM_data/TVT_10cm/Val/IMG\")\n",
    "    for i in ValLBL_ls:\n",
    "        shutil.copy(i, r\"/home/mnt/HOTOSM_data/TVT_10cm/Val/LBL\")\n",
    "    for i in TestIMG_ls:\n",
    "        shutil.copy(i, r\"/home/mnt/HOTOSM_data/TVT_10cm/Test/IMG\")\n",
    "    for i in TestLBL_ls:\n",
    "        shutil.copy(i, r\"/home/mnt/HOTOSM_data/TVT_10cm/Test/LBL\")\n",
    "\n",
    "# Data Augmentation\n",
    "InRGB_Train = BuildingDataset(TrainIMG_ls, TrainLBL_ls, transform = \"InRGB\")\n",
    "HFLIP_Train = BuildingDataset(TrainIMG_ls, TrainLBL_ls, transform = \"Hflip\")\n",
    "VFLIP_Train = BuildingDataset(TrainIMG_ls, TrainLBL_ls, transform = \"Vflip\")\n",
    "BLUR_Train = BuildingDataset(TrainIMG_ls, TrainLBL_ls, transform = \"Blur\")\n",
    "CON_Train = BuildingDataset(TrainIMG_ls, TrainLBL_ls, transform = \"Contrast\")\n",
    "SOL_Train = BuildingDataset(TrainIMG_ls, TrainLBL_ls, transform = \"Solarize\")\n",
    "\n",
    "HFLIP_Val = BuildingDataset(ValIMG_ls, ValLBL_ls, transform = \"Hflip\")\n",
    "VFLIP_Val = BuildingDataset(ValIMG_ls, ValLBL_ls, transform = \"Vflip\")\n",
    "\n",
    "Train = BuildingDataset(png_dir = TrainIMG_ls,\n",
    "                        lbl_dir = TrainLBL_ls)\n",
    "\n",
    "Val = BuildingDataset(png_dir = ValIMG_ls,\n",
    "                      lbl_dir = ValLBL_ls)\n",
    "\n",
    "Test = BuildingDataset(png_dir = TestIMG_ls,\n",
    "                       lbl_dir = TestLBL_ls)\n",
    "\n",
    "#assert len(Train.png_dir) == len(Train.lbl_dir)\n",
    "#assert len(Val.png_dir) == len(Val.lbl_dir)\n",
    "\n",
    "print(\"Total images and labels pair in DataLoader: {0}\".format(len(Train.png_dir*7) + len(Val.png_dir*3) + len(Test.png_dir)))\n",
    "\n",
    "print(\"Concatenated TRAINING images and labels pair: {0} :\".format(len(Train.png_dir*7)))\n",
    "#for x, y in zip(Train.png_dir, Train.lbl_dir):    \n",
    "#    print(f\"Image: {x}\", f\"Label: {y}\")\n",
    "\n",
    "print(\"Concatenated VALIDATION images and labels pair: {0} :\".format(len(Val.png_dir*3)))\n",
    "#for x, y in zip(Val.png_dir, Val.lbl_dir):\n",
    "#    print(f\"Image: {x}\", f\"Label: {y}\")\n",
    "\n",
    "print(\"Concatenated TESTING images and labels pair: {0} :\".format(len(Test.png_dir)))\n",
    "#for x, y in zip(Test.png_dir, Test.lbl_dir):\n",
    "#    print(f\"Image: {x}\", f\"Label: {y}\")\n",
    "\n",
    "# plot a sample\n",
    "train_pair = random.choice(InRGB_Train)\n",
    "print(train_pair[0].shape, train_pair[1].shape)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "RGB = plt.imshow(train_pair[0].detach().cpu().numpy().transpose(1, 2, 0))\n",
    "ax.set_title('Train Sample')\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "LBL = plt.imshow(train_pair[1].detach().cpu().numpy().transpose(1, 2, 0))\n",
    "ax.set_title('Train Label')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c717152d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trimmed down version\n",
    "def rayHPO_loop(n_epochs, optimizer, model, xp_name,\n",
    "                loss_fn, in_channels, out_channels, train_loader,\n",
    "                checkpoint_freq):\n",
    "\n",
    "    model = model.train()\n",
    "\n",
    "    for epoch in tqdm(range(1, n_epochs + 1)):\n",
    "         \n",
    "        log_dir = os.path.abspath(\"/home/mnt/HOTOSM_data/log\")\n",
    "        checkpointdir = os.path.abspath(\"/home/mnt/HOTOSM_data/ray_checkpoints\")\n",
    "        loss_train = 0.0\n",
    "\n",
    "        for i, (imgs, labels) in tqdm(enumerate(train_loader), total = len(train_loader)):\n",
    "            imgs = imgs.to(device = device, dtype = torch.float32)\n",
    "            labels = labels.to(device = device, dtype = torch.float32)\n",
    "\n",
    "            prediction = model(imgs)\n",
    "            loss = loss_fn(prediction.squeeze(0), labels.squeeze(0))\n",
    "            \n",
    "            lbl = labels.squeeze(0)\n",
    "            pred_BOOL = (prediction.squeeze(0) > 0.5).to(torch.float32)\n",
    "\n",
    "            # Dice Loss\n",
    "            dice_pred = pred_BOOL.sum(dim = [0, 1, 2])\n",
    "            dice_lbl = lbl.sum(dim = [0, 1, 2])\n",
    "            dice_score = 0\n",
    "            dice_score += (2 * (pred_BOOL * lbl).sum(dim = [0, 1, 2]) + 1e-8) / (dice_pred + dice_lbl + 1e-8)\n",
    "            dice_score = dice_score.detach().cpu().numpy()\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss_train += loss.item()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            global_step = epoch * len(train_loader) + i\n",
    "\n",
    "        # Validation\n",
    "        #if epoch % val_freq == 0:\n",
    "        #    \n",
    "        #    model = model.eval()\n",
    "        #    val_loss = 0.0\n",
    "\n",
    "        #    with torch.no_grad():\n",
    "        #        for i, (imgs, labels) in tqdm(enumerate(val_loader), total = len(val_loader)):\n",
    "        #            imgs = imgs.to(device = device, dtype = torch.float32)\n",
    "        #            labels = labels.to(device = device, dtype = torch.float32)\n",
    "\n",
    "        #            prediction = model(imgs)\n",
    "        #            val_loss += loss_fn(prediction.squeeze(0), labels.squeeze(0))\n",
    "        #            assert val_loss.requires_grad == False\n",
    "\n",
    "        if epoch % checkpoint_freq == 0:\n",
    "            with tune.checkpoint_dir(step = global_step) as checkpoint_dir:\n",
    "                checkpoint_dir = checkpointdir\n",
    "                os.makedirs(os.path.join(checkpoint_dir, xp_name), exist_ok = True)\n",
    "                checkpoint_file = os.path.join(checkpoint_dir, xp_name, xp_name + \"_iter_\" + str(global_step).zfill(6) + \".pth\")\n",
    "                model_state = {}\n",
    "\n",
    "                model_state = model.state_dict()\n",
    "\n",
    "                state = {\"Model:\": model_state, \"Epoch:\": epoch, \"Steps:\": global_step}\n",
    "                torch.save(state, checkpoint_file)\n",
    "\n",
    "            tune.report(loss = (loss_train / global_step), dice_score = dice_score)\n",
    "    \n",
    "    print(\"Finished Training\")\n",
    "\n",
    "###########################################\n",
    "###########################################\n",
    "###########################################\n",
    "\n",
    "def training_loop(n_epochs, optimizer, model, xp_name,\n",
    "                  loss_fn, in_channels, out_channels, train_loader,\n",
    "                  val_loader, val_freq, checkpoint_freq):\n",
    "\n",
    "    model = model.train()\n",
    "    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode = \"min\", patience = 20,\n",
    "                                                     factor = 0.1, verbose = True, min_lr = 1e-8)\n",
    "\n",
    "    for epoch in tqdm(range(1, n_epochs + 1)):\n",
    "        \n",
    "        log_dir = os.path.abspath(\"/home/mnt/HOTOSM_data/log\")\n",
    "        checkpointdir = os.path.abspath(\"/home/mnt/HOTOSM_data/checkpoints\")\n",
    "        writer = SummaryWriter(os.path.join(log_dir, xp_name))\n",
    "\n",
    "        loss_train = 0.0\n",
    "\n",
    "        for i, (imgs, labels) in tqdm(enumerate(train_loader), total = len(train_loader)):\n",
    "            imgs = imgs.to(device = device, dtype = torch.float32)\n",
    "            labels = labels.to(device = device, dtype = torch.float32)\n",
    "\n",
    "            prediction = model(imgs)\n",
    "            loss = loss_fn(prediction.squeeze(0), labels.squeeze(0))\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            loss_train += loss.item()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            global_step = epoch * len(train_loader) + i\n",
    "\n",
    "            if global_step % 10 == 0:\n",
    "                writer.add_scalar(\"Train/Loss\", loss.item(), global_step = global_step)\n",
    "                writer.add_scalar(\"Batch/Loss\", loss_train/len(train_loader), global_step = global_step)\n",
    "        # Validation\n",
    "\n",
    "        if epoch % val_freq == 0:\n",
    "            \n",
    "            model = model.eval()\n",
    "            val_loss = 0.0\n",
    "\n",
    "            with torch.no_grad():\n",
    "                for i, (imgs, labels) in tqdm(enumerate(val_loader), total = len(val_loader)):\n",
    "                    imgs = imgs.to(device = device, dtype = torch.float32)\n",
    "                    labels = labels.to(device = device, dtype = torch.float32)\n",
    "\n",
    "                    prediction = model(imgs)\n",
    "                    val_loss += loss_fn(prediction.squeeze(0), labels.squeeze(0))\n",
    "                    assert val_loss.requires_grad == False\n",
    "                    scheduler.step(val_loss)\n",
    "\n",
    "                    #writer.add_images(\"Val/Sample_LBL\", labels.squeeze(0), dataformats = \"CHW\",\n",
    "                    #                  global_step = global_step)\n",
    "\n",
    "                    #writer.add_images(\"Val/Sample_pred\", prediction.squeeze(0), dataformats = \"CHW\",\n",
    "                    #                  global_step = global_step)\n",
    "\n",
    "                    #predBOOL = (prediction > 0.5)\n",
    "\n",
    "                    #writer.add_images(\"Val/Sample_segmentation\", predBOOL.squeeze(0), dataformats = \"CHW\",\n",
    "                    #                      global_step = global_step)\n",
    "\n",
    "                writer.add_scalar(\"Val/Loss\", val_loss.item(), global_step = global_step)\n",
    "\n",
    "\n",
    "        if epoch % checkpoint_freq == 0:\n",
    "            os.makedirs(os.path.join(checkpointdir, xp_name), exist_ok = True)\n",
    "            checkpoint_file = os.path.join(checkpointdir, xp_name, xp_name + \"_iter_\" + str(global_step).zfill(6) + \".pth\")\n",
    "            model_state = {}\n",
    "\n",
    "            model_state = model.state_dict()\n",
    "\n",
    "            state = {\"Model:\": model_state, \"Epoch:\": epoch, \"Steps:\": global_step}\n",
    "            torch.save(state, checkpoint_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "141acde1-7c7d-438f-bc83-aec86c900a4d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth\" to /home/chris/.cache/torch/hub/checkpoints/efficientnet-b0-355c32eb.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7e9f7ff0c8240a3a021e3d008d51ab1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/20.4M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unet(\n",
      "  (encoder): EfficientNetEncoder(\n",
      "    (_conv_stem): Conv2dStaticSamePadding(\n",
      "      3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False\n",
      "      (static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)\n",
      "    )\n",
      "    (_bn0): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "    (_blocks): ModuleList(\n",
      "      (0): MBConvBlock(\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          32, 32, kernel_size=(3, 3), stride=[1, 1], groups=32, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          32, 8, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          8, 32, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(16, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (1): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          96, 96, kernel_size=(3, 3), stride=[2, 2], groups=96, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          96, 4, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          4, 96, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (2): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          144, 144, kernel_size=(3, 3), stride=(1, 1), groups=144, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          144, 6, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          6, 144, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (3): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          144, 144, kernel_size=(5, 5), stride=[2, 2], groups=144, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          144, 6, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          6, 144, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (4): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          240, 240, kernel_size=(5, 5), stride=(1, 1), groups=240, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          240, 10, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          10, 240, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (5): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          240, 240, kernel_size=(3, 3), stride=[2, 2], groups=240, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          240, 10, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          10, 240, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (6): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          480, 480, kernel_size=(3, 3), stride=(1, 1), groups=480, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          480, 20, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          20, 480, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (7): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          480, 480, kernel_size=(3, 3), stride=(1, 1), groups=480, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          480, 20, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          20, 480, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (8): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          480, 480, kernel_size=(5, 5), stride=[1, 1], groups=480, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          480, 20, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          20, 480, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (9): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          672, 672, kernel_size=(5, 5), stride=(1, 1), groups=672, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          672, 28, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          28, 672, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (10): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          672, 672, kernel_size=(5, 5), stride=(1, 1), groups=672, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          672, 28, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          28, 672, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (11): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          672, 672, kernel_size=(5, 5), stride=[2, 2], groups=672, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          672, 28, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          28, 672, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (12): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          1152, 48, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          48, 1152, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (13): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          1152, 48, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          48, 1152, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (14): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          1152, 48, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          48, 1152, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "      (15): MBConvBlock(\n",
      "        (_expand_conv): Conv2dStaticSamePadding(\n",
      "          192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
      "          1152, 1152, kernel_size=(3, 3), stride=[1, 1], groups=1152, bias=False\n",
      "          (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
      "        )\n",
      "        (_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_se_reduce): Conv2dStaticSamePadding(\n",
      "          1152, 48, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_se_expand): Conv2dStaticSamePadding(\n",
      "          48, 1152, kernel_size=(1, 1), stride=(1, 1)\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_project_conv): Conv2dStaticSamePadding(\n",
      "          1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (static_padding): Identity()\n",
      "        )\n",
      "        (_bn2): BatchNorm2d(320, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "        (_swish): MemoryEfficientSwish()\n",
      "      )\n",
      "    )\n",
      "    (_conv_head): Conv2dStaticSamePadding(\n",
      "      320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "      (static_padding): Identity()\n",
      "    )\n",
      "    (_bn1): BatchNorm2d(1280, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "    (_avg_pooling): AdaptiveAvgPool2d(output_size=1)\n",
      "    (_dropout): Dropout(p=0.2, inplace=False)\n",
      "    (_swish): MemoryEfficientSwish()\n",
      "  )\n",
      "  (decoder): UnetDecoder(\n",
      "    (center): Identity()\n",
      "    (blocks): ModuleList(\n",
      "      (0): DecoderBlock(\n",
      "        (conv1): Conv2dReLU(\n",
      "          (0): Conv2d(152, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "        )\n",
      "        (attention1): Attention(\n",
      "          (attention): Identity()\n",
      "        )\n",
      "        (conv2): Conv2dReLU(\n",
      "          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "        )\n",
      "        (attention2): Attention(\n",
      "          (attention): Identity()\n",
      "        )\n",
      "      )\n",
      "      (1): DecoderBlock(\n",
      "        (conv1): Conv2dReLU(\n",
      "          (0): Conv2d(152, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "        )\n",
      "        (attention1): Attention(\n",
      "          (attention): Identity()\n",
      "        )\n",
      "        (conv2): Conv2dReLU(\n",
      "          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "        )\n",
      "        (attention2): Attention(\n",
      "          (attention): Identity()\n",
      "        )\n",
      "      )\n",
      "      (2): DecoderBlock(\n",
      "        (conv1): Conv2dReLU(\n",
      "          (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "        )\n",
      "        (attention1): Attention(\n",
      "          (attention): Identity()\n",
      "        )\n",
      "        (conv2): Conv2dReLU(\n",
      "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "        )\n",
      "        (attention2): Attention(\n",
      "          (attention): Identity()\n",
      "        )\n",
      "      )\n",
      "      (3): DecoderBlock(\n",
      "        (conv1): Conv2dReLU(\n",
      "          (0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "        )\n",
      "        (attention1): Attention(\n",
      "          (attention): Identity()\n",
      "        )\n",
      "        (conv2): Conv2dReLU(\n",
      "          (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "        )\n",
      "        (attention2): Attention(\n",
      "          (attention): Identity()\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (segmentation_head): SegmentationHead(\n",
      "    (0): Conv2d(16, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): Identity()\n",
      "    (2): Activation(\n",
      "      (activation): Sigmoid()\n",
      "    )\n",
      "  )\n",
      ")\n",
      "Trainable parameters in current model: [864, 32, 32, 288, 32, 32, 256, 8, 256, 32, 512, 16, 16, 1536, 96, 96, 864, 96, 96, 384, 4, 384, 96, 2304, 24, 24, 3456, 144, 144, 1296, 144, 144, 864, 6, 864, 144, 3456, 24, 24, 3456, 144, 144, 3600, 144, 144, 864, 6, 864, 144, 5760, 40, 40, 9600, 240, 240, 6000, 240, 240, 2400, 10, 2400, 240, 9600, 40, 40, 9600, 240, 240, 2160, 240, 240, 2400, 10, 2400, 240, 19200, 80, 80, 38400, 480, 480, 4320, 480, 480, 9600, 20, 9600, 480, 38400, 80, 80, 38400, 480, 480, 4320, 480, 480, 9600, 20, 9600, 480, 38400, 80, 80, 38400, 480, 480, 12000, 480, 480, 9600, 20, 9600, 480, 53760, 112, 112, 75264, 672, 672, 16800, 672, 672, 18816, 28, 18816, 672, 75264, 112, 112, 75264, 672, 672, 16800, 672, 672, 18816, 28, 18816, 672, 75264, 112, 112, 75264, 672, 672, 16800, 672, 672, 18816, 28, 18816, 672, 129024, 192, 192, 221184, 1152, 1152, 28800, 1152, 1152, 55296, 48, 55296, 1152, 221184, 192, 192, 221184, 1152, 1152, 28800, 1152, 1152, 55296, 48, 55296, 1152, 221184, 192, 192, 221184, 1152, 1152, 28800, 1152, 1152, 55296, 48, 55296, 1152, 221184, 192, 192, 221184, 1152, 1152, 10368, 1152, 1152, 55296, 48, 55296, 1152, 368640, 320, 320, 409600, 1280, 1280, 175104, 128, 128, 147456, 128, 128, 87552, 64, 64, 36864, 64, 64, 27648, 32, 32, 9216, 32, 32, 4608, 16, 16, 2304, 16, 16, 144, 1]\n"
     ]
    }
   ],
   "source": [
    "Net = smp.Unet(encoder_name = 'efficientnet-b0', encoder_depth = 4, encoder_weights = \"imagenet\", decoder_use_batchnorm = True, decoder_channels = (128, 64, 32, 16), decoder_attention_type = None, in_channels = 3, classes = 1, activation = \"sigmoid\", aux_params = None)\n",
    "#qubvel_weights = os.path.abspath(\"/home/chris/Dropbox/HOTOSM/qubvel_UNet/weights/stage3/effb1-f0/checkpoints/best.pth\")\n",
    "#qubvel_weights = torch.load(qubvel_weights, map_location = device)\n",
    "#Net.load_state_dict(qubvel_weights[\"state_dict\"])\n",
    "\n",
    "#Net = smp.Unet(encoder_name = 'efficientnet-b1', encoder_depth = 4, encoder_weights = None, decoder_use_batchnorm = True, decoder_channels=(128, 64, 32, 16), decoder_attention_type = None, in_channels= 3 , classes = 1, activation = \"sigmoid\", aux_params = None)\n",
    "\n",
    "#Net = Five_UNet()\n",
    "\n",
    "#Net = torch.hub.load('mateuszbuda/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)\n",
    "\n",
    "Net = Net.to(device = device)\n",
    "\n",
    "n_params = [p.numel() for p in Net.parameters() if p.requires_grad == True]\n",
    "\n",
    "print(Net)\n",
    "print('Trainable parameters in current model:', n_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df93dd09",
   "metadata": {},
   "source": [
    "### RayTune Hyperparameter Optimisation experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35920f54",
   "metadata": {},
   "source": [
    "# Ray tune Hyperparameter Optimsisation experiment using RayHPO_loop\n",
    "xp_name = \"9472:1184oc1_EB1-UNet-IMN_RAY_HYPERBANDBOHB_ep100_BCE\"\n",
    "\n",
    "\n",
    "\n",
    "#config = {\"lr\": tune.loguniform(1e-5, 1e-2),\n",
    "#          \"wd\": tune.loguniform(1e-6, 1e-2),\n",
    "#          \"batch_size\": tune.choice([4, 6])}\n",
    "\n",
    "def main(num_samples = 5, max_epochs = 1, gpus_per_trial = 1):\n",
    "\n",
    "    search_algo = TuneBOHB(mode = \"min\",\n",
    "                           metric = \"mean_loss\")\n",
    "\n",
    "    scheduler = HyperBandForBOHB(time_attr = \"training_iteration\",\n",
    "                                 metric = \"mean_loss\",\n",
    "                                 mode = \"min\",\n",
    "                                 max_t = max_epochs)\n",
    "\n",
    "    config = {\"lr\": np.exp(np.random.uniform(1e-5, 1e-2)),\n",
    "              \"wd\": np.exp(np.random.uniform(1e-6, 1e-2)),\n",
    "              \"batch_size\": int(np.random.choice([6, 8]))}\n",
    "\n",
    "    Loop = rayHPO_loop(n_epochs = max_epochs, \n",
    "                       optimizer = torch.optim.Adam(Net.parameters(), lr = config[\"lr\"], weight_decay = config[\"wd\"]),\n",
    "                       model = Net,\n",
    "                       in_channels = 3,\n",
    "                       out_channels = 1,\n",
    "                       xp_name = xp_name,\n",
    "                       loss_fn = nn.BCELoss(reduction = 'mean'),\n",
    "                       train_loader = DataLoader(Train, batch_size = config[\"batch_size\"], shuffle = True),\n",
    "                       checkpoint_freq = 1)\n",
    "\n",
    "    result = tune.run(tune.with_parameters(Loop),\n",
    "                      resources_per_trial = {\"cpu\": 4, \"gpu\": gpus_per_trial},\n",
    "                      config = config,\n",
    "                      num_samples = num_samples,\n",
    "                      search_alg = search_algo,\n",
    "                      scheduler = scheduler)\n",
    "\n",
    "    best_trial = result.get_best_trial(\"loss\", \"min\", \"last\")\n",
    "    print(\"Best trial config: {}\".format(best_trial.config))\n",
    "    print(\"Best trial final validation loss: {}\".format(best_trial.last_result[\"loss\"]))\n",
    "    \n",
    "    return best_trial\n",
    "\n",
    "main()\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf60c9f1",
   "metadata": {},
   "source": [
    "torch.cuda.empty_cache()\n",
    "\n",
    "print(best_trial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "098875cb-a2b4-4309-9d6c-a2976f7a88fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 256, 256]) torch.Size([1, 256, 256])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
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      "Epoch    22: reducing learning rate of group 0 to 1.0000e-04.\n"
     ]
    },
    {
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    {
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      "Epoch    43: reducing learning rate of group 0 to 1.0000e-05.\n"
     ]
    },
    {
     "name": "stderr",
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    },
    {
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      "Epoch    64: reducing learning rate of group 0 to 1.0000e-06.\n"
     ]
    },
    {
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      "Epoch   100: reducing learning rate of group 0 to 1.0000e-07.\n"
     ]
    },
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      "Epoch   121: reducing learning rate of group 0 to 1.0000e-08.\n"
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     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp_name = \"KBY5719:1224_256oc_EB0-Unet-IMN_lr1e-3_wd1e-5_b32_ep500_BCE_RLRonPlateau(min1e-8)\"\n",
    "\n",
    "ChainedTrain = ConcatDataset([Train, InRGB_Train, HFLIP_Train, VFLIP_Train, BLUR_Train, CON_Train, SOL_Train])\n",
    "ChainedVal = ConcatDataset([Val, HFLIP_Val, VFLIP_Val])\n",
    "\n",
    "# plot a sample\n",
    "train_pair = random.choice(ChainedTrain)\n",
    "print(train_pair[0].shape, train_pair[1].shape)\n",
    " \n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "RGB = plt.imshow(train_pair[0].detach().cpu().numpy().transpose(1, 2, 0))\n",
    "ax.set_title('Train Sample')\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "LBL = plt.imshow(train_pair[1].detach().cpu().numpy().transpose(1, 2, 0))\n",
    "ax.set_title('Train Label')\n",
    "\n",
    "training_loop(n_epochs = 500,\n",
    "             optimizer = torch.optim.Adam(Net.parameters(), lr = 1e-3, weight_decay = 1e-5),\n",
    "             model = Net,\n",
    "             in_channels = 3,\n",
    "             out_channels = 1,\n",
    "             xp_name = xp_name,\n",
    "             loss_fn = nn.BCELoss(reduction = 'mean'),\n",
    "             train_loader = DataLoader(ChainedTrain,\n",
    "                                       batch_size = 32, shuffle = True),\n",
    "             val_loader = DataLoader(ChainedVal,\n",
    "                                     batch_size = 32, shuffle = True),\n",
    "             checkpoint_freq = 10,\n",
    "             val_freq = 10)\n",
    "\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "561a2d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f34d1d48",
   "metadata": {},
   "source": [
    "## Prediction on Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de47f60c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'LBL')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a sample\n",
    "test_pair = random.choice(Test)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "RGB = plt.imshow(test_pair[0].detach().cpu().numpy().transpose(1, 2, 0))\n",
    "ax.set_title('RGB')\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "LBL = plt.imshow(test_pair[1].detach().cpu().numpy().transpose(1, 2, 0))\n",
    "ax.set_title('LBL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d1fd594",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 256, 256]) torch.Size([1, 256, 256])\n",
      "Minimum: 2.142128323612269e-07, Maximum: 0.004829976242035627, Mean: 0.0003991887206211686, St dev: 0.0011060712859034538\n",
      "True Positive = 6505, True Negative = 57960, False Negative = 265 False Positive = 806\n",
      "Precision = 0.8897551894187927, Recall = 0.9608567357063293, Specificity = 0.986284613609314, Overall Accuracy = 0.9836578369140625, Intersection-over-Union = 0.858632504940033, Dice-Score = 0.9239400625228882\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Binary Segmentation')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "Net = smp.Unet(encoder_name = 'efficientnet-b0', encoder_depth = 4,\n",
    "               encoder_weights = None, decoder_use_batchnorm = True, \n",
    "               decoder_channels = (128, 64, 32, 16), decoder_attention_type = None,\n",
    "               in_channels = 3, classes = 1, activation = \"sigmoid\", aux_params = None)\n",
    "\n",
    "#Net = Four_UNet()\n",
    "#Net = Five_UNet()\n",
    "\n",
    "checkpoint_dir = os.path.abspath(\"/home/mnt/HOTOSM_data/checkpoints\")\n",
    "#checkpoint_dir = os.path.abspath(\"/home/chris/Dropbox/HOTOSM/SAMPLE/best_weights\")\n",
    "best_iter = \"_iter_028818.pth\"\n",
    "\n",
    "NN_weights = os.path.join(checkpoint_dir, xp_name, xp_name + best_iter)\n",
    "\n",
    "NN_weights = torch.load(NN_weights, map_location = device)\n",
    "\n",
    "Net.load_state_dict(NN_weights[\"Model:\"])\n",
    "Net.to(device = \"cpu\")\n",
    "Net.eval()\n",
    "\n",
    "img, lbl = test_pair[0], test_pair[1]\n",
    "img = img.to(device = \"cpu\")\n",
    "lbl = lbl.to(device = \"cpu\")\n",
    "print(img.shape, lbl.shape)\n",
    "\n",
    "with torch.no_grad():\n",
    "    prediction = Net(img.unsqueeze(0))\n",
    "    prediction = prediction.squeeze(0)\n",
    "\n",
    "    print(\"Minimum: {0}, Maximum: {1}, Mean: {2}, St dev: {3}\".format(prediction.min(), prediction.max(), prediction.mean(), prediction.std()))\n",
    "\n",
    "    pred_BOOL = (prediction > 0.001).to(torch.float32)\n",
    "\n",
    "    # Booleanise\n",
    "    pred_BOOL = pred_BOOL.bool()\n",
    "    lbl = lbl.bool()\n",
    "\n",
    "    # 1st order statistics\n",
    "    TP = (pred_BOOL * lbl).sum(dim = [0, 1, 2])\n",
    "    TN = ((~pred_BOOL) * (~lbl)).sum(dim = [0, 1, 2])\n",
    "    FN = ((~pred_BOOL) * lbl).sum(dim = [0, 1, 2])\n",
    "    FP = (pred_BOOL * (~lbl)).sum(dim = [0, 1, 2])\n",
    "\n",
    "    # 2nd order statistics\n",
    "    Precision = (TP / (TP + FP))\n",
    "    Recall = (TP / (TP + FN)) #True Positive Rate / Sensitivity\n",
    "    Specificity = (TN / (FP + TN)) #True Negative Rate\n",
    "    OA = ((TP + TN) / (TP + TN + FP + FN))\n",
    "    IoU = (TP / (TP + FN + FP))\n",
    "    Dice = (2 * ((Precision * Recall) / (Precision + Recall)))\n",
    "\n",
    "\n",
    "print(\"True Positive = {0}, True Negative = {1}, False Negative = {2} False Positive = {3}\".format(TP, TN, FN, FP))\n",
    "print(\"Precision = {0}, Recall = {1}, Specificity = {2}, Overall Accuracy = {3}, Intersection-over-Union = {4}, Dice-Score = {5}\".format(Precision, Recall, Specificity, OA, IoU, Dice))\n",
    "\n",
    "# Plotting the RGB, Prediction, and Confidence\n",
    "fig = plt.figure(figsize = (15, 6))\n",
    "ax = fig.add_subplot(1, 4, 1)\n",
    "fig.tight_layout(w_pad = 3)\n",
    "fig.suptitle(\"256x256(15cm) 4-layer UNet | Dice Score: {0}\".format(Dice))\n",
    "RGB = plt.imshow(test_pair[0].detach().cpu().numpy().transpose(1, 2, 0))\n",
    "ax.set_title('RGB')\n",
    "\n",
    "ax = fig.add_subplot(1, 4, 2)\n",
    "PILlbl = lbl.detach().cpu().numpy().transpose(1, 2, 0)\n",
    "plt.imshow(PILlbl)\n",
    "ax.set_title('Label')\n",
    "\n",
    "ax = fig.add_subplot(1, 4, 3)\n",
    "PILpred = prediction.detach().cpu().numpy().transpose(1, 2, 0)\n",
    "plt.imshow(PILpred, cmap = \"brg\")\n",
    "plt.colorbar(boundaries = np.arange(0, 1.0, 0.1), ticks = np.arange(0, 1.0, 0.1),\n",
    "             orientation = \"horizontal\")\n",
    "ax.set_title('Prediction')\n",
    "\n",
    "ax = fig.add_subplot(1, 4, 4)\n",
    "PILbin = pred_BOOL.detach().cpu().numpy().transpose(1, 2, 0)\n",
    "plt.imshow(PILbin, cmap = \"hot\")\n",
    "ax.set_title('Binary Segmentation')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a437ed1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 136/136 [00:01<00:00, 92.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KBY5719:1224_256oc_EB0-Unet-IMN_lr1e-3_wd1e-5_b32_ep500_BCE_RLRonPlateau(min1e-8)_iter_028818.pth per Window statistics: mean Dice Score = 0.88250732421875, mean Precision = 0.8241199254989624, mean Recall = 0.8846703171730042, mean Specificity = 0.9899119138717651, mean OA = 0.9879526495933533, mean IoU = 0.7777359485626221\n",
      "\n",
      "\n",
      "KBY5719:1224_256oc_EB0-Unet-IMN_lr1e-3_wd1e-5_b32_ep500_BCE_RLRonPlateau(min1e-8)_iter_028818.pth GLOBAL statistics: Dice Score = 0.9277514656338879, Precision = 0.8968135035850686, Recall = 0.9609002708115846, Specificity = 0.9903209388947953, OA = 0.9879526250502643, IoU = 0.8652392014528167\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Loading testing batch\n",
    "test_loader = DataLoader(Test, batch_size = 1, shuffle = True)\n",
    "Net.to(device = device)\n",
    "\n",
    "idx = []\n",
    "png_ls = []\n",
    "Dice_ls = []\n",
    "TP_ls = []\n",
    "TN_ls = []\n",
    "FN_ls = []\n",
    "FP_ls = []\n",
    "Precision_ls = []\n",
    "Recall_ls = []\n",
    "Specificity_ls = []\n",
    "OA_ls = []\n",
    "IoU_ls = []\n",
    "\n",
    "for i, (img, lbl) in tqdm(enumerate(test_loader), total = len(test_loader)):\n",
    "    img = img.to(device = device)\n",
    "    lbl = lbl.to(device = device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        prediction = Net(img)\n",
    "        prediction = prediction.squeeze(0)\n",
    "        lbl = lbl.squeeze(0)\n",
    "\n",
    "        pred_BOOL = (prediction > 0.001).to(torch.float32)\n",
    "\n",
    "        # Booleanise\n",
    "        pred_BOOL = pred_BOOL.bool()\n",
    "        lbl = lbl.bool()\n",
    "\n",
    "        # 1st order statistics\n",
    "        TP = (pred_BOOL * lbl).sum(dim = [0, 1, 2])\n",
    "        TN = ((~pred_BOOL) * (~lbl)).sum(dim = [0, 1, 2])\n",
    "        FN = ((~pred_BOOL) * lbl).sum(dim = [0, 1, 2])\n",
    "        FP = (pred_BOOL * (~lbl)).sum(dim = [0, 1, 2])\n",
    "\n",
    "        # 2nd order statistics\n",
    "        Precision = (TP / (TP + FP))\n",
    "        Recall = (TP / (TP + FN)) #True Positive Rate / Sensitivity\n",
    "        Specificity = (TN / (FP + TN)) #True Negative Rate\n",
    "        OA = ((TP + TN) /(TP + TN + FP + FN))\n",
    "        IoU = (TP / (TP + FN + FP))\n",
    "        Dice = (2 * ((Precision * Recall) / (Precision + Recall)))\n",
    "\n",
    "        TP = TP.detach().cpu().numpy()\n",
    "        TN = TN.detach().cpu().numpy()\n",
    "        FN = FN.detach().cpu().numpy()\n",
    "        FP = FP.detach().cpu().numpy()\n",
    "        Precision = Precision.detach().cpu().numpy()\n",
    "        Recall = Recall.detach().cpu().numpy()\n",
    "        Specificity = Specificity.detach().cpu().numpy()\n",
    "        OA = OA.detach().cpu().numpy()\n",
    "        IoU = IoU.detach().cpu().numpy()\n",
    "        Dice = Dice.detach().cpu().numpy()\n",
    "\n",
    "        idx.append(i)\n",
    "        TP_ls.append(TP)\n",
    "        TN_ls.append(TN)\n",
    "        FN_ls.append(FN)\n",
    "        FP_ls.append(FP)\n",
    "        Precision_ls.append(Precision)\n",
    "        Recall_ls.append(Recall)\n",
    "        Specificity_ls.append(Specificity)\n",
    "        OA_ls.append(OA)\n",
    "        IoU_ls.append(IoU)\n",
    "        Dice_ls.append(Dice)\n",
    "\n",
    "for j in Test.png_dir:\n",
    "    png_ls.append(os.path.basename(j))\n",
    "\n",
    "df = {\"id\": idx,\n",
    "      \"png\": png_ls,\n",
    "      \"Dice_score\": Dice_ls,\n",
    "      \"True_Positive\": TP_ls,\n",
    "      \"True_Negative\": TN_ls,\n",
    "      \"False_Negative\": FN_ls,\n",
    "      \"False_Positive\": FP_ls,\n",
    "      \"Precision\": Precision_ls,\n",
    "      \"Recall\": Recall_ls,\n",
    "      \"Specificity\": Specificity_ls,\n",
    "      \"Overall Accuracy\": OA_ls,\n",
    "      \"IoU\": IoU_ls}\n",
    "    \n",
    "TP_SUM = np.sum(df[\"True_Positive\"])\n",
    "TN_SUM = np.sum(df[\"True_Negative\"])\n",
    "FP_SUM = np.sum(df[\"False_Positive\"])\n",
    "FN_SUM = np.sum(df[\"False_Negative\"])\n",
    "\n",
    "# 2nd order statistics\n",
    "GLOB_Precision = (TP_SUM / (TP_SUM + FP_SUM))\n",
    "GLOB_Recall = (TP_SUM / (TP_SUM + FN_SUM)) #True Positive Rate / Sensitivity\n",
    "GLOB_Specificity = (TN_SUM / (FP_SUM + TN_SUM)) #True Negative Rate\n",
    "GLOB_OA = ((TP_SUM + TN_SUM) / (TP_SUM + TN_SUM + FP_SUM + FN_SUM))\n",
    "GLOB_IoU = (TP_SUM / (TP_SUM + FN_SUM + FP_SUM))\n",
    "GLOB_Dice = (2 * ((GLOB_Precision * GLOB_Recall) / (GLOB_Precision + GLOB_Recall)))\n",
    "\n",
    "results_csv = pd.DataFrame(df)\n",
    "\n",
    "print(xp_name + best_iter, \"per Window statistics: mean Dice Score = {0}, mean Precision = {1}, mean Recall = {2}, mean Specificity = {3}, mean OA = {4}, mean IoU = {5}\".format(np.nanmean(Dice_ls), np.nanmean(Precision_ls), np.nanmean(Recall_ls), np.nanmean(Specificity_ls), np.nanmean(OA_ls), np.nanmean(IoU_ls)))\n",
    "print(\"\\n\")\n",
    "print(xp_name + best_iter, \"GLOBAL statistics: Dice Score = {0}, Precision = {1}, Recall = {2}, Specificity = {3}, OA = {4}, IoU = {5}\".format(GLOB_Dice, GLOB_Precision, GLOB_Recall, GLOB_Specificity, GLOB_OA, GLOB_IoU))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "97c022f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 136 entries, 0 to 135\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   id                136 non-null    int64 \n",
      " 1   png               136 non-null    object\n",
      " 2   Dice_score        136 non-null    object\n",
      " 3   True_Positive     136 non-null    object\n",
      " 4   True_Negative     136 non-null    object\n",
      " 5   False_Negative    136 non-null    object\n",
      " 6   False_Positive    136 non-null    object\n",
      " 7   Precision         136 non-null    object\n",
      " 8   Recall            136 non-null    object\n",
      " 9   Specificity       136 non-null    object\n",
      " 10  Overall Accuracy  136 non-null    object\n",
      " 11  IoU               136 non-null    object\n",
      "dtypes: int64(1), object(11)\n",
      "memory usage: 12.9+ KB\n",
      "None count           136\n",
      "unique          136\n",
      "top       0.8439326\n",
      "freq              1\n",
      "Name: Dice_score, dtype: object    id                        png  Dice_score True_Positive True_Negative  \\\n",
      "0   0  KBY15_IMG_29925-23256.png   0.8439326          4837         58910   \n",
      "1   1   KBY15_IMG_3078-14535.png  0.93183476          6384         58218   \n",
      "2   2   KBY15_IMG_11286-9747.png   0.9514947          1687         63677   \n",
      "3   3   KBY15_IMG_3933-13509.png   0.9654992          1847         63557   \n",
      "4   4  KBY15_IMG_19836-15732.png   0.7368421            77         65404   \n",
      "\n",
      "  False_Negative False_Positive   Precision      Recall Specificity  \\\n",
      "0            353           1436   0.7710824   0.9319846   0.9762039   \n",
      "1            113            821  0.88605136  0.98260736  0.98609394   \n",
      "2             82             90  0.94935286   0.9536461   0.9985886   \n",
      "3             53             79  0.95898235  0.97210526  0.99875855   \n",
      "4             47              8  0.90588236  0.62096775   0.9998777   \n",
      "\n",
      "  Overall Accuracy         IoU  \n",
      "0         0.972702    0.730003  \n",
      "1        0.9857483   0.8723695  \n",
      "2        0.9973755  0.90747714  \n",
      "3       0.99798584  0.93329966  \n",
      "4       0.99916077   0.5833333  \n"
     ]
    }
   ],
   "source": [
    "print(results_csv.info(), results_csv[\"Dice_score\"].describe(), results_csv.head())\n",
    "\n",
    "log_path = os.path.abspath(\"/home/chris/Dropbox/HOTOSM/HOTOSM_OAM_codeV2/iter_log\")\n",
    "\n",
    "results_csv.to_csv(os.path.join(log_path, xp_name + best_iter + \".csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d613d7",
   "metadata": {},
   "source": []
  }
 ],
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