resnet_bwmask_ver2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "98cd38f0-0427-4eae-b087-e5d7fcb2f84d",
   "metadata": {},
   "source": [
    "# New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04164e71-a3ce-4896-a4a7-3a5cfd79ca22",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import zipfile\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "import gc\n",
    "import argparse\n",
    "import time\n",
    "import pandas as pd\n",
    "import torch.optim as optim\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.transforms import ToPILImage\n",
    "from tqdm import tqdm\n",
    "from torchmetrics import JaccardIndex\n",
    "import shutil\n",
    "from torchvision import models\n",
    "from torchsummary import summary\n",
    "from torchvision.models.segmentation import deeplabv3_resnet50\n",
    "from torchvision.models.segmentation.deeplabv3 import DeepLabHead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7cc5d53-69dc-4306-b7b8-47368c28c368",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset extracted to: extracted_dataset\n"
     ]
    }
   ],
   "source": [
    "# Define function to extract the dataset from ZIP\n",
    "def extract_zip(zip_path, extract_to):\n",
    "    \"\"\"\n",
    "    Extracts a ZIP file to the specified directory.\n",
    "    \"\"\"\n",
    "    if not os.path.exists(extract_to):\n",
    "        os.makedirs(extract_to)\n",
    "    \n",
    "    with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
    "        zip_ref.extractall(extract_to)\n",
    "        print(f\"Dataset extracted to: {extract_to}\")\n",
    "\n",
    "# Path to the ZIP file and extraction directory\n",
    "zip_file_path = 'archive_3.zip'\n",
    "extracted_data_path = 'extracted_dataset'\n",
    "\n",
    "# Call the function to extract the ZIP file\n",
    "extract_zip(zip_file_path, extracted_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0ca7ccd-34a6-4366-b84f-a0ec61aa1709",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "H = 448\n",
    "W = 448"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f4e03de7-0886-4b4b-a268-6b5ac430c1ff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "import matplotlib.colors as colors\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import tensorflow as tf\n",
    "Label = namedtuple( 'Label' , ['name' ,'id','color'] )\n",
    "labels = [\n",
    "    Label(  'ground'  ,  0 ,  (0,  0,  0)     ),\n",
    "    Label(  'crack'   ,  1 ,  (255, 255, 255) )\n",
    "]\n",
    "\n",
    "max_label_id = max([l.id for l in labels])\n",
    "\n",
    "# turn to arr of numbers between 0 to 1\n",
    "LabelCmap = colors.ListedColormap([np.array(l.color) / 255 for l in labels])\n",
    "bounds = np.linspace(0, max_label_id, len(labels))\n",
    "\n",
    "def display(display_list):\n",
    "  fig, cols = plt.subplots(1, len(display_list), sharey=True)\n",
    "  fig.set_size_inches(15, 5)\n",
    "  title = ['Input Image', 'Ground-truth Labels', 'Stage 1 Prediction Labels']\n",
    "  im = None\n",
    "  for i in range(len(display_list)):\n",
    "    cols[i].set_title(title[i])\n",
    "    y = display_list[i]\n",
    "    \n",
    "    if 'Label' in title[i]:\n",
    "      w,h,_ = y.shape\n",
    "      y = y.reshape(w,h)\n",
    "      im = cols[i].imshow(y, cmap=LabelCmap, vmin=0, vmax=max_label_id)\n",
    "    else:\n",
    "      im = cols[i].imshow(tf.keras.preprocessing.image.array_to_img(y)) # this look better\n",
    "      #im = cols[i].imshow(y)\n",
    "    cols[i].axis('off')\n",
    "  cbar = plt.colorbar(im, ax=cols.ravel().tolist(), cmap=LabelCmap, norm=colors.BoundaryNorm(bounds, LabelCmap.N), \n",
    "                      orientation='horizontal',spacing='proportional', ticks=bounds, boundaries=bounds, format='%1i')\n",
    "  cbar.ax.set_xticklabels([l.name for l in labels], rotation=90)\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "53d4e022-b3d7-4ecc-9079-19b30f5c7175",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Plotting Loss, IoU, and Correct in separate subplots\n",
    "def plot_combined_metrics(train_loss, val_loss, train_iou, val_iou, train_correct, val_correct, save_path='metrics_plot.png'):\n",
    "    epochs = range(1, len(train_loss) + 1)\n",
    "\n",
    "    # Create subplots for Loss, IoU, and Correct Predictions\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(21, 6))\n",
    "\n",
    "    # Plotting Loss\n",
    "    ax1.plot(epochs, train_loss, label='Training Loss', color='tab:blue', linestyle='-')\n",
    "    ax1.plot(epochs, val_loss, label='Validation Loss', color='tab:orange', linestyle='--')\n",
    "    ax1.set_title('Loss Over Epochs')\n",
    "    ax1.set_xlabel('Epochs')\n",
    "    ax1.set_ylabel('Loss')\n",
    "    ax1.legend()\n",
    "\n",
    "    # Plotting IoU\n",
    "    ax2.plot(epochs, train_iou, label='Training IoU', color='tab:green', linestyle='-')\n",
    "    ax2.plot(epochs, val_iou, label='Validation IoU', color='tab:red', linestyle='--')\n",
    "    ax2.set_title('IoU Over Epochs')\n",
    "    ax2.set_xlabel('Epochs')\n",
    "    ax2.set_ylabel('IoU')\n",
    "    ax2.legend()\n",
    "\n",
    "    # Plotting Correct Predictions\n",
    "    ax3.plot(epochs, train_correct, label='Training mPA', color='tab:purple', linestyle='-')\n",
    "    ax3.plot(epochs, val_correct, label='Validation mPA', color='tab:brown', linestyle='--')\n",
    "    ax3.set_title('Mean Pixel Accuracy (mPA) Over Epochs')\n",
    "    ax3.set_xlabel('Epochs')\n",
    "    ax3.set_ylabel('mPA')\n",
    "    ax3.legend()\n",
    "\n",
    "    # Adjust layout to prevent overlap\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Save the plot to a file\n",
    "    plt.savefig(save_path)\n",
    "    plt.close()  # Close the plot to free memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9f58616f-c8dd-48bf-bfac-09bb8d49c3aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torchvision import transforms, datasets\n",
    "import os\n",
    "import os.path as osp\n",
    "import random\n",
    "from glob import glob\n",
    "from PIL import Image\n",
    "import scipy.io as scio\n",
    "from torch.utils.data import Dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "import cv2\n",
    "from PIL import Image\n",
    "import torch\n",
    "\n",
    "class CrackDataSet(torch.utils.data.Dataset):\n",
    "    # data[0] is image, data[1] is mask\n",
    "    def __init__(self, data, data_transforms):\n",
    "        self.data_transforms = data_transforms\n",
    "        self.data = data\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.data[0]) # images size\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        # OpenCV's default = BGR => RGB and then adding normalization here\n",
    "        image = cv2.imread(self.data[0][index], cv2.IMREAD_COLOR)\n",
    "        image = self.data_transforms(Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype('uint8'), 'RGB'))\n",
    "\n",
    "        # masks are black and white images, so pixels closer to 255 consider = crack = label them\n",
    "        mask = cv2.imread(self.data[1][index], cv2.IMREAD_GRAYSCALE)\n",
    "        mask_label = np.zeros(mask.shape)\n",
    "        mask_label[mask >= 128] = 1\n",
    "        mask_label = np.asarray(mask_label, np.compat.long)\n",
    "        mask_label = torch.LongTensor(mask_label)\n",
    "        return image, mask_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bd430100-7c88-4149-8761-3b793e1ad927",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import argparse\n",
    "import os\n",
    "import time\n",
    "import pandas as pd\n",
    "import torch.optim as optim\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.transforms import ToPILImage\n",
    "from tqdm import tqdm\n",
    "from torchmetrics import JaccardIndex\n",
    "import shutil\n",
    "from torchvision import models\n",
    "from torchsummary import summary\n",
    "\n",
    "def load_data(path=''):\n",
    "    images = sorted(glob(os.path.join(path, \"images/*\")))\n",
    "    masks = sorted(glob(os.path.join(path, \"masks/*\")))\n",
    "    train_x, valid_x, train_y, valid_y = train_test_split(images, masks, test_size=0.2, random_state=42)\n",
    "    return (train_x, train_y), (valid_x, valid_y)\n",
    "\n",
    "class Trainer_Wrapper():\n",
    "    def __init__(self, model, input_path, batch_size, max_epochs, num_class=2):\n",
    "        self.num_class = num_class\n",
    "        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        self.batch_size = batch_size\n",
    "        self.epoch = 1\n",
    "        self.max_epochs = max_epochs\n",
    "        self.lr = 0.001\n",
    "        data_train, data_eval = load_data(path=input_path)\n",
    "        self.image_datasets = {\n",
    "            'train': CrackDataSet(data_train, data_transforms['train']),\n",
    "            'validation': CrackDataSet(data_eval, data_transforms['validation'])\n",
    "        }\n",
    "        self.train_loader = torch.utils.data.DataLoader(self.image_datasets['train'],\n",
    "                                                        batch_size=batch_size, shuffle=True)\n",
    "        self.val_loader = torch.utils.data.DataLoader(self.image_datasets['validation'],\n",
    "                                                      batch_size=batch_size, shuffle=False)\n",
    "        self.model = model.to(self.device)\n",
    "        self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)\n",
    "        self.scaler = torch.amp.GradScaler('cuda')  # Add mixed precision scaler\n",
    "\n",
    "        self.fiou = JaccardIndex(task='multiclass', num_classes=self.num_class).to(self.device)\n",
    "        self.loss_criterion = nn.CrossEntropyLoss()\n",
    "        self.train_loss_history, self.train_iou_history, self.train_correct_history = [], [], []\n",
    "        self.eval_loss_history, self.eval_iou_history, self.eval_correct_history = [], [], []\n",
    "\n",
    "    def begin_train(self):\n",
    "        for self.epoch in range(1, self.max_epochs + 1):\n",
    "            self.training()\n",
    "            self.evaluate_epoch('Validation', self.val_loader)\n",
    "            \n",
    "            # Clear memory after each epoch\n",
    "            torch.cuda.empty_cache()\n",
    "            gc.collect()\n",
    "\n",
    "        plot_combined_metrics(\n",
    "            self.train_loss_history, self.eval_loss_history,\n",
    "            self.train_iou_history, self.eval_iou_history,\n",
    "            self.train_correct_history, self.eval_correct_history\n",
    "        )\n",
    "\n",
    "    def training(self):\n",
    "        self.model.train()\n",
    "        total_correct, total_iou, total_loss, total_num_of_images = 0.0, 0.0, 0.0, 0.0\n",
    "        num_of_batches = 0\n",
    "\n",
    "        for data, target in tqdm(self.train_loader, desc=f\"Training Epoch {self.epoch}/{self.max_epochs}\"):\n",
    "            num_of_batches += 1\n",
    "            data, target = data.to(self.device), target.to(self.device)\n",
    "\n",
    "            with torch.amp.autocast('cuda'):  # Enable mixed precision\n",
    "                out = self.model(data)['out']\n",
    "                loss = self.loss_criterion(out, target)\n",
    "\n",
    "            # Backpropagation with scaled gradients\n",
    "            self.optimizer.zero_grad()\n",
    "            self.scaler.scale(loss).backward()\n",
    "            self.scaler.step(self.optimizer)\n",
    "            self.scaler.update()\n",
    "\n",
    "            # Update metrics\n",
    "            prediction = torch.argmax(out, dim=1)\n",
    "            num_of_samples = data.size(0)\n",
    "            total_loss += loss.item() * num_of_samples\n",
    "            total_iou += self.fiou(out, target).item()\n",
    "            total_num_of_images += num_of_samples\n",
    "            total_correct += torch.sum(prediction == target).item() / target.numel() * num_of_samples\n",
    "\n",
    "            # Clear memory after each batch\n",
    "            torch.cuda.empty_cache()\n",
    "            gc.collect()\n",
    "\n",
    "        avg_loss = total_loss / total_num_of_images\n",
    "        avg_iou = total_iou / num_of_batches\n",
    "        avg_correct = total_correct / total_num_of_images\n",
    "\n",
    "        self.train_loss_history.append(avg_loss)\n",
    "        self.train_iou_history.append(avg_iou)\n",
    "        self.train_correct_history.append(avg_correct)\n",
    "\n",
    "        print(f\"Training - Epoch [{self.epoch}/{self.max_epochs}] - \"\n",
    "              f\"Loss: {avg_loss:.4f} | mPA: {avg_correct * 100:.2f}% | IoU: {avg_iou:.4f}\")\n",
    "\n",
    "    def evaluate_epoch(self, epoch_mode, loader):\n",
    "        self.model.eval()\n",
    "        total_iou, total_loss, total_correct, total_num_of_images = 0.0, 0.0, 0.0, 0\n",
    "        num_of_batches = 0\n",
    "\n",
    "        with torch.no_grad():  # Disable gradient calculation during evaluation\n",
    "            for data, target in tqdm(loader, desc=f\"Evaluating {epoch_mode}\"):\n",
    "                num_of_batches += 1\n",
    "                data, target = data.to(self.device), target.to(self.device)\n",
    "\n",
    "                out = self.model(data)['out']\n",
    "                prediction = torch.argmax(out, dim=1)\n",
    "\n",
    "                # Calculate loss, IoU, and correct predictions\n",
    "                loss = self.loss_criterion(out, target)\n",
    "                num_of_samples = data.size(0)\n",
    "                total_loss += loss.item() * num_of_samples\n",
    "                total_iou += self.fiou(out, target).item()\n",
    "                total_num_of_images += num_of_samples\n",
    "                total_correct += torch.sum(prediction == target).item() / target.numel() * num_of_samples\n",
    "\n",
    "        # Use safe division to prevent division by zero\n",
    "        avg_loss = total_loss / total_num_of_images if total_num_of_images > 0 else 0\n",
    "        avg_iou = total_iou / num_of_batches if num_of_batches > 0 else 0\n",
    "        avg_correct = total_correct / total_num_of_images if total_num_of_images > 0 else 0\n",
    "\n",
    "        # Append metrics to history lists\n",
    "        if epoch_mode == 'Training':\n",
    "            self.train_loss_history.append(avg_loss)\n",
    "            self.train_iou_history.append(avg_iou)\n",
    "            self.train_correct_history.append(avg_correct)\n",
    "        else:\n",
    "            self.eval_loss_history.append(avg_loss)\n",
    "            self.eval_iou_history.append(avg_iou)\n",
    "            self.eval_correct_history.append(avg_correct)\n",
    "\n",
    "        print(f\"{epoch_mode} - Epoch [{self.epoch}/{self.max_epochs}] - \"\n",
    "              f\"Loss: {avg_loss:.4f} | mPA: {avg_correct * 100:.2f}% | IoU: {avg_iou:.4f}\")\n",
    "\n",
    "        # Clear memory after evaluation\n",
    "        torch.cuda.empty_cache()\n",
    "        gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "204ead11-71d0-4b7a-bdb3-1e9666a9df96",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import gc\n",
    "\n",
    "def clear_memory():\n",
    "    torch.cuda.empty_cache()\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bafccfea-c829-438c-ab77-5000334ab4bc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4523"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.cuda.empty_cache()  # Clear cached memory\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "37bca157-fab0-4c28-af31-9f84c5d60ea6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torchvision.models.segmentation import deeplabv3_resnet50\n",
    "from torchvision.models.segmentation.deeplabv3 import DeepLabHead\n",
    "from torchvision.models.segmentation import deeplabv3_resnet101\n",
    "from torchvision.models.segmentation import deeplabv3_mobilenet_v3_large\n",
    "from torchvision.models.segmentation import DeepLabV3_ResNet50_Weights\n",
    "\n",
    "input_path = 'extracted_dataset/crack_segmentation_dataset'\n",
    "batch_size = 8\n",
    "max_epochs = 25\n",
    "num_of_classes = 2\n",
    "\n",
    "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([transforms.ToTensor(), normalize]),\n",
    "    'validation': transforms.Compose([transforms.ToTensor(), normalize]),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d057f1ec-4a7a-4043-9140-087a9cf73360",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torchvision.models.segmentation import DeepLabV3_ResNet50_Weights, deeplabv3_resnet50\n",
    "from torchvision.models.segmentation.deeplabv3 import DeepLabHead\n",
    "\n",
    "def custom_DeepLabv3():\n",
    "    weights = DeepLabV3_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1\n",
    "    model = deeplabv3_resnet50(weights=weights, progress=True)\n",
    "    model.classifier = DeepLabHead(2048, 2)\n",
    "    model.train()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27a0e6-78da-4319-a733-8c9aeca547b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epoch 1/25: 100%|██████████| 1130/1130 [13:55<00:00,  1.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training - Epoch [1/25] - Loss: 0.0784 | mPA: 97.11% | IoU: 0.6760\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating Validation: 100%|██████████| 283/283 [03:03<00:00,  1.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation - Epoch [1/25] - Loss: 0.0627 | mPA: 97.69% | IoU: 0.7036\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epoch 2/25: 100%|██████████| 1130/1130 [13:58<00:00,  1.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training - Epoch [2/25] - Loss: 0.0603 | mPA: 97.73% | IoU: 0.7241\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating Validation: 100%|██████████| 283/283 [03:02<00:00,  1.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation - Epoch [2/25] - Loss: 0.0639 | mPA: 97.58% | IoU: 0.7214\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epoch 3/25: 100%|██████████| 1130/1130 [13:58<00:00,  1.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
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     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2100x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "RuntimeError",
     "evalue": "Parent directory extracted_dataset/crack_segmentation_dataset/weights does not exist.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[70], line 10\u001b[0m\n\u001b[1;32m      8\u001b[0m filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mres_net_best_weight.pth\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      9\u001b[0m save_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(weight_dir, filename)\n\u001b[0;32m---> 10\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstate_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_path\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/serialization.py:651\u001b[0m, in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    648\u001b[0m _check_save_filelike(f)\n\u001b[1;32m    650\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _use_new_zipfile_serialization:\n\u001b[0;32m--> 651\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43m_open_zipfile_writer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m opened_zipfile:\n\u001b[1;32m    652\u001b[0m         _save(obj, opened_zipfile, pickle_module, pickle_protocol, _disable_byteorder_record)\n\u001b[1;32m    653\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/serialization.py:525\u001b[0m, in \u001b[0;36m_open_zipfile_writer\u001b[0;34m(name_or_buffer)\u001b[0m\n\u001b[1;32m    523\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    524\u001b[0m     container \u001b[38;5;241m=\u001b[39m _open_zipfile_writer_buffer\n\u001b[0;32m--> 525\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcontainer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname_or_buffer\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/serialization.py:496\u001b[0m, in \u001b[0;36m_open_zipfile_writer_file.__init__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    494\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39mPyTorchFileWriter(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfile_stream))\n\u001b[1;32m    495\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 496\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPyTorchFileWriter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Parent directory extracted_dataset/crack_segmentation_dataset/weights does not exist."
     ]
    }
   ],
   "source": [
    "from torch.cuda.amp import GradScaler, autocast\n",
    "\n",
    "model = custom_DeepLabv3()\n",
    "trainer = Trainer_Wrapper(model, input_path, batch_size, max_epochs, num_of_classes)\n",
    "trainer.begin_train()\n",
    "\n",
    "weight_dir = input_path + \"/weights\"\n",
    "filename = 'res_net_best_weight.pth'\n",
    "save_path = os.path.join(weight_dir, filename)\n",
    "torch.save(trainer.model.state_dict(), save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b0b8e8b8-54b1-4035-83df-3b87ac3609d9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torch.cuda.amp import GradScaler, autocast\n",
    "\n",
    "\n",
    "weight_dir = \"extracted_dataset/crack_segmentation_dataset/weights\"\n",
    "filename = 'res_net_best_weight.pth'\n",
    "save_path = os.path.join(weight_dir, filename)\n",
    "torch.save(trainer.model.state_dict(), save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f138b414-54d6-4803-9c50-2004f89e46c6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "plot_combined_metrics(\n",
    "    trainer.train_loss_history, trainer.eval_loss_history,\n",
    "    trainer.train_iou_history, trainer.eval_iou_history,\n",
    "    trainer.train_correct_history, trainer.eval_correct_history,\n",
    "    save_path='training_validation_metrics.png'  # Save the plot as a PNG file\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d5f95fd1-69fe-48c7-84b3-4e1953292896",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics exported to 'training_metrics_report.csv'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Create a DataFrame from the training metrics\n",
    "metrics_df = pd.DataFrame({\n",
    "    'Epoch': range(1, len(trainer.train_loss_history) + 1),\n",
    "    'Train Loss': trainer.train_loss_history,\n",
    "    'Validation Loss': trainer.eval_loss_history,\n",
    "    'Train IoU': trainer.train_iou_history,\n",
    "    'Validation IoU': trainer.eval_iou_history,\n",
    "    'Train mPA': trainer.train_correct_history,\n",
    "    'Validation mPA': trainer.eval_correct_history\n",
    "})\n",
    "\n",
    "# Export to CSV\n",
    "metrics_df.to_csv('training_metrics_report.csv', index=False)\n",
    "\n",
    "print(\"Metrics exported to 'training_metrics_report.csv'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b444b14-9be3-41a5-9e11-240c5fd41733",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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