run.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0AUZqEQHz08N"
   },
   "source": [
    "# Semantic Segmentation on Aerial Images using Drone\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project Repository: [https://github.com/mr-ravin/Aerial-Semantic-Segmentation-Drone-using-Pytorch/](https://github.com/mr-ravin/Aerial-Semantic-Segmentation-Drone-using-Pytorch/)\n",
    "\n",
    "- Aerial Image Dataset: https://www.kaggle.com/datasets/santurini/semantic-segmentation-drone-dataset\n",
    "\n",
    "- Number of Raw Classes: 24, indexed from 0 to 23. After processing this data, these classes are grouped into 5 classes, indexed from 0 to 4.\n",
    "\n",
    "|  index | Class         |\n",
    "|---------|--------------|\n",
    "|    0    |  unlabeled   |\n",
    "|    1    |  paved-area  |\n",
    "|    2    |  dirt        |\n",
    "|    3    |  grass       |\n",
    "|    4    |  gravel      |\n",
    "|    5    |  water       |\n",
    "|    6    |  rocks       |\n",
    "|    7    |  pool        |\n",
    "|    8    |  vegetation  |\n",
    "|    9    |  roof        |\n",
    "|    10   |  wall        |\n",
    "|    11   |  window      |\n",
    "|    12   |  door        |\n",
    "|    13   |  fence       |\n",
    "|    14   |  fence-pole  |\n",
    "|    15   |  person      |\n",
    "|    16   |  dog         |\n",
    "|    17   |  car         |\n",
    "|    18   |  bicycle     |\n",
    "|    19   |  tree        |\n",
    "|    20   |  bald-tree   |\n",
    "|    21   |  ar-marker   |\n",
    "|    22   |  obstacle    |\n",
    "|    23   |  conflicting |\n",
    "\n",
    "\n",
    "### Final Number of Grouped Classes: 5\n",
    "\n",
    "| grouped class id | individual classes                   | group color in rgb  | grouped class name  |\n",
    "|------------------|--------------------------------------|---------------------|---------------------|\n",
    "|        0         | 0, 6, 10, 11, 12, 13, 14, 21, 22, 23 |    [155,38,182]     | obstacles           |\n",
    "|        1         | 5, 7                                 |    [14,135,204]     | water               |\n",
    "|        2         | 2, 3, 8, 19, 20                      |    [124,252,0]      | nature              |\n",
    "|        3         | 15, 16, 17, 18                       |    [255,20,147]     | moving              |\n",
    "|        4         | 1, 4, 9                              |    [169,169,169]    | landable            |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Package Installations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 103273,
     "status": "ok",
     "timestamp": 1721902744517,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "GC32IrKQzdC5",
    "outputId": "81e981b0-2997-45b6-f45b-86ba7bb171f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (11.0.2.54)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch->efficientnet-pytorch==0.7.1->segmentation_models_pytorch) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch->efficientnet-pytorch==0.7.1->segmentation_models_pytorch) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch->efficientnet-pytorch==0.7.1->segmentation_models_pytorch) (2.19.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch->efficientnet-pytorch==0.7.1->segmentation_models_pytorch) (12.1.105)\n",
      "Requirement already satisfied: triton==2.2.0 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch->efficientnet-pytorch==0.7.1->segmentation_models_pytorch) (2.2.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->efficientnet-pytorch==0.7.1->segmentation_models_pytorch) (12.5.82)\n",
      "Requirement already satisfied: packaging>=20.9 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from huggingface-hub->timm==0.9.2->segmentation_models_pytorch) (24.1)\n",
      "Requirement already satisfied: requests in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from huggingface-hub->timm==0.9.2->segmentation_models_pytorch) (2.32.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from jinja2->torch->efficientnet-pytorch==0.7.1->segmentation_models_pytorch) (2.1.5)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from requests->huggingface-hub->timm==0.9.2->segmentation_models_pytorch) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from requests->huggingface-hub->timm==0.9.2->segmentation_models_pytorch) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from requests->huggingface-hub->timm==0.9.2->segmentation_models_pytorch) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from requests->huggingface-hub->timm==0.9.2->segmentation_models_pytorch) (2024.7.4)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from sympy->torch->efficientnet-pytorch==0.7.1->segmentation_models_pytorch) (1.3.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install torch\n",
    "!pip install torchvision\n",
    "!pip install numpy\n",
    "!pip install opencv-python\n",
    "!pip install albumentations\n",
    "!pip install tqdm\n",
    "!pip install segmentation_models_pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 463,
     "status": "ok",
     "timestamp": 1721902821923,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "tSztwyANFfK5"
   },
   "outputs": [],
   "source": [
    "root_dir = '.' # directory path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 700,
     "status": "ok",
     "timestamp": 1721902824885,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "085zWjeM4TYc"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import os\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "sys.path.append(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-q0PyENzZt4w"
   },
   "source": [
    "## Initialisation of Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1721902825848,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "O-6nhFzE7xCj"
   },
   "outputs": [],
   "source": [
    "LR = 2e-3\n",
    "DIMy = 512\n",
    "DIMx = 512\n",
    "EPOCH = 25\n",
    "DEVICE = \"cuda\"\n",
    "individual_class_count=24\n",
    "total_channels = 5 # total 5 grouped classes, indexed ranging from 0 to 4.\n",
    "MODE = \"train\"\n",
    "DO_DATA_SPLIT = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LZb2dbjGFGZl"
   },
   "source": [
    "## Create Data Split for train and val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information regarding the images present in our train set is present inside `train_files.txt`and image information for validation set is present inside `valid_files.txt`. It's provided so that one can reproduce the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import utils.preprocess as utils_preprocess\n",
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1721902836901,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "k0iyXtqbFJs5"
   },
   "outputs": [],
   "source": [
    "if MODE == \"train\" and DO_DATA_SPLIT == True:\n",
    "    print(\"Generating Dataset...\")\n",
    "    utils_preprocess.data_split(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ASCaaZURgeKG"
   },
   "source": [
    "## Visualise Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 1431,
     "status": "ok",
     "timestamp": 1721902844030,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "PgnXyxkMhMPJ"
   },
   "outputs": [],
   "source": [
    "import utils.graphics as utils_graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 569
    },
    "executionInfo": {
     "elapsed": 9944,
     "status": "ok",
     "timestamp": 1721902857373,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "RJlXbQb7hPUN",
    "outputId": "646eb430-7b20-412c-e41c-b67d3ef10115"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_dimx, show_dimy = 512, 512\n",
    "rgb_image = cv2.imread(root_dir+'/dataset/images/000.jpg')\n",
    "rgb_image = cv2.resize(rgb_image, (show_dimx, show_dimy))\n",
    "gt_label = cv2.imread(root_dir+'/dataset/masks/000.png', cv2.IMREAD_UNCHANGED)\n",
    "gt_label = cv2.resize(gt_label, (show_dimx, show_dimy))\n",
    "visualise_mask = utils_graphics.generate_rgb_mask(gt_label, total_channels, individual_class_count)\n",
    "tagged_input_image = utils_graphics.add_tag_on_image(rgb_image, \"Input Image\")\n",
    "tagged_label_image = utils_graphics.add_tag_on_image(visualise_mask, \"Ground Truth Image\")\n",
    "combined_image = cv2.hconcat([tagged_input_image, tagged_label_image])\n",
    "plt.imshow(combined_image)\n",
    "combined_image = cv2.cvtColor(combined_image, cv2.COLOR_RGB2BGR)\n",
    "cv2.imwrite(root_dir+\"/results/view_dataset.jpg\", combined_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JoT-hicI7-nv"
   },
   "source": [
    "## Model Architecture: FPN with EfficientNet-B3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 511,
     "status": "ok",
     "timestamp": 1721902971366,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "rnEyInbZ-nMA",
    "outputId": "3eb36dfb-0541-46aa-cc87-7f5454d0d2af"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:albumentations.check_version:A new version of Albumentations is available: 1.4.12 (you have 1.4.11). Upgrade using: pip install --upgrade albumentations\n"
     ]
    }
   ],
   "source": [
    "%autoreload 3\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "import albumentations as A\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "from tqdm import tqdm\n",
    "from dataloader import CustomImageDataset\n",
    "import utils.compute as compute\n",
    "import segmentation_models_pytorch as smp\n",
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1257,
     "status": "ok",
     "timestamp": 1721902976779,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "bEDmGfFp8SOo",
    "outputId": "e63cfcdc-cda3-43c4-90b7-ca96b0ffa72c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Trainable parameters:  12475309\n"
     ]
    }
   ],
   "source": [
    "\n",
    "segmentation_model = smp.FPN(\n",
    "    encoder_name=\"efficientnet-b3\",\n",
    "    in_channels=3,\n",
    "    classes=total_channels,\n",
    ")\n",
    "sigmoid = nn.Sigmoid()\n",
    "segmentation_model.to(DEVICE)\n",
    "\n",
    "trainable_parameters = sum(p.numel() for p in segmentation_model.parameters() if p.requires_grad)\n",
    "print(\"Number of Trainable parameters: \",trainable_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fVHeMGes9fx7"
   },
   "source": [
    "## Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 447,
     "status": "ok",
     "timestamp": 1721902987624,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "4vswAnie9i8h"
   },
   "outputs": [],
   "source": [
    "IMAGE_HT, IMAGE_WT = DIMy, DIMx\n",
    "train_transform = A.Compose(\n",
    "            [   A.Resize(height=IMAGE_HT, width=IMAGE_WT, interpolation=cv2.INTER_NEAREST, p=1.0),\n",
    "                A.Rotate(limit=45,p=0.35),\n",
    "                A.HorizontalFlip(p=0.25),\n",
    "                A.VerticalFlip(p=0.25),\n",
    "                A.GridDistortion(p=0.20),\n",
    "                A.RandomBrightnessContrast (brightness_limit=(-0.15, 0.15), contrast_limit=(-0.15, 0.15), p=0.25),\n",
    "                A.GaussNoise(p=0.10),\n",
    "                A.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5], max_pixel_value=255.0,),\n",
    "                ToTensorV2(),\n",
    "            ],\n",
    "            )\n",
    "validation_transform = A.Compose(\n",
    "            [   A.Resize(height=IMAGE_HT, width=IMAGE_WT, interpolation=cv2.INTER_NEAREST),\n",
    "                A.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5],max_pixel_value=255.0,),\n",
    "                ToTensorV2(),\n",
    "            ],\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jMNYKqPU9SZB"
   },
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 553,
     "status": "ok",
     "timestamp": 1721902991955,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "LuTqfaizFxfp"
   },
   "outputs": [],
   "source": [
    "epoch_tr_loss, epoch_vl_loss = [], []\n",
    "epoch_tr_iou, epoch_vl_iou = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loss function\n",
    "loss_fn = compute.CombinedLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1721902993881,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "tohjd2Cp9Br2"
   },
   "outputs": [],
   "source": [
    "def train_model(root_dir, total_channels, train_transform, validation_transform, lr, total_epoch):\n",
    "    global segmentation_model\n",
    "    global epoch_tr_loss, epoch_vl_loss\n",
    "    global epoch_tr_iou, epoch_vl_iou\n",
    "    valid_loss_min = np.Inf\n",
    "    valid_acc_max_iou_mean = -1\n",
    "\n",
    "    # Optimizer and Scheduler\n",
    "    optimizer = optim.Adam(segmentation_model.parameters(), lr=lr)\n",
    "    scheduler = StepLR(optimizer, step_size=4, gamma=0.5)\n",
    "\n",
    "    # Data Loading\n",
    "    training_data = CustomImageDataset(root_dir=root_dir, total_channels = total_channels, transform=train_transform, mode=\"train\", device=DEVICE)\n",
    "    validation_data = CustomImageDataset(root_dir=root_dir, total_channels = total_channels, transform=validation_transform, mode=\"val\", device=DEVICE)\n",
    "    train_dataloader = DataLoader(training_data, batch_size=4, shuffle=True, pin_memory=True, num_workers=4)\n",
    "    validation_dataloader = DataLoader(validation_data, batch_size=3, shuffle=True, pin_memory=True, num_workers=3)\n",
    "\n",
    "    # training loop\n",
    "    for ep in range(total_epoch):\n",
    "        train_acc = 0.0\n",
    "        valid_acc = 0.0\n",
    "        valid_batch_run = 0\n",
    "        train_batch_run = 0\n",
    "        train_losses = []\n",
    "        valid_losses = []\n",
    "        segmentation_model.train()\n",
    "        with tqdm(train_dataloader, unit=\" Train batch\") as tepoch:\n",
    "            tepoch.set_description(f\"Train Epoch {ep+1}\")\n",
    "            for input_images, target_masks, gt_paths in tepoch:\n",
    "                input_images = input_images.to(DEVICE)\n",
    "                target_masks = target_masks.to(DEVICE)\n",
    "                train_batch_run += 1\n",
    "                optimizer.zero_grad()\n",
    "                output_masks = segmentation_model(input_images)\n",
    "                output_masks = sigmoid(output_masks)\n",
    "                output_masks = output_masks.squeeze(dim=1)\n",
    "                loss_value = loss_fn(output_masks, target_masks.float())\n",
    "                loss_value.backward()\n",
    "                optimizer.step()\n",
    "                train_losses.append(loss_value.item())\n",
    "                accuracy_value = compute.get_iou_score(output_masks, target_masks.float())\n",
    "                train_acc += accuracy_value\n",
    "            scheduler.step()\n",
    "\n",
    "        segmentation_model.eval()\n",
    "        with tqdm(validation_dataloader, unit=\" Valid batch\") as vepoch:\n",
    "            for input_images, target_masks, gt_paths in vepoch:\n",
    "                valid_batch_run += 1\n",
    "                input_images = input_images.to(DEVICE)\n",
    "                target_masks = target_masks.to(DEVICE)\n",
    "                with torch.no_grad():\n",
    "                    output_masks = segmentation_model(input_images)\n",
    "                    output_masks = sigmoid(output_masks)\n",
    "                    output_masks = output_masks.squeeze(dim=1)\n",
    "                    loss_value = loss_fn(output_masks, target_masks.float())\n",
    "                    valid_losses.append(loss_value.item())\n",
    "                    accuracy_value = compute.get_iou_score(output_masks, target_masks.float())\n",
    "                    valid_acc += accuracy_value\n",
    "        # matrices log\n",
    "        epoch_train_loss = np.mean(train_losses)\n",
    "        epoch_val_loss = np.mean(valid_losses)\n",
    "\n",
    "        epoch_train_iou_mean = train_acc/train_batch_run\n",
    "        epoch_valid_iou_mean = valid_acc/valid_batch_run\n",
    "\n",
    "        epoch_tr_loss.append(epoch_train_loss)\n",
    "        epoch_vl_loss.append(epoch_val_loss)\n",
    "\n",
    "        epoch_tr_iou.append(epoch_train_iou_mean)\n",
    "        epoch_vl_iou.append(epoch_valid_iou_mean)\n",
    "\n",
    "        print(f'Epoch {ep+1}')\n",
    "        print(f'train_loss : {epoch_train_loss} val_loss : {epoch_val_loss}')\n",
    "        print(f'train_iou_mean : {epoch_train_iou_mean*100} val_iou_mean : {epoch_valid_iou_mean*100}')\n",
    "        if epoch_val_loss <= valid_loss_min or valid_acc_max_iou_mean <= epoch_valid_iou_mean*100 :\n",
    "            if epoch_val_loss <= valid_loss_min:\n",
    "                valid_loss_min = epoch_val_loss\n",
    "            if valid_acc_max_iou_mean <= epoch_valid_iou_mean*100:\n",
    "                valid_acc_max_iou_mean = epoch_valid_iou_mean*100\n",
    "            os.system(\"rm \"+root_dir+\"/weights/*pt\")\n",
    "            print(\"removing stored weights of previous epoch\")\n",
    "            torch.save(segmentation_model.state_dict(), root_dir+\"/weights/FPN_efficientnet_b3_\"+str(ep+1)+\".pt\")\n",
    "            print(\"storing weights of current epoch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "executionInfo": {
     "elapsed": 16950,
     "status": "error",
     "timestamp": 1721903016056,
     "user": {
      "displayName": "Ravin Kumar",
      "userId": "09011051242953458989"
     },
     "user_tz": -330
    },
    "id": "CDz0wJCJBhrh",
    "outputId": "a2d9bbad-93c8-415e-baf1-74d55e44eb53",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 1:   0%|          | 0/85 [00:00<?, ? Train batch/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 1: 100%|██████████| 85/85 [07:29<00:00,  5.29s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.25s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "train_loss : 0.3465876815950169 val_loss : 0.6127519562840462\n",
      "train_iou_mean : 39.009403228759766 val_iou_mean : 25.12979507446289\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 2: 100%|██████████| 85/85 [07:26<00:00,  5.26s/ Train batch]\n",
      "100%|██████████| 20/20 [01:25<00:00,  4.28s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2\n",
      "train_loss : 0.2638934166992412 val_loss : 0.2943224012851715\n",
      "train_iou_mean : 41.94923782348633 val_iou_mean : 40.60930252075195\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 3: 100%|██████████| 85/85 [07:29<00:00,  5.29s/ Train batch]\n",
      "100%|██████████| 20/20 [01:25<00:00,  4.28s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3\n",
      "train_loss : 0.23234650597852818 val_loss : 0.40093152672052385\n",
      "train_iou_mean : 43.27360534667969 val_iou_mean : 37.33104705810547\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 4: 100%|██████████| 85/85 [07:32<00:00,  5.32s/ Train batch]\n",
      "100%|██████████| 20/20 [01:25<00:00,  4.26s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4\n",
      "train_loss : 0.22996789497487685 val_loss : 0.23491987586021423\n",
      "train_iou_mean : 43.49102783203125 val_iou_mean : 43.17238998413086\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 5: 100%|██████████| 85/85 [07:27<00:00,  5.27s/ Train batch]\n",
      "100%|██████████| 20/20 [01:25<00:00,  4.27s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5\n",
      "train_loss : 0.19688489139080048 val_loss : 0.16983861178159715\n",
      "train_iou_mean : 44.877479553222656 val_iou_mean : 45.90470886230469\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 6: 100%|██████████| 85/85 [07:29<00:00,  5.28s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.24s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6\n",
      "train_loss : 0.1886415511369705 val_loss : 0.170610860735178\n",
      "train_iou_mean : 45.20906066894531 val_iou_mean : 45.888973236083984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 7: 100%|██████████| 85/85 [07:23<00:00,  5.22s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.25s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7\n",
      "train_loss : 0.184398354677593 val_loss : 0.1621469471603632\n",
      "train_iou_mean : 45.393070220947266 val_iou_mean : 46.3244514465332\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 8: 100%|██████████| 85/85 [07:23<00:00,  5.22s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.24s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8\n",
      "train_loss : 0.17849029302597047 val_loss : 0.17590115927159786\n",
      "train_iou_mean : 45.66164016723633 val_iou_mean : 45.72431564331055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 9: 100%|██████████| 85/85 [07:25<00:00,  5.24s/ Train batch]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9\n",
      "train_loss : 0.1709595575052149 val_loss : 0.1567074567079544\n",
      "train_iou_mean : 45.98933410644531 val_iou_mean : 46.52188491821289\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 10: 100%|██████████| 85/85 [07:26<00:00,  5.26s/ Train batch]\n",
      "100%|██████████| 20/20 [01:25<00:00,  4.25s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10\n",
      "train_loss : 0.16277573354103986 val_loss : 0.15135067962110044\n",
      "train_iou_mean : 46.363250732421875 val_iou_mean : 46.762535095214844\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 11: 100%|██████████| 85/85 [07:25<00:00,  5.25s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.22s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11\n",
      "train_loss : 0.16254765926038517 val_loss : 0.1511682778596878\n",
      "train_iou_mean : 46.37569808959961 val_iou_mean : 46.80076599121094\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Train Epoch 12: 100%|██████████| 85/85 [07:26<00:00,  5.25s/ Train batch]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12\n",
      "train_loss : 0.15772172983954935 val_loss : 0.151217719912529\n",
      "train_iou_mean : 46.567588806152344 val_iou_mean : 46.804115295410156\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 13: 100%|██████████| 85/85 [07:26<00:00,  5.26s/ Train batch]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13\n",
      "train_loss : 0.15600435234167997 val_loss : 0.14726029932498932\n",
      "train_iou_mean : 46.648685455322266 val_iou_mean : 46.953025817871094\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 14: 100%|██████████| 85/85 [07:23<00:00,  5.22s/ Train batch]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14\n",
      "train_loss : 0.15359174311161042 val_loss : 0.14669913798570633\n",
      "train_iou_mean : 46.758365631103516 val_iou_mean : 46.97883987426758\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 15: 100%|██████████| 85/85 [07:26<00:00,  5.25s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.23s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15\n",
      "train_loss : 0.153826943127548 val_loss : 0.14575043134391308\n",
      "train_iou_mean : 46.7356071472168 val_iou_mean : 47.0330924987793\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 16: 100%|██████████| 85/85 [07:25<00:00,  5.25s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.25s/ Valid batch]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16\n",
      "train_loss : 0.1508741665412398 val_loss : 0.14571891836822032\n",
      "train_iou_mean : 46.8706169128418 val_iou_mean : 47.01591873168945\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 17: 100%|██████████| 85/85 [07:21<00:00,  5.19s/ Train batch]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17\n",
      "train_loss : 0.15009038088952795 val_loss : 0.1457198817282915\n",
      "train_iou_mean : 46.9054069519043 val_iou_mean : 47.0295295715332\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Train Epoch 18: 100%|██████████| 85/85 [07:23<00:00,  5.22s/ Train batch]\n",
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 18\n",
      "train_loss : 0.14963397594059216 val_loss : 0.1448302071541548\n",
      "train_iou_mean : 46.92444610595703 val_iou_mean : 47.07440948486328\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Train Epoch 19: 100%|██████████| 85/85 [07:24<00:00,  5.23s/ Train batch]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19\n",
      "train_loss : 0.1490981292198686 val_loss : 0.14394139796495437\n",
      "train_iou_mean : 46.952640533447266 val_iou_mean : 47.10255432128906\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 20: 100%|██████████| 85/85 [07:24<00:00,  5.23s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.21s/ Valid batch]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20\n",
      "train_loss : 0.14816019070499084 val_loss : 0.14437246993184089\n",
      "train_iou_mean : 46.99121856689453 val_iou_mean : 47.125640869140625\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
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     "text": [
      "Train Epoch 21: 100%|██████████| 85/85 [07:22<00:00,  5.20s/ Train batch]\n",
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    {
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     "text": [
      "Epoch 21\n",
      "train_loss : 0.14743098204626756 val_loss : 0.1441610526293516\n",
      "train_iou_mean : 47.02164077758789 val_iou_mean : 47.12577819824219\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
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     "text": [
      "Train Epoch 22: 100%|██████████| 85/85 [07:22<00:00,  5.20s/ Train batch]\n",
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     "text": [
      "Epoch 22\n",
      "train_loss : 0.14773018298780216 val_loss : 0.1438143879175186\n",
      "train_iou_mean : 47.00358200073242 val_iou_mean : 47.147377014160156\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 23: 100%|██████████| 85/85 [07:25<00:00,  5.24s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.21s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23\n",
      "train_loss : 0.14669262091903126 val_loss : 0.14350877441465854\n",
      "train_iou_mean : 47.063072204589844 val_iou_mean : 47.179840087890625\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 24: 100%|██████████| 85/85 [07:21<00:00,  5.20s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.22s/ Valid batch]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24\n",
      "train_loss : 0.14648259816800846 val_loss : 0.14296996518969535\n",
      "train_iou_mean : 47.0690803527832 val_iou_mean : 47.18739700317383\n",
      "removing stored weights of previous epoch\n",
      "storing weights of current epoch\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Epoch 25: 100%|██████████| 85/85 [07:23<00:00,  5.22s/ Train batch]\n",
      "100%|██████████| 20/20 [01:24<00:00,  4.22s/ Valid batch]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25\n",
      "train_loss : 0.1448951930684202 val_loss : 0.14300221018493176\n",
      "train_iou_mean : 47.140472412109375 val_iou_mean : 47.18614196777344\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "if MODE == \"train\":\n",
    "    print(\"Training...\")\n",
    "    train_model(root_dir, total_channels, train_transform, validation_transform, LR, EPOCH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise Train and Val Losses and mean IOUs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn_process_epoch_iou_list(iou_list):\n",
    "    result = []\n",
    "    for elem in iou_list:\n",
    "        tmp = float(elem.detach().cpu().numpy())\n",
    "        result.append(tmp)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_epoch_tr_iou = fn_process_epoch_iou_list(epoch_tr_iou)\n",
    "processed_epoch_vl_iou = fn_process_epoch_iou_list(epoch_vl_iou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch_tr_loss:  [0.3465876815950169, 0.2638934166992412, 0.23234650597852818, 0.22996789497487685, 0.19688489139080048, 0.1886415511369705, 0.184398354677593, 0.17849029302597047, 0.1709595575052149, 0.16277573354103986, 0.16254765926038517, 0.15772172983954935, 0.15600435234167997, 0.15359174311161042, 0.153826943127548, 0.1508741665412398, 0.15009038088952795, 0.14963397594059216, 0.1490981292198686, 0.14816019070499084, 0.14743098204626756, 0.14773018298780216, 0.14669262091903126, 0.14648259816800846, 0.1448951930684202]\n",
      "epoch_vl_loss:  [0.6127519562840462, 0.2943224012851715, 0.40093152672052385, 0.23491987586021423, 0.16983861178159715, 0.170610860735178, 0.1621469471603632, 0.17590115927159786, 0.1567074567079544, 0.15135067962110044, 0.1511682778596878, 0.151217719912529, 0.14726029932498932, 0.14669913798570633, 0.14575043134391308, 0.14571891836822032, 0.1457198817282915, 0.1448302071541548, 0.14394139796495437, 0.14437246993184089, 0.1441610526293516, 0.1438143879175186, 0.14350877441465854, 0.14296996518969535, 0.14300221018493176]\n",
      "epoch_tr_iou:  [0.3900940418243408, 0.4194923937320709, 0.43273603916168213, 0.43491026759147644, 0.4487748146057129, 0.4520905911922455, 0.45393070578575134, 0.4566164016723633, 0.4598933458328247, 0.4636324942111969, 0.4637569785118103, 0.46567589044570923, 0.4664868712425232, 0.46758365631103516, 0.4673560857772827, 0.4687061607837677, 0.46905407309532166, 0.46924445033073425, 0.46952641010284424, 0.46991220116615295, 0.47021642327308655, 0.4700358211994171, 0.4706307053565979, 0.4706907868385315, 0.4714047312736511]\n",
      "epoch_vl_iou:  [0.2512979507446289, 0.406093031167984, 0.37331047654151917, 0.4317238926887512, 0.4590470790863037, 0.4588897228240967, 0.4632445275783539, 0.4572431743144989, 0.46521884202957153, 0.46762534976005554, 0.4680076539516449, 0.46804115176200867, 0.46953025460243225, 0.46978840231895447, 0.4703309237957001, 0.4701592028141022, 0.4702952802181244, 0.4707441031932831, 0.4710255563259125, 0.47125640511512756, 0.4712577760219574, 0.4714737832546234, 0.4717983901500702, 0.4718739688396454, 0.47186142206192017]\n"
     ]
    }
   ],
   "source": [
    "print(\"epoch_tr_loss: \",epoch_tr_loss)\n",
    "print(\"epoch_vl_loss: \",epoch_vl_loss)\n",
    "print(\"epoch_tr_iou: \",processed_epoch_tr_iou)\n",
    "print(\"epoch_vl_iou: \",processed_epoch_vl_iou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 6000x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils.graphics import plot_graph\n",
    "plot_graph(root_dir, EPOCH, epoch_tr_loss, epoch_vl_loss, processed_epoch_tr_iou, processed_epoch_vl_iou)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FPN(\n",
       "  (encoder): EfficientNetEncoder(\n",
       "    (_conv_stem): Conv2dStaticSamePadding(\n",
       "      3, 40, kernel_size=(3, 3), stride=(2, 2), bias=False\n",
       "      (static_padding): ZeroPad2d((0, 1, 0, 1))\n",
       "    )\n",
       "    (_bn0): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "    (_blocks): ModuleList(\n",
       "      (0): MBConvBlock(\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          40, 40, kernel_size=(3, 3), stride=[1, 1], groups=40, bias=False\n",
       "          (static_padding): ZeroPad2d((1, 1, 1, 1))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          40, 10, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          10, 40, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          40, 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",
       "      (1): MBConvBlock(\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          24, 24, kernel_size=(3, 3), stride=(1, 1), groups=24, bias=False\n",
       "          (static_padding): ZeroPad2d((1, 1, 1, 1))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          24, 6, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          6, 24, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          24, 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=[2, 2], groups=144, bias=False\n",
       "          (static_padding): ZeroPad2d((0, 1, 0, 1))\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, 32, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (3-4): 2 x MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          192, 192, kernel_size=(3, 3), stride=(1, 1), groups=192, bias=False\n",
       "          (static_padding): ZeroPad2d((1, 1, 1, 1))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          192, 8, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          8, 192, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (5): MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          192, 192, kernel_size=(5, 5), stride=[2, 2], groups=192, bias=False\n",
       "          (static_padding): ZeroPad2d((2, 2, 2, 2))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          192, 8, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          8, 192, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (6-7): 2 x MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(288, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          288, 288, kernel_size=(5, 5), stride=(1, 1), groups=288, bias=False\n",
       "          (static_padding): ZeroPad2d((2, 2, 2, 2))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(288, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          288, 12, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          12, 288, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (8): MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(288, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          288, 288, kernel_size=(3, 3), stride=[2, 2], groups=288, bias=False\n",
       "          (static_padding): ZeroPad2d((0, 1, 0, 1))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(288, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          288, 12, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          12, 288, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (9-12): 4 x MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          576, 576, kernel_size=(3, 3), stride=(1, 1), groups=576, bias=False\n",
       "          (static_padding): ZeroPad2d((1, 1, 1, 1))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          576, 24, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          24, 576, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (13): MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          576, 576, kernel_size=(5, 5), stride=[1, 1], groups=576, bias=False\n",
       "          (static_padding): ZeroPad2d((2, 2, 2, 2))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          576, 24, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          24, 576, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          576, 136, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(136, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (14-17): 4 x MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          816, 816, kernel_size=(5, 5), stride=(1, 1), groups=816, bias=False\n",
       "          (static_padding): ZeroPad2d((2, 2, 2, 2))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          816, 34, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          34, 816, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          816, 136, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(136, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (18): MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          816, 816, kernel_size=(5, 5), stride=[2, 2], groups=816, bias=False\n",
       "          (static_padding): ZeroPad2d((2, 2, 2, 2))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          816, 34, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          34, 816, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          816, 232, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (19-23): 5 x MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False\n",
       "          (static_padding): ZeroPad2d((2, 2, 2, 2))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          1392, 58, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          58, 1392, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (24): MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          1392, 1392, kernel_size=(3, 3), stride=[1, 1], groups=1392, bias=False\n",
       "          (static_padding): ZeroPad2d((1, 1, 1, 1))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          1392, 58, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          58, 1392, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          1392, 384, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "      (25): MBConvBlock(\n",
       "        (_expand_conv): Conv2dStaticSamePadding(\n",
       "          384, 2304, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn0): BatchNorm2d(2304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_depthwise_conv): Conv2dStaticSamePadding(\n",
       "          2304, 2304, kernel_size=(3, 3), stride=(1, 1), groups=2304, bias=False\n",
       "          (static_padding): ZeroPad2d((1, 1, 1, 1))\n",
       "        )\n",
       "        (_bn1): BatchNorm2d(2304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_se_reduce): Conv2dStaticSamePadding(\n",
       "          2304, 96, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_se_expand): Conv2dStaticSamePadding(\n",
       "          96, 2304, kernel_size=(1, 1), stride=(1, 1)\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_project_conv): Conv2dStaticSamePadding(\n",
       "          2304, 384, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (static_padding): Identity()\n",
       "        )\n",
       "        (_bn2): BatchNorm2d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "        (_swish): MemoryEfficientSwish()\n",
       "      )\n",
       "    )\n",
       "    (_conv_head): Conv2dStaticSamePadding(\n",
       "      384, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "      (static_padding): Identity()\n",
       "    )\n",
       "    (_bn1): BatchNorm2d(1536, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
       "    (_avg_pooling): AdaptiveAvgPool2d(output_size=1)\n",
       "    (_dropout): Dropout(p=0.3, inplace=False)\n",
       "    (_swish): MemoryEfficientSwish()\n",
       "  )\n",
       "  (decoder): FPNDecoder(\n",
       "    (p5): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (p4): FPNBlock(\n",
       "      (skip_conv): Conv2d(136, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "    (p3): FPNBlock(\n",
       "      (skip_conv): Conv2d(48, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "    (p2): FPNBlock(\n",
       "      (skip_conv): Conv2d(32, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "    (seg_blocks): ModuleList(\n",
       "      (0): SegmentationBlock(\n",
       "        (block): Sequential(\n",
       "          (0): Conv3x3GNReLU(\n",
       "            (block): Sequential(\n",
       "              (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
       "              (2): ReLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "          (1): Conv3x3GNReLU(\n",
       "            (block): Sequential(\n",
       "              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
       "              (2): ReLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "          (2): Conv3x3GNReLU(\n",
       "            (block): Sequential(\n",
       "              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
       "              (2): ReLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (1): SegmentationBlock(\n",
       "        (block): Sequential(\n",
       "          (0): Conv3x3GNReLU(\n",
       "            (block): Sequential(\n",
       "              (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
       "              (2): ReLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "          (1): Conv3x3GNReLU(\n",
       "            (block): Sequential(\n",
       "              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
       "              (2): ReLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (2-3): 2 x SegmentationBlock(\n",
       "        (block): Sequential(\n",
       "          (0): Conv3x3GNReLU(\n",
       "            (block): Sequential(\n",
       "              (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
       "              (2): ReLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (merge): MergeBlock()\n",
       "    (dropout): Dropout2d(p=0.2, inplace=True)\n",
       "  )\n",
       "  (segmentation_head): SegmentationHead(\n",
       "    (0): Conv2d(128, 5, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (1): UpsamplingBilinear2d(scale_factor=4.0, mode='bilinear')\n",
       "    (2): Activation(\n",
       "      (activation): Identity()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import glob\n",
    "model_weight_path = glob.glob(root_dir+\"/weights/*pt\")[0]\n",
    "segmentation_model.load_state_dict(torch.load(model_weight_path,map_location=DEVICE))\n",
    "segmentation_model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils.graphics as utils_graphics\n",
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/20 [00:00<?, ? Valid batch/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/20 [00:24<?, ? Valid batch/s]\n"
     ]
    }
   ],
   "source": [
    "inference_results = None\n",
    "input_images_path = None\n",
    "validation_data = CustomImageDataset(root_dir=root_dir, total_channels = total_channels, transform=validation_transform, mode=\"val\", device=DEVICE)\n",
    "validation_dataloader = DataLoader(validation_data, batch_size=3, shuffle=True, pin_memory=True, num_workers=3)\n",
    "with tqdm(validation_dataloader, unit=\" Valid batch\") as vepoch:\n",
    "    for input_images, target_masks, gt_paths in vepoch:\n",
    "        input_images = input_images.to(DEVICE)\n",
    "        target_masks = target_masks.to(DEVICE)\n",
    "        with torch.no_grad():\n",
    "            output_masks = segmentation_model(input_images)\n",
    "            output_masks = sigmoid(output_masks)\n",
    "            output_masks = output_masks.squeeze(dim=1)\n",
    "            inference_results = output_masks.detach().cpu().numpy()\n",
    "            inference_results = (inference_results>0.5)*1\n",
    "            input_images_path = gt_paths\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_dimx, show_dimy = 512, 512\n",
    "instance_results = []\n",
    "for idx in range(inference_results.shape[0]):\n",
    "    image_path = input_images_path[idx]\n",
    "    tmp_var = image_path.split(\"/\")[-1]\n",
    "    gt_mask_path = root_dir+\"/dataset/masks/\"+tmp_var.replace(\".jpg\",\".png\")\n",
    "    image = cv2.imread(image_path)\n",
    "    image = cv2.resize(image, (show_dimx,show_dimy))\n",
    "    gt_mask = cv2.imread(gt_mask_path,cv2.IMREAD_UNCHANGED)\n",
    "    gt_mask = cv2.resize(gt_mask, (show_dimx,show_dimy))\n",
    "    gt_mask = utils_graphics.generate_rgb_mask(gt_mask, total_channels, individual_class_count)\n",
    "    pred_mask_numpy = inference_results[idx]\n",
    "    pred_mask = utils_graphics.generate_mask_from_model_output(pred_mask_numpy, total_channels)\n",
    "    pred_mask = cv2.resize(pred_mask, (show_dimx,show_dimy))\n",
    "    image = utils_graphics.add_tag_on_image(image, \"Input Image\")\n",
    "    gt_mask = utils_graphics.add_tag_on_image(gt_mask, \"Ground Truth\")\n",
    "    pred_mask = utils_graphics.add_tag_on_image(pred_mask, \"Prediction\")\n",
    "    single_instance = cv2.hconcat([image, gt_mask, pred_mask])\n",
    "    instance_results.append(single_instance.copy())\n",
    "\n",
    "if inference_results.shape[0] == 1:\n",
    "    combined_results = instance_results[0]\n",
    "else:\n",
    "    combined_results = cv2.vconcat(instance_results)\n",
    "\n",
    "plt.imshow(combined_results)\n",
    "combined_results = cv2.cvtColor(combined_results, cv2.COLOR_RGB2BGR)\n",
    "cv2.imwrite(root_dir+\"/results/inference_results.jpg\",combined_results)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}