Train_Segmentation_Model_FPN+Resnet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "96b27a78",
   "metadata": {},
   "source": [
    "# IMPORTS\n",
    "\n",
    "First we started with some python imports. For model building we used the <mark>**segmentation_models_pytorch library**</mark>. This has library has a lot segmentation model architechtures. Before you can import the library it needs to be ensured that the library is installed in the conda environment. For this in the anaconda terminal just type the below command to install the library:-\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Conda Command:</b> conda install -q segmentation-models-pytorch\n",
    "</div>\n",
    "\n",
    "Once, the libray is installed it can be imported in the script safely. We also imported the utility script Trainer for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8eef06ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import segmentation_models_pytorch as smp\n",
    "from Utilities.Trainer import Trainer\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torchvision import models\n",
    "import torch.multiprocessing as mp\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4576d175",
   "metadata": {},
   "source": [
    "The below statement helps in better error tracking when training is done in cuda. More details about this can be found in the below url:-\n",
    "\n",
    "https://pytorch.org/docs/stable/notes/cuda.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cfa0091",
   "metadata": {},
   "outputs": [],
   "source": [
    "CUDA_LAUNCH_BLOCKING=\"1\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694bfa11",
   "metadata": {},
   "source": [
    "# Model Building\n",
    "\n",
    "Next a FPN model with Resnet34 encoder was created with imagenet weights from the smp library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94aca1c3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FPN(\n",
      "  (encoder): ResNetEncoder(\n",
      "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (relu): ReLU(inplace=True)\n",
      "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "    (layer1): Sequential(\n",
      "      (0): BasicBlock(\n",
      "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (1): BasicBlock(\n",
      "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (2): BasicBlock(\n",
      "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (layer2): Sequential(\n",
      "      (0): BasicBlock(\n",
      "        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (downsample): Sequential(\n",
      "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): BasicBlock(\n",
      "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (2): BasicBlock(\n",
      "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (3): BasicBlock(\n",
      "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (layer3): Sequential(\n",
      "      (0): BasicBlock(\n",
      "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (downsample): Sequential(\n",
      "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): BasicBlock(\n",
      "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (2): BasicBlock(\n",
      "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (3): BasicBlock(\n",
      "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (4): BasicBlock(\n",
      "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (5): BasicBlock(\n",
      "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (layer4): Sequential(\n",
      "      (0): BasicBlock(\n",
      "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (downsample): Sequential(\n",
      "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): BasicBlock(\n",
      "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (2): BasicBlock(\n",
      "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (decoder): FPNDecoder(\n",
      "    (p5): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (p4): FPNBlock(\n",
      "      (skip_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    )\n",
      "    (p3): FPNBlock(\n",
      "      (skip_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    )\n",
      "    (p2): FPNBlock(\n",
      "      (skip_conv): Conv2d(64, 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): 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",
      "      (3): 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, 6, 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",
      ")\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "ENCODER = 'resnet34'\n",
    "ENCODER_WEIGHTS = 'imagenet'\n",
    "CLASSES = 6\n",
    "ACTIVATION = None # could be None for logits or 'softmax2d' for multiclass segmentation\n",
    "\n",
    "# create segmentation model with pretrained encoder\n",
    "model = smp.FPN(\n",
    "    encoder_name=ENCODER, \n",
    "    encoder_weights=ENCODER_WEIGHTS, \n",
    "    in_channels=3,\n",
    "    classes=CLASSES, \n",
    "    activation=ACTIVATION,\n",
    ")\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651bb33b",
   "metadata": {},
   "source": [
    "# Model Training\n",
    "\n",
    "Next the model training was started with 20 epochs and initial learning rate as 5e-04. The model checkpoint was saved whenever the validation dice was reported the greater than the previous best validation dice.\n",
    "\n",
    "In the below results you can see the training starting from epoch 6. This is because the kernel was initally interuptted after epoch 5. So, I had to start all over again. Since, checkpoint was saved upto epoch number 5 so it started from epoch 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "300328a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 5e-04\n",
    "epochs = 20\n",
    "path = \"./Models/model_fpn_resnet.pth\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e29de8da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current Device is GPU: 0\n",
      "Total memory allocated in GB: 0.3\n",
      "Total memory cached in GB: 0.6\n",
      "Learning Rate =  0.0005\n",
      "Starting epoch: 6 | phase: train | ⏰: 21:23:36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:34<00:00,  1.13it/s, loss=0.00984]\n",
      "C:\\Users\\SIDDHARTA\\OneDrive\\Documents\\NEU-DET\\Utilities\\Meter.py:40: RuntimeWarning: Mean of empty slice\n",
      "  dice_neg = np.nanmean(self.dice_neg_scores)\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0787 | IoU: 0.5754 | dice: 0.6997 | dice_neg: nan | dice_pos: 0.6997\n",
      "Starting epoch: 6 | phase: val | ⏰: 21:28:11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:42<00:00,  3.10it/s, loss=0.00756]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0605 | IoU: 0.6266 | dice: 0.7500 | dice_neg: nan | dice_pos: 0.7500\n",
      "******** New optimal found, saving state ********\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Learning Rate =  0.0005\n",
      "Starting epoch: 7 | phase: train | ⏰: 21:28:55\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:27<00:00,  1.16it/s, loss=0.00943]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0755 | IoU: 0.5887 | dice: 0.7123 | dice_neg: nan | dice_pos: 0.7123\n",
      "Starting epoch: 7 | phase: val | ⏰: 21:33:22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:43<00:00,  3.03it/s, loss=0.00779]\n",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0623 | IoU: 0.6139 | dice: 0.7323 | dice_neg: nan | dice_pos: 0.7323\n",
      "\n",
      "Learning Rate =  0.0005\n",
      "Starting epoch: 8 | phase: train | ⏰: 21:34:06\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:15<00:00,  1.22it/s, loss=0.00792]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0634 | IoU: 0.6269 | dice: 0.7497 | dice_neg: nan | dice_pos: 0.7497\n",
      "Starting epoch: 8 | phase: val | ⏰: 21:38:21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:35<00:00,  3.79it/s, loss=0.00722]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0578 | IoU: 0.6515 | dice: 0.7718 | dice_neg: nan | dice_pos: 0.7718\n",
      "******** New optimal found, saving state ********\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Learning Rate =  0.0005\n",
      "Starting epoch: 9 | phase: train | ⏰: 21:38:57\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████| 310/310 [04:25<00:00,  1.17it/s, loss=0.0076]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0608 | IoU: 0.6423 | dice: 0.7638 | dice_neg: nan | dice_pos: 0.7638\n",
      "Starting epoch: 9 | phase: val | ⏰: 21:43:23\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:41<00:00,  3.19it/s, loss=0.00705]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0564 | IoU: 0.6606 | dice: 0.7793 | dice_neg: nan | dice_pos: 0.7793\n",
      "******** New optimal found, saving state ********\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Learning Rate =  0.0005\n",
      "Starting epoch: 10 | phase: train | ⏰: 21:44:05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:26<00:00,  1.16it/s, loss=0.00706]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0565 | IoU: 0.6605 | dice: 0.7796 | dice_neg: nan | dice_pos: 0.7796\n",
      "Starting epoch: 10 | phase: val | ⏰: 21:48:32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████| 133/133 [00:41<00:00,  3.19it/s, loss=0.0074]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0592 | IoU: 0.6653 | dice: 0.7833 | dice_neg: nan | dice_pos: 0.7833\n",
      "******** New optimal found, saving state ********\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Learning Rate =  0.0005\n",
      "Starting epoch: 11 | phase: train | ⏰: 21:49:14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:26<00:00,  1.16it/s, loss=0.00688]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0551 | IoU: 0.6751 | dice: 0.7926 | dice_neg: nan | dice_pos: 0.7926\n",
      "Starting epoch: 11 | phase: val | ⏰: 21:53:40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:41<00:00,  3.19it/s, loss=0.00833]\n",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0667 | IoU: 0.6281 | dice: 0.7439 | dice_neg: nan | dice_pos: 0.7439\n",
      "\n",
      "Learning Rate =  0.0005\n",
      "Starting epoch: 12 | phase: train | ⏰: 21:54:22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:25<00:00,  1.17it/s, loss=0.00674]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0539 | IoU: 0.6721 | dice: 0.7882 | dice_neg: nan | dice_pos: 0.7882\n",
      "Starting epoch: 12 | phase: val | ⏰: 21:58:47\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:41<00:00,  3.19it/s, loss=0.00711]\n",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0569 | IoU: 0.6609 | dice: 0.7795 | dice_neg: nan | dice_pos: 0.7795\n",
      "\n",
      "Learning Rate =  0.0005\n",
      "Starting epoch: 13 | phase: train | ⏰: 21:59:29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:40<00:00,  1.10it/s, loss=0.00702]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0561 | IoU: 0.6688 | dice: 0.7851 | dice_neg: nan | dice_pos: 0.7851\n",
      "Starting epoch: 13 | phase: val | ⏰: 22:04:10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████| 133/133 [00:41<00:00,  3.18it/s, loss=0.0073]\n",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0584 | IoU: 0.6530 | dice: 0.7722 | dice_neg: nan | dice_pos: 0.7722\n",
      "\n",
      "Learning Rate =  0.0005\n",
      "Starting epoch: 14 | phase: train | ⏰: 22:04:51\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:24<00:00,  1.17it/s, loss=0.00763]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0610 | IoU: 0.6478 | dice: 0.7664 | dice_neg: nan | dice_pos: 0.7664\n",
      "Starting epoch: 14 | phase: val | ⏰: 22:09:16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:41<00:00,  3.19it/s, loss=0.00738]\n",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0590 | IoU: 0.6520 | dice: 0.7703 | dice_neg: nan | dice_pos: 0.7703\n",
      "Epoch     9: reducing learning rate of group 0 to 5.0000e-05.\n",
      "\n",
      "Learning Rate =  5e-05\n",
      "Starting epoch: 15 | phase: train | ⏰: 22:09:58\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [13:04<00:00,  2.53s/it, loss=0.00678]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0543 | IoU: 0.6792 | dice: 0.7949 | dice_neg: nan | dice_pos: 0.7949\n",
      "Starting epoch: 15 | phase: val | ⏰: 22:23:02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:17<00:00,  7.47it/s, loss=0.00673]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0538 | IoU: 0.6685 | dice: 0.7846 | dice_neg: nan | dice_pos: 0.7846\n",
      "******** New optimal found, saving state ********\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Learning Rate =  5e-05\n",
      "Starting epoch: 16 | phase: train | ⏰: 22:23:21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [01:49<00:00,  2.83it/s, loss=0.00628]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0502 | IoU: 0.6910 | dice: 0.8052 | dice_neg: nan | dice_pos: 0.8052\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting epoch: 16 | phase: val | ⏰: 22:25:11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:37<00:00,  3.54it/s, loss=0.00651]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0520 | IoU: 0.6784 | dice: 0.7935 | dice_neg: nan | dice_pos: 0.7935\n",
      "******** New optimal found, saving state ********\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Learning Rate =  5e-05\n",
      "Starting epoch: 17 | phase: train | ⏰: 22:25:49\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:25<00:00,  1.17it/s, loss=0.00605]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0484 | IoU: 0.7015 | dice: 0.8139 | dice_neg: nan | dice_pos: 0.8139\n",
      "Starting epoch: 17 | phase: val | ⏰: 22:30:14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:41<00:00,  3.19it/s, loss=0.00654]\n",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0523 | IoU: 0.6717 | dice: 0.7875 | dice_neg: nan | dice_pos: 0.7875\n",
      "\n",
      "Learning Rate =  5e-05\n",
      "Starting epoch: 18 | phase: train | ⏰: 22:30:56\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:24<00:00,  1.17it/s, loss=0.00589]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0471 | IoU: 0.7075 | dice: 0.8189 | dice_neg: nan | dice_pos: 0.8189\n",
      "Starting epoch: 18 | phase: val | ⏰: 22:35:21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:41<00:00,  3.19it/s, loss=0.00667]\n",
      "  0%|                                                                                          | 0/310 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0534 | IoU: 0.6712 | dice: 0.7866 | dice_neg: nan | dice_pos: 0.7866\n",
      "\n",
      "Learning Rate =  5e-05\n",
      "Starting epoch: 19 | phase: train | ⏰: 22:36:02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 310/310 [04:25<00:00,  1.17it/s, loss=0.00581]\n",
      "  0%|                                                                                          | 0/133 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0465 | IoU: 0.7046 | dice: 0.8163 | dice_neg: nan | dice_pos: 0.8163\n",
      "Starting epoch: 19 | phase: val | ⏰: 22:40:28\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████| 133/133 [00:41<00:00,  3.18it/s, loss=0.00659]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0527 | IoU: 0.6818 | dice: 0.7963 | dice_neg: nan | dice_pos: 0.7963\n",
      "******** New optimal found, saving state ********\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_trainer = Trainer(model, lr, epochs, path)\n",
    "print(f\"Current Device is GPU: {torch.cuda.current_device()}\")\n",
    "print(f\"Total memory allocated in GB: {round(torch.cuda.memory_allocated(0)/1024**3,1)}\")\n",
    "print(f\"Total memory cached in GB: {round(torch.cuda.memory_reserved(0)/1024**3,1)}\")\n",
    "model_trainer.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3b92f78",
   "metadata": {},
   "source": [
    "After the model training was done the loss, dice_scores and iou_scores were plotted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f162068b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = model_trainer.losses\n",
    "dice_scores = model_trainer.dice_scores # overall dice\n",
    "iou_scores = model_trainer.iou_scores\n",
    "\n",
    "def plot(scores, name):\n",
    "    plt.figure(figsize=(15,5))\n",
    "    plt.plot(range(len(scores[\"train\"])), scores[\"train\"], label=f'train {name}')\n",
    "    plt.plot(range(len(scores[\"train\"])), scores[\"val\"], label=f'val {name}')\n",
    "    plt.title(f'{name} plot'); plt.xlabel('Epoch'); plt.ylabel(f'{name}');\n",
    "    plt.legend(); \n",
    "    plt.show()\n",
    "\n",
    "plot(losses, \"BCE loss\")\n",
    "plot(dice_scores, \"Dice score\")\n",
    "plot(iou_scores, \"IoU score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6babd8d",
   "metadata": {},
   "source": [
    "The above results the overall training and validation dice and IoU goes up with epochs and training and validation loss goes down with epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69252d4a",
   "metadata": {},
   "source": [
    "# Convert to ONNX\n",
    "\n",
    "Lastly the trained model is converted to ONNX format using the Utility script ONNX_Converter.py."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ab71b3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model name :FPN\n",
      "Model checkpoint path is :./Models/model_fpn_resnet.pth\n",
      "Model output path is :./ONNX_models/fpn_resnet.onnx\n",
      "Successfully converted the model to onnx format.\n"
     ]
    }
   ],
   "source": [
    "! python ./Utilities/ONNX_converter.py FPN resnet34  imagenet  6 ./Models/model_fpn_resnet.pth ./ONNX_models/fpn_resnet.onnx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2983e9c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}