fire_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import torch\n",
    "import torchvision.transforms as transforms \n",
    "import torchvision.datasets as datasets\n",
    "import torchvision.models as models\n",
    "import torch.nn as nn \n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim \n",
    "import numpy as np \n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify transformations\n",
    "transformations = transforms.Compose([\n",
    "    transforms.Resize(255),\n",
    "    transforms.CenterCrop(224),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create training and testing data loasers\n",
    "train_set = datasets.ImageFolder('data/train', transform=transformations)\n",
    "train_loader = torch.utils.data.DataLoader(train_set, batch_size=32, shuffle=True)\n",
    "test_set = datasets.ImageFolder('data/test', transform=transformations)\n",
    "test_loader = torch.utils.data.DataLoader(test_set, batch_size=32, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get pretrained model\n",
    "model = models.densenet161(pretrained=True)\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use custom classifier \n",
    "classifier_input = model.classifier.in_features\n",
    "num_labels = 2\n",
    "class FireClassifier(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(FireClassifier, self).__init__()\n",
    "        self.linear1 = nn.Linear(classifier_input, 1024)\n",
    "        self.linear2 = nn.Linear(1024, 512)\n",
    "        self.linear3 = nn.Linear(512, 256)\n",
    "        self.linear4 = nn.Linear(256, num_labels)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.linear1(x))\n",
    "        x = F.relu(self.linear2(x))\n",
    "        x = F.relu(self.linear3(x))\n",
    "        x = F.log_softmax(self.linear4(x), dim=1)\n",
    "        return x\n",
    "        \n",
    "fire_classifier = FireClassifier()\n",
    "model.classifier = fire_classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseNet(\n",
       "  (features): Sequential(\n",
       "    (conv0): Conv2d(3, 96, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (norm0): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu0): ReLU(inplace=True)\n",
       "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (denseblock1): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(96, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(144, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(240, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(288, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(336, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition1): _Transition(\n",
       "      (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock2): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(240, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(288, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(336, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(432, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(432, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(528, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(528, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(624, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(624, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(720, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(720, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition2): _Transition(\n",
       "      (norm): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(768, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock3): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(432, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(432, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(528, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(528, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(624, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(624, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(720, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(720, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(816, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(864, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(912, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(912, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(960, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1008, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1008, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1056, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1104, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1200, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1248, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1296, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1296, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1344, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1392, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1440, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1488, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1488, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer25): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1536, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer26): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1584, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1584, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer27): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1632, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer28): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1680, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1680, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer29): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1728, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer30): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1776, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1776, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer31): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1824, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer32): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1872, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1872, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer33): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1920, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer34): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1968, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1968, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer35): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(2016, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(2016, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer36): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(2064, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(2064, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition3): _Transition(\n",
       "      (norm): BatchNorm2d(2112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(2112, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock4): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1056, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1104, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1200, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1248, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1296, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1296, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1344, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1392, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1440, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1488, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1488, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1536, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1584, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1584, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1632, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1680, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1680, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1728, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1776, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1776, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1824, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1872, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1872, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1920, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1968, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1968, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(2016, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(2016, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(2064, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(2064, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(2112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(2112, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(2160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(2160, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (norm5): BatchNorm2d(2208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): FireClassifier(\n",
       "    (linear1): Linear(in_features=2208, out_features=1024, bias=True)\n",
       "    (linear2): Linear(in_features=1024, out_features=512, bias=True)\n",
       "    (linear3): Linear(in_features=512, out_features=256, bias=True)\n",
       "    (linear4): Linear(in_features=256, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use CUDA if available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set error function and optimizer for training \n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.classifier.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  1 / 3\n",
      "Training Batch:  1 / 7\n",
      "Training Batch:  2 / 7\n",
      "Training Batch:  3 / 7\n",
      "Training Batch:  4 / 7\n",
      "Training Batch:  5 / 7\n",
      "Training Batch:  6 / 7\n",
      "Training Batch:  7 / 7\n",
      "Testing Batch:  1 / 3\n",
      "Testing Batch:  2 / 3\n",
      "Testing Batch:  3 / 3\n",
      "Accuracy:  0.7083333333333334\n",
      "Training Loss: 0.691142\n",
      "Testing Loss: 0.643803\n",
      "\n",
      "Epoch:  2 / 3\n",
      "Training Batch:  1 / 7\n",
      "Training Batch:  2 / 7\n",
      "Training Batch:  3 / 7\n",
      "Training Batch:  4 / 7\n",
      "Training Batch:  5 / 7\n",
      "Training Batch:  6 / 7\n",
      "Training Batch:  7 / 7\n",
      "Testing Batch:  1 / 3\n",
      "Testing Batch:  2 / 3\n",
      "Testing Batch:  3 / 3\n",
      "Accuracy:  0.9479166666666666\n",
      "Training Loss: 0.546291\n",
      "Testing Loss: 0.342724\n",
      "\n",
      "Epoch:  3 / 3\n",
      "Training Batch:  1 / 7\n",
      "Training Batch:  2 / 7\n",
      "Training Batch:  3 / 7\n",
      "Training Batch:  4 / 7\n",
      "Training Batch:  5 / 7\n",
      "Training Batch:  6 / 7\n",
      "Training Batch:  7 / 7\n",
      "Testing Batch:  1 / 3\n",
      "Testing Batch:  2 / 3\n",
      "Testing Batch:  3 / 3\n",
      "Accuracy:  0.9479166666666666\n",
      "Training Loss: 0.277649\n",
      "Testing Loss: 0.179197\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# train and evaluate the model, monitor loss and accuracy \n",
    "epochs = 3\n",
    "for epoch in range(epochs):\n",
    "    train_loss = 0\n",
    "    test_loss = 0\n",
    "    accuracy = 0\n",
    "    \n",
    "    model.train()\n",
    "    counter = 0\n",
    "    print('Epoch: ', epoch + 1, '/', epochs)\n",
    "    for inputs, labels in train_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        output = model.forward(inputs)\n",
    "\n",
    "        loss = criterion(output, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()*inputs.size(0)\n",
    "        \n",
    "        counter += 1\n",
    "        print('Training Batch: ', counter, '/', len(train_loader)) \n",
    "        \n",
    "    model.eval()\n",
    "    counter = 0\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in test_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            output = model.forward(inputs)\n",
    "\n",
    "            testloss = criterion(output, labels)\n",
    "            test_loss += testloss.item()*inputs.size(0)\n",
    "           \n",
    "            output = torch.exp(output)\n",
    "            top_p, top_class = output.topk(1, dim=1)\n",
    "\n",
    "            equals = top_class == labels.view(*top_class.shape)\n",
    "            accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "            \n",
    "            counter += 1\n",
    "            print ('Testing Batch: ', counter, \"/\", len(test_loader))    \n",
    "            \n",
    "    train_loss = train_loss/len(train_loader.dataset)\n",
    "    test_loss = test_loss/len(test_loader.dataset)\n",
    "    \n",
    "    print('Accuracy: ', accuracy/len(test_loader))\n",
    "    print('Training Loss: {:.6f}'.format(train_loss))\n",
    "    print('Testing Loss: {:.6f}'.format(test_loss))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocess input image \n",
    "def process_image(image_path):\n",
    "    image_path = urllib.request.urlopen(image_path)\n",
    "    img = Image.open(image_path)\n",
    "    width, height = img.size\n",
    "    img = img.resize((255, int(255*(height/width))) if width < height else (int(255*(width/height)), 255))\n",
    "    width, height = img.size \n",
    "    \n",
    "    left = (width - 224)/2\n",
    "    top = (height - 224)/2\n",
    "    right = (width + 224) / 2\n",
    "    bottom = (height + 224) / 2\n",
    "    img = img.crop((left, top, right, bottom))\n",
    "    \n",
    "    img = np.array(img)\n",
    "    \n",
    "    img = img.transpose((2, 0, 1))\n",
    "    \n",
    "    img = img/255\n",
    "    \n",
    "    img[0] = (img[0] - 0.485)/0.229\n",
    "    img[1] = (img[1] - 0.456)/0.224\n",
    "    img[2] = (img[2] - 0.406)/0.225\n",
    "    \n",
    "    img = img[np.newaxis,:]\n",
    "    image = torch.from_numpy(img)\n",
    "    image = image.float()\n",
    "    return image[:, :3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use model to predict label \n",
    "def predict(image, model):\n",
    "    output = model.forward(image)\n",
    "    output = torch.exp(output)\n",
    "    \n",
    "    probs, classes = output.topk(1, dim=1)\n",
    "    return probs.item(), classes.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show image \n",
    "def show_image(image):\n",
    "    image = image.numpy()\n",
    "    image[0] = image[0] * 0.226 + 0.445\n",
    "    \n",
    "    fig = plt.figure(figsize=(25, 4))\n",
    "    plt.imshow(np.transpose(image[0], (1, 2, 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model is  97.49863147735596 % certain that the image has a predicted class of  1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# process image (no fire)\n",
    "image_url = 'https://image.shutterstock.com/image-photo/beautiful-aerial-shot-hills-260nw-1317841274.jpg'\n",
    "image = process_image(image_url)\n",
    "top_prob, top_class = predict(image, model)\n",
    "show_image(image)\n",
    "print(\"The model is \", top_prob*100, \"% certain that the image has a predicted class of \", top_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model is  99.22680258750916 % certain that the image has a predicted class of  0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# process image (fire)\n",
    "image_url = 'https://i.guim.co.uk/img/media/ffa44643eb2ad3f816acfa3a21ec8b7ae2f50ac5/0_98_3000_1800/master/3000.jpg?width=445&quality=45&auto=format&fit=max&dpr=2&s=842f1c97089ccab575e2043d8358e48c'\n",
    "image = process_image(image_url)\n",
    "top_prob, top_class = predict(image, model)\n",
    "show_image(image)\n",
    "print(\"The model is \", top_prob*100, \"% certain that the image has a predicted class of \", top_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.classifier.state_dict(), 'fire_classifier')"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}