Agro-detect.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:10.634853Z",
     "iopub.status.busy": "2020-10-24T08:49:10.634237Z",
     "iopub.status.idle": "2020-10-24T08:49:13.414899Z",
     "shell.execute_reply": "2020-10-24T08:49:13.413751Z"
    },
    "id": "9Pec-P6UCbch",
    "papermill": {
     "duration": 2.804045,
     "end_time": "2020-10-24T08:49:13.415029",
     "exception": false,
     "start_time": "2020-10-24T08:49:10.610984",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from collections import OrderedDict\n",
    "from torch import optim\n",
    "from torchvision import datasets, transforms, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:13.456989Z",
     "iopub.status.busy": "2020-10-24T08:49:13.454801Z",
     "iopub.status.idle": "2020-10-24T08:49:19.654876Z",
     "shell.execute_reply": "2020-10-24T08:49:19.653529Z"
    },
    "id": "zT6nAcC_Cbcl",
    "papermill": {
     "duration": 6.224171,
     "end_time": "2020-10-24T08:49:19.654995",
     "exception": false,
     "start_time": "2020-10-24T08:49:13.430824",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# load the data from the folders\n",
    "data_dir = 'data/'\n",
    "\n",
    "train_transforms = transforms.Compose([transforms.RandomRotation(30),\n",
    "                                       transforms.RandomResizedCrop(224),\n",
    "                                       transforms.RandomHorizontalFlip(),\n",
    "                                       transforms.ToTensor(),\n",
    "                                       transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                            [0.229, 0.224, 0.225])])\n",
    "\n",
    "val_transforms = transforms.Compose([transforms.Resize(255),\n",
    "                                      transforms.CenterCrop(224),\n",
    "                                      transforms.ToTensor(),\n",
    "                                      transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                           [0.229, 0.224, 0.225])])\n",
    "\n",
    "\n",
    "train_data = datasets.ImageFolder(data_dir + '/train', transform=train_transforms)\n",
    "val_data = datasets.ImageFolder(data_dir + '/val', transform=val_transforms)\n",
    "\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\n",
    "val_loader = torch.utils.data.DataLoader(val_data, batch_size=64)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:19.693906Z",
     "iopub.status.busy": "2020-10-24T08:49:19.693209Z",
     "iopub.status.idle": "2020-10-24T08:49:20.137503Z",
     "shell.execute_reply": "2020-10-24T08:49:20.138008Z"
    },
    "id": "kLhAwAubvi8T",
    "outputId": "e4cafaf6-f15b-43fd-d079-77f3947325ab",
    "papermill": {
     "duration": 0.467874,
     "end_time": "2020-10-24T08:49:20.138142",
     "exception": false,
     "start_time": "2020-10-24T08:49:19.670268",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'torch.Tensor'>\n",
      "torch.Size([64, 3, 224, 224])\n",
      "torch.Size([64])\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()\n",
    "print(type(images))\n",
    "print(images.shape)\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:20.180002Z",
     "iopub.status.busy": "2020-10-24T08:49:20.176601Z",
     "iopub.status.idle": "2020-10-24T08:49:20.182594Z",
     "shell.execute_reply": "2020-10-24T08:49:20.182087Z"
    },
    "id": "9VqkNTuEvn2N",
    "papermill": {
     "duration": 0.028937,
     "end_time": "2020-10-24T08:49:20.182696",
     "exception": false,
     "start_time": "2020-10-24T08:49:20.153759",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def imshow(image, ax=None, normalize=True):\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    image = image.numpy().transpose((1, 2, 0))\n",
    "\n",
    "    if normalize:\n",
    "        # if the data loader has transform.normalize\n",
    "        # undo preprocessing\n",
    "        mean = np.array([0.485, 0.456, 0.406])\n",
    "        std = np.array([0.229, 0.224, 0.225])\n",
    "        image = std * image + mean\n",
    "        image = np.clip(image, 0, 1)\n",
    "\n",
    "    ax.imshow(image)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "    ax.tick_params(axis='both', length=0)\n",
    "    ax.set_xticklabels('')\n",
    "    ax.set_yticklabels('')\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:20.218909Z",
     "iopub.status.busy": "2020-10-24T08:49:20.218288Z",
     "iopub.status.idle": "2020-10-24T08:49:20.675110Z",
     "shell.execute_reply": "2020-10-24T08:49:20.675619Z"
    },
    "id": "N5lKyX1gvqiA",
    "outputId": "920ac7f3-a17b-4ad4-ca2f-40bffac85f9a",
    "papermill": {
     "duration": 0.477802,
     "end_time": "2020-10-24T08:49:20.675755",
     "exception": false,
     "start_time": "2020-10-24T08:49:20.197953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(images[1]);\n",
    "imshow(images[4]);\n",
    "imshow(images[6]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:20.743679Z",
     "iopub.status.busy": "2020-10-24T08:49:20.743049Z",
     "iopub.status.idle": "2020-10-24T08:49:27.551515Z",
     "shell.execute_reply": "2020-10-24T08:49:27.551999Z"
    },
    "id": "XG-i248SjoyI",
    "outputId": "c8b80be9-0ad4-4cc8-a4d5-7758ca65e71f",
    "papermill": {
     "duration": 6.837963,
     "end_time": "2020-10-24T08:49:27.552145",
     "exception": false,
     "start_time": "2020-10-24T08:49:20.714182",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/densenet201-c1103571.pth\" to /root/.cache/torch/hub/checkpoints/densenet201-c1103571.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "967bc04a9b9d497189483f4dcba1507d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=81131730.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# use pretraned model\n",
    "model = models.densenet201(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:27.614764Z",
     "iopub.status.busy": "2020-10-24T08:49:27.613970Z",
     "iopub.status.idle": "2020-10-24T08:49:27.620537Z",
     "shell.execute_reply": "2020-10-24T08:49:27.621129Z"
    },
    "id": "gyvhzooT9jB4",
    "outputId": "b3755bbd-b86a-4fd1-8237-26e3cfbc0ea5",
    "papermill": {
     "duration": 0.044899,
     "end_time": "2020-10-24T08:49:27.621257",
     "exception": false,
     "start_time": "2020-10-24T08:49:27.576358",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseNet(\n",
       "  (features): Sequential(\n",
       "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (norm0): BatchNorm2d(64, 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(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition1): _Transition(\n",
       "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(256, 128, 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(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, 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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition2): _Transition(\n",
       "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(512, 256, 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(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, 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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer25): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer26): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer27): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer28): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer29): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer30): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer31): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer32): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer33): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer34): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer35): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer36): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer37): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer38): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer39): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer40): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer41): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer42): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer43): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer44): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer45): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer46): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer47): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer48): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition3): _Transition(\n",
       "      (norm): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(1792, 896, 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(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer25): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer26): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer27): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer28): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer29): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1792, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer30): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer31): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1856, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1856, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer32): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1888, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1888, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (norm5): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): Linear(in_features=1920, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jLwO_hqM-AcC",
    "papermill": {
     "duration": 0.025479,
     "end_time": "2020-10-24T08:49:27.672459",
     "exception": false,
     "start_time": "2020-10-24T08:49:27.646980",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The above model was trained on imageNet which has 1000 different categories, For our model we have 38 categories.We will have to change the classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:27.731205Z",
     "iopub.status.busy": "2020-10-24T08:49:27.730445Z",
     "iopub.status.idle": "2020-10-24T08:49:27.733439Z",
     "shell.execute_reply": "2020-10-24T08:49:27.732987Z"
    },
    "id": "8c1_kLY5-kHY",
    "papermill": {
     "duration": 0.035471,
     "end_time": "2020-10-24T08:49:27.733531",
     "exception": false,
     "start_time": "2020-10-24T08:49:27.698060",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for param in model.parameters():\n",
    "  param.required_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:27.792772Z",
     "iopub.status.busy": "2020-10-24T08:49:27.792048Z",
     "iopub.status.idle": "2020-10-24T08:49:27.802502Z",
     "shell.execute_reply": "2020-10-24T08:49:27.803095Z"
    },
    "id": "cIs2zROcAPGU",
    "papermill": {
     "duration": 0.043994,
     "end_time": "2020-10-24T08:49:27.803203",
     "exception": false,
     "start_time": "2020-10-24T08:49:27.759209",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# change the classifier\n",
    "from collections import OrderedDict\n",
    "\n",
    "classifier = nn.Sequential(OrderedDict([\n",
    "              ('fc1', nn.Linear(1920, 500)),\n",
    "              ('relu1', nn.ReLU()),\n",
    "              ('dropout1', nn.Dropout(p=0.2)),\n",
    "              ('fc2', nn.Linear(500, 256)),\n",
    "              ('relu2', nn.ReLU()),\n",
    "              ('dropout2', nn.Dropout(p=0.2)),\n",
    "              ('fc3', nn.Linear(256, 39)),\n",
    "              ('output', nn.LogSoftmax(dim=1))\n",
    "              \n",
    "]))\n",
    "model.classifier = classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:28.209145Z",
     "iopub.status.busy": "2020-10-24T08:49:28.208444Z",
     "iopub.status.idle": "2020-10-24T08:49:28.213094Z",
     "shell.execute_reply": "2020-10-24T08:49:28.212598Z"
    },
    "id": "r4QyrrlACd4c",
    "papermill": {
     "duration": 0.38413,
     "end_time": "2020-10-24T08:49:28.213203",
     "exception": false,
     "start_time": "2020-10-24T08:49:27.829073",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Train either on GPU or CPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:28.279433Z",
     "iopub.status.busy": "2020-10-24T08:49:28.278823Z",
     "iopub.status.idle": "2020-10-24T08:49:32.805037Z",
     "shell.execute_reply": "2020-10-24T08:49:32.805515Z"
    },
    "id": "DysyM8GKGONp",
    "outputId": "26c66246-eb30-461f-a038-4f564b613b7a",
    "papermill": {
     "duration": 4.563044,
     "end_time": "2020-10-24T08:49:32.805667",
     "exception": false,
     "start_time": "2020-10-24T08:49:28.242623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseNet(\n",
       "  (features): Sequential(\n",
       "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (norm0): BatchNorm2d(64, 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(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition1): _Transition(\n",
       "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(256, 128, 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(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, 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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition2): _Transition(\n",
       "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(512, 256, 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(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, 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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer25): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer26): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer27): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer28): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer29): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer30): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer31): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer32): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer33): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer34): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer35): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer36): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer37): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer38): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer39): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer40): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer41): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer42): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer43): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer44): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer45): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer46): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer47): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer48): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition3): _Transition(\n",
       "      (norm): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(1792, 896, 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(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer25): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer26): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer27): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer28): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer29): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1792, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer30): _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, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer31): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1856, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1856, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer32): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1888, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(1888, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (norm5): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (fc1): Linear(in_features=1920, out_features=500, bias=True)\n",
       "    (relu1): ReLU()\n",
       "    (dropout1): Dropout(p=0.2, inplace=False)\n",
       "    (fc2): Linear(in_features=500, out_features=256, bias=True)\n",
       "    (relu2): ReLU()\n",
       "    (dropout2): Dropout(p=0.2, inplace=False)\n",
       "    (fc3): Linear(in_features=256, out_features=39, bias=True)\n",
       "    (output): LogSoftmax(dim=1)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "criterion = nn.NLLLoss()\n",
    "\n",
    "optimizer = optim.SGD(model.classifier.parameters(), lr = 0.01, momentum=0.9)\n",
    "\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T08:49:32.876018Z",
     "iopub.status.busy": "2020-10-24T08:49:32.875231Z",
     "iopub.status.idle": "2020-10-24T10:37:50.264177Z",
     "shell.execute_reply": "2020-10-24T10:37:50.264963Z"
    },
    "id": "K3f7LcrOUINL",
    "papermill": {
     "duration": 6497.431524,
     "end_time": "2020-10-24T10:37:50.265175",
     "exception": false,
     "start_time": "2020-10-24T08:49:32.833651",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/10..  Training Loss: 1.276..  Valid Loss: 0.332..  Valid Accuracy: 0.898\n",
      "Epoch: 2/10..  Training Loss: 0.528..  Valid Loss: 0.214..  Valid Accuracy: 0.933\n",
      "Epoch: 3/10..  Training Loss: 0.440..  Valid Loss: 0.179..  Valid Accuracy: 0.944\n",
      "Epoch: 4/10..  Training Loss: 0.382..  Valid Loss: 0.167..  Valid Accuracy: 0.945\n",
      "Epoch: 5/10..  Training Loss: 0.357..  Valid Loss: 0.138..  Valid Accuracy: 0.958\n",
      "Epoch: 6/10..  Training Loss: 0.341..  Valid Loss: 0.125..  Valid Accuracy: 0.961\n",
      "Epoch: 7/10..  Training Loss: 0.316..  Valid Loss: 0.118..  Valid Accuracy: 0.961\n",
      "Epoch: 8/10..  Training Loss: 0.305..  Valid Loss: 0.108..  Valid Accuracy: 0.966\n",
      "Epoch: 9/10..  Training Loss: 0.300..  Valid Loss: 0.115..  Valid Accuracy: 0.964\n",
      "Epoch: 10/10..  Training Loss: 0.291..  Valid Loss: 0.115..  Valid Accuracy: 0.962\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    running_loss = 0\n",
    "    for images, labels in train_loader:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        \n",
    "    else:\n",
    "        validation_loss = 0\n",
    "        accuracy = 0\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            model.eval()\n",
    "            for images, labels in val_loader:\n",
    "                images, labels = images.to(device), labels.to(device)\n",
    "                outputs = model(images)\n",
    "                loss = criterion(outputs, labels)\n",
    "                validation_loss += loss.item()\n",
    "                \n",
    "                ps = torch.exp(outputs)\n",
    "                top_p, top_class = ps.topk(1, dim=1)\n",
    "                equals = top_class == labels.view(*top_class.shape)\n",
    "                accuracy += torch.mean(equals.type(torch.FloatTensor))\n",
    "        \n",
    "        model.train()\n",
    "\n",
    "        print(\"Epoch: {}/{}.. \".format(epoch+1, epochs),\n",
    "              \"Training Loss: {:.3f}.. \".format(running_loss/len(train_loader)),\n",
    "              \"Valid Loss: {:.3f}.. \".format(validation_loss/len(val_loader)),\n",
    "              \"Valid Accuracy: {:.3f}\".format(accuracy/len(val_loader)))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T10:37:50.334020Z",
     "iopub.status.busy": "2020-10-24T10:37:50.332313Z",
     "iopub.status.idle": "2020-10-24T10:37:50.334693Z",
     "shell.execute_reply": "2020-10-24T10:37:50.335138Z"
    },
    "papermill": {
     "duration": 0.03842,
     "end_time": "2020-10-24T10:37:50.335243",
     "exception": false,
     "start_time": "2020-10-24T10:37:50.296823",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.class_to_idx = train_data.class_to_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T10:37:50.402968Z",
     "iopub.status.busy": "2020-10-24T10:37:50.402157Z",
     "iopub.status.idle": "2020-10-24T10:37:50.609502Z",
     "shell.execute_reply": "2020-10-24T10:37:50.608932Z"
    },
    "id": "KeG_ZnXgv5_7",
    "papermill": {
     "duration": 0.243145,
     "end_time": "2020-10-24T10:37:50.609632",
     "exception": false,
     "start_time": "2020-10-24T10:37:50.366487",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# save model\n",
    "torch.save({\"state_dict\":model.state_dict(),\n",
    "           \"class_to_idx\":model.class_to_idx}, 'classifier.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T10:37:50.683742Z",
     "iopub.status.busy": "2020-10-24T10:37:50.683005Z",
     "iopub.status.idle": "2020-10-24T10:37:50.686367Z",
     "shell.execute_reply": "2020-10-24T10:37:50.685867Z"
    },
    "id": "ppgJyRp1v-Sa",
    "papermill": {
     "duration": 0.044793,
     "end_time": "2020-10-24T10:37:50.686456",
     "exception": false,
     "start_time": "2020-10-24T10:37:50.641663",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# load the model\n",
    "def load_checkpoint(file_path):\n",
    "    state_dict = torch.load(file_path, map_location=\"cpu\")\n",
    "    \n",
    "    model = models.densenet201(pretrained=True)\n",
    "    model.class_to_idx = state_dict[\"class_to_idx\"]\n",
    "    \n",
    "    classifier = classifier = nn.Sequential(OrderedDict([\n",
    "              ('fc1', nn.Linear(1920, 500)),\n",
    "              ('relu1', nn.ReLU()),\n",
    "              ('dropout1', nn.Dropout(p=0.2)),\n",
    "              ('fc2', nn.Linear(500, 256)),\n",
    "              ('relu2', nn.ReLU()),\n",
    "              ('dropout2', nn.Dropout(p=0.2)),\n",
    "              ('fc3', nn.Linear(256, 39)),\n",
    "              ('output', nn.LogSoftmax(dim=1))\n",
    "              \n",
    "        ]))\n",
    "    model.classifier = classifier\n",
    "    \n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "        \n",
    "    model.load_state_dict(state_dict[\"state_dict\"], strict=False)\n",
    "    \n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T10:37:50.754314Z",
     "iopub.status.busy": "2020-10-24T10:37:50.753486Z",
     "iopub.status.idle": "2020-10-24T10:37:51.813283Z",
     "shell.execute_reply": "2020-10-24T10:37:51.812747Z"
    },
    "id": "y6ZRNluvwIOv",
    "outputId": "257748ce-2df3-4e59-f5f7-2721decb6a86",
    "papermill": {
     "duration": 1.09504,
     "end_time": "2020-10-24T10:37:51.813401",
     "exception": false,
     "start_time": "2020-10-24T10:37:50.718361",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# laod the model\n",
    "model = load_checkpoint(\"classifier.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T10:37:51.885683Z",
     "iopub.status.busy": "2020-10-24T10:37:51.884900Z",
     "iopub.status.idle": "2020-10-24T10:37:51.889358Z",
     "shell.execute_reply": "2020-10-24T10:37:51.888776Z"
    },
    "id": "PLmUG_TyhRaN",
    "papermill": {
     "duration": 0.043174,
     "end_time": "2020-10-24T10:37:51.889469",
     "exception": false,
     "start_time": "2020-10-24T10:37:51.846295",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def process_image(image_path):\n",
    "    \n",
    "    test_transform = transforms.Compose([transforms.Resize(255),\n",
    "                                      transforms.CenterCrop(224),\n",
    "                                      transforms.ToTensor(),\n",
    "                                      transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                           [0.229, 0.224, 0.225])])\n",
    "    from PIL import Image\n",
    "    im = Image.open(image_path)\n",
    "    im = test_transform(im)\n",
    "\n",
    "    return im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T10:37:51.961637Z",
     "iopub.status.busy": "2020-10-24T10:37:51.961019Z",
     "iopub.status.idle": "2020-10-24T10:37:51.965242Z",
     "shell.execute_reply": "2020-10-24T10:37:51.964753Z"
    },
    "id": "kqdScDFryP3x",
    "papermill": {
     "duration": 0.043625,
     "end_time": "2020-10-24T10:37:51.965331",
     "exception": false,
     "start_time": "2020-10-24T10:37:51.921706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def predict(image, model):\n",
    "    # we have to process the image as we did while training the others\n",
    "    image = process_image(image)\n",
    "    \n",
    "    image_input = image.unsqueeze(0)\n",
    "    \n",
    "    model.eval()\n",
    "    outputs = model(image_input)\n",
    "    ps = torch.exp(outputs)\n",
    "    \n",
    "    top_p, top_cls = ps.topk(5, dim=1)\n",
    "    top_cls = top_cls.detach().numpy().tolist()[0]\n",
    "    \n",
    "    # covert indices to classes\n",
    "    idx_to_class = {v: k for k, v in model.class_to_idx.items()}\n",
    "    \n",
    "    top_cls = [idx_to_class[top_class] for top_class in top_cls]\n",
    "    \n",
    "    return top_p, top_cls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T10:37:52.087404Z",
     "iopub.status.busy": "2020-10-24T10:37:52.086761Z",
     "iopub.status.idle": "2020-10-24T10:37:52.090380Z",
     "shell.execute_reply": "2020-10-24T10:37:52.089890Z"
    },
    "id": "xCoIwjAbyUnw",
    "papermill": {
     "duration": 0.093069,
     "end_time": "2020-10-24T10:37:52.090471",
     "exception": false,
     "start_time": "2020-10-24T10:37:51.997402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_solution(image_path, ps, classes):\n",
    "    plt.figure(figsize = (6,10))\n",
    "    ax = plt.subplot(2,1,1)\n",
    "    image = process_image(image_path)\n",
    "    imshow(image, ax)\n",
    "\n",
    "    plt.subplot(2,1,2)\n",
    "    sns.barplot(x=ps, y=classes, color=sns.color_palette()[2]);\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-24T10:37:52.160106Z",
     "iopub.status.busy": "2020-10-24T10:37:52.159309Z",
     "iopub.status.idle": "2020-10-24T10:37:52.757754Z",
     "shell.execute_reply": "2020-10-24T10:37:52.758259Z"
    },
    "id": "PVZs2W9hyh6R",
    "outputId": "6fa76566-2c3a-473b-e100-a3ab17b2223e",
    "papermill": {
     "duration": 0.635766,
     "end_time": "2020-10-24T10:37:52.758393",
     "exception": false,
     "start_time": "2020-10-24T10:37:52.122627",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9998658895492554, 4.3901472963625565e-05, 2.727953869907651e-05, 2.5136672775261104e-05, 5.5230475481948815e-06]\n",
      "['Apple___Apple_scab', 'Tomato___Septoria_leaf_spot', 'Tomato___Late_blight', 'Apple___Cedar_apple_rust', 'Peach___Bacterial_spot']\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = \"data/val/Apple___Apple_scab/image (102).JPG\"\n",
    "ps, classes = predict(image, model)\n",
    "ps = ps.detach().numpy().tolist()[0]\n",
    "print(ps)\n",
    "print(classes)\n",
    "\n",
    "plot_solution(image, ps, classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Fzev-baXywo-",
    "papermill": {
     "duration": 0.034967,
     "end_time": "2020-10-24T10:37:52.828949",
     "exception": false,
     "start_time": "2020-10-24T10:37:52.793982",
     "status": "completed"
    },
    "tags": []
   },
   "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": "3.7.6"
  },
  "papermill": {
   "duration": 6528.722213,
   "end_time": "2020-10-24T10:37:54.866689",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2020-10-24T08:49:06.144476",
   "version": "2.1.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "02392518444f48c280db471348ab431e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "18ed9c63b3294016b82a3f51dcb1c0c9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_8e058936947a4f55b8eb287a489ea113",
       "placeholder": "​",
       "style": "IPY_MODEL_02392518444f48c280db471348ab431e",
       "value": " 77.4M/77.4M [00:05&lt;00:00, 13.9MB/s]"
      }
     },
     "3cd78a1622fa45e2b14bc7b700ce2f90": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "89a67bb1c66d4f959bef8f54a4a510ad": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "100%",
       "description_tooltip": null,
       "layout": "IPY_MODEL_3cd78a1622fa45e2b14bc7b700ce2f90",
       "max": 81131730,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_ff33223b308249778955cb2b329b2c3c",
       "value": 81131730
      }
     },
     "8e058936947a4f55b8eb287a489ea113": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "967bc04a9b9d497189483f4dcba1507d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_89a67bb1c66d4f959bef8f54a4a510ad",
        "IPY_MODEL_18ed9c63b3294016b82a3f51dcb1c0c9"
       ],
       "layout": "IPY_MODEL_f760ac9b06db4f1c8d10b0015fed5324"
      }
     },
     "f760ac9b06db4f1c8d10b0015fed5324": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "ff33223b308249778955cb2b329b2c3c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": "initial"
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}