Image Classifier Project-zh.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 开发 AI 应用\n",
    "\n",
    "未来，AI 算法在日常生活中的应用将越来越广泛。例如，你可能想要在智能手机应用中包含图像分类器。为此，在整个应用架构中，你将使用一个用成百上千个图像训练过的深度学习模型。未来的软件开发很大一部分将是使用这些模型作为应用的常用部分。\n",
    "\n",
    "在此项目中，你将训练一个图像分类器来识别不同的花卉品种。可以想象有这么一款手机应用，当你对着花卉拍摄时，它能够告诉你这朵花的名称。在实际操作中，你会训练此分类器，然后导出它以用在你的应用中。我们将使用[此数据集](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html)，其中包含 102 个花卉类别。你可以在下面查看几个示例。 \n",
    "\n",
    "<img src='assets/Flowers.png' width=500px>\n",
    "\n",
    "该项目分为多个步骤：\n",
    "\n",
    "* 加载和预处理图像数据集\n",
    "* 用数据集训练图像分类器\n",
    "* 使用训练的分类器预测图像内容\n",
    "\n",
    "我们将指导你完成每一步，你将用 Python 实现这些步骤。建议在基本完成提示代码的情况下再开启GPU调试。\n",
    "\n",
    "完成此项目后，你将拥有一个可以用任何带标签图像的数据集进行训练的应用。你的网络将学习花卉，并成为一个命令行应用。但是，你对新技能的应用取决于你的想象力和构建数据集的精力。例如，想象有一款应用能够拍摄汽车，告诉你汽车的制造商和型号，然后查询关于该汽车的信息。构建你自己的数据集并开发一款新型应用吧。\n",
    "\n",
    "首先，导入你所需的软件包。建议在代码开头导入所有软件包。当你创建此 notebook 时，如果发现你需要导入某个软件包，确保在开头导入该软件包。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OK：Imports here\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets, transforms, models\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from torch.autograd import Variable\n",
    "\n",
    "import matplotlib as mlb\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg # mpimg 用于读取图片\n",
    "import seaborn as sb\n",
    "\n",
    "import json\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据\n",
    "\n",
    "在此项目中，你将使用 `torchvision` 加载数据（[文档](http://pytorch.org/docs/master/torchvision/transforms.html#)）。数据应该和此 notebook 一起包含在内，否则你可以[在此处下载数据](https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz)。数据集分成了三部分：训练集、验证集和测试集。对于训练集，你需要变换数据，例如随机缩放、剪裁和翻转。这样有助于网络泛化，并带来更好的效果。你还需要确保将输入数据的大小调整为 224x224 像素，因为预训练的网络需要这么做。\n",
    "\n",
    "验证集和测试集用于衡量模型对尚未见过的数据的预测效果。对此步骤，你不需要进行任何缩放或旋转变换，但是需要将图像剪裁到合适的大小。\n",
    "\n",
    "对于所有三个数据集，你都需要将均值和标准差标准化到网络期望的结果。均值为 `[0.485, 0.456, 0.406]`，标准差为 `[0.229, 0.224, 0.225]`。这样使得每个颜色通道的值位于 -1 到 1 之间，而不是 0 到 1 之间。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = '/data/flowers/train'\n",
    "valid_dir = '/data/flowers/valid'\n",
    "test_dir = '/data/flowers/test'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提示：\n",
    "- train数据集的transforms需要进行裁剪，均值和标准化，并且要对图像进行随机缩放、剪裁和翻转等等，有助于网络泛化，test和valid数据集的transforms只需要裁剪到合适的大小并进行均值和标准化。综上所述，valid数据集和test数据集使用同一个transform即可\n",
    "- 注意需要先resize图片，再crop，否则对于较大图片可能crop不到花朵部分\n",
    "- datasets设置时注意参数的变化（目录的不同）\n",
    "- loader设置时，train的loader的参数注意设置：shuffle = True，来增加训练图像时的随机性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OK: 定义transforms\n",
    "#标准化normalize类实例\n",
    "t_normalize=transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])\n",
    "#训练集-转换器\n",
    "train_transforms = transforms.Compose([transforms.Resize(224),#先设置大小\n",
    "                                       transforms.RandomResizedCrop(224),#随机长宽比裁剪\n",
    "                                       transforms.RandomHorizontalFlip(),#随机水平翻转\n",
    "                                       transforms.ToTensor(),\n",
    "                                       t_normalize])\n",
    "\n",
    "#测试与验证集-转换器\n",
    "test_transforms = transforms.Compose([transforms.Resize(224),\n",
    "                                      transforms.CenterCrop(224),\n",
    "                                      transforms.ToTensor(),\n",
    "                                      t_normalize])\n",
    "# TODO: 使用dir路径定义datasets，注意配置相对应的transforms\n",
    "train_datasets = datasets.ImageFolder(train_dir,transform=train_transforms)\n",
    "valid_datasets = datasets.ImageFolder(valid_dir,transform=test_transforms)\n",
    "test_dataset =datasets.ImageFolder(test_dir,transform=test_transforms)\n",
    "# TODO: 使用datasets定义dataloaders\n",
    "train_loader =  torch.utils.data.DataLoader(train_datasets, batch_size=64, shuffle=True)\n",
    "valid_loader = torch.utils.data.DataLoader(valid_datasets, batch_size=64, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 标签映射\n",
    "\n",
    "你还需要加载从类别标签到类别名称的映射。你可以在文件 `cat_to_name.json` 中找到此映射。它是一个 JSON 对象，可以使用 [`json` 模块](https://docs.python.org/2/library/json.html)读取它。这样可以获得一个从整数编码的类别到实际花卉名称的映射字典。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OK\n",
    "with open('cat_to_name.json', 'r') as f:\n",
    "    cat_to_name = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "{'21': 'fire lily', '3': 'canterbury bells', '45': 'bolero deep blue', '1': 'pink primrose', '34': 'mexican aster', '27': 'prince of wales feathers', '7': 'moon orchid', '16': 'globe-flower', '25': 'grape hyacinth', '26': 'corn poppy', '79': 'toad lily', '39': 'siam tulip', '24': 'red ginger', '67': 'spring crocus', '35': 'alpine sea holly', '32': 'garden phlox', '10': 'globe thistle', '6': 'tiger lily', '93': 'ball moss', '33': 'love in the mist', '9': 'monkshood', '102': 'blackberry lily', '14': 'spear thistle', '19': 'balloon flower', '100': 'blanket flower', '13': 'king protea', '49': 'oxeye daisy', '15': 'yellow iris', '61': 'cautleya spicata', '31': 'carnation', '64': 'silverbush', '68': 'bearded iris', '63': 'black-eyed susan', '69': 'windflower', '62': 'japanese anemone', '20': 'giant white arum lily', '38': 'great masterwort', '4': 'sweet pea', '86': 'tree mallow', '101': 'trumpet creeper', '42': 'daffodil', '22': 'pincushion flower', '2': 'hard-leaved pocket orchid', '54': 'sunflower', '66': 'osteospermum', '70': 'tree poppy', '85': 'desert-rose', '99': 'bromelia', '87': 'magnolia', '5': 'english marigold', '92': 'bee balm', '28': 'stemless gentian', '97': 'mallow', '57': 'gaura', '40': 'lenten rose', '47': 'marigold', '59': 'orange dahlia', '48': 'buttercup', '55': 'pelargonium', '36': 'ruby-lipped cattleya', '91': 'hippeastrum', '29': 'artichoke', '71': 'gazania', '90': 'canna lily', '18': 'peruvian lily', '98': 'mexican petunia', '8': 'bird of paradise', '30': 'sweet william', '17': 'purple coneflower', '52': 'wild pansy', '84': 'columbine', '12': \"colt's foot\", '11': 'snapdragon', '96': 'camellia', '23': 'fritillary', '50': 'common dandelion', '44': 'poinsettia', '53': 'primula', '72': 'azalea', '65': 'californian poppy', '80': 'anthurium', '76': 'morning glory', '37': 'cape flower', '56': 'bishop of llandaff', '60': 'pink-yellow dahlia', '82': 'clematis', '58': 'geranium', '75': 'thorn apple', '41': 'barbeton daisy', '95': 'bougainvillea', '43': 'sword lily', '83': 'hibiscus', '78': 'lotus lotus', '88': 'cyclamen', '94': 'foxglove', '81': 'frangipani', '74': 'rose', '89': 'watercress', '73': 'water lily', '46': 'wallflower', '77': 'passion flower', '51': 'petunia'}\n"
     ]
    }
   ],
   "source": [
    "print(type(cat_to_name))\n",
    "print(cat_to_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 构建和训练分类器\n",
    "\n",
    "数据准备好后，就开始构建和训练分类器了。和往常一样，你应该使用 `torchvision.models` 中的某个预训练模型获取图像特征。使用这些特征构建和训练新的前馈分类器。\n",
    "\n",
    "这部分将由你来完成。如果你想与他人讨论这部分，欢迎与你的同学讨论！你还可以在论坛上提问或在工作时间内咨询我们的课程经理和助教导师。\n",
    "\n",
    "请参阅[审阅标准](https://review.udacity.com/#!/rubrics/1663/view)，了解如何成功地完成此部分。你需要执行以下操作：\n",
    "\n",
    "* 加载[预训练的网络](https://pytorch.org/docs/stable/torchvision/models.html)\n",
    "* 使用 ReLU 激活函数和丢弃定义新的未训练前馈网络作为分类器\n",
    "* 使用反向传播训练分类器层，并使用预训练的网络获取特征\n",
    "* 跟踪验证集的损失和准确率，以确定最佳超参数\n",
    "\n",
    "按照提示逐步完成项目，检查确保每部分都达到预期效果，然后再完成下个部分。你可能会发现，当你实现每部分时，可能需要回去修改之前的代码，这很正常！\n",
    "\n",
    "训练时，确保仅更新前馈网络的权重。如果一切构建正确的话，验证准确率应该能够超过 70%。确保尝试不同的超参数（学习速率、分类器中的单元、周期等），寻找最佳模型。保存这些超参数并用作项目下个部分的默认值。\n",
    "\n",
    "下面让我们开始吧！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提示1：\n",
    "- 挑选一个模型，加载预训练模型（既然是*预*训练的模型，那么注意参数pretrained应为True）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "Downloading: \"https://download.pytorch.org/models/densenet201-c1103571.pth\" to /root/.torch/models/densenet201-c1103571.pth\n",
      "100%|██████████| 81131730/81131730 [00:01<00:00, 68746490.30it/s]\n"
     ]
    },
    {
     "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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OK\n",
    "model = models.densenet201(pretrained=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提示2：\n",
    "- [冻结模型梯度权重](https://pytorch.org/docs/stable/notes/autograd.html?highlight=param%20requires_grad)，设置每一层的param.requires_grad标志"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OK\n",
    "for param in model.parameters():\n",
    "    param.requires_grad=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提示3：\n",
    "- 增加全连接层，dropout层，以及Relu激活函数\n",
    "- 修改classifier层的主要目的是为了适应我们的分类要求，即最终的输出层要输出102种花卉分类（即要有一层全连接层输出为102，**不要**在输出层后加dropout）\n",
    "- 全连接层后使用[LogSoftmax](https://pytorch.org/docs/stable/nn.html?highlight=nllloss#torch.nn.LogSoftmax)或者[softmax](https://pytorch.org/docs/stable/nn.html?highlight=nllloss#torch.nn.softmax)，来得出每种花的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "# ok\n",
    "classifier = nn.Sequential(nn.Linear(1920,256),\n",
    "                           nn.ReLU(),\n",
    "                           nn.Dropout(0.2),\n",
    "                           nn.Linear(256,102),\n",
    "                           nn.LogSoftmax(dim=1)) \n",
    "\n",
    "model.classifier = classifier\n",
    "# 将模型转入gpu--我这里加了判断，方便在本地调试，未启用gpu模式不报错\n",
    "if torch.cuda.is_available():\n",
    "    model.cuda()\n",
    "#torch变量\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提示4：\n",
    "- 设置[损失函数](https://pytorch.org/docs/stable/nn.html?highlight=nllloss#torch.nn.NLLLoss)\n",
    "- 设置[优化器](https://pytorch.org/docs/stable/optim.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ok 定义损失函数与优化器\n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.classifier.parameters(),lr=0.004)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提示5：\n",
    "- 分别写成两个函数用于训练模型和测试模型(课程中写到了一个代码中)，便于调试代码，以及增加程序的可读性\n",
    "- 因为模型已经转入gpu，这里注意需要将数据也转入gpu\n",
    "- 注意输出训练测试过程，把控模型是否欠拟合或者过拟合\n",
    "- 模型训练时要model.train()，模型测试时要model.eval()，让模型进入训练或者测试的模式中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: 训练网络，需要有loss和accuracy打印输出--每次训练打印一次\n",
    "\n",
    "def train(model, loader):\n",
    "    train_loss=0\n",
    "    for images,labels in loader:\n",
    "        \n",
    "        images,labels=images.to(device),labels.to(device)\n",
    "        #每次优化器梯度重置\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        logits=model.forward(images)\n",
    "        loss=criterion(logits,labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        train_loss+=loss.item()\n",
    "        \n",
    "    print(f\"Train loss: {train_loss:.3f}.. \")\n",
    "        #print(f\"Epoch {epoch+1}/{epochs}.. \"\n",
    "         #         f\"Train loss: {running_loss/print_every:.3f}.. \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO:测试网络，使用验证集或者测试集，训练模型时使用验证集，测试模型最终结果使用测试集\n",
    "# 需要有loss和accuracy打印输出--每次训练打印一次\n",
    "def test(model, loader):\n",
    "    test_loss = 0\n",
    "    accuracy = 0\n",
    "    #模型测试时要model.eval()\n",
    "    model.eval()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for images,labels in loader:\n",
    "            images,labels=images.to(device),labels.to(device)\n",
    "            logits=model.forward(images)\n",
    "            loss=criterion(logits,labels)\n",
    "            test_loss+=loss.item()\n",
    "            \n",
    "            #计算精准度\n",
    "            ps=torch.exp(logits)\n",
    "            top_p,top_class=ps.topk(1,dim=1)\n",
    "            equals=top_class==labels.view(*top_class.shape)\n",
    "            if torch.cuda.is_available():\n",
    "                accuracy+=torch.mean(equals.type(torch.cuda.FloatTensor)).item()\n",
    "            else:\n",
    "                accuracy+=torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "            \n",
    "    \n",
    "    #模型训练时要model.train()        \n",
    "    model.train()\n",
    "    \n",
    "    print(f\"Test loss: {test_loss/len(loader):.3f}.. \"\n",
    "          f\"Test accuracy: {accuracy/len(loader):.3f}\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count:1:\n",
      "Train loss: 290.956.. \n",
      "Test loss: 1.093.. Test accuracy: 0.733\n",
      "count:2:\n",
      "Train loss: 126.253.. \n",
      "Test loss: 0.544.. Test accuracy: 0.855\n",
      "count:3:\n",
      "Train loss: 97.204.. \n",
      "Test loss: 0.448.. Test accuracy: 0.869\n",
      "count:4:\n",
      "Train loss: 88.238.. \n",
      "Test loss: 0.358.. Test accuracy: 0.907\n"
     ]
    }
   ],
   "source": [
    "epoch = 5 #TODO 修改epoch次数\n",
    "for i in range(1, epoch):\n",
    "    print(\"count:{}:\".format(i))\n",
    "    train(model,train_loader)#训练集\n",
    "    test(model, valid_loader)#验证集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试网络\n",
    "\n",
    "建议使用网络在训练或验证过程中从未见过的测试数据测试训练的网络。这样，可以很好地判断模型预测全新图像的效果。用网络预测测试图像，并测量准确率，就像验证过程一样。如果模型训练良好的话，你应该能够达到大约 70% 的准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.416.. Test accuracy: 0.879\n"
     ]
    }
   ],
   "source": [
    "# OK: 在测试集上进行测试，需要输出结果\n",
    "test(model, test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存检查点\n",
    "\n",
    "训练好网络后，[保存模型](https://pytorch.org/docs/stable/torch.html?highlight=torch%20save#torch.save)，以便稍后加载它并进行预测。你可能还需要保存其他内容，例如从类别到索引的映射，索引是从某个图像数据集中获取的：`image_datasets['train'].class_to_idx`。你可以将其作为属性附加到模型上，这样稍后推理会更轻松。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提示：\n",
    "- 创建checkpoint字典，字典中最少需要包含模型model相关和题目中要求的image_datasets['train'].class_to_idx\n",
    "- 使用torch.save保存checkpoint字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#存储path\n",
    "checkpoint_pth='checkpoint.pth'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "attributes": {
     "": "",
     "classes": [],
     "id": ""
    }
   },
   "outputs": [],
   "source": [
    "# OK: Save the checkpoint \n",
    "checkpoint={'input_size':1920,\n",
    "            'hidden_layers':[256],\n",
    "            'output_size':102,\n",
    "            'drop_out_size':0.2,\n",
    "            'state_dict':model.state_dict(),\n",
    "            'class_to_idx':train_datasets.class_to_idx}\n",
    "\n",
    "torch.save(checkpoint,checkpoint_pth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载检查点\n",
    "\n",
    "此刻，建议写一个可以加载检查点并重新构建模型的函数。这样的话，你可以回到此项目并继续完善它，而不用重新训练网络。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提示：\n",
    "- 加载”保存检查点“所保存的文件，用于恢复模型\n",
    "- 注意torch的save会原模原样保存你需要保存的数据，因为你保存的是字典，从文件加载出来的就是一个字典类型的数据，按照字典方式操作即可。比如\n",
    "```\n",
    "#保存：\n",
    "dict1 = {\"A\" : 1, \"B\": 2}\n",
    "torch.save(dict1, filepath)\n",
    "#加载：\n",
    "checkpoint = torch.load(filepath)\n",
    "A = checkpoint[\"A\"]\n",
    "B = checkpoint[\"B\"]\n",
    "```\n",
    "- image_datasets['train'].class_to_idx，这个是指花朵本身对应的文件夹名称（数字）到模型预测出来的名称（数字）的一个对应关系，是一个字典形式，为了后面方便查询（通过预测出来的名称来得到真正的花朵所在文件夹的名称），这里需要将class_to_idx这个字典键key和值value颠倒过来\n",
    "- 题目要求是函数，所以函数最终需要返回可以用于预测的模型以及颠倒后的class_to_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "def load_checkpoint(filepath):\n",
    "    checkpoint=torch.load(filepath)\n",
    "    #,map_location=torch.device(device)\n",
    "    input_size=checkpoint[\"input_size\"]\n",
    "    output_size=checkpoint[\"output_size\"]\n",
    "    hidden_layers=checkpoint[\"hidden_layers\"]\n",
    "    drop_out_size=checkpoint[\"drop_out_size\"]\n",
    "    state_dict=checkpoint[\"state_dict\"]\n",
    "    class_to_idx=checkpoint[\"class_to_idx\"]\n",
    "    \n",
    "    #print(\"checkpoint:{}\".format(checkpoint))\n",
    "    \n",
    "    classifier = nn.Sequential(nn.Linear(input_size,hidden_layers[0]),\n",
    "                           nn.ReLU(),\n",
    "                           nn.Dropout(drop_out_size),\n",
    "                           nn.Linear(hidden_layers[-1],output_size),\n",
    "                           nn.LogSoftmax(dim=1))\n",
    "\n",
    "    model=models.densenet201(pretrained=True)\n",
    "    model.classifier = classifier\n",
    "    model.load_state_dict(state_dict)\n",
    "    #load on device\n",
    "    model.to(device)\n",
    "    #翻转class_to_idx\n",
    "    class_to_idx=dict(zip(class_to_idx.values(), class_to_idx.keys()))\n",
    "    \n",
    "    return model,class_to_idx\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n"
     ]
    }
   ],
   "source": [
    "model, class_to_idx = load_checkpoint(checkpoint_pth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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",
       "    (0): Linear(in_features=1920, out_features=256, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Dropout(p=0.2)\n",
       "    (3): Linear(in_features=256, out_features=102, bias=True)\n",
       "    (4): LogSoftmax()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_to_idx[0] #应该返回 ‘1’"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 类别推理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先，你需要处理输入图像，使其可以用于你的网络。\n",
    "\n",
    "## 图像处理\n",
    "\n",
    "你需要使用 `PIL` 加载图像（[文档](https://pillow.readthedocs.io/en/latest/reference/Image.html)）。建议写一个函数来处理图像，使图像可以作为模型的输入。该函数应该按照训练的相同方式处理图像。\n",
    "\n",
    "首先，调整图像大小，使最小的边为 256 像素，并保持宽高比。为此，可以使用 [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) 或 [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) 方法。然后，你需要从图像的中心裁剪出 224x224 的部分。\n",
    "\n",
    "图像的颜色通道通常编码为整数 0-255，但是该模型要求值为浮点数 0-1。你需要变换值。使用 Numpy 数组最简单，你可以从 PIL 图像中获取，例如 `np_image = np.array(pil_image)`。\n",
    "\n",
    "和之前一样，网络要求图像按照特定的方式标准化。均值应标准化为 `[0.485, 0.456, 0.406]`，标准差应标准化为 `[0.229, 0.224, 0.225]`。你需要用每个颜色通道减去均值，然后除以标准差。\n",
    "\n",
    "最后，PyTorch 要求颜色通道为第一个维度，但是在 PIL 图像和 Numpy 数组中是第三个维度。你可以使用 [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html)对维度重新排序。颜色通道必须是第一个维度，并保持另外两个维度的顺序。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提示：\n",
    "- 按照上述要求来写即可，特别提示维度排序：np_image.transpose((2, 0, 1))，这个样子是指将第三个维度（颜色）换到了第一个维度上，其他两个维度保持顺序\n",
    "- 另外要注意，np和PIL的很多操作都是在返回值中进行修改，而不会对原数据进行修改，务必要进行赋值操作\n",
    "- 运行下面的测试，如果出现下面的图，则编写正确：\n",
    "<img src='assets/1_flower.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OK: Process a PIL image for use in a PyTorch model\n",
    "def process_image(image):\n",
    "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
    "        returns an Numpy array\n",
    "    '''\n",
    "    #加载图像\n",
    "    im=Image.open(image)\n",
    "    \n",
    "    #设置像素最低是256\n",
    "    #im=im.point(lambda i: 256 if i<256 else i)\n",
    "    #resize()函数会返回一个Image对象,原图不会修改, thumbnail()函数返回None，会修改原图\n",
    "    #这里使用resize，调整图像大小244*244\n",
    "    im=im.resize((224,224))\n",
    "\n",
    "    np_image=np.array(im)\n",
    "    #图像的颜色通道通常编码为整数 0-255，但是该模型要求值为浮点数 0-1\n",
    "    np_image=np_image/256\n",
    "    \n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    #每个颜色通道减去均值，然后除以标准差\n",
    "    np_image=np.divide((np_image-mean),std)\n",
    "\n",
    "    #将第三个维度（颜色）换到了第一个维度上，其他两个维度保持顺序\n",
    "    np_image=np_image.transpose((2, 0, 1))\n",
    "    \n",
    "    img_tensor=torch.from_numpy(np_image)\n",
    "    return img_tensor\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要检查你的项目，可以使用以下函数来转换 PyTorch 张量并将其显示在  notebook 中。如果 `process_image` 函数可行，用该函数运行输出应该会返回原始图像（但是剪裁掉的部分除外）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imshow(image, ax=None, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    # PyTorch tensors assume the color channel is the first dimension\n",
    "    # but matplotlib assumes is the third dimension\n",
    "    image = image.numpy().transpose((1, 2, 0))\n",
    "    \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",
    "    \n",
    "    # Image needs to be clipped between 0 and 1 or it looks like noise when displayed\n",
    "    image = np.clip(image, 0, 1)\n",
    "    \n",
    "    ax.imshow(image)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试一下，跟提示中出现同样的花朵后才可进行下一道题："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1c59110a90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1c58e71668>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 259
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_tensor = process_image(train_dir+\"/1/image_06735.jpg\")\n",
    "imshow(img_tensor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 类别预测\n",
    "\n",
    "可以获得格式正确的图像后 \n",
    "\n",
    "现在，使用训练的网络进行推理的函数。即你将向网络中传入一个图像路径，并预测图像中的花卉类别。这里实现了 `predict` 的函数，该函数会接受图像和模型，然后返回概率在前 $K$ 的类别及其概率。\n",
    "\n",
    "要获得前 $K$ 个值，在张量中使用 [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk)。该函数会返回前 `k` 个概率和对应的类别索引。使用  `class_to_idx`将这些索引转换为实际类别标签，或者从用来加载数据的[ `ImageFolder`](https://pytorch.org/docs/master/torchvision/datasets.html?highlight=imagefolder#torchvision.datasets.ImageFolder)进行转换。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 只是示例，不要运行，可以看出predict接收了图像的path以及加载出来的模型，返回了预测的种类索引和各个种类的概率\n",
    "probs, classes = predict(image_path, model)\n",
    "print(probs)\n",
    "print(classes)\n",
    "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
    "> ['70', '3', '45', '62', '55']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不需要修改，运行即可\n",
    "def predict(image_path, model, topk=5):\n",
    "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
    "    '''\n",
    "    model.eval()\n",
    "    img_tensor = process_image(image_path)\n",
    "    img_tensor.unsqueeze_(0)\n",
    "    img_tensor = img_tensor.type(torch.cuda.FloatTensor)\n",
    "    output = model(Variable(img_tensor.cuda(), volatile=True))\n",
    "    ps = torch.exp(output)\n",
    "    probs, index = ps.topk(topk)\n",
    "    probs = probs.cpu().detach().numpy().tolist()[0]\n",
    "    index = index.cpu().detach().numpy().tolist()[0]\n",
    "    index = [class_to_idx[i] for i in index]\n",
    "    return probs, index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "尝试调用预测一下，如果你的模型测出来概率最高的那个种类是1，即可进行下一道题的操作，否则你可能需要反复修改之前所有涉及到模型的问题，或者检查下加载检查点中的class_to_idx是否正确颠倒。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7867199182510376, 0.12403368204832077, 0.0655478686094284, 0.009930584579706192, 0.002579021267592907]\n",
      "['76', '86', '1', '51', '43']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "probs, classes = predict(train_dir+\"/1/image_06735.jpg\", model)\n",
    "print(probs)\n",
    "print(classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 检查运行状况\n",
    "\n",
    "你已经可以使用训练的模型做出预测，现在检查模型的性能如何。即使测试准确率很高，始终有必要检查是否存在明显的错误。使用 `matplotlib` 将前 5 个类别的概率以及输入图像绘制为条形图，应该如下所示：\n",
    "\n",
    "<img src='assets/inference_example.png' width=300px>\n",
    "\n",
    "你可以使用 `cat_to_name.json` 文件（应该之前已经在 notebook 中加载该文件）将类别整数编码转换为实际花卉名称。要将 PyTorch 张量显示为图像，请使用定义如下的 `imshow` 函数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提示：\n",
    "- cat_to_name.json是文件夹的目录和真实花朵名称的对应关系，通过上一步predict得出的index对应到cat_to_name即可得到名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "# 参数分别为测试花朵的路径，花朵种类的文件夹名称，模型，以及topk\n",
    "def show_result(path, classname, model, topk):\n",
    "    \n",
    "    #返回种各个种类的概率 和 类索引\n",
    "    probs, classes = predict(path, model,topk)\n",
    "    #真实的图片名称以及图片展示\n",
    "    title=cat_to_name[classname]\n",
    "    print(\"真实的图片名称以及图片展示:{}\".format(title))\n",
    "    print(probs)\n",
    "    print(classes)\n",
    "    \n",
    "    #获取对应索引花卉类别名称\n",
    "    class_names=[cat_to_name[name] for name in classes]\n",
    "    print(class_names)\n",
    "    probs_dict={}\n",
    "    for name,prob in zip(class_names,probs):\n",
    "        probs_dict[name]=pd.Series(data = [prob],index=['probs'])\n",
    "    \n",
    "    #测试花朵图片展示\n",
    "    img = mpimg.imread(path) \n",
    "    plt.title(title) # 显示标题\n",
    "    plt.imshow(img) # 显示图片\n",
    "    plt.axis('off') # 不显示坐标轴\n",
    "    plt.show() #展示原始图片\n",
    "    \n",
    "    #print(\"概率类别名称索引{}\".format(probs_dict))\n",
    "    topk_data=pd.DataFrame(probs_dict,index=[\"probs\"])\n",
    "    #print(topk_data)\n",
    "    sb.barplot( data = topk_data,color=sb.color_palette()[0]);  \n",
    "    #pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来测试一下，希望你绘制的图中，概率最高的花朵种类名称为trumpet creeper。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实的图片名称以及图片展示:rose\n",
      "[0.9585458636283875, 0.023213960230350494, 0.0051452298648655415, 0.004714189562946558, 0.004386178683489561]\n",
      "['74', '43', '96', '90', '31']\n",
      "['rose', 'sword lily', 'camellia', 'canna lily', 'carnation']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1c58d7e518>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 332
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1c58b815f8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 373
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#直接运行\n",
    "show_result(test_dir+\"/74/image_01256.jpg\", \"74\", model, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#换了别的几个图片测试的还挺准确"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}