ResNeXt-101_32x16d.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "from torchvision import models\n",
    "from tqdm import tqdm\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import numpy as np\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"4,6,7\"\n",
    "\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "\n",
    "torch.backends.cudnn.deterministic = True\n",
    "torch.backends.cudnn.benchmark = False\n",
    "\n",
    "# font = {'weight' : 'bold',\n",
    "#         'size'   : 90}\n",
    "\n",
    "# plt.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "#IMG_SIZE = [224,224]\n",
    "#IMG_SIZE = [320,320]  ## Best so far with this resolution and 32x48d\n",
    "IMG_SIZE = [384,384]\n",
    "num_classes = 42\n",
    "device = \"cuda\"\n",
    "target_names = [str(i) for i in range(42)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105392\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.Resize(IMG_SIZE),\n",
    "     transforms.ToTensor(),\n",
    "     transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])])\n",
    "\n",
    "trainvalset = torchvision.datasets.ImageFolder(\"train/train/\",transform=transform)\n",
    "print(len(trainvalset))\n",
    "\n",
    "trainset, valset = torch.utils.data.random_split(trainvalset, [len(trainvalset)-30000, 30000])\n",
    "\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
    "                                          shuffle=True, num_workers=1)\n",
    "\n",
    "valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size,\n",
    "                                          shuffle=True, num_workers=1)\n",
    "\n",
    "testset = torchvision.datasets.ImageFolder(\"test/\",transform=transform)\n",
    "\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
    "                                         shuffle=False, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([14, 40,  7, 21, 25, 11, 41, 21,  8,  8, 32, 15, 34,  8,  9, 22])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "print(labels)\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "#print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /root/.cache/torch/hub/facebookresearch_WSL-Images_master\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataParallel(\n",
      "  (module): ResNet(\n",
      "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (relu): ReLU(inplace=True)\n",
      "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "    (layer1): Sequential(\n",
      "      (0): Bottleneck(\n",
      "        (conv1): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (downsample): Sequential(\n",
      "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): Bottleneck(\n",
      "        (conv1): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (2): Bottleneck(\n",
      "        (conv1): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "    (layer2): Sequential(\n",
      "      (0): Bottleneck(\n",
      "        (conv1): Conv2d(256, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (downsample): Sequential(\n",
      "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): Bottleneck(\n",
      "        (conv1): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (2): Bottleneck(\n",
      "        (conv1): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (3): Bottleneck(\n",
      "        (conv1): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "    (layer3): Sequential(\n",
      "      (0): Bottleneck(\n",
      "        (conv1): Conv2d(512, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (downsample): Sequential(\n",
      "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (2): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (3): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (4): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (5): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (6): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (7): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (8): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (9): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (10): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (11): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (12): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (13): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (14): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (15): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (16): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (17): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (18): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (19): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (20): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (21): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (22): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 6144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(6144, 6144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(6144, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "    (layer4): Sequential(\n",
      "      (0): Bottleneck(\n",
      "        (conv1): Conv2d(1024, 12288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(12288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(12288, 12288, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(12288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(12288, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (downsample): Sequential(\n",
      "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): Bottleneck(\n",
      "        (conv1): Conv2d(2048, 12288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(12288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(12288, 12288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(12288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(12288, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "      (2): Bottleneck(\n",
      "        (conv1): Conv2d(2048, 12288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(12288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv2): Conv2d(12288, 12288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "        (bn2): BatchNorm2d(12288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (conv3): Conv2d(12288, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "    (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "    (fc): Linear(in_features=2048, out_features=42, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#model = models.resnet18(pretrained=True)\n",
    "# model = torchvision.models.wide_resnet101_2(pretrained=True)\n",
    "#model = torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x32d_wsl')\n",
    "model = torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x48d_wsl')\n",
    "#print(model)\n",
    "num_ftrs = model.fc.in_features\n",
    "model.fc = nn.Linear(num_ftrs, num_classes)\n",
    "model = nn.DataParallel(model)\n",
    "#model.load_state_dict(torch.load(\"ResNeXt-101_32x48d.pth\"))\n",
    "model.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.00001, momentum=0.9)\n",
    "print(model)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm,\n",
    "                          target_names,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=None,\n",
    "                          normalize=True):\n",
    "    \"\"\"\n",
    "    given a sklearn confusion matrix (cm), make a nice plot\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
    "\n",
    "    target_names: given classification classes such as [0, 1, 2]\n",
    "                  the class names, for example: ['high', 'medium', 'low']\n",
    "\n",
    "    title:        the text to display at the top of the matrix\n",
    "\n",
    "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
    "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "                  plt.get_cmap('jet') or plt.cm.Blues\n",
    "\n",
    "    normalize:    If False, plot the raw numbers\n",
    "                  If True, plot the proportions\n",
    "\n",
    "    Usage\n",
    "    -----\n",
    "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
    "                                                              # sklearn.metrics.confusion_matrix\n",
    "                          normalize    = True,                # show proportions\n",
    "                          target_names = y_labels_vals,       # list of names of the classes\n",
    "                          title        = best_estimator_name) # title of graph\n",
    "\n",
    "    Citiation\n",
    "    ---------\n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "\n",
    "    plt.figure(figsize=(80, 60))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    #plt.rc('xtick', labelsize=20) \n",
    "    #plt.rc('ytick', labelsize=20) \n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45,fontsize=50)\n",
    "        plt.yticks(tick_marks, target_names,fontsize=50)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(net):\n",
    "    net.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    y_true = []\n",
    "    y_pred = []\n",
    "    with torch.no_grad():\n",
    "        pbar = tqdm(valloader)\n",
    "        i_batch = 0\n",
    "        for data in pbar:\n",
    "            images, labels = data\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "            outputs = net(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            \n",
    "            for output,label in zip(predicted,labels):\n",
    "                y_pred.append(output.item())\n",
    "                y_true.append(label.item())\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "            pbar.set_description(\"Val Accuracy %.4f\" % (correct/float(total)))\n",
    "            i_batch+=1\n",
    "#             if(i_batch==10):\n",
    "#                 break\n",
    "\n",
    "    print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "        100 * correct / total))\n",
    "    \n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    \n",
    "    plot_confusion_matrix(cm= cm, \n",
    "                      normalize    = True,\n",
    "                      target_names = target_names,\n",
    "                      title        = \"Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started Training\n",
      "[1,    20] loss: 3.785\n",
      "[1,    40] loss: 3.766\n",
      "[1,    60] loss: 3.755\n",
      "[1,    80] loss: 3.624\n",
      "[1,   100] loss: 3.611\n",
      "[1,   120] loss: 3.552\n",
      "[1,   140] loss: 3.521\n",
      "[1,   160] loss: 3.476\n",
      "[1,   180] loss: 3.404\n",
      "[1,   200] loss: 3.350\n",
      "[1,   220] loss: 3.323\n",
      "[1,   240] loss: 3.190\n",
      "[1,   260] loss: 3.186\n",
      "[1,   280] loss: 3.108\n",
      "[1,   300] loss: 3.034\n",
      "[1,   320] loss: 3.037\n",
      "[1,   340] loss: 2.934\n",
      "[1,   360] loss: 2.893\n",
      "[1,   380] loss: 2.831\n",
      "[1,   400] loss: 2.676\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-9a9268596317>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m    196\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m         \"\"\"\n\u001b[0;32m--> 198\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m     98\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m     99\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "print(\"Started Training\")\n",
    "for epoch in range(2):  # loop over the dataset multiple times\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs; data is a list of [inputs, labels]\n",
    "        inputs, labels = data\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        if i % 20 == 19:    # print every 2000 mini-batches\n",
    "            print('[%d, %5d] loss: %.3f' %\n",
    "                  (epoch + 1, i + 1, running_loss / 20))\n",
    "            running_loss = 0.0\n",
    "        #if i % 200 == 199:\n",
    "    evaluate(model)\n",
    "    model.train()\n",
    "    torch.save(model.state_dict(), \"ResNeXt-101_32x48d.pth\")\n",
    "\n",
    "print('Finished Training')"
   ]
  }
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