{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate the Model #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import torch.backends.cudnn as cudnn\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.RandomCrop(32, padding=4),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "])\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(\n",
    "    root='./data', train=True, download=True, transform=transform_train)\n",
    "trainloader = torch.utils.data.DataLoader(\n",
    "    trainset, batch_size=128, shuffle=True, num_workers=4, pin_memory=True)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(\n",
    "    root='./data', train=False, download=True, transform=transform_test)\n",
    "testloader = torch.utils.data.DataLoader(\n",
    "    testset, batch_size=100, shuffle=False, num_workers=4, pin_memory=True)\n",
    "\n",
    "classes  = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Test Loss: 0.2659624905884266, Accuracy: 94.24%\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import torch.backends.cudnn as cudnn\n",
    "from collections import OrderedDict\n",
    "from resnet import *\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "net, name = ResNet18_with_name()\n",
    "\n",
    "# load checkpoint\n",
    "checkpoint = torch.load('./checkpoint/' + 'ResNet18.pth', weights_only=True)\n",
    "\n",
    "# remove 'module.' prefix\n",
    "new_state_dict = OrderedDict()\n",
    "for k, v in checkpoint['net'].items():\n",
    "    name = k[7:] if k.startswith('module.') else k  # remove 'module.' prefix\n",
    "    new_state_dict[name] = v\n",
    "\n",
    "# load best state_dict\n",
    "net.load_state_dict(new_state_dict)\n",
    "\n",
    "# move to  GPU if supported\n",
    "net = net.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# test function\n",
    "def test(epoch):\n",
    "    net.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for batch_idx, (inputs, targets) in enumerate(testloader):\n",
    "            inputs, targets = inputs.to(device), targets.to(device)\n",
    "            outputs = net(inputs)\n",
    "            loss = criterion(outputs, targets)\n",
    "\n",
    "            test_loss += loss.item()\n",
    "            _, predicted = outputs.max(1)\n",
    "            total += targets.size(0)\n",
    "            correct += predicted.eq(targets).sum().item()\n",
    "\n",
    "    print(f'Epoch: {epoch}, Test Loss: {test_loss / (batch_idx + 1)}, Accuracy: {100. * correct / total}%')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "test(0)"
   ]
  }
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