File size: 4,721 Bytes
a2c9640 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
{
"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)"
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|