Final_Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch, os\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision.datasets as datasets\n",
    "import torch.utils.data as data\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.models as models\n",
    "from train_model import train_model\n",
    "from test_model import test_model\n",
    "from AlexNet import alexnet\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "    ])\n",
    "}\n",
    "\n",
    "data_dir = 'tiny-imagenet-200/'\n",
    "num_workers = {\n",
    "    'train' : 100,\n",
    "    'val'   : 0,\n",
    "    'test'  : 0\n",
    "}\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val','test']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=100,\n",
    "                                             shuffle=True, num_workers=num_workers[x])\n",
    "              for x in ['train', 'val', 'test']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 50/50, Loss: 256.13112449645996.\n",
      "Test Loss: 2.7296 Acc: 0.3588\n",
      "\n",
      "Test complete in 0m 2s\n"
     ]
    }
   ],
   "source": [
    "#Load AlexNet\n",
    "model_ft = alexnet()\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft.load_state_dict(torch.load('models/Alex/model_17_epoch.pt'))\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "#Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "#Test\n",
    "test_model(model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'tiny-imagenet-200/'\n",
    "num_workers = {'train' : 100,'val'   : 0,'test'  : 0}\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomRotation(20),\n",
    "        transforms.RandomHorizontalFlip(0.5),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ])\n",
    "}\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) \n",
    "                  for x in ['train', 'val','test']}\n",
    "dataloaders = {x: data.DataLoader(image_datasets[x], batch_size=100, shuffle=True, num_workers=num_workers[x])\n",
    "                  for x in ['train', 'val', 'test']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 50/50, Loss: 201.29690170288086.\n",
      "Test Loss: 2.0182 Acc: 0.5358\n",
      "\n",
      "Test complete in 0m 2s\n"
     ]
    }
   ],
   "source": [
    "#Test Resnet18-64\n",
    "model_ft = models.resnet18()\n",
    "#Finetune Final few layers to adjust for tiny imagenet input\n",
    "model_ft.avgpool = nn.AdaptiveAvgPool2d(1)\n",
    "model_ft.fc.out_features = 200\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft.load_state_dict(torch.load('models/64/model_17_epoch.pt'))\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "#Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "#Test\n",
    "test_model(model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'tiny-224/'\n",
    "num_workers = {'train' : 100,'val'   : 0,'test'  : 0}\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomRotation(20),\n",
    "        transforms.RandomHorizontalFlip(0.5),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ])\n",
    "}\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) \n",
    "                  for x in ['train', 'val','test']}\n",
    "dataloaders = {x: data.DataLoader(image_datasets[x], batch_size=100, shuffle=True, num_workers=num_workers[x])\n",
    "                  for x in ['train', 'val', 'test']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 50/50, Loss: 137.87623643875122.\n",
      "Test Loss: 1.2549 Acc: 0.6962\n",
      "\n",
      "Test complete in 0m 12s\n"
     ]
    }
   ],
   "source": [
    "#Test Resnet18-224\n",
    "model_ft = models.resnet18()\n",
    "#Finetune Final few layers to adjust for tiny imagenet input\n",
    "model_ft.avgpool = nn.AdaptiveAvgPool2d(1)\n",
    "model_ft.fc.out_features = 200\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft.load_state_dict(torch.load('models/224/model_19_epoch.pt'))\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "#Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "#Test\n",
    "test_model(model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'tiny-imagenet-200/'\n",
    "num_workers = {'train' : 100,'val'   : 0,'test'  : 0}\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomRotation(20),\n",
    "        transforms.RandomHorizontalFlip(0.5),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "    ])\n",
    "}\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) \n",
    "                  for x in ['train', 'val','test']}\n",
    "dataloaders = {x: data.DataLoader(image_datasets[x], batch_size=100, shuffle=True, num_workers=num_workers[x])\n",
    "                  for x in ['train', 'val', 'test']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 50/50, Loss: 137.0599389076233..\n",
      "Test Loss: 1.2996 Acc: 0.6980\n",
      "\n",
      "Test complete in 0m 4s\n"
     ]
    }
   ],
   "source": [
    "#Test Resnet18-64\n",
    "model_ft = models.resnet18()\n",
    "#Finetune Final few layers to adjust for tiny imagenet input\n",
    "model_ft.conv1 = nn.Conv2d(3,64, kernel_size=(3,3), stride=(1,1), padding=(1,1), bias=False)\n",
    "model_ft.maxpool = nn.Sequential()\n",
    "model_ft.avgpool = nn.AdaptiveAvgPool2d(1)\n",
    "model_ft.fc.out_features = 200\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft.load_state_dict(torch.load('models/Fine/model_12_epoch.pt'))\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "#Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "#Test\n",
    "test_model(model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft)"
   ]
  }
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