vgg-net.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.Compose([\n",
    "    transforms.Resize(256),\n",
    "    transforms.CenterCrop(224),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "])\n",
    "\n",
    "dataset = datasets.ImageFolder(root='./imagenet/ILSVRC/Data/CLS-LOC/train', transform=transform)\n",
    "\n",
    "total_len = len(dataset)\n",
    "train_len = int(0.8 * total_len)\n",
    "test_len = total_len - train_len\n",
    "\n",
    "train_dataset, test_dataset = random_split(dataset, [train_len, test_len])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.layer1 = nn.Sequential(\n",
    "            nn.Conv2d(3,64,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(64,64,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2,2)\n",
    "        )\n",
    "        self.layer2 = nn.Sequential(\n",
    "            nn.Conv2d(64,128,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(128,128,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2,2)\n",
    "        )\n",
    "        self.layer3 = nn.Sequential(\n",
    "            nn.Conv2d(128,256,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(256,256,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(256,256,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2,2)\n",
    "        )\n",
    "        self.layer4 = nn.Sequential(\n",
    "            nn.Conv2d(256,512,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(512,512,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(512,512,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2,2)\n",
    "        )\n",
    "        self.layer5 = nn.Sequential(\n",
    "            nn.Conv2d(512,512,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(512,512,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(512,512,3,1,1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2,2)\n",
    "        )\n",
    "\n",
    "        self.linear= nn.Sequential(\n",
    "            nn.Linear(25088,4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(4096,4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(4096,1000),\n",
    "        )\n",
    "\n",
    "        self.avg = nn.AdaptiveAvgPool2d((6,6))\n",
    "\n",
    "    def forward(self,x):\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "        x = self.layer5(x)\n",
    "\n",
    "        x = x.reshape(x.size(0), -1)\n",
    "\n",
    "        x = self.linear(x)\n",
    "        output = F.log_softmax(x, dim=1) \n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Net().to(\"mps\")\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "losses = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for epoch in range(5):\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(\"mps\"), target.to(\"mps\")\n",
    "        output = model(data)\n",
    "        loss = criterion(output, target)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if batch_idx % 100 == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item()))\n",
    "            checkpoint = {\n",
    "                'epoch': epoch,\n",
    "                'model_state_dict': model.state_dict(),\n",
    "                'optimizer_state_dict': optimizer.state_dict(),\n",
    "                'losses': losses\n",
    "            }\n",
    "            torch.save(checkpoint, f'./checkpoints/vgg/checkpoint-epoch{epoch}-batch{batch_idx}.pth')\n",
    "\n",
    "        losses.append(loss.item())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# When you use model.eval(), the dropout and batch norm is turned off. This ensures that the model behaves consistently.\n",
    "model.eval()\n",
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for images, labels in test_loader: \n",
    "        images, labels = images.to(\"mps\"), labels.to(\"mps\")\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "accuracy = 100 * correct /total\n",
    "print(f\"Accuracy on the test set: {accuracy}%\")"
   ]
  }
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