PyTorch_Classifier_Xena .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "destroyed-seller",
   "metadata": {},
   "source": [
    "# Image Classification with PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pending-silence",
   "metadata": {},
   "source": [
    "This notebook serves as an extension to the PyTorch tutorial that will get you started with basic image classification. It is based on the following tutorial (https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#) and includes modifications that you will need to get the tutorial working."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "assured-frost",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intellectual-complex",
   "metadata": {},
   "source": [
    "To use torchvision to your notebook, you'll need to first go back to your terminal and run the command 'conda install torchvision'. Once the installation is finished, run the below codeblock to import it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "introductory-change",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "american-nebraska",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "egyptian-simon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/users/mdunlavy/.local/lib/python3.9/site-packages/torch/utils/data/dataloader.py:478: UserWarning: This DataLoader will create 2 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "batch_size = 4\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
    "                                         shuffle=False, num_workers=2)\n",
    "\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "arabic-policy",
   "metadata": {},
   "source": [
    "To use matplotlib, you'll need to first go back to your terminal and run the command 'conda install matplotlib'. You'll then be able to use the plot function for the below training and classification. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "japanese-peninsula",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plane deer  car   cat  \n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "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 = next(dataiter)\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "acting-couple",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = torch.flatten(x, 1) # flatten all dimensions except batch\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "net = Net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "thorough-chile",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "coupled-helping",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/users/mdunlavy/.local/lib/python3.9/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,  2000] loss: 2.211\n",
      "[1,  4000] loss: 1.886\n",
      "[1,  6000] loss: 1.695\n",
      "[1,  8000] loss: 1.596\n",
      "[1, 10000] loss: 1.533\n",
      "[1, 12000] loss: 1.478\n",
      "[2,  2000] loss: 1.428\n",
      "[2,  4000] loss: 1.370\n",
      "[2,  6000] loss: 1.379\n",
      "[2,  8000] loss: 1.342\n",
      "[2, 10000] loss: 1.326\n",
      "[2, 12000] loss: 1.294\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(2):  # loop over the dataset multiple times\n",
    "\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",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        if i % 2000 == 1999:    # print every 2000 mini-batches\n",
    "            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')\n",
    "            running_loss = 0.0\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "convinced-andrews",
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = './cifar_net.pth'\n",
    "torch.save(net.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "corrected-hardwood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GroundTruth:  cat   ship  ship  plane\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(testloader)\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "# print images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print('GroundTruth: ', ' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "pending-canon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Net()\n",
    "net.load_state_dict(torch.load(PATH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "running-arbor",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = net(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "occupied-pacific",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:  cat   plane plane plane\n"
     ]
    }
   ],
   "source": [
    "_, predicted = torch.max(outputs, 1)\n",
    "\n",
    "print('Predicted: ', ' '.join(f'{classes[predicted[j]]:5s}'\n",
    "                              for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "union-width",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 52 %\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "# since we're not training, we don't need to calculate the gradients for our outputs\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        # calculate outputs by running images through the network\n",
    "        outputs = net(images)\n",
    "        # the class with the highest energy is what we choose as prediction\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fabulous-alexander",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for class: plane is 70.6 %\n",
      "Accuracy for class: car   is 68.9 %\n",
      "Accuracy for class: bird  is 43.7 %\n",
      "Accuracy for class: cat   is 27.8 %\n",
      "Accuracy for class: deer  is 29.6 %\n",
      "Accuracy for class: dog   is 28.6 %\n",
      "Accuracy for class: frog  is 85.9 %\n",
      "Accuracy for class: horse is 62.1 %\n",
      "Accuracy for class: ship  is 62.0 %\n",
      "Accuracy for class: truck is 50.0 %\n"
     ]
    }
   ],
   "source": [
    "# prepare to count predictions for each class\n",
    "correct_pred = {classname: 0 for classname in classes}\n",
    "total_pred = {classname: 0 for classname in classes}\n",
    "\n",
    "# again no gradients needed\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = net(images)\n",
    "        _, predictions = torch.max(outputs, 1)\n",
    "        # collect the correct predictions for each class\n",
    "        for label, prediction in zip(labels, predictions):\n",
    "            if label == prediction:\n",
    "                correct_pred[classes[label]] += 1\n",
    "            total_pred[classes[label]] += 1\n",
    "\n",
    "\n",
    "# print accuracy for each class\n",
    "for classname, correct_count in correct_pred.items():\n",
    "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
    "    print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "disturbed-rental",
   "metadata": {},
   "outputs": [
   
   ],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
    "\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "spectacular-costs",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
       "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
       "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "acknowledged-cable",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs, labels = data[0].to(device), data[1].to(device)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-pytorch-1.9-cuda-11-K40]",
   "language": "python",
   "name": "conda-env-.conda-pytorch-1.9-cuda-11-K40-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}