MPS Test CIFAR10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d2e7853",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.12.1\n",
      "True\n",
      "True\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plane frog  bird  ship  deer  deer  frog  truck bird  cat   car   ship  dog   ship  plane car   dog   dog   deer  dog   car   ship  truck truck ship  horse bird  dog   dog   frog  car   cat   deer  frog  cat   truck plane cat   cat   cat   horse horse cat   bird  cat   ship  horse ship  bird  horse deer  bird  cat   ship  cat   cat   dog   dog   dog   car   truck deer  car   horse frog  dog   ship  car   dog   horse truck plane bird  horse deer  cat   dog   plane horse car   horse ship  cat   plane car   ship  deer  deer  cat   cat   bird  car   truck dog   ship  deer  car   cat   truck cat   frog  dog   deer  bird  cat   dog   truck car   plane ship  ship  horse horse horse frog  deer  bird  ship  plane plane bird  dog   deer  dog   truck truck deer  ship  horse car   bird  ship  bird  horse truck car   truck horse deer  car   bird  plane car   deer  car   truck truck horse horse truck horse horse horse horse dog   truck dog   deer  bird  deer  cat   horse plane ship  truck plane horse bird  horse car   plane cat   frog  dog   ship  dog   car   ship  dog   truck ship  dog   bird  truck cat   dog   bird  frog  truck truck plane ship  car   ship  frog  bird  dog   car   bird  bird  plane cat   deer  frog  dog   horse car   deer  car   deer  bird  truck car   plane ship  horse frog  cat   ship  ship  dog   ship  ship  car   frog  ship  truck deer  dog   ship  horse horse car   plane truck horse truck frog  cat   car   ship  ship  horse plane bird  car   ship  plane car   dog   cat   deer  truck cat   bird  horse ship  horse car   ship  ship  cat   deer  bird  frog  dog   frog  bird  deer  truck plane frog  ship  dog   horse cat   dog   bird  plane ship  horse plane car   dog   dog   dog   dog   ship  dog   plane car   plane horse bird  cat   frog  bird  frog  truck bird  dog   car   plane frog  bird  cat   horse car   truck ship  cat   dog   frog  car   cat   dog   plane bird  horse ship  frog  horse truck frog  frog  bird  car   plane frog  plane cat   plane cat   frog  frog  frog  horse cat   dog   plane horse ship  ship  cat   horse dog   car   bird  cat   dog   truck truck car   bird  car   frog  plane plane cat   car   plane ship  plane ship  truck deer  plane truck bird  car   horse deer  horse frog  truck car   truck horse frog  ship  cat   dog   horse dog   plane deer  deer  plane ship  dog   cat   deer  deer  car   bird  deer  dog   frog  horse cat   car   horse plane frog  dog   deer  bird  plane car   frog  truck plane horse truck truck bird  frog  cat   cat   truck frog  bird  bird  deer  car   car   horse deer  truck ship  truck deer  car   truck dog   frog  horse horse bird  ship  horse horse truck ship  plane frog  car   dog   cat   ship  ship  dog   deer  dog   ship  frog  plane cat   frog  plane cat   horse car   ship  deer  truck horse deer  plane cat   car   frog  car   frog  deer  deer  dog   truck dog   deer  horse bird  plane bird  dog   deer  dog   car   bird  frog  horse bird  frog  car   truck horse deer  ship  plane ship \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12,   101] loss: 0.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [00:13<00:00,  7.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13,   101] loss: 0.017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "[20,   101] loss: 0.015\n",
      "Finished Training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "print(torch.__version__)\n",
    "print(torch.backends.mps.is_built())\n",
    "print(torch.backends.mps.is_available())\n",
    "\n",
    "device = torch.device(\"mps\")\n",
    "# device = torch.device(\"cpu\")\n",
    "\n",
    "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 = 500\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",
    "           \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",
    "images, labels = next(iter(trainloader))\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)))\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()\n",
    "net = net.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "for epoch in range(20):  # loop over the dataset multiple times\n",
    "\n",
    "    running_loss = 0.0\n",
    "    i = 0 \n",
    "    for data in tqdm(trainloader):\n",
    "        # get the inputs; data is a list of [inputs, labels]\n",
    "        inputs, labels = data\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\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",
    "        i += 1\n",
    "        running_loss += loss.item()\n",
    "\n",
    "    print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 12500:.3f}')\n",
    "\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b410dc5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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