pytorch-lightning_ipynb/data-augmentation/autoaugment/with-autoaugment.ipynb

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   "source": [
    "# With AutoAugment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6194920a-f622-4f3f-b17b-4cc817289226",
   "metadata": {},
   "source": [
    "This notebook trains a ResNet-18 model with the AutoAugment procedure on CIFAR-10. \n",
    "\n",
    "Compare the performance to the baseline [./without-augmentation.ipynb](./without-augmentation.ipynb).\n",
    "\n",
    "## References\n",
    "\n",
    "AutoAugment paper:\n",
    "- [AutoAugment: Learning Augmentation Policies from Data](https://arxiv.org/abs/1805.09501)\n",
    "\n",
    "PyTorch/torchvision documentation:\n",
    "- [transforms.AutoAugment](https://pytorch.org/vision/main/generated/torchvision.transforms.AutoAugment.html)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4af58769",
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   "outputs": [
    {
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     "text": [
      "torch      : 1.13.1\n",
      "lightning  : 1.9.0\n",
      "torchvision: 0.14.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning,torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4363916",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightning as L\n",
    "import time\n",
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchmetrics\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "from helper_utilities import LightningModel,Cifar10DataModule,plot_loss_and_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66486cc5-7c7e-497c-bfb9-fc5e918256f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 123\n"
     ]
    }
   ],
   "source": [
    "from torchvision import transforms\n",
    "\n",
    "train_transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(32),\n",
    "        transforms.AutoAugment(transforms.autoaugment.AutoAugmentPolicy.CIFAR10),\n",
    "        transforms.ToTensor(),\n",
    "        \n",
    "    ]\n",
    ")\n",
    "L.seed_everything(123)\n",
    "dm = Cifar10DataModule(\n",
    "    height_width=(32, 32),\n",
    "    batch_size=256, \n",
    "    train_transform=train_transform, \n",
    "    num_workers=4\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cd186d4",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n",
      "\n",
      "  | Name      | Type               | Params\n",
      "-------------------------------------------------\n",
      "0 | model     | ResNet             | 11.2 M\n",
      "1 | train_acc | MulticlassAccuracy | 0     \n",
      "2 | val_acc   | MulticlassAccuracy | 0     \n",
      "3 | test_acc  | MulticlassAccuracy | 0     \n",
      "-------------------------------------------------\n",
      "11.2 M    Trainable params\n",
      "0         Non-trainable params\n",
      "11.2 M    Total params\n",
      "44.727    Total estimated model params size (MB)\n"
     ]
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=500` reached.\n"
     ]
    }
   ],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "pytorch_model = torchvision.models.resnet18(weights=None)\n",
    "pytorch_model.fc = torch.nn.Linear(512, 10)\n",
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.05)\n",
    "\n",
    "\n",
    "start_1 = time.time()\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=500,\n",
    "    accelerator=\"gpu\",\n",
    "    devices=[1],\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"baseline\"),\n",
    "    deterministic=True,\n",
    ")\n",
    "\n",
    "trainer.fit(model=lightning_model, datamodule=dm)\n",
    "\n",
    "end_1 = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "011bd890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 51.74 min\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Training time: {(end_1-start_1)/60:.2f} min\")\n",
    "\n",
    "plot_loss_and_acc(\n",
    "    trainer.logger.log_dir, \n",
    "    acc_ylim=(0.3, 1.1), \n",
    "    save_loss=\"1_autoaugment-loss.pdf\", \n",
    "    save_acc=\"1_autoaugment-acc.pdf\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f38e1c7-10de-4184-9e99-2c30f89097a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3af8fe5dabcf49a78dc0f3d49e4b269c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7829999923706055     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7829999923706055    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.7829999923706055}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cc95e3c-b97b-43a1-a3a2-df15e3bfccba",
   "metadata": {},
   "outputs": [],
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