{"cells": [{"cell_type": "markdown", "id": "ff99ed92", "metadata": {"papermill": {"duration": 0.003453, "end_time": "2024-09-01T12:43:59.006121", "exception": false, "start_time": "2024-09-01T12:43:59.002668", "status": "completed"}, "tags": []}, "source": ["\n", "# PyTorch Lightning CIFAR10 ~94% Baseline Tutorial\n", "\n", "* **Author:** Lightning.ai\n", "* **License:** CC BY-SA\n", "* **Generated:** 2024-09-01T12:43:52.217738\n", "\n", "Train a Resnet to 94% accuracy on Cifar10!\n", "\n", "\n", "---\n", "Open in [![Open In Colab](data:image/png;base64,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){height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/lightning_examples/cifar10-baseline.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/Lightning-AI/lightning/)\n", "| Check out [the documentation](https://lightning.ai/docs/)\n", "| Join us [on Discord](https://discord.com/invite/tfXFetEZxv)"]}, {"cell_type": "markdown", "id": "8ec66364", "metadata": {"papermill": {"duration": 0.002515, "end_time": "2024-09-01T12:43:59.011401", "exception": false, "start_time": "2024-09-01T12:43:59.008886", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "3555009e", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2024-09-01T12:43:59.017536Z", "iopub.status.busy": "2024-09-01T12:43:59.017346Z", "iopub.status.idle": "2024-09-01T12:44:00.293113Z", "shell.execute_reply": "2024-09-01T12:44:00.292206Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 1.28091, "end_time": "2024-09-01T12:44:00.294868", "exception": false, "start_time": "2024-09-01T12:43:59.013958", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\r\n", "\u001b[0m"]}], "source": ["! pip install --quiet \"matplotlib\" \"numpy <3.0\" \"pytorch-lightning >=2.0,<2.5\" \"torchmetrics>=1.0, <1.5\" \"seaborn\" \"torch>=1.8.1, <2.5\" \"pandas\" \"torchvision\""]}, {"cell_type": "code", "execution_count": 2, "id": "9cb666a5", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:44:00.301663Z", "iopub.status.busy": "2024-09-01T12:44:00.301475Z", "iopub.status.idle": "2024-09-01T12:44:00.305267Z", "shell.execute_reply": "2024-09-01T12:44:00.304505Z"}, "papermill": {"duration": 0.008891, "end_time": "2024-09-01T12:44:00.306622", "exception": false, "start_time": "2024-09-01T12:44:00.297731", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# Run this if you intend to use TPUs\n", "# !pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.8-cp37-cp37m-linux_x86_64.whl"]}, {"cell_type": "code", "execution_count": 3, "id": "edb7b267", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:44:00.313580Z", "iopub.status.busy": "2024-09-01T12:44:00.312977Z", "iopub.status.idle": "2024-09-01T12:44:04.116250Z", "shell.execute_reply": "2024-09-01T12:44:04.115188Z"}, "papermill": {"duration": 3.80945, "end_time": "2024-09-01T12:44:04.118816", "exception": false, "start_time": "2024-09-01T12:44:00.309366", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Seed set to 7\n"]}], "source": ["import os\n", "\n", "import pandas as pd\n", "import pytorch_lightning as pl\n", "import seaborn as sn\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torchvision\n", "from IPython.display import display\n", "from pytorch_lightning.callbacks import LearningRateMonitor\n", "from pytorch_lightning.loggers import CSVLogger\n", "from torch.optim.lr_scheduler import OneCycleLR\n", "from torch.optim.swa_utils import AveragedModel\n", "from torch.utils.data import DataLoader, random_split\n", "from torchmetrics.functional import accuracy\n", "from torchvision.datasets import CIFAR10\n", "\n", "pl.seed_everything(7)\n", "\n", "PATH_DATASETS = os.environ.get(\"PATH_DATASETS\", \".\")\n", "BATCH_SIZE = 256 if torch.cuda.is_available() else 64\n", "NUM_WORKERS = int(os.cpu_count() / 2)"]}, {"cell_type": "markdown", "id": "242d49cc", "metadata": {"papermill": {"duration": 0.006084, "end_time": "2024-09-01T12:44:04.131319", "exception": false, "start_time": "2024-09-01T12:44:04.125235", "status": "completed"}, "tags": []}, "source": ["### CIFAR10 DataLoaders\n"]}, {"cell_type": "code", "execution_count": 4, "id": "b61b9797", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:44:04.145554Z", "iopub.status.busy": "2024-09-01T12:44:04.144909Z", "iopub.status.idle": "2024-09-01T12:44:13.181619Z", "shell.execute_reply": "2024-09-01T12:44:13.179880Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 9.047098, "end_time": "2024-09-01T12:44:13.184398", "exception": false, "start_time": "2024-09-01T12:44:04.137300", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /__w/13/s/.datasets/cifar-10-python.tar.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 0/170498071 [00:00<?, ?it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 229376/170498071 [00:00<01:30, 1878945.44it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  1%|          | 1998848/170498071 [00:00<00:16, 10393585.66it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  4%|\u258d         | 6946816/170498071 [00:00<00:05, 27333811.88it/s]"]}, 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"cifar10_normalization = torchvision.transforms.Normalize(\n", "    mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],\n", "    std=[x / 255.0 for x in [63.0, 62.1, 66.7]],\n", ")\n", "\n", "\n", "def split_dataset(dataset, val_split=0.2, train=True):\n", "    \"\"\"Splits the dataset into train and validation set.\"\"\"\n", "    len_dataset = len(dataset)\n", "    splits = get_splits(len_dataset, val_split)\n", "    dataset_train, dataset_val = random_split(dataset, splits, generator=torch.Generator().manual_seed(42))\n", "\n", "    if train:\n", "        return dataset_train\n", "    return dataset_val\n", "\n", "\n", "def get_splits(len_dataset, val_split):\n", "    \"\"\"Computes split lengths for train and validation set.\"\"\"\n", "    if isinstance(val_split, int):\n", "        train_len = len_dataset - val_split\n", "        splits = [train_len, val_split]\n", "    elif isinstance(val_split, float):\n", "        val_len = int(val_split * len_dataset)\n", "        train_len = len_dataset - val_len\n", "        splits = [train_len, val_len]\n", "    else:\n", "        raise ValueError(f\"Unsupported type {type(val_split)}\")\n", "\n", "    return splits\n", "\n", "\n", "train_transforms = torchvision.transforms.Compose(\n", "    [\n", "        torchvision.transforms.RandomCrop(32, padding=4),\n", "        torchvision.transforms.RandomHorizontalFlip(),\n", "        torchvision.transforms.ToTensor(),\n", "        cifar10_normalization,\n", "    ]\n", ")\n", "test_transforms = torchvision.transforms.Compose(\n", "    [\n", "        torchvision.transforms.ToTensor(),\n", "        cifar10_normalization,\n", "    ]\n", ")\n", "\n", "dataset_train = CIFAR10(PATH_DATASETS, train=True, download=True, transform=train_transforms)\n", "dataset_val = CIFAR10(PATH_DATASETS, train=True, download=True, transform=test_transforms)\n", "dataset_train = split_dataset(dataset_train)\n", "dataset_val = split_dataset(dataset_val, train=False)\n", "dataset_test = CIFAR10(PATH_DATASETS, train=False, download=True, transform=test_transforms)\n", "\n", "train_dataloader = DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS)\n", "val_dataloader = DataLoader(dataset_val, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_WORKERS)\n", "test_dataloader = DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_WORKERS)"]}, {"cell_type": "markdown", "id": "08a4c835", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.012376, "end_time": "2024-09-01T12:44:13.208705", "exception": false, "start_time": "2024-09-01T12:44:13.196329", "status": "completed"}, "tags": []}, "source": ["### Resnet\n", "Modify the pre-existing Resnet architecture from TorchVision. The pre-existing architecture is based on ImageNet\n", "images (224x224) as input. So we need to modify it for CIFAR10 images (32x32)."]}, {"cell_type": "code", "execution_count": 5, "id": "119f610d", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:44:13.233537Z", "iopub.status.busy": "2024-09-01T12:44:13.233265Z", "iopub.status.idle": "2024-09-01T12:44:13.239377Z", "shell.execute_reply": "2024-09-01T12:44:13.238672Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.021627, "end_time": "2024-09-01T12:44:13.241652", "exception": false, "start_time": "2024-09-01T12:44:13.220025", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def create_model():\n", "    model = torchvision.models.resnet18(pretrained=False, num_classes=10)\n", "    model.conv1 = nn.Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", "    model.maxpool = nn.Identity()\n", "    return model"]}, {"cell_type": "markdown", "id": "ebb720bc", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.011242, "end_time": "2024-09-01T12:44:13.264561", "exception": false, "start_time": "2024-09-01T12:44:13.253319", "status": "completed"}, "tags": []}, "source": ["### Lightning Module\n", "Check out the [`configure_optimizers`](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#configure-optimizers)\n", "method to use custom Learning Rate schedulers. The OneCycleLR with SGD will get you to around 92-93% accuracy\n", "in 20-30 epochs and 93-94% accuracy in 40-50 epochs. Feel free to experiment with different\n", "LR schedules from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate"]}, {"cell_type": "code", "execution_count": 6, "id": "962e795f", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:44:13.281665Z", "iopub.status.busy": "2024-09-01T12:44:13.281406Z", "iopub.status.idle": "2024-09-01T12:44:13.292033Z", "shell.execute_reply": "2024-09-01T12:44:13.291242Z"}, "papermill": {"duration": 0.020658, "end_time": "2024-09-01T12:44:13.293355", "exception": false, "start_time": "2024-09-01T12:44:13.272697", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class LitResnet(pl.LightningModule):\n", "    def __init__(self, lr=0.05):\n", "        super().__init__()\n", "\n", "        self.save_hyperparameters()\n", "        self.model = create_model()\n", "\n", "    def forward(self, x):\n", "        out = self.model(x)\n", "        return F.log_softmax(out, dim=1)\n", "\n", "    def training_step(self, batch, batch_idx):\n", "        x, y = batch\n", "        logits = self(x)\n", "        loss = F.nll_loss(logits, y)\n", "        self.log(\"train_loss\", loss)\n", "        return loss\n", "\n", "    def evaluate(self, batch, stage=None):\n", "        x, y = batch\n", "        logits = self(x)\n", "        loss = F.nll_loss(logits, y)\n", "        preds = torch.argmax(logits, dim=1)\n", "        acc = accuracy(preds, y, task=\"multiclass\", num_classes=10)\n", "\n", "        if stage:\n", "            self.log(f\"{stage}_loss\", loss, prog_bar=True)\n", "            self.log(f\"{stage}_acc\", acc, prog_bar=True)\n", "\n", "    def validation_step(self, batch, batch_idx):\n", "        self.evaluate(batch, \"val\")\n", "\n", "    def test_step(self, batch, batch_idx):\n", "        self.evaluate(batch, \"test\")\n", "\n", "    def configure_optimizers(self):\n", "        optimizer = torch.optim.SGD(\n", "            self.parameters(),\n", "            lr=self.hparams.lr,\n", "            momentum=0.9,\n", "            weight_decay=5e-4,\n", "        )\n", "        steps_per_epoch = 45000 // BATCH_SIZE\n", "        scheduler_dict = {\n", "            \"scheduler\": OneCycleLR(\n", "                optimizer,\n", "                0.1,\n", "                epochs=self.trainer.max_epochs,\n", "                steps_per_epoch=steps_per_epoch,\n", "            ),\n", "            \"interval\": \"step\",\n", "        }\n", "        return {\"optimizer\": optimizer, \"lr_scheduler\": scheduler_dict}"]}, {"cell_type": "code", "execution_count": 7, "id": "0d8605a1", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:44:13.304765Z", "iopub.status.busy": "2024-09-01T12:44:13.304511Z", "iopub.status.idle": "2024-09-01T12:45:19.309865Z", "shell.execute_reply": "2024-09-01T12:45:19.309121Z"}, "papermill": {"duration": 66.012717, "end_time": "2024-09-01T12:45:19.311301", "exception": false, "start_time": "2024-09-01T12:44:13.298584", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", "  warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", "  warnings.warn(msg)\n", "GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["You are using a CUDA device ('NVIDIA GeForce RTX 3090') 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"]}, {"name": "stderr", "output_type": "stream", "text": ["Missing logger folder: logs/lightning_logs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\n", "  | Name  | Type   | Params | Mode \n", "-----------------------------------------\n", "0 | model | ResNet | 11.2 M | train\n", "-----------------------------------------\n", "11.2 M    Trainable params\n", "0         Non-trainable params\n", "11.2 M    Total params\n", "44.696    Total estimated model params size (MB)\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "e2cc0bff21904845bb79e02ceed76c59", "version_major": 2, "version_minor": 0}, "text/plain": ["Sanity Checking: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "2e34076000684b128e0ebf895f823494", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "43bdcd08be7d4508b2d62873ceb933c0", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "876a20842a5644be87d40b4158a0534b", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "5b70199998b54a6e8ac4a5a3d18a469f", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "9086edd5975d420e8e306fab3ad916e2", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "f874840f372640fcb7107bfd7d0cd8fa", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["`Trainer.fit` stopped: `max_epochs=5` reached.\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "595d9dcc5df9407c824f15390dba646d", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "       Test metric             DataLoader 0\n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "        test_acc            0.8141999840736389\n", "        test_loss           0.5329769253730774\n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n"]}, {"data": {"text/plain": ["[{'test_loss': 0.5329769253730774, 'test_acc': 0.8141999840736389}]"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["model = LitResnet(lr=0.05)\n", "\n", "trainer = pl.Trainer(\n", "    max_epochs=5,\n", "    accelerator=\"auto\",\n", "    devices=1,\n", "    logger=CSVLogger(save_dir=\"logs/\"),\n", "    callbacks=[LearningRateMonitor(logging_interval=\"step\")],\n", ")\n", "\n", "trainer.fit(model, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)\n", "trainer.test(model, dataloaders=test_dataloader)"]}, {"cell_type": "code", "execution_count": 8, "id": "9ea7e0ca", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:45:19.327340Z", "iopub.status.busy": "2024-09-01T12:45:19.327136Z", "iopub.status.idle": "2024-09-01T12:45:19.978883Z", "shell.execute_reply": "2024-09-01T12:45:19.977842Z"}, "papermill": {"duration": 0.661154, "end_time": "2024-09-01T12:45:19.980389", "exception": false, "start_time": "2024-09-01T12:45:19.319235", "status": "completed"}, "tags": []}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>lr-SGD</th>\n", "      <th>test_acc</th>\n", "      <th>test_loss</th>\n", "      <th>train_loss</th>\n", "      <th>val_acc</th>\n", "      <th>val_loss</th>\n", "    </tr>\n", "    <tr>\n", "      <th>epoch</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>NaN</th>\n", "      <td>0.012079</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>0.0</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.818587</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>NaN</th>\n", "      <td>0.034132</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>0.0</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.639136</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>NaN</th>\n", "      <td>0.062440</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["         lr-SGD  test_acc  test_loss  train_loss  val_acc  val_loss\n", "epoch                                                              \n", "NaN    0.012079       NaN        NaN         NaN      NaN       NaN\n", "0.0         NaN       NaN        NaN    1.818587      NaN       NaN\n", "NaN    0.034132       NaN        NaN         NaN      NaN       NaN\n", "0.0         NaN       NaN        NaN    1.639136      NaN       NaN\n", "NaN    0.062440       NaN        NaN         NaN      NaN       NaN"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["<seaborn.axisgrid.FacetGrid at 0x7f4d44ce2440>"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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", "text/plain": ["<Figure size 616.125x500 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["\n", "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n", "del metrics[\"step\"]\n", "metrics.set_index(\"epoch\", inplace=True)\n", "display(metrics.dropna(axis=1, how=\"all\").head())\n", "sn.relplot(data=metrics, kind=\"line\")"]}, {"cell_type": "markdown", "id": "5fa79504", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.007563, "end_time": "2024-09-01T12:45:19.995994", "exception": false, "start_time": "2024-09-01T12:45:19.988431", "status": "completed"}, "tags": []}, "source": ["### Bonus: Use [Stochastic Weight Averaging](https://arxiv.org/abs/1803.05407) to get a boost on performance\n", "\n", "Use SWA from torch.optim to get a quick performance boost. Also shows a couple of cool features from Lightning:\n", "- Use `training_epoch_end` to run code after the end of every epoch\n", "- Use a pretrained model directly with this wrapper for SWA"]}, {"cell_type": "code", "execution_count": 9, "id": "4571d655", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:45:20.012237Z", "iopub.status.busy": "2024-09-01T12:45:20.012032Z", "iopub.status.idle": "2024-09-01T12:45:20.019619Z", "shell.execute_reply": "2024-09-01T12:45:20.018719Z"}, "papermill": {"duration": 0.017128, "end_time": "2024-09-01T12:45:20.020772", "exception": false, "start_time": "2024-09-01T12:45:20.003644", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class SWAResnet(LitResnet):\n", "    def __init__(self, trained_model, lr=0.01):\n", "        super().__init__()\n", "\n", "        self.save_hyperparameters(\"lr\")\n", "        self.model = trained_model\n", "        self.swa_model = AveragedModel(self.model)\n", "\n", "    def forward(self, x):\n", "        out = self.swa_model(x)\n", "        return F.log_softmax(out, dim=1)\n", "\n", "    def on_train_epoch_end(self):\n", "        self.swa_model.update_parameters(self.model)\n", "\n", "    def validation_step(self, batch, batch_idx, stage=None):\n", "        x, y = batch\n", "        logits = F.log_softmax(self.model(x), dim=1)\n", "        loss = F.nll_loss(logits, y)\n", "        preds = torch.argmax(logits, dim=1)\n", "        acc = accuracy(preds, y, task=\"multiclass\", num_classes=10)\n", "\n", "        self.log(\"val_loss\", loss, prog_bar=True)\n", "        self.log(\"val_acc\", acc, prog_bar=True)\n", "\n", "    def configure_optimizers(self):\n", "        optimizer = torch.optim.SGD(self.model.parameters(), lr=self.hparams.lr, momentum=0.9, weight_decay=5e-4)\n", "        return optimizer\n", "\n", "    # def on_train_end(self):  # todo: failing as trainer has only dataloaders, not datamodules\n", "    #     update_bn(self.trainer.datamodule.train_dataloader(), self.swa_model, device=self.device)"]}, {"cell_type": "code", "execution_count": 10, "id": "db5cbcee", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:45:20.036879Z", "iopub.status.busy": "2024-09-01T12:45:20.036616Z", "iopub.status.idle": "2024-09-01T13:37:04.675517Z", "shell.execute_reply": "2024-09-01T13:37:04.674590Z"}, "papermill": {"duration": 3104.669901, "end_time": "2024-09-01T13:37:04.698159", "exception": false, "start_time": "2024-09-01T12:45:20.028258", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/parsing.py:208: Attribute 'trained_model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['trained_model'])`.\n", "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", "  warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", "  warnings.warn(msg)\n"]}, {"name": "stderr", "output_type": "stream", "text": ["GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\n", "  | Name      | Type          | Params | Mode \n", "----------------------------------------------------\n", "0 | model     | ResNet        | 11.2 M | train\n", "1 | swa_model | AveragedModel | 11.2 M | train\n", "----------------------------------------------------\n", "22.3 M    Trainable params\n", "0         Non-trainable params\n", "22.3 M    Total params\n", "89.392    Total estimated model params size (MB)\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c3712fac076a47fe9ce9671ac43730e2", "version_major": 2, "version_minor": 0}, "text/plain": ["Sanity Checking: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "8d395f05e4604f62ba956c95189ca146", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "929583048945449d99fd87527f808253", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "886633f6fafd437aa7fb0a920d0a050c", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b579138542c14fcf838a4aae6af33e54", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "f778675e831c4b189c8c484d0050fa60", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "63933785103c487ea244584a9dba9497", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["`Trainer.fit` stopped: `max_epochs=5` reached.\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "e06bfefa8a3047dca6c128844c94616b", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "       Test metric             DataLoader 0\n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "        test_acc            0.8141999840736389\n", "        test_loss           0.5329769253730774\n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n"]}, {"data": {"text/plain": ["[{'test_loss': 0.5329769253730774, 'test_acc': 0.8141999840736389}]"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["swa_model = SWAResnet(model.model, lr=0.01)\n", "\n", "swa_trainer = pl.Trainer(\n", "    max_epochs=5,\n", "    accelerator=\"auto\",\n", "    devices=1,\n", "    logger=CSVLogger(save_dir=\"logs/\"),\n", ")\n", "\n", "swa_trainer.fit(swa_model, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)\n", "swa_trainer.test(swa_model, dataloaders=test_dataloader)"]}, {"cell_type": "code", "execution_count": 11, "id": "4c384b42", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T13:37:04.735955Z", "iopub.status.busy": "2024-09-01T13:37:04.735739Z", "iopub.status.idle": "2024-09-01T13:37:05.139265Z", "shell.execute_reply": "2024-09-01T13:37:05.138662Z"}, "papermill": {"duration": 0.424004, "end_time": "2024-09-01T13:37:05.141934", "exception": false, "start_time": "2024-09-01T13:37:04.717930", "status": "completed"}, "tags": []}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: 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", "text/plain": ["<Figure size 616.125x500 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["\n", "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n", "del metrics[\"step\"]\n", "metrics.set_index(\"epoch\", inplace=True)\n", "display(metrics.dropna(axis=1, how=\"all\").head())\n", "sn.relplot(data=metrics, kind=\"line\")"]}, {"cell_type": "markdown", "id": "11a9dc47", "metadata": {"papermill": {"duration": 0.021561, "end_time": "2024-09-01T13:37:05.185723", "exception": false, "start_time": "2024-09-01T13:37:05.164162", "status": "completed"}, "tags": []}, "source": ["## Congratulations - Time to Join the Community!\n", "\n", "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n", "movement, you can do so in the following ways!\n", "\n", "### Star [Lightning](https://github.com/Lightning-AI/lightning) on GitHub\n", "The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool\n", "tools we're building.\n", "\n", "### Join our [Discord](https://discord.com/invite/tfXFetEZxv)!\n", "The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n", "and share your interests in `#general` channel\n", "\n", "\n", "### Contributions !\n", "The best way to contribute to our community is to become a code contributor! At any time you can go to\n", "[Lightning](https://github.com/Lightning-AI/lightning) or [Bolt](https://github.com/Lightning-AI/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/Lightning-AI/lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/Lightning-AI/lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* You can also contribute your own notebooks with useful examples !\n", "\n", "### Great thanks from the entire Pytorch Lightning Team for your interest !\n", "\n", "[![Pytorch Lightning](data:image/png;base64,NDA0OiBOb3QgRm91bmQ=){height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"]}, {"cell_type": "raw", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. customcarditem::\n", "   :header: PyTorch Lightning CIFAR10 ~94% Baseline Tutorial\n", "   :card_description: Train a Resnet to 94% accuracy on Cifar10!\n", "   :tags: Image,GPU/TPU,Lightning-Examples"]}], "metadata": {"jupytext": {"cell_metadata_filter": "colab_type,id,colab,-all", "formats": "ipynb,py:percent", "main_language": "python"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12"}, "papermill": {"default_parameters": {}, "duration": 3189.334017, "end_time": "2024-09-01T13:37:07.489933", "environment_variables": {}, "exception": null, "input_path": "lightning_examples/cifar10-baseline/baseline.ipynb", "output_path": ".notebooks/lightning_examples/cifar10-baseline.ipynb", "parameters": {}, "start_time": "2024-09-01T12:43:58.155916", "version": "2.6.0"}, "widgets": {"application/vnd.jupyter.widget-state+json": {"state": {"038f10f02da745cf827075ef59dce721": {"model_module": "@jupyter-widgets/controls", 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