{"cells": [{"cell_type": "markdown", "id": "d35f9851", "metadata": {"papermill": {"duration": 0.008754, "end_time": "2021-08-31T12:14:19.291440", "exception": false, "start_time": "2021-08-31T12:14:19.282686", "status": "completed"}, "tags": []}, "source": ["\n", "# PyTorch Lightning CIFAR10 ~94% Baseline Tutorial\n", "\n", "* **Author:** PL team\n", "* **License:** CC BY-SA\n", "* **Generated:** 2021-08-31T13:56:05.361261\n", "\n", "Train a Resnet to 94% accuracy on Cifar10!\n", "\n", "\n", "---\n", "Open in [{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/PytorchLightning/pytorch-lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/latest/)\n", "| Join us [on Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)"]}, {"cell_type": "markdown", "id": "a7145bb7", "metadata": {"papermill": {"duration": 0.007423, "end_time": "2021-08-31T12:14:19.306673", "exception": false, "start_time": "2021-08-31T12:14:19.299250", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "95bb8cde", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2021-08-31T12:14:19.328129Z", "iopub.status.busy": "2021-08-31T12:14:19.327661Z", "iopub.status.idle": "2021-08-31T12:14:21.742719Z", "shell.execute_reply": "2021-08-31T12:14:21.743132Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 2.429292, "end_time": "2021-08-31T12:14:21.743374", "exception": false, "start_time": "2021-08-31T12:14:19.314082", "status": "completed"}, "tags": []}, "outputs": [], "source": ["! pip install --quiet \"torch>=1.6, <1.9\" \"lightning-bolts\" \"pytorch-lightning>=1.3\" \"torchmetrics>=0.3\" \"torchvision\""]}, {"cell_type": "code", "execution_count": 2, "id": "5489941c", "metadata": {"execution": {"iopub.execute_input": "2021-08-31T12:14:21.765013Z", "iopub.status.busy": "2021-08-31T12:14:21.764544Z", "iopub.status.idle": "2021-08-31T12:14:21.766680Z", "shell.execute_reply": "2021-08-31T12:14:21.766219Z"}, "papermill": {"duration": 0.015095, "end_time": "2021-08-31T12:14:21.766774", "exception": false, "start_time": "2021-08-31T12:14:21.751679", "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": "e6cc7baa", "metadata": {"execution": {"iopub.execute_input": "2021-08-31T12:14:21.788731Z", "iopub.status.busy": "2021-08-31T12:14:21.788256Z", "iopub.status.idle": "2021-08-31T12:14:23.371389Z", "shell.execute_reply": "2021-08-31T12:14:23.370948Z"}, "papermill": {"duration": 1.596209, "end_time": "2021-08-31T12:14:23.371511", "exception": false, "start_time": "2021-08-31T12:14:21.775302", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 7\n"]}], "source": ["import os\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torchvision\n", "from pl_bolts.datamodules import CIFAR10DataModule\n", "from pl_bolts.transforms.dataset_normalizations import cifar10_normalization\n", "from pytorch_lightning import LightningModule, Trainer, seed_everything\n", "from pytorch_lightning.callbacks import LearningRateMonitor\n", "from pytorch_lightning.loggers import TensorBoardLogger\n", "from torch.optim.lr_scheduler import OneCycleLR\n", "from torch.optim.swa_utils import AveragedModel, update_bn\n", "from torchmetrics.functional import accuracy\n", "\n", "seed_everything(7)\n", "\n", "PATH_DATASETS = os.environ.get(\"PATH_DATASETS\", \".\")\n", "AVAIL_GPUS = min(1, torch.cuda.device_count())\n", "BATCH_SIZE = 256 if AVAIL_GPUS else 64\n", "NUM_WORKERS = int(os.cpu_count() / 2)"]}, {"cell_type": "markdown", "id": "9739d5a3", "metadata": {"papermill": {"duration": 0.007885, "end_time": "2021-08-31T12:14:23.388066", "exception": false, "start_time": "2021-08-31T12:14:23.380181", "status": "completed"}, "tags": []}, "source": ["### CIFAR10 Data Module\n", "\n", "Import the existing data module from `bolts` and modify the train and test transforms."]}, {"cell_type": "code", "execution_count": 4, "id": "9350b4b6", "metadata": {"execution": {"iopub.execute_input": "2021-08-31T12:14:23.408772Z", "iopub.status.busy": "2021-08-31T12:14:23.408301Z", "iopub.status.idle": "2021-08-31T12:14:23.410478Z", "shell.execute_reply": "2021-08-31T12:14:23.410010Z"}, "papermill": {"duration": 0.014644, "end_time": "2021-08-31T12:14:23.410573", "exception": false, "start_time": "2021-08-31T12:14:23.395929", "status": "completed"}, "tags": []}, "outputs": [], "source": ["\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", "\n", "test_transforms = torchvision.transforms.Compose(\n", " [\n", " torchvision.transforms.ToTensor(),\n", " cifar10_normalization(),\n", " ]\n", ")\n", "\n", "cifar10_dm = CIFAR10DataModule(\n", " data_dir=PATH_DATASETS,\n", " batch_size=BATCH_SIZE,\n", " num_workers=NUM_WORKERS,\n", " train_transforms=train_transforms,\n", " test_transforms=test_transforms,\n", " val_transforms=test_transforms,\n", ")"]}, {"cell_type": "markdown", "id": "0d8aa0b9", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.0079, "end_time": "2021-08-31T12:14:23.427416", "exception": false, "start_time": "2021-08-31T12:14:23.419516", "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": "e27d2780", "metadata": {"execution": {"iopub.execute_input": "2021-08-31T12:14:23.447054Z", "iopub.status.busy": "2021-08-31T12:14:23.446587Z", "iopub.status.idle": "2021-08-31T12:14:23.448708Z", "shell.execute_reply": "2021-08-31T12:14:23.448312Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.013446, "end_time": "2021-08-31T12:14:23.448801", "exception": false, "start_time": "2021-08-31T12:14:23.435355", "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": "90972c30", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008008, "end_time": "2021-08-31T12:14:23.465022", "exception": false, "start_time": "2021-08-31T12:14:23.457014", "status": "completed"}, "tags": []}, "source": ["### Lightning Module\n", "Check out the [`configure_optimizers`](https://pytorch-lightning.readthedocs.io/en/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": "b90db99c", "metadata": {"execution": {"iopub.execute_input": "2021-08-31T12:14:23.488955Z", "iopub.status.busy": "2021-08-31T12:14:23.488483Z", "iopub.status.idle": "2021-08-31T12:14:23.491052Z", "shell.execute_reply": "2021-08-31T12:14:23.491430Z"}, "papermill": {"duration": 0.0185, "end_time": "2021-08-31T12:14:23.491541", "exception": false, "start_time": "2021-08-31T12:14:23.473041", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class LitResnet(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)\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": "003d760b", "metadata": {"execution": {"iopub.execute_input": "2021-08-31T12:14:23.512433Z", "iopub.status.busy": "2021-08-31T12:14:23.511963Z", "iopub.status.idle": "2021-08-31T12:25:48.089202Z", "shell.execute_reply": "2021-08-31T12:25:48.089585Z"}, "papermill": {"duration": 684.590073, "end_time": "2021-08-31T12:25:48.089746", "exception": false, "start_time": "2021-08-31T12:14:23.499673", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["GPU available: True, used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\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\n", "---------------------------------\n", "0 | model | ResNet | 11.2 M\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": "bad547af4ba942c4847e4af43d715e91", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation sanity check: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["Global seed set to 7\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "8128183385dd468a8da7ec3f7f402b16", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: -1it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c26d898fb11d41f994423bcca68c37dd", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "03d44f3f29984b6fbf54b95a3df034b7", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "cde2d70eb2a549c0be790b7cd2ae96be", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "ec41f20cd1f24c008a7f81065ec6ae62", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "aa9dd523bd8e4cd3bb3122f8a2f74b69", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "22e5c11e518348e5b0ad385fefb960f7", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "edfcce1675354088b669c67c53bb457f", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "58597b07ef7b496f874e7106f4a33346", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "0e6dbfc845554711ab854c9e3011343e", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "801f598d5c4a473285f6667dcb3ea02a", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "a04b89107ff34e598ea7d3efaa65a6f9", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "ba7445d796cd473e909c7bbbece50126", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "3c4a29a0753a40328e3f56ad449b653b", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "8a1211c6a0cf4a4fa46d1cddaee77a4e", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "3ed9d9e18e374f1c9856e4aec0ae765b", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "7c877f67f3554deea1a023cb21499541", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c06fb17f6dc44bb8a7340af518c368d9", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "a0f8e950566a4d0b891ff8b19b7ba26d", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "13f4d68b9ce5422db1d8f60604242c10", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "a9933511132a488c9024fee20106f012", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "e917ba7c8a144ec0913e6408a75b27af", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "dd6c75b5c6694dda9c852b5767c3b0b7", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "eef113541fb448499d74838cad56da5b", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "03f600ef7125489e8b3bf2f7719bc163", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "568ab45cdcf142e9bba8c5c92466a5af", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "2f89c68a1b0f4d25966888bb126d7b3a", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "2f0295cbe9044e63928ea2b466fa6f74", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "072526be01ee48d1b78096cd3966940a", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "f9a0a71571924a70b3a869d80d198a29", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "29f9a70b1c554193a660e21ea3c8e554", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "7fce88e76d3b439ca94ea08da4f804ef", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["--------------------------------------------------------------------------------\n", "DATALOADER:0 TEST RESULTS\n", "{'test_acc': 0.9193999767303467, 'test_loss': 0.2923180162906647}\n", "--------------------------------------------------------------------------------\n"]}, {"data": {"text/plain": ["[{'test_loss': 0.2923180162906647, 'test_acc': 0.9193999767303467}]"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["model = LitResnet(lr=0.05)\n", "model.datamodule = cifar10_dm\n", "\n", "trainer = Trainer(\n", " progress_bar_refresh_rate=10,\n", " max_epochs=30,\n", " gpus=AVAIL_GPUS,\n", " logger=TensorBoardLogger(\"lightning_logs/\", name=\"resnet\"),\n", " callbacks=[LearningRateMonitor(logging_interval=\"step\")],\n", ")\n", "\n", "trainer.fit(model, cifar10_dm)\n", "trainer.test(model, datamodule=cifar10_dm)"]}, {"cell_type": "markdown", "id": "cf58db9f", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.020054, "end_time": "2021-08-31T12:25:48.130650", "exception": false, "start_time": "2021-08-31T12:25:48.110596", "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": 8, "id": "4a19fa47", "metadata": {"execution": {"iopub.execute_input": "2021-08-31T12:25:48.178647Z", "iopub.status.busy": "2021-08-31T12:25:48.178174Z", "iopub.status.idle": "2021-08-31T12:25:48.180299Z", "shell.execute_reply": "2021-08-31T12:25:48.179903Z"}, "papermill": {"duration": 0.029478, "end_time": "2021-08-31T12:25:48.180397", "exception": false, "start_time": "2021-08-31T12:25:48.150919", "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 training_epoch_end(self, training_step_outputs):\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)\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):\n", " update_bn(self.datamodule.train_dataloader(), self.swa_model, device=self.device)"]}, {"cell_type": "code", "execution_count": 9, "id": "cf08d3fe", "metadata": {"execution": {"iopub.execute_input": "2021-08-31T12:25:48.225403Z", "iopub.status.busy": "2021-08-31T12:25:48.224934Z", "iopub.status.idle": "2021-08-31T12:33:31.681450Z", "shell.execute_reply": "2021-08-31T12:33:31.681050Z"}, "papermill": {"duration": 463.480767, "end_time": "2021-08-31T12:33:31.681568", "exception": false, "start_time": "2021-08-31T12:25:48.200801", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["GPU available: True, used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py:423: LightningDeprecationWarning: DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup.\n", " rank_zero_deprecation(\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\n", " | Name | Type | Params\n", "--------------------------------------------\n", "0 | model | ResNet | 11.2 M\n", "1 | swa_model | AveragedModel | 11.2 M\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": "034400d70c05459b8d13805da1de84bf", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation sanity check: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["Global seed set to 7\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b9034ff5b45c447ebb83d14cfef2944b", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: -1it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "5cce503f63494d4a96fc7ab6526801a9", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b4e3dcf5b1a648a7b69da020a35825f2", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "576370d7ac56429299e985439c077ffe", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "813e287a28b6426fa008ad83d1f336dd", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b13b159b89bd4bce8d25c34e147dfa98", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "24b4d14666ad4c759d02938cadb9f5de", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "d601ab24947d4d509320694f8cbd9ebf", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "2ef0182de04b4883b59069b066ad574c", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "fc127d7a444144db941b2ee4ebc3fcdc", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "68c377591a5946f09aaa98d74def7d5d", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "bc829ba2b2f049a9930a01d353828d0c", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "123f4159101040d88d68aaee06c573f3", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "d3f704b4dbd442af90f39453385db685", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c3e60330511b4f9cb4c40d8e9adf22a9", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "67c5c8bc24fe4717b9104618e939182b", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "81bbe4fa0a224544b46b1de58a4d7a77", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c6b7fc5fd81c42fab08128289a319edd", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "e8cb13d44a1f4ced890808b2b3dd8958", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "7f039e0a2f16451aa87cdbca84b64198", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "59b56dbdde064412813ba576f0a8d4e6", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["/tmp/ipykernel_3806/3826980810.py:31: LightningDeprecationWarning: The `LightningModule.datamodule` property is deprecated in v1.3 and will be removed in v1.5. Access the datamodule through using `self.trainer.datamodule` instead.\n", " update_bn(self.datamodule.train_dataloader(), self.swa_model, device=self.device)\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py:423: LightningDeprecationWarning: DataModule.teardown has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.teardown.\n", " rank_zero_deprecation(\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "24e81691330943bdbecf2b4aab28ab6d", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["--------------------------------------------------------------------------------\n", "DATALOADER:0 TEST RESULTS\n", "{'test_acc': 0.9193999767303467, 'test_loss': 0.2627083957195282}\n", "--------------------------------------------------------------------------------\n"]}, {"data": {"text/plain": ["[{'test_loss': 0.2627083957195282, 'test_acc': 0.9193999767303467}]"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["swa_model = SWAResnet(model.model, lr=0.01)\n", "swa_model.datamodule = cifar10_dm\n", "\n", "swa_trainer = Trainer(\n", " progress_bar_refresh_rate=20,\n", " max_epochs=20,\n", " gpus=AVAIL_GPUS,\n", " logger=TensorBoardLogger(\"lightning_logs/\", name=\"swa_resnet\"),\n", ")\n", "\n", "swa_trainer.fit(swa_model, cifar10_dm)\n", "swa_trainer.test(swa_model, datamodule=cifar10_dm)"]}, {"cell_type": "code", "execution_count": 10, "id": "1b60cc93", "metadata": {"execution": {"iopub.execute_input": "2021-08-31T12:33:31.744141Z", "iopub.status.busy": "2021-08-31T12:33:31.743692Z", "iopub.status.idle": "2021-08-31T12:33:33.288066Z", "shell.execute_reply": "2021-08-31T12:33:33.288441Z"}, "papermill": {"duration": 1.577462, "end_time": "2021-08-31T12:33:33.288582", "exception": false, "start_time": "2021-08-31T12:33:31.711120", "status": "completed"}, "tags": []}, "outputs": [{"data": {"text/html": ["\n", " \n", " \n", " "], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Start tensorboard.\n", "%reload_ext tensorboard\n", "%tensorboard --logdir lightning_logs/"]}, {"cell_type": "markdown", "id": "d219f5d7", "metadata": {"papermill": {"duration": 0.029271, "end_time": "2021-08-31T12:33:33.348372", "exception": false, "start_time": "2021-08-31T12:33:33.319101", "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/PyTorchLightning/pytorch-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 [Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)!\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/PyTorchLightning/pytorch-lightning) or [Bolt](https://github.com/PyTorchLightning/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/PyTorchLightning/pytorch-lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/PyTorchLightning/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", "{height=\"60px\" width=\"240px\"}"]}, {"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,id,colab_type,-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": "3.9.6"}, "papermill": {"default_parameters": {}, "duration": 1156.464693, "end_time": "2021-08-31T12:33:34.697194", "environment_variables": {}, "exception": null, "input_path": "lightning_examples/cifar10-baseline/baseline.ipynb", "output_path": ".notebooks/lightning_examples/cifar10-baseline.ipynb", "parameters": {}, "start_time": "2021-08-31T12:14:18.232501", "version": "2.3.3"}, "widgets": {"application/vnd.jupyter.widget-state+json": {"state": {"000fd9c8ed4d4defa54023a2c4d4186d": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", 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