{ "cells": [ { "cell_type": "markdown", "id": "c896512d", "metadata": { "papermill": { "duration": 0.008246, "end_time": "2021-07-08T20:31:58.526317", "exception": false, "start_time": "2021-07-08T20:31:58.518071", "status": "completed" }, "tags": [] }, "source": [ "\n", "# PyTorch Lightning CIFAR10 ~94% Baseline Tutorial\n", "\n", "* **Author:** PL team\n", "* **License:** CC BY-SA\n", "* **Generated:** 2021-07-08T20:31:46.347606\n", "\n", "Train a Resnet to 94% accuracy on Cifar10!\n", "\n", "\n", "---\n", "Open in [![Open In Colab](https://colab.research.google.com/assets/colab-badge.png){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 ⭐ [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": "55204502", "metadata": { "papermill": { "duration": 0.006697, "end_time": "2021-07-08T20:31:58.540162", "exception": false, "start_time": "2021-07-08T20:31:58.533465", "status": "completed" }, "tags": [] }, "source": [ "### Setup\n", "This notebook requires some packages besides pytorch-lightning." ] }, { "cell_type": "code", "execution_count": 1, "id": "4b7e0eb0", "metadata": { "colab": {}, "colab_type": "code", "execution": { "iopub.execute_input": "2021-07-08T20:31:58.560864Z", "iopub.status.busy": "2021-07-08T20:31:58.560397Z", "iopub.status.idle": "2021-07-08T20:32:00.881073Z", "shell.execute_reply": "2021-07-08T20:32:00.881477Z" }, "id": "LfrJLKPFyhsK", "papermill": { "duration": 2.33475, "end_time": "2021-07-08T20:32:00.881751", "exception": false, "start_time": "2021-07-08T20:31:58.547001", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "! pip install --quiet \"torchmetrics>=0.3\" \"torch>=1.6, <1.9\" \"torchvision\" \"lightning-bolts\" \"pytorch-lightning>=1.3\"" ] }, { "cell_type": "code", "execution_count": 2, "id": "ac168cb0", "metadata": { "execution": { "iopub.execute_input": "2021-07-08T20:32:00.914814Z", "iopub.status.busy": "2021-07-08T20:32:00.914357Z", "iopub.status.idle": "2021-07-08T20:32:00.916496Z", "shell.execute_reply": "2021-07-08T20:32:00.916095Z" }, "papermill": { "duration": 0.026972, "end_time": "2021-07-08T20:32:00.916594", "exception": false, "start_time": "2021-07-08T20:32:00.889622", "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": "e12c9a40", "metadata": { "execution": { "iopub.execute_input": "2021-07-08T20:32:00.935287Z", "iopub.status.busy": "2021-07-08T20:32:00.934829Z", "iopub.status.idle": "2021-07-08T20:32:02.873925Z", "shell.execute_reply": "2021-07-08T20:32:02.874313Z" }, "papermill": { "duration": 1.950905, "end_time": "2021-07-08T20:32:02.874485", "exception": false, "start_time": "2021-07-08T20:32:00.923580", "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, seed_everything, Trainer\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": "09c3f865", "metadata": { "papermill": { "duration": 0.007284, "end_time": "2021-07-08T20:32:02.890083", "exception": false, "start_time": "2021-07-08T20:32:02.882799", "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": "6c8dd3d1", "metadata": { "execution": { "iopub.execute_input": "2021-07-08T20:32:02.909520Z", "iopub.status.busy": "2021-07-08T20:32:02.909026Z", "iopub.status.idle": "2021-07-08T20:32:02.911187Z", "shell.execute_reply": "2021-07-08T20:32:02.910802Z" }, "papermill": { "duration": 0.013994, "end_time": "2021-07-08T20:32:02.911284", "exception": false, "start_time": "2021-07-08T20:32:02.897290", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "\n", "train_transforms = torchvision.transforms.Compose([\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", " torchvision.transforms.ToTensor(),\n", " cifar10_normalization(),\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": "d9acf9e7", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.007356, "end_time": "2021-07-08T20:32:02.925857", "exception": false, "start_time": "2021-07-08T20:32:02.918501", "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": "0232a912", "metadata": { "execution": { "iopub.execute_input": "2021-07-08T20:32:02.944131Z", "iopub.status.busy": "2021-07-08T20:32:02.943680Z", "iopub.status.idle": "2021-07-08T20:32:02.945672Z", "shell.execute_reply": "2021-07-08T20:32:02.946033Z" }, "lines_to_next_cell": 2, "papermill": { "duration": 0.012989, "end_time": "2021-07-08T20:32:02.946143", "exception": false, "start_time": "2021-07-08T20:32:02.933154", "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": "9efc84b7", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.010824, "end_time": "2021-07-08T20:32:02.964550", "exception": false, "start_time": "2021-07-08T20:32:02.953726", "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": "ff8fbb2e", "metadata": { "execution": { "iopub.execute_input": "2021-07-08T20:32:02.988235Z", "iopub.status.busy": "2021-07-08T20:32:02.987767Z", "iopub.status.idle": "2021-07-08T20:32:02.989492Z", "shell.execute_reply": "2021-07-08T20:32:02.989897Z" }, "papermill": { "duration": 0.017734, "end_time": "2021-07-08T20:32:02.990017", "exception": false, "start_time": "2021-07-08T20:32:02.972283", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class LitResnet(LightningModule):\n", "\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": "0bec8e17", "metadata": { "execution": { "iopub.execute_input": "2021-07-08T20:32:03.010605Z", "iopub.status.busy": "2021-07-08T20:32:03.010150Z", "iopub.status.idle": "2021-07-08T20:43:29.675713Z", "shell.execute_reply": "2021-07-08T20:43:29.675246Z" }, "papermill": { "duration": 686.676973, "end_time": "2021-07-08T20:43:29.675836", "exception": false, "start_time": "2021-07-08T20:32:02.998863", "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": "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": "f20a4532afbc46fe88843611ab66cde9", "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": "bf88db9734b145d4a43a65f2819bff6b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "66613c5f789f4cfbae042eef41dde0f9", "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": [ "/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:610: LightningDeprecationWarning: Relying on `self.log('val_loss', ...)` to set the ModelCheckpoint monitor is deprecated in v1.2 and will be removed in v1.4. Please, create your own `mc = ModelCheckpoint(monitor='your_monitor')` and use it as `Trainer(callbacks=[mc])`.\n", " warning_cache.deprecation(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "12a30be7cbe04b83924aba5c1f7000e5", "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": "956a414ab16a43f8bdcd3ce09f8e7ac6", "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": "51669e1ac6a0454587702d1e27dcbed8", "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": 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"display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "341959785d71474084a85989f777612a", "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": "7361ed131b414e5abe0d172724b0733a", "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": "9250b89e78504403a27459b96d513337", "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": "6f25d9c987ad46649f865b0e06246ece", "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": "7a0e42ff9852472b832796966bbb0c96", "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": "31e5c8563ca84984b393431e5f559710", "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": "9988daa6019a46c98bf81f13f7556e57", "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': 0.9176999926567078, 'test_loss': 0.2892821431159973}\n", "--------------------------------------------------------------------------------\n" ] }, { "data": { "text/plain": [ "[{'test_loss': 0.2892821431159973, 'test_acc': 0.9176999926567078}]" ] }, "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": "de0d84aa", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.019408, "end_time": "2021-07-08T20:43:29.715116", "exception": false, "start_time": "2021-07-08T20:43:29.695708", "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": "480b76dc", "metadata": { "execution": { "iopub.execute_input": "2021-07-08T20:43:29.760968Z", "iopub.status.busy": "2021-07-08T20:43:29.760529Z", "iopub.status.idle": "2021-07-08T20:43:29.762610Z", "shell.execute_reply": "2021-07-08T20:43:29.762180Z" }, "papermill": { "duration": 0.028439, "end_time": "2021-07-08T20:43:29.762703", "exception": false, "start_time": "2021-07-08T20:43:29.734264", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class SWAResnet(LitResnet):\n", "\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(\n", " self.model.parameters(), lr=self.hparams.lr, momentum=0.9, weight_decay=5e-4\n", " )\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": "0ce6cb13", "metadata": { "execution": { "iopub.execute_input": "2021-07-08T20:43:29.805002Z", "iopub.status.busy": "2021-07-08T20:43:29.804569Z", "iopub.status.idle": "2021-07-08T20:51:09.654183Z", "shell.execute_reply": "2021-07-08T20:51:09.654547Z" }, "papermill": { "duration": 459.872964, "end_time": "2021-07-08T20:51:09.654713", "exception": false, "start_time": "2021-07-08T20:43:29.781749", "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": [ "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": "32bf871cd6cb4241ba61f1a6abda1d2f", "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": "3b42553465b84a79bc327e314a5fba65", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b918c18559bc4cf78cf7b1096a05521a", "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": "72e2003006fb4a53b63e25b782a4123c", "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": "fd09e50e2fea440f9c08b89d4964595c", "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": "d33792d6ca2d499f944da25b23490538", "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": "7a99b5253fd840cea87b2c11fdf5da49", "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": "2eed5c543b624b3ca3a9594c3fb9008a", "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": "e78a9487518a4054a253f3a3be856bac", "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": "36ae2fd54054444f8dbec98074daf52b", "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": "f8bff0c63f1b4d8ca49d9839d2bcff2a", "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": "98b66f361bb74a9d9a942aec10cd6f2c", "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": "b70d885dd80e4e6e8da695e2c1bb5034", "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": "9e761ed63a8a4b838a165c15cc2f9f66", "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": "3022f4e95dea434780ec910d86560e53", "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": "cdc39d99c06840ef8e3e22fcbe6fe666", "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": "3b7a41b91fb24572ae829a9a968aedc3", "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": "e41a15c5f3e241cfb1be060ffc32f9b2", "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": "e82bc5d772aa443d95008398bd50b759", "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": "dabd629726fd4b548f83597e692989ea", "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": "ebc5a681fee347df8e09ffa76b0c1bbb", "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": "10ba4239f5e448399026e6981c683550", "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": [ "/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py:168: 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", " rank_zero_deprecation(\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": "5c3c49eb222e4718abf860f3b7f2f997", "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': 0.9178000092506409, 'test_loss': 0.2622945308685303}\n", "--------------------------------------------------------------------------------\n" ] }, { "data": { "text/plain": [ "[{'test_loss': 0.2622945308685303, 'test_acc': 0.9178000092506409}]" ] }, "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": "7588a1cd", "metadata": { "execution": { "iopub.execute_input": "2021-07-08T20:51:09.716644Z", "iopub.status.busy": "2021-07-08T20:51:09.715945Z", "iopub.status.idle": "2021-07-08T20:51:11.260368Z", "shell.execute_reply": "2021-07-08T20:51:11.259960Z" }, "papermill": { "duration": 1.577074, "end_time": "2021-07-08T20:51:11.260497", "exception": false, "start_time": "2021-07-08T20:51:09.683423", "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": "4571b460", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.029224, "end_time": "2021-07-08T20:51:11.320196", "exception": false, "start_time": "2021-07-08T20:51:11.290972", "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", "![Pytorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/docs/source/_static/images/logo.png?raw=true){height=\"60px\" height=\"60px\" width=\"240px\"}" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "colab_type,colab,id,-all", "formats": "ipynb,py:percent", 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