{"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 [![Open In Colab](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAHUAAAAUCAYAAACzrHJDAAAIuUlEQVRoQ+1ZaVRURxb+qhdolmbTUVSURpZgmLhHbQVFZIlGQBEXcMvJhKiTEzfigjQg7oNEJ9GMGidnjnNMBs2czIzajksEFRE1xklCTKJiQLRFsUGkoUWw+82pamn79etGYoKek1B/4NW99/tu3e/dquJBAGD27NkHALxKf39WY39gyrOi+i3xqGtUoePJrFmznrmgtModorbTu8YRNZk5cybXTvCtwh7o6NR2KzuZMWNGh6jtVt7nA0ymT5/eJlF9POrh7PAQl6s8bGYa3PUum//htmebVtLRqW0q01M5keTk5FZFzU0oRle3+zxwg5Hgtb+PZiL/ZVohxCI+hL5JgjmfjPxZ26+33BG3dA+ealHPM4gQAo5rU59gsI8bRvl54t3Ca62mvHyUAhtOlLd5WSQpKcluBjumnoCLs1EARkVd9E8l3p9y2i7RbQ1B6pFwu/YDgW8KbHJHMTQrwnjz2oZm9M4pavOCfo5jWrgCaaMVcMs6/pNhDr0+AMN93XlxV7R6DNpyzi7W/OE+yIrsjU6rTrbKV5cd/pNyItOmTbMp6sbBB+EqaYJY4cWE3VUciNt1TpgfcRFv71Fi54xT5kSoyLvOBEJMOMxWXkFlBeBSX4u6Zkcs+3KszYRtiapbNRqF31UgetVuc8z9vBXIv1qD+F1f83B6uDlCUyfsZGepGPpmg01OB7EITQbhS9ribKy+DmP1DUiClLz4bnIHVOqa7BY+Z1wg5g3zgUvyehiNpnJKxSLc/ts76LKm0BzX3c0RNy1yXjDcB5lWoro4iNHQxM+f1kWeWQARAWQS++trISJTp061Kep25X/MycwtjuctSC5rxo7ppi7VNUox5+PhPHtrsS2O1qJ6yx1QujQUzm9sh6hbkBlvvGcN8hYnwjUjH6kjfZEd5c/jitz5Jc5U3ENnFynKl4eB7nyEgP2UZ+Yz3/rVEbyYr27qELrtC4FIC0J7sc7xWnmccdHfRRTs0VB+cA4lt+oFcRR/wUeH8FG5w2Mbx8FQ8TXEvv1xYf4wBP3O2WyL3/UVjpXWgIqaFeUPr+wTmDvUB7njH6/bOv+HRg4SqioAg5GDe1aB3ZeMTJkyRSBqkLsWqSEm0fZVBEN94zEZnYvrdx1JL5cxe+a+AbhSJecRRHW/ikTFRTa38dtQlNZ5CRKwFvUtZU/kvBoEF9Uxni/XqIM+dwKbTw3rhcxIf7gmr2M+H6SMwx8iBzJbw5oxeG3Lv5FX9B3AGaHPS8e8z77H7v9VMpvPG5ug1enh7eGK8h0LBTwUb+GInqzInlRUK65DmTPQu4c3+uQKjwKK77zwUxBX4Tq7yR1RuiwUsqlrABCM6esHdXoy47fk4+prYKy8ZF574x4V5BnHQBuf4g9Z9ld8U36L2aktZNNplNfw7zotwWTy5MkCUft4aLEopJj5/OPHl1BQqeAVOnHgNSQOqmBzq9V9cfEm/yx5ubMGKS9cYPZ3vx2OS/c6PVHUuUO7Y1Pci3BO/1zgq18byebfGemLtNF+6JRtOvMk926ibussZqM+1mNz4TWkH7rCbM5phwGRGDAaoF8fY5OHFnlldAA8sgoEXKnDukA1NgSeNjqkJT9brbN4pC9WRweYXyLugR73c+MYvyWfu0yC6+mjzN1Isfw3FKJS98CU/zI1IHFkFPR52cHL2FJk0sB6kMTERIGo9GzcPkLNfA0cwdwi/hfEYO86ZMd9w+y1egfM2T2Eh/vesMNwljSzuZRT420SW3eqy8N6aHMmwmnFUZ7/PGVPbIoNZvNU1BURdHs0bT2+HjL8sDSM2e6vi4Lj5NW8WOLVA6RTT2azxLV+bglaFNqLieqemS/gWkw7NyoAHo+2dEsiivengjKsPFoqWOvbSh/kxPaxyW/JRzH2Fl3EzD9/xjAefJqB3usKUFn/0Gb+S/d/jy3FN2yLOmnSJJtn6oehByEiHPSeXnDxFGPRnoFoaBJjcdQlbDwcjL1zTNuQpoxD7R0OG0uUTMi0fkVwdzBdYIwcwZunxrVJVLplNm54BZp7jfDfYLoNyqQi1K6KxIdHzmN+QQ2WjFIwUT2zTGdlRXo4NFXVUO4sgX5dFC7f0aP/ZlNeUjFBuL8Xjl6uRuP6aMjSjpjzsH62FDU7JhBuGccEXIvDfJFFBc/gHw80dklfCVYnRaDfpiJcutPA4F7qJsfJeUPQI+1fqMlNhFx1FM0GDqkjFVg7NojlQ0Vt4aM5ReSqcbpaCg8nCW5lRsBvbT4T1TLfFptsfh7gItzuKTdJSEiwKSrt1vcmnEXXrsLbYnWDA1bu+z2WKy9Arq+1KRqdfKsoBo0GcdtEpS/B1bO4v0cFiUhkjskvKcMrWwtAPHuwQq8Z+4LZ1vTQANfXt4J0DwZX9gWa9qh4XDM/voC9JXfwYEMMHJcfNtusn82ihvliVUwg5KrPGVf6GH94ZJpEZBen6EC4qYTHA1dXhW0JIex8txzv//c8lhzXIi/BFxOH9jGbQhZsRalTIBZZ8KkGyZAxeRQvXkFF1TWz/Hm46jNYUnjPbt3JxIkT7f6dSj8qfJJyVvBxgaIlblOyjtysNHWN9fjjqWi7glJfW3/S0Hlj2XnA8PhKT9w6g3Qx3XiXhvuxQsuT1proxBKI/AaZqY1Xz5muvY8G8XkRRCaHsfQsRAFDH/tZPbcYuHotOG0FRIqB4HR3wNVoIPLtz8ycTguu+jpEigE218vd1YCr5m+HpHMvEI9u4LTXwNWaLjl0iPwGAmIpeHx1VeCqTJdPs1/vweweQPO3HC24NhOhnTphwoQnfv6QSY2ICbkNmdSA4h87oaLaiYfn5diIEd4att2erOwJXbPUHp953p6orQVSUVWRAXBT8c/dJ5L9xhzaJGp71GR/wFP8P5V2z10NSC9T93QM2xUg8fHxT+zU9ijeU4naHon8CjFJXFzc8/kn+dN06q9QgF98SYSo2Xen2NjYZy5sR6f+4nLSK5Iam2PH/x87a1YN/t5sBgAAAABJRU5ErkJggg==){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": 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"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", "![Pytorch Lightning](data:image/png;base64,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