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PyTorch Lightning CIFAR10 ~94% Baseline Tutorial

  • Author: PL team

  • License: CC BY-SA

  • Generated: 2021-07-08T20:31:46.347606

Train a Resnet to 94% accuracy on Cifar10!


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Setup

This notebook requires some packages besides pytorch-lightning.

[1]:
! pip install --quiet "torchmetrics>=0.3" "torch>=1.6, <1.9" "torchvision" "lightning-bolts" "pytorch-lightning>=1.3"
[2]:
# Run this if you intend to use TPUs
# !pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.8-cp37-cp37m-linux_x86_64.whl
[3]:
import os

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.transforms.dataset_normalizations import cifar10_normalization
from pytorch_lightning import LightningModule, seed_everything, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
from torch.optim.lr_scheduler import OneCycleLR
from torch.optim.swa_utils import AveragedModel, update_bn
from torchmetrics.functional import accuracy

seed_everything(7)

PATH_DATASETS = os.environ.get('PATH_DATASETS', '.')
AVAIL_GPUS = min(1, torch.cuda.device_count())
BATCH_SIZE = 256 if AVAIL_GPUS else 64
NUM_WORKERS = int(os.cpu_count() / 2)
Global seed set to 7

CIFAR10 Data Module

Import the existing data module from bolts and modify the train and test transforms.

[4]:

train_transforms = torchvision.transforms.Compose([
    torchvision.transforms.RandomCrop(32, padding=4),
    torchvision.transforms.RandomHorizontalFlip(),
    torchvision.transforms.ToTensor(),
    cifar10_normalization(),
])

test_transforms = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor(),
    cifar10_normalization(),
])

cifar10_dm = CIFAR10DataModule(
    data_dir=PATH_DATASETS,
    batch_size=BATCH_SIZE,
    num_workers=NUM_WORKERS,
    train_transforms=train_transforms,
    test_transforms=test_transforms,
    val_transforms=test_transforms,
)

Resnet

Modify the pre-existing Resnet architecture from TorchVision. The pre-existing architecture is based on ImageNet images (224x224) as input. So we need to modify it for CIFAR10 images (32x32).

[5]:
def create_model():
    model = torchvision.models.resnet18(pretrained=False, num_classes=10)
    model.conv1 = nn.Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    model.maxpool = nn.Identity()
    return model

Lightning Module

Check out the `configure_optimizers <https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#configure-optimizers>`__ method to use custom Learning Rate schedulers. The OneCycleLR with SGD will get you to around 92-93% accuracy in 20-30 epochs and 93-94% accuracy in 40-50 epochs. Feel free to experiment with different LR schedules from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate

[6]:
class LitResnet(LightningModule):

    def __init__(self, lr=0.05):
        super().__init__()

        self.save_hyperparameters()
        self.model = create_model()

    def forward(self, x):
        out = self.model(x)
        return F.log_softmax(out, dim=1)

    def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = F.nll_loss(logits, y)
        self.log('train_loss', loss)
        return loss

    def evaluate(self, batch, stage=None):
        x, y = batch
        logits = self(x)
        loss = F.nll_loss(logits, y)
        preds = torch.argmax(logits, dim=1)
        acc = accuracy(preds, y)

        if stage:
            self.log(f'{stage}_loss', loss, prog_bar=True)
            self.log(f'{stage}_acc', acc, prog_bar=True)

    def validation_step(self, batch, batch_idx):
        self.evaluate(batch, 'val')

    def test_step(self, batch, batch_idx):
        self.evaluate(batch, 'test')

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(
            self.parameters(),
            lr=self.hparams.lr,
            momentum=0.9,
            weight_decay=5e-4,
        )
        steps_per_epoch = 45000 // BATCH_SIZE
        scheduler_dict = {
            'scheduler': OneCycleLR(
                optimizer,
                0.1,
                epochs=self.trainer.max_epochs,
                steps_per_epoch=steps_per_epoch,
            ),
            'interval': 'step',
        }
        return {'optimizer': optimizer, 'lr_scheduler': scheduler_dict}
[7]:
model = LitResnet(lr=0.05)
model.datamodule = cifar10_dm

trainer = Trainer(
    progress_bar_refresh_rate=10,
    max_epochs=30,
    gpus=AVAIL_GPUS,
    logger=TensorBoardLogger('lightning_logs/', name='resnet'),
    callbacks=[LearningRateMonitor(logging_interval='step')],
)

trainer.fit(model, cifar10_dm)
trainer.test(model, datamodule=cifar10_dm)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
Files already downloaded and verified
Files already downloaded and verified
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]

  | Name  | Type   | Params
---------------------------------
0 | model | ResNet | 11.2 M
---------------------------------
11.2 M    Trainable params
0         Non-trainable params
11.2 M    Total params
44.696    Total estimated model params size (MB)
Global seed set to 7
/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])`.
  warning_cache.deprecation(
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
--------------------------------------------------------------------------------
DATALOADER:0 TEST RESULTS
{'test_acc': 0.9176999926567078, 'test_loss': 0.2892821431159973}
--------------------------------------------------------------------------------
[7]:
[{'test_loss': 0.2892821431159973, 'test_acc': 0.9176999926567078}]

Bonus: Use Stochastic Weight Averaging to get a boost on performance

Use SWA from torch.optim to get a quick performance boost. Also shows a couple of cool features from Lightning: - Use training_epoch_end to run code after the end of every epoch - Use a pretrained model directly with this wrapper for SWA

[8]:
class SWAResnet(LitResnet):

    def __init__(self, trained_model, lr=0.01):
        super().__init__()

        self.save_hyperparameters('lr')
        self.model = trained_model
        self.swa_model = AveragedModel(self.model)

    def forward(self, x):
        out = self.swa_model(x)
        return F.log_softmax(out, dim=1)

    def training_epoch_end(self, training_step_outputs):
        self.swa_model.update_parameters(self.model)

    def validation_step(self, batch, batch_idx, stage=None):
        x, y = batch
        logits = F.log_softmax(self.model(x), dim=1)
        loss = F.nll_loss(logits, y)
        preds = torch.argmax(logits, dim=1)
        acc = accuracy(preds, y)

        self.log('val_loss', loss, prog_bar=True)
        self.log('val_acc', acc, prog_bar=True)

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(
            self.model.parameters(), lr=self.hparams.lr, momentum=0.9, weight_decay=5e-4
        )
        return optimizer

    def on_train_end(self):
        update_bn(self.datamodule.train_dataloader(), self.swa_model, device=self.device)
[9]:
swa_model = SWAResnet(model.model, lr=0.01)
swa_model.datamodule = cifar10_dm

swa_trainer = Trainer(
    progress_bar_refresh_rate=20,
    max_epochs=20,
    gpus=AVAIL_GPUS,
    logger=TensorBoardLogger('lightning_logs/', name='swa_resnet'),
)

swa_trainer.fit(swa_model, cifar10_dm)
swa_trainer.test(swa_model, datamodule=cifar10_dm)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]

  | Name      | Type          | Params
--------------------------------------------
0 | model     | ResNet        | 11.2 M
1 | swa_model | AveragedModel | 11.2 M
--------------------------------------------
22.3 M    Trainable params
0         Non-trainable params
22.3 M    Total params
89.392    Total estimated model params size (MB)
Global seed set to 7
/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.
  rank_zero_deprecation(
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
--------------------------------------------------------------------------------
DATALOADER:0 TEST RESULTS
{'test_acc': 0.9178000092506409, 'test_loss': 0.2622945308685303}
--------------------------------------------------------------------------------
[9]:
[{'test_loss': 0.2622945308685303, 'test_acc': 0.9178000092506409}]
[10]:
# Start tensorboard.
%reload_ext tensorboard
%tensorboard --logdir lightning_logs/

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