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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You can also contribute your own notebooks with useful examples !
Great thanks from the entire Pytorch Lightning Team for your interest !¶
