PyTorch Lightning CIFAR10 ~94% Baseline Tutorial¶
Author: PL team
License: CC BY-SA
Generated: 2021-08-31T13:56:05.361261
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 "torch>=1.6, <1.9" "lightning-bolts" "pytorch-lightning>=1.3" "torchmetrics>=0.3" "torchvision"
[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, Trainer, seed_everything
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
IPU available: False, using: 0 IPUs
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
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
--------------------------------------------------------------------------------
DATALOADER:0 TEST RESULTS
{'test_acc': 0.9193999767303467, 'test_loss': 0.2923180162906647}
--------------------------------------------------------------------------------
[7]:
[{'test_loss': 0.2923180162906647, 'test_acc': 0.9193999767303467}]
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
IPU available: False, using: 0 IPUs
/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.
rank_zero_deprecation(
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
/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.
update_bn(self.datamodule.train_dataloader(), self.swa_model, device=self.device)
/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.
rank_zero_deprecation(
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
--------------------------------------------------------------------------------
DATALOADER:0 TEST RESULTS
{'test_acc': 0.9193999767303467, 'test_loss': 0.2627083957195282}
--------------------------------------------------------------------------------
[9]:
[{'test_loss': 0.2627083957195282, 'test_acc': 0.9193999767303467}]
[10]:
# Start tensorboard.
%reload_ext tensorboard
%tensorboard --logdir lightning_logs/
Congratulations - Time to Join the Community!¶
Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning movement, you can do so in the following ways!
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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 !¶
