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pytorch_lightning_mnist.py
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pytorch_lightning_mnist.py
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import os
import torch
import torch.nn.functional as F
from torchvision.datasets import MNIST
from torchvision import transforms
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.profiler import SimpleProfiler
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from torch.utils.data import random_split
import logging
logging.basicConfig(format='%(asctime)s %(message)s',
datefmt='%m/%d/%Y %I:%M:%S %p')
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
class LitModel(pl.LightningModule):
def __init__(self, hparams):
super().__init__()
self.hparams = hparams
self.layer_1 = torch.nn.Linear(28 * 28, 128)
self.layer_2 = torch.nn.Linear(128, 10)
def forward(self, x):
x = x.view(x.size(0), -1)
x = self.layer_1(x)
x = F.relu(x)
x = self.layer_2(x)
return x
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
return optimizer
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
# (log keyword is optional)
# return {'loss': loss, 'log': {'train_loss': loss}}
result = pl.TrainResult(minimize=loss)
result.log('train_loss', loss)
return result
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
result = pl.EvalResult()
result.log('val_loss', loss)
return result
def test_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
result = pl.EvalResult()
result.log('val_loss', loss)
return result
if __name__ == '__main__':
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('--gpus', type=int, default=None)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--auto_lr', type=int, default=0)
parser.add_argument('--max_epochs', type=int, default=10)
parser.add_argument('--patience', type=int, default=None)
parser.add_argument('--backend', type=str, default='ddp')
args = parser.parse_args()
logger.info("Creating train/val datasets...")
train_dataset = MNIST(os.getcwd(),
train=True,
download=True,
transform=transforms.ToTensor())
n_train = int(len(train_dataset) * 0.9)
n_val = int(len(train_dataset) * 0.1)
assert n_train + n_val == len(train_dataset), \
f"Mismatch: {n_train} + {n_val} != {len(train_dataset)}"
train_set, val_set = random_split(train_dataset, lengths=[n_train, n_val])
train_loader = DataLoader(train_set)
val_loader = DataLoader(val_set)
# Create test dataset
logger.info("Done! Creating test dataset...")
test_dataset = MNIST(os.getcwd(),
train=False,
download=True,
transform=transforms.ToTensor())
test_loader = DataLoader(test_dataset)
logger.info(f"Done!"
f"\n# of train examples: {n_train}"
f"\n# of val examples: {n_val}"
f"\n# of test examples: {len(test_dataset)}")
# init model
model = LitModel(args)
if args.patience is not None:
early_stop_ckpt = EarlyStopping(monitor='val_loss',
verbose=True,
patience=args.patience)
else:
early_stop_ckpt = None
profiler = SimpleProfiler()
lightning_log_pth = '/lightning_logs'
if not os.path.isdir(lightning_log_pth):
logger.warning(f"Unable to find {lightning_log_pth} to log to! "
f"If not running Grid then ignore.")
save_dir = ''
else:
save_dir = lightning_log_pth
tensorboard = TensorBoardLogger(save_dir=save_dir)
mdl_ckpt = ModelCheckpoint(filepath=save_dir,
save_top_k = 1,
verbose = True,
monitor = 'val_loss')
# most basic trainer, uses good defaults (auto-tensorboard, checkpoints, logs, and more)
trainer = pl.Trainer(gpus=args.gpus if torch.cuda.is_available() else None,
auto_lr_find=bool(args.auto_lr),
logger=tensorboard,
checkpoint_callback=mdl_ckpt,
early_stop_callback=early_stop_ckpt,
distributed_backend=args.backend if torch.cuda.is_available() else None,
max_epochs=args.max_epochs)
logger.info("Beginning training...")
trainer.fit(model,
train_dataloader=train_loader,
val_dataloaders=val_loader)
logger.info("Done! Beginning testing...")
trainer.test(model, test_loader)
logger.info("Done - job complete!")