-
Notifications
You must be signed in to change notification settings - Fork 0
/
sample.py
67 lines (52 loc) · 2.08 KB
/
sample.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
import os
import pytorch_lightning as pl
import torch
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from torch.utils.data import DataLoader, random_split
from torchvision import datasets, transforms
from torchvision.datasets import MNIST
from torch.nn import functional as F
class Demo(pl.LightningModule):
def __init__(self, classes=10):
super().__init__()
self.save_hyperparameters()
self.l1 = torch.nn.Linear(28 * 28, self.hparams.classes)
def forward(self, x):
return torch.relu(self.l1(x.view(x.size(0), -1)))
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
tensorboard_logs = {'train_loss': loss}
return {'loss': loss, 'log': tensorboard_logs}
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
return {'val_loss': loss}
def validation_epoch_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
return {'val_loss': avg_loss, 'log': {'hits@1': 1}}
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=0.001)
mnist_train = MNIST(os.getcwd(), train=True, download=True,
transform=transforms.ToTensor())
mnist_train = DataLoader(mnist_train, batch_size=32, num_workers=4)
mnist_val = MNIST(os.getcwd(), train=True, download=True,
transform=transforms.ToTensor())
mnist_val = DataLoader(mnist_val, batch_size=32, num_workers=4)
model = Demo()
checkpoint_callback = ModelCheckpoint(
filepath=os.getcwd()+'/{epoch}_{val_loss:.3f}',
verbose=True,
monitor='val_loss',
mode='min',
save_top_k=1)
trainer = Trainer(checkpoint_callback=checkpoint_callback,
val_check_interval=0.1,
gpus='3',
profiler=True)
trainer.fit(model, mnist_train, mnist_val)