PyTorch Lightning Basic GAN Tutorial¶
Author: PL team
License: CC BY-SA
Generated: 2021-06-28T09:27:42.776969
How to train a GAN!
Main takeaways: 1. Generator and discriminator are arbitrary PyTorch modules. 2. training_step does both the generator and discriminator training.
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Setup¶
This notebook requires some packages besides pytorch-lightning.
[1]:
! pip install --quiet "torchvision" "pytorch-lightning>=1.3" "torch>=1.6, <1.9" "torchmetrics>=0.3"
[2]:
import os
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from pytorch_lightning import LightningDataModule, LightningModule, Trainer
from torch.utils.data import DataLoader, random_split
from torchvision.datasets import MNIST
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)
/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/metrics/__init__.py:43: LightningDeprecationWarning: `pytorch_lightning.metrics.*` module has been renamed to `torchmetrics.*` and split off to its own package (https://github.com/PyTorchLightning/metrics) since v1.3 and will be removed in v1.5
rank_zero_deprecation(
MNIST DataModule¶
Below, we define a DataModule for the MNIST Dataset. To learn more about DataModules, check out our tutorial on them or see the latest docs.
[3]:
class MNISTDataModule(LightningDataModule):
def __init__(
self,
data_dir: str = PATH_DATASETS,
batch_size: int = BATCH_SIZE,
num_workers: int = NUM_WORKERS,
):
super().__init__()
self.data_dir = data_dir
self.batch_size = batch_size
self.num_workers = num_workers
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, )),
])
# self.dims is returned when you call dm.size()
# Setting default dims here because we know them.
# Could optionally be assigned dynamically in dm.setup()
self.dims = (1, 28, 28)
self.num_classes = 10
def prepare_data(self):
# download
MNIST(self.data_dir, train=True, download=True)
MNIST(self.data_dir, train=False, download=True)
def setup(self, stage=None):
# Assign train/val datasets for use in dataloaders
if stage == 'fit' or stage is None:
mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])
# Assign test dataset for use in dataloader(s)
if stage == 'test' or stage is None:
self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)
def train_dataloader(self):
return DataLoader(
self.mnist_train,
batch_size=self.batch_size,
num_workers=self.num_workers,
)
def val_dataloader(self):
return DataLoader(self.mnist_val, batch_size=self.batch_size, num_workers=self.num_workers)
def test_dataloader(self):
return DataLoader(self.mnist_test, batch_size=self.batch_size, num_workers=self.num_workers)
A. Generator¶
[4]:
class Generator(nn.Module):
def __init__(self, latent_dim, img_shape):
super().__init__()
self.img_shape = img_shape
def block(in_feat, out_feat, normalize=True):
layers = [nn.Linear(in_feat, out_feat)]
if normalize:
layers.append(nn.BatchNorm1d(out_feat, 0.8))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.model = nn.Sequential(
*block(latent_dim, 128, normalize=False),
*block(128, 256),
*block(256, 512),
*block(512, 1024),
nn.Linear(1024, int(np.prod(img_shape))),
nn.Tanh(),
)
def forward(self, z):
img = self.model(z)
img = img.view(img.size(0), *self.img_shape)
return img
B. Discriminator¶
[5]:
class Discriminator(nn.Module):
def __init__(self, img_shape):
super().__init__()
self.model = nn.Sequential(
nn.Linear(int(np.prod(img_shape)), 512),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(512, 256),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(256, 1),
nn.Sigmoid(),
)
def forward(self, img):
img_flat = img.view(img.size(0), -1)
validity = self.model(img_flat)
return validity
C. GAN¶
A couple of cool features to check out in this example…¶
We use
some_tensor.type_as(another_tensor)to make sure we initialize new tensors on the right device (i.e. GPU, CPU).Lightning will put your dataloader data on the right device automatically
In this example, we pull from latent dim on the fly, so we need to dynamically add tensors to the right device.
type_asis the way we recommend to do this.
This example shows how to use multiple dataloaders in your
LightningModule.
[6]:
class GAN(LightningModule):
def __init__(
self,
channels,
width,
height,
latent_dim: int = 100,
lr: float = 0.0002,
b1: float = 0.5,
b2: float = 0.999,
batch_size: int = BATCH_SIZE,
**kwargs
):
super().__init__()
self.save_hyperparameters()
# networks
data_shape = (channels, width, height)
self.generator = Generator(latent_dim=self.hparams.latent_dim, img_shape=data_shape)
self.discriminator = Discriminator(img_shape=data_shape)
self.validation_z = torch.randn(8, self.hparams.latent_dim)
self.example_input_array = torch.zeros(2, self.hparams.latent_dim)
def forward(self, z):
return self.generator(z)
def adversarial_loss(self, y_hat, y):
return F.binary_cross_entropy(y_hat, y)
def training_step(self, batch, batch_idx, optimizer_idx):
imgs, _ = batch
# sample noise
z = torch.randn(imgs.shape[0], self.hparams.latent_dim)
z = z.type_as(imgs)
# train generator
if optimizer_idx == 0:
# generate images
self.generated_imgs = self(z)
# log sampled images
sample_imgs = self.generated_imgs[:6]
grid = torchvision.utils.make_grid(sample_imgs)
self.logger.experiment.add_image('generated_images', grid, 0)
# ground truth result (ie: all fake)
# put on GPU because we created this tensor inside training_loop
valid = torch.ones(imgs.size(0), 1)
valid = valid.type_as(imgs)
# adversarial loss is binary cross-entropy
g_loss = self.adversarial_loss(self.discriminator(self(z)), valid)
tqdm_dict = {'g_loss': g_loss}
output = OrderedDict({'loss': g_loss, 'progress_bar': tqdm_dict, 'log': tqdm_dict})
return output
# train discriminator
if optimizer_idx == 1:
# Measure discriminator's ability to classify real from generated samples
# how well can it label as real?
valid = torch.ones(imgs.size(0), 1)
valid = valid.type_as(imgs)
real_loss = self.adversarial_loss(self.discriminator(imgs), valid)
# how well can it label as fake?
fake = torch.zeros(imgs.size(0), 1)
fake = fake.type_as(imgs)
fake_loss = self.adversarial_loss(self.discriminator(self(z).detach()), fake)
# discriminator loss is the average of these
d_loss = (real_loss + fake_loss) / 2
tqdm_dict = {'d_loss': d_loss}
output = OrderedDict({'loss': d_loss, 'progress_bar': tqdm_dict, 'log': tqdm_dict})
return output
def configure_optimizers(self):
lr = self.hparams.lr
b1 = self.hparams.b1
b2 = self.hparams.b2
opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=(b1, b2))
opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))
return [opt_g, opt_d], []
def on_epoch_end(self):
z = self.validation_z.type_as(self.generator.model[0].weight)
# log sampled images
sample_imgs = self(z)
grid = torchvision.utils.make_grid(sample_imgs)
self.logger.experiment.add_image('generated_images', grid, self.current_epoch)
[7]:
dm = MNISTDataModule()
model = GAN(*dm.size())
trainer = Trainer(gpus=AVAIL_GPUS, max_epochs=5, progress_bar_refresh_rate=20)
trainer.fit(model, dm)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/configuration_validator.py:99: UserWarning: you passed in a val_dataloader but have no validation_step. Skipping val loop
rank_zero_warn(f'you passed in a {loader_name} but have no {step_name}. Skipping {stage} loop')
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
| Name | Type | Params | In sizes | Out sizes
----------------------------------------------------------------------------
0 | generator | Generator | 1.5 M | [2, 100] | [2, 1, 28, 28]
1 | discriminator | Discriminator | 533 K | ? | ?
----------------------------------------------------------------------------
2.0 M Trainable params
0 Non-trainable params
2.0 M Total params
8.174 Total estimated model params size (MB)
[8]:
# Start tensorboard.
%load_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 !¶
