def on_train_batch_start(self, trainer, pl_module, batch, batch_idx,
dataloader_idx):
opt = pl_module.optimizers(use_pl_optimizer=False)
self.lrs.append(opt.param_groups[0]['lr'])
self.momentums.append(opt.param_groups[0]['betas'][0])
if __name__ == '__main__':
dm = MNISTDataModule()
dm.batch_size = 512
print(dm.batch_size)
dm.setup()
dm.prepare_data()
dl = dm.train_dataloader()
steps_per_epoch = len(dl)
model = CustomModel(steps_per_epoch, 0.06)
all_hooks = []
for i, k in enumerate(model.model):
if isinstance(k, ConvBlock):
for l in k:
hook = ActivationHistHook(i, type(l))
hook.add_hook(l)
all_hooks.append(hook)
callback = CustomCallback(all_hooks)
trainer = pl.Trainer(
gpus=0,
max_epochs=1,
class VAE(LightningModule):
def __init__(self,
hidden_dim: int = 128,
latent_dim: int = 32,
input_channels: int = 3,
input_width: int = 224,
input_height: int = 224,
batch_size: int = 32,
learning_rate: float = 0.001,
data_dir: str = '.',
datamodule: pl_bolts.datamodules.LightningDataModule = None,
pretrained: str = None,
**kwargs):
"""
Standard VAE with Gaussian Prior and approx posterior.
Model is available pretrained on different datasets:
Example::
# not pretrained
vae = VAE()
# pretrained on imagenet
vae = VAE(pretrained='imagenet')
# pretrained on cifar10
vae = VAE(pretrained='cifar10')
Args:
hidden_dim: encoder and decoder hidden dims
latent_dim: latenet code dim
input_channels: num of channels of the input image.
input_width: image input width
input_height: image input height
batch_size: the batch size
learning_rate" the learning rate
data_dir: the directory to store data
datamodule: The Lightning DataModule
pretrained: Load weights pretrained on a dataset
"""
super().__init__()
self.save_hyperparameters()
self.datamodule = datamodule
self.__set_pretrained_dims(pretrained)
# use mnist as the default module
self.__set_default_datamodule(data_dir)
# init actual model
self.__init_system()
if pretrained:
self.load_pretrained(pretrained)
def __init_system(self):
self.encoder = self.init_encoder()
self.decoder = self.init_decoder()
def __set_pretrained_dims(self, pretrained):
if pretrained == 'imagenet2012':
self.datamodule = ImagenetDataModule(
data_dir=self.hparams.data_dir)
(self.hparams.input_channels, self.hparams.input_height,
self.hparams.input_width) = self.datamodule.size()
def __set_default_datamodule(self, data_dir):
if self.datamodule is None:
self.datamodule = MNISTDataModule(data_dir=data_dir)
(self.hparams.input_channels, self.hparams.input_height,
self.hparams.input_width) = self.datamodule.size()
def load_pretrained(self, pretrained):
available_weights = {'imagenet2012'}
if pretrained in available_weights:
weights_name = f'vae-{pretrained}'
load_pretrained(self, weights_name)
def init_encoder(self):
encoder = Encoder(self.hparams.hidden_dim, self.hparams.latent_dim,
self.hparams.input_channels,
self.hparams.input_width, self.hparams.input_height)
return encoder
def init_decoder(self):
decoder = Decoder(self.hparams.hidden_dim, self.hparams.latent_dim,
self.hparams.input_width, self.hparams.input_height,
self.hparams.input_channels)
return decoder
def get_prior(self, z_mu, z_std):
# Prior ~ Normal(0,1)
P = distributions.normal.Normal(loc=torch.zeros_like(z_mu),
scale=torch.ones_like(z_std))
return P
def get_approx_posterior(self, z_mu, z_std):
# Approx Posterior ~ Normal(mu, sigma)
Q = distributions.normal.Normal(loc=z_mu, scale=z_std)
return Q
def elbo_loss(self, x, P, Q):
# Reconstruction loss
z = Q.rsample()
pxz = self(z)
pxz = torch.tanh(pxz)
recon_loss = F.mse_loss(pxz, x, reduction='none')
# sum across dimensions because sum of log probabilities of iid univariate gaussians is the same as
# multivariate gaussian
recon_loss = recon_loss.sum(dim=-1)
# KL divergence loss
log_qz = Q.log_prob(z)
log_pz = P.log_prob(z)
kl_div = (log_qz - log_pz).sum(dim=1)
# ELBO = reconstruction + KL
loss = recon_loss + kl_div
# average over batch
loss = loss.mean()
recon_loss = recon_loss.mean()
kl_div = kl_div.mean()
return loss, recon_loss, kl_div, pxz
def forward(self, z):
return self.decoder(z)
def _run_step(self, batch):
x, _ = batch
z_mu, z_log_var = self.encoder(x)
z_std = torch.exp(z_log_var / 2)
P = self.get_prior(z_mu, z_std)
Q = self.get_approx_posterior(z_mu, z_std)
x = x.view(x.size(0), -1)
loss, recon_loss, kl_div, pxz = self.elbo_loss(x, P, Q)
return loss, recon_loss, kl_div, pxz
def training_step(self, batch, batch_idx):
loss, recon_loss, kl_div, pxz = self._run_step(batch)
tensorboard_logs = {
'train_elbo_loss': loss,
'train_recon_loss': recon_loss,
'train_kl_loss': kl_div
}
return {'loss': loss, 'log': tensorboard_logs}
def validation_step(self, batch, batch_idx):
loss, recon_loss, kl_div, pxz = self._run_step(batch)
return {
'val_loss': loss,
'val_recon_loss': recon_loss,
'val_kl_div': kl_div,
'pxz': pxz
}
def validation_epoch_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
recon_loss = torch.stack([x['val_recon_loss'] for x in outputs]).mean()
kl_loss = torch.stack([x['val_kl_div'] for x in outputs]).mean()
tensorboard_logs = {
'val_elbo_loss': avg_loss,
'val_recon_loss': recon_loss,
'val_kl_loss': kl_loss
}
return {'val_loss': avg_loss, 'log': tensorboard_logs}
def test_step(self, batch, batch_idx):
loss, recon_loss, kl_div, pxz = self._run_step(batch)
return {
'test_loss': loss,
'test_recon_loss': recon_loss,
'test_kl_div': kl_div,
'pxz': pxz
}
def test_epoch_end(self, outputs):
avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
recon_loss = torch.stack([x['test_recon_loss']
for x in outputs]).mean()
kl_loss = torch.stack([x['test_kl_div'] for x in outputs]).mean()
tensorboard_logs = {
'test_elbo_loss': avg_loss,
'test_recon_loss': recon_loss,
'test_kl_loss': kl_loss
}
return {'test_loss': avg_loss, 'log': tensorboard_logs}
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(),
lr=self.hparams.learning_rate)
def prepare_data(self):
self.datamodule.prepare_data()
def train_dataloader(self):
return self.datamodule.train_dataloader(self.hparams.batch_size)
def val_dataloader(self):
return self.datamodule.val_dataloader(self.hparams.batch_size)
def test_dataloader(self):
return self.datamodule.test_dataloader(self.hparams.batch_size)
@staticmethod
def add_model_specific_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False)
parser.add_argument(
'--hidden_dim',
type=int,
default=128,
help=
'itermediate layers dimension before embedding for default encoder/decoder'
)
parser.add_argument('--latent_dim',
type=int,
default=32,
help='dimension of latent variables z')
parser.add_argument(
'--input_width',
type=int,
default=224,
help='input width (used Imagenet downsampled size)')
parser.add_argument(
'--input_height',
type=int,
default=224,
help='input width (used Imagenet downsampled size)')
parser.add_argument('--input_channels',
type=int,
default=3,
help='number of input channels')
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--pretrained', type=str, default=None)
parser.add_argument('--data_dir', type=str, default=os.getcwd())
parser.add_argument('--learning_rate', type=float, default=1e-3)
return parser
class AE(LightningModule):
def __init__(
self,
hidden_dim=128,
latent_dim=32,
input_channels=1,
input_width=28,
input_height=28,
batch_size=32,
learning_rate=0.001,
data_dir=os.getcwd(),
**kwargs
):
super().__init__()
self.save_hyperparameters()
self.dataloaders = MNISTDataModule(data_dir=data_dir)
self.img_dim = self.dataloaders.size()
self.encoder = self.init_encoder(self.hparams.hidden_dim, self.hparams.latent_dim,
self.hparams.input_width, self.hparams.input_height)
self.decoder = self.init_decoder(self.hparams.hidden_dim, self.hparams.latent_dim)
def init_encoder(self, hidden_dim, latent_dim, input_width, input_height):
encoder = AEEncoder(hidden_dim, latent_dim, input_width, input_height)
return encoder
def init_decoder(self, hidden_dim, latent_dim):
c, h, w = self.img_dim
decoder = Decoder(hidden_dim, latent_dim, w, h, c)
return decoder
def forward(self, z):
return self.decoder(z)
def _run_step(self, batch):
x, _ = batch
z = self.encoder(x)
x_hat = self(z)
loss = F.mse_loss(x.view(x.size(0), -1), x_hat)
return loss
def training_step(self, batch, batch_idx):
loss = self._run_step(batch)
tensorboard_logs = {
'mse_loss': loss,
}
return {'loss': loss, 'log': tensorboard_logs}
def validation_step(self, batch, batch_idx):
loss = self._run_step(batch)
return {
'val_loss': loss,
}
def validation_epoch_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
tensorboard_logs = {'mse_loss': avg_loss}
return {
'val_loss': avg_loss,
'log': tensorboard_logs
}
def test_step(self, batch, batch_idx):
loss = self._run_step(batch)
return {
'test_loss': loss,
}
def test_epoch_end(self, outputs):
avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
tensorboard_logs = {'mse_loss': avg_loss}
return {
'test_loss': avg_loss,
'log': tensorboard_logs
}
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
def prepare_data(self):
self.dataloaders.prepare_data()
def train_dataloader(self):
return self.dataloaders.train_dataloader(self.hparams.batch_size)
def val_dataloader(self):
return self.dataloaders.val_dataloader(self.hparams.batch_size)
def test_dataloader(self):
return self.dataloaders.test_dataloader(self.hparams.batch_size)
@staticmethod
def add_model_specific_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False)
parser.add_argument('--hidden_dim', type=int, default=128,
help='itermediate layers dimension before embedding for default encoder/decoder')
parser.add_argument('--latent_dim', type=int, default=32,
help='dimension of latent variables z')
parser.add_argument('--input_width', type=int, default=28,
help='input image width - 28 for MNIST (must be even)')
parser.add_argument('--input_height', type=int, default=28,
help='input image height - 28 for MNIST (must be even)')
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--learning_rate', type=float, default=1e-3)
parser.add_argument('--data_dir', type=str, default='')
return parser