Autoencoders¶
This section houses autoencoders and variational autoencoders.
Note
We rely on the community to keep these updated and working. If something doesn’t work, we’d really appreciate a contribution to fix!
Basic AE¶
This is the simplest autoencoder. You can use it like so
from pl_bolts.models.autoencoders import AE
model = AE()
trainer = Trainer()
trainer.fit(model)
You can override any part of this AE to build your own variation.
from pl_bolts.models.autoencoders import AE
class MyAEFlavor(AE):
def init_encoder(self, hidden_dim, latent_dim, input_width, input_height):
encoder = YourSuperFancyEncoder(...)
return encoder
You can use the pretrained models present in bolts.
CIFAR-10 pretrained model:
from pl_bolts.models.autoencoders import AE
ae = AE(input_height=32)
print(AE.pretrained_weights_available())
ae = ae.from_pretrained('cifar10-resnet18')
ae.freeze()
Training:

Reconstructions:
Both input and generated images are normalized versions as the training was done with such images.


- class pl_bolts.models.autoencoders.AE(input_height, enc_type='resnet18', first_conv=False, maxpool1=False, enc_out_dim=512, latent_dim=256, lr=0.0001, **kwargs)[source]
Bases:
pytorch_lightning.core.module.LightningModule
Warning
The feature AE is currently marked under review. The compatibility with other Lightning projects is not guaranteed and API may change at any time. The API and functionality may change without warning in future releases. More details: https://lightning-bolts.readthedocs.io/en/latest/stability.html
Standard AE.
Model is available pretrained on different datasets:
Example:
# not pretrained ae = AE() # pretrained on cifar10 ae = AE(input_height=32).from_pretrained('cifar10-resnet18')
- Parameters
first_conv¶ (
bool
) – use standard kernel_size 7, stride 2 at start or replace it with kernel_size 3, stride 1 convmaxpool1¶ (
bool
) – use standard maxpool to reduce spatial dim of feat by a factor of 2enc_out_dim¶ (
int
) – set according to the out_channel count of encoder used (512 for resnet18, 2048 for resnet50)
- configure_optimizers()[source]
Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need one. But in the case of GANs or similar you might have multiple.
- Returns
Any of these 6 options.
Single optimizer.
List or Tuple of optimizers.
Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple
lr_scheduler_config
).Dictionary, with an
"optimizer"
key, and (optionally) a"lr_scheduler"
key whose value is a single LR scheduler orlr_scheduler_config
.Tuple of dictionaries as described above, with an optional
"frequency"
key.None - Fit will run without any optimizer.
The
lr_scheduler_config
is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.lr_scheduler_config = { # REQUIRED: The scheduler instance "scheduler": lr_scheduler, # The unit of the scheduler's step size, could also be 'step'. # 'epoch' updates the scheduler on epoch end whereas 'step' # updates it after a optimizer update. "interval": "epoch", # How many epochs/steps should pass between calls to # `scheduler.step()`. 1 corresponds to updating the learning # rate after every epoch/step. "frequency": 1, # Metric to to monitor for schedulers like `ReduceLROnPlateau` "monitor": "val_loss", # If set to `True`, will enforce that the value specified 'monitor' # is available when the scheduler is updated, thus stopping # training if not found. If set to `False`, it will only produce a warning "strict": True, # If using the `LearningRateMonitor` callback to monitor the # learning rate progress, this keyword can be used to specify # a custom logged name "name": None, }
When there are schedulers in which the
.step()
method is conditioned on a value, such as thetorch.optim.lr_scheduler.ReduceLROnPlateau
scheduler, Lightning requires that thelr_scheduler_config
contains the keyword"monitor"
set to the metric name that the scheduler should be conditioned on.# The ReduceLROnPlateau scheduler requires a monitor def configure_optimizers(self): optimizer = Adam(...) return { "optimizer": optimizer, "lr_scheduler": { "scheduler": ReduceLROnPlateau(optimizer, ...), "monitor": "metric_to_track", "frequency": "indicates how often the metric is updated" # If "monitor" references validation metrics, then "frequency" should be set to a # multiple of "trainer.check_val_every_n_epoch". }, } # In the case of two optimizers, only one using the ReduceLROnPlateau scheduler def configure_optimizers(self): optimizer1 = Adam(...) optimizer2 = SGD(...) scheduler1 = ReduceLROnPlateau(optimizer1, ...) scheduler2 = LambdaLR(optimizer2, ...) return ( { "optimizer": optimizer1, "lr_scheduler": { "scheduler": scheduler1, "monitor": "metric_to_track", }, }, {"optimizer": optimizer2, "lr_scheduler": scheduler2}, )
Metrics can be made available to monitor by simply logging it using
self.log('metric_to_track', metric_val)
in yourLightningModule
.Note
The
frequency
value specified in a dict along with theoptimizer
key is an int corresponding to the number of sequential batches optimized with the specific optimizer. It should be given to none or to all of the optimizers. There is a difference between passing multiple optimizers in a list, and passing multiple optimizers in dictionaries with a frequency of 1:In the former case, all optimizers will operate on the given batch in each optimization step.
In the latter, only one optimizer will operate on the given batch at every step.
This is different from the
frequency
value specified in thelr_scheduler_config
mentioned above.def configure_optimizers(self): optimizer_one = torch.optim.SGD(self.model.parameters(), lr=0.01) optimizer_two = torch.optim.SGD(self.model.parameters(), lr=0.01) return [ {"optimizer": optimizer_one, "frequency": 5}, {"optimizer": optimizer_two, "frequency": 10}, ]
In this example, the first optimizer will be used for the first 5 steps, the second optimizer for the next 10 steps and that cycle will continue. If an LR scheduler is specified for an optimizer using the
lr_scheduler
key in the above dict, the scheduler will only be updated when its optimizer is being used.Examples:
# most cases. no learning rate scheduler def configure_optimizers(self): return Adam(self.parameters(), lr=1e-3) # multiple optimizer case (e.g.: GAN) def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) return gen_opt, dis_opt # example with learning rate schedulers def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) dis_sch = CosineAnnealing(dis_opt, T_max=10) return [gen_opt, dis_opt], [dis_sch] # example with step-based learning rate schedulers # each optimizer has its own scheduler def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) gen_sch = { 'scheduler': ExponentialLR(gen_opt, 0.99), 'interval': 'step' # called after each training step } dis_sch = CosineAnnealing(dis_opt, T_max=10) # called every epoch return [gen_opt, dis_opt], [gen_sch, dis_sch] # example with optimizer frequencies # see training procedure in `Improved Training of Wasserstein GANs`, Algorithm 1 # https://arxiv.org/abs/1704.00028 def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) n_critic = 5 return ( {'optimizer': dis_opt, 'frequency': n_critic}, {'optimizer': gen_opt, 'frequency': 1} )
Note
Some things to know:
Lightning calls
.backward()
and.step()
on each optimizer as needed.If learning rate scheduler is specified in
configure_optimizers()
with key"interval"
(default “epoch”) in the scheduler configuration, Lightning will call the scheduler’s.step()
method automatically in case of automatic optimization.If you use 16-bit precision (
precision=16
), Lightning will automatically handle the optimizers.If you use multiple optimizers,
training_step()
will have an additionaloptimizer_idx
parameter.If you use
torch.optim.LBFGS
, Lightning handles the closure function automatically for you.If you use multiple optimizers, gradients will be calculated only for the parameters of current optimizer at each training step.
If you need to control how often those optimizers step or override the default
.step()
schedule, override theoptimizer_step()
hook.
- forward(x)[source]
Same as
torch.nn.Module.forward()
.
- training_step(batch, batch_idx)[source]
Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.
- Parameters
batch¶ (
Tensor
| (Tensor
, …) | [Tensor
, …]) – The output of yourDataLoader
. A tensor, tuple or list.batch_idx¶ (
int
) – Integer displaying index of this batchoptimizer_idx¶ (
int
) – When using multiple optimizers, this argument will also be present.hiddens¶ (
Any
) – Passed in iftruncated_bptt_steps
> 0.
- Returns
Any of.
Tensor
- The loss tensordict
- A dictionary. Can include any keys, but must include the key'loss'
None
- Training will skip to the next batch. This is only for automatic optimization.This is not supported for multi-GPU, TPU, IPU, or DeepSpeed.
In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.
Example:
def training_step(self, batch, batch_idx): x, y, z = batch out = self.encoder(x) loss = self.loss(out, x) return loss
If you define multiple optimizers, this step will be called with an additional
optimizer_idx
parameter.# Multiple optimizers (e.g.: GANs) def training_step(self, batch, batch_idx, optimizer_idx): if optimizer_idx == 0: # do training_step with encoder ... if optimizer_idx == 1: # do training_step with decoder ...
If you add truncated back propagation through time you will also get an additional argument with the hidden states of the previous step.
# Truncated back-propagation through time def training_step(self, batch, batch_idx, hiddens): # hiddens are the hidden states from the previous truncated backprop step out, hiddens = self.lstm(data, hiddens) loss = ... return {"loss": loss, "hiddens": hiddens}
Note
The loss value shown in the progress bar is smoothed (averaged) over the last values, so it differs from the actual loss returned in train/validation step.
Note
When
accumulate_grad_batches
> 1, the loss returned here will be automatically normalized byaccumulate_grad_batches
internally.
- validation_step(batch, batch_idx)[source]
Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.
# the pseudocode for these calls val_outs = [] for val_batch in val_data: out = validation_step(val_batch) val_outs.append(out) validation_epoch_end(val_outs)
- Parameters
batch¶ – The output of your
DataLoader
.batch_idx¶ – The index of this batch.
dataloader_idx¶ – The index of the dataloader that produced this batch. (only if multiple val dataloaders used)
- Returns
Any object or value
None
- Validation will skip to the next batch
# pseudocode of order val_outs = [] for val_batch in val_data: out = validation_step(val_batch) if defined("validation_step_end"): out = validation_step_end(out) val_outs.append(out) val_outs = validation_epoch_end(val_outs)
# if you have one val dataloader: def validation_step(self, batch, batch_idx): ... # if you have multiple val dataloaders: def validation_step(self, batch, batch_idx, dataloader_idx=0): ...
Examples:
# CASE 1: A single validation dataset def validation_step(self, batch, batch_idx): x, y = batch # implement your own out = self(x) loss = self.loss(out, y) # log 6 example images # or generated text... or whatever sample_imgs = x[:6] grid = torchvision.utils.make_grid(sample_imgs) self.logger.experiment.add_image('example_images', grid, 0) # calculate acc labels_hat = torch.argmax(out, dim=1) val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # log the outputs! self.log_dict({'val_loss': loss, 'val_acc': val_acc})
If you pass in multiple val dataloaders,
validation_step()
will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.# CASE 2: multiple validation dataloaders def validation_step(self, batch, batch_idx, dataloader_idx=0): # dataloader_idx tells you which dataset this is. ...
Note
If you don’t need to validate you don’t need to implement this method.
Note
When the
validation_step()
is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.
Variational Autoencoders¶
Basic VAE¶
Use the VAE like so.
from pl_bolts.models.autoencoders import VAE
model = VAE()
trainer = Trainer()
trainer.fit(model)
You can override any part of this VAE to build your own variation.
from pl_bolts.models.autoencoders import VAE
class MyVAEFlavor(VAE):
def get_posterior(self, mu, std):
# do something other than the default
# P = self.get_distribution(self.prior, loc=torch.zeros_like(mu), scale=torch.ones_like(std))
return P
You can use the pretrained models present in bolts.
CIFAR-10 pretrained model:
from pl_bolts.models.autoencoders import VAE
vae = VAE(input_height=32)
print(VAE.pretrained_weights_available())
vae = vae.from_pretrained('cifar10-resnet18')
vae.freeze()
Training:


Reconstructions:
Both input and generated images are normalized versions as the training was done with such images.


STL-10 pretrained model:
from pl_bolts.models.autoencoders import VAE
vae = VAE(input_height=96, first_conv=True)
print(VAE.pretrained_weights_available())
vae = vae.from_pretrained('cifar10-resnet18')
vae.freeze()
Training:


- class pl_bolts.models.autoencoders.VAE(input_height, enc_type='resnet18', first_conv=False, maxpool1=False, enc_out_dim=512, kl_coeff=0.1, latent_dim=256, lr=0.0001, **kwargs)[source]
Bases:
pytorch_lightning.core.module.LightningModule
Warning
The feature VAE is currently marked under review. The compatibility with other Lightning projects is not guaranteed and API may change at any time. The API and functionality may change without warning in future releases. More details: https://lightning-bolts.readthedocs.io/en/latest/stability.html
Standard VAE with Gaussian Prior and approx posterior.
Model is available pretrained on different datasets:
Example:
# not pretrained vae = VAE() # pretrained on cifar10 vae = VAE(input_height=32).from_pretrained('cifar10-resnet18') # pretrained on stl10 vae = VAE(input_height=32).from_pretrained('stl10-resnet18')
- Parameters
first_conv¶ (
bool
) – use standard kernel_size 7, stride 2 at start or replace it with kernel_size 3, stride 1 convmaxpool1¶ (
bool
) – use standard maxpool to reduce spatial dim of feat by a factor of 2enc_out_dim¶ (
int
) – set according to the out_channel count of encoder used (512 for resnet18, 2048 for resnet50)
- configure_optimizers()[source]
Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need one. But in the case of GANs or similar you might have multiple.
- Returns
Any of these 6 options.
Single optimizer.
List or Tuple of optimizers.
Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple
lr_scheduler_config
).Dictionary, with an
"optimizer"
key, and (optionally) a"lr_scheduler"
key whose value is a single LR scheduler orlr_scheduler_config
.Tuple of dictionaries as described above, with an optional
"frequency"
key.None - Fit will run without any optimizer.
The
lr_scheduler_config
is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.lr_scheduler_config = { # REQUIRED: The scheduler instance "scheduler": lr_scheduler, # The unit of the scheduler's step size, could also be 'step'. # 'epoch' updates the scheduler on epoch end whereas 'step' # updates it after a optimizer update. "interval": "epoch", # How many epochs/steps should pass between calls to # `scheduler.step()`. 1 corresponds to updating the learning # rate after every epoch/step. "frequency": 1, # Metric to to monitor for schedulers like `ReduceLROnPlateau` "monitor": "val_loss", # If set to `True`, will enforce that the value specified 'monitor' # is available when the scheduler is updated, thus stopping # training if not found. If set to `False`, it will only produce a warning "strict": True, # If using the `LearningRateMonitor` callback to monitor the # learning rate progress, this keyword can be used to specify # a custom logged name "name": None, }
When there are schedulers in which the
.step()
method is conditioned on a value, such as thetorch.optim.lr_scheduler.ReduceLROnPlateau
scheduler, Lightning requires that thelr_scheduler_config
contains the keyword"monitor"
set to the metric name that the scheduler should be conditioned on.# The ReduceLROnPlateau scheduler requires a monitor def configure_optimizers(self): optimizer = Adam(...) return { "optimizer": optimizer, "lr_scheduler": { "scheduler": ReduceLROnPlateau(optimizer, ...), "monitor": "metric_to_track", "frequency": "indicates how often the metric is updated" # If "monitor" references validation metrics, then "frequency" should be set to a # multiple of "trainer.check_val_every_n_epoch". }, } # In the case of two optimizers, only one using the ReduceLROnPlateau scheduler def configure_optimizers(self): optimizer1 = Adam(...) optimizer2 = SGD(...) scheduler1 = ReduceLROnPlateau(optimizer1, ...) scheduler2 = LambdaLR(optimizer2, ...) return ( { "optimizer": optimizer1, "lr_scheduler": { "scheduler": scheduler1, "monitor": "metric_to_track", }, }, {"optimizer": optimizer2, "lr_scheduler": scheduler2}, )
Metrics can be made available to monitor by simply logging it using
self.log('metric_to_track', metric_val)
in yourLightningModule
.Note
The
frequency
value specified in a dict along with theoptimizer
key is an int corresponding to the number of sequential batches optimized with the specific optimizer. It should be given to none or to all of the optimizers. There is a difference between passing multiple optimizers in a list, and passing multiple optimizers in dictionaries with a frequency of 1:In the former case, all optimizers will operate on the given batch in each optimization step.
In the latter, only one optimizer will operate on the given batch at every step.
This is different from the
frequency
value specified in thelr_scheduler_config
mentioned above.def configure_optimizers(self): optimizer_one = torch.optim.SGD(self.model.parameters(), lr=0.01) optimizer_two = torch.optim.SGD(self.model.parameters(), lr=0.01) return [ {"optimizer": optimizer_one, "frequency": 5}, {"optimizer": optimizer_two, "frequency": 10}, ]
In this example, the first optimizer will be used for the first 5 steps, the second optimizer for the next 10 steps and that cycle will continue. If an LR scheduler is specified for an optimizer using the
lr_scheduler
key in the above dict, the scheduler will only be updated when its optimizer is being used.Examples:
# most cases. no learning rate scheduler def configure_optimizers(self): return Adam(self.parameters(), lr=1e-3) # multiple optimizer case (e.g.: GAN) def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) return gen_opt, dis_opt # example with learning rate schedulers def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) dis_sch = CosineAnnealing(dis_opt, T_max=10) return [gen_opt, dis_opt], [dis_sch] # example with step-based learning rate schedulers # each optimizer has its own scheduler def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) gen_sch = { 'scheduler': ExponentialLR(gen_opt, 0.99), 'interval': 'step' # called after each training step } dis_sch = CosineAnnealing(dis_opt, T_max=10) # called every epoch return [gen_opt, dis_opt], [gen_sch, dis_sch] # example with optimizer frequencies # see training procedure in `Improved Training of Wasserstein GANs`, Algorithm 1 # https://arxiv.org/abs/1704.00028 def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) n_critic = 5 return ( {'optimizer': dis_opt, 'frequency': n_critic}, {'optimizer': gen_opt, 'frequency': 1} )
Note
Some things to know:
Lightning calls
.backward()
and.step()
on each optimizer as needed.If learning rate scheduler is specified in
configure_optimizers()
with key"interval"
(default “epoch”) in the scheduler configuration, Lightning will call the scheduler’s.step()
method automatically in case of automatic optimization.If you use 16-bit precision (
precision=16
), Lightning will automatically handle the optimizers.If you use multiple optimizers,
training_step()
will have an additionaloptimizer_idx
parameter.If you use
torch.optim.LBFGS
, Lightning handles the closure function automatically for you.If you use multiple optimizers, gradients will be calculated only for the parameters of current optimizer at each training step.
If you need to control how often those optimizers step or override the default
.step()
schedule, override theoptimizer_step()
hook.
- forward(x)[source]
Same as
torch.nn.Module.forward()
.
- training_step(batch, batch_idx)[source]
Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.
- Parameters
batch¶ (
Tensor
| (Tensor
, …) | [Tensor
, …]) – The output of yourDataLoader
. A tensor, tuple or list.batch_idx¶ (
int
) – Integer displaying index of this batchoptimizer_idx¶ (
int
) – When using multiple optimizers, this argument will also be present.hiddens¶ (
Any
) – Passed in iftruncated_bptt_steps
> 0.
- Returns
Any of.
Tensor
- The loss tensordict
- A dictionary. Can include any keys, but must include the key'loss'
None
- Training will skip to the next batch. This is only for automatic optimization.This is not supported for multi-GPU, TPU, IPU, or DeepSpeed.
In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.
Example:
def training_step(self, batch, batch_idx): x, y, z = batch out = self.encoder(x) loss = self.loss(out, x) return loss
If you define multiple optimizers, this step will be called with an additional
optimizer_idx
parameter.# Multiple optimizers (e.g.: GANs) def training_step(self, batch, batch_idx, optimizer_idx): if optimizer_idx == 0: # do training_step with encoder ... if optimizer_idx == 1: # do training_step with decoder ...
If you add truncated back propagation through time you will also get an additional argument with the hidden states of the previous step.
# Truncated back-propagation through time def training_step(self, batch, batch_idx, hiddens): # hiddens are the hidden states from the previous truncated backprop step out, hiddens = self.lstm(data, hiddens) loss = ... return {"loss": loss, "hiddens": hiddens}
Note
The loss value shown in the progress bar is smoothed (averaged) over the last values, so it differs from the actual loss returned in train/validation step.
Note
When
accumulate_grad_batches
> 1, the loss returned here will be automatically normalized byaccumulate_grad_batches
internally.
- validation_step(batch, batch_idx)[source]
Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.
# the pseudocode for these calls val_outs = [] for val_batch in val_data: out = validation_step(val_batch) val_outs.append(out) validation_epoch_end(val_outs)
- Parameters
batch¶ – The output of your
DataLoader
.batch_idx¶ – The index of this batch.
dataloader_idx¶ – The index of the dataloader that produced this batch. (only if multiple val dataloaders used)
- Returns
Any object or value
None
- Validation will skip to the next batch
# pseudocode of order val_outs = [] for val_batch in val_data: out = validation_step(val_batch) if defined("validation_step_end"): out = validation_step_end(out) val_outs.append(out) val_outs = validation_epoch_end(val_outs)
# if you have one val dataloader: def validation_step(self, batch, batch_idx): ... # if you have multiple val dataloaders: def validation_step(self, batch, batch_idx, dataloader_idx=0): ...
Examples:
# CASE 1: A single validation dataset def validation_step(self, batch, batch_idx): x, y = batch # implement your own out = self(x) loss = self.loss(out, y) # log 6 example images # or generated text... or whatever sample_imgs = x[:6] grid = torchvision.utils.make_grid(sample_imgs) self.logger.experiment.add_image('example_images', grid, 0) # calculate acc labels_hat = torch.argmax(out, dim=1) val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # log the outputs! self.log_dict({'val_loss': loss, 'val_acc': val_acc})
If you pass in multiple val dataloaders,
validation_step()
will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.# CASE 2: multiple validation dataloaders def validation_step(self, batch, batch_idx, dataloader_idx=0): # dataloader_idx tells you which dataset this is. ...
Note
If you don’t need to validate you don’t need to implement this method.
Note
When the
validation_step()
is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.