Self-supervised learning¶
These transforms are used in various self-supervised learning approaches.
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!
CPC transforms¶
Transforms used for CPC
CIFAR-10 Train (c)¶
- class pl_bolts.transforms.self_supervised.cpc_transforms.CPCTrainTransformsCIFAR10(patch_size=8, overlap=4)[source]
Bases:
object
Warning
The feature CPCTrainTransformsCIFAR10 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
Transforms used for CPC:
Transforms:
random_flip img_jitter col_jitter rnd_gray transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2)
Example:
# in a regular dataset CIFAR10(..., transforms=CPCTrainTransformsCIFAR10()) # in a DataModule module = CIFAR10DataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCTrainTransformsCIFAR10())
- Parameters:
CIFAR-10 Eval (c)¶
- class pl_bolts.transforms.self_supervised.cpc_transforms.CPCEvalTransformsCIFAR10(patch_size=8, overlap=4)[source]
Bases:
object
Warning
The feature CPCEvalTransformsCIFAR10 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
Transforms used for CPC:
Transforms:
random_flip transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=overlap)
Example:
# in a regular dataset CIFAR10(..., transforms=CPCEvalTransformsCIFAR10()) # in a DataModule module = CIFAR10DataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCEvalTransformsCIFAR10())
- Parameters:
Imagenet Train (c)¶
- class pl_bolts.transforms.self_supervised.cpc_transforms.CPCTrainTransformsImageNet128(patch_size=32, overlap=16)[source]
Bases:
object
Warning
The feature CPCTrainTransformsImageNet128 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
Transforms used for CPC:
Transforms:
random_flip transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2)
Example:
# in a regular dataset Imagenet(..., transforms=CPCTrainTransformsImageNet128()) # in a DataModule module = ImagenetDataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCTrainTransformsImageNet128())
- Parameters:
Imagenet Eval (c)¶
- class pl_bolts.transforms.self_supervised.cpc_transforms.CPCEvalTransformsImageNet128(patch_size=32, overlap=16)[source]
Bases:
object
Warning
The feature CPCEvalTransformsImageNet128 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
Transforms used for CPC:
Transforms:
random_flip transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2)
Example:
# in a regular dataset Imagenet(..., transforms=CPCEvalTransformsImageNet128()) # in a DataModule module = ImagenetDataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCEvalTransformsImageNet128())
- Parameters:
STL-10 Train (c)¶
- class pl_bolts.transforms.self_supervised.cpc_transforms.CPCTrainTransformsSTL10(patch_size=16, overlap=8)[source]
Bases:
object
Warning
The feature CPCTrainTransformsSTL10 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
Transforms used for CPC:
Transforms:
random_flip img_jitter col_jitter rnd_gray transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2)
Example:
# in a regular dataset STL10(..., transforms=CPCTrainTransformsSTL10()) # in a DataModule module = STL10DataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCTrainTransformsSTL10())
- Parameters:
STL-10 Eval (c)¶
- class pl_bolts.transforms.self_supervised.cpc_transforms.CPCEvalTransformsSTL10(patch_size=16, overlap=8)[source]
Bases:
object
Warning
The feature CPCEvalTransformsSTL10 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
Transforms used for CPC:
Transforms:
random_flip transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2)
Example:
# in a regular dataset STL10(..., transforms=CPCEvalTransformsSTL10()) # in a DataModule module = STL10DataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCEvalTransformsSTL10())
- Parameters:
AMDIM transforms¶
Transforms used for AMDIM
CIFAR-10 Train (a)¶
- class pl_bolts.transforms.self_supervised.amdim_transforms.AMDIMTrainTransformsCIFAR10[source]
Bases:
object
Warning
The feature AMDIMTrainTransformsCIFAR10 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
Transforms applied to AMDIM.
Transforms:
img_jitter, col_jitter, rnd_gray, transforms.ToTensor(), normalize
Example:
x = torch.rand(5, 3, 32, 32) transform = AMDIMTrainTransformsCIFAR10() (view1, view2) = transform(x)
CIFAR-10 Eval (a)¶
- class pl_bolts.transforms.self_supervised.amdim_transforms.AMDIMEvalTransformsCIFAR10[source]
Bases:
object
Warning
The feature AMDIMEvalTransformsCIFAR10 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
Transforms applied to AMDIM.
Transforms:
transforms.ToTensor(), normalize
Example:
x = torch.rand(5, 3, 32, 32) transform = AMDIMEvalTransformsCIFAR10() (view1, view2) = transform(x)
Imagenet Train (a)¶
- class pl_bolts.transforms.self_supervised.amdim_transforms.AMDIMTrainTransformsImageNet128(height=128)[source]
Bases:
object
Warning
The feature AMDIMTrainTransformsImageNet128 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
Transforms applied to AMDIM.
Transforms:
img_jitter, col_jitter, rnd_gray, transforms.ToTensor(), normalize
Example:
x = torch.rand(5, 3, 128, 128) transform = AMDIMTrainTransformsSTL10() (view1, view2) = transform(x)
Imagenet Eval (a)¶
- class pl_bolts.transforms.self_supervised.amdim_transforms.AMDIMEvalTransformsImageNet128(height=128)[source]
Bases:
object
Warning
The feature AMDIMEvalTransformsImageNet128 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
Transforms applied to AMDIM.
Transforms:
transforms.Resize(height + 6, interpolation=InterpolationMode.BICUBIC), transforms.CenterCrop(height), transforms.ToTensor(), normalize
Example:
x = torch.rand(5, 3, 128, 128) transform = AMDIMEvalTransformsImageNet128() view1 = transform(x)
STL-10 Train (a)¶
- class pl_bolts.transforms.self_supervised.amdim_transforms.AMDIMTrainTransformsSTL10(height=64)[source]
Bases:
object
Warning
The feature AMDIMTrainTransformsSTL10 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
Transforms applied to AMDIM.
Transforms:
img_jitter, col_jitter, rnd_gray, transforms.ToTensor(), normalize
Example:
x = torch.rand(5, 3, 64, 64) transform = AMDIMTrainTransformsSTL10() (view1, view2) = transform(x)
STL-10 Eval (a)¶
- class pl_bolts.transforms.self_supervised.amdim_transforms.AMDIMEvalTransformsSTL10(height=64)[source]
Bases:
object
Warning
The feature AMDIMEvalTransformsSTL10 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
Transforms applied to AMDIM.
Transforms:
transforms.Resize(height + 6, interpolation=InterpolationMode.BICUBIC), transforms.CenterCrop(height), transforms.ToTensor(), normalize
Example:
x = torch.rand(5, 3, 64, 64) transform = AMDIMTrainTransformsSTL10() view1 = transform(x)
MOCO V2 transforms¶
Transforms used for MOCO V2
CIFAR-10 Train (m2)¶
- class pl_bolts.transforms.self_supervised.moco_transforms.MoCo2TrainCIFAR10Transforms(size=32)[source]
Bases:
object
Warning
The feature MoCo2TrainCIFAR10Transforms 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
MoCo v2 transforms.
Transform:
RandomResizedCrop(size=self.input_size)
Example:
from pl_bolts.transforms.self_supervised.MoCo_transforms import MoCo2TrainCIFAR10Transforms transform = MoCo2TrainCIFAR10Transforms(input_size=32) x = sample() (xi, xj) = transform(x)
MoCo 2 augmentation:
CIFAR-10 Eval (m2)¶
- class pl_bolts.transforms.self_supervised.moco_transforms.MoCo2EvalCIFAR10Transforms(size=32)[source]
Bases:
object
Warning
The feature MoCo2EvalCIFAR10Transforms 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
MoCo 2 augmentation:
Imagenet Train (m2)¶
- class pl_bolts.transforms.self_supervised.moco_transforms.MoCo2TrainSTL10Transforms(size=64)[source]
Bases:
object
Warning
The feature MoCo2TrainSTL10Transforms 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
MoCo 2 augmentation:
Imagenet Eval (m2)¶
- class pl_bolts.transforms.self_supervised.moco_transforms.MoCo2EvalSTL10Transforms(size=64)[source]
Bases:
object
Warning
The feature MoCo2EvalSTL10Transforms 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
MoCo 2 augmentation:
STL-10 Train (m2)¶
- class pl_bolts.transforms.self_supervised.moco_transforms.MoCo2TrainImagenetTransforms(size=224)[source]
Bases:
object
Warning
The feature MoCo2TrainImagenetTransforms 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
MoCo 2 augmentation:
STL-10 Eval (m2)¶
- class pl_bolts.transforms.self_supervised.moco_transforms.MoCo2EvalImagenetTransforms(size=128)[source]
Bases:
object
Warning
The feature MoCo2EvalImagenetTransforms 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
Transforms for MoCo during training step.
SimCLR transforms¶
Transforms used for SimCLR
Train (sc)¶
- class pl_bolts.transforms.self_supervised.simclr_transforms.SimCLRTrainDataTransform(input_height=224, gaussian_blur=True, jitter_strength=1.0, normalize=None)[source]
Bases:
object
Transforms for SimCLR during training step of the pre-training stage.
- Parameters:
input_height¶ (int, optional) – expected output size of image. Defaults to 224.
gaussian_blur¶ (bool, optional) – applies Gaussian blur if True. Defaults to True.
jitter_strength¶ (float, optional) – color jitter multiplier. Defaults to 1.0.
normalize¶ (Callable, optional) – optional transform to normalize. Defaults to None.
Transform:
RandomResizedCrop(size=self.input_height) RandomHorizontalFlip() RandomApply([color_jitter], p=0.8) RandomGrayscale(p=0.2) RandomApply([GaussianBlur(kernel_size=int(0.1 * self.input_height))], p=0.5) transforms.ToTensor()
Example:
from pl_bolts.transforms.self_supervised.simclr_transforms import SimCLRTrainDataTransform transform = SimCLRTrainDataTransform(input_height=32) x = sample() (xi, xj, xk) = transform(x) # xk is only for the online evaluator if used
Eval (sc)¶
- class pl_bolts.transforms.self_supervised.simclr_transforms.SimCLREvalDataTransform(input_height=224, gaussian_blur=True, jitter_strength=1.0, normalize=None)[source]
Bases:
SimCLRTrainDataTransform
Transforms for SimCLR during the validation step of the pre-training stage.
- Parameters:
input_height¶ (int, optional) – expected output size of image. Defaults to 224.
gaussian_blur¶ (bool, optional) – applies Gaussian blur if True. Defaults to True.
jitter_strength¶ (float, optional) – color jitter multiplier. Defaults to 1.0.
normalize¶ (Callable, optional) – optional transform to normalize. Defaults to None.
Transform:
Resize(input_height + 10, interpolation=3) transforms.CenterCrop(input_height), transforms.ToTensor()
Example:
from pl_bolts.transforms.self_supervised.simclr_transforms import SimCLREvalDataTransform transform = SimCLREvalDataTransform(input_height=32) x = sample() (xi, xj, xk) = transform(x) # xk is only for the online evaluator if used
Identity class¶
Example:
from pl_bolts.utils import Identity
- class pl_bolts.utils.self_supervised.Identity[source]
Bases:
Module
Warning
The feature Identity 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
An identity class to replace arbitrary layers in pretrained models.
Example:
from pl_bolts.utils import Identity model = resnet18() model.fc = Identity()
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.- Return type:
SSL-ready resnets¶
Torchvision resnets with the fc layers removed and with the ability to return all feature maps instead of just the last one.
Example:
from pl_bolts.utils.self_supervised import torchvision_ssl_encoder
resnet = torchvision_ssl_encoder('resnet18', pretrained=False, return_all_feature_maps=True)
x = torch.rand(3, 3, 32, 32)
feat_maps = resnet(x)
- pl_bolts.utils.self_supervised.torchvision_ssl_encoder(name, pretrained=False, return_all_feature_maps=False)[source]
Warning
The feature torchvision_ssl_encoder 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
- Return type:
SSL backbone finetuner¶
- class pl_bolts.models.self_supervised.ssl_finetuner.SSLFineTuner(backbone, in_features=2048, num_classes=1000, epochs=100, hidden_dim=None, dropout=0.0, learning_rate=0.1, weight_decay=1e-06, nesterov=False, scheduler_type='cosine', decay_epochs=(60, 80), gamma=0.1, final_lr=0.0)[source]
Bases:
LightningModule
Finetunes a self-supervised learning backbone using the standard evaluation protocol of a singler layer MLP with 1024 units.
Example:
from pl_bolts.utils.self_supervised import SSLFineTuner from pl_bolts.models.self_supervised import CPC_v2 from pl_bolts.datamodules import CIFAR10DataModule from pl_bolts.models.self_supervised.cpc.transforms import CPCEvalTransformsCIFAR10, CPCTrainTransformsCIFAR10 # pretrained model backbone = CPC_v2.load_from_checkpoint(PATH, strict=False) # dataset + transforms dm = CIFAR10DataModule(data_dir='.') dm.train_transforms = CPCTrainTransformsCIFAR10() dm.val_transforms = CPCEvalTransformsCIFAR10() # finetuner finetuner = SSLFineTuner(backbone, in_features=backbone.z_dim, num_classes=backbone.num_classes) # train trainer = pl.Trainer() trainer.fit(finetuner, dm) # test trainer.test(datamodule=dm)
- Parameters:
- 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.
- on_train_epoch_start()[source]
Called in the training loop at the very beginning of the epoch.
- Return type:
- test_step(batch, batch_idx)[source]
Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.
# the pseudocode for these calls test_outs = [] for test_batch in test_data: out = test_step(test_batch) test_outs.append(out) test_epoch_end(test_outs)
- Parameters:
batch¶ – The output of your
DataLoader
.batch_idx¶ – The index of this batch.
dataloader_id¶ – The index of the dataloader that produced this batch. (only if multiple test dataloaders used).
- Returns:
Any of.
Any object or value
None
- Testing will skip to the next batch
# if you have one test dataloader: def test_step(self, batch, batch_idx): ... # if you have multiple test dataloaders: def test_step(self, batch, batch_idx, dataloader_idx=0): ...
Examples:
# CASE 1: A single test dataset def test_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) test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # log the outputs! self.log_dict({'test_loss': loss, 'test_acc': test_acc})
If you pass in multiple test dataloaders,
test_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 test dataloaders def test_step(self, batch, batch_idx, dataloader_idx=0): # dataloader_idx tells you which dataset this is. ...
Note
If you don’t need to test you don’t need to implement this method.
Note
When the
test_step()
is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.
- 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.