lightly.utils

.io

I/O operations to save and load embeddings.

class lightly.utils.io.COCO_ANNOTATION_KEYS

Enum of coco annotation keys complemented with a key for custom metadata.

lightly.utils.io.check_embeddings(path: str, remove_additional_columns: bool = False)

Raises an error if the embeddings csv file has not the correct format

Use this check whenever you want to upload an embedding to the Lightly Platform. This method only checks whether the header row matches the specs: https://docs.lightly.ai/self-supervised-learning/getting_started/command_line_tool.html#id1

Parameters

  • path – Path to the embedding csv file

  • remove_additional_columns – If True, all additional columns which are not in {filenames, embeddings_x, labels} are removed. If false, they are kept unchanged.

Raises

RuntimeError –

lightly.utils.io.check_filenames(filenames: List[str])

Raises an error if one of the filenames is misformatted

Parameters

filenames – A list of string being filenames

lightly.utils.io.format_custom_metadata(custom_metadata: List[Tuple[str, Dict]])

Transforms custom metadata into a format which can be handled by Lightly.

Parameters

custom_metadata – List of tuples (filename, metadata) where metadata is a dictionary.

Returns

A dictionary of formatted custom metadata.

Examples

>>> custom_metadata = [
>>>     ('hello.png', {'number_of_people': 1}),
>>>     ('world.png', {'number_of_people': 3}),
>>> ]
>>>
>>> format_custom_metadata(custom_metadata)
>>> > {
>>> >   'images': [{'id': 0, 'file_name': 'hello.png'}, {'id': 1, 'file_name': 'world.png'}],
>>> >   'metadata': [{'image_id': 0, 'number_of_people': 1}, {'image_id': 1, 'number_of_people': 3}]
>>> > }
lightly.utils.io.load_embeddings(path: str)

Loads embeddings from a csv file in a Lightly compatible format.

Parameters

path – Path to the csv file.

Returns

The embeddings as a numpy array, labels as a list of integers, and filenames as a list of strings in the order they were saved.

The embeddings will always be of the Float32 datatype.

Examples

>>> import lightly.utils.io as io
>>> embeddings, labels, filenames = io.load_embeddings(
>>>     'path/to/my/embeddings.csv')
lightly.utils.io.load_embeddings_as_dict(path: str, embedding_name: str = 'default', return_all: bool = False)

Loads embeddings from csv and store it in a dictionary for transfer.

Loads embeddings to a dictionary which can be serialized and sent to the Lightly servers. It is recommended that the embedding_name is always specified because the Lightly web-app does not allow two embeddings with the same name.

Parameters

  • path – Path to the csv file.

  • embedding_name – Name of the embedding for the platform.

  • return_all – If true, return embeddings, labels, and filenames, too.

Returns

A dictionary containing the embedding information (see load_embeddings)

Examples

>>> import lightly.utils.io as io
>>> embedding_dict = io.load_embeddings_as_dict(
>>>     'path/to/my/embeddings.csv',
>>>     embedding_name='MyEmbeddings')
>>>
>>> result = io.load_embeddings_as_dict(
>>>     'path/to/my/embeddings.csv',
>>>     embedding_name='MyEmbeddings',
>>>     return_all=True)
>>> embedding_dict, embeddings, labels, filenames = result
lightly.utils.io.save_custom_metadata(path: str, custom_metadata: List[Tuple[str, Dict]])

Saves custom metadata in a .json.

Parameters

  • path – Filename of the .json file where the data should be stored.

  • custom_metadata – List of tuples (filename, metadata) where metadata is a dictionary.

lightly.utils.io.save_embeddings(path: str, embeddings: numpy.ndarray, labels: List[int], filenames: List[str])

Saves embeddings in a csv file in a Lightly compatible format.

Creates a csv file at the location specified by path and saves embeddings, labels, and filenames.

Parameters

  • path – Path to the csv file.

  • embeddings – Embeddings of the images as a numpy array (n x d).

  • labels – List of integer labels.

  • filenames – List of filenames.

Raises

ValueError – If embeddings, labels, and filenames have different lengths.

Examples

>>> import lightly.utils.io as io
>>> io.save_embeddings(
>>>     'path/to/my/embeddings.csv',
>>>     embeddings,
>>>     labels,
>>>     filenames)
lightly.utils.io.save_schema(path: str, task_type: str, ids: List[int], names: List[str])

Saves a prediction schema in the right format.

Parameters

  • path – Where to store the schema.

  • task_type – Task type (e.g. classification, object-detection).

  • ids – List of category ids.

  • names – List of category names.

lightly.utils.io.save_tasks(path: str, tasks: List[str])

Saves a list of prediction task names in the right format.

Parameters

  • path – Where to store the task names.

  • tasks – List of task names.

.embeddings_2d

Transform embeddings to two-dimensional space for visualization.

class lightly.utils.embeddings_2d.PCA(n_components: int = 2, eps: float = 1e-10)

Handmade PCA to bypass sklearn dependency.

n_components

Number of principal components to keep.

eps

Epsilon for numerical stability.

fit(X: numpy.ndarray)

Fits PCA to data in X.

Parameters

X – Datapoints stored in numpy array of size n x d.

Returns

PCA object to transform datapoints.

transform(X: numpy.ndarray)

Uses PCA to transform data in X.

Parameters

X – Datapoints stored in numpy array of size n x d.

Returns

Numpy array of n x p datapoints where p <= d.

lightly.utils.embeddings_2d.fit_pca(embeddings: numpy.ndarray, n_components: int = 2, fraction: Optional[float] = None)

Fits PCA to randomly selected subset of embeddings.

For large datasets, it can be unfeasible to perform PCA on the whole data. This method can fit a PCA on a fraction of the embeddings in order to save computational resources.

Parameters

  • embeddings – Datapoints stored in numpy array of size n x d.

  • n_components – Number of principal components to keep.

  • fraction – Fraction of the dataset to fit PCA on.

Returns

A transformer which can be used to transform embeddings to lower dimensions.

Raises

ValueError – If fraction < 0 or fraction > 1.

.benchmarking

Helper modules for benchmarking SSL models

class lightly.utils.benchmarking.BenchmarkModule(dataloader_kNN: torch.utils.data.dataloader.DataLoader, num_classes: int, knn_k: int = 200, knn_t: float = 0.1)

A PyTorch Lightning Module for automated kNN callback

At the end of every training epoch we create a feature bank by feeding the dataloader_kNN passed to the module through the backbone. At every validation step we predict features on the validation data. After all predictions on validation data (validation_epoch_end) we evaluate the predictions on a kNN classifier on the validation data using the feature_bank features from the train data.

We can access the highest test accuracy during a kNN prediction using the max_accuracy attribute.

backbone

The backbone model used for kNN validation. Make sure that you set the backbone when inheriting from BenchmarkModule.

max_accuracy

Floating point number between 0.0 and 1.0 representing the maximum test accuracy the benchmarked model has achieved.

dataloader_kNN

Dataloader to be used after each training epoch to create feature bank.

num_classes

Number of classes. E.g. for cifar10 we have 10 classes. (default: 10)

knn_k

Number of nearest neighbors for kNN

knn_t

Temperature parameter for kNN

Examples

>>> class SimSiamModel(BenchmarkingModule):
>>>     def __init__(dataloader_kNN, num_classes):
>>>         super().__init__(dataloader_kNN, num_classes)
>>>         resnet = lightly.models.ResNetGenerator('resnet-18')
>>>         self.backbone = nn.Sequential(
>>>             *list(resnet.children())[:-1],
>>>             nn.AdaptiveAvgPool2d(1),
>>>         )
>>>         self.resnet_simsiam =
>>>             lightly.models.SimSiam(self.backbone, num_ftrs=512)
>>>         self.criterion = lightly.loss.SymNegCosineSimilarityLoss()
>>>
>>>     def forward(self, x):
>>>         self.resnet_simsiam(x)
>>>
>>>     def training_step(self, batch, batch_idx):
>>>         (x0, x1), _, _ = batch
>>>         x0, x1 = self.resnet_simsiam(x0, x1)
>>>         loss = self.criterion(x0, x1)
>>>         return loss
>>>     def configure_optimizers(self):
>>>         optim = torch.optim.SGD(
>>>             self.resnet_simsiam.parameters(), lr=6e-2, momentum=0.9
>>>         )
>>>         return [optim]
>>>
>>> model = SimSiamModel(dataloader_train_kNN)
>>> trainer = pl.Trainer()
>>> trainer.fit(
>>>     model,
>>>     train_dataloader=dataloader_train_ssl,
>>>     val_dataloaders=dataloader_test
>>> )
>>> # you can get the peak accuracy using
>>> print(model.max_accuracy)
training_epoch_end(outputs)

Called at the end of the training epoch with the outputs of all training steps. Use this in case you need to do something with all the outputs returned by training_step().

# the pseudocode for these calls
train_outs = []
for train_batch in train_data:
    out = training_step(train_batch)
    train_outs.append(out)
training_epoch_end(train_outs)
Parameters

outputs – List of outputs you defined in training_step(). If there are multiple optimizers or when using truncated_bptt_steps > 0, the lists have the dimensions (n_batches, tbptt_steps, n_optimizers). Dimensions of length 1 are squeezed.

Returns

None

Note

If this method is not overridden, this won’t be called.

def training_epoch_end(self, training_step_outputs):
    # do something with all training_step outputs
    for out in training_step_outputs:
        ...
validation_epoch_end(outputs)

Called at the end of the validation epoch with the outputs of all validation steps.

# 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

outputs – List of outputs you defined in validation_step(), or if there are multiple dataloaders, a list containing a list of outputs for each dataloader.

Returns

None

Note

If you didn’t define a validation_step(), this won’t be called.

Examples

With a single dataloader:

def validation_epoch_end(self, val_step_outputs):
    for out in val_step_outputs:
        ...

With multiple dataloaders, outputs will be a list of lists. The outer list contains one entry per dataloader, while the inner list contains the individual outputs of each validation step for that dataloader.

def validation_epoch_end(self, outputs):
    for dataloader_output_result in outputs:
        dataloader_outs = dataloader_output_result.dataloader_i_outputs

    self.log("final_metric", final_value)
validation_step(batch, batch_idx)

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.

lightly.utils.benchmarking.knn_predict(feature: torch.Tensor, feature_bank: torch.Tensor, feature_labels: torch.Tensor, num_classes: int, knn_k: int = 200, knn_t: float = 0.1) → torch.Tensor

Run kNN predictions on features based on a feature bank

This method is commonly used to monitor performance of self-supervised learning methods.

The default parameters are the ones used in https://arxiv.org/pdf/1805.01978v1.pdf.

Parameters

  • feature – Tensor of shape [N, D] for which you want predictions

  • feature_bank – Tensor of a database of features used for kNN

  • feature_labels – Labels for the features in our feature_bank

  • num_classes – Number of classes (e.g. 10 for CIFAR-10)

  • knn_k – Number of k neighbors used for kNN

  • knn_t – Temperature parameter to reweights similarities for kNN

Returns

A tensor containing the kNN predictions

Examples

>>> images, targets, _ = batch
>>> feature = backbone(images).squeeze()
>>> # we recommend to normalize the features
>>> feature = F.normalize(feature, dim=1)
>>> pred_labels = knn_predict(
>>>     feature,
>>>     feature_bank,
>>>     targets_bank,
>>>     num_classes=10,
>>> )

.debug

lightly.utils.debug.apply_transform_without_normalize(image: PIL.Image.Image, transform)

Applies the transform to the image but skips ToTensor and Normalize.

lightly.utils.debug.generate_grid_of_augmented_images(input_images: List[PIL.Image.Image], collate_function: Union[lightly.data.collate.BaseCollateFunction, lightly.data.collate.MultiViewCollateFunction])

Returns a grid of augmented images. Images in a column belong together.

This function ignores the transforms ToTensor and Normalize for visualization purposes.

Parameters

  • input_images – List of PIL images for which the augmentations should be plotted.

  • collate_function – The collate function of the self-supervised learning algorithm. Must be of type BaseCollateFunction or MultiViewCollateFunction.

Returns

A grid of augmented images. Images in a column belong together.

lightly.utils.debug.plot_augmented_images(input_images: List[PIL.Image.Image], collate_function: Union[lightly.data.collate.BaseCollateFunction, lightly.data.collate.MultiViewCollateFunction])

Returns a figure showing original images in the left column and augmented images to their right.

This function ignores the transforms ToTensor and Normalize for visualization purposes.

Parameters

  • input_images – List of PIL images for which the augmentations should be plotted.

  • collate_function – The collate function of the self-supervised learning algorithm. Must be of type BaseCollateFunction or MultiViewCollateFunction.

Returns

A figure showing the original images in the left column and the augmented images to their right. If the collate_function is an instance of the BaseCollateFunction, two example augmentations are shown. For MultiViewCollateFunctions all the generated views are shown.

lightly.utils.debug.std_of_l2_normalized(z: torch.Tensor) → torch.Tensor

Calculates the mean of the standard deviation of z along each dimension.

This measure was used by [0] to determine the level of collapse of the learned representations. If the returned number is 0., the outputs z have collapsed to a constant vector. “If the output z has a zero-mean isotropic Gaussian distribution” [0], the returned number should be close to 1/sqrt(d) where d is the dimensionality of the output.

[0]: https://arxiv.org/abs/2011.10566

Parameters

z – A torch tensor of shape batch_size x dimension.

Returns

The mean of the standard deviation of the l2 normalized tensor z along each dimension.

.dist

class lightly.utils.dist.GatherLayer(*args, **kwargs)

Gather tensors from all processes, supporting backward propagation.

This code was taken and adapted from here: https://github.com/Spijkervet/SimCLR

static backward(ctx, *grads: torch.Tensor) → torch.Tensor

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

It must accept a context ctx as the first argument, followed by as many outputs as the forward() returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs to forward(). Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.

The context can be used to retrieve tensors saved during the forward pass. It also has an attribute ctx.needs_input_grad as a tuple of booleans representing whether each input needs gradient. E.g., backward() will have ctx.needs_input_grad[0] = True if the first input to forward() needs gradient computated w.r.t. the output.

static forward(ctx, input: torch.Tensor) → Tuple[torch.Tensor, ...]

Performs the operation.

This function is to be overridden by all subclasses.

It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).

The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with ctx.save_for_backward() if they are intended to be used in backward (equivalently, vjp) or ctx.save_for_forward() if they are intended to be used for in jvp.

lightly.utils.dist.eye_rank(n: int, device: Optional[torch.device] = None) → torch.Tensor

Returns an (n, n * world_size) zero matrix with the diagonal for the rank of this process set to 1.

Example output where n=3, the current process has rank 1, and there are 4 processes in total:

rank0 rank1 rank2 rank3 0 0 0 | 1 0 0 | 0 0 0 | 0 0 0 0 0 0 | 0 1 0 | 0 0 0 | 0 0 0 0 0 0 | 0 0 1 | 0 0 0 | 0 0 0

Equivalent to torch.eye for undistributed settings or if world size == 1.

Parameters

  • n – Size of the square matrix on a single process.

  • device – Device on which the matrix should be created.

lightly.utils.dist.gather(input: torch.Tensor) → Tuple[torch.Tensor]

Gathers this tensor from all processes. Supports backprop.

lightly.utils.dist.rank() → int

Returns the rank of the current process.

lightly.utils.dist.world_size() → int

Returns the current world size (number of distributed processes).

.reordering

lightly.utils.reordering.sort_items_by_keys(keys: List[any], items: List[any], sorted_keys: List[any])

Sorts the items in the same order as the sorted keys.

Parameters

  • keys – Keys by which items can be identified.

  • items – Items to sort.

  • sorted_keys – Keys in sorted order.

Returns

The list of sorted items.

Examples

>>> keys = [3, 2, 1]
>>> items = ['!', 'world', 'hello']
>>> sorted_keys = [1, 2, 3]
>>> sorted_items = sort_items_by_keys(
>>>     keys,
>>>     items,
>>>     sorted_keys,
>>> )
>>> print(sorted_items)
>>> > ['hello', 'world', '!']

.version_compare

Utility method for comparing versions of libraries

lightly.utils.version_compare.version_compare(v0: str, v1: str)

Returns 1 if version of v0 is larger than v1 and -1 otherwise

Use this method to compare Python package versions and see which one is newer.

Examples

>>> # compare two versions
>>> version_compare('1.2.0', '1.1.2')
>>> 1