PyTorch-Lightning-Bolts documentation¶

Introduction Guide¶

Welcome to PyTorch Lightning Bolts!

Bolts is a Deep learning research and production toolbox of:

SOTA pretrained models.
Model components.
Callbacks.
Losses.
Datasets.

The Main goal of bolts is to enable trying new ideas as fast as possible!

All models are tested (daily), benchmarked, documented and work on CPUs, TPUs, GPUs and 16-bit precision.

some examples!

from pl_bolts.models import VAE, GPT2, ImageGPT, PixelCNN
from pl_bolts.models.self_supervised import AMDIM, CPCV2, SimCLR, MocoV2
from pl_bolts.models import LinearRegression, LogisticRegression
from pl_bolts.models.gans import GAN
from pl_bolts.callbacks import PrintTableMetricsCallback
from pl_bolts.datamodules import FashionMNISTDataModule, CIFAR10DataModule, ImagenetDataModule

Bolts are built for rapid idea iteration - subclass, override and train!

from pl_bolts.models import ImageGPT
from pl_bolts.self_supervised import SimCLR

class VideoGPT(ImageGPT):

    def training_step(self, batch, batch_idx):
        x, y = batch
        x = _shape_input(x)

        logits = self.gpt(x)
        simclr_features = self.simclr(x)

        # -----------------
        # do something new with GPT logits + simclr_features
        # -----------------

        loss = self.criterion(logits.view(-1, logits.size(-1)), x.view(-1).long())

        logs = {"loss": loss}
        return {"loss": loss, "log": logs}

Mix and match data, modules and components as you please!

model = GAN(datamodule=ImagenetDataModule(PATH))
model = GAN(datamodule=FashionMNISTDataModule(PATH))
model = ImageGPT(datamodule=FashionMNISTDataModule(PATH))

And train on any hardware accelerator

import pytorch_lightning as pl

model = ImageGPT(datamodule=FashionMNISTDataModule(PATH))

# cpus
pl.Trainer.fit(model)

# gpus
pl.Trainer(gpus=8).fit(model)

# tpus
pl.Trainer(tpu_cores=8).fit(model)

Or pass in any dataset of your choice

model = ImageGPT()
Trainer().fit(
    model,
    train_dataloader=DataLoader(...),
    val_dataloader=DataLoader(...)
)

Community Built¶

Bolts are built-by the Lightning community and contributed to bolts. The lightning team guarantees that contributions are:

Rigorously Tested (CPUs, GPUs, TPUs).
Rigorously Documented.
Standardized via PyTorch Lightning.
Optimized for speed.
Checked for correctness.

How to contribute¶

We accept contributions directly to Bolts or via your own repository.

Note

We encourage you to have your own repository so we can link to it via our docs!

To contribute:

Submit a pull request to Bolts (we will help you finish it!).
We’ll help you add tests.
We’ll help you refactor models to work on (GPU, TPU, CPU)..
We’ll help you remove bottlenecks in your model.
We’ll help you write up documentation.
We’ll help you pretrain expensive models and host weights for you.
We’ll create proper attribution for you and link to your repo.
Once all of this is ready, we will merge into bolts.

After your model or other contribution is in bolts, our team will make sure it maintains compatibility with the other components of the library!

Contribution ideas¶

Don’t have something to contribute? Ping us on Slack or look at our Github issues!

We’ll help and guide you through the implementation / conversion

When to use Bolts¶

For pretrained models¶

Most bolts have pretrained weights trained on various datasets or algorithms. This is useful when you don’t have enough data, time or money to do your own training.

For example, you could use a pretrained VAE to generate features for an image dataset.

from pl_bolts.models.autoencoders import VAE
from pl_bolts.models.self_supervised import CPCV2

model1 = VAE(pretrained='imagenet2012')
encoder = model1.encoder
encoder.freeze()

# bolts are pretrained on different datasets
model2 = CPCV2(encoder='resnet18', pretrained='imagenet128').freeze()
model3 = CPCV2(encoder='resnet18', pretrained='stl10').freeze()

for (x, y) in own_data
    features = encoder(x)
    feat2 = model2(x)
    feat3 = model3(x)

# which is better?

To finetune on your data¶

If you have your own data, finetuning can often increase the performance. Since this is pure PyTorch you can use any finetuning protocol you prefer.

Example 1: Unfrozen finetune

# unfrozen finetune
model = CPCV2(encoder='resnet18', pretrained='imagenet128')
resnet18 = model.encoder
# don't call .freeze()

classifier = LogisticRegression()

for (x, y) in own_data:
    feats = resnet18(x)
    y_hat = classifier(feats)

Example 2: Freeze then unfreeze

# FREEZE!
model = CPCV2(encoder='resnet18', pretrained='imagenet128')
resnet18 = model.encoder
resnet18.freeze()

classifier = LogisticRegression()

for epoch in epochs:
    for (x, y) in own_data:
        feats = resnet18(x)
        y_hat = classifier(feats)
        loss = cross_entropy_with_logits(y_hat, y)

    # UNFREEZE after 10 epochs
    if epoch == 10:
        resnet18.unfreeze()

For research¶

Here is where bolts is very different than other libraries with models. It’s not just designed for production, but each module is written to be easily extended for research.

from pl_bolts.models import ImageGPT
from pl_bolts.self_supervised import SimCLR

class VideoGPT(ImageGPT):

    def training_step(self, batch, batch_idx):
        x, y = batch
        x = _shape_input(x)

        logits = self.gpt(x)
        simclr_features = self.simclr(x)

        # -----------------
        # do something new with GPT logits + simclr_features
        # -----------------

        loss = self.criterion(logits.view(-1, logits.size(-1)), x.view(-1).long())

        logs = {"loss": loss}
        return {"loss": loss, "log": logs}

Or perhaps your research is in self_supervised_learning and you want to do a new SimCLR. In this case, the only thing you want to change is the loss.

By subclassing you can focus on changing a single piece of a system without worrying that the other parts work (because if they are in Bolts, then they do and we’ve tested it).

# subclass SimCLR and change ONLY what you want to try
class ComplexCLR(SimCLR):

    def init_loss(self):
        return self.new_xent_loss

    def new_xent_loss(self):
        out = torch.cat([out_1, out_2], dim=0) n_samples = len(out)

        # Full similarity matrix
        cov = torch.mm(out, out.t().contiguous())
        sim = torch.exp(cov / temperature)

        # Negative similarity
        mask = ~torch.eye(n_samples, device=sim.device).bool()
        neg = sim.masked_select(mask).view(n_samples, -1).sum(dim=-1)

        # ------------------
        # some new thing we want to do
        # ------------------

        # Positive similarity :
        pos = torch.exp(torch.sum(out_1 * out_2, dim=-1) / temperature)
        pos = torch.cat([pos, pos], dim=0)
        loss = -torch.log(pos / neg).mean()

        return loss

Callbacks¶

Callbacks are arbitrary programs which can run at any points in time within a training loop in Lightning.

Bolts houses a collection of callbacks that are community contributed and can work in any Lightning Module!

from pl_bolts.callbacks import PrintTableMetricsCallback
import pytorch_lightning as pl

trainer = pl.Trainer(callbacks=[PrintTableMetricsCallback()])

DataModules¶

In PyTorch, working with data has these major elements.

Downloading, saving and preparing the dataset.

Splitting into train, val and test.

For each split, applying different transforms

A DataModule groups together those actions into a single reproducible DataModule that can be shared around to guarantee:

Consistent data preprocessing (download, splits, etc…)

The same exact splits

The same exact transforms

from pl_bolts.datamodules import ImagenetDataModule

dm = ImagenetDataModule(data_dir=PATH)

# standard PyTorch!
train_loader = dm.train_dataloader()
val_loader = dm.val_dataloader()
test_loader = dm.test_dataloader()

Trainer().fit(
    model,
    train_loader,
    val_loader
)

But when paired with PyTorch LightningModules (all bolts models), you can plug and play full dataset definitions with the same splits, transforms, etc…

imagenet = ImagenetDataModule(PATH)
model = VAE(datamodule=imagenet)
model = ImageGPT(datamodule=imagenet)
model = GAN(datamodule=imagenet)

We even have prebuilt modules to bridge the gap between Numpy, Sklearn and PyTorch

from sklearn.datasets import load_boston
from pl_bolts.datamodules import SklearnDataModule

X, y = load_boston(return_X_y=True)
datamodule = SklearnDataModule(X, y)

model = LitModel(datamodule)

Regression Heroes¶

In case your job or research doesn’t need a “hammer”, we offer implementations of Classic ML models which benefit from lightning’s multi-GPU and TPU support.

So, now you can run huge workloads scalably, without needing to do any engineering. For instance, here we can run Logistic Regression on Imagenet (each epoch takes about 3 minutes)!

from pl_bolts.models.regression import LogisticRegression

imagenet = ImagenetDataModule(PATH)

# 224 x 224 x 3
pixels_per_image = 150528
model = LogisticRegression(input_dim=pixels_per_image, num_classes=1000)
model.prepare_data = imagenet.prepare_data

trainer = Trainer(gpus=2)
trainer.fit(
    model,
    imagenet.train_dataloader(batch_size=256),
    imagenet.val_dataloader(batch_size=256)
)

Linear Regression¶

Here’s an example for Linear regression

import pytorch_lightning as pl
from pl_bolts.datamodules import SklearnDataModule
from sklearn.datasets import load_boston

# link the numpy dataset to PyTorch
X, y = load_boston(return_X_y=True)
loaders = SklearnDataModule(X, y)

# training runs training batches while validating against a validation set
model = LinearRegression()
trainer = pl.Trainer(num_gpus=8)
trainer.fit(model, loaders.train_dataloader(), loaders.val_dataloader())

Once you’re done, you can run the test set if needed.

trainer.test(test_dataloaders=loaders.test_dataloader())

But more importantly, you can scale up to many GPUs, TPUs or even CPUs

# 8 GPUs
trainer = pl.Trainer(num_gpus=8)

# 8 TPU cores
trainer = pl.Trainer(tpu_cores=8)

# 32 GPUs
trainer = pl.Trainer(num_gpus=8, num_nodes=4)

# 128 CPUs
trainer = pl.Trainer(num_processes=128)

Logistic Regression¶

Here’s an example for Logistic regression

from sklearn.datasets import load_iris
from pl_bolts.models.regression import LogisticRegression
from pl_bolts.datamodules import SklearnDataModule
import pytorch_lightning as pl

# use any numpy or sklearn dataset
X, y = load_iris(return_X_y=True)
dm = SklearnDataModule(X, y)

# build model
model = LogisticRegression(input_dim=4, num_classes=3)

# fit
trainer = pl.Trainer(tpu_cores=8, precision=16)
trainer.fit(model, dm.train_dataloader(), dm.val_dataloader())

trainer.test(test_dataloaders=dm.test_dataloader(batch_size=12))

Any input will be flattened across all dimensions except the firs one (batch). This means images, sound, etc… work out of the box.

# create dataset
dm = MNISTDataModule(num_workers=0, data_dir=tmpdir)

model = LogisticRegression(input_dim=28 * 28, num_classes=10, learning_rate=0.001)
model.prepare_data = dm.prepare_data
model.train_dataloader = dm.train_dataloader
model.val_dataloader = dm.val_dataloader
model.test_dataloader = dm.test_dataloader

trainer = pl.Trainer(max_epochs=2)
trainer.fit(model)
trainer.test(model)
# {test_acc: 0.92}

But more importantly, you can scale up to many GPUs, TPUs or even CPUs

# 8 GPUs
trainer = pl.Trainer(num_gpus=8)

# 8 TPUs
trainer = pl.Trainer(tpu_cores=8)

# 32 GPUs
trainer = pl.Trainer(num_gpus=8, num_nodes=4)

# 128 CPUs
trainer = pl.Trainer(num_processes=128)

Regular PyTorch¶

Everything in bolts also works with regular PyTorch since they are all just nn.Modules!

However, if you train using Lightning you don’t have to deal with engineering code :)

Command line support¶

Any bolt module can also be trained from the command line

cd pl_bolts/models/autoencoders/basic_vae
python basic_vae_pl_module.py

Each script accepts Argparse arguments for both the lightning trainer and the model

python basic_vae_pl_module.py --latent_dim 32 --batch_size 32 --gpus 4 --max_epochs 12

Model quality control¶

For bolts to be added to the library we have a rigorous quality control checklist

Bolts vs my own repo¶

We hope you keep your own repo still! We want to link to it to let people know. However, by adding your contribution to bolts you get these additional benefits!

More visibility! (more people all over the world use your code)

We test your code on every PR (CPUs, GPUs, TPUs).

We host the docs (and test on every PR).

We help you build thorough, beautiful documentation.

We help you build robust tests.

We’ll pretrain expensive models for you and host weights.

We will improve the speed of your models!

Eligible for invited talks to discuss your implementation.

Lightning Swag + involvement in the broader contributor community :)

Note

You still get to keep your attribution and be recognized for your work!

Note

Bolts is a community library built by incredible people like you!

Contribution requirements¶

Benchmarked¶

Models have known performance results on common baseline datasets.

Device agnostic¶

Models must work on CPUs, GPUs and TPUs without changing code. We help authors with this.

# bad
encoder.to(device)

Fast¶

We inspect models for computational inefficiencies and help authors meet the bar. Granted, sometimes the approaches are slow for mathematical reasons. But anything related to engineering we help overcome.

# bad
mtx = ...
for xi in rows:
    for yi in cols
        mxt[xi, yi] = ...

# good
x = x.item().numpy()
x = np.some_fx(x)
x = torch.tensor(x)

Tested¶

Models are tested on every PR (on CPUs, GPUs and soon TPUs).

Modular¶

Models are modularized to be extended and reused easily.

# GOOD!
class LitVAE(pl.LightningModule):

    def init_prior(self, ...):
        # enable users to override interesting parts of each model

    def init_posterior(self, ...):
        # enable users to override interesting parts of each model

# BAD
class LitVAE(pl.LightningModule):

    def __init__(self):
        self.prior = ...
        self.posterior = ...

Attribution¶

Any models and weights that are contributed are attributed to you as the author(s).

We request that each contribution have:

The original paper link

The list of paper authors

The link to the original paper code (if available)

The link to your repo

Your name and your team’s name as the implementation authors.

Your team’s affiliation

Any generated examples, or result plots.

Hyperparameters configurations for the results.

Thank you for all your amazing contributions!

The bar seems high¶

If your model doesn’t yet meet this bar, no worries! Please open the PR and our team of core contributors will help you get there!

Do you have contribution ideas?¶

Yes! Check the Github issues for requests from the Lightning team and the community! We’ll even work with you to finish your implementation! Then we’ll help you pretrain it and cover the compute costs when possible.

Build a Callback¶

This module houses a collection of callbacks that can be passed into the trainer

from pl_bolts.callbacks import PrintTableMetricsCallback
import pytorch_lightning as pl

trainer = pl.Trainer(callbacks=[PrintTableMetricsCallback()])

# loss│train_loss│val_loss│epoch
# ──────────────────────────────
# 2.2541470527648926│2.2541470527648926│2.2158432006835938│0

What is a Callback¶

A callback is a self-contained program that can be intertwined into a training pipeline without polluting the main research logic.

Create a Callback¶

Creating a callback is simple:

from pytorch_lightning.callbacks import Callback

class MyCallback(Callback)
    def on_epoch_end(self, trainer, pl_module):
        # do something

Please refer to Callback docs for a full list of the 20+ hooks available.

Info Callbacks¶

These callbacks give all sorts of useful information during training.

Print Table Metrics¶

This callbacks prints training metrics to a table. It’s very bare-bones for speed purposes.

class pl_bolts.callbacks.printing.PrintTableMetricsCallback[source]

Bases: pytorch_lightning.callbacks.Callback

Prints a table with the metrics in columns on every epoch end

Example:

from pl_bolts.callbacks import PrintTableMetricsCallback

callback = PrintTableMetricsCallback()

pass into trainer like so:

trainer = pl.Trainer(callbacks=[callback])
trainer.fit(...)

# ------------------------------
# at the end of every epoch it will print
# ------------------------------

# loss│train_loss│val_loss│epoch
# ──────────────────────────────
# 2.2541470527648926│2.2541470527648926│2.2158432006835938│0

Self-supervised Callbacks¶

Useful callbacks for self-supervised learning models

BYOLMAWeightUpdate¶

The exponential moving average weight-update rule from Bring Your Own Latent (BYOL).

class pl_bolts.callbacks.self_supervised.BYOLMAWeightUpdate(initial_tau=0.996)[source]

Bases: pytorch_lightning.Callback

Weight update rule from BYOL.

Your model should have a:

self.online_network.

self.target_network.

Updates the target_network params using an exponential moving average update rule weighted by tau. BYOL claims this keeps the online_network from collapsing.

Note

Automatically increases tau from initial_tau to 1.0 with every training step

Example:

from pl_bolts.callbacks.self_supervised import BYOLMAWeightUpdate

# model must have 2 attributes
model = Model()
model.online_network = ...
model.target_network = ...

# make sure to set max_steps in Trainer
trainer = Trainer(callbacks=[BYOLMAWeightUpdate()], max_steps=1000)

Parameters: initial_tau¶ – starting tau. Auto-updates with every training step

SSLOnlineEvaluator¶

Appends a MLP for fine-tuning to the given model. Callback has its own mini-inner loop.

class pl_bolts.callbacks.self_supervised.SSLOnlineEvaluator(drop_p=0.2, hidden_dim=1024, z_dim=None, num_classes=None)[source]

Bases: pytorch_lightning.Callback

Attaches a MLP for finetuning using the standard self-supervised protocol.

Example:

from pl_bolts.callbacks.self_supervised import SSLOnlineEvaluator

# your model must have 2 attributes
model = Model()
model.z_dim = ... # the representation dim
model.num_classes = ... # the num of classes in the model

Parameters

drop_p¶ (float) – (0.2) dropout probability
hidden_dim¶ (int) –
1. the hidden dimension for the finetune MLP

get_representations(pl_module, x)[source]: Override this to customize for the particular model :param _sphinx_paramlinks_pl_bolts.callbacks.self_supervised.SSLOnlineEvaluator.get_representations.pl_module: :param _sphinx_paramlinks_pl_bolts.callbacks.self_supervised.SSLOnlineEvaluator.get_representations.x:

Variational Callbacks¶

Useful callbacks for GANs, variational-autoencoders or anything with latent spaces.

Latent Dim Interpolator¶

Interpolates latent dims.

Example output:

class pl_bolts.callbacks.variational.LatentDimInterpolator(interpolate_epoch_interval=20, range_start=-5, range_end=5, num_samples=2)[source]

Bases: pytorch_lightning.callbacks.Callback

Interpolates the latent space for a model by setting all dims to zero and stepping through the first two dims increasing one unit at a time.

Default interpolates between [-5, 5] (-5, -4, -3, …, 3, 4, 5)

Example:

from pl_bolts.callbacks import LatentDimInterpolator

Trainer(callbacks=[LatentDimInterpolator()])

Parameters

interpolate_epoch_interval¶ –
range_start¶ – default -5
range_end¶ – default 5
num_samples¶ – default 2

Vision Callbacks¶

Useful callbacks for vision models

Confused Logit¶

Shows how the input would have to change to move the prediction from one logit to the other

Example outputs:

class pl_bolts.callbacks.vision.confused_logit.ConfusedLogitCallback(top_k, projection_factor=3, min_logit_value=5.0, logging_batch_interval=20, max_logit_difference=0.1)[source]

Bases: pytorch_lightning.Callback

Takes the logit predictions of a model and when the probabilities of two classes are very close, the model doesn’t have high certainty that it should pick one vs the other class.

This callback shows how the input would have to change to swing the model from one label prediction to the other.

In this case, the network predicts a 5… but gives almost equal probability to an 8. The images show what about the original 5 would have to change to make it more like a 5 or more like an 8.

For each confused logit the confused images are generated by taking the gradient from a logit wrt an input for the top two closest logits.

Example:

from pl_bolts.callbacks.vision import ConfusedLogitCallback
trainer = Trainer(callbacks=[ConfusedLogitCallback()])

Note

whenever called, this model will look for self.last_batch and self.last_logits in the LightningModule

Note

this callback supports tensorboard only right now

Parameters

top_k¶ – How many “offending” images we should plot
projection_factor¶ – How much to multiply the input image to make it look more like this logit label
min_logit_value¶ – Only consider logit values above this threshold
logging_batch_interval¶ – how frequently to inspect/potentially plot something
max_logit_difference¶ – when the top 2 logits are within this threshold we consider them confused

Authored by:

Alfredo Canziani

Tensorboard Image Generator¶

Generates images from a generative model and plots to tensorboard

class pl_bolts.callbacks.vision.image_generation.TensorboardGenerativeModelImageSampler(num_samples=3)[source]

Bases: pytorch_lightning.Callback

Generates images and logs to tensorboard. Your model must implement the forward function for generation

Requirements:

# model must have img_dim arg
model.img_dim = (1, 28, 28)

# model forward must work for sampling
z = torch.rand(batch_size, latent_dim)
img_samples = your_model(z)

Example:

from pl_bolts.callbacks import TensorboardGenerativeModelImageSampler

trainer = Trainer(callbacks=[TensorboardGenerativeModelImageSampler()])

DataModules¶

DataModules (introduced in PyTorch Lightning 0.9.0) decouple the data from a model. A DataModule is simply a collection of a training dataloder, val dataloader and test dataloader. In addition, it specifies how to:

Downloading/preparing data.
Train/val/test splits.
Transforms

Then you can use it like this:

Example:

dm = MNISTDataModule('path/to/data')
model = LitModel()

trainer = Trainer()
trainer.fit(model, dm)

Or use it manually with plain PyTorch

Example:

dm = MNISTDataModule('path/to/data')
for batch in dm.train_dataloader():
    ...
for batch in dm.val_dataloader():
    ...
for batch in dm.test_dataloader():
    ...

Please visit the PyTorch Lightning documentation for more details on DataModules

Sklearn Datamodule¶

Utilities to map sklearn or numpy datasets to PyTorch Dataloaders with automatic data splits and GPU/TPU support.

from sklearn.datasets import load_boston
from pl_bolts.datamodules import SklearnDataModule

X, y = load_boston(return_X_y=True)
loaders = SklearnDataModule(X, y)

train_loader = loaders.train_dataloader(batch_size=32)
val_loader = loaders.val_dataloader(batch_size=32)
test_loader = loaders.test_dataloader(batch_size=32)

Or build your own torch datasets

from sklearn.datasets import load_boston
from pl_bolts.datamodules import SklearnDataset

X, y = load_boston(return_X_y=True)
dataset = SklearnDataset(X, y)
loader = DataLoader(dataset)

Sklearn Dataset Class¶

Transforms a sklearn or numpy dataset to a PyTorch Dataset.

class pl_bolts.datamodules.sklearn_datamodule.SklearnDataset(X, y, X_transform=None, y_transform=None)[source]

Bases: torch.utils.data.Dataset

Mapping between numpy (or sklearn) datasets to PyTorch datasets.

Parameters

X¶ (ndarray) – Numpy ndarray
y¶ (ndarray) – Numpy ndarray
X_transform¶ (Optional[Any]) – Any transform that works with Numpy arrays
y_transform¶ (Optional[Any]) – Any transform that works with Numpy arrays

Example

>>> from sklearn.datasets import load_boston
>>> from pl_bolts.datamodules import SklearnDataset
...
>>> X, y = load_boston(return_X_y=True)
>>> dataset = SklearnDataset(X, y)
>>> len(dataset)
506

Sklearn DataModule Class¶

Automatically generates the train, validation and test splits for a Numpy dataset. They are set up as dataloaders for convenience. Optionally, you can pass in your own validation and test splits.

class pl_bolts.datamodules.sklearn_datamodule.SklearnDataModule(X, y, x_val=None, y_val=None, x_test=None, y_test=None, val_split=0.2, test_split=0.1, num_workers=2, random_state=1234, shuffle=True, *args, **kwargs)[source]

Bases: pytorch_lightning.LightningDataModule

Automatically generates the train, validation and test splits for a Numpy dataset. They are set up as dataloaders for convenience. Optionally, you can pass in your own validation and test splits.

Example

>>> from sklearn.datasets import load_boston
>>> from pl_bolts.datamodules import SklearnDataModule
...
>>> X, y = load_boston(return_X_y=True)
>>> loaders = SklearnDataModule(X, y)
...
>>> # train set
>>> train_loader = loaders.train_dataloader(batch_size=32)
>>> len(train_loader.dataset)
355
>>> len(train_loader)
11
>>> # validation set
>>> val_loader = loaders.val_dataloader(batch_size=32)
>>> len(val_loader.dataset)
100
>>> len(val_loader)
3
>>> # test set
>>> test_loader = loaders.test_dataloader(batch_size=32)
>>> len(test_loader.dataset)
51
>>> len(test_loader)
1

Vision DataModules¶

The following are pre-built datamodules for computer-vision.

Supervised learning¶

These are standard vision datasets with the train, test, val splits pre-generated in DataLoaders with the standard transforms (and Normalization) values

BinaryMNIST¶

class pl_bolts.datamodules.binary_mnist_datamodule.BinaryMNISTDataModule(data_dir, val_split=5000, num_workers=16, normalize=False, seed=42, *args, **kwargs)[source]

Bases: pytorch_lightning.LightningDataModule

Specs:

10 classes (1 per digit)
Each image is (1 x 28 x 28)

Binary MNIST, train, val, test splits and transforms

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor()
])

Example:

from pl_bolts.datamodules import BinaryMNISTDataModule

dm = BinaryMNISTDataModule('.')
model = LitModel()

Trainer().fit(model, dm)

Parameters

data_dir¶ (str) – where to save/load the data
val_split¶ (int) – how many of the training images to use for the validation split
num_workers¶ (int) – how many workers to use for loading data
normalize¶ (bool) – If true applies image normalize

prepare_data()[source]: Saves MNIST files to data_dir

test_dataloader(batch_size=32, transforms=None)[source]

MNIST test set uses the test split

Parameters

batch_size¶ – size of batch
transforms¶ – custom transforms

train_dataloader(batch_size=32, transforms=None)[source]

MNIST train set removes a subset to use for validation

Parameters

batch_size¶ – size of batch
transforms¶ – custom transforms

val_dataloader(batch_size=32, transforms=None)[source]

MNIST val set uses a subset of the training set for validation

Parameters

batch_size¶ – size of batch
transforms¶ – custom transforms

property num_classes[source]: Return: 10

CityScapes¶

class pl_bolts.datamodules.cityscapes_datamodule.CityscapesDataModule(data_dir, val_split=5000, num_workers=16, batch_size=32, seed=42, *args, **kwargs)[source]

Bases: pytorch_lightning.LightningDataModule

Standard Cityscapes, train, val, test splits and transforms

Specs:

30 classes (road, person, sidewalk, etc…)
(image, target) - image dims: (3 x 32 x 32), target dims: (3 x 32 x 32)

Transforms:

transforms = transform_lib.Compose([
    transform_lib.ToTensor(),
    transform_lib.Normalize(
        mean=[0.28689554, 0.32513303, 0.28389177],
        std=[0.18696375, 0.19017339, 0.18720214]
    )
])

Example:

from pl_bolts.datamodules import CityscapesDataModule

dm = CityscapesDataModule(PATH)
model = LitModel()

Trainer().fit(model, dm)

Or you can set your own transforms

Example:

dm.train_transforms = ...
dm.test_transforms = ...
dm.val_transforms  = ...

Parameters

data_dir¶ – where to save/load the data
val_split¶ – how many of the training images to use for the validation split
num_workers¶ – how many workers to use for loading data
batch_size¶ – number of examples per training/eval step

prepare_data()[source]: Saves Cityscapes files to data_dir

test_dataloader()[source]: Cityscapes test set uses the test split

train_dataloader()[source]: Cityscapes train set with removed subset to use for validation

val_dataloader()[source]: Cityscapes val set uses a subset of the training set for validation

property num_classes[source]: Return: 30

CIFAR-10¶

class pl_bolts.datamodules.cifar10_datamodule.CIFAR10DataModule(data_dir=None, val_split=5000, num_workers=16, batch_size=32, seed=42, *args, **kwargs)[source]

Bases: pytorch_lightning.LightningDataModule

Specs:

10 classes (1 per class)
Each image is (3 x 32 x 32)

Standard CIFAR10, train, val, test splits and transforms

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor(),
    transforms.Normalize(
        mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
        std=[x / 255.0 for x in [63.0, 62.1, 66.7]]
    )
])

Example:

from pl_bolts.datamodules import CIFAR10DataModule

dm = CIFAR10DataModule(PATH)
model = LitModel()

Trainer().fit(model, dm)

Or you can set your own transforms

Example:

dm.train_transforms = ...
dm.test_transforms = ...
dm.val_transforms  = ...

Parameters

data_dir¶ (Optional[str]) – where to save/load the data
val_split¶ (int) – how many of the training images to use for the validation split
num_workers¶ (int) – how many workers to use for loading data
batch_size¶ (int) – number of examples per training/eval step

prepare_data()[source]: Saves CIFAR10 files to data_dir

test_dataloader()[source]: CIFAR10 test set uses the test split

train_dataloader()[source]: CIFAR train set removes a subset to use for validation

val_dataloader()[source]: CIFAR10 val set uses a subset of the training set for validation

property num_classes[source]: Return: 10

FashionMNIST¶

class pl_bolts.datamodules.fashion_mnist_datamodule.FashionMNISTDataModule(data_dir, val_split=5000, num_workers=16, seed=42, *args, **kwargs)[source]

Bases: pytorch_lightning.LightningDataModule

Specs:

10 classes (1 per type)
Each image is (1 x 28 x 28)

Standard FashionMNIST, train, val, test splits and transforms

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor()
])

Example:

from pl_bolts.datamodules import FashionMNISTDataModule

dm = FashionMNISTDataModule('.')
model = LitModel()

Trainer().fit(model, dm)

Parameters

data_dir¶ (str) – where to save/load the data
val_split¶ (int) – how many of the training images to use for the validation split
num_workers¶ (int) – how many workers to use for loading data

prepare_data()[source]: Saves FashionMNIST files to data_dir

test_dataloader(batch_size=32, transforms=None)[source]

FashionMNIST test set uses the test split

Parameters

batch_size¶ – size of batch
transforms¶ – custom transforms

train_dataloader(batch_size=32, transforms=None)[source]

FashionMNIST train set removes a subset to use for validation

Parameters

batch_size¶ – size of batch
transforms¶ – custom transforms

val_dataloader(batch_size=32, transforms=None)[source]

FashionMNIST val set uses a subset of the training set for validation

Parameters

batch_size¶ – size of batch
transforms¶ – custom transforms

property num_classes[source]: Return: 10

Imagenet¶

class pl_bolts.datamodules.imagenet_datamodule.ImagenetDataModule(data_dir, meta_dir=None, num_imgs_per_val_class=50, image_size=224, num_workers=16, batch_size=32, *args, **kwargs)[source]

Bases: pytorch_lightning.LightningDataModule

Specs:

1000 classes
Each image is (3 x varies x varies) (here we default to 3 x 224 x 224)

Imagenet train, val and test dataloaders.

The train set is the imagenet train.

The val set is taken from the train set with num_imgs_per_val_class images per class. For example if num_imgs_per_val_class=2 then there will be 2,000 images in the validation set.

The test set is the official imagenet validation set.

Example:

from pl_bolts.datamodules import ImagenetDataModule

dm = ImagenetDataModule(IMAGENET_PATH)
model = LitModel()

Trainer().fit(model, dm)

Parameters

data_dir¶ (str) – path to the imagenet dataset file
meta_dir¶ (Optional[str]) – path to meta.bin file
num_imgs_per_val_class¶ (int) – how many images per class for the validation set
image_size¶ (int) – final image size
num_workers¶ (int) – how many data workers
batch_size¶ (int) – batch_size

prepare_data()[source]: This method already assumes you have imagenet2012 downloaded. It validates the data using the meta.bin.

Warning

Please download imagenet on your own first.

test_dataloader()[source]: Uses the validation split of imagenet2012 for testing

train_dataloader()[source]: Uses the train split of imagenet2012 and puts away a portion of it for the validation split

train_transform()[source]

The standard imagenet transforms

transform_lib.Compose([
    transform_lib.RandomResizedCrop(self.image_size),
    transform_lib.RandomHorizontalFlip(),
    transform_lib.ToTensor(),
    transform_lib.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    ),
])

val_dataloader()[source]

Uses the part of the train split of imagenet2012 that was not used for training via num_imgs_per_val_class

Parameters

batch_size¶ – the batch size
transforms¶ – the transforms

val_transform()[source]

The standard imagenet transforms for validation

transform_lib.Compose([
    transform_lib.Resize(self.image_size + 32),
    transform_lib.CenterCrop(self.image_size),
    transform_lib.ToTensor(),
    transform_lib.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    ),
])

property num_classes[source]

Return:

1000

MNIST¶

class pl_bolts.datamodules.mnist_datamodule.MNISTDataModule(data_dir='./', val_split=5000, num_workers=16, normalize=False, seed=42, batch_size=32, *args, **kwargs)[source]

Bases: pytorch_lightning.LightningDataModule

Specs:

10 classes (1 per digit)
Each image is (1 x 28 x 28)

Standard MNIST, train, val, test splits and transforms

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor()
])

Example:

from pl_bolts.datamodules import MNISTDataModule

dm = MNISTDataModule('.')
model = LitModel()

Trainer().fit(model, dm)

Parameters

data_dir¶ (str) – where to save/load the data
val_split¶ (int) – how many of the training images to use for the validation split
num_workers¶ (int) – how many workers to use for loading data
normalize¶ (bool) – If true applies image normalize

prepare_data()[source]: Saves MNIST files to data_dir

test_dataloader(batch_size=32, transforms=None)[source]

MNIST test set uses the test split

Parameters

batch_size¶ – size of batch
transforms¶ – custom transforms

train_dataloader(batch_size=32, transforms=None)[source]

MNIST train set removes a subset to use for validation

Parameters

batch_size¶ – size of batch
transforms¶ – custom transforms

val_dataloader(batch_size=32, transforms=None)[source]

MNIST val set uses a subset of the training set for validation

Parameters

batch_size¶ – size of batch
transforms¶ – custom transforms

property num_classes[source]: Return: 10

Semi-supervised learning¶

The following datasets have support for unlabeled training and semi-supervised learning where only a few examples are labeled.

Imagenet (ssl)¶

class pl_bolts.datamodules.ssl_imagenet_datamodule.SSLImagenetDataModule(data_dir, meta_dir=None, num_workers=16, *args, **kwargs)[source]: Bases: pytorch_lightning.LightningDataModule

STL-10¶

class pl_bolts.datamodules.stl10_datamodule.STL10DataModule(data_dir=None, unlabeled_val_split=5000, train_val_split=500, num_workers=16, batch_size=32, seed=42, *args, **kwargs)[source]

Bases: pytorch_lightning.LightningDataModule

Specs:

10 classes (1 per type)
Each image is (3 x 96 x 96)

Standard STL-10, train, val, test splits and transforms. STL-10 has support for doing validation splits on the labeled or unlabeled splits

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor(),
    transforms.Normalize(
        mean=(0.43, 0.42, 0.39),
        std=(0.27, 0.26, 0.27)
    )
])

Example:

from pl_bolts.datamodules import STL10DataModule

dm = STL10DataModule(PATH)
model = LitModel()

Trainer().fit(model, dm)

Parameters

data_dir¶ (Optional[str]) – where to save/load the data
unlabeled_val_split¶ (int) – how many images from the unlabeled training split to use for validation
train_val_split¶ (int) – how many images from the labeled training split to use for validation
num_workers¶ (int) – how many workers to use for loading data
batch_size¶ (int) – the batch size

prepare_data()[source]: Downloads the unlabeled, train and test split

test_dataloader()[source]

Loads the test split of STL10

Parameters

batch_size¶ – the batch size
transforms¶ – the transforms

train_dataloader()[source]: Loads the ‘unlabeled’ split minus a portion set aside for validation via unlabeled_val_split.

train_dataloader_mixed()[source]

Loads a portion of the ‘unlabeled’ training data and ‘train’ (labeled) data. both portions have a subset removed for validation via unlabeled_val_split and train_val_split

Parameters

batch_size¶ – the batch size
transforms¶ – a sequence of transforms

val_dataloader()[source]

Loads a portion of the ‘unlabeled’ training data set aside for validation The val dataset = (unlabeled - train_val_split)

Parameters

batch_size¶ – the batch size
transforms¶ – a sequence of transforms

val_dataloader_mixed()[source]

Loads a portion of the ‘unlabeled’ training data set aside for validation along with the portion of the ‘train’ dataset to be used for validation

unlabeled_val = (unlabeled - train_val_split)

labeled_val = (train- train_val_split)

full_val = unlabeled_val + labeled_val

Parameters

batch_size¶ – the batch size
transforms¶ – a sequence of transforms

AsynchronousLoader¶

This dataloader behaves identically to the standard pytorch dataloader, but will transfer data asynchronously to the GPU with training. You can also use it to wrap an existing dataloader.

Example::

dataloader = AsynchronousLoader(DataLoader(ds, batch_size=16), device=device)

for b in dataloader:: …

class pl_bolts.datamodules.async_dataloader.AsynchronousLoader(data, device=torch.device, q_size=10, num_batches=None, **kwargs)[source]

Bases: object

Class for asynchronously loading from CPU memory to device memory with DataLoader.

Note that this only works for single GPU training, multiGPU uses PyTorch’s DataParallel or DistributedDataParallel which uses its own code for transferring data across GPUs. This could just break or make things slower with DataParallel or DistributedDataParallel.

Parameters

data¶ – The PyTorch Dataset or DataLoader we’re using to load.
device¶ – The PyTorch device we are loading to
q_size¶ – Size of the queue used to store the data loaded to the device
num_batches¶ – Number of batches to load. This must be set if the dataloader doesn’t have a finite __len__. It will also override DataLoader.__len__ if set and DataLoader has a __len__. Otherwise it can be left as None
**kwargs¶ – Any additional arguments to pass to the dataloader if we’re constructing one here

DummyDataset¶

class pl_bolts.datamodules.dummy_dataset.DummyDataset(*shapes, num_samples=10000)[source]

Bases: torch.utils.data.Dataset

Generate a dummy dataset

Parameters

*shapes¶ – list of shapes
num_samples¶ – how many samples to use in this dataset

Example:

from pl_bolts.datamodules import DummyDataset

# mnist dims
>>> ds = DummyDataset((1, 28, 28), (1,))
>>> dl = DataLoader(ds, batch_size=7)
...
>>> batch = next(iter(dl))
>>> x, y = batch
>>> x.size()
torch.Size([7, 1, 28, 28])
>>> y.size()
torch.Size([7, 1])

Losses¶

This package lists common losses across research domains (This is a work in progress. If you have any losses you want to contribute, please submit a PR!)

Note

this module is a work in progress

Your Loss¶

We’re cleaning up many of our losses, but in the meantime, submit a PR to add your loss here!

How to use models¶

Models are meant to be “bolted” onto your research or production cases.

Bolts are meant to be used in the following ways

Predicting on your data¶

Most bolts have pretrained weights trained on various datasets or algorithms. This is useful when you don’t have enough data, time or money to do your own training.

For example, you could use a pretrained VAE to generate features for an image dataset.

from pl_bolts.models.autoencoders import VAE

model = VAE(pretrained='imagenet2012')
encoder = model.encoder
encoder.freeze()

for (x, y) in own_data
    features = encoder(x)

The advantage of bolts is that each system can be decomposed and used in interesting ways. For instance, this resnet18 was trained using self-supervised learning (no labels) on Imagenet, and thus might perform better than the same resnet18 trained with labels

# trained without labels
from pl_bolts.models.self_supervised import CPCV2

model = CPCV2(encoder='resnet18', pretrained='imagenet128')
resnet18_unsupervised = model.encoder.freeze()

# trained with labels
from torchvision.models import resnet18
resnet18_supervised = resnet18(pretrained=True)

# perhaps the features when trained without labels are much better for classification or other tasks
x = image_sample()
unsup_feats = resnet18_unsupervised(x)
sup_feats = resnet18_supervised(x)

# which one will be better?

Bolts are often trained on more than just one dataset.

model = CPCV2(encoder='resnet18', pretrained='stl10')

Finetuning on your data¶

If you have a little bit of data and can pay for a bit of training, it’s often better to finetune on your own data.

To finetune you have two options unfrozen finetuning or unfrozen later.

Unfrozen Finetuning¶

In this approach, we load the pretrained model and unfreeze from the beginning

model = CPCV2(encoder='resnet18', pretrained='imagenet128')
resnet18 = model.encoder
# don't call .freeze()

classifier = LogisticRegression()

for (x, y) in own_data:
    feats = resnet18(x)
    y_hat = classifier(feats)
    ...

Or as a LightningModule

class FineTuner(pl.LightningModule):

    def __init__(self, encoder):
        self.encoder = encoder
        self.classifier = LogisticRegression()

    def training_step(self, batch, batch_idx):
        (x, y) = batch
        feats = self.encoder(x)
        y_hat = self.classifier(feats)
        loss = cross_entropy_with_logits(y_hat, y)
        return loss

trainer = Trainer(gpus=2)
model = FineTuner(resnet18)
trainer.fit(model)

Sometimes this works well, but more often it’s better to keep the encoder frozen for a while

Freeze then unfreeze¶

The approach that works best most often is to freeze first then unfreeze later

# freeze!
model = CPCV2(encoder='resnet18', pretrained='imagenet128')
resnet18 = model.encoder
resnet18.freeze()

classifier = LogisticRegression()

for epoch in epochs:
    for (x, y) in own_data:
        feats = resnet18(x)
        y_hat = classifier(feats)
        loss = cross_entropy_with_logits(y_hat, y)

    # unfreeze after 10 epochs
    if epoch == 10:
        resnet18.unfreeze()

Note

In practice, unfreezing later works MUCH better.

Or in Lightning as a Callback so you don’t pollute your research code.

class UnFreezeCallback(Callback):

    def on_epoch_end(self, trainer, pl_module):
        if trainer.current_epoch == 10.
            encoder.unfreeze()

trainer = Trainer(gpus=2, callbacks=[UnFreezeCallback()])
model = FineTuner(resnet18)
trainer.fit(model)

Unless you still need to mix it into your research code.

class FineTuner(pl.LightningModule):

    def __init__(self, encoder):
        self.encoder = encoder
        self.classifier = LogisticRegression()

    def training_step(self, batch, batch_idx):

        # option 1 - (not recommended because it's messy)
        if self.trainer.current_epoch == 10:
            self.encoder.unfreeze()

        (x, y) = batch
        feats = self.encoder(x)
        y_hat = self.classifier(feats)
        loss = cross_entropy_with_logits(y_hat, y)
        return loss

    def on_epoch_end(self, trainer, pl_module):
        # a hook is cleaner (but a callback is much better)
        if self.trainer.current_epoch == 10:
            self.encoder.unfreeze()

Hyperparameter search¶

For finetuning to work well, you should try many versions of the model hyperparameters. Otherwise you’re unlikely to get the most value out of your data.

learning_rates = [0.01, 0.001, 0.0001]
hidden_dim = [128, 256, 512]

for lr in learning_rates:
    for hd in hidden_dim:
        vae = VAE(hidden_dim=hd, learning_rate=lr)
        trainer = Trainer()
        trainer.fit(vae)

Train from scratch¶

If you do have enough data and compute resources, then you could try training from scratch.

# get data
train_data = DataLoader(YourDataset)
val_data = DataLoader(YourDataset)

# use any bolts model without pretraining
model = VAE()

# fit!
trainer = Trainer(gpus=2)
trainer.fit(model, train_data, val_data)

Note

For this to work well, make sure you have enough data and time to train these models!

For research¶

What separates bolts from all the other libraries out there is that bolts is built by and used by AI researchers. This means every single bolt is modularized so that it can be easily extended or mixed with arbitrary parts of the rest of the code-base.

Extending work¶

Perhaps a research project requires modifying a part of a know approach. In this case, you’re better off only changing that part of a system that is already know to perform well. Otherwise, you risk not implementing the work correctly.

Example 1: Changing the prior or approx posterior of a VAE

from pl_bolts.models.autoencoders import VAE

class MyVAEFlavor(VAE):

    def init_prior(self, z_mu, z_std):
        P = MyPriorDistribution

        # default is standard normal
        # P = distributions.normal.Normal(loc=torch.zeros_like(z_mu), scale=torch.ones_like(z_std))
        return P

    def init_posterior(self, z_mu, z_std):
        Q = MyPosteriorDistribution
        # default is normal(z_mu, z_sigma)
        # Q = distributions.normal.Normal(loc=z_mu, scale=z_std)
        return Q

And of course train it with lightning.

model = MyVAEFlavor()
trainer = Trainer()
trainer.fit(model)

In just a few lines of code you changed something fundamental about a VAE… This means you can iterate through ideas much faster knowing that the bolt implementation and the training loop are CORRECT and TESTED.

If your model doesn’t work with the new P, Q, then you can discard that research idea much faster than trying to figure out if your VAE implementation was correct, or if your training loop was correct.

Example 2: Changing the generator step of a GAN

from pl_bolts.models.gans import GAN

class FancyGAN(GAN):

    def generator_step(self, x):
        # sample noise
        z = torch.randn(x.shape[0], self.hparams.latent_dim)
        z = z.type_as(x)

        # generate images
        self.generated_imgs = self(z)

        # ground truth result (ie: all real)
        real = torch.ones(x.size(0), 1)
        real = real.type_as(x)
        g_loss = self.generator_loss(real)

        tqdm_dict = {'g_loss': g_loss}
        output = OrderedDict({
            'loss': g_loss,
            'progress_bar': tqdm_dict,
            'log': tqdm_dict
        })
        return output

Example 3: Changing the way the loss is calculated in a contrastive self-supervised learning approach

from pl_bolts.models.self_supervised import AMDIM

class MyDIM(AMDIM):

    def validation_step(self, batch, batch_nb):
        [img_1, img_2], labels = batch

        # generate features
        r1_x1, r5_x1, r7_x1, r1_x2, r5_x2, r7_x2 = self.forward(img_1, img_2)

        # Contrastive task
        loss, lgt_reg = self.contrastive_task((r1_x1, r5_x1, r7_x1), (r1_x2, r5_x2, r7_x2))
        unsupervised_loss = loss.sum() + lgt_reg

        result = {
            'val_nce': unsupervised_loss
        }
        return result

Importing parts¶

All the bolts are modular. This means you can also arbitrarily mix and match fundamental blocks from across approaches.

Example 1: Use the VAE encoder for a GAN as a generator

from pl_bolts.models.gans import GAN
from pl_bolts.models.autoencoders.basic_vae import Encoder

class FancyGAN(GAN):

    def init_generator(self, img_dim):
        generator = Encoder(...)
        return generator

trainer = Trainer(...)
trainer.fit(FancyGAN())

Example 2: Use the contrastive task of AMDIM in CPC

from pl_bolts.models.self_supervised import AMDIM, CPCV2

default_amdim_task = AMDIM().contrastive_task
model = CPCV2(contrastive_task=default_amdim_task, encoder='cpc_default')
# you might need to modify the cpc encoder depending on what you use

Compose new ideas¶

You may also be interested in creating completely new approaches that mix and match all sorts of different pieces together

# this model is for illustration purposes, it makes no research sense but it's intended to show
# that you can be as creative and expressive as you want.
class MyNewContrastiveApproach(pl.LightningModule):

    def __init__(self):
        suoer().__init_()

        self.gan = GAN()
        self.vae = VAE()
        self.amdim = AMDIM()
        self.cpc = CPCV2

    def training_step(self, batch, batch_idx):
        (x, y) = batch

        feat_a = self.gan.generator(x)
        feat_b = self.vae.encoder(x)

        unsup_loss = self.amdim(feat_a) + self.cpc(feat_b)

        vae_loss = self.vae._step(batch)
        gan_loss = self.gan.generator_loss(x)

        return unsup_loss + vae_loss + gan_loss

Autoencoders¶

This section houses autoencoders and variational autoencoders.

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()

Tensorboard for AE on CIFAR-10

Training:

class pl_bolts.models.autoencoders.AE(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.LightningModule

Standard AE

Model is available pretrained on different datasets:

Example:

# not pretrained
ae = AE()

# pretrained on imagenet
ae = AE.from_pretrained('resnet50-imagenet')

# pretrained on cifar10
ae = AE.from_pretrained('resnet18-cifar10')

Parameters

input_height¶ – height of the images
enc_type¶ – option between resnet18 or resnet50
first_conv¶ – use standard kernel_size 7, stride 2 at start or replace it with kernel_size 3, stride 1 conv
maxpool1¶ – use standard maxpool to reduce spatial dim of feat by a factor of 2
enc_out_dim¶ – set according to the out_channel count of encoder used (512 for resnet18, 2048 for resnet50)
latent_dim¶ – dim of latent space
lr¶ – learning rate for Adam

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()

Tensorboard for VAE on CIFAR-10

Training:

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()

Tensorboard for VAE on STL-10

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.LightningModule

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.from_pretrained('resnet50-imagenet')

# pretrained on cifar10
vae = VAE.from_pretrained('resnet18-cifar10')

Parameters

input_height¶ – height of the images
enc_type¶ – option between resnet18 or resnet50
first_conv¶ – use standard kernel_size 7, stride 2 at start or replace it with kernel_size 3, stride 1 conv
maxpool1¶ – use standard maxpool to reduce spatial dim of feat by a factor of 2
enc_out_dim¶ – set according to the out_channel count of encoder used (512 for resnet18, 2048 for resnet50)
kl_coeff¶ – coefficient for kl term of the loss
latent_dim¶ – dim of latent space
lr¶ – learning rate for Adam

Classic ML Models¶

This module implements classic machine learning models in PyTorch Lightning, including linear regression and logistic regression. Unlike other libraries that implement these models, here we use PyTorch to enable multi-GPU, multi-TPU and half-precision training.

Linear Regression¶

Linear regression fits a linear model between a real-valued target variable $y$ and one or more features $X$ . We estimate the regression coefficients that minimizes the mean squared error between the predicted and true target values.

We formulate the linear regression model as a single-layer neural network. By default we include only one neuron in the output layer, although you can specify the output_dim yourself.

Add either L1 or L2 regularization, or both, by specifying the regularization strength (default 0).

from pl_bolts.models.regression import LinearRegression
import pytorch_lightning as pl
from pl_bolts.datamodules import SklearnDataModule
from sklearn.datasets import load_boston

X, y = load_boston(return_X_y=True)
loaders = SklearnDataModule(X, y)

model = LinearRegression(input_dim=13)
trainer = pl.Trainer()
trainer.fit(model, loaders.train_dataloader(), loaders.val_dataloader())
trainer.test(test_dataloaders=loaders.test_dataloader())

class pl_bolts.models.regression.linear_regression.LinearRegression(input_dim, output_dim=1, bias=True, learning_rate=0.0001, optimizer=torch.optim.Adam, l1_strength=0.0, l2_strength=0.0, **kwargs)[source]

Bases: pytorch_lightning.LightningModule

Linear regression model implementing - with optional L1/L2 regularization $$min_{W} ||(Wx + b) - y ||_2^2 $$

Parameters

input_dim¶ (int) – number of dimensions of the input (1+)
output_dim¶ (int) – number of dimensions of the output (default=1)
bias¶ (bool) – If false, will not use $+b$
learning_rate¶ (float) – learning_rate for the optimizer
optimizer¶ (Optimizer) – the optimizer to use (default=’Adam’)
l1_strength¶ (float) – L1 regularization strength (default=None)
l2_strength¶ (float) – L2 regularization strength (default=None)

Logistic Regression¶

Logistic regression is a linear model used for classification, i.e. when we have a categorical target variable. This implementation supports both binary and multi-class classification.

In the binary case, we formulate the logistic regression model as a one-layer neural network with one neuron in the output layer and a sigmoid activation function. In the multi-class case, we use a single-layer neural network but now with $k$ neurons in the output, where $k$ is the number of classes. This is also referred to as multinomial logistic regression.

Add either L1 or L2 regularization, or both, by specifying the regularization strength (default 0).

from sklearn.datasets import load_iris
from pl_bolts.models.regression import LogisticRegression
from pl_bolts.datamodules import SklearnDataModule
import pytorch_lightning as pl

# use any numpy or sklearn dataset
X, y = load_iris(return_X_y=True)
dm = SklearnDataModule(X, y)

# build model
model = LogisticRegression(input_dim=4, num_classes=3)

# fit
trainer = pl.Trainer(tpu_cores=8, precision=16)
trainer.fit(model, dm.train_dataloader(), dm.val_dataloader())

trainer.test(test_dataloaders=dm.test_dataloader(batch_size=12))

Any input will be flattened across all dimensions except the firs one (batch). This means images, sound, etc… work out of the box.

# create dataset
dm = MNISTDataModule(num_workers=0, data_dir=tmpdir)

model = LogisticRegression(input_dim=28 * 28, num_classes=10, learning_rate=0.001)
model.prepare_data = dm.prepare_data
model.train_dataloader = dm.train_dataloader
model.val_dataloader = dm.val_dataloader
model.test_dataloader = dm.test_dataloader

trainer = pl.Trainer(max_epochs=2)
trainer.fit(model)
trainer.test(model)
# {test_acc: 0.92}

class pl_bolts.models.regression.logistic_regression.LogisticRegression(input_dim, num_classes, bias=True, learning_rate=0.0001, optimizer=torch.optim.Adam, l1_strength=0.0, l2_strength=0.0, **kwargs)[source]

Bases: pytorch_lightning.LightningModule

Logistic regression model

Parameters

input_dim¶ (int) – number of dimensions of the input (at least 1)
num_classes¶ (int) – number of class labels (binary: 2, multi-class: >2)
bias¶ (bool) – specifies if a constant or intercept should be fitted (equivalent to fit_intercept in sklearn)
learning_rate¶ (float) – learning_rate for the optimizer
optimizer¶ (Optimizer) – the optimizer to use (default=’Adam’)
l1_strength¶ (float) – L1 regularization strength (default=None)
l2_strength¶ (float) – L2 regularization strength (default=None)

Convolutional Architectures¶

This package lists contributed convolutional architectures.

GPT-2¶

class pl_bolts.models.vision.image_gpt.gpt2.GPT2(embed_dim, heads, layers, num_positions, vocab_size, num_classes)[source]

Bases: pytorch_lightning.LightningModule

GPT-2 from language Models are Unsupervised Multitask Learners

Paper by: Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever

Implementation contributed by:

Teddy Koker

Example:

from pl_bolts.models import GPT2

seq_len = 17
batch_size = 32
vocab_size = 16
x = torch.randint(0, vocab_size, (seq_len, batch_size))
model = GPT2(embed_dim=32, heads=2, layers=2, num_positions=seq_len, vocab_size=vocab_size, num_classes=4)
results = model(x)

forward(x, classify=False)[source]: Expect input as shape [sequence len, batch] If classify, return classification logits

Image GPT¶

class pl_bolts.models.vision.image_gpt.igpt_module.ImageGPT(datamodule=None, embed_dim=16, heads=2, layers=2, pixels=28, vocab_size=16, num_classes=10, classify=False, batch_size=64, learning_rate=0.01, steps=25000, data_dir='.', num_workers=8, **kwargs)[source]

Bases: pytorch_lightning.LightningModule

Paper: Generative Pretraining from Pixels [original paper code].

Paper by: Mark Che, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, Prafulla Dhariwal, David Luan, Ilya Sutskever

Implementation contributed by:

Teddy Koker

Original repo with results and more implementation details:

https://github.com/teddykoker/image-gpt

Example Results (Photo credits: Teddy Koker):

Default arguments:

Argument Defaults¶
Argument	Default	iGPT-S (Chen et al.)
–embed_dim	16	512
–heads	2	8
–layers	8	24
–pixels	28	32
–vocab_size	16	512
–num_classes	10	10
–batch_size	64	128
–learning_rate	0.01	0.01
–steps	25000	1000000

Example:

import pytorch_lightning as pl
from pl_bolts.models.vision import ImageGPT

dm = MNISTDataModule('.')
model = ImageGPT(dm)

pl.Trainer(gpu=4).fit(model)

As script:

cd pl_bolts/models/vision/image_gpt
python igpt_module.py --learning_rate 1e-2 --batch_size 32 --gpus 4

Parameters

datamodule¶ (Optional[LightningDataModule]) – LightningDataModule
embed_dim¶ (int) – the embedding dim
heads¶ (int) – number of attention heads
layers¶ (int) – number of layers
pixels¶ (int) – number of input pixels
vocab_size¶ (int) – vocab size
num_classes¶ (int) – number of classes in the input
classify¶ (bool) – true if should classify
batch_size¶ (int) – the batch size
learning_rate¶ (float) – learning rate
steps¶ (int) – number of steps for cosine annealing
data_dir¶ (str) – where to store data
num_workers¶ (int) – num_data workers

Pixel CNN¶

class pl_bolts.models.vision.pixel_cnn.PixelCNN(input_channels, hidden_channels=256, num_blocks=5)[source]

Bases: torch.nn.Module

Implementation of Pixel CNN.

Paper authors: Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu

Implemented by:

William Falcon

Example:

>>> from pl_bolts.models.vision import PixelCNN
>>> import torch
...
>>> model = PixelCNN(input_channels=3)
>>> x = torch.rand(5, 3, 64, 64)
>>> out = model(x)
...
>>> out.shape
torch.Size([5, 3, 64, 64])

GANs¶

Collection of Generative Adversarial Networks

Basic GAN¶

This is a vanilla GAN. This model can work on any dataset size but results are shown for MNIST. Replace the encoder, decoder or any part of the training loop to build a new method, or simply finetune on your data.

Implemented by:

William Falcon

Example outputs:

Loss curves:

from pl_bolts.models.gans import GAN
...
gan = GAN()
trainer = Trainer()
trainer.fit(gan)

class pl_bolts.models.gans.GAN(input_channels, input_height, input_width, latent_dim=32, learning_rate=0.0002, **kwargs)[source]

Bases: pytorch_lightning.LightningModule

Vanilla GAN implementation.

Example:

from pl_bolts.models.gan import GAN

m = GAN()
Trainer(gpus=2).fit(m)

Example CLI:

# mnist
python  basic_gan_module.py --gpus 1

# imagenet
python  basic_gan_module.py --gpus 1 --dataset 'imagenet2012'
--data_dir /path/to/imagenet/folder/ --meta_dir ~/path/to/meta/bin/folder
--batch_size 256 --learning_rate 0.0001

Parameters

datamodule¶ – the datamodule (train, val, test splits)
latent_dim¶ (int) – emb dim for encoder
batch_size¶ – the batch size
learning_rate¶ (float) – the learning rate
data_dir¶ – where to store data
num_workers¶ – data workers

forward(z)[source]

Generates an image given input noise z

Example:

z = torch.rand(batch_size, latent_dim)
gan = GAN.load_from_checkpoint(PATH)
img = gan(z)

Self-supervised Learning¶

This bolts module houses a collection of all self-supervised learning models.

Self-supervised learning extracts representations of an input by solving a pretext task. In this package, we implement many of the current state-of-the-art self-supervised algorithms.

Self-supervised models are trained with unlabeled datasets

Use cases¶

Here are some use cases for the self-supervised package.

Extracting image features¶

The models in this module are trained unsupervised and thus can capture better image representations (features).

In this example, we’ll load a resnet 18 which was pretrained on imagenet using CPC as the pretext task.

Example:

from pl_bolts.models.self_supervised import CPCV2

# load resnet18 pretrained using CPC on imagenet
model = CPCV2(pretrained='resnet18')
cpc_resnet18 = model.encoder
cpc_resnet18.freeze()

# it supports any torchvision resnet
model = CPCV2(pretrained='resnet50')

This means you can now extract image representations that were pretrained via unsupervised learning.

Example:

my_dataset = SomeDataset()
for batch in my_dataset:
    x, y = batch
    out = cpc_resnet18(x)

Train with unlabeled data¶

These models are perfect for training from scratch when you have a huge set of unlabeled images

from pl_bolts.models.self_supervised import SimCLR
from pl_bolts.models.self_supervised.simclr import SimCLREvalDataTransform, SimCLRTrainDataTransform


train_dataset = MyDataset(transforms=SimCLRTrainDataTransform())
val_dataset = MyDataset(transforms=SimCLREvalDataTransform())

# simclr needs a lot of compute!
model = SimCLR()
trainer = Trainer(tpu_cores=128)
trainer.fit(
    model,
    DataLoader(train_dataset),
    DataLoader(val_dataset),
)

Research¶

Mix and match any part, or subclass to create your own new method

from pl_bolts.models.self_supervised import CPCV2
from pl_bolts.losses.self_supervised_learning import FeatureMapContrastiveTask

amdim_task = FeatureMapContrastiveTask(comparisons='01, 11, 02', bidirectional=True)
model = CPCV2(contrastive_task=amdim_task)

Contrastive Learning Models¶

Contrastive self-supervised learning (CSL) is a self-supervised learning approach where we generate representations of instances such that similar instances are near each other and far from dissimilar ones. This is often done by comparing triplets of positive, anchor and negative representations.

In this section, we list Lightning implementations of popular contrastive learning approaches.

AMDIM¶

class pl_bolts.models.self_supervised.AMDIM(datamodule='cifar10', encoder='amdim_encoder', contrastive_task=torch.nn.Module, image_channels=3, image_height=32, encoder_feature_dim=320, embedding_fx_dim=1280, conv_block_depth=10, use_bn=False, tclip=20.0, learning_rate=0.0002, data_dir='', num_classes=10, batch_size=200, **kwargs)[source]

Bases: pytorch_lightning.LightningModule

PyTorch Lightning implementation of Augmented Multiscale Deep InfoMax (AMDIM)

Paper authors: Philip Bachman, R Devon Hjelm, William Buchwalter.

Model implemented by: William Falcon

This code is adapted to Lightning using the original author repo (the original repo).

Example

>>> from pl_bolts.models.self_supervised import AMDIM
...
>>> model = AMDIM(encoder='resnet18')

Train:

trainer = Trainer()
trainer.fit(model)

Parameters

datamodule¶ (Union[str, LightningDataModule]) – A LightningDatamodule
encoder¶ (Union[str, Module, LightningModule]) – an encoder string or model
image_channels¶ (int) – 3
image_height¶ (int) – pixels
encoder_feature_dim¶ (int) – Called ndf in the paper, this is the representation size for the encoder.
embedding_fx_dim¶ (int) – Output dim of the embedding function (nrkhs in the paper) (Reproducing Kernel Hilbert Spaces).
conv_block_depth¶ (int) – Depth of each encoder block,
use_bn¶ (bool) – If true will use batchnorm.
tclip¶ (int) – soft clipping non-linearity to the scores after computing the regularization term and before computing the log-softmax. This is the ‘second trick’ used in the paper
learning_rate¶ (int) – The learning rate
data_dir¶ (str) – Where to store data
num_classes¶ (int) – How many classes in the dataset
batch_size¶ (int) – The batch size

BYOL¶

class pl_bolts.models.self_supervised.BYOL(num_classes, learning_rate=0.2, weight_decay=1.5e-06, input_height=32, batch_size=32, num_workers=0, warmup_epochs=10, max_epochs=1000, **kwargs)[source]

Bases: pytorch_lightning.LightningModule

PyTorch Lightning implementation of Bootstrap Your Own Latent (BYOL)

Paper authors: Jean-Bastien Grill ,Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko.

Model implemented by:

Annika Brundyn

Warning

Work in progress. This implementation is still being verified.

TODOs:

verify on CIFAR-10
verify on STL-10
pre-train on imagenet

Example:

import pytorch_lightning as pl
from pl_bolts.models.self_supervised import BYOL
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.models.self_supervised.simclr.simclr_transforms import (
    SimCLREvalDataTransform, SimCLRTrainDataTransform)

# model
model = BYOL(num_classes=10)

# data
dm = CIFAR10DataModule(num_workers=0)
dm.train_transforms = SimCLRTrainDataTransform(32)
dm.val_transforms = SimCLREvalDataTransform(32)

trainer = pl.Trainer()
trainer.fit(model, dm)

Train:

trainer = Trainer()
trainer.fit(model)

CLI command:

# cifar10
python byol_module.py --gpus 1

# imagenet
python byol_module.py
    --gpus 8
    --dataset imagenet2012
    --data_dir /path/to/imagenet/
    --meta_dir /path/to/folder/with/meta.bin/
    --batch_size 32

Parameters

datamodule¶ – The datamodule
learning_rate¶ (float) – the learning rate
weight_decay¶ (float) – optimizer weight decay
input_height¶ (int) – image input height
batch_size¶ (int) – the batch size
num_workers¶ (int) – number of workers
warmup_epochs¶ (int) – num of epochs for scheduler warm up
max_epochs¶ (int) – max epochs for scheduler

CPC (V2)¶

PyTorch Lightning implementation of Data-Efficient Image Recognition with Contrastive Predictive Coding

Paper authors: (Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Eslami, Aaron van den Oord).

Model implemented by:

William Falcon

Tullie Murrell

To Train:

import pytorch_lightning as pl
from pl_bolts.models.self_supervised import CPCV2
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.models.self_supervised.cpc import (
    CPCTrainTransformsCIFAR10, CPCEvalTransformsCIFAR10)

# data
dm = CIFAR10DataModule(num_workers=0)
dm.train_transforms = CPCTrainTransformsCIFAR10()
dm.val_transforms = CPCEvalTransformsCIFAR10()

# model
model = CPCV2()

# fit
trainer = pl.Trainer()
trainer.fit(model, dm)

To finetune:

python cpc_finetuner.py
    --ckpt_path path/to/checkpoint.ckpt
    --dataset cifar10
    --gpus 1

CIFAR-10 and STL-10 baselines¶

CPCv2 does not report baselines on CIFAR-10 and STL-10 datasets. Results in table are reported from the YADIM paper.

CPCv2 implementation results¶
Dataset	test acc	Encoder	Optimizer	Batch	Epochs	Hardware	LR
CIFAR-10	84.52	CPCresnet101	Adam	64	1000 (upto 24 hours)	1 V100 (32GB)	4e-5
STL-10	78.36	CPCresnet101	Adam	144	1000 (upto 72 hours)	4 V100 (32GB)	1e-4
ImageNet	54.82	CPCresnet101	Adam	3072	1000 (upto 21 days)	64 V100 (32GB)	4e-5

CIFAR-10 pretrained model:

from pl_bolts.models.self_supervised import CPCV2

weight_path = 'https://pl-bolts-weights.s3.us-east-2.amazonaws.com/cpc/cpc-cifar10-v4-exp3/epoch%3D474.ckpt'
cpc_v2 = CPCV2.load_from_checkpoint(weight_path, strict=False)

cpc_v2.freeze()

Tensorboard for CIFAR10

Pre-training:

Fine-tuning:

STL-10 pretrained model:

from pl_bolts.models.self_supervised import CPCV2

weight_path = 'https://pl-bolts-weights.s3.us-east-2.amazonaws.com/cpc/cpc-stl10-v0-exp3/epoch%3D624.ckpt'
cpc_v2 = CPCV2.load_from_checkpoint(weight_path, strict=False)

cpc_v2.freeze()

Tensorboard for STL10

Pre-training:

Fine-tuning:

ImageNet pretrained model:

from pl_bolts.models.self_supervised import CPCV2

weight_path = 'https://pl-bolts-weights.s3.us-east-2.amazonaws.com/cpc/cpcv2_weights/checkpoints/epoch%3D526.ckpt'
cpc_v2 = CPCV2.load_from_checkpoint(weight_path, strict=False)

cpc_v2.freeze()

Tensorboard for ImageNet

Pre-training:

Fine-tuning:

CPCV2 API¶

class pl_bolts.models.self_supervised.CPCV2(datamodule=None, encoder='cpc_encoder', patch_size=8, patch_overlap=4, online_ft=True, task='cpc', num_workers=4, learning_rate=0.0001, data_dir='', batch_size=32, pretrained=None, **kwargs)[source]

Bases: pytorch_lightning.LightningModule

Parameters

datamodule¶ (Optional[LightningDataModule]) – A Datamodule (optional). Otherwise set the dataloaders directly
encoder¶ (Union[str, Module, LightningModule]) – A string for any of the resnets in torchvision, or the original CPC encoder, or a custon nn.Module encoder
patch_size¶ (int) – How big to make the image patches
patch_overlap¶ (int) – How much overlap should each patch have.
online_ft¶ (int) – Enable a 1024-unit MLP to fine-tune online
task¶ (str) – Which self-supervised task to use (‘cpc’, ‘amdim’, etc…)
num_workers¶ (int) – num dataloader worksers
learning_rate¶ (int) – what learning rate to use
data_dir¶ (str) – where to store data
batch_size¶ (int) – batch size
pretrained¶ (Optional[str]) – If true, will use the weights pretrained (using CPC) on Imagenet

Moco (V2)¶

class pl_bolts.models.self_supervised.MocoV2(base_encoder='resnet18', emb_dim=128, num_negatives=65536, encoder_momentum=0.999, softmax_temperature=0.07, learning_rate=0.03, momentum=0.9, weight_decay=0.0001, datamodule=None, data_dir='./', batch_size=256, use_mlp=False, num_workers=8, *args, **kwargs)[source]

Bases: pytorch_lightning.LightningModule

PyTorch Lightning implementation of Moco

Paper authors: Xinlei Chen, Haoqi Fan, Ross Girshick, Kaiming He.

Code adapted from facebookresearch/moco to Lightning by:

William Falcon

Example

>>> from pl_bolts.models.self_supervised import MocoV2
...
>>> model = MocoV2()

Train:

trainer = Trainer()
trainer.fit(model)

CLI command:

# cifar10
python moco2_module.py --gpus 1

# imagenet
python moco2_module.py
    --gpus 8
    --dataset imagenet2012
    --data_dir /path/to/imagenet/
    --meta_dir /path/to/folder/with/meta.bin/
    --batch_size 32

Parameters

base_encoder¶ (Union[str, Module]) – torchvision model name or torch.nn.Module
emb_dim¶ (int) – feature dimension (default: 128)
num_negatives¶ (int) – queue size; number of negative keys (default: 65536)
encoder_momentum¶ (float) – moco momentum of updating key encoder (default: 0.999)
softmax_temperature¶ (float) – softmax temperature (default: 0.07)
learning_rate¶ (float) – the learning rate
momentum¶ (float) – optimizer momentum
weight_decay¶ (float) – optimizer weight decay
datamodule¶ (Optional[LightningDataModule]) – the DataModule (train, val, test dataloaders)
data_dir¶ (str) – the directory to store data
batch_size¶ (int) – batch size
use_mlp¶ (bool) – add an mlp to the encoders
num_workers¶ (int) – workers for the loaders

_batch_shuffle_ddp(x)[source]: Batch shuffle, for making use of BatchNorm. * Only support DistributedDataParallel (DDP) model. *

_batch_unshuffle_ddp(x, idx_unshuffle)[source]: Undo batch shuffle. * Only support DistributedDataParallel (DDP) model. *

_momentum_update_key_encoder()[source]: Momentum update of the key encoder

forward(img_q, img_k)[source]

Input:: im_q: a batch of query images im_k: a batch of key images
Output:: logits, targets

init_encoders(base_encoder)[source]: Override to add your own encoders

SimCLR¶

PyTorch Lightning implementation of SimCLR

Paper authors: Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton.

Model implemented by:

William Falcon

Tullie Murrell

To Train:

import pytorch_lightning as pl
from pl_bolts.models.self_supervised import SimCLR
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.models.self_supervised.simclr.simclr_transforms import (
    SimCLREvalDataTransform, SimCLRTrainDataTransform)

# data
dm = CIFAR10DataModule(num_workers=0)
dm.train_transforms = SimCLRTrainDataTransform(32)
dm.val_transforms = SimCLREvalDataTransform(32)

# model
model = SimCLR(num_samples=dm.num_samples, batch_size=dm.batch_size)

# fit
trainer = pl.Trainer()
trainer.fit(model, dm)

CIFAR-10 baseline¶

Cifar-10 implementation results¶
Implementation	test acc	Encoder	Optimizer	Batch	Epochs	Hardware	LR
Original	92.00?	resnet50	LARS	512	1000	1 V100 (32GB)	1.0
Ours	85.68	resnet50	LARS	512	960 (12 hr)	1 V100 (32GB)	1e-6

CIFAR-10 pretrained model:

from pl_bolts.models.self_supervised import SimCLR

weight_path = 'https://pl-bolts-weights.s3.us-east-2.amazonaws.com/simclr/simclr-cifar10-v1-exp12_87_52/epoch%3D960.ckpt'
simclr = SimCLR.load_from_checkpoint(weight_path, strict=False)

simclr.freeze()

Pre-training:

Fine-tuning (Single layer MLP, 1024 hidden units):

To reproduce:

# pretrain
python simclr_module.py
    --gpus 1
    --dataset cifar10
    --batch_size 512
    --learning_rate 1e-06
    --num_workers 8

# finetune
python simclr_finetuner.py
    --ckpt_path path/to/epoch=xyz.ckpt
    --gpus 1

SimCLR API¶

class pl_bolts.models.self_supervised.SimCLR(batch_size, num_samples, warmup_epochs=10, lr=0.0001, opt_weight_decay=1e-06, loss_temperature=0.5, **kwargs)[source]

Bases: pytorch_lightning.LightningModule

Parameters

batch_size¶ – the batch size
num_samples¶ – num samples in the dataset
warmup_epochs¶ – epochs to warmup the lr for
lr¶ – the optimizer learning rate
opt_weight_decay¶ – the optimizer weight decay
loss_temperature¶ – the loss temperature

Self-supervised learning Transforms¶

These transforms are used in various self-supervised learning approaches.

CPC transforms¶

Transforms used for CPC

CIFAR-10 Train (c)¶

class pl_bolts.models.self_supervised.cpc.transforms.CPCTrainTransformsCIFAR10(patch_size=8, overlap=4)[source]

Bases: object

Transforms used for CPC:

Parameters

patch_size¶ – size of patches when cutting up the image into overlapping patches
overlap¶ – how much to overlap patches

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())

__call__(inp)[source]: Call self as a function.

CIFAR-10 Eval (c)¶

class pl_bolts.models.self_supervised.cpc.transforms.CPCEvalTransformsCIFAR10(patch_size=8, overlap=4)[source]

Bases: object

Transforms used for CPC:

Parameters

patch_size¶ – size of patches when cutting up the image into overlapping patches
overlap¶ – how much to overlap patches

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())

__call__(inp)[source]: Call self as a function.

Imagenet Train (c)¶

class pl_bolts.models.self_supervised.cpc.transforms.CPCTrainTransformsImageNet128(patch_size=32, overlap=16)[source]

Bases: object

Transforms used for CPC:

Parameters

patch_size¶ – size of patches when cutting up the image into overlapping patches
overlap¶ – how much to overlap patches

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())

__call__(inp)[source]: Call self as a function.

Imagenet Eval (c)¶

class pl_bolts.models.self_supervised.cpc.transforms.CPCEvalTransformsImageNet128(patch_size=32, overlap=16)[source]

Bases: object

Transforms used for CPC:

Parameters

patch_size¶ – size of patches when cutting up the image into overlapping patches
overlap¶ – how much to overlap patches

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())

__call__(inp)[source]: Call self as a function.

STL-10 Train (c)¶

class pl_bolts.models.self_supervised.cpc.transforms.CPCTrainTransformsSTL10(patch_size=16, overlap=8)[source]

Bases: object

Transforms used for CPC:

Parameters

patch_size¶ – size of patches when cutting up the image into overlapping patches
overlap¶ – how much to overlap patches

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())

__call__(inp)[source]: Call self as a function.

STL-10 Eval (c)¶

class pl_bolts.models.self_supervised.cpc.transforms.CPCEvalTransformsSTL10(patch_size=16, overlap=8)[source]

Bases: object

Transforms used for CPC:

Parameters

patch_size¶ – size of patches when cutting up the image into overlapping patches
overlap¶ – how much to overlap patches

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())

__call__(inp)[source]: Call self as a function.

AMDIM transforms¶

Transforms used for AMDIM

CIFAR-10 Train (a)¶

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMTrainTransformsCIFAR10[source]

Bases: object

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)

__call__(inp)[source]: Call self as a function.

CIFAR-10 Eval (a)¶

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMEvalTransformsCIFAR10[source]

Bases: object

Transforms applied to AMDIM

Transforms:

transforms.ToTensor(),
normalize

Example:

x = torch.rand(5, 3, 32, 32)

transform = AMDIMEvalTransformsCIFAR10()
(view1, view2) = transform(x)

__call__(inp)[source]: Call self as a function.

Imagenet Train (a)¶

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMTrainTransformsImageNet128(height=128)[source]

Bases: object

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)

__call__(inp)[source]: Call self as a function.

Imagenet Eval (a)¶

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMEvalTransformsImageNet128(height=128)[source]

Bases: object

Transforms applied to AMDIM

Transforms:

transforms.Resize(height + 6, interpolation=3),
transforms.CenterCrop(height),
transforms.ToTensor(),
normalize

Example:

x = torch.rand(5, 3, 128, 128)

transform = AMDIMEvalTransformsImageNet128()
view1 = transform(x)

__call__(inp)[source]: Call self as a function.

STL-10 Train (a)¶

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMTrainTransformsSTL10(height=64)[source]

Bases: object

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)

__call__(inp)[source]: Call self as a function.

STL-10 Eval (a)¶

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMEvalTransformsSTL10(height=64)[source]

Bases: object

Transforms applied to AMDIM

Transforms:

transforms.Resize(height + 6, interpolation=3),
transforms.CenterCrop(height),
transforms.ToTensor(),
normalize

Example:

x = torch.rand(5, 3, 64, 64)

transform = AMDIMTrainTransformsSTL10()
view1 = transform(x)

__call__(inp)[source]: Call self as a function.

MOCO V2 transforms¶

Transforms used for MOCO V2

CIFAR-10 Train (m2)¶

class pl_bolts.models.self_supervised.moco.transforms.Moco2TrainCIFAR10Transforms(height=32)[source]

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]: Call self as a function.

CIFAR-10 Eval (m2)¶

class pl_bolts.models.self_supervised.moco.transforms.Moco2EvalCIFAR10Transforms(height=32)[source]

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]: Call self as a function.

Imagenet Train (m2)¶

class pl_bolts.models.self_supervised.moco.transforms.Moco2TrainSTL10Transforms(height=64)[source]

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]: Call self as a function.

Imagenet Eval (m2)¶

class pl_bolts.models.self_supervised.moco.transforms.Moco2EvalSTL10Transforms(height=64)[source]

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]: Call self as a function.

STL-10 Train (m2)¶

class pl_bolts.models.self_supervised.moco.transforms.Moco2TrainImagenetTransforms(height=128)[source]

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]: Call self as a function.

STL-10 Eval (m2)¶

class pl_bolts.models.self_supervised.moco.transforms.Moco2EvalImagenetTransforms(height=128)[source]

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]: Call self as a function.

SimCLR transforms¶

Transforms used for SimCLR

Train (sc)¶

class pl_bolts.models.self_supervised.simclr.simclr_transforms.SimCLRTrainDataTransform(input_height, s=1)[source]

Bases: object

Transforms for SimCLR

Transform:

RandomResizedCrop(size=self.input_height)
RandomHorizontalFlip()
RandomApply([color_jitter], p=0.8)
RandomGrayscale(p=0.2)
GaussianBlur(kernel_size=int(0.1 * self.input_height))
transforms.ToTensor()

Example:

from pl_bolts.models.self_supervised.simclr.transforms import SimCLRTrainDataTransform

transform = SimCLRTrainDataTransform(input_height=32)
x = sample()
(xi, xj) = transform(x)

__call__(sample)[source]: Call self as a function.

Eval (sc)¶

class pl_bolts.models.self_supervised.simclr.simclr_transforms.SimCLREvalDataTransform(input_height, s=1)[source]

Bases: object

Transforms for SimCLR

Transform:

Resize(input_height + 10, interpolation=3)
transforms.CenterCrop(input_height),
transforms.ToTensor()

Example:

from pl_bolts.models.self_supervised.simclr.transforms import SimCLREvalDataTransform

transform = SimCLREvalDataTransform(input_height=32)
x = sample()
(xi, xj) = transform(x)

__call__(sample)[source]: Call self as a function.

Self-supervised learning¶

Collection of useful functions for self-supervised learning

Identity class¶

Example:

from pl_bolts.utils import Identity

class pl_bolts.utils.self_supervised.Identity[source]

Bases: torch.nn.Module

An identity class to replace arbitrary layers in pretrained models

Example:

from pl_bolts.utils import Identity

model = resnet18()
model.fc = Identity()

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]

SSL backbone finetuner¶

class pl_bolts.models.self_supervised.ssl_finetuner.SSLFineTuner(backbone, in_features, num_classes, hidden_dim=1024)[source]

Bases: pytorch_lightning.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 CPCV2
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.models.self_supervised.cpc.transforms import CPCEvalTransformsCIFAR10,
                                                            CPCTrainTransformsCIFAR10

# pretrained model
backbone = CPCV2.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

backbone¶ – a pretrained model
in_features¶ – feature dim of backbone outputs
num_classes¶ – classes of the dataset
hidden_dim¶ – dim of the MLP (1024 default used in self-supervised literature)

Semi-supervised learning¶

Collection of utilities for semi-supervised learning where some part of the data is labeled and the other part is not.

Balanced classes¶

Example:

from pl_bolts.utils.semi_supervised import balance_classes

pl_bolts.utils.semi_supervised.balance_classes(X, Y, batch_size)[source]

Makes sure each batch has an equal amount of data from each class. Perfect balance

Parameters

X¶ (ndarray) – input features
Y¶ (list) – mixed labels (ints)
batch_size¶ (int) – the ultimate batch size

half labeled batches¶

Example:

from pl_bolts.utils.semi_supervised import balance_classes

pl_bolts.utils.semi_supervised.generate_half_labeled_batches(smaller_set_X, smaller_set_Y, larger_set_X, larger_set_Y, batch_size)[source]: Given a labeled dataset and an unlabeled dataset, this function generates a joint pair where half the batches are labeled and the other half is not

Self-supervised Learning Contrastive tasks¶

This section implements popular contrastive learning tasks used in self-supervised learning.

FeatureMapContrastiveTask¶

This task compares sets of feature maps.

In general the feature map comparison pretext task uses triplets of features. Here are the abstract steps of comparison.

Generate multiple views of the same image

x1_view_1 = data_augmentation(x1)
x1_view_2 = data_augmentation(x1)

Use a different example to generate additional views (usually within the same batch or a pool of candidates)

x2_view_1 = data_augmentation(x2)
x2_view_2 = data_augmentation(x2)

Pick 3 views to compare, these are the anchor, positive and negative features

anchor = x1_view_1
positive = x1_view_2
negative = x2_view_1

Generate feature maps for each view

(a0, a1, a2) = encoder(anchor)
(p0, p1, p2) = encoder(positive)

Make a comparison for a set of feature maps

phi = some_score_function()

# the '01' comparison
score = phi(a0, p1)

# and can be bidirectional
score = phi(p0, a1)

In practice the contrastive task creates a BxB matrix where B is the batch size. The diagonals for set 1 of feature maps are the anchors, the diagonals of set 2 of the feature maps are the positives, the non-diagonals of set 1 are the negatives.

class pl_bolts.losses.self_supervised_learning.FeatureMapContrastiveTask(comparisons='00, 11', tclip=10.0, bidirectional=True)[source]

Bases: torch.nn.Module

Performs an anchor, positive negative pair comparison for each each tuple of feature maps passed.

# extract feature maps
pos_0, pos_1, pos_2 = encoder(x_pos)
anc_0, anc_1, anc_2 = encoder(x_anchor)

# compare only the 0th feature maps
task = FeatureMapContrastiveTask('00')
loss, regularizer = task((pos_0), (anc_0))

# compare (pos_0 to anc_1) and (pos_0, anc_2)
task = FeatureMapContrastiveTask('01, 02')
losses, regularizer = task((pos_0, pos_1, pos_2), (anc_0, anc_1, anc_2))
loss = losses.sum()

# compare (pos_1 vs a anc_random)
task = FeatureMapContrastiveTask('0r')
loss, regularizer = task((pos_0, pos_1, pos_2), (anc_0, anc_1, anc_2))

Parameters

comparisons¶ (str) – groupings of feature map indices to compare (zero indexed, ‘r’ means random) ex: ‘00, 1r’
tclip¶ (float) – stability clipping value
bidirectional¶ (bool) – if true, does the comparison both ways

# with bidirectional the comparisons are done both ways
task = FeatureMapContrastiveTask('01, 02')

# will compare the following:
# 01: (pos_0, anc_1), (anc_0, pos_1)
# 02: (pos_0, anc_2), (anc_0, pos_2)

forward(anchor_maps, positive_maps)[source]

Takes in a set of tuples, each tuple has two feature maps with all matching dimensions

Example

>>> import torch
>>> from pytorch_lightning import seed_everything
>>> seed_everything(0)
0
>>> a1 = torch.rand(3, 5, 2, 2)
>>> a2 = torch.rand(3, 5, 2, 2)
>>> b1 = torch.rand(3, 5, 2, 2)
>>> b2 = torch.rand(3, 5, 2, 2)
...
>>> task = FeatureMapContrastiveTask('01, 11')
...
>>> losses, regularizer = task((a1, a2), (b1, b2))
>>> losses
tensor([2.2351, 2.1902])
>>> regularizer
tensor(0.0324)

static parse_map_indexes(comparisons)[source]

Example:

>>> FeatureMapContrastiveTask.parse_map_indexes('11')
[(1, 1)]
>>> FeatureMapContrastiveTask.parse_map_indexes('11,59')
[(1, 1), (5, 9)]
>>> FeatureMapContrastiveTask.parse_map_indexes('11,59, 2r')
[(1, 1), (5, 9), (2, -1)]

Context prediction tasks¶

The following tasks aim to predict a target using a context representation.

CPCContrastiveTask¶

This is the predictive task from CPC (v2).

task = CPCTask(num_input_channels=32)

# (batch, channels, rows, cols)
# this should be thought of as 49 feature vectors, each with 32 dims
Z = torch.random.rand(3, 32, 7, 7)

loss = task(Z)

class pl_bolts.losses.self_supervised_learning.CPCTask(num_input_channels, target_dim=64, embed_scale=0.1)[source]

Bases: torch.nn.Module

Loss used in CPC

Indices and tables¶

Logo

PyTorch Lightning Bolts¶

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

Continuous Integration¶

Install¶

Simple installation from PyPI

pip install pytorch-lightning-bolts

Install bleeding-edge (no guarantees)

pip install git+https://github.com/PytorchLightning/pytorch-lightning-bolts.git@master --upgrade

Docs¶

What is Bolts¶

Bolts is a Deep learning research and production toolbox of:

SOTA pretrained models.
Model components.
Callbacks.
Losses.
Datasets.

Main Goals of Bolts¶

The main goal of Bolts is to enable rapid model idea iteration.

Example 1: Finetuning on data¶

from pl_bolts.models.self_supervised import SimCLR
from pl_bolts.models.self_supervised.simclr.transforms import SimCLRTrainDataTransform, SimCLREvalDataTransform
import pytorch_lightning as pl

# data
train_data = DataLoader(MyDataset(transforms=SimCLRTrainDataTransform(input_height=32)))
val_data = DataLoader(MyDataset(transforms=SimCLREvalDataTransform(input_height=32)))

# model
weight_path = 'https://pl-bolts-weights.s3.us-east-2.amazonaws.com/simclr/simclr-cifar10-v1-exp12_87_52/epoch%3D960.ckpt'
simclr = SimCLR.load_from_checkpoint(weight_path, strict=False)

simclr.freeze()

# finetune

Example 2: Subclass and ideate¶

from pl_bolts.models import ImageGPT
from pl_bolts.self_supervised import SimCLR

class VideoGPT(ImageGPT):

    def training_step(self, batch, batch_idx):
        x, y = batch
        x = _shape_input(x)

        logits = self.gpt(x)
        simclr_features = self.simclr(x)

        # -----------------
        # do something new with GPT logits + simclr_features
        # -----------------

        loss = self.criterion(logits.view(-1, logits.size(-1)), x.view(-1).long())

        logs = {"loss": loss}
        return {"loss": loss, "log": logs}

Who is Bolts for?¶

Corporate production teams
Professional researchers
Ph.D. students
Linear + Logistic regression heroes

I don’t need deep learning¶

Great! We have LinearRegression and LogisticRegression implementations with numpy and sklearn bridges for datasets! But our implementations work on multiple GPUs, TPUs and scale dramatically…

Check out our Linear Regression on TPU demo

from pl_bolts.models.regression import LinearRegression
from pl_bolts.datamodules import SklearnDataModule
from sklearn.datasets import load_boston
import pytorch_lightning as pl

# sklearn dataset
X, y = load_boston(return_X_y=True)
loaders = SklearnDataModule(X, y)

model = LinearRegression(input_dim=13)

# try with gpus=4!
# trainer = pl.Trainer(gpus=4)
trainer = pl.Trainer()
trainer.fit(model, loaders.train_dataloader(), loaders.val_dataloader())
trainer.test(test_dataloaders=loaders.test_dataloader())

Is this another model zoo?¶

No!

Bolts is unique because models are implemented using PyTorch Lightning and structured so that they can be easily subclassed and iterated on.

For example, you can override the elbo loss of a VAE, or the generator_step of a GAN to quickly try out a new idea. The best part is that all the models are benchmarked so you won’t waste time trying to “reproduce” or find the bugs with your implementation.

Team¶

Bolts is supported by the PyTorch Lightning team and the PyTorch Lightning community!

pl_bolts.callbacks package¶

Collection of PyTorchLightning callbacks

Subpackages¶

pl_bolts.callbacks.vision package¶

Submodules¶

pl_bolts.callbacks.vision.confused_logit module¶

class pl_bolts.callbacks.vision.confused_logit.ConfusedLogitCallback(top_k, projection_factor=3, min_logit_value=5.0, logging_batch_interval=20, max_logit_difference=0.1)[source]¶

Bases: pytorch_lightning.Callback

Takes the logit predictions of a model and when the probabilities of two classes are very close, the model doesn’t have high certainty that it should pick one vs the other class.

This callback shows how the input would have to change to swing the model from one label prediction to the other.

In this case, the network predicts a 5… but gives almost equal probability to an 8. The images show what about the original 5 would have to change to make it more like a 5 or more like an 8.

For each confused logit the confused images are generated by taking the gradient from a logit wrt an input for the top two closest logits.

Example:

from pl_bolts.callbacks.vision import ConfusedLogitCallback
trainer = Trainer(callbacks=[ConfusedLogitCallback()])

Note

whenever called, this model will look for self.last_batch and self.last_logits in the LightningModule

Note

this callback supports tensorboard only right now

Parameters

top_k – How many “offending” images we should plot
projection_factor – How much to multiply the input image to make it look more like this logit label
min_logit_value – Only consider logit values above this threshold
logging_batch_interval – how frequently to inspect/potentially plot something
max_logit_difference – when the top 2 logits are within this threshold we consider them confused

Authored by:

Alfredo Canziani

static _ConfusedLogitCallback__draw_sample(fig, axarr, row_idx, col_idx, img, title)[source]¶

_plot(confusing_x, confusing_y, trainer, model, mask_idxs)[source]¶

on_train_batch_end(trainer, pl_module, batch, batch_idx, dataloader_idx)[source]¶

pl_bolts.callbacks.vision.image_generation module¶

class pl_bolts.callbacks.vision.image_generation.TensorboardGenerativeModelImageSampler(num_samples=3)[source]¶

Bases: pytorch_lightning.Callback

Generates images and logs to tensorboard. Your model must implement the forward function for generation

Requirements:

# model must have img_dim arg
model.img_dim = (1, 28, 28)

# model forward must work for sampling
z = torch.rand(batch_size, latent_dim)
img_samples = your_model(z)

Example:

from pl_bolts.callbacks import TensorboardGenerativeModelImageSampler

trainer = Trainer(callbacks=[TensorboardGenerativeModelImageSampler()])

on_epoch_end(trainer, pl_module)[source]¶

Submodules¶

pl_bolts.callbacks.printing module¶

class pl_bolts.callbacks.printing.PrintTableMetricsCallback[source]¶

Bases: pytorch_lightning.callbacks.Callback

Prints a table with the metrics in columns on every epoch end

Example:

from pl_bolts.callbacks import PrintTableMetricsCallback

callback = PrintTableMetricsCallback()

pass into trainer like so:

trainer = pl.Trainer(callbacks=[callback])
trainer.fit(...)

# ------------------------------
# at the end of every epoch it will print
# ------------------------------

# loss│train_loss│val_loss│epoch
# ──────────────────────────────
# 2.2541470527648926│2.2541470527648926│2.2158432006835938│0

on_epoch_end(trainer, pl_module)[source]¶

pl_bolts.callbacks.printing.dicts_to_table(dicts, keys=None, pads=None, fcodes=None, convert_headers=None, header_names=None, skip_none_lines=False, replace_values=None)[source]¶

Generate ascii table from dictionary Taken from (https://stackoverflow.com/questions/40056747/print-a-list-of-dictionaries-in-table-form)

Parameters

dicts (List[Dict]) – input dictionary list; empty lists make keys OR header_names mandatory
keys (Optional[List[str]]) – order list of keys to generate columns for; no key/dict-key should suffix with ‘____’ else adjust code-suffix
pads (Optional[List[str]]) – indicate padding direction and size, eg <10 to right pad alias left-align
fcodes (Optional[List[str]]) – formating codes for respective column type, eg .3f
convert_headers (Optional[Dict[str, Callable]]) – apply converters(dict) on column keys k, eg timestamps
header_names (Optional[List[str]]) – supply for custom column headers instead of keys
skip_none_lines (bool) – skip line if contains None
replace_values (Optional[Dict[str, Any]]) – specify per column keys k a map from seen value to new value; new value must comply with the columns fcode; CAUTION: modifies input (due speed)

Example

>>> a = {'a': 1, 'b': 2}
>>> b = {'a': 3, 'b': 4}
>>> print(dicts_to_table([a, b]))
a│b
───
1│2
3│4

pl_bolts.callbacks.self_supervised module¶

class pl_bolts.callbacks.self_supervised.BYOLMAWeightUpdate(initial_tau=0.996)[source]¶

Bases: pytorch_lightning.Callback

Weight update rule from BYOL.

Your model should have a:

self.online_network.

self.target_network.

Updates the target_network params using an exponential moving average update rule weighted by tau. BYOL claims this keeps the online_network from collapsing.

Note

Automatically increases tau from initial_tau to 1.0 with every training step

Example:

from pl_bolts.callbacks.self_supervised import BYOLMAWeightUpdate

# model must have 2 attributes
model = Model()
model.online_network = ...
model.target_network = ...

# make sure to set max_steps in Trainer
trainer = Trainer(callbacks=[BYOLMAWeightUpdate()], max_steps=1000)

Parameters: initial_tau – starting tau. Auto-updates with every training step

on_train_batch_end(trainer, pl_module, batch, batch_idx, dataloader_idx)[source]¶

update_tau(pl_module, trainer)[source]¶

update_weights(online_net, target_net)[source]¶

class pl_bolts.callbacks.self_supervised.SSLOnlineEvaluator(drop_p=0.2, hidden_dim=1024, z_dim=None, num_classes=None)[source]¶

Bases: pytorch_lightning.Callback

Attaches a MLP for finetuning using the standard self-supervised protocol.

Example:

from pl_bolts.callbacks.self_supervised import SSLOnlineEvaluator

# your model must have 2 attributes
model = Model()
model.z_dim = ... # the representation dim
model.num_classes = ... # the num of classes in the model

Parameters

drop_p (float) – (0.2) dropout probability
hidden_dim (int) –
1. the hidden dimension for the finetune MLP

get_representations(pl_module, x)[source]¶: Override this to customize for the particular model :param _sphinx_paramlinks_pl_bolts.callbacks.self_supervised.SSLOnlineEvaluator.get_representations.pl_module: :param _sphinx_paramlinks_pl_bolts.callbacks.self_supervised.SSLOnlineEvaluator.get_representations.x:

on_pretrain_routine_start(trainer, pl_module)[source]¶

on_train_batch_end(trainer, pl_module, batch, batch_idx, dataloader_idx)[source]¶

to_device(batch, device)[source]¶

pl_bolts.callbacks.variational module¶

class pl_bolts.callbacks.variational.LatentDimInterpolator(interpolate_epoch_interval=20, range_start=-5, range_end=5, num_samples=2)[source]¶

Bases: pytorch_lightning.callbacks.Callback

Interpolates the latent space for a model by setting all dims to zero and stepping through the first two dims increasing one unit at a time.

Default interpolates between [-5, 5] (-5, -4, -3, …, 3, 4, 5)

Example:

from pl_bolts.callbacks import LatentDimInterpolator

Trainer(callbacks=[LatentDimInterpolator()])

Parameters

interpolate_epoch_interval –
range_start – default -5
range_end – default 5
num_samples – default 2

interpolate_latent_space(pl_module, latent_dim)[source]¶

on_epoch_end(trainer, pl_module)[source]¶

pl_bolts.datamodules package¶

Submodules¶

pl_bolts.datamodules.async_dataloader module¶

class pl_bolts.datamodules.async_dataloader.AsynchronousLoader(data, device=torch.device, q_size=10, num_batches=None, **kwargs)[source]¶

Bases: object

Class for asynchronously loading from CPU memory to device memory with DataLoader.

Note that this only works for single GPU training, multiGPU uses PyTorch’s DataParallel or DistributedDataParallel which uses its own code for transferring data across GPUs. This could just break or make things slower with DataParallel or DistributedDataParallel.

Parameters

data – The PyTorch Dataset or DataLoader we’re using to load.
device – The PyTorch device we are loading to
q_size – Size of the queue used to store the data loaded to the device
num_batches – Number of batches to load. This must be set if the dataloader doesn’t have a finite __len__. It will also override DataLoader.__len__ if set and DataLoader has a __len__. Otherwise it can be left as None
**kwargs – Any additional arguments to pass to the dataloader if we’re constructing one here

load_instance(sample)[source]¶

load_loop()[source]¶

pl_bolts.datamodules.base_dataset module¶

class pl_bolts.datamodules.base_dataset.LightDataset(*args, **kwargs)[source]¶

Bases: abc.ABC, torch.utils.data.Dataset

_download_from_url(base_url, data_folder, file_name)[source]¶

static _prepare_subset(full_data, full_targets, num_samples, labels)[source]¶

Prepare a subset of a common dataset.

Return type: Tuple[Tensor, Tensor]

DATASET_NAME = 'light'[source]¶

cache_folder_name: str = None[source]¶

property cached_folder_path[source]¶

Return type: str

data: torch.Tensor = None[source]¶

dir_path: str = None[source]¶

normalize: tuple = None[source]¶

targets: torch.Tensor = None[source]¶

pl_bolts.datamodules.binary_mnist_datamodule module¶

class pl_bolts.datamodules.binary_mnist_datamodule.BinaryMNIST(*args, **kwargs)[source]¶: Bases: torchvision.datasets.MNIST

class pl_bolts.datamodules.binary_mnist_datamodule.BinaryMNISTDataModule(data_dir, val_split=5000, num_workers=16, normalize=False, seed=42, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningDataModule

Specs:

10 classes (1 per digit)
Each image is (1 x 28 x 28)

Binary MNIST, train, val, test splits and transforms

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor()
])

Example:

from pl_bolts.datamodules import BinaryMNISTDataModule

dm = BinaryMNISTDataModule('.')
model = LitModel()

Trainer().fit(model, dm)

Parameters

data_dir (str) – where to save/load the data
val_split (int) – how many of the training images to use for the validation split
num_workers (int) – how many workers to use for loading data
normalize (bool) – If true applies image normalize

_default_transforms()[source]¶

prepare_data()[source]¶: Saves MNIST files to data_dir

test_dataloader(batch_size=32, transforms=None)[source]¶

MNIST test set uses the test split

Parameters

batch_size – size of batch
transforms – custom transforms

train_dataloader(batch_size=32, transforms=None)[source]¶

MNIST train set removes a subset to use for validation

Parameters

batch_size – size of batch
transforms – custom transforms

val_dataloader(batch_size=32, transforms=None)[source]¶

MNIST val set uses a subset of the training set for validation

Parameters

batch_size – size of batch
transforms – custom transforms

name = 'mnist'[source]¶

property num_classes[source]¶: Return: 10

pl_bolts.datamodules.cifar10_datamodule module¶

class pl_bolts.datamodules.cifar10_datamodule.CIFAR10DataModule(data_dir=None, val_split=5000, num_workers=16, batch_size=32, seed=42, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningDataModule

Specs:

10 classes (1 per class)
Each image is (3 x 32 x 32)

Standard CIFAR10, train, val, test splits and transforms

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor(),
    transforms.Normalize(
        mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
        std=[x / 255.0 for x in [63.0, 62.1, 66.7]]
    )
])

Example:

from pl_bolts.datamodules import CIFAR10DataModule

dm = CIFAR10DataModule(PATH)
model = LitModel()

Trainer().fit(model, dm)

Or you can set your own transforms

Example:

dm.train_transforms = ...
dm.test_transforms = ...
dm.val_transforms  = ...

Parameters

data_dir (Optional[str]) – where to save/load the data
val_split (int) – how many of the training images to use for the validation split
num_workers (int) – how many workers to use for loading data
batch_size (int) – number of examples per training/eval step

default_transforms()[source]¶

prepare_data()[source]¶: Saves CIFAR10 files to data_dir

test_dataloader()[source]¶: CIFAR10 test set uses the test split

train_dataloader()[source]¶: CIFAR train set removes a subset to use for validation

val_dataloader()[source]¶: CIFAR10 val set uses a subset of the training set for validation

extra_args = {}[source]¶

name = 'cifar10'[source]¶

property num_classes[source]¶: Return: 10

class pl_bolts.datamodules.cifar10_datamodule.TinyCIFAR10DataModule(data_dir, val_split=50, num_workers=16, num_samples=100, labels=(1, 5, 8), *args, **kwargs)[source]¶

Bases: pl_bolts.datamodules.cifar10_datamodule.CIFAR10DataModule

Standard CIFAR10, train, val, test splits and transforms

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor(),
    transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
                         std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
])

Example:

from pl_bolts.datamodules import CIFAR10DataModule

dm = CIFAR10DataModule(PATH)
model = LitModel(datamodule=dm)

Parameters

data_dir (str) – where to save/load the data
val_split (int) – how many of the training images to use for the validation split
num_workers (int) – how many workers to use for loading data
num_samples (int) – number of examples per selected class/label
labels (Optional[Sequence]) – list selected CIFAR10 classes/labels

property num_classes[source]¶

Return number of classes.

Return type: int

pl_bolts.datamodules.cifar10_dataset module¶

class pl_bolts.datamodules.cifar10_dataset.CIFAR10(data_dir='.', train=True, transform=None, download=True)[source]¶

Bases: pl_bolts.datamodules.base_dataset.LightDataset

Customized CIFAR10 dataset for testing Pytorch Lightning without the torchvision dependency.

Part of the code was copied from https://github.com/pytorch/vision/blob/build/v0.5.0/torchvision/datasets/

Parameters

data_dir (str) – Root directory of dataset where CIFAR10/processed/training.pt and CIFAR10/processed/test.pt exist.
train (bool) – If True, creates dataset from training.pt, otherwise from test.pt.
download (bool) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

Examples

>>> from torchvision import transforms
>>> from pl_bolts.transforms.dataset_normalizations import cifar10_normalization
>>> cf10_transforms = transforms.Compose([transforms.ToTensor(), cifar10_normalization()])
>>> dataset = CIFAR10(download=True, transform=cf10_transforms)
>>> len(dataset)
50000
>>> torch.bincount(dataset.targets)
tensor([5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000])
>>> data, label = dataset[0]
>>> data.shape
torch.Size([3, 32, 32])
>>> label
6

Labels:

airplane: 0
automobile: 1
bird: 2
cat: 3
deer: 4
dog: 5
frog: 6
horse: 7
ship: 8
truck: 9

classmethod _check_exists(data_folder, file_names)[source]¶

Return type: bool

_extract_archive_save_torch(download_path)[source]¶

_unpickle(path_folder, file_name)[source]¶

Return type: Tuple[Tensor, Tensor]

download(data_folder)[source]¶

Download the data if it doesn’t exist in cached_folder_path already.

Return type: None

prepare_data(download)[source]¶

BASE_URL = 'https://www.cs.toronto.edu/~kriz/'[source]¶

DATASET_NAME = 'CIFAR10'[source]¶

FILE_NAME = 'cifar-10-python.tar.gz'[source]¶

TEST_FILE_NAME = 'test.pt'[source]¶

TRAIN_FILE_NAME = 'training.pt'[source]¶

cache_folder_name: str = 'complete'[source]¶

data = None[source]¶

dir_path = None[source]¶

labels = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}[source]¶

normalize = None[source]¶

relabel = False[source]¶

targets = None[source]¶

class pl_bolts.datamodules.cifar10_dataset.TrialCIFAR10(data_dir='.', train=True, transform=None, download=False, num_samples=100, labels=(1, 5, 8), relabel=True)[source]¶

Bases: pl_bolts.datamodules.cifar10_dataset.CIFAR10

Customized CIFAR10 dataset for testing Pytorch Lightning without the torchvision dependency.

Parameters

data_dir (str) – Root directory of dataset where CIFAR10/processed/training.pt and CIFAR10/processed/test.pt exist.
train (bool) – If True, creates dataset from training.pt, otherwise from test.pt.
download (bool) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.
num_samples (int) – number of examples per selected class/digit
labels (Optional[Sequence]) – list selected CIFAR10 digits/classes

Examples

>>> dataset = TrialCIFAR10(download=True, num_samples=150, labels=(1, 5, 8))
>>> len(dataset)
450
>>> sorted(set([d.item() for d in dataset.targets]))
[1, 5, 8]
>>> torch.bincount(dataset.targets)
tensor([  0, 150,   0,   0,   0, 150,   0,   0, 150])
>>> data, label = dataset[0]
>>> data.shape
torch.Size([3, 32, 32])

prepare_data(download)[source]¶

Return type: None

data = None[source]¶

dir_path = None[source]¶

normalize = None[source]¶

targets = None[source]¶

pl_bolts.datamodules.cityscapes_datamodule module¶

class pl_bolts.datamodules.cityscapes_datamodule.CityscapesDataModule(data_dir, val_split=5000, num_workers=16, batch_size=32, seed=42, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningDataModule

Standard Cityscapes, train, val, test splits and transforms

Specs:

30 classes (road, person, sidewalk, etc…)
(image, target) - image dims: (3 x 32 x 32), target dims: (3 x 32 x 32)

Transforms:

transforms = transform_lib.Compose([
    transform_lib.ToTensor(),
    transform_lib.Normalize(
        mean=[0.28689554, 0.32513303, 0.28389177],
        std=[0.18696375, 0.19017339, 0.18720214]
    )
])

Example:

from pl_bolts.datamodules import CityscapesDataModule

dm = CityscapesDataModule(PATH)
model = LitModel()

Trainer().fit(model, dm)

Or you can set your own transforms

Example:

dm.train_transforms = ...
dm.test_transforms = ...
dm.val_transforms  = ...

Parameters

data_dir – where to save/load the data
val_split – how many of the training images to use for the validation split
num_workers – how many workers to use for loading data
batch_size – number of examples per training/eval step

default_transforms()[source]¶

prepare_data()[source]¶: Saves Cityscapes files to data_dir

test_dataloader()[source]¶: Cityscapes test set uses the test split

train_dataloader()[source]¶: Cityscapes train set with removed subset to use for validation

val_dataloader()[source]¶: Cityscapes val set uses a subset of the training set for validation

extra_args = {}[source]¶

name = 'Cityscapes'[source]¶

property num_classes[source]¶: Return: 30

pl_bolts.datamodules.concat_dataset module¶

class pl_bolts.datamodules.concat_dataset.ConcatDataset(*datasets)[source]¶: Bases: torch.utils.data.Dataset

pl_bolts.datamodules.dummy_dataset module¶

class pl_bolts.datamodules.dummy_dataset.DummyDataset(*shapes, num_samples=10000)[source]¶

Bases: torch.utils.data.Dataset

Generate a dummy dataset

Parameters

*shapes – list of shapes
num_samples – how many samples to use in this dataset

Example:

from pl_bolts.datamodules import DummyDataset

# mnist dims
>>> ds = DummyDataset((1, 28, 28), (1,))
>>> dl = DataLoader(ds, batch_size=7)
...
>>> batch = next(iter(dl))
>>> x, y = batch
>>> x.size()
torch.Size([7, 1, 28, 28])
>>> y.size()
torch.Size([7, 1])

class pl_bolts.datamodules.dummy_dataset.DummyDetectionDataset(img_shape=(3, 256, 256), num_boxes=1, num_classes=2, num_samples=10000)[source]¶

Bases: torch.utils.data.Dataset

_random_bbox()[source]¶

pl_bolts.datamodules.fashion_mnist_datamodule module¶

class pl_bolts.datamodules.fashion_mnist_datamodule.FashionMNISTDataModule(data_dir, val_split=5000, num_workers=16, seed=42, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningDataModule

Specs:

10 classes (1 per type)
Each image is (1 x 28 x 28)

Standard FashionMNIST, train, val, test splits and transforms

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor()
])

Example:

from pl_bolts.datamodules import FashionMNISTDataModule

dm = FashionMNISTDataModule('.')
model = LitModel()

Trainer().fit(model, dm)

Parameters

data_dir (str) – where to save/load the data
val_split (int) – how many of the training images to use for the validation split
num_workers (int) – how many workers to use for loading data

_default_transforms()[source]¶

prepare_data()[source]¶: Saves FashionMNIST files to data_dir

test_dataloader(batch_size=32, transforms=None)[source]¶

FashionMNIST test set uses the test split

Parameters

batch_size – size of batch
transforms – custom transforms

train_dataloader(batch_size=32, transforms=None)[source]¶

FashionMNIST train set removes a subset to use for validation

Parameters

batch_size – size of batch
transforms – custom transforms

val_dataloader(batch_size=32, transforms=None)[source]¶

FashionMNIST val set uses a subset of the training set for validation

Parameters

batch_size – size of batch
transforms – custom transforms

name = 'fashion_mnist'[source]¶

property num_classes[source]¶: Return: 10

pl_bolts.datamodules.imagenet_datamodule module¶

class pl_bolts.datamodules.imagenet_datamodule.ImagenetDataModule(data_dir, meta_dir=None, num_imgs_per_val_class=50, image_size=224, num_workers=16, batch_size=32, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningDataModule

Specs:

1000 classes
Each image is (3 x varies x varies) (here we default to 3 x 224 x 224)

Imagenet train, val and test dataloaders.

The train set is the imagenet train.

The val set is taken from the train set with num_imgs_per_val_class images per class. For example if num_imgs_per_val_class=2 then there will be 2,000 images in the validation set.

The test set is the official imagenet validation set.

Example:

from pl_bolts.datamodules import ImagenetDataModule

dm = ImagenetDataModule(IMAGENET_PATH)
model = LitModel()

Trainer().fit(model, dm)

Parameters

data_dir (str) – path to the imagenet dataset file
meta_dir (Optional[str]) – path to meta.bin file
num_imgs_per_val_class (int) – how many images per class for the validation set
image_size (int) – final image size
num_workers (int) – how many data workers
batch_size (int) – batch_size

_verify_splits(data_dir, split)[source]¶

prepare_data()[source]¶: This method already assumes you have imagenet2012 downloaded. It validates the data using the meta.bin.

Warning

Please download imagenet on your own first.

test_dataloader()[source]¶: Uses the validation split of imagenet2012 for testing

train_dataloader()[source]¶: Uses the train split of imagenet2012 and puts away a portion of it for the validation split

train_transform()[source]¶

The standard imagenet transforms

transform_lib.Compose([
    transform_lib.RandomResizedCrop(self.image_size),
    transform_lib.RandomHorizontalFlip(),
    transform_lib.ToTensor(),
    transform_lib.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    ),
])

val_dataloader()[source]¶

Uses the part of the train split of imagenet2012 that was not used for training via num_imgs_per_val_class

Parameters

batch_size – the batch size
transforms – the transforms

val_transform()[source]¶

The standard imagenet transforms for validation

transform_lib.Compose([
    transform_lib.Resize(self.image_size + 32),
    transform_lib.CenterCrop(self.image_size),
    transform_lib.ToTensor(),
    transform_lib.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    ),
])

name = 'imagenet'[source]¶

property num_classes[source]¶

Return:

1000

pl_bolts.datamodules.imagenet_dataset module¶

class pl_bolts.datamodules.imagenet_dataset.UnlabeledImagenet(root, split='train', num_classes=-1, num_imgs_per_class=-1, num_imgs_per_class_val_split=50, meta_dir=None, **kwargs)[source]¶

Bases: torchvision.datasets.ImageNet

Official train set gets split into train, val. (using nb_imgs_per_val_class for each class). Official validation becomes test set

Within each class, we further allow limiting the number of samples per class (for semi-sup lng)

Parameters

root – path of dataset
split (str) –
num_classes (int) – Sets the limit of classes
num_imgs_per_class (int) – Limits the number of images per class
num_imgs_per_class_val_split (int) – How many images per class to generate the val split
download –
kwargs –

classmethod generate_meta_bins(devkit_dir)[source]¶

partition_train_set(imgs, nb_imgs_in_val)[source]¶

pl_bolts.datamodules.imagenet_dataset._calculate_md5(fpath, chunk_size=1048576)[source]¶

pl_bolts.datamodules.imagenet_dataset._check_integrity(fpath, md5=None)[source]¶

pl_bolts.datamodules.imagenet_dataset._check_md5(fpath, md5, **kwargs)[source]¶

pl_bolts.datamodules.imagenet_dataset._is_gzip(filename)[source]¶

pl_bolts.datamodules.imagenet_dataset._is_tar(filename)[source]¶

pl_bolts.datamodules.imagenet_dataset._is_targz(filename)[source]¶

pl_bolts.datamodules.imagenet_dataset._is_tarxz(filename)[source]¶

pl_bolts.datamodules.imagenet_dataset._is_zip(filename)[source]¶

pl_bolts.datamodules.imagenet_dataset._verify_archive(root, file, md5)[source]¶

pl_bolts.datamodules.imagenet_dataset.extract_archive(from_path, to_path=None, remove_finished=False)[source]¶

pl_bolts.datamodules.imagenet_dataset.parse_devkit_archive(root, file=None)[source]¶

Parse the devkit archive of the ImageNet2012 classification dataset and save the meta information in a binary file.

Parameters

root (str) – Root directory containing the devkit archive
file (str, optional) – Name of devkit archive. Defaults to ‘ILSVRC2012_devkit_t12.tar.gz’

pl_bolts.datamodules.mnist_datamodule module¶

class pl_bolts.datamodules.mnist_datamodule.MNISTDataModule(data_dir='./', val_split=5000, num_workers=16, normalize=False, seed=42, batch_size=32, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningDataModule

Specs:

10 classes (1 per digit)
Each image is (1 x 28 x 28)

Standard MNIST, train, val, test splits and transforms

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor()
])

Example:

from pl_bolts.datamodules import MNISTDataModule

dm = MNISTDataModule('.')
model = LitModel()

Trainer().fit(model, dm)

Parameters

data_dir (str) – where to save/load the data
val_split (int) – how many of the training images to use for the validation split
num_workers (int) – how many workers to use for loading data
normalize (bool) – If true applies image normalize

_default_transforms()[source]¶

prepare_data()[source]¶: Saves MNIST files to data_dir

test_dataloader(batch_size=32, transforms=None)[source]¶

MNIST test set uses the test split

Parameters

batch_size – size of batch
transforms – custom transforms

train_dataloader(batch_size=32, transforms=None)[source]¶

MNIST train set removes a subset to use for validation

Parameters

batch_size – size of batch
transforms – custom transforms

val_dataloader(batch_size=32, transforms=None)[source]¶

MNIST val set uses a subset of the training set for validation

Parameters

batch_size – size of batch
transforms – custom transforms

name = 'mnist'[source]¶

property num_classes[source]¶: Return: 10

pl_bolts.datamodules.sklearn_datamodule module¶

class pl_bolts.datamodules.sklearn_datamodule.SklearnDataModule(X, y, x_val=None, y_val=None, x_test=None, y_test=None, val_split=0.2, test_split=0.1, num_workers=2, random_state=1234, shuffle=True, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningDataModule

Automatically generates the train, validation and test splits for a Numpy dataset. They are set up as dataloaders for convenience. Optionally, you can pass in your own validation and test splits.

Example

>>> from sklearn.datasets import load_boston
>>> from pl_bolts.datamodules import SklearnDataModule
...
>>> X, y = load_boston(return_X_y=True)
>>> loaders = SklearnDataModule(X, y)
...
>>> # train set
>>> train_loader = loaders.train_dataloader(batch_size=32)
>>> len(train_loader.dataset)
355
>>> len(train_loader)
11
>>> # validation set
>>> val_loader = loaders.val_dataloader(batch_size=32)
>>> len(val_loader.dataset)
100
>>> len(val_loader)
3
>>> # test set
>>> test_loader = loaders.test_dataloader(batch_size=32)
>>> len(test_loader.dataset)
51
>>> len(test_loader)
1

_init_datasets(X, y, x_val, y_val, x_test, y_test)[source]¶

test_dataloader(batch_size=16)[source]¶

train_dataloader(batch_size=16)[source]¶

val_dataloader(batch_size=16)[source]¶

name = 'sklearn'[source]¶

class pl_bolts.datamodules.sklearn_datamodule.SklearnDataset(X, y, X_transform=None, y_transform=None)[source]¶

Bases: torch.utils.data.Dataset

Mapping between numpy (or sklearn) datasets to PyTorch datasets.

Parameters

X (ndarray) – Numpy ndarray
y (ndarray) – Numpy ndarray
X_transform (Optional[Any]) – Any transform that works with Numpy arrays
y_transform (Optional[Any]) – Any transform that works with Numpy arrays

Example

>>> from sklearn.datasets import load_boston
>>> from pl_bolts.datamodules import SklearnDataset
...
>>> X, y = load_boston(return_X_y=True)
>>> dataset = SklearnDataset(X, y)
>>> len(dataset)
506

class pl_bolts.datamodules.sklearn_datamodule.TensorDataModule(X, y, x_val=None, y_val=None, x_test=None, y_test=None, val_split=0.2, test_split=0.1, num_workers=2, random_state=1234, shuffle=True, *args, **kwargs)[source]¶

Bases: pl_bolts.datamodules.sklearn_datamodule.SklearnDataModule

Automatically generates the train, validation and test splits for a PyTorch tensor dataset. They are set up as dataloaders for convenience. Optionally, you can pass in your own validation and test splits.

Example

>>> from pl_bolts.datamodules import TensorDataModule
>>> import torch
...
>>> # create dataset
>>> X = torch.rand(100, 3)
>>> y = torch.rand(100)
>>> loaders = TensorDataModule(X, y)
...
>>> # train set
>>> train_loader = loaders.train_dataloader(batch_size=10)
>>> len(train_loader.dataset)
70
>>> len(train_loader)
7
>>> # validation set
>>> val_loader = loaders.val_dataloader(batch_size=10)
>>> len(val_loader.dataset)
20
>>> len(val_loader)
2
>>> # test set
>>> test_loader = loaders.test_dataloader(batch_size=10)
>>> len(test_loader.dataset)
10
>>> len(test_loader)
1

Automatically generates the train, validation and test splits for a Numpy dataset. They are set up as dataloaders for convenience. Optionally, you can pass in your own validation and test splits.

Example

>>> from sklearn.datasets import load_boston
>>> from pl_bolts.datamodules import SklearnDataModule
...
>>> X, y = load_boston(return_X_y=True)
>>> loaders = SklearnDataModule(X, y)
...
>>> # train set
>>> train_loader = loaders.train_dataloader(batch_size=32)
>>> len(train_loader.dataset)
355
>>> len(train_loader)
11
>>> # validation set
>>> val_loader = loaders.val_dataloader(batch_size=32)
>>> len(val_loader.dataset)
100
>>> len(val_loader)
3
>>> # test set
>>> test_loader = loaders.test_dataloader(batch_size=32)
>>> len(test_loader.dataset)
51
>>> len(test_loader)
1

class pl_bolts.datamodules.sklearn_datamodule.TensorDataset(X, y, X_transform=None, y_transform=None)[source]¶

Bases: torch.utils.data.Dataset

Prepare PyTorch tensor dataset for data loaders.

Parameters

X (Tensor) – PyTorch tensor
y (Tensor) – PyTorch tensor
X_transform (Optional[Any]) – Any transform that works with PyTorch tensors
y_transform (Optional[Any]) – Any transform that works with PyTorch tensors

Example

>>> from pl_bolts.datamodules import TensorDataset
...
>>> X = torch.rand(10, 3)
>>> y = torch.rand(10)
>>> dataset = TensorDataset(X, y)
>>> len(dataset)
10

pl_bolts.datamodules.ssl_imagenet_datamodule module¶

class pl_bolts.datamodules.ssl_imagenet_datamodule.SSLImagenetDataModule(data_dir, meta_dir=None, num_workers=16, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningDataModule

_default_transforms()[source]¶

_verify_splits(data_dir, split)[source]¶

prepare_data()[source]¶

test_dataloader(batch_size, num_images_per_class, add_normalize=False)[source]¶

train_dataloader(batch_size, num_images_per_class=-1, add_normalize=False)[source]¶

val_dataloader(batch_size, num_images_per_class=50, add_normalize=False)[source]¶

name = 'imagenet'[source]¶

property num_classes[source]¶

pl_bolts.datamodules.stl10_datamodule module¶

class pl_bolts.datamodules.stl10_datamodule.STL10DataModule(data_dir=None, unlabeled_val_split=5000, train_val_split=500, num_workers=16, batch_size=32, seed=42, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningDataModule

Specs:

10 classes (1 per type)
Each image is (3 x 96 x 96)

Standard STL-10, train, val, test splits and transforms. STL-10 has support for doing validation splits on the labeled or unlabeled splits

Transforms:

mnist_transforms = transform_lib.Compose([
    transform_lib.ToTensor(),
    transforms.Normalize(
        mean=(0.43, 0.42, 0.39),
        std=(0.27, 0.26, 0.27)
    )
])

Example:

from pl_bolts.datamodules import STL10DataModule

dm = STL10DataModule(PATH)
model = LitModel()

Trainer().fit(model, dm)

Parameters

data_dir (Optional[str]) – where to save/load the data
unlabeled_val_split (int) – how many images from the unlabeled training split to use for validation
train_val_split (int) – how many images from the labeled training split to use for validation
num_workers (int) – how many workers to use for loading data
batch_size (int) – the batch size

default_transforms()[source]¶

prepare_data()[source]¶: Downloads the unlabeled, train and test split

test_dataloader()[source]¶

Loads the test split of STL10

Parameters

batch_size – the batch size
transforms – the transforms

train_dataloader()[source]¶: Loads the ‘unlabeled’ split minus a portion set aside for validation via unlabeled_val_split.

train_dataloader_labeled()[source]¶

train_dataloader_mixed()[source]¶

Loads a portion of the ‘unlabeled’ training data and ‘train’ (labeled) data. both portions have a subset removed for validation via unlabeled_val_split and train_val_split

Parameters

batch_size – the batch size
transforms – a sequence of transforms

val_dataloader()[source]¶

Loads a portion of the ‘unlabeled’ training data set aside for validation The val dataset = (unlabeled - train_val_split)

Parameters

batch_size – the batch size
transforms – a sequence of transforms

val_dataloader_labeled()[source]¶

val_dataloader_mixed()[source]¶

Loads a portion of the ‘unlabeled’ training data set aside for validation along with the portion of the ‘train’ dataset to be used for validation

unlabeled_val = (unlabeled - train_val_split)

labeled_val = (train- train_val_split)

full_val = unlabeled_val + labeled_val

Parameters

batch_size – the batch size
transforms – a sequence of transforms

name = 'stl10'[source]¶

property num_classes[source]¶

pl_bolts.datamodules.vocdetection_datamodule module¶

class pl_bolts.datamodules.vocdetection_datamodule.Compose(transforms)[source]¶

Bases: object

Like torchvision.transforms.compose but works for (image, target)

__call__(image, target)[source]¶: Call self as a function.

class pl_bolts.datamodules.vocdetection_datamodule.VOCDetectionDataModule(data_dir, year='2012', num_workers=16, normalize=False, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningDataModule

TODO(teddykoker) docstring

_default_transforms()[source]¶

prepare_data()[source]¶: Saves VOCDetection files to data_dir

train_dataloader(batch_size=1, transforms=None)[source]¶

VOCDetection train set uses the train subset

Parameters

batch_size – size of batch
transforms – custom transforms

val_dataloader(batch_size=1, transforms=None)[source]¶

VOCDetection val set uses the val subset

Parameters

batch_size – size of batch
transforms – custom transforms

name = 'vocdetection'[source]¶

property num_classes[source]¶: Return: 21

pl_bolts.datamodules.vocdetection_datamodule._collate_fn(batch)[source]¶

pl_bolts.datamodules.vocdetection_datamodule._prepare_voc_instance(image, target)[source]¶

Prepares VOC dataset into appropriate target for fasterrcnn

https://github.com/pytorch/vision/issues/1097#issuecomment-508917489

pl_bolts.metrics package¶

Submodules¶

pl_bolts.metrics.aggregation module¶

pl_bolts.metrics.aggregation.accuracy(preds, labels)[source]¶

pl_bolts.metrics.aggregation.mean(res, key)[source]¶

pl_bolts.metrics.aggregation.precision_at_k(output, target, top_k=(1, ))[source]¶: Computes the accuracy over the k top predictions for the specified values of k

pl_bolts.models package¶

Collection of PyTorchLightning models

Subpackages¶

pl_bolts.models.autoencoders package¶

Here are a VAE and GAN

Subpackages¶

pl_bolts.models.autoencoders.basic_ae package¶

AE Template¶

This is a basic template for implementing an Autoencoder in PyTorch Lightning.

A default encoder and decoder have been provided but can easily be replaced by custom models.

This template uses the CIFAR10 dataset but image data of any dimension can be fed in as long as the image: width and image height are even values. For other types of data, such as sound, it will be necessary to change the Encoder and Decoder.
The default encoder is a resnet18 backbone followed by linear layers which map representations to latent space.: The default decoder mirrors the encoder architecture and is similar to an inverted resnet18.

from pl_bolts.models.autoencoders import AE

model = AE()
trainer = pl.Trainer()
trainer.fit(model)

Submodules¶

pl_bolts.models.autoencoders.basic_ae.basic_ae_module module¶

class pl_bolts.models.autoencoders.basic_ae.basic_ae_module.AE(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.LightningModule

Standard AE

Model is available pretrained on different datasets:

Example:

# not pretrained
ae = AE()

# pretrained on imagenet
ae = AE.from_pretrained('resnet50-imagenet')

# pretrained on cifar10
ae = AE.from_pretrained('resnet18-cifar10')

Parameters

input_height – height of the images
enc_type – option between resnet18 or resnet50
first_conv – use standard kernel_size 7, stride 2 at start or replace it with kernel_size 3, stride 1 conv
maxpool1 – use standard maxpool to reduce spatial dim of feat by a factor of 2
enc_out_dim – set according to the out_channel count of encoder used (512 for resnet18, 2048 for resnet50)
latent_dim – dim of latent space
lr – learning rate for Adam

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

forward(z)[source]¶

from_pretrained(checkpoint_name)[source]¶

static pretrained_weights_available()[source]¶

step(batch, batch_idx)[source]¶

training_step(batch, batch_idx)[source]¶

validation_step(batch, batch_idx)[source]¶

pretrained_urls = {'cifar10-resnet18': 'https://pl-bolts-weights.s3.us-east-2.amazonaws.com/ae/ae-cifar10/checkpoints/epoch%3D96.ckpt'}[source]¶

pl_bolts.models.autoencoders.basic_ae.basic_ae_module.cli_main(args=None)[source]¶

pl_bolts.models.autoencoders.basic_vae package¶

VAE Template¶

This is a basic template for implementing a Variational Autoencoder in PyTorch Lightning.

A default encoder and decoder have been provided but can easily be replaced by custom models.

This template uses the CIFAR10 dataset but image data of any dimension can be fed in as long as the image: width and image height are even values. For other types of data, such as sound, it will be necessary to change the Encoder and Decoder.
The default encoder is a resnet18 backbone followed by linear layers which map representations: to mu and var. The default decoder mirrors the encoder architecture and is similar to an inverted resnet18. The model also assumes a Gaussian prior and a Gaussian approximate posterior distribution.

Submodules¶

pl_bolts.models.autoencoders.basic_vae.basic_vae_module module¶

class pl_bolts.models.autoencoders.basic_vae.basic_vae_module.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.LightningModule

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.from_pretrained('resnet50-imagenet')

# pretrained on cifar10
vae = VAE.from_pretrained('resnet18-cifar10')

Parameters

input_height – height of the images
enc_type – option between resnet18 or resnet50
first_conv – use standard kernel_size 7, stride 2 at start or replace it with kernel_size 3, stride 1 conv
maxpool1 – use standard maxpool to reduce spatial dim of feat by a factor of 2
enc_out_dim – set according to the out_channel count of encoder used (512 for resnet18, 2048 for resnet50)
kl_coeff – coefficient for kl term of the loss
latent_dim – dim of latent space
lr – learning rate for Adam

_run_step(x)[source]¶

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

forward(z)[source]¶

from_pretrained(checkpoint_name)[source]¶

static pretrained_weights_available()[source]¶

sample(mu, log_var)[source]¶

step(batch, batch_idx)[source]¶

training_step(batch, batch_idx)[source]¶

validation_step(batch, batch_idx)[source]¶

pretrained_urls = {'cifar10-resnet18': 'https://pl-bolts-weights.s3.us-east-2.amazonaws.com/vae/vae-cifar10/checkpoints/epoch%3D89.ckpt', 'stl10-resnet18': 'https://pl-bolts-weights.s3.us-east-2.amazonaws.com/vae/vae-stl10/checkpoints/epoch%3D89.ckpt'}[source]¶

pl_bolts.models.autoencoders.basic_vae.basic_vae_module.cli_main(args=None)[source]¶

Submodules¶

pl_bolts.models.autoencoders.components module¶

class pl_bolts.models.autoencoders.components.DecoderBlock(inplanes, planes, scale=1, upsample=None)[source]¶

Bases: torch.nn.Module

ResNet block, but convs replaced with resize convs, and channel increase is in second conv, not first

forward(x)[source]¶

expansion = 1[source]¶

class pl_bolts.models.autoencoders.components.DecoderBottleneck(inplanes, planes, scale=1, upsample=None)[source]¶

Bases: torch.nn.Module

ResNet bottleneck, but convs replaced with resize convs

forward(x)[source]¶

expansion = 4[source]¶

class pl_bolts.models.autoencoders.components.EncoderBlock(inplanes, planes, stride=1, downsample=None)[source]¶

Bases: torch.nn.Module

ResNet block, copied from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py#L35

forward(x)[source]¶

expansion = 1[source]¶

class pl_bolts.models.autoencoders.components.EncoderBottleneck(inplanes, planes, stride=1, downsample=None)[source]¶

Bases: torch.nn.Module

ResNet bottleneck, copied from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py#L75

forward(x)[source]¶

expansion = 4[source]¶

class pl_bolts.models.autoencoders.components.Interpolate(size=None, scale_factor=None)[source]¶

Bases: torch.nn.Module

nn.Module wrapper for F.interpolate

forward(x)[source]¶

class pl_bolts.models.autoencoders.components.ResNetDecoder(block, layers, latent_dim, input_height, first_conv=False, maxpool1=False)[source]¶

Bases: torch.nn.Module

Resnet in reverse order

_make_layer(block, planes, blocks, scale=1)[source]¶

forward(x)[source]¶

class pl_bolts.models.autoencoders.components.ResNetEncoder(block, layers, first_conv=False, maxpool1=False)[source]¶

Bases: torch.nn.Module

_make_layer(block, planes, blocks, stride=1)[source]¶

forward(x)[source]¶

pl_bolts.models.autoencoders.components.conv1x1(in_planes, out_planes, stride=1)[source]¶: 1x1 convolution

pl_bolts.models.autoencoders.components.conv3x3(in_planes, out_planes, stride=1)[source]¶: 3x3 convolution with padding

pl_bolts.models.autoencoders.components.resize_conv1x1(in_planes, out_planes, scale=1)[source]¶: upsample + 1x1 convolution with padding to avoid checkerboard artifact

pl_bolts.models.autoencoders.components.resize_conv3x3(in_planes, out_planes, scale=1)[source]¶: upsample + 3x3 convolution with padding to avoid checkerboard artifact

pl_bolts.models.autoencoders.components.resnet18_decoder(latent_dim, input_height, first_conv, maxpool1)[source]¶

pl_bolts.models.autoencoders.components.resnet18_encoder(first_conv, maxpool1)[source]¶

pl_bolts.models.autoencoders.components.resnet50_decoder(latent_dim, input_height, first_conv, maxpool1)[source]¶

pl_bolts.models.autoencoders.components.resnet50_encoder(first_conv, maxpool1)[source]¶

pl_bolts.models.detection package¶

Submodules¶

pl_bolts.models.detection.faster_rcnn module¶

class pl_bolts.models.detection.faster_rcnn.FasterRCNN(learning_rate=0.0001, num_classes=91, pretrained=False, pretrained_backbone=True, trainable_backbone_layers=3, replace_head=True, **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

PyTorch Lightning implementation of Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Paper authors: Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

Model implemented by:

Teddy Koker <https://github.com/teddykoker>

During training, the model expects both the input tensors, as well as targets (list of dictionary), containing:

boxes (FloatTensor[N, 4]): the ground truth boxes in [x1, y1, x2, y2] format.
labels (Int64Tensor[N]): the class label for each ground truh box

CLI command:

# PascalVOC
python faster_rcnn.py --gpus 1 --pretrained True

Parameters

learning_rate (float) – the learning rate
num_classes (int) – number of detection classes (including background)
pretrained (bool) – if true, returns a model pre-trained on COCO train2017
pretrained_backbone (bool) – if true, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int) – number of trainable resnet layers starting from final block

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

forward(x)[source]¶

training_step(batch, batch_idx)[source]¶

validation_epoch_end(outs)[source]¶

validation_step(batch, batch_idx)[source]¶

pl_bolts.models.detection.faster_rcnn._evaluate_iou(target, pred)[source]¶: Evaluate intersection over union (IOU) for target from dataset and output prediction from model

pl_bolts.models.detection.faster_rcnn.run_cli()[source]¶

pl_bolts.models.gans package¶

Subpackages¶

pl_bolts.models.gans.basic package¶

Submodules¶

pl_bolts.models.gans.basic.basic_gan_module module¶

class pl_bolts.models.gans.basic.basic_gan_module.GAN(input_channels, input_height, input_width, latent_dim=32, learning_rate=0.0002, **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

Vanilla GAN implementation.

Example:

from pl_bolts.models.gan import GAN

m = GAN()
Trainer(gpus=2).fit(m)

Example CLI:

# mnist
python  basic_gan_module.py --gpus 1

# imagenet
python  basic_gan_module.py --gpus 1 --dataset 'imagenet2012'
--data_dir /path/to/imagenet/folder/ --meta_dir ~/path/to/meta/bin/folder
--batch_size 256 --learning_rate 0.0001

Parameters

datamodule – the datamodule (train, val, test splits)
latent_dim (int) – emb dim for encoder
batch_size – the batch size
learning_rate (float) – the learning rate
data_dir – where to store data
num_workers – data workers

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

discriminator_loss(x)[source]¶

discriminator_step(x)[source]¶

forward(z)[source]¶

Generates an image given input noise z

Example:

z = torch.rand(batch_size, latent_dim)
gan = GAN.load_from_checkpoint(PATH)
img = gan(z)

generator_loss(x)[source]¶

generator_step(x)[source]¶

init_discriminator(img_dim)[source]¶

init_generator(img_dim)[source]¶

training_step(batch, batch_idx, optimizer_idx)[source]¶

pl_bolts.models.gans.basic.basic_gan_module.cli_main(args=None)[source]¶

pl_bolts.models.gans.basic.components module¶

class pl_bolts.models.gans.basic.components.Discriminator(img_shape, hidden_dim=1024)[source]¶

Bases: torch.nn.Module

forward(img)[source]¶

class pl_bolts.models.gans.basic.components.Generator(latent_dim, img_shape, hidden_dim=256)[source]¶

Bases: torch.nn.Module

forward(z)[source]¶

pl_bolts.models.regression package¶

Submodules¶

pl_bolts.models.regression.linear_regression module¶

class pl_bolts.models.regression.linear_regression.LinearRegression(input_dim, output_dim=1, bias=True, learning_rate=0.0001, optimizer=torch.optim.Adam, l1_strength=0.0, l2_strength=0.0, **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

Linear regression model implementing - with optional L1/L2 regularization $$min_{W} ||(Wx + b) - y ||_2^2 $$

Parameters

input_dim (int) – number of dimensions of the input (1+)
output_dim (int) – number of dimensions of the output (default=1)
bias (bool) – If false, will not use $+b$
learning_rate (float) – learning_rate for the optimizer
optimizer (Optimizer) – the optimizer to use (default=’Adam’)
l1_strength (float) – L1 regularization strength (default=None)
l2_strength (float) – L2 regularization strength (default=None)

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

forward(x)[source]¶

test_epoch_end(outputs)[source]¶

test_step(batch, batch_idx)[source]¶

training_step(batch, batch_idx)[source]¶

validation_epoch_end(outputs)[source]¶

validation_step(batch, batch_idx)[source]¶

pl_bolts.models.regression.linear_regression.cli_main()[source]¶

pl_bolts.models.regression.logistic_regression module¶

class pl_bolts.models.regression.logistic_regression.LogisticRegression(input_dim, num_classes, bias=True, learning_rate=0.0001, optimizer=torch.optim.Adam, l1_strength=0.0, l2_strength=0.0, **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

Logistic regression model

Parameters

input_dim (int) – number of dimensions of the input (at least 1)
num_classes (int) – number of class labels (binary: 2, multi-class: >2)
bias (bool) – specifies if a constant or intercept should be fitted (equivalent to fit_intercept in sklearn)
learning_rate (float) – learning_rate for the optimizer
optimizer (Optimizer) – the optimizer to use (default=’Adam’)
l1_strength (float) – L1 regularization strength (default=None)
l2_strength (float) – L2 regularization strength (default=None)

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

forward(x)[source]¶

test_epoch_end(outputs)[source]¶

test_step(batch, batch_idx)[source]¶

training_step(batch, batch_idx)[source]¶

validation_epoch_end(outputs)[source]¶

validation_step(batch, batch_idx)[source]¶

pl_bolts.models.regression.logistic_regression.cli_main()[source]¶

pl_bolts.models.self_supervised package¶

These models have been pre-trained using self-supervised learning. The models can also be used without pre-training and overwritten for your own research.

Here’s an example for using these as pretrained models.

from pl_bolts.models.self_supervised import CPCV2

images = get_imagenet_batch()

# extract unsupervised representations
pretrained = CPCV2(pretrained=True)
representations = pretrained(images)

# use these in classification or any downstream task
classifications = classifier(representations)

Subpackages¶

pl_bolts.models.self_supervised.amdim package¶

Submodules¶

pl_bolts.models.self_supervised.amdim.amdim_module module¶

class pl_bolts.models.self_supervised.amdim.amdim_module.AMDIM(datamodule='cifar10', encoder='amdim_encoder', contrastive_task=torch.nn.Module, image_channels=3, image_height=32, encoder_feature_dim=320, embedding_fx_dim=1280, conv_block_depth=10, use_bn=False, tclip=20.0, learning_rate=0.0002, data_dir='', num_classes=10, batch_size=200, **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

PyTorch Lightning implementation of Augmented Multiscale Deep InfoMax (AMDIM)

Paper authors: Philip Bachman, R Devon Hjelm, William Buchwalter.

Model implemented by: William Falcon

This code is adapted to Lightning using the original author repo (the original repo).

Example

>>> from pl_bolts.models.self_supervised import AMDIM
...
>>> model = AMDIM(encoder='resnet18')

Train:

trainer = Trainer()
trainer.fit(model)

Parameters

datamodule (Union[str, LightningDataModule]) – A LightningDatamodule
encoder (Union[str, Module, LightningModule]) – an encoder string or model
image_channels (int) – 3
image_height (int) – pixels
encoder_feature_dim (int) – Called ndf in the paper, this is the representation size for the encoder.
embedding_fx_dim (int) – Output dim of the embedding function (nrkhs in the paper) (Reproducing Kernel Hilbert Spaces).
conv_block_depth (int) – Depth of each encoder block,
use_bn (bool) – If true will use batchnorm.
tclip (int) – soft clipping non-linearity to the scores after computing the regularization term and before computing the log-softmax. This is the ‘second trick’ used in the paper
learning_rate (int) – The learning rate
data_dir (str) – Where to store data
num_classes (int) – How many classes in the dataset
batch_size (int) – The batch size

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

forward(img_1, img_2)[source]¶

init_encoder()[source]¶

train_dataloader()[source]¶

training_step(batch, batch_nb)[source]¶

training_step_end(outputs)[source]¶

val_dataloader()[source]¶

validation_epoch_end(outputs)[source]¶

validation_step(batch, batch_nb)[source]¶

pl_bolts.models.self_supervised.amdim.amdim_module.cli_main()[source]¶

pl_bolts.models.self_supervised.amdim.datasets module¶

class pl_bolts.models.self_supervised.amdim.datasets.AMDIMPatchesPretraining[source]¶

Bases: object

” For pretraining we use the train transform for both train and val.

static cifar10(dataset_root, patch_size, patch_overlap, split='train')[source]¶

static imagenet(dataset_root, nb_classes, patch_size, patch_overlap, split='train')[source]¶

static stl(dataset_root, patch_size, patch_overlap, split=None)[source]¶

class pl_bolts.models.self_supervised.amdim.datasets.AMDIMPretraining[source]¶

Bases: object

” For pretraining we use the train transform for both train and val.

static cifar10(dataset_root, split='train')[source]¶

static cifar10_tiny(dataset_root, split='train')[source]¶

static get_dataset(datamodule, data_dir, split='train', **kwargs)[source]¶

static imagenet(dataset_root, nb_classes, split='train')[source]¶

static stl(dataset_root, split=None)[source]¶

pl_bolts.models.self_supervised.amdim.networks module¶

class pl_bolts.models.self_supervised.amdim.networks.AMDIMEncoder(dummy_batch, num_channels=3, encoder_feature_dim=64, embedding_fx_dim=512, conv_block_depth=3, encoder_size=32, use_bn=False)[source]¶

Bases: torch.nn.Module

_config_modules(x, output_widths, n_rkhs, use_bn)[source]¶: Configure the modules for extracting fake rkhs embeddings for infomax.

_forward_acts(x)[source]¶: Return activations from all layers.

forward(x)[source]¶

init_weights(init_scale=1.0)[source]¶: Run custom weight init for modules…

class pl_bolts.models.self_supervised.amdim.networks.Conv3x3(n_in, n_out, n_kern, n_stride, n_pad, use_bn=True, pad_mode='constant')[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

class pl_bolts.models.self_supervised.amdim.networks.ConvResBlock(n_in, n_out, width, stride, pad, depth, use_bn)[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

init_weights(init_scale=1.0)[source]¶: Do a fixup-ish init for each ConvResNxN in this block.

class pl_bolts.models.self_supervised.amdim.networks.ConvResNxN(n_in, n_out, width, stride, pad, use_bn=False)[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

init_weights(init_scale=1.0)[source]¶

class pl_bolts.models.self_supervised.amdim.networks.FakeRKHSConvNet(n_input, n_output, use_bn=False)[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

init_weights(init_scale=1.0)[source]¶

class pl_bolts.models.self_supervised.amdim.networks.MaybeBatchNorm2d(n_ftr, affine, use_bn)[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

class pl_bolts.models.self_supervised.amdim.networks.NopNet(norm_dim=None)[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

pl_bolts.models.self_supervised.amdim.ssl_datasets module¶

class pl_bolts.models.self_supervised.amdim.ssl_datasets.CIFAR10Mixed(root, split='val', transform=None, target_transform=None, download=False, nb_labeled_per_class=None, val_pct=0.1)[source]¶: Bases: pl_bolts.models.self_supervised.amdim.ssl_datasets.SSLDatasetMixin, torchvision.datasets.CIFAR10

class pl_bolts.models.self_supervised.amdim.ssl_datasets.SSLDatasetMixin[source]¶

Bases: abc.ABC

classmethod deterministic_shuffle(x, y)[source]¶

classmethod generate_train_val_split(examples, labels, pct_val)[source]¶: Splits dataset uniformly across classes :param _sphinx_paramlinks_pl_bolts.models.self_supervised.amdim.ssl_datasets.SSLDatasetMixin.generate_train_val_split.examples: :param _sphinx_paramlinks_pl_bolts.models.self_supervised.amdim.ssl_datasets.SSLDatasetMixin.generate_train_val_split.labels: :param _sphinx_paramlinks_pl_bolts.models.self_supervised.amdim.ssl_datasets.SSLDatasetMixin.generate_train_val_split.pct_val: :return:

classmethod select_nb_imgs_per_class(examples, labels, nb_imgs_in_val)[source]¶: Splits a dataset into two parts. The labeled split has nb_imgs_in_val per class :param _sphinx_paramlinks_pl_bolts.models.self_supervised.amdim.ssl_datasets.SSLDatasetMixin.select_nb_imgs_per_class.examples: :param _sphinx_paramlinks_pl_bolts.models.self_supervised.amdim.ssl_datasets.SSLDatasetMixin.select_nb_imgs_per_class.labels: :param _sphinx_paramlinks_pl_bolts.models.self_supervised.amdim.ssl_datasets.SSLDatasetMixin.select_nb_imgs_per_class.nb_imgs_in_val: :return:

pl_bolts.models.self_supervised.amdim.transforms module¶

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMEvalTransformsCIFAR10[source]¶

Bases: object

Transforms applied to AMDIM

Transforms:

transforms.ToTensor(),
normalize

Example:

x = torch.rand(5, 3, 32, 32)

transform = AMDIMEvalTransformsCIFAR10()
(view1, view2) = transform(x)

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMEvalTransformsImageNet128(height=128)[source]¶

Bases: object

Transforms applied to AMDIM

Transforms:

transforms.Resize(height + 6, interpolation=3),
transforms.CenterCrop(height),
transforms.ToTensor(),
normalize

Example:

x = torch.rand(5, 3, 128, 128)

transform = AMDIMEvalTransformsImageNet128()
view1 = transform(x)

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMEvalTransformsSTL10(height=64)[source]¶

Bases: object

Transforms applied to AMDIM

Transforms:

transforms.Resize(height + 6, interpolation=3),
transforms.CenterCrop(height),
transforms.ToTensor(),
normalize

Example:

x = torch.rand(5, 3, 64, 64)

transform = AMDIMTrainTransformsSTL10()
view1 = transform(x)

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMTrainTransformsCIFAR10[source]¶

Bases: object

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)

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMTrainTransformsImageNet128(height=128)[source]¶

Bases: object

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)

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.amdim.transforms.AMDIMTrainTransformsSTL10(height=64)[source]¶

Bases: object

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)

__call__(inp)[source]¶: Call self as a function.

pl_bolts.models.self_supervised.byol package¶

Submodules¶

pl_bolts.models.self_supervised.byol.byol_module module¶

class pl_bolts.models.self_supervised.byol.byol_module.BYOL(num_classes, learning_rate=0.2, weight_decay=1.5e-06, input_height=32, batch_size=32, num_workers=0, warmup_epochs=10, max_epochs=1000, **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

PyTorch Lightning implementation of Bootstrap Your Own Latent (BYOL)

Paper authors: Jean-Bastien Grill ,Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko.

Model implemented by:

Annika Brundyn

Warning

Work in progress. This implementation is still being verified.

TODOs:

verify on CIFAR-10
verify on STL-10
pre-train on imagenet

Example:

import pytorch_lightning as pl
from pl_bolts.models.self_supervised import BYOL
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.models.self_supervised.simclr.simclr_transforms import (
    SimCLREvalDataTransform, SimCLRTrainDataTransform)

# model
model = BYOL(num_classes=10)

# data
dm = CIFAR10DataModule(num_workers=0)
dm.train_transforms = SimCLRTrainDataTransform(32)
dm.val_transforms = SimCLREvalDataTransform(32)

trainer = pl.Trainer()
trainer.fit(model, dm)

Train:

trainer = Trainer()
trainer.fit(model)

CLI command:

# cifar10
python byol_module.py --gpus 1

# imagenet
python byol_module.py
    --gpus 8
    --dataset imagenet2012
    --data_dir /path/to/imagenet/
    --meta_dir /path/to/folder/with/meta.bin/
    --batch_size 32

Parameters

datamodule – The datamodule
learning_rate (float) – the learning rate
weight_decay (float) – optimizer weight decay
input_height (int) – image input height
batch_size (int) – the batch size
num_workers (int) – number of workers
warmup_epochs (int) – num of epochs for scheduler warm up
max_epochs (int) – max epochs for scheduler

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

cosine_similarity(a, b)[source]¶

forward(x)[source]¶

on_train_batch_end(batch, batch_idx, dataloader_idx)[source]¶

Return type: None

shared_step(batch, batch_idx)[source]¶

training_step(batch, batch_idx)[source]¶

validation_step(batch, batch_idx)[source]¶

pl_bolts.models.self_supervised.byol.byol_module.cli_main()[source]¶

pl_bolts.models.self_supervised.byol.models module¶

class pl_bolts.models.self_supervised.byol.models.MLP(input_dim=2048, hidden_size=4096, output_dim=256)[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

class pl_bolts.models.self_supervised.byol.models.SiameseArm(encoder=None)[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

pl_bolts.models.self_supervised.cpc package¶

Submodules¶

pl_bolts.models.self_supervised.cpc.cpc_finetuner module¶

pl_bolts.models.self_supervised.cpc.cpc_finetuner.cli_main()[source]¶

pl_bolts.models.self_supervised.cpc.cpc_module module¶

CPC V2¶

class pl_bolts.models.self_supervised.cpc.cpc_module.CPCV2(datamodule=None, encoder='cpc_encoder', patch_size=8, patch_overlap=4, online_ft=True, task='cpc', num_workers=4, learning_rate=0.0001, data_dir='', batch_size=32, pretrained=None, **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

Parameters

datamodule (Optional[LightningDataModule]) – A Datamodule (optional). Otherwise set the dataloaders directly
encoder (Union[str, Module, LightningModule]) – A string for any of the resnets in torchvision, or the original CPC encoder, or a custon nn.Module encoder
patch_size (int) – How big to make the image patches
patch_overlap (int) – How much overlap should each patch have.
online_ft (int) – Enable a 1024-unit MLP to fine-tune online
task (str) – Which self-supervised task to use (‘cpc’, ‘amdim’, etc…)
num_workers (int) – num dataloader worksers
learning_rate (int) – what learning rate to use
data_dir (str) – where to store data
batch_size (int) – batch size
pretrained (Optional[str]) – If true, will use the weights pretrained (using CPC) on Imagenet

_CPCV2__compute_final_nb_c(patch_size)[source]¶

_CPCV2__recover_z_shape(Z, b)[source]¶

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

forward(img_1)[source]¶

init_encoder()[source]¶

load_pretrained(encoder)[source]¶

shared_step(batch)[source]¶

training_step(batch, batch_nb)[source]¶

validation_step(batch, batch_nb)[source]¶

pl_bolts.models.self_supervised.cpc.networks module¶

class pl_bolts.models.self_supervised.cpc.networks.CPCResNet(sample_batch, block, layers, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None)[source]¶

Bases: torch.nn.Module

_make_layer(sample_batch, block, planes, blocks, stride=1, dilate=False, expansion=4)[source]¶

flatten(x)[source]¶

forward(x)[source]¶

class pl_bolts.models.self_supervised.cpc.networks.LNBottleneck(sample_batch, inplanes, planes, stride=1, downsample_conv=None, groups=1, base_width=64, dilation=1, norm_layer=None, expansion=4)[source]¶

Bases: torch.nn.Module

_LNBottleneck__init_layer_norms(x, conv1, conv2, conv3, downsample_conv)[source]¶

forward(x)[source]¶

pl_bolts.models.self_supervised.cpc.networks.conv1x1(in_planes, out_planes, stride=1)[source]¶: 1x1 convolution

pl_bolts.models.self_supervised.cpc.networks.conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1)[source]¶: 3x3 convolution with padding

pl_bolts.models.self_supervised.cpc.networks.cpc_resnet101(sample_batch, **kwargs)[source]¶

pl_bolts.models.self_supervised.cpc.networks.cpc_resnet50(sample_batch, **kwargs)[source]¶

pl_bolts.models.self_supervised.cpc.transforms module¶

class pl_bolts.models.self_supervised.cpc.transforms.CPCEvalTransformsCIFAR10(patch_size=8, overlap=4)[source]¶

Bases: object

Transforms used for CPC:

Parameters

patch_size – size of patches when cutting up the image into overlapping patches
overlap – how much to overlap patches

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())

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.cpc.transforms.CPCEvalTransformsImageNet128(patch_size=32, overlap=16)[source]¶

Bases: object

Transforms used for CPC:

Parameters

patch_size – size of patches when cutting up the image into overlapping patches
overlap – how much to overlap patches

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())

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.cpc.transforms.CPCEvalTransformsSTL10(patch_size=16, overlap=8)[source]¶

Bases: object

Transforms used for CPC:

Parameters

patch_size – size of patches when cutting up the image into overlapping patches
overlap – how much to overlap patches

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())

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.cpc.transforms.CPCTrainTransformsCIFAR10(patch_size=8, overlap=4)[source]¶

Bases: object

Transforms used for CPC:

Parameters

patch_size – size of patches when cutting up the image into overlapping patches
overlap – how much to overlap patches

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())

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.cpc.transforms.CPCTrainTransformsImageNet128(patch_size=32, overlap=16)[source]¶

Bases: object

Transforms used for CPC:

Parameters

patch_size – size of patches when cutting up the image into overlapping patches
overlap – how much to overlap patches

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())

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.cpc.transforms.CPCTrainTransformsSTL10(patch_size=16, overlap=8)[source]¶

Bases: object

Transforms used for CPC:

Parameters

patch_size – size of patches when cutting up the image into overlapping patches
overlap – how much to overlap patches

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())

__call__(inp)[source]¶: Call self as a function.

pl_bolts.models.self_supervised.moco package¶

Submodules¶

pl_bolts.models.self_supervised.moco.callbacks module¶

class pl_bolts.models.self_supervised.moco.callbacks.MocoLRScheduler(initial_lr=0.03, use_cosine_scheduler=False, schedule=(120, 160), max_epochs=200)[source]¶

Bases: pytorch_lightning.Callback

on_epoch_start(trainer, pl_module)[source]¶

pl_bolts.models.self_supervised.moco.moco2_module module¶

Adapted from: https://github.com/facebookresearch/moco

class pl_bolts.models.self_supervised.moco.moco2_module.MocoV2(base_encoder='resnet18', emb_dim=128, num_negatives=65536, encoder_momentum=0.999, softmax_temperature=0.07, learning_rate=0.03, momentum=0.9, weight_decay=0.0001, datamodule=None, data_dir='./', batch_size=256, use_mlp=False, num_workers=8, *args, **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

PyTorch Lightning implementation of Moco

Paper authors: Xinlei Chen, Haoqi Fan, Ross Girshick, Kaiming He.

Code adapted from facebookresearch/moco to Lightning by:

William Falcon

Example

>>> from pl_bolts.models.self_supervised import MocoV2
...
>>> model = MocoV2()

Train:

trainer = Trainer()
trainer.fit(model)

CLI command:

# cifar10
python moco2_module.py --gpus 1

# imagenet
python moco2_module.py
    --gpus 8
    --dataset imagenet2012
    --data_dir /path/to/imagenet/
    --meta_dir /path/to/folder/with/meta.bin/
    --batch_size 32

Parameters

base_encoder (Union[str, Module]) – torchvision model name or torch.nn.Module
emb_dim (int) – feature dimension (default: 128)
num_negatives (int) – queue size; number of negative keys (default: 65536)
encoder_momentum (float) – moco momentum of updating key encoder (default: 0.999)
softmax_temperature (float) – softmax temperature (default: 0.07)
learning_rate (float) – the learning rate
momentum (float) – optimizer momentum
weight_decay (float) – optimizer weight decay
datamodule (Optional[LightningDataModule]) – the DataModule (train, val, test dataloaders)
data_dir (str) – the directory to store data
batch_size (int) – batch size
use_mlp (bool) – add an mlp to the encoders
num_workers (int) – workers for the loaders

_batch_shuffle_ddp(x)[source]¶: Batch shuffle, for making use of BatchNorm. * Only support DistributedDataParallel (DDP) model. *

_batch_unshuffle_ddp(x, idx_unshuffle)[source]¶: Undo batch shuffle. * Only support DistributedDataParallel (DDP) model. *

_dequeue_and_enqueue(keys)[source]¶

_momentum_update_key_encoder()[source]¶: Momentum update of the key encoder

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

forward(img_q, img_k)[source]¶

Input:: im_q: a batch of query images im_k: a batch of key images
Output:: logits, targets

init_encoders(base_encoder)[source]¶: Override to add your own encoders

training_step(batch, batch_idx)[source]¶

validation_epoch_end(outputs)[source]¶

validation_step(batch, batch_idx)[source]¶

pl_bolts.models.self_supervised.moco.moco2_module.cli_main()[source]¶

pl_bolts.models.self_supervised.moco.moco2_module.concat_all_gather(tensor)[source]¶: Performs all_gather operation on the provided tensors. * Warning *: torch.distributed.all_gather has no gradient.

pl_bolts.models.self_supervised.moco.transforms module¶

class pl_bolts.models.self_supervised.moco.transforms.GaussianBlur(sigma=(0.1, 2.0))[source]¶

Bases: object

Gaussian blur augmentation in SimCLR https://arxiv.org/abs/2002.05709

__call__(x)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.moco.transforms.Moco2EvalCIFAR10Transforms(height=32)[source]¶

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.moco.transforms.Moco2EvalImagenetTransforms(height=128)[source]¶

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.moco.transforms.Moco2EvalSTL10Transforms(height=64)[source]¶

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.moco.transforms.Moco2TrainCIFAR10Transforms(height=32)[source]¶

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.moco.transforms.Moco2TrainImagenetTransforms(height=128)[source]¶

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.moco.transforms.Moco2TrainSTL10Transforms(height=64)[source]¶

Bases: object

Moco 2 augmentation: https://arxiv.org/pdf/2003.04297.pdf

__call__(inp)[source]¶: Call self as a function.

pl_bolts.models.self_supervised.simclr package¶

Submodules¶

pl_bolts.models.self_supervised.simclr.simclr_finetuner module¶

pl_bolts.models.self_supervised.simclr.simclr_finetuner.cli_main()[source]¶

pl_bolts.models.self_supervised.simclr.simclr_module module¶

class pl_bolts.models.self_supervised.simclr.simclr_module.DensenetEncoder[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

class pl_bolts.models.self_supervised.simclr.simclr_module.Projection(input_dim=2048, hidden_dim=2048, output_dim=128)[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

class pl_bolts.models.self_supervised.simclr.simclr_module.SimCLR(batch_size, num_samples, warmup_epochs=10, lr=0.0001, opt_weight_decay=1e-06, loss_temperature=0.5, **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

Parameters

batch_size – the batch size
num_samples – num samples in the dataset
warmup_epochs – epochs to warmup the lr for
lr – the optimizer learning rate
opt_weight_decay – the optimizer weight decay
loss_temperature – the loss temperature

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

exclude_from_wt_decay(named_params, weight_decay, skip_list=['bias', 'bn'])[source]¶

forward(x)[source]¶

init_encoder()[source]¶

setup(stage)[source]¶

shared_step(batch, batch_idx)[source]¶

training_step(batch, batch_idx)[source]¶

validation_step(batch, batch_idx)[source]¶

pl_bolts.models.self_supervised.simclr.simclr_module.cli_main()[source]¶

pl_bolts.models.self_supervised.simclr.simclr_transforms module¶

class pl_bolts.models.self_supervised.simclr.simclr_transforms.GaussianBlur(kernel_size, min=0.1, max=2.0)[source]¶

Bases: object

__call__(sample)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.simclr.simclr_transforms.SimCLREvalDataTransform(input_height, s=1)[source]¶

Bases: object

Transforms for SimCLR

Transform:

Resize(input_height + 10, interpolation=3)
transforms.CenterCrop(input_height),
transforms.ToTensor()

Example:

from pl_bolts.models.self_supervised.simclr.transforms import SimCLREvalDataTransform

transform = SimCLREvalDataTransform(input_height=32)
x = sample()
(xi, xj) = transform(x)

__call__(sample)[source]¶: Call self as a function.

class pl_bolts.models.self_supervised.simclr.simclr_transforms.SimCLRTrainDataTransform(input_height, s=1)[source]¶

Bases: object

Transforms for SimCLR

Transform:

RandomResizedCrop(size=self.input_height)
RandomHorizontalFlip()
RandomApply([color_jitter], p=0.8)
RandomGrayscale(p=0.2)
GaussianBlur(kernel_size=int(0.1 * self.input_height))
transforms.ToTensor()

Example:

from pl_bolts.models.self_supervised.simclr.transforms import SimCLRTrainDataTransform

transform = SimCLRTrainDataTransform(input_height=32)
x = sample()
(xi, xj) = transform(x)

__call__(sample)[source]¶: Call self as a function.

Submodules¶

pl_bolts.models.self_supervised.evaluator module¶

class pl_bolts.models.self_supervised.evaluator.Flatten[source]¶

Bases: torch.nn.Module

forward(input_tensor)[source]¶

class pl_bolts.models.self_supervised.evaluator.SSLEvaluator(n_input, n_classes, n_hidden=512, p=0.1)[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

pl_bolts.models.self_supervised.resnets module¶

class pl_bolts.models.self_supervised.resnets.ResNet(block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, return_all_feature_maps=False)[source]¶

Bases: torch.nn.Module

_make_layer(block, planes, blocks, stride=1, dilate=False)[source]¶

forward(x)[source]¶

pl_bolts.models.self_supervised.resnets.resnet18(pretrained=False, progress=True, **kwargs)[source]¶: ResNet-18 model from “Deep Residual Learning for Image Recognition” :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet18.pretrained: If True, returns a model pre-trained on ImageNet :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet18.pretrained: bool :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet18.progress: If True, displays a progress bar of the download to stderr :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet18.progress: bool

pl_bolts.models.self_supervised.resnets.resnet34(pretrained=False, progress=True, **kwargs)[source]¶: ResNet-34 model from “Deep Residual Learning for Image Recognition” :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet34.pretrained: If True, returns a model pre-trained on ImageNet :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet34.pretrained: bool :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet34.progress: If True, displays a progress bar of the download to stderr :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet34.progress: bool

pl_bolts.models.self_supervised.resnets.resnet50(pretrained=False, progress=True, **kwargs)[source]¶: ResNet-50 model from “Deep Residual Learning for Image Recognition” :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet50.pretrained: If True, returns a model pre-trained on ImageNet :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet50.pretrained: bool :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet50.progress: If True, displays a progress bar of the download to stderr :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet50.progress: bool

pl_bolts.models.self_supervised.resnets.resnet50_bn(pretrained=False, progress=True, **kwargs)[source]¶: ResNet-50 model from “Deep Residual Learning for Image Recognition” :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet50_bn.pretrained: If True, returns a model pre-trained on ImageNet :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet50_bn.pretrained: bool :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet50_bn.progress: If True, displays a progress bar of the download to stderr :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet50_bn.progress: bool

pl_bolts.models.self_supervised.resnets.resnet101(pretrained=False, progress=True, **kwargs)[source]¶: ResNet-101 model from “Deep Residual Learning for Image Recognition” :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet101.pretrained: If True, returns a model pre-trained on ImageNet :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet101.pretrained: bool :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet101.progress: If True, displays a progress bar of the download to stderr :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet101.progress: bool

pl_bolts.models.self_supervised.resnets.resnet152(pretrained=False, progress=True, **kwargs)[source]¶: ResNet-152 model from “Deep Residual Learning for Image Recognition” :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet152.pretrained: If True, returns a model pre-trained on ImageNet :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet152.pretrained: bool :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet152.progress: If True, displays a progress bar of the download to stderr :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnet152.progress: bool

pl_bolts.models.self_supervised.resnets.resnext50_32x4d(pretrained=False, progress=True, **kwargs)[source]¶: ResNeXt-50 32x4d model from “Aggregated Residual Transformation for Deep Neural Networks” :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnext50_32x4d.pretrained: If True, returns a model pre-trained on ImageNet :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnext50_32x4d.pretrained: bool :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnext50_32x4d.progress: If True, displays a progress bar of the download to stderr :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnext50_32x4d.progress: bool

pl_bolts.models.self_supervised.resnets.resnext101_32x8d(pretrained=False, progress=True, **kwargs)[source]¶: ResNeXt-101 32x8d model from “Aggregated Residual Transformation for Deep Neural Networks” :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnext101_32x8d.pretrained: If True, returns a model pre-trained on ImageNet :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnext101_32x8d.pretrained: bool :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnext101_32x8d.progress: If True, displays a progress bar of the download to stderr :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.resnext101_32x8d.progress: bool

pl_bolts.models.self_supervised.resnets.wide_resnet50_2(pretrained=False, progress=True, **kwargs)[source]¶: Wide ResNet-50-2 model from “Wide Residual Networks” The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.wide_resnet50_2.pretrained: If True, returns a model pre-trained on ImageNet :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.wide_resnet50_2.pretrained: bool :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.wide_resnet50_2.progress: If True, displays a progress bar of the download to stderr :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.wide_resnet50_2.progress: bool

pl_bolts.models.self_supervised.resnets.wide_resnet101_2(pretrained=False, progress=True, **kwargs)[source]¶: Wide ResNet-101-2 model from “Wide Residual Networks” The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.wide_resnet101_2.pretrained: If True, returns a model pre-trained on ImageNet :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.wide_resnet101_2.pretrained: bool :param _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.wide_resnet101_2.progress: If True, displays a progress bar of the download to stderr :type _sphinx_paramlinks_pl_bolts.models.self_supervised.resnets.wide_resnet101_2.progress: bool

pl_bolts.models.self_supervised.ssl_finetuner module¶

class pl_bolts.models.self_supervised.ssl_finetuner.SSLFineTuner(backbone, in_features, num_classes, hidden_dim=1024)[source]¶

Bases: pytorch_lightning.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 CPCV2
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.models.self_supervised.cpc.transforms import CPCEvalTransformsCIFAR10,
                                                            CPCTrainTransformsCIFAR10

# pretrained model
backbone = CPCV2.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

backbone – a pretrained model
in_features – feature dim of backbone outputs
num_classes – classes of the dataset
hidden_dim – dim of the MLP (1024 default used in self-supervised literature)

configure_optimizers()[source]¶

on_train_epoch_start()[source]¶

Return type: None

shared_step(batch)[source]¶

test_step(batch, batch_idx)[source]¶

training_step(batch, batch_idx)[source]¶

validation_step(batch, batch_idx)[source]¶

pl_bolts.models.vision package¶

Subpackages¶

pl_bolts.models.vision.image_gpt package¶

Submodules¶

pl_bolts.models.vision.image_gpt.gpt2 module¶

class pl_bolts.models.vision.image_gpt.gpt2.Block(embed_dim, heads)[source]¶

Bases: torch.nn.Module

forward(x)[source]¶

class pl_bolts.models.vision.image_gpt.gpt2.GPT2(embed_dim, heads, layers, num_positions, vocab_size, num_classes)[source]¶

Bases: pytorch_lightning.LightningModule

GPT-2 from language Models are Unsupervised Multitask Learners

Paper by: Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever

Implementation contributed by:

Teddy Koker

Example:

from pl_bolts.models import GPT2

seq_len = 17
batch_size = 32
vocab_size = 16
x = torch.randint(0, vocab_size, (seq_len, batch_size))
model = GPT2(embed_dim=32, heads=2, layers=2, num_positions=seq_len, vocab_size=vocab_size, num_classes=4)
results = model(x)

_init_embeddings()[source]¶

_init_layers()[source]¶

_init_sos_token()[source]¶

forward(x, classify=False)[source]¶: Expect input as shape [sequence len, batch] If classify, return classification logits

pl_bolts.models.vision.image_gpt.igpt_module module¶

class pl_bolts.models.vision.image_gpt.igpt_module.ImageGPT(datamodule=None, embed_dim=16, heads=2, layers=2, pixels=28, vocab_size=16, num_classes=10, classify=False, batch_size=64, learning_rate=0.01, steps=25000, data_dir='.', num_workers=8, **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

Paper: Generative Pretraining from Pixels [original paper code].

Paper by: Mark Che, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, Prafulla Dhariwal, David Luan, Ilya Sutskever

Implementation contributed by:

Teddy Koker

Original repo with results and more implementation details:

https://github.com/teddykoker/image-gpt

Example Results (Photo credits: Teddy Koker):

Default arguments:

Argument Defaults¶
Argument	Default	iGPT-S (Chen et al.)
–embed_dim	16	512
–heads	2	8
–layers	8	24
–pixels	28	32
–vocab_size	16	512
–num_classes	10	10
–batch_size	64	128
–learning_rate	0.01	0.01
–steps	25000	1000000

Example:

import pytorch_lightning as pl
from pl_bolts.models.vision import ImageGPT

dm = MNISTDataModule('.')
model = ImageGPT(dm)

pl.Trainer(gpu=4).fit(model)

As script:

cd pl_bolts/models/vision/image_gpt
python igpt_module.py --learning_rate 1e-2 --batch_size 32 --gpus 4

Parameters

datamodule (Optional[LightningDataModule]) – LightningDataModule
embed_dim (int) – the embedding dim
heads (int) – number of attention heads
layers (int) – number of layers
pixels (int) – number of input pixels
vocab_size (int) – vocab size
num_classes (int) – number of classes in the input
classify (bool) – true if should classify
batch_size (int) – the batch size
learning_rate (float) – learning rate
steps (int) – number of steps for cosine annealing
data_dir (str) – where to store data
num_workers (int) – num_data workers

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

forward(x, classify=False)[source]¶

test_epoch_end(outs)[source]¶

test_step(batch, batch_idx)[source]¶

training_step(batch, batch_idx)[source]¶

validation_epoch_end(outs)[source]¶

validation_step(batch, batch_idx)[source]¶

pl_bolts.models.vision.image_gpt.igpt_module._shape_input(x)[source]¶: shape batch of images for input into GPT2 model

pl_bolts.models.vision.image_gpt.igpt_module.cli_main()[source]¶

Submodules¶

pl_bolts.models.vision.pixel_cnn module¶

PixelCNN Implemented by: William Falcon Reference: https://arxiv.org/pdf/1905.09272.pdf (page 15) Accessed: May 14, 2020

class pl_bolts.models.vision.pixel_cnn.PixelCNN(input_channels, hidden_channels=256, num_blocks=5)[source]¶

Bases: torch.nn.Module

Implementation of Pixel CNN.

Paper authors: Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu

Implemented by:

William Falcon

Example:

>>> from pl_bolts.models.vision import PixelCNN
>>> import torch
...
>>> model = PixelCNN(input_channels=3)
>>> x = torch.rand(5, 3, 64, 64)
>>> out = model(x)
...
>>> out.shape
torch.Size([5, 3, 64, 64])

conv_block(input_channels)[source]¶

forward(z)[source]¶

Submodules¶

pl_bolts.models.mnist_module module¶

class pl_bolts.models.mnist_module.LitMNIST(hidden_dim=128, learning_rate=0.001, batch_size=32, num_workers=4, data_dir='', **kwargs)[source]¶

Bases: pytorch_lightning.LightningModule

static add_model_specific_args(parent_parser)[source]¶

configure_optimizers()[source]¶

forward(x)[source]¶

prepare_data()[source]¶

test_dataloader()[source]¶

test_epoch_end(outputs)[source]¶

test_step(batch, batch_idx)[source]¶

train_dataloader()[source]¶

training_step(batch, batch_idx)[source]¶

val_dataloader()[source]¶

validation_epoch_end(outputs)[source]¶

validation_step(batch, batch_idx)[source]¶

pl_bolts.models.mnist_module.cli_main()[source]¶

pl_bolts.losses package¶

Submodules¶

pl_bolts.losses.self_supervised_learning module¶

class pl_bolts.losses.self_supervised_learning.AmdimNCELoss(tclip)[source]¶

Bases: torch.nn.Module

forward(anchor_representations, positive_representations, mask_mat)[source]¶

Compute the NCE scores for predicting r_src->r_trg. :param _sphinx_paramlinks_pl_bolts.losses.self_supervised_learning.AmdimNCELoss.forward.anchor_representations: (batch_size, emb_dim) :param _sphinx_paramlinks_pl_bolts.losses.self_supervised_learning.AmdimNCELoss.forward.positive_representations: (emb_dim, n_batch * w* h) (ie: nb_feat_vectors x embedding_dim) :param _sphinx_paramlinks_pl_bolts.losses.self_supervised_learning.AmdimNCELoss.forward.mask_mat: (n_batch_gpu, n_batch)

Output:: raw_scores : (n_batch_gpu, n_locs) nce_scores : (n_batch_gpu, n_locs) lgt_reg : scalar

class pl_bolts.losses.self_supervised_learning.CPCTask(num_input_channels, target_dim=64, embed_scale=0.1)[source]¶

Bases: torch.nn.Module

Loss used in CPC

compute_loss_h(targets, preds, i)[source]¶

forward(Z)[source]¶

class pl_bolts.losses.self_supervised_learning.FeatureMapContrastiveTask(comparisons='00, 11', tclip=10.0, bidirectional=True)[source]¶

Bases: torch.nn.Module

Performs an anchor, positive negative pair comparison for each each tuple of feature maps passed.

# extract feature maps
pos_0, pos_1, pos_2 = encoder(x_pos)
anc_0, anc_1, anc_2 = encoder(x_anchor)

# compare only the 0th feature maps
task = FeatureMapContrastiveTask('00')
loss, regularizer = task((pos_0), (anc_0))

# compare (pos_0 to anc_1) and (pos_0, anc_2)
task = FeatureMapContrastiveTask('01, 02')
losses, regularizer = task((pos_0, pos_1, pos_2), (anc_0, anc_1, anc_2))
loss = losses.sum()

# compare (pos_1 vs a anc_random)
task = FeatureMapContrastiveTask('0r')
loss, regularizer = task((pos_0, pos_1, pos_2), (anc_0, anc_1, anc_2))

Parameters

comparisons (str) – groupings of feature map indices to compare (zero indexed, ‘r’ means random) ex: ‘00, 1r’
tclip (float) – stability clipping value
bidirectional (bool) – if true, does the comparison both ways

# with bidirectional the comparisons are done both ways
task = FeatureMapContrastiveTask('01, 02')

# will compare the following:
# 01: (pos_0, anc_1), (anc_0, pos_1)
# 02: (pos_0, anc_2), (anc_0, pos_2)

_FeatureMapContrastiveTask__cache_dimension_masks(*args)[source]¶

_FeatureMapContrastiveTask__compare_maps(m1, m2)[source]¶

_sample_src_ftr(r_cnv, masks)[source]¶

feat_size_w_mask(w, feature_map)[source]¶

forward(anchor_maps, positive_maps)[source]¶

Takes in a set of tuples, each tuple has two feature maps with all matching dimensions

Example

>>> import torch
>>> from pytorch_lightning import seed_everything
>>> seed_everything(0)
0
>>> a1 = torch.rand(3, 5, 2, 2)
>>> a2 = torch.rand(3, 5, 2, 2)
>>> b1 = torch.rand(3, 5, 2, 2)
>>> b2 = torch.rand(3, 5, 2, 2)
...
>>> task = FeatureMapContrastiveTask('01, 11')
...
>>> losses, regularizer = task((a1, a2), (b1, b2))
>>> losses
tensor([2.2351, 2.1902])
>>> regularizer
tensor(0.0324)

static parse_map_indexes(comparisons)[source]¶

Example:

>>> FeatureMapContrastiveTask.parse_map_indexes('11')
[(1, 1)]
>>> FeatureMapContrastiveTask.parse_map_indexes('11,59')
[(1, 1), (5, 9)]
>>> FeatureMapContrastiveTask.parse_map_indexes('11,59, 2r')
[(1, 1), (5, 9), (2, -1)]

pl_bolts.losses.self_supervised_learning.nt_xent_loss(out_1, out_2, temperature)[source]¶: Loss used in SimCLR

pl_bolts.losses.self_supervised_learning.tanh_clip(x, clip_val=10.0)[source]¶: soft clip values to the range [-clip_val, +clip_val]

pl_bolts.optimizers package¶

Submodules¶

pl_bolts.optimizers.lars_scheduling module¶

References

class pl_bolts.optimizers.lars_scheduling.LARSWrapper(optimizer, eta=0.02, clip=True, eps=1e-08)[source]¶

Bases: object

Wrapper that adds LARS scheduling to any optimizer. This helps stability with huge batch sizes.

Parameters

optimizer – torch optimizer
eta – LARS coefficient (trust)
clip – True to clip LR
eps – adaptive_lr stability coefficient

step()[source]¶

update_p(p, group, weight_decay)[source]¶

property param_groups[source]¶

property state[source]¶

pl_bolts.optimizers.lr_scheduler module¶

class pl_bolts.optimizers.lr_scheduler.LinearWarmupCosineAnnealingLR(optimizer, warmup_epochs, max_epochs, warmup_start_lr=0.0, eta_min=0.0, last_epoch=-1)[source]¶

Bases: torch.optim.lr_scheduler._LRScheduler

Sets the learning rate of each parameter group to follow a linear warmup schedule between warmup_start_lr and base_lr followed by a cosine annealing schedule between base_lr and eta_min.

Warning

It is recommended to call step() for LinearWarmupCosineAnnealingLR after each iteration as calling it after each epoch will keep the starting lr at warmup_start_lr for the first epoch which is 0 in most cases.

Warning

passing epoch to step() is being deprecated and comes with an EPOCH_DEPRECATION_WARNING. It calls the _get_closed_form_lr() method for this scheduler instead of get_lr(). Though this does not change the behavior of the scheduler, when passing epoch param to step(), the user should call the step() function before calling train and validation methods.

Parameters

optimizer (Optimizer) – Wrapped optimizer.
warmup_epochs (int) – Maximum number of iterations for linear warmup
max_epochs (int) – Maximum number of iterations
warmup_start_lr (float) – Learning rate to start the linear warmup. Default: 0.
eta_min (float) – Minimum learning rate. Default: 0.
last_epoch (int) – The index of last epoch. Default: -1.

Example

>>> layer = nn.Linear(10, 1)
>>> optimizer = Adam(layer.parameters(), lr=0.02)
>>> scheduler = LinearWarmupCosineAnnealingLR(optimizer, warmup_epochs=10, max_epochs=40)
>>> #
>>> # the default case
>>> for epoch in range(40):
...     # train(...)
...     # validate(...)
...     scheduler.step()
>>> #
>>> # passing epoch param case
>>> for epoch in range(40):
...     scheduler.step(epoch)
...     # train(...)
...     # validate(...)

_get_closed_form_lr()[source]¶

Called when epoch is passed as a param to the step function of the scheduler.

Return type: List[float]

get_lr()[source]¶

Compute learning rate using chainable form of the scheduler

Return type: List[float]

pl_bolts.transforms package¶

Subpackages¶

pl_bolts.transforms.self_supervised package¶

Submodules¶

pl_bolts.transforms.self_supervised.ssl_transforms module¶

class pl_bolts.transforms.self_supervised.ssl_transforms.Patchify(patch_size, overlap_size)[source]¶

Bases: object

__call__(x)[source]¶: Call self as a function.

class pl_bolts.transforms.self_supervised.ssl_transforms.RandomTranslateWithReflect(max_translation)[source]¶

Bases: object

Translate image randomly Translate vertically and horizontally by n pixels where n is integer drawn uniformly independently for each axis from [-max_translation, max_translation]. Fill the uncovered blank area with reflect padding.

__call__(old_image)[source]¶: Call self as a function.

Submodules¶

pl_bolts.transforms.dataset_normalizations module¶

pl_bolts.transforms.dataset_normalizations.cifar10_normalization()[source]¶

pl_bolts.transforms.dataset_normalizations.imagenet_normalization()[source]¶

pl_bolts.transforms.dataset_normalizations.stl10_normalization()[source]¶

pl_bolts.utils package¶

Submodules¶

pl_bolts.utils.pretrained_weights module¶

pl_bolts.utils.pretrained_weights.load_pretrained(model, class_name=None)[source]¶

pl_bolts.utils.self_supervised module¶

pl_bolts.utils.self_supervised.torchvision_ssl_encoder(name, pretrained=False, return_all_feature_maps=False)[source]¶

pl_bolts.utils.semi_supervised module¶

class pl_bolts.utils.semi_supervised.Identity[source]¶

Bases: torch.nn.Module

An identity class to replace arbitrary layers in pretrained models

Example:

from pl_bolts.utils import Identity

model = resnet18()
model.fc = Identity()

forward(x)[source]¶

pl_bolts.utils.semi_supervised.balance_classes(X, Y, batch_size)[source]¶

Makes sure each batch has an equal amount of data from each class. Perfect balance

Parameters

X (ndarray) – input features
Y (list) – mixed labels (ints)
batch_size (int) – the ultimate batch size

pl_bolts.utils.semi_supervised.generate_half_labeled_batches(smaller_set_X, smaller_set_Y, larger_set_X, larger_set_Y, batch_size)[source]¶: Given a labeled dataset and an unlabeled dataset, this function generates a joint pair where half the batches are labeled and the other half is not

pl_bolts.utils.shaping module¶

pl_bolts.utils.shaping.tile(a, dim, n_tile)[source]¶