Awesome
Pywick
<div style="text-align:center"> </div>High-Level Training framework for Pytorch
Pywick is a high-level Pytorch training framework that aims to get you up and running quickly with state of the art neural networks. Does the world need another Pytorch framework? Probably not. But we started this project when no good frameworks were available and it just kept growing. So here we are.
Pywick tries to stay on the bleeding edge of research into neural networks. If you just wish to run a vanilla CNN, this is probably going to be overkill. However, if you want to get lost in the world of neural networks, fine-tuning and hyperparameter optimization for months on end then this is probably the right place for you :)
Among other things Pywick includes:
- State of the art normalization, activation, loss functions and optimizers not included in the standard Pytorch library (AdaBelief, Addsign, Apollo, Eve, Lookahead, Radam, Ralamb, RangerLARS etc).
- A high-level module for training with callbacks, constraints, metrics, conditions and regularizers.
- Hundreds of popular object classification and semantic segmentation models!
- Comprehensive data loading, augmentation, transforms, and sampling capability.
- Utility tensor functions.
- Useful meters.
- Basic GridSearch (exhaustive and random).
Docs
Hey, check this out, we now have docs! They're still a work in progress though so apologies for anything that's broken.
What's New (highlights)
v0.6.5 - Docker all the things!
Another great improvement to the framework - docker! You can now run the 17flowers demo right out of the box!
- Grab our docker image at docker hub:
docker pull achaiah/pywick:latest
. Pytorch 1.8 and cuda dependencies are pre-installed. - Run 17flowers demo with:
docker run --rm -it --ipc=host -v your_local_out_dir:/jobs/17flowers --init -e demo=true achaiah/pywick:latest
- Or run the container in standalone mode so you can use your own data (don't forget to map your local dir to container):
docker run --rm -it \ --ipc=host \ -v <your_local_data_dir>:<container_data_dir> \ -v <your_local_out_dir>:<container_out_dir> \ --init \ achaiah/pywick:latest
Older Notes
- Oct. 11, 2021 - We thought ya might like YAML!
- So you're saying you like configuration files? You're saying you like examples too? Well, we've got you covered! Huge release today with a configuration-based training example! All you have to do is:
- Get your favorite dataset (or download 17 flowers to get started and
pywick/examples/17flowers_split.py
to convert) - Adjust the
configs/train_classifier.yaml
file to fit your workspace - Then simply run:
python3 train_classifier.py configs/train_classifier.yaml
and watch it train!
- Get your favorite dataset (or download 17 flowers to get started and
- So you're saying you like configuration files? You're saying you like examples too? Well, we've got you covered! Huge release today with a configuration-based training example! All you have to do is:
- May 6, 2021
- Many SoTA classification and segmentation models added: Swin-Transformer variants, NFNet variants (L0, L1), Halo nets, Lambda nets, ECA variants, Rexnet + others
- Many new loss functions added: RecallLoss, SoftInvDiceLoss, OhemBCEDicePenalizeBorderLoss, RMIBCEDicePenalizeBorderLoss + others
- Bug fixes
- Jun. 15, 2020
- 700+ models added from rwightman's repo via
torch.hub
! See docs for all the variants! - Some minor bug fixes
- 700+ models added from rwightman's repo via
- Jan. 20, 2020
- New release: 0.5.6 (minor fix from 0.5.5 for pypi)
- Mish activation function (SoTA)
- rwightman's models of pretrained/ported variants for classification (44 total)
- efficientnet Tensorflow port b0-b8, with and without AP, el/em/es, cc
- mixnet L/M/S
- mobilenetv3
- mnasnet
- spnasnet
- Additional loss functions
- Aug. 1, 2019
- New segmentation NNs: BiSeNet, DANet, DenseASPP, DUNet, OCNet, PSANet
- New Loss Functions: Focal Tversky Loss, OHEM CrossEntropy Loss, various combination losses
- Major restructuring and standardization of NN models and loading functionality
- General bug fixes and code improvements
Install
Pywick requires pytorch >= 1.4
pip install pywick
or specific version from git:
pip install git+https://github.com/achaiah/pywick.git@v0.6.5
ModuleTrainer
The ModuleTrainer
class provides a high-level training interface which abstracts away the training loop while providing callbacks, constraints, initializers, regularizers,
and more.
See the train_classifier.py
example for a pretty complete configuration example. To get up and running with your own data quickly simply edit the configs/train_classifier.yaml
file with your desired parameters and dataset location(s).
Note: <i>Dataset needs to be organized for classification where each directory name is the name of a class and contains all images pertaining to that class</i>
PyWick provides a wide range of <b>callbacks</b>, generally mimicking the interface found in Keras
:
CSVLogger
- Logs epoch-level metrics to a CSV fileCyclicLRScheduler
- Cycles through min-max learning rateEarlyStopping
- Provides ability to stop training early based on supplied criteriaHistory
- Keeps history of metrics etc. during the learning processLambdaCallback
- Allows you to implement your own callbacks on the flyLRScheduler
- Simple learning rate scheduler based on function or supplied scheduleModelCheckpoint
- Comprehensive model saverReduceLROnPlateau
- Reduces learning rate (LR) when a plateau has been reachedSimpleModelCheckpoint
- Simple model saver- Additionally, a
TensorboardLogger
is incredibly easy to implement via tensorboardX (now part of pytorch 1.1 release!)
from pywick.callbacks import EarlyStopping
callbacks = [EarlyStopping(monitor='val_loss', patience=5)]
trainer.set_callbacks(callbacks)
PyWick also provides <b>regularizers</b>:
L1Regularizer
L2Regularizer
L1L2Regularizer
and <b>constraints</b>:
UnitNorm
MaxNorm
NonNeg
Both regularizers and constraints can be selectively applied on layers using regular expressions and the module_filter
argument. Constraints can be explicit (hard) constraints applied at an arbitrary batch or
epoch frequency, or they can be implicit (soft) constraints similar to regularizers
where the constraint deviation is added as a penalty to the total model loss.
from pywick.constraints import MaxNorm, NonNeg
from pywick.regularizers import L1Regularizer
# hard constraint applied every 5 batches
hard_constraint = MaxNorm(value=2., frequency=5, unit='batch', module_filter='*fc*')
# implicit constraint added as a penalty term to model loss
soft_constraint = NonNeg(lagrangian=True, scale=1e-3, module_filter='*fc*')
constraints = [hard_constraint, soft_constraint]
trainer.set_constraints(constraints)
regularizers = [L1Regularizer(scale=1e-4, module_filter='*conv*')]
trainer.set_regularizers(regularizers)
You can also fit directly on a torch.utils.data.DataLoader
and can have
a validation set as well :
from pywick import TensorDataset
from torch.utils.data import DataLoader
train_dataset = TensorDataset(x_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=32)
val_dataset = TensorDataset(x_val, y_val)
val_loader = DataLoader(val_dataset, batch_size=32)
trainer.fit_loader(loader, val_loader=val_loader, num_epoch=100)
Extensive Library of Image Classification Models (most are pretrained!)
- All standard models from Pytorch:
- BatchNorm Inception
- Deep High-Resolution Representation Learning for Human Pose Estimation
- Deep Layer Aggregation
- Dual Path Networks
- EfficientNet variants (b0-b8, el, em, es, lite1-lite4, pruned, AP/NS)
- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
- FBResnet
- FBNet-C
- Inception v4
- InceptionResnet v2
- Mixnet variants (l, m, s, xl, xxl)
- MnasNet
- MobileNet V3
- NasNet variants (mnas, pnas, mobile)
- PNASNet
- Polynet
- Pyramid Resnet
- RegNet - Designing Network Design Spaces
- Resnet variants (gluon, res2net, se, ssl, tv, wide)
- ResNeSt: Split-Attention Networks
- ResNext variants (ig, se, ssl, swsl, tv)
- SE Net variants (gluon, resnet, resnext, inception)
- SelecSLS Convolutional Net
- Selective Kernel Networks
- Semi-Supervised and Semi-Weakly Supervised ImageNet Models
- Single-Pass NAS Net
- TResNet: High Performance GPU-Dedicated Architecture
- Wide Resnet
- XCeption
- All the newest classification models (200+) from rwightman's repo ECA-NFNet, GERNet, RegNet, SKResnext, SWIN-Transformer, VIT etc.)
Image Segmentation Models
- BiSeNet (Bilateral Segmentation Network for Real-time Semantic Segmentation)
- DANet (Dual Attention Network for Scene Segmentation)
- Deeplab v2 (DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs)
- Deeplab v3 (Rethinking Atrous Convolution for Semantic Image Segmentation)
- DenseASPP (DenseASPP for Semantic Segmentation in Street Scenes)
- DRNNet (Dilated Residual Networks)
- DUC, HDC (understanding convolution for semantic segmentation)
- DUNet (Decoders Matter for Semantic Segmentation)
- ENet (ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation)
- Vanilla FCN: FCN32, FCN16, FCN8, in the versions of VGG, ResNet and OptDenseNet respectively (Fully convolutional networks for semantic segmentation)
- FRRN (Full Resolution Residual Networks for Semantic Segmentation in Street Scenes)
- FusionNet (FusionNet in Tensorflow by Hyungjoo Andrew Cho)
- GALDNet
- GCN (Large Kernel Matters)
- LinkNet (Link-Net)
- OCNet (Object Context Network for Scene Parsing)
- PSPNet (Pyramid scene parsing network)
- RefineNet (RefineNet)
- SegNet (Segnet: A deep convolutional encoder-decoder architecture for image segmentation)
- Tiramisu (The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation)
- U-Net (U-net: Convolutional networks for biomedical image segmentation)
- Additional variations of many of the above
To load one of these models:
Read the docs for useful details! Then dive in:
# use the `get_model` utility
from pywick.models.model_utils import get_model, ModelType
model = get_model(model_type=ModelType.CLASSIFICATION, model_name='resnet18', num_classes=1000, pretrained=True)
For a complete list of models (including many experimental ones) you can call the get_supported_models
method e.g.
pywick.models.model_utils.get_supported_models(ModelType.SEGMENTATION)
Data Augmentation and Datasets
The PyWick package provides wide variety of good data augmentation and transformation
tools which can be applied during data loading. The package also provides the flexible
TensorDataset
, FolderDataset
and MultiFolderDataset
classes to handle most dataset needs.
Torch Transforms
These transforms work directly on torch tensors
AddChannel
ChannelsFirst
ChannelsLast
Compose
ExpandAxis
Pad
PadNumpy
RandomChoiceCompose
RandomCrop
RandomFlip
RandomOrder
RangeNormalize
Slice2D
SpecialCrop
StdNormalize
ToFile
ToNumpyType
ToTensor
Transpose
TypeCast
Additionally, we provide image-specific manipulations directly on tensors:
Brightness
Contrast
Gamma
Grayscale
RandomBrightness
RandomChoiceBrightness
RandomChoiceContrast
RandomChoiceGamma
RandomChoiceSaturation
RandomContrast
RandomGamma
RandomGrayscale
RandomSaturation
Saturation
Affine Transforms (perform affine or affine-like transforms on torch tensors)
RandomAffine
RandomChoiceRotate
RandomChoiceShear
RandomChoiceTranslate
RandomChoiceZoom
RandomRotate
RandomShear
RandomSquareZoom
RandomTranslate
RandomZoom
Rotate
Shear
Translate
Zoom
We also provide a class for stringing multiple affine transformations together so that only one interpolation takes place:
Affine
AffineCompose
Blur and Scramble transforms (for tensors)
Blur
RandomChoiceBlur
RandomChoiceScramble
Scramble
Datasets and Sampling
We provide the following datasets which provide general structure and iterators for sampling from and using transforms on in-memory or out-of-memory data. In particular, the FolderDataset has been designed to fit most of your dataset needs. It has extensive options for data filtering and manipulation. It supports loading images for classification, segmentation and even arbitrary source/target mapping. Take a good look at its documentation for more info.
ClonedDataset
CSVDataset
FolderDataset
MultiFolderDataset
TensorDataset
tnt.BatchDataset
tnt.ConcatDataset
tnt.ListDataset
tnt.MultiPartitionDataset
tnt.ResampleDataset
tnt.ShuffleDataset
tnt.TensorDataset
tnt.TransformDataset
Imbalanced Datasets
In many scenarios it is important to ensure that your traing set is properly balanced,
however, it may not be practical in real life to obtain such a perfect dataset. In these cases
you can use the ImbalancedDatasetSampler
as a drop-in replacement for the basic sampler provided
by the DataLoader. More information can be found here
from pywick.samplers import ImbalancedDatasetSampler
train_loader = torch.utils.data.DataLoader(train_dataset,
sampler=ImbalancedDatasetSampler(train_dataset),
batch_size=args.batch_size, **kwargs)
Utility Functions
PyWick provides a few utility functions not commonly found:
Tensor Functions
th_iterproduct
(mimics itertools.product)th_gather_nd
(N-dimensional version of torch.gather)th_random_choice
(mimics np.random.choice)th_pearsonr
(mimics scipy.stats.pearsonr)th_corrcoef
(mimics np.corrcoef)th_affine2d
andth_affine3d
(affine transforms on torch.Tensors)
Acknowledgements and References
We stand on the shoulders of (github?) giants and couldn't have done this without the rich github ecosystem and community. This framework is based in part on the excellent Torchsample framework originally published by @ncullen93. Additionally, many models have been gently borrowed/modified from @Cadene pretrained models repo as well as @Tramac segmentation repo.
Thank you to the following people and the projects they maintain:
- @ncullen93
- @cadene
- @deallynomore
- @recastrodiaz
- @zijundeng
- @Tramac
- And many others! (attributions listed in the codebase as they occur)
Thank you to the following projects from which we gently borrowed code and models
- PyTorchNet
- pretrained-models.pytorch
- DeepLab_pytorch
- Pytorch for Semantic Segmentation
- Binseg Pytorch
- awesome-semantic-segmentation-pytorch
- And many others! (attributions listed in the codebase as they occur)
Thangs are broken matey! Arrr!!! |
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We're working on this project as time permits so you might discover bugs here and there. Feel free to report them, or better yet, to submit a pull request! |