Pywick

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.
  • A high-level module for training with callbacks, constraints, metrics,
    conditions and regularizers.
  • Dozens 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.

Install

pip install pywick

or specific version from git:

pip install git+https://github.com/achaiah/[email protected]

ModuleTrainer

The ModuleTrainer class provides a high-level training interface which abstracts
away the training loop while providing callbacks, constraints, initializers, regularizers,
and more.

Example:

from pywick.modules import ModuleTrainer
from pywick.initializers import XavierUniform
from pywick.metrics import CategoricalAccuracySingleInput
import torch.nn as nn
import torch.functional as F

# Define your model EXACTLY as normal
class Network(nn.Module):
    def __init__(self):
        super(Network, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.fc1 = nn.Linear(1600, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 1600)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)

model = Network()
trainer = ModuleTrainer(model)   # optionally supply cuda_devices as a parameter

initializers = [XavierUniform(bias=False, module_filter='fc*')]

# initialize metrics with top1 and top5 
metrics = [CategoricalAccuracySingleInput(top_k=1), CategoricalAccuracySingleInput(top_k=5)]

trainer.compile(loss='cross_entropy',
                # callbacks=callbacks,          # define your callbacks here (e.g. model saver, LR scheduler)
                # regularizers=regularizers,    # define regularizers
                # constraints=constraints,      # define constraints
                optimizer='sgd',
                initializers=initializers,
                metrics=metrics)

trainer.fit_loader(train_dataset_loader, 
            val_loader=val_dataset_loader,
            num_epoch=20,
            verbose=1)

You also have access to the standard evaluation and prediction functions:

loss = trainer.evaluate(x_train, y_train)
y_pred = trainer.predict(x_train)

PyWick provides a wide range of callbacks, generally mimicking the interface
found in Keras:

  • CSVLogger - Logs epoch-level metrics to a CSV file
  • CyclicLRScheduler - Cycles through min-max learning rate
  • EarlyStopping - Provides ability to stop training early based on supplied criteria
  • History - Keeps history of metrics etc. during the learning process
  • LambdaCallback - Allows you to implement your own callbacks on the fly
  • LRScheduler - Simple learning rate scheduler based on function or supplied schedule
  • ModelCheckpoint - Comprehensive model saver
  • ReduceLROnPlateau - Reduces learning rate (LR) when a plateau has been reached
  • SimpleModelCheckpoint - Simple model saver
  • Additionally, a TensorboardLogger is incredibly easy to implement
    via the 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 regularizers:

  • L1Regularizer
  • L2Regularizer
  • L1L2Regularizer

and constraints:

  • 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 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)

Data Augmentation and Datasets

The PyWick package provides a ton 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 and th_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.

GitHub