High-Level Training, Data Augmentation, and Utilities for Pytorch
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v0.1.3 JUST RELEASED - contains significant improvements, bug fixes, and additional support. Get it from the releases, or pull the master branch.
This package provides a few things:
Have any feature requests? Submit an issue! I'll make it happen. Specifically, any data augmentation, data loading, or sampling functions.
Want to contribute? Check the issues page for those tagged with [contributions welcome].
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 torchsample.modules import ModuleTrainer
# 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)
trainer.compile(loss='nll_loss',
optimizer='adadelta')
trainer.fit(x_train, y_train,
val_data=(x_test, y_test),
num_epoch=20,
batch_size=128,
verbose=1)
You also have access to the standard evaluation and prediction functions:
loss = model.evaluate(x_train, y_train)
y_pred = model.predict(x_train)
Torchsample provides a wide range of callbacks, generally mimicking the interface
found in Keras
:
EarlyStopping
ModelCheckpoint
LearningRateScheduler
ReduceLROnPlateau
CSVLogger
from torchsample.callbacks import EarlyStopping
callbacks = [EarlyStopping(monitor='val_loss', patience=5)]
model.set_callbacks(callbacks)
Torchsample 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 torchsample.constraints import MaxNorm, NonNeg
from torchsample.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]
model.set_constraints(constraints)
regularizers = [L1Regularizer(scale=1e-4, module_filter='*conv*')]
model.set_regularizers(regularizers)
You can also fit directly on a torch.utils.data.DataLoader
and can have
a validation set as well :
from torchsample 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)
Finally, torchsample provides a few utility functions not commonly found:
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)F_affine2d
and F_affine3d
F_map_coordinates2d
and F_map_coordinates3d
The torchsample 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
and FolderDataset
classes to handle most dataset needs.
These transforms work directly on torch tensors
Compose()
AddChannel()
SwapDims()
RangeNormalize()
StdNormalize()
Slice2D()
RandomCrop()
SpecialCrop()
Pad()
RandomFlip()
ToTensor()
The following transforms perform affine (or affine-like) transforms on torch tensors.
Rotate()
Translate()
Shear()
Zoom()
We also provide a class for stringing multiple affine transformations together so that only one interpolation takes place:
Affine()
AffineCompose()
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:
TensorDataset()
FolderDataset()
Thank you to the following people and contributors: