homura 
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homura is a fast prototyping library for DL research.
Requirements
Minimal requirements
Python>=3.9
PyTorch>=1.9.0
torchvision>=0.10.0
Installation
pip install -U homura-coreor
pip uninstall homura
pip install -U git+https://github.com/moskomule/homuraOptional
faiss (for faster kNN)
accimage (for faster image pre-processing)
cupy (homura)
opt_einsum (for accelerated einsum)
test
pytest .
APIs
Basics
homura aims abstract (e.g., device-agnostic) simple prototyping.
from homura import optim, lr_scheduler
from homura import trainers, reporters
from homura.vision import MODEL_REGISTRY, DATASET_REGISTRY
from torch.nn import functional as F
train_loader, test_loader, num_classes = DATASET_REGISTRY('dataset_name')(...)
# User does not need to care about the device
model = MODEL_REGISTRY('model_name')(num_classes=num_classes)
# Model is registered in optimizer lazily. This is convenient for distributed training and other complicated scenes.
optimizer = optim.SGD(lr=0.1, momentum=0.9)
scheduler = lr_scheduler.MultiStepLR(milestones=[30, 80], gamma=0.1)
with trainers.SupervisedTrainer(model,
optimizer,
F.cross_entropy,
reporters=[reporters.TensorboardReporter(...)],
scheduler=scheduler) as trainer:
# epoch-based training
for _ in trainer.epoch_iterator(num_epochs):
trainer.train(train_loader)
trainer.scheduler.step()
trainer.test(test_loader)
trainer.scheduler.step()
# otherwise, iteration-based training
trainer.run(train_loader, test_loader,
total_iterations=1_000, val_intervals=10)
print(f"Max Accuracy={max(trainer.history['accuracy']['tests'])}")You can customize iteration of trainer as follows.
from homura.trainers import TrainerBase, SupervisedTrainer
from homura.metrics import accuracy
trainer = SupervisedTrainer(...)
# from v2020.08, iteration is much simpler
def iteration(trainer: TrainerBase,
data: Tuple[torch.Tensor, torch.Tensor]
) -> None:
input, target = data
output = trainer.model(input)
loss = trainer.loss_f(output, target)
trainer.reporter.add('loss', loss.detach())
trainer.reporter.add('accuracy', accuracy(input, target))
trainer.reporter.add('')
if trainer.is_train:
trainer.optimizer.zero_grad()
loss.backward()
trainer.optimizer.step()
# in case schedule is step-wise
trainer.scheduler.step()
SupervisedTrainer.iteration = iteration
# or
trainer.update_iteration(iteration) dict of models, optimizers, loss functions are supported. This is useful for GANs, for example.
trainer = CustomTrainer({"generator": generator, "discriminator": discriminator},
{"generator": gen_opt, "discriminator": dis_opt},
{"reconstruction": recon_loss, "generator": gen_loss},
**kwargs)reporter internally tracks the values during each epoch and reduces after every epoch. Therefore, users can compute
mIoU, for example, as
from homura.metrics import confusion_matrix
def cm_to_miou(cms: List[torch.Tensor]) -> torch.Tensor:
# cms: list of confusion matrices
cm = sum(cms).float()
miou = cm.diag() / (cm.sum(0) + cm.sum(1) - cm.diag())
return miou.mean().item()
def iteration(trainer: TrainerBase,
data: Tuple[torch.Tensor, torch.Tensor]
) -> None:
input, target = data
output = trainer.model(input)
trainer.reporter.add('miou', confusion_matrix(output, target), reduction=cm_to_miou)
...Distributed training
Distributed training is complicated at glance. homura has simple APIs, to hide the messy codes for DDP, such
as homura.init_distributed for the initialization and homura.is_master for checking if the process is master or not.
For details, see examples/imagenet.py.
Reproducibility
These methods make randomness deterministic in its context.
from homura.utils.reproducibility import set_deterministic, set_seed
with set_deterministic(seed):
# suppress nondeterministic computation
# but will affect the performance
something()
with set_seed(seed):
# only set random seed of Python, PyTorch and Numpy
other_thing()Registry System
Following major libraries, homura also has a simple registry system.
from homura import Registry
MODEL_REGISTRY = Registry("language_models")
@MODEL_REGISTRY.register
class Transformer(nn.Module):
...
# or
MODEL_REGISTRY.register(bert_model, 'bert')
# magic
MODEL_REGISTRY.import_modules(".")
transformer = MODEL_REGISTRY('Transformer')(...)
# or
bert = MODEL_REGISTRY('bert', ...)Examples
See examples.
Citing
@misc{homura,
author = {Ryuichiro Hataya},
title = {homura},
year = {2018},
howpublished = {\url{https:/github.com/moskomule/homura}},
}
