def _callbacks_loader(r: Registry): from catalyst.core.callback import Callback, CallbackWrapper r.add(Callback) r.add(CallbackWrapper) from catalyst import callbacks as m # noqa: WPS347 r.add_from_module(m)
def _runners_loader(r: Registry): from catalyst.core.runner import IRunner, IStageBasedRunner r.add(IRunner) r.add(IStageBasedRunner) from catalyst import runners as m # noqa: WPS347 r.add_from_module(m)
def _experiments_loader(r: Registry): from catalyst.core.experiment import IExperiment r.add(IExperiment) from catalyst import experiments as m r.add_from_module(m) # noqa: WPS347 r.add_from_module(m)
def test_add_module(): """@TODO: Docs. Contribution is welcome.""" r = Registry("") r.add_from_module(module) r.get("foo") with pytest.raises(RegistryException): r.get_instance("bar")
def _samplers_loader(r: Registry): from torch.utils.data import sampler as s factories = { k: v for k, v in s.__dict__.items() if "Sampler" in k and k != "Sampler" } r.add(**factories) from catalyst.data import sampler r.add_from_module(sampler)
def _experiments_loader(r: Registry): from catalyst.core import IExperiment, IStageBasedRunner r.add(IExperiment) r.add(IStageBasedRunner) from catalyst.dl import experiment as m # noqa: WPS347 r.add_from_module(m) from catalyst.contrib.dl import experiment as m # noqa: WPS347 r.add_from_module(m)
def _model_loader(r: Registry): from catalyst.contrib import models as m r.add_from_module(m) try: import segmentation_models_pytorch as smp r.add_from_module(smp, prefix="smp.") except ImportError as ex: if SETTINGS.segmentation_models_required: logger.warning("segmentation_models_pytorch not available," " to install segmentation_models_pytorch," " run `pip install segmentation-models-pytorch`.") raise ex
def _transforms_loader(r: Registry): from torch.jit.frontend import UnsupportedNodeError from catalyst.data.cv.transforms import torch as t r.add_from_module(t, prefix=["catalyst.", "C."]) try: import albumentations as m r.add_from_module(m, prefix=["A.", "albu.", "albumentations."]) from albumentations import pytorch as p r.add_from_module(p, prefix=["A.", "albu.", "albumentations."]) from catalyst.data.cv.transforms import albumentations as t r.add_from_module(t, prefix=["catalyst.", "C."]) except ImportError as ex: if SETTINGS.albumentations_required: logger.warning( "albumentations not available, to install albumentations, " "run `pip install albumentations`." ) raise ex try: from kornia import augmentation as k r.add_from_module(k, prefix=["kornia."]) except ImportError as ex: if SETTINGS.kornia_required: logger.warning( "kornia not available, to install kornia, " "run `pip install kornia`." ) raise ex except UnsupportedNodeError as ex: logger.warning( "kornia has requirement torch>=1.5.0, probably you have" " an old version of torch which is incompatible.\n" "To update pytorch, run `pip install -U 'torch>=1.5.0'`." ) if SETTINGS.kornia_required: raise ex
def _callbacks_loader(r: Registry): from catalyst.core import callbacks as m r.add_from_module(m) from catalyst.dl import callbacks as m # noqa: WPS347 r.add_from_module(m) from catalyst.contrib.dl import callbacks as m # noqa: WPS347 r.add_from_module(m)
def _grad_clip_loader(r: Registry): from torch.nn.utils import clip_grad as m r.add_from_module(m)
def _schedulers_loader(r: Registry): from catalyst.contrib.nn import schedulers as m r.add_from_module(m)
def _optimizers_loader(r: Registry): from catalyst.contrib.nn import optimizers as m r.add_from_module(m)
def _criterion_loader(r: Registry): from catalyst.contrib.nn import criterion as m r.add_from_module(m)
def _modules_loader(r: Registry): from catalyst.contrib.nn import modules as m r.add_from_module(m)