def _datasets_loader(r: registry.Registry): from catalyst.data import dataset as m # noqa: WPS347 r.add_from_module(m) from catalyst.contrib import datasets as m_contrib # noqa: WPS347 r.add_from_module(m_contrib)
def _engines_loader(r: registry.Registry): from catalyst.core.engine import IEngine r.add(IEngine) from catalyst import engines as m # noqa: WPS347 r.add_from_module(m)
def _runners_loader(r: registry.Registry): from catalyst.core.runner import IRunner r.add(IRunner) r.add(IRunner) from catalyst import runners as m # noqa: WPS347 r.add_from_module(m)
def _callbacks_loader(r: registry.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 _optimizers_loader(r: registry.Registry): from catalyst.contrib.nn import optimizers as m r.add_from_module(m) if SETTINGS.fairscale_required: from fairscale import optim as m2 r.add_from_module(m2, prefix=["fairscale."])
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 _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.Registry): from catalyst.data import transforms as t # add `'transform.'` prefix to avoid nameing conflicts with other catalyst modules r.add_from_module(t, prefix=["transform."]) if SETTINGS.albu_required: 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."])
def _transforms_loader(r: Registry): from torch.jit.frontend import UnsupportedNodeError 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.contrib.data.cv import transforms 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 _transforms_loader(r: Registry): 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.contrib.data.cv import transforms 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
def _modules_loader(r: Registry): from catalyst.contrib.nn import modules as m 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 _loggers_loader(r: registry.Registry): from catalyst import loggers as m # noqa: WPS347 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 _torch_functional_loader(r: registry.Registry): import torch.nn.functional as F r.add_from_module(F, ["F."])
def _torch_loader(r: registry.Registry): import torch as m r.add_from_module(m, ["torch."], ignore_all=True)
def _model_loader(r: registry.Registry): from catalyst.contrib import models as m r.add_from_module(m)
def _callbacks_loader(r: Registry): from catalyst.dl import callbacks as m # noqa: WPS347 r.add_from_module(m)
def _callbacks_loader(r: Registry): from catalyst.core import callbacks as m r.add_from_module(m)