def _samplers_loader(r: hydra_slayer.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 _engines_loader(r: hydra_slayer.Registry):
from catalyst.core.engine import IEngine
r.add(IEngine)
from catalyst import engines as m
r.add_from_module(m)
def _datasets_loader(r: hydra_slayer.Registry):
from catalyst.data import dataset as m
r.add_from_module(m)
from catalyst.contrib import datasets as m_contrib
r.add_from_module(m_contrib)
def _optimizers_loader(r: hydra_slayer.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 _callbacks_loader(r: hydra_slayer.Registry):
from catalyst.core.callback import Callback, CallbackWrapper
r.add(Callback)
r.add(CallbackWrapper)
from catalyst import callbacks as m
r.add_from_module(m)
def _runners_loader(r: hydra_slayer.Registry):
from catalyst.core.runner import IRunner
r.add(IRunner)
r.add(IRunner)
from catalyst import runners as m
r.add_from_module(m)
def _transforms_loader(r: hydra_slayer.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 _criterion_loader(r: hydra_slayer.Registry):
from catalyst.contrib.nn import criterion as m
r.add_from_module(m)
def _model_loader(r: hydra_slayer.Registry):
from catalyst.contrib import models as m
r.add_from_module(m)
def _grad_clip_loader(r: hydra_slayer.Registry):
from torch.nn.utils import clip_grad as m
r.add_from_module(m)
def _torch_loader(r: hydra_slayer.Registry):
import torch as m
r.add_from_module(m, ["torch."], ignore_all=True)
def _torch_functional_loader(r: hydra_slayer.Registry):
import torch.nn.functional as F
r.add_from_module(F, ["F."])
def _loggers_loader(r: hydra_slayer.Registry):
from catalyst import loggers as m
r.add_from_module(m)
def _schedulers_loader(r: hydra_slayer.Registry):
from catalyst.contrib.nn import schedulers as m
r.add_from_module(m)