from vedastr.utils import Registry
TRANSFORMER_ENCODER_LAYERS = Registry('transformer_encoder_layer')
TRANSFORMER_DECODER_LAYERS = Registry('transformer_decoder_layer')
from vedastr.utils import Registry
DECODERS = Registry('decoder')
from vedastr.utils import Registry
SEQUENCE_DECODERS = Registry('sequence_decoder')
from vedastr.utils import Registry
TRANSFORMS = Registry('transforms')
from vedastr.utils import Registry
BRICKS = Registry('brick')
from vedastr.utils import Registry
CONVERTERS = Registry('convert')
from vedastr.utils import Registry
DATASETS = Registry('dataset')
from vedastr.utils import Registry
SAMPLER = Registry('sampler')
from vedastr.utils import Registry
TRANSFORMER_ATTENTIONS = Registry('transformer_attention')
from vedastr.utils import Registry
BACKBONES = Registry('backbone')
from vedastr.utils import Registry
SEQUENCE_ENCODERS = Registry('sequence_encoder')
from vedastr.utils import Registry
POSITION_ENCODERS = Registry('position_encoder')
from vedastr.utils import Registry
DATALOADERS = Registry('dataloader')
from vedastr.utils import Registry
TRANSFORMER_FEEDFORWARDS = Registry('transformer_feedforward')
from vedastr.utils import Registry
UTILS = Registry('utils')
import torch.nn as nn
from vedastr.utils import Registry
CRITERIA = Registry('criterion')
# CTCLoss = nn.CTCLoss
# CRITERIA.register_module(CTCLoss)
#
#
# CrossEntropyLoss = nn.CrossEntropyLoss
# CRITERIA.register_module(CrossEntropyLoss)
from torch.optim import lr_scheduler
from vedastr.utils import Registry
LR_SCHEDULERS = Registry('lr_scheduler')
from vedastr.utils import Registry
RECTIFICATORS = Registry('Rectificator')
from vedastr.utils import Registry
METRICS = Registry('metrics')
from vedastr.utils import Registry
RUNNERS = Registry('runner')
from vedastr.utils import Registry
ENHANCE_MODULES = Registry('enhance_module')
from vedastr.utils import Registry
COMPONENT = Registry('component')
BODIES = Registry('body')