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| | | from funasr.iterators.multiple_iter_factory import MultipleIterFactory |
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| | | from funasr.iterators.sequence_iter_factory import SequenceIterFactory |
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| | | from funasr.optimizers.sgd import SGD |
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| | | from funasr.optimizers.fairseq_adam import FairseqAdam |
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| | | from funasr.samplers.build_batch_sampler import BATCH_TYPES |
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| | | from funasr.samplers.build_batch_sampler import build_batch_sampler |
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| | | from funasr.samplers.unsorted_batch_sampler import UnsortedBatchSampler |
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| | | from funasr.schedulers.noam_lr import NoamLR |
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| | | from funasr.schedulers.warmup_lr import WarmupLR |
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| | | from funasr.schedulers.tri_stage_scheduler import TriStageLR |
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| | | from funasr.torch_utils.load_pretrained_model import load_pretrained_model |
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| | | from funasr.torch_utils.model_summary import model_summary |
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| | | from funasr.torch_utils.pytorch_version import pytorch_cudnn_version |
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| | | optim_classes = dict( |
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| | | adam=torch.optim.Adam, |
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| | | fairseq_adam=FairseqAdam, |
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| | | adamw=torch.optim.AdamW, |
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| | | sgd=SGD, |
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| | | adadelta=torch.optim.Adadelta, |
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| | |
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| | | CosineAnnealingLR=torch.optim.lr_scheduler.CosineAnnealingLR, |
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| | | noamlr=NoamLR, |
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| | | warmuplr=WarmupLR, |
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| | | tri_stage=TriStageLR, |
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| | | cycliclr=torch.optim.lr_scheduler.CyclicLR, |
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| | | onecyclelr=torch.optim.lr_scheduler.OneCycleLR, |
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| | | CosineAnnealingWarmRestarts=torch.optim.lr_scheduler.CosineAnnealingWarmRestarts, |
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