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220 lines
8.2 KiB
220 lines
8.2 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Contains U2 model."""
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import paddle
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from paddle import distributed as dist
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from paddle.io import DataLoader
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from paddlespeech.s2t.io.collator import SpeechCollator
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from paddlespeech.s2t.io.dataset import ManifestDataset
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from paddlespeech.s2t.io.sampler import SortagradBatchSampler
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from paddlespeech.s2t.io.sampler import SortagradDistributedBatchSampler
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from paddlespeech.s2t.models.u2 import U2Evaluator
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from paddlespeech.s2t.models.u2 import U2Model
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from paddlespeech.s2t.models.u2 import U2Updater
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from paddlespeech.s2t.training.extensions.snapshot import Snapshot
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from paddlespeech.s2t.training.extensions.visualizer import VisualDL
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from paddlespeech.s2t.training.optimizer import OptimizerFactory
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from paddlespeech.s2t.training.scheduler import LRSchedulerFactory
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from paddlespeech.s2t.training.timer import Timer
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from paddlespeech.s2t.training.trainer import Trainer
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from paddlespeech.s2t.training.updaters.trainer import Trainer as NewTrainer
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from paddlespeech.s2t.utils import layer_tools
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from paddlespeech.s2t.utils.log import Log
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from paddlespeech.s2t.utils.utility import UpdateConfig
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logger = Log(__name__).getlog()
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class U2Trainer(Trainer):
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def __init__(self, config, args):
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super().__init__(config, args)
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def setup_dataloader(self):
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config = self.config.clone()
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config.defrost()
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config.keep_transcription_text = False
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# train/valid dataset, return token ids
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config.manifest = config.train_manifest
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train_dataset = ManifestDataset.from_config(config)
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config.manifest = config.dev_manifest
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dev_dataset = ManifestDataset.from_config(config)
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collate_fn_train = SpeechCollator.from_config(config)
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collate_fn_dev = SpeechCollator.from_config(config)
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if self.parallel:
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batch_sampler = SortagradDistributedBatchSampler(
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train_dataset,
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batch_size=config.batch_size,
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num_replicas=None,
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rank=None,
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shuffle=True,
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drop_last=True,
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sortagrad=config.sortagrad,
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shuffle_method=config.shuffle_method)
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else:
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batch_sampler = SortagradBatchSampler(
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train_dataset,
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shuffle=True,
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batch_size=config.batch_size,
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drop_last=True,
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sortagrad=config.sortagrad,
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shuffle_method=config.shuffle_method)
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self.train_loader = DataLoader(
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train_dataset,
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batch_sampler=batch_sampler,
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collate_fn=collate_fn_train,
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num_workers=config.num_workers, )
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self.valid_loader = DataLoader(
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dev_dataset,
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batch_size=config.batch_size,
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shuffle=False,
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drop_last=False,
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collate_fn=collate_fn_dev,
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num_workers=config.num_workers, )
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# test dataset, return raw text
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config.manifest = config.test_manifest
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# filter test examples, will cause less examples, but no mismatch with training
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# and can use large batch size , save training time, so filter test egs now.
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config.min_input_len = 0.0 # second
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config.max_input_len = float('inf') # second
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config.min_output_len = 0.0 # tokens
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config.max_output_len = float('inf') # tokens
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config.min_output_input_ratio = 0.00
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config.max_output_input_ratio = float('inf')
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test_dataset = ManifestDataset.from_config(config)
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# return text ord id
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config.keep_transcription_text = True
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self.test_loader = DataLoader(
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test_dataset,
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batch_size=config.decode.batch_size,
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shuffle=False,
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drop_last=False,
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collate_fn=SpeechCollator.from_config(config))
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# return text token id
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config.keep_transcription_text = False
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self.align_loader = DataLoader(
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test_dataset,
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batch_size=config.decode.batch_size,
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shuffle=False,
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drop_last=False,
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collate_fn=SpeechCollator.from_config(config))
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logger.info("Setup train/valid/test/align Dataloader!")
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def setup_model(self):
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config = self.config
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model_conf = config
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with UpdateConfig(model_conf):
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model_conf.input_dim = self.train_loader.collate_fn.feature_size
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model_conf.output_dim = self.train_loader.collate_fn.vocab_size
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model = U2Model.from_config(model_conf)
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if self.parallel:
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model = paddle.DataParallel(model)
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model.train()
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logger.info(f"{model}")
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layer_tools.print_params(model, logger.info)
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train_config = config
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optim_type = train_config.optim
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optim_conf = train_config.optim_conf
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scheduler_type = train_config.scheduler
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scheduler_conf = train_config.scheduler_conf
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scheduler_args = {
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"learning_rate": optim_conf.lr,
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"verbose": False,
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"warmup_steps": scheduler_conf.warmup_steps,
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"gamma": scheduler_conf.lr_decay,
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"d_model": model_conf.encoder_conf.output_size,
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}
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lr_scheduler = LRSchedulerFactory.from_args(scheduler_type,
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scheduler_args)
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def optimizer_args(
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config,
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parameters,
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lr_scheduler=None, ):
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train_config = config
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optim_type = train_config.optim
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optim_conf = train_config.optim_conf
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scheduler_type = train_config.scheduler
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scheduler_conf = train_config.scheduler_conf
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return {
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"grad_clip": train_config.global_grad_clip,
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"weight_decay": optim_conf.weight_decay,
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"learning_rate": lr_scheduler
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if lr_scheduler else optim_conf.lr,
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"parameters": parameters,
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"epsilon": 1e-9 if optim_type == 'noam' else None,
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"beta1": 0.9 if optim_type == 'noam' else None,
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"beat2": 0.98 if optim_type == 'noam' else None,
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}
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optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler)
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optimizer = OptimizerFactory.from_args(optim_type, optimzer_args)
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self.model = model
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self.optimizer = optimizer
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self.lr_scheduler = lr_scheduler
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logger.info("Setup model/optimizer/lr_scheduler!")
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def setup_updater(self):
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output_dir = self.output_dir
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config = self.config
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updater = U2Updater(
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model=self.model,
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optimizer=self.optimizer,
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scheduler=self.lr_scheduler,
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dataloader=self.train_loader,
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output_dir=output_dir,
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accum_grad=config.accum_grad)
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trainer = NewTrainer(updater, (config.n_epoch, 'epoch'), output_dir)
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evaluator = U2Evaluator(self.model, self.valid_loader)
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trainer.extend(evaluator, trigger=(1, "epoch"))
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if dist.get_rank() == 0:
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trainer.extend(VisualDL(output_dir), trigger=(1, "iteration"))
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num_snapshots = config.checkpoint.kbest_n
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trainer.extend(
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Snapshot(
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mode='kbest',
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max_size=num_snapshots,
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indicator='VALID/LOSS',
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less_better=True),
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trigger=(1, 'epoch'))
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# print(trainer.extensions)
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# trainer.run()
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self.trainer = trainer
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def run(self):
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"""The routine of the experiment after setup. This method is intended
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to be used by the user.
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"""
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self.setup_updater()
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with Timer("Training Done: {}"):
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self.trainer.run()
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