如何解决这个问题？

  File "train.py", line 98, in validation_epoch_end    self.scheduler.step(avg_loss)AttributeError: 'dict' object has no attribute 'step'

我的源代码：

import osimport astimport torchimport torchaudioimport torch.nn as nnfrom torch.nn import functional as Fimport torch.optim as optimfrom torch.utils.data import DataLoaderfrom pytorch_lightning.core.lightning import LightningModulefrom pytorch_lightning import Trainerfrom argparse import ArgumentParserfrom model import SpeechRecognitionfrom dataset import Data, collate_fn_paddfrom pytorch_lightning.callbacks import ModelCheckpointnodes = int(1)gpus = int(1)data_workers = int(0)train_file  = 'path/to/your/train.json'valid_file = 'path/to/your/test.json'valid_every = int(1000)save_model_path = 'path/where/you/want/to/save/your/model'logdir = 'path/where/you/want/to/save/your/logs'epochs = int(10)batch_size = int(64)pct_start = float(0.3)div_factor = int(100)dparams_override = str("{}")hparams_override = str("{}")load_model_from = Noneresume_from_checkpoint = Noneclass SpeechModule(LightningModule):    def __init__(self, model, args):        super(SpeechModule, self).__init__()        self.model = model        self.criterion = nn.CTCLoss(blank=28, zero_infinity=True)        self.args = args    def forward(self, x, hidden):        return self.model(x, hidden)    def configure_optimizers(self):        self.optimizer = optim.AdamW(self.model.parameters(), self.args.learning_rate)        lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(            self.optimizer, mode='min',            factor=0.50, patience=6        )        self.scheduler = {            'scheduler': lr_scheduler,            'reduce_on_plateau': True,            'monitor': 'val_checkpoint_on'        }        return [self.optimizer], [self.scheduler]    def step(self, batch):        spectrograms, labels, input_lengths, label_lengths = batch         bs = spectrograms.shape[0]        hidden = self.model._init_hidden(bs)        hn, c0 = hidden[0].to(self.device), hidden[1].to(self.device)        output, _ = self(spectrograms, (hn, c0))        output = F.log_softmax(output, dim=2)        loss = self.criterion(output, labels, input_lengths, label_lengths)        return loss    def training_step(self, batch, batch_idx):        loss = self.step(batch)        logs = {'loss': loss, 'lr': self.optimizer.param_groups[0]['lr'] }        return {'loss': loss, 'log': logs}    def train_dataloader(self):        d_params = Data.parameters        d_params.update(dparams_override)        train_dataset = Data(json_path=self.train_file, **d_params)        return DataLoader(dataset=train_dataset,                            batch_size= batch_size,                            num_workers= data_workers,                            pin_memory=True,                            collate_fn=collate_fn_padd)    def validation_step(self, batch, batch_idx):        loss = self.step(batch)        return {'val_loss': loss}    def validation_epoch_end(self, outputs):        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()        self.scheduler.step(avg_loss)        tensorboard_logs = {'val_loss': avg_loss}        return {'val_loss': avg_loss, 'log': tensorboard_logs}    def val_dataloader(self):        d_params = Data.parameters        d_params.update(dparams_override)        test_dataset = Data(json_path=valid_file, **d_params, valid=True)        return DataLoader(dataset=test_dataset,                            batch_size= batch_size,                            num_workers= data_workers,                            collate_fn=collate_fn_padd,                            pin_memory=True)def checkpoint_callback():    return ModelCheckpoint(        filepath= save_model_path,        save_top_k=True,        verbose=True,        monitor='val_loss',        mode='min',        prefix=''    )def main(args):    h_params = SpeechRecognition.hyper_parameters    h_params.update(hparams_override)    model = SpeechRecognition(**h_params)    if load_model_from:        speech_module = SpeechModule.load_from_checkpoint(load_model_from, model=model)    else:        speech_module = SpeechModule(model, args)    trainer = Trainer()    trainer = Trainer(                        max_epochs= epochs, gpus= gpus,        num_nodes= nodes, distributed_backend=None,        gradient_clip_val=1.0,                             val_check_interval= valid_every,        checkpoint_callback=checkpoint_callback,        resume_from_checkpoint= resume_from_checkpoint    )    trainer.fit(speech_module)if __name__ == "__main__":    parser = ArgumentParser()    parser.add_argument('--learning_rate', default=1e-3, type=float, help='learning rate')    args = parser.parse_args()    hparams_override = ast.literal_eval(hparams_override)    dparams_override = ast.literal_eval(dparams_override)    if save_model_path:        if not os.path.isdir(os.path.dirname(save_model_path)):            raise Exception("the directory for path {} does not exist".format(save_model_path))    main(args)

回答：

self.scheduler.step(avg_loss)

应该改为

self.step(avg_loss)