class_docstr: A custom evaluator for a pytorch LM.
class_docstr_tok: ["A","custom","evaluator","for","a","pytorch","LM","."]
docstr: Evaluate the model.
docstr_tok: ["Evaluate","the","model","."]
code: def evaluate(self):
      """Evaluate the model."""
      val_iter = self.get_iterator("main")
      loss = 0
      nll = 0
      count = 0
      self.model.eval()
      with torch.no_grad():
          for batch in copy.copy(val_iter):
              x, t = concat_examples(
                  batch, device=self.device[0], padding=(0, -100)
              )
              if self.device[0] == -1:
                  l, n, c = self.model(x, t)
              else:
                  # apex does not support torch.nn.DataParallel
                  l, n, c = data_parallel(
                      self.model, (x, t), self.device
                  )
              loss += float(l.sum())
              nll += float(n.sum())
              count += int(c.sum())
      self.model.train()
      # report validation loss
      observation = {}
      with reporter.report_scope(observation):
          reporter.report({"loss": loss}, self.model.reporter)
          reporter.report({"nll": nll}, self.model.reporter)
          reporter.report({"count": count}, self.model.reporter)
      return observation
  
imports: import copy
  import json
  import logging
  import numpy
  import torch
  import torch.nn
  from chainer import Chain, reporter, training
  from chainer.dataset import convert
  from chainer.training import extensions
  from torch.nn.parallel import data_parallel
  from espnet.asr.asr_utils import snapshot_object, torch_load, torch_resume, torch_snapshot
  from espnet.lm.lm_utils import MakeSymlinkToBestModel, ParallelSentenceIterator, count_tokens, load_dataset, read_tokens
  from espnet.nets.lm_interface import LMInterface, dynamic_import_lm
  from espnet.optimizer.factory import dynamic_import_optimizer
  from espnet.scheduler.pytorch import PyTorchScheduler
  from espnet.scheduler.scheduler import dynamic_import_scheduler
  from espnet.utils.deterministic_utils import set_deterministic_pytorch
  from espnet.utils.training.evaluator import BaseEvaluator
  from espnet.utils.training.iterators import ShufflingEnabler
  from espnet.utils.training.tensorboard_logger import TensorboardLogger
  from espnet.utils.training.train_utils import check_early_stop, set_early_stop
label_desc: Use image node_id 2 for calling the LMEvaluator obj's underlying member method code with example usage: obj.evaluate() and returns: observation