def eval(self):
if self.src_data == None:
self.src_data, self.ref_data, self.src_batches, self.ref_batches = \
xnmt.input_reader.read_parallel_corpus(self.model.src_reader, self.model.trg_reader,
self.src_file, self.ref_file, batcher=self.batcher,
max_src_len=self.max_src_len, max_trg_len=self.max_trg_len)
loss_val = LossScalarBuilder()
ref_words_cnt = 0
for src, trg in zip(self.src_batches, self.ref_batches):
dy.renew_cg(immediate_compute=settings.IMMEDIATE_COMPUTE, check_validity=settings.CHECK_VALIDITY)
loss_builder = LossBuilder()
standard_loss = self.model.calc_loss(src, trg, self.loss_calculator)
additional_loss = self.model.calc_additional_loss(standard_loss)
loss_builder.add_loss("standard_loss", standard_loss)
loss_builder.add_loss("additional_loss", additional_loss)
ref_words_cnt += self.model.trg_reader.count_words(trg)
loss_val += loss_builder.get_loss_stats()
loss_stats = {k: v/ref_words_cnt for k, v in loss_val.items()}
try:
return LossScore(loss_stats[self.model.get_primary_loss()], loss_stats=loss_stats, desc=self.desc), ref_words_cnt
except KeyError:
raise RuntimeError("Did you wrap your loss calculation with LossBuilder({'primary_loss': loss_value}) ?")
def training_step(self, src, trg):
"""
Performs forward pass, backward pass, parameter update for the given minibatch
"""
loss_builder = LossBuilder()
standard_loss = self.model.calc_loss(src, trg, self.loss_calculator)
additional_loss = self.model.calc_additional_loss(standard_loss)
loss_builder.add_loss("standard_loss", standard_loss)
loss_builder.add_loss("additional_loss", additional_loss)
loss_value = loss_builder.compute()
self.logger.update_epoch_loss(src, trg, loss_builder.get_loss_stats())
self.logger.report_train_process()
return loss_value
