hooks.py 文件源码

def __call__(self, epoch):
        if self._batches is None:
            logger.info("Preparing evaluation data...")
            self._batches = self.reader.input_module.batch_generator(self._dataset, self._batch_size, is_eval=True)

        logger.info("Started evaluation %s" % self._info)
        metrics = defaultdict(lambda: list())
        bar = progressbar.ProgressBar(
            max_value=len(self._dataset) // self._batch_size + 1,
            widgets=[' [', progressbar.Timer(), '] ', progressbar.Bar(), ' (', progressbar.ETA(), ') '])
        for i, batch in bar(enumerate(self._batches)):
            inputs = self._dataset[i * self._batch_size:(i + 1) * self._batch_size]
            predictions = self.reader.model_module(batch, self._ports)
            m = self.apply_metrics(inputs, predictions)
            for k in self._metrics:
                metrics[k].append(m[k])

        metrics = self.combine_metrics(metrics)
        super().add_to_history(metrics, self._iter, epoch)

        printmetrics = sorted(metrics.keys())
        res = "Epoch %d\tIter %d\ttotal %d" % (epoch, self._iter, self._total)
        for m in printmetrics:
            res += '\t%s: %.3f' % (m, metrics[m])
            self.update_summary(self._iter, self._info + '_' + m, metrics[m])
            if self._write_metrics_to is not None:
                with open(self._write_metrics_to, 'a') as f:
                    f.write("{0} {1} {2:.5}\n".format(datetime.now(), self._info + '_' + m,
                                                      np.round(metrics[m], 5)))
        res += '\t' + self._info
        logger.info(res)

        if self._side_effect is not None:
            self._side_effect_state = self._side_effect(metrics, self._side_effect_state)