Method evaluation_forward

utils/pl_utils.py:1281–1311

(self, model, batch, batch_idx, dataloader_idx, test=False)

                                                  logs=self.callback_metrics)

    def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False):
        # make dataloader_idx arg in validation_step optional
        args = [batch, batch_idx]

        if test and len(self.get_test_dataloaders()) > 1:
            args.append(dataloader_idx)

        elif not test and len(self.get_val_dataloaders()) > 1:
            args.append(dataloader_idx)

        # handle DP, DDP forward
        if self.use_ddp or self.use_dp:
            output = model(*args)
            return output

        # single GPU
        if self.single_gpu:
            # for single GPU put inputs on gpu manually
            root_gpu = 0
            if isinstance(self.data_parallel_device_ids, list):
                root_gpu = self.data_parallel_device_ids[0]
            batch = self.transfer_batch_to_gpu(batch, root_gpu)
            args[0] = batch

        # CPU
        if test:
            output = model.test_step(*args)
        else:
            output = model.validation_step(*args)

        return output

    def train(self):
        model = self.get_model()