workaround for train_set batching during inference time

ufal · Jan 9, 2019 · 299c1bc · 299c1bc
1 parent b384686
commit 299c1bc
Showing 1 changed file with 4 additions and 1 deletion.
diff --git a/neuralmonkey/learning_utils.py b/neuralmonkey/learning_utils.py
@@ -13,7 +13,7 @@
 from termcolor import colored
 
 from neuralmonkey.logging import log, log_print, warn
-from neuralmonkey.dataset import Dataset
+from neuralmonkey.dataset import Dataset, BatchingScheme
 from neuralmonkey.tf_manager import TensorFlowManager
 from neuralmonkey.runners.base_runner import (
     BaseRunner, ExecutionResult, GraphExecutor, OutputSeries)
@@ -85,6 +85,9 @@ def training_loop(cfg: Namespace) -> None:
                     trainer_result = cfg.tf_manager.execute(
                         batch, feedables, cfg.trainers, train=True,
                         summaries=True)
+                    # workaround: we need to use validation batching scheme
+                    #             during evaluation
+                    batch.batching = BatchingScheme(batch_size=cfg.batch_size)
                     train_results, train_outputs, f_batch = run_on_dataset(
                         cfg.tf_manager, cfg.runners, cfg.dataset_runner, batch,
                         cfg.postprocess, write_out=False)