fix

cnn_e2e/dali_cnn_val.py

@@ -59,10 +59,10 @@ def main():
     for epoch in range(args.num_epochs):
         model_load_dir = os.path.join(args.model_load_dir, 'snapshot_epoch_{}'.format(epoch+1))
         snapshot = Snapshot(args.model_save_dir, model_load_dir)
-        metric = Metric(desc='validataion', calculate_batches=num_val_steps, summary=summary,
+        metric = Metric(desc='validation', calculate_batches=num_val_steps, summary=summary,
                         save_summary_steps=num_val_steps, batch_size=val_batch_size)
         val_data_iter.reset()
-        for i in range(num_val_steps):
+        for i, batches in enumerate(val_data_iter):
             images, labels = batches
             InferenceNet(images, labels).async_get(predict_callback(epoch, i))
         summary.save()

cnn_e2e/of_cnn_train_val.py

@@ -93,7 +93,7 @@ def main():
         #        break
         #break
         if args.val_data_dir:
-            metric = Metric(desc='validataion', calculate_batches=num_val_steps, summary=summary,
+            metric = Metric(desc='validation', calculate_batches=num_val_steps, summary=summary,
                             save_summary_steps=num_val_steps, batch_size=val_batch_size)
             for i in range(num_val_steps):
                 InferenceNet().async_get(metric.metric_cb(epoch, i))

cnn_e2e/of_cnn_val.py

@@ -66,7 +66,7 @@ def main():
     for epoch in range(args.num_epochs):
         model_load_dir = os.path.join(args.model_load_dir, 'snapshot_epoch_{}'.format(epoch))
         snapshot = Snapshot(args.model_save_dir, model_load_dir)
-        metric = Metric(desc='validataion', calculate_batches=num_val_steps, summary=summary,
+        metric = Metric(desc='validation', calculate_batches=num_val_steps, summary=summary,
                         save_summary_steps=num_val_steps, batch_size=val_batch_size)
         for i in range(num_val_steps):
             InferenceNet().async_get(metric.metric_cb(epoch, i))