update related examples

examples/classification/eval_imagenet.py

@@ -11,7 +11,7 @@
 from chainercv.links import FeaturePredictor
 from chainercv.links import VGG16
 
-from chainercv.utils import apply_prediction_to_iterator
+from chainercv.utils import apply_to_batch
 from chainercv.utils import ProgressHook
 
 
@@ -42,12 +42,12 @@ def main():
         model.to_gpu()
 
     print('Model has been prepared. Evaluation starts.')
-    imgs, pred_values, gt_values = apply_prediction_to_iterator(
+    in_values, out_values, rest_values = apply_to_batch(
         model.predict, iterator, hook=ProgressHook(len(dataset)))
-    del imgs
+    del in_values
 
-    pred_probs, = pred_values
-    gt_labels, = gt_values
+    pred_probs, = out_values
+    gt_labels, = rest_values
 
     accuracy = F.accuracy(
         np.array(list(pred_probs)), np.array(list(gt_labels))).data

examples/detection/eval_voc07.py

@@ -9,7 +9,7 @@
 from chainercv.links import FasterRCNNVGG16
 from chainercv.links import SSD300
 from chainercv.links import SSD512
-from chainercv.utils import apply_prediction_to_iterator
+from chainercv.utils import apply_to_batch
 from chainercv.utils import ProgressHook
 
 
@@ -56,13 +56,13 @@ def main():
     iterator = iterators.SerialIterator(
         dataset, args.batchsize, repeat=False, shuffle=False)
 
-    imgs, pred_values, gt_values = apply_prediction_to_iterator(
+    in_values, out_values, rest_values = apply_to_batch(
         model.predict, iterator, hook=ProgressHook(len(dataset)))
-    # delete unused iterator explicitly
-    del imgs
+    # delete unused iterators explicitly
+    del in_values
 
-    pred_bboxes, pred_labels, pred_scores = pred_values
-    gt_bboxes, gt_labels, gt_difficults = gt_values
+    pred_bboxes, pred_labels, pred_scores = out_values
+    gt_bboxes, gt_labels, gt_difficults = rest_values
 
     result = eval_detection_voc(
         pred_bboxes, pred_labels, pred_scores,

examples/segnet/eval_camvid.py

@@ -11,7 +11,7 @@
 from chainercv.datasets import CamVidDataset
 from chainercv.evaluations import eval_semantic_segmentation
 from chainercv.links import SegNetBasic
-from chainercv.utils import apply_prediction_to_iterator
+from chainercv.utils import apply_to_batch
 from chainercv.utils import ProgressHook
 
 
@@ -60,12 +60,12 @@ def main():
     it = chainer.iterators.SerialIterator(test, batch_size=args.batchsize,
                                           repeat=False, shuffle=False)
 
-    imgs, pred_values, gt_values = apply_prediction_to_iterator(
+    in_values, out_values, rest_values = apply_to_batch(
         model.predict, it, hook=ProgressHook(len(test)))
     # Delete an iterator of images to save memory usage.
-    del imgs
-    pred_labels, = pred_values
-    gt_labels, = gt_values
+    del in_values
+    pred_labels, = out_values
+    gt_labels, = rest_values
 
     result = eval_semantic_segmentation(pred_labels, gt_labels)