From 4179b00ea2efa27388aafd77c043315e7e72e66d Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Tue, 27 Feb 2018 11:22:45 +0900
Subject: [PATCH 01/18] add n_input option

---
 .../iterator/apply_prediction_to_iterator.py  | 65 ++++++++++---------
 1 file changed, 35 insertions(+), 30 deletions(-)

diff --git a/chainercv/utils/iterator/apply_prediction_to_iterator.py b/chainercv/utils/iterator/apply_prediction_to_iterator.py
index 60a95f11a9..19088b0ecb 100644
--- a/chainercv/utils/iterator/apply_prediction_to_iterator.py
+++ b/chainercv/utils/iterator/apply_prediction_to_iterator.py
@@ -1,7 +1,7 @@
 from chainercv.utils.iterator.unzip import unzip
 
 
-def apply_prediction_to_iterator(predict, iterator, hook=None):
+def apply_prediction_to_iterator(func, iterator, n_input=1, hook=None):
     """Apply a prediction function/method to an iterator.
 
     This function applies a prediction function/method to an iterator.
@@ -89,52 +89,57 @@ def apply_prediction_to_iterator(predict, iterator, hook=None):
             will be empty.
     """
 
-    imgs, pred_values, gt_values = unzip(
-        _apply(predict, iterator, hook))
+    in_values, out_values, rest_values = unzip(
+        _apply(func, iterator, n_input, hook))
 
-    # imgs: iter of [img] -> iter of img
-    imgs = _flatten(imgs)
+    # in_values: iter of ([in_val0], [in_val1], ...)
+    #     -> (iter of in_val0, iter of in_val1, ...)
+    in_values = _flatten(in_values)
 
-    # pred_values: iter of ([pred_val0], [pred_val1], ...)
-    #    -> (iter of pred_val0, iter of pred_val1, ...)
-    pred_values = tuple(map(_flatten, unzip(pred_values)))
+    # out_values: iter of ([out_val0], [out_val1], ...)
+    #     -> (iter of out_val0, iter of out_val1, ...)
+    out_values = tuple(map(_flatten, unzip(out_values)))
 
-    # gt_values: iter of ([gt_val0], [gt_val1], ...)
-    #    -> (iter of gt_val0, iter of gt_val1, ...)
-    gt_values = tuple(map(_flatten, unzip(gt_values)))
+    # rest_values: iter of ([rest_val0], [rest_val1], ...)
+    #     -> (iter of rest_val0, iter of rest_val1, ...)
+    rest_values = tuple(map(_flatten, unzip(rest_values)))
 
-    return imgs, pred_values, gt_values
+    return in_values, out_values, rest_values
 
 
-def _apply(predict, iterator, hook):
+def _apply(func, iterator, n_input, hook):
     for batch in iterator:
-        # batch: [(img, gt_val0, gt_val1, ...)] or [img]
+        # batch: [(in_val0, in_val1, ... , rest_val0, rest_val1, ...)] or
+        #     [in_val]
 
-        imgs = list()
-        gt_values = list()
+        in_values = list()
+        rest_values = list()
         for sample in batch:
             if isinstance(sample, tuple):
-                imgs.append(sample[0])
-                gt_values.append(sample[1:])
+                in_values.append(sample[0:n_input])
+                rest_values.append(sample[n_input:])
             else:
-                imgs.append(sample)
-                gt_values.append(tuple())
+                in_values.append((sample,))
+                rest_values.append(tuple())
 
-        # imgs: [img]
+        # in_values: [(in_val0, in_val1, ...)]
+        #     ->  ([in_val0], [in_val1], ...)
+        in_values = tuple(list(v) for v in zip(*in_values))
 
-        # gt_values: [(gt_val0, gt_val1, ...)] -> ([gt_val0], [gt_val1], ...)
-        gt_values = tuple(list(v) for v in zip(*gt_values))
+        # rest_values: [(rest_val0, rest_val1, ...)]
+        #     -> ([rest_val0], [rest_val1], ...)
+        rest_values = tuple(list(v) for v in zip(*rest_values))
 
-        # pred_values: ([pred_val0], [pred_val1], ...) or [pred_val]
-        pred_values = predict(imgs)
-        if not isinstance(pred_values, tuple):
-            # pred_values: [pred_val] -> ([pred_val],)
-            pred_values = pred_values,
+        # out_values: ([out_val0], [out_val1], ...) or [out_val]
+        out_values = func(*in_values)
+        if not isinstance(out_values, tuple):
+            # pred_values: [out_val] -> ([out_val],)
+            out_values = out_values,
 
         if hook:
-            hook(imgs, pred_values, gt_values)
+            hook(in_values, out_values, rest_values)
 
-        yield imgs, pred_values, gt_values
+        yield in_values, out_values, rest_values
 
 
 def _flatten(iterator):

From 6ebf6e6f2e8c4e2be5e4778089a8d4a66432c88b Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Tue, 27 Feb 2018 11:24:15 +0900
Subject: [PATCH 02/18] update ProgressHook

---
 chainercv/utils/iterator/progress_hook.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/chainercv/utils/iterator/progress_hook.py b/chainercv/utils/iterator/progress_hook.py
index f895361e20..4835752a33 100644
--- a/chainercv/utils/iterator/progress_hook.py
+++ b/chainercv/utils/iterator/progress_hook.py
@@ -19,16 +19,16 @@ def __init__(self, n_total=None):
         self.start = time.time()
         self.n_processed = 0
 
-    def __call__(self, imgs, pred_values, gt_values):
-        self.n_processed += len(imgs)
+    def __call__(self, in_values, out_values, rest_values):
+        self.n_processed += len(in_values[0])
         fps = self.n_processed / (time.time() - self.start)
         if self.n_total is not None:
             sys.stdout.write(
-                '\r{:d} of {:d} images, {:.2f} FPS'.format(
+                '\r{:d} of {:d} samples, {:.2f} samples/sec'.format(
                     self.n_processed, self.n_total, fps))
         else:
             sys.stdout.write(
-                '\r{:d} images, {:.2f} FPS'.format(
+                '\r{:d} samples, {:.2f} samples/sec'.format(
                     self.n_processed, fps))
 
         sys.stdout.flush()

From 4afd154a36654e64bf1ae778c8f2889365cdda4f Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Tue, 27 Feb 2018 12:58:52 +0900
Subject: [PATCH 03/18] update test

---
 .../iterator/apply_prediction_to_iterator.py  |   2 +-
 .../test_apply_prediction_to_iterator.py      | 130 +++++++++++-------
 .../iterator_tests/test_progress_hook.py      |  20 +--
 3 files changed, 89 insertions(+), 63 deletions(-)

diff --git a/chainercv/utils/iterator/apply_prediction_to_iterator.py b/chainercv/utils/iterator/apply_prediction_to_iterator.py
index 19088b0ecb..666ec63f48 100644
--- a/chainercv/utils/iterator/apply_prediction_to_iterator.py
+++ b/chainercv/utils/iterator/apply_prediction_to_iterator.py
@@ -94,7 +94,7 @@ def apply_prediction_to_iterator(func, iterator, n_input=1, hook=None):
 
     # in_values: iter of ([in_val0], [in_val1], ...)
     #     -> (iter of in_val0, iter of in_val1, ...)
-    in_values = _flatten(in_values)
+    in_values = tuple(map(_flatten, unzip(in_values)))
 
     # out_values: iter of ([out_val0], [out_val1], ...)
     #     -> (iter of out_val0, iter of out_val1, ...)
diff --git a/tests/utils_tests/iterator_tests/test_apply_prediction_to_iterator.py b/tests/utils_tests/iterator_tests/test_apply_prediction_to_iterator.py
index 54b0006bff..18580a6894 100644
--- a/tests/utils_tests/iterator_tests/test_apply_prediction_to_iterator.py
+++ b/tests/utils_tests/iterator_tests/test_apply_prediction_to_iterator.py
@@ -10,83 +10,109 @@
 
 
 @testing.parameterize(*testing.product({
-    'multi_pred_values': [False, True],
-    'with_gt_values': [False, True],
+    'multi_in_values': [False, True],
+    'multi_out_values': [False, True],
+    'with_rest_values': [False, True],
     'with_hook': [False, True],
 }))
 class TestApplyPredictionToIterator(unittest.TestCase):
 
     def test_apply_prediction_to_iterator(self):
-        if self.multi_pred_values:
-            def predict(imgs):
-                n_img = len(imgs)
+        if self.multi_in_values:
+            n_input = 2
+        else:
+            n_input = 1
+
+        in_values_expect = list()
+        for _ in range(n_input):
+            in_value = list()
+            for _ in range(5):
+                H, W = np.random.randint(8, 16, size=2)
+                in_value.append(np.random.randint(0, 256, size=(3, H, W)))
+            in_values_expect.append(in_value)
+
+        if self.multi_out_values:
+            def func(*in_values):
+                n_sample = len(in_values[0])
                 return (
-                    [np.random.uniform(size=(10, 4)) for _ in range(n_img)],
-                    [np.random.uniform(size=10) for _ in range(n_img)],
-                    [np.random.uniform(size=10) for _ in range(n_img)])
+                    [np.random.uniform(size=(10, 4)) for _ in range(n_sample)],
+                    [np.random.uniform(size=10) for _ in range(n_sample)],
+                    [np.random.uniform(size=10) for _ in range(n_sample)])
 
-            n_pred_values = 3
+            n_output = 3
         else:
-            def predict(imgs):
-                n_img = len(imgs)
-                return [np.random.uniform(size=(48, 64)) for _ in range(n_img)]
+            def func(*in_values):
+                n_sample = len(in_values[0])
+                return [np.random.uniform(size=(48, 64))
+                        for _ in range(n_sample)]
 
-            n_pred_values = 1
+            n_output = 1
 
-        dataset_imgs = list()
-        for _ in range(5):
-            H, W = np.random.randint(8, 16, size=2)
-            dataset_imgs.append(np.random.randint(0, 256, size=(3, H, W)))
-
-        if self.with_gt_values:
+        if self.with_rest_values:
             strs = ['a', 'bc', 'def', 'ghij', 'klmno']
             nums = [0, 1, 2, 3, 4]
             arrays = [np.random.uniform(size=10) for _ in range(5)]
+            rest_values_expect = (strs, nums, arrays)
+            n_rest = 3
 
             dataset = chainer.datasets.TupleDataset(
-                dataset_imgs, strs, nums, arrays)
-            dataset_gt_values = (strs, nums, arrays)
+                *in_values_expect, *rest_values_expect)
         else:
-            dataset = dataset_imgs
-            dataset_gt_values = tuple()
+            rest_values_expect = tuple()
+            n_rest = 0
+
+            dataset = list(zip(*in_values_expect))
+
         iterator = SerialIterator(dataset, 2, repeat=False, shuffle=False)
 
         if self.with_hook:
-            def hook(imgs, pred_values, gt_values):
-                self.assertEqual(len(pred_values), n_pred_values)
-                for pred_vals in pred_values:
-                    self.assertEqual(len(pred_vals), len(imgs))
-
-                self.assertEqual(len(gt_values), len(dataset_gt_values))
-                for gt_vals in gt_values:
-                    self.assertEqual(len(gt_vals), len(imgs))
-        else:
-            hook = None
+            def hook(in_values, out_values, rest_values):
+                n_sample = len(in_values[0])
 
-        imgs, pred_values, gt_values = apply_prediction_to_iterator(
-            predict, iterator, hook=hook)
+                self.assertEqual(len(in_values), n_input)
+                for in_vals in in_values:
+                    self.assertEqual(len(in_vals), n_sample)
 
-        for img, dataset_img in zip_longest(imgs, dataset_imgs):
-            np.testing.assert_equal(img, dataset_img)
+                self.assertEqual(len(out_values), n_output)
+                for out_vals in out_values:
+                    self.assertEqual(len(out_vals), n_sample)
 
-        self.assertEqual(len(pred_values), n_pred_values)
-        for vals in pred_values:
-            self.assertEqual(len(list(vals)), len(dataset_imgs))
+                self.assertEqual(len(rest_values), n_rest)
+                for rest_vals in rest_values:
+                    self.assertEqual(len(rest_vals), n_sample)
+        else:
+            hook = None
 
-        for vals, dataset_vals in zip_longest(gt_values, dataset_gt_values):
-            for val, dataset_val in zip_longest(vals, dataset_vals):
-                if isinstance(dataset_val, np.ndarray):
-                    np.testing.assert_equal(val, dataset_val)
+        in_values, out_values, rest_values = apply_prediction_to_iterator(
+            func, iterator, n_input=n_input, hook=hook)
+
+        self.assertEqual(len(in_values), n_input)
+        for in_vals, in_vals_expect in \
+                zip_longest(in_values, in_values_expect):
+            for in_val, in_val_expect in zip_longest(in_vals, in_vals_expect):
+                np.testing.assert_equal(in_val, in_val_expect)
+
+        self.assertEqual(len(out_values), n_output)
+        for out_vals in out_values:
+            self.assertEqual(len(list(out_vals)), len(dataset))
+
+        self.assertEqual(len(rest_values), n_rest)
+        for rest_vals, rest_vals_expect in \
+                zip_longest(rest_values, rest_values_expect):
+            for rest_val, rest_val_expect in \
+                    zip_longest(rest_vals, rest_vals_expect):
+                if isinstance(rest_val_expect, np.ndarray):
+                    np.testing.assert_equal(rest_val, rest_val_expect)
                 else:
-                    self.assertEqual(val, dataset_val)
+                    self.assertEqual(rest_val, rest_val_expect)
 
 
 class TestApplyPredictionToIteratorWithInfiniteIterator(unittest.TestCase):
 
     def test_apply_prediction_to_iterator_with_infinite_iterator(self):
-        def predict(imgs):
-            n_img = len(imgs)
-            return [np.random.uniform(size=(48, 64)) for _ in range(n_img)]
+        def func(*in_values):
+            n_sample = len(in_values[0])
+            return [np.random.uniform(size=(48, 64)) for _ in range(n_sample)]
 
         dataset = list()
         for _ in range(5):
@@ -95,14 +121,14 @@ def predict(imgs):
 
         iterator = SerialIterator(dataset, 2)
 
-        imgs, pred_values, gt_values = apply_prediction_to_iterator(
-            predict, iterator)
+        in_values, out_values, rest_values = apply_prediction_to_iterator(
+            func, iterator)
 
         for _ in range(10):
-            next(imgs)
+            next(in_values[0])
 
         for _ in range(10):
-            next(pred_values[0])
+            next(out_values[0])
 
 
 testing.run_module(__name__, __file__)
diff --git a/tests/utils_tests/iterator_tests/test_progress_hook.py b/tests/utils_tests/iterator_tests/test_progress_hook.py
index c3e569f1c0..90c242e503 100644
--- a/tests/utils_tests/iterator_tests/test_progress_hook.py
+++ b/tests/utils_tests/iterator_tests/test_progress_hook.py
@@ -11,11 +11,11 @@
 class TestProgressHook(unittest.TestCase):
 
     def setUp(self):
-        def predict(imgs):
-            n_img = len(imgs)
-            return [np.random.uniform() for _ in range(n_img)]
+        def func(*in_values):
+            n_sample = len(in_values[0])
+            return [np.random.uniform() for _ in range(n_sample)]
 
-        self.predict = predict
+        self.func = func
 
         self.dataset = list()
         for _ in range(5):
@@ -25,22 +25,22 @@ def predict(imgs):
     def test_progress_hook(self):
         iterator = SerialIterator(self.dataset, 2, repeat=False)
 
-        imgs, pred_values, gt_values = apply_prediction_to_iterator(
-            self.predict, iterator,
+        in_values, out_values, rest_values = apply_prediction_to_iterator(
+            self.func, iterator,
             hook=ProgressHook(n_total=len(self.dataset)))
 
         # consume all data
-        for _ in imgs:
+        for _ in in_values[0]:
             pass
 
     def test_progress_hook_with_infinite_iterator(self):
         iterator = SerialIterator(self.dataset, 2)
 
-        imgs, pred_values, gt_values = apply_prediction_to_iterator(
-            self.predict, iterator, hook=ProgressHook())
+        in_values, out_values, rest_values = apply_prediction_to_iterator(
+            self.func, iterator, hook=ProgressHook())
 
         for _ in range(10):
-            next(imgs)
+            next(in_values[0])
 
 
 testing.run_module(__name__, __file__)

From 14d0973eb96dca4cb2f15746ebbb474ee7560358 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Tue, 27 Feb 2018 13:31:00 +0900
Subject: [PATCH 04/18] rename and update docs

---
 chainercv/utils/__init__.py                   |   2 +-
 chainercv/utils/iterator/__init__.py          |   2 +-
 ...ction_to_iterator.py => apply_to_batch.py} | 109 ++++++++++--------
 docs/source/reference/utils.rst               |   6 +-
 ..._to_iterator.py => test_apply_to_batch.py} |  11 +-
 .../iterator_tests/test_progress_hook.py      |   6 +-
 6 files changed, 71 insertions(+), 65 deletions(-)
 rename chainercv/utils/iterator/{apply_prediction_to_iterator.py => apply_to_batch.py} (50%)
 rename tests/utils_tests/iterator_tests/{test_apply_prediction_to_iterator.py => test_apply_to_batch.py} (92%)

diff --git a/chainercv/utils/__init__.py b/chainercv/utils/__init__.py
index 47f48e3e77..7d4c5eeea4 100644
--- a/chainercv/utils/__init__.py
+++ b/chainercv/utils/__init__.py
@@ -6,7 +6,7 @@
 from chainercv.utils.image import read_image  # NOQA
 from chainercv.utils.image import tile_images  # NOQA
 from chainercv.utils.image import write_image  # NOQA
-from chainercv.utils.iterator import apply_prediction_to_iterator  # NOQA
+from chainercv.utils.iterator import apply_to_batch  # NOQA
 from chainercv.utils.iterator import ProgressHook  # NOQA
 from chainercv.utils.iterator import unzip  # NOQA
 from chainercv.utils.testing import assert_is_bbox  # NOQA
diff --git a/chainercv/utils/iterator/__init__.py b/chainercv/utils/iterator/__init__.py
index f7c736a059..3a9d3f2d7b 100644
--- a/chainercv/utils/iterator/__init__.py
+++ b/chainercv/utils/iterator/__init__.py
@@ -1,3 +1,3 @@
-from chainercv.utils.iterator.apply_prediction_to_iterator import apply_prediction_to_iterator  # NOQA
+from chainercv.utils.iterator.apply_to_batch import apply_to_batch  # NOQA
 from chainercv.utils.iterator.progress_hook import ProgressHook  # NOQA
 from chainercv.utils.iterator.unzip import unzip  # NOQA
diff --git a/chainercv/utils/iterator/apply_prediction_to_iterator.py b/chainercv/utils/iterator/apply_to_batch.py
similarity index 50%
rename from chainercv/utils/iterator/apply_prediction_to_iterator.py
rename to chainercv/utils/iterator/apply_to_batch.py
index 666ec63f48..963575ad12 100644
--- a/chainercv/utils/iterator/apply_prediction_to_iterator.py
+++ b/chainercv/utils/iterator/apply_to_batch.py
@@ -1,32 +1,31 @@
 from chainercv.utils.iterator.unzip import unzip
 
 
-def apply_prediction_to_iterator(func, iterator, n_input=1, hook=None):
-    """Apply a prediction function/method to an iterator.
+def apply_to_batch(func, iterator, n_input=1, hook=None):
+    """Apply a function/method to an iterator of batches.
 
-    This function applies a prediction function/method to an iterator.
-    It assumes that the iterator returns a batch of images or
-    a batch of tuples whose first element is an image. In the case that
-    it returns a batch of tuples, the rests are treated as ground truth
-    values.
+    This function applies a function/method to an iterator of batches.
+    It assumes that the iterator returns a batch of input data or
+    a batch of tuples whose first elements ara input data.
 
-    >>> imgs = next(iterator)
-    >>> # imgs: [img]
-    or
     >>> batch = next(iterator)
-    >>> # batch: [(img, gt_val0, gt_val1)]
+    >>> # batch: [in_val]
+    or
+    >>> # batch: [(in_val0, in_val1, ...)]
+    or
+    >>> # batch: [(in_val0, in_val1, ..., rest_val0, rest_val1, ...)]
 
-    This function applys :func:`predict` to a batch of images and gets
-    predicted value(s). :func:`predict` should take a batch of images and
-    return a batch of prediction values
-    or a tuple of batches of prediction values.
+    This function applies :func:`func` to batch(es) of input data and gets
+    computed value(s). :func:`func` should take a batch of data and
+    return a batch of computed values
+    or a tuple of batches of computed values.
 
-    >>> pred_vals0 = predict(imgs)
-    >>> # pred_vals0: [pred_val0]
+    >>> out_vals = func(in_val0, in_val1, ...)
+    >>> # out_vals: [out_val]
     or
-    >>> pred_vals0, pred_vals1 = predict(imgs)
-    >>> # pred_vals0: [pred_val0]
-    >>> # pred_vals1: [pred_val1]
+    >>> out_vals0, out_vals1, ... = func(in_val0, in_val1)
+    >>> # out_vals0: [out_val0]
+    >>> # out_vals1: [out_val1]
 
     Here is an exmple, which applies a pretrained Faster R-CNN to
     PASCAL VOC dataset.
@@ -35,7 +34,7 @@ def apply_prediction_to_iterator(func, iterator, n_input=1, hook=None):
     >>>
     >>> from chainercv.datasets import VOCDetectionDataset
     >>> from chainercv.links import FasterRCNNVGG16
-    >>> from chainercv.utils import apply_prediction_to_iterator
+    >>> from chainercv.utils import apply_to_batch
     >>>
     >>> dataset = VOCDetectionDataset(year='2007', split='test')
     >>> # next(iterator) -> [(img, gt_bbox, gt_label)]
@@ -45,47 +44,55 @@ def apply_prediction_to_iterator(func, iterator, n_input=1, hook=None):
     >>> # model.predict([img]) -> ([pred_bbox], [pred_label], [pred_score])
     >>> model = FasterRCNNVGG16(pretrained_model='voc07')
     >>>
-    >>> imgs, pred_values, gt_values = apply_prediction_to_iterator(
+    >>> in_values, out_values, rest_values = apply_to_batch(
     ...     model.predict, iterator)
     >>>
-    >>> # pred_values contains three iterators
-    >>> pred_bboxes, pred_labels, pred_scores = pred_values
-    >>> # gt_values contains two iterators
-    >>> gt_bboxes, gt_labels = gt_values
+    >>> # in_values contains one iterator
+    >>> imgs, = in_values
+    >>> # out_values contains three iterators
+    >>> pred_bboxes, pred_labels, pred_scores = out_values
+    >>> # rest_values contains two iterators
+    >>> gt_bboxes, gt_labels = rest_values
 
     Args:
-        predict: A callable that takes a batch of images and returns
-            prediction.
-        iterator (chainer.Iterator): An iterator. Each sample should have
-            an image as its first element. This image is passed to
-            :func:`predict` as an argument.
-            The rests are treated as ground truth values.
+        func: A callable that takes batch(es) of input data and returns
+            computed data.
+        iterator (chainer.Iterator): An iterator of batch.
+            The first :obj:`n_input` elements in each sample are
+            treated as input values. They are passed to :obj:`func`.
+        n_input (int): The number of input data. The default value is :obj:`1`.
         hook: A callable that is called after each iteration.
-            :obj:`imgs`, :obj:`pred_values` and :obj:`gt_values` are passed as
-            arguments.
+            :obj:`in_values`, :obj:`out_values`, and :obj:`rest_values`
+            are passed as arguments.
             Note that these values do not contain data from the previous
             iterations.
 
     Returns:
-        An iterator and two tuples of iterators:
-        This function returns an iterator and two tuples of iterators:
-        :obj:`imgs`, :obj:`pred_values` and :obj:`gt_values`.
-
-        * :obj:`imgs`: An iterator that returns an image.
-        * :obj:`pred_values`: A tuple of iterators. Each iterator \
-            returns a corresponding predicted value. \
-            For example, if :func:`predict` returns \
-            :obj:`([pred_val0], [pred_val1])`, :obj:`next(pred_values[0])` \
-            and :obj:`next(pred_values[1])` will be \
-            :obj:`pred_val0` and :obj:`pred_val1`.
-        * :obj:`gt_values`: A tuple of iterators. Each iterator \
-            returns a corresponding ground truth value. \
+        Three tuples of iterators:
+        This function returns three tuples of iterators:
+        :obj:`in_values`, :obj:`out_values` and :obj:`rest_values`.
+
+        * :obj:`in_values`: A tuple of iterators. Each iterator \
+            returns a corresponding input value. \
+            For example, if :func:`func` takes \
+            :obj:`[in_val0], [in_val1]`, :obj:`next(in_values[0])` \
+            and :obj:`next(in_values[1])` will be \
+            :obj:`in_val0` and :obj:`in_val1`.
+        * :obj:`out_values`: A tuple of iterators. Each iterator \
+            returns a corresponding computed value. \
+            For example, if :func:`func` returns \
+            :obj:`([out_val0], [out_val1])`, :obj:`next(out_values[0])` \
+            and :obj:`next(out_values[1])` will be \
+            :obj:`out_val0` and :obj:`out_val1`.
+        * :obj:`rest_values`: A tuple of iterators. Each iterator \
+            returns a corresponding rest value. \
             For example, if the :obj:`iterator` returns \
-            :obj:`[(img, gt_val0, gt_val1)]`, :obj:`next(gt_values[0])` \
-            and :obj:`next(gt_values[1])` will be \
-            :obj:`gt_val0` and :obj:`gt_val1`. \
+            :obj:`[(in_val0, in_val1, rest_val0, rest_val1)]`, \
+            :obj:`next(rest_values[0])` \
+            and :obj:`next(rest_values[1])` will be \
+            :obj:`rest_val0` and :obj:`rest_val1`. \
             If the input \
-            iterator does not give any ground truth values, this tuple \
+            iterator does not give any rest values, this tuple \
             will be empty.
     """
 
diff --git a/docs/source/reference/utils.rst b/docs/source/reference/utils.rst
index 3b952002b4..4e863715b8 100644
--- a/docs/source/reference/utils.rst
+++ b/docs/source/reference/utils.rst
@@ -51,9 +51,9 @@ write_image
 Iterator Utilities
 ------------------
 
-apply_prediction_to_iterator
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autofunction:: apply_prediction_to_iterator
+apply_to_batch
+~~~~~~~~~~~~~~
+.. autofunction:: apply_to_batch
 
 ProgressHook
 ~~~~~~~~~~~~
diff --git a/tests/utils_tests/iterator_tests/test_apply_prediction_to_iterator.py b/tests/utils_tests/iterator_tests/test_apply_to_batch.py
similarity index 92%
rename from tests/utils_tests/iterator_tests/test_apply_prediction_to_iterator.py
rename to tests/utils_tests/iterator_tests/test_apply_to_batch.py
index 18580a6894..938e5f0555 100644
--- a/tests/utils_tests/iterator_tests/test_apply_prediction_to_iterator.py
+++ b/tests/utils_tests/iterator_tests/test_apply_to_batch.py
@@ -6,7 +6,7 @@
 from chainer.iterators import SerialIterator
 from chainer import testing
 
-from chainercv.utils import apply_prediction_to_iterator
+from chainercv.utils import apply_to_batch
 
 
 @testing.parameterize(*testing.product({
@@ -17,7 +17,7 @@
 }))
 class TestApplyPredictionToIterator(unittest.TestCase):
 
-    def test_apply_prediction_to_iterator(self):
+    def test_apply_to_batch(self):
         if self.multi_in_values:
             n_input = 2
         else:
@@ -83,7 +83,7 @@ def hook(in_values, out_values, rest_values):
         else:
             hook = None
 
-        in_values, out_values, rest_values = apply_prediction_to_iterator(
+        in_values, out_values, rest_values = apply_to_batch(
             func, iterator, n_input=n_input, hook=hook)
 
         self.assertEqual(len(in_values), n_input)
@@ -109,7 +109,7 @@ def hook(in_values, out_values, rest_values):
 
 class TestApplyPredictionToIteratorWithInfiniteIterator(unittest.TestCase):
 
-    def test_apply_prediction_to_iterator_with_infinite_iterator(self):
+    def test_apply_to_batch_with_infinite_iterator(self):
         def func(*in_values):
             n_sample = len(in_values[0])
             return [np.random.uniform(size=(48, 64)) for _ in range(n_sample)]
@@ -121,8 +121,7 @@ def func(*in_values):
 
         iterator = SerialIterator(dataset, 2)
 
-        in_values, out_values, rest_values = apply_prediction_to_iterator(
-            func, iterator)
+        in_values, out_values, rest_values = apply_to_batch(func, iterator)
 
         for _ in range(10):
             next(in_values[0])
diff --git a/tests/utils_tests/iterator_tests/test_progress_hook.py b/tests/utils_tests/iterator_tests/test_progress_hook.py
index 90c242e503..0d3128daa9 100644
--- a/tests/utils_tests/iterator_tests/test_progress_hook.py
+++ b/tests/utils_tests/iterator_tests/test_progress_hook.py
@@ -4,7 +4,7 @@
 from chainer.iterators import SerialIterator
 from chainer import testing
 
-from chainercv.utils import apply_prediction_to_iterator
+from chainercv.utils import apply_to_batch
 from chainercv.utils import ProgressHook
 
 
@@ -25,7 +25,7 @@ def func(*in_values):
     def test_progress_hook(self):
         iterator = SerialIterator(self.dataset, 2, repeat=False)
 
-        in_values, out_values, rest_values = apply_prediction_to_iterator(
+        in_values, out_values, rest_values = apply_to_batch(
             self.func, iterator,
             hook=ProgressHook(n_total=len(self.dataset)))
 
@@ -36,7 +36,7 @@ def test_progress_hook(self):
     def test_progress_hook_with_infinite_iterator(self):
         iterator = SerialIterator(self.dataset, 2)
 
-        in_values, out_values, rest_values = apply_prediction_to_iterator(
+        in_values, out_values, rest_values = apply_to_batch(
             self.func, iterator, hook=ProgressHook())
 
         for _ in range(10):

From cdbc657cc8ac5f389a8a2a80c1cc1fe4e0a2f2c0 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Tue, 27 Feb 2018 13:34:19 +0900
Subject: [PATCH 05/18] update related evaluator

---
 .../evaluator/detection_voc_evaluator.py      | 19 +++++++++----------
 .../semantic_segmentation_evaluator.py        | 13 ++++++-------
 2 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/chainercv/extensions/evaluator/detection_voc_evaluator.py b/chainercv/extensions/evaluator/detection_voc_evaluator.py
index d7b73b1613..654c1f0721 100644
--- a/chainercv/extensions/evaluator/detection_voc_evaluator.py
+++ b/chainercv/extensions/evaluator/detection_voc_evaluator.py
@@ -5,7 +5,7 @@
 import chainer.training.extensions
 
 from chainercv.evaluations import eval_detection_voc
-from chainercv.utils import apply_prediction_to_iterator
+from chainercv.utils import apply_to_batch
 
 
 class DetectionVOCEvaluator(chainer.training.extensions.Evaluator):
@@ -72,17 +72,16 @@ def evaluate(self):
         else:
             it = copy.copy(iterator)
 
-        imgs, pred_values, gt_values = apply_prediction_to_iterator(
-            target.predict, it)
-        # delete unused iterator explicitly
-        del imgs
+        in_values, out_values, rest_values = apply_to_batch(target.predict, it)
+        # delete unused iterators explicitly
+        del in_values
 
-        pred_bboxes, pred_labels, pred_scores = pred_values
+        pred_bboxes, pred_labels, pred_scores = out_values
 
-        if len(gt_values) == 3:
-            gt_bboxes, gt_labels, gt_difficults = gt_values
-        elif len(gt_values) == 2:
-            gt_bboxes, gt_labels = gt_values
+        if len(rest_values) == 3:
+            gt_bboxes, gt_labels, gt_difficults = rest_values
+        elif len(rest_values) == 2:
+            gt_bboxes, gt_labels = rest_values
             gt_difficults = None
 
         result = eval_detection_voc(
diff --git a/chainercv/extensions/evaluator/semantic_segmentation_evaluator.py b/chainercv/extensions/evaluator/semantic_segmentation_evaluator.py
index 25872f865d..a9035c7ab9 100644
--- a/chainercv/extensions/evaluator/semantic_segmentation_evaluator.py
+++ b/chainercv/extensions/evaluator/semantic_segmentation_evaluator.py
@@ -5,7 +5,7 @@
 import chainer.training.extensions
 
 from chainercv.evaluations import eval_semantic_segmentation
-from chainercv.utils import apply_prediction_to_iterator
+from chainercv.utils import apply_to_batch
 
 
 class SemanticSegmentationEvaluator(chainer.training.extensions.Evaluator):
@@ -79,13 +79,12 @@ def evaluate(self):
         else:
             it = copy.copy(iterator)
 
-        imgs, pred_values, gt_values = apply_prediction_to_iterator(
-            target.predict, it)
-        # delete unused iterator explicitly
-        del imgs
+        in_values, out_values, rest_values = apply_to_batch(target.predict, it)
+        # delete unused iterators explicitly
+        del in_values
 
-        pred_labels, = pred_values
-        gt_labels, = gt_values
+        pred_labels, = out_values
+        gt_labels, = rest_values
 
         result = eval_semantic_segmentation(pred_labels, gt_labels)
 

From 592d30a6f70e73a8cadffc3e2a251fdc0a4c30a0 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Tue, 27 Feb 2018 13:37:22 +0900
Subject: [PATCH 06/18] update related examples

---
 examples/classification/eval_imagenet.py | 10 +++++-----
 examples/detection/eval_voc07.py         | 12 ++++++------
 examples/segnet/eval_camvid.py           | 10 +++++-----
 3 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/examples/classification/eval_imagenet.py b/examples/classification/eval_imagenet.py
index 90215c60e3..c6bdece997 100644
--- a/examples/classification/eval_imagenet.py
+++ b/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
diff --git a/examples/detection/eval_voc07.py b/examples/detection/eval_voc07.py
index e5d9c81452..e8fe6fef6b 100644
--- a/examples/detection/eval_voc07.py
+++ b/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,
diff --git a/examples/segnet/eval_camvid.py b/examples/segnet/eval_camvid.py
index 2e25d3c7db..7fcf91ed33 100644
--- a/examples/segnet/eval_camvid.py
+++ b/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)
 

From 2a6a4a284ed901dfbc78609de7949e64aed6ead2 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Tue, 27 Feb 2018 14:32:48 +0900
Subject: [PATCH 07/18] for Python2

---
 tests/utils_tests/iterator_tests/test_apply_to_batch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/utils_tests/iterator_tests/test_apply_to_batch.py b/tests/utils_tests/iterator_tests/test_apply_to_batch.py
index 57e820ec39..89bebd4169 100644
--- a/tests/utils_tests/iterator_tests/test_apply_to_batch.py
+++ b/tests/utils_tests/iterator_tests/test_apply_to_batch.py
@@ -56,7 +56,7 @@ def func(*in_values):
             n_rest = 3
 
             dataset = chainer.datasets.TupleDataset(
-                *in_values_expect, *rest_values_expect)
+                *(in_values_expect + rest_values_expect))
         else:
             rest_values_expect = tuple()
             n_rest = 0

From 7a57c6c6284cda877b8c0ce254ea209d6285f594 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Tue, 27 Feb 2018 15:08:24 +0900
Subject: [PATCH 08/18] fix

---
 tests/utils_tests/iterator_tests/test_apply_to_batch.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/utils_tests/iterator_tests/test_apply_to_batch.py b/tests/utils_tests/iterator_tests/test_apply_to_batch.py
index 89bebd4169..ccde2ae094 100644
--- a/tests/utils_tests/iterator_tests/test_apply_to_batch.py
+++ b/tests/utils_tests/iterator_tests/test_apply_to_batch.py
@@ -30,6 +30,7 @@ def test_apply_to_batch(self):
                 H, W = np.random.randint(8, 16, size=2)
                 in_value.append(np.random.randint(0, 256, size=(3, H, W)))
             in_values_expect.append(in_value)
+        in_values_expect = tuple(in_values_expect)
 
         if self.multi_out_values:
             def func(*in_values):

From 77acf2e49bb1d3af5b424c6fe981750d2c5d1ced Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Tue, 27 Feb 2018 17:37:05 +0900
Subject: [PATCH 09/18] apply_to_batch -> apply_to_iterator

---
 .../evaluator/detection_voc_evaluator.py           |  5 +++--
 .../evaluator/semantic_segmentation_evaluator.py   |  5 +++--
 chainercv/utils/__init__.py                        |  2 +-
 chainercv/utils/iterator/__init__.py               |  2 +-
 .../{apply_to_batch.py => apply_to_iterator.py}    |  6 +++---
 docs/source/reference/utils.rst                    |  6 +++---
 examples/classification/eval_imagenet.py           |  4 ++--
 examples/detection/eval_voc07.py                   |  4 ++--
 examples/segnet/eval_camvid.py                     |  4 ++--
 ...apply_to_batch.py => test_apply_to_iterator.py} | 14 +++++++-------
 .../iterator_tests/test_progress_hook.py           |  6 +++---
 11 files changed, 30 insertions(+), 28 deletions(-)
 rename chainercv/utils/iterator/{apply_to_batch.py => apply_to_iterator.py} (97%)
 rename tests/utils_tests/iterator_tests/{test_apply_to_batch.py => test_apply_to_iterator.py} (91%)

diff --git a/chainercv/extensions/evaluator/detection_voc_evaluator.py b/chainercv/extensions/evaluator/detection_voc_evaluator.py
index c2a34f4b82..1bd4b38df7 100644
--- a/chainercv/extensions/evaluator/detection_voc_evaluator.py
+++ b/chainercv/extensions/evaluator/detection_voc_evaluator.py
@@ -5,7 +5,7 @@
 import chainer.training.extensions
 
 from chainercv.evaluations import eval_detection_voc
-from chainercv.utils import apply_to_batch
+from chainercv.utils import apply_to_iterator
 
 
 class DetectionVOCEvaluator(chainer.training.extensions.Evaluator):
@@ -72,7 +72,8 @@ def evaluate(self):
         else:
             it = copy.copy(iterator)
 
-        in_values, out_values, rest_values = apply_to_batch(target.predict, it)
+        in_values, out_values, rest_values = apply_to_iterator(
+            target.predict, it)
         # delete unused iterators explicitly
         del in_values
 
diff --git a/chainercv/extensions/evaluator/semantic_segmentation_evaluator.py b/chainercv/extensions/evaluator/semantic_segmentation_evaluator.py
index 0472578b86..9ac53552e5 100644
--- a/chainercv/extensions/evaluator/semantic_segmentation_evaluator.py
+++ b/chainercv/extensions/evaluator/semantic_segmentation_evaluator.py
@@ -5,7 +5,7 @@
 import chainer.training.extensions
 
 from chainercv.evaluations import eval_semantic_segmentation
-from chainercv.utils import apply_to_batch
+from chainercv.utils import apply_to_iterator
 
 
 class SemanticSegmentationEvaluator(chainer.training.extensions.Evaluator):
@@ -79,7 +79,8 @@ def evaluate(self):
         else:
             it = copy.copy(iterator)
 
-        in_values, out_values, rest_values = apply_to_batch(target.predict, it)
+        in_values, out_values, rest_values = apply_to_iterator(
+            target.predict, it)
         # delete unused iterators explicitly
         del in_values
 
diff --git a/chainercv/utils/__init__.py b/chainercv/utils/__init__.py
index 7d4c5eeea4..9ee7dd02ab 100644
--- a/chainercv/utils/__init__.py
+++ b/chainercv/utils/__init__.py
@@ -6,7 +6,7 @@
 from chainercv.utils.image import read_image  # NOQA
 from chainercv.utils.image import tile_images  # NOQA
 from chainercv.utils.image import write_image  # NOQA
-from chainercv.utils.iterator import apply_to_batch  # NOQA
+from chainercv.utils.iterator import apply_to_iterator  # NOQA
 from chainercv.utils.iterator import ProgressHook  # NOQA
 from chainercv.utils.iterator import unzip  # NOQA
 from chainercv.utils.testing import assert_is_bbox  # NOQA
diff --git a/chainercv/utils/iterator/__init__.py b/chainercv/utils/iterator/__init__.py
index 3a9d3f2d7b..fb657c7e86 100644
--- a/chainercv/utils/iterator/__init__.py
+++ b/chainercv/utils/iterator/__init__.py
@@ -1,3 +1,3 @@
-from chainercv.utils.iterator.apply_to_batch import apply_to_batch  # NOQA
+from chainercv.utils.iterator.apply_to_iterator import apply_to_iterator  # NOQA
 from chainercv.utils.iterator.progress_hook import ProgressHook  # NOQA
 from chainercv.utils.iterator.unzip import unzip  # NOQA
diff --git a/chainercv/utils/iterator/apply_to_batch.py b/chainercv/utils/iterator/apply_to_iterator.py
similarity index 97%
rename from chainercv/utils/iterator/apply_to_batch.py
rename to chainercv/utils/iterator/apply_to_iterator.py
index 28779a3162..67e8a35ef2 100644
--- a/chainercv/utils/iterator/apply_to_batch.py
+++ b/chainercv/utils/iterator/apply_to_iterator.py
@@ -1,7 +1,7 @@
 from chainercv.utils.iterator.unzip import unzip
 
 
-def apply_to_batch(func, iterator, n_input=1, hook=None):
+def apply_to_iterator(func, iterator, n_input=1, hook=None):
     """Apply a function/method to an iterator of batches.
 
     This function applies a function/method to an iterator of batches.
@@ -34,7 +34,7 @@ def apply_to_batch(func, iterator, n_input=1, hook=None):
     >>>
     >>> from chainercv.datasets import VOCDetectionDataset
     >>> from chainercv.links import FasterRCNNVGG16
-    >>> from chainercv.utils import apply_to_batch
+    >>> from chainercv.utils import apply_to_iterator
     >>>
     >>> dataset = VOCDetectionDataset(year='2007', split='test')
     >>> # next(iterator) -> [(img, gt_bbox, gt_label)]
@@ -44,7 +44,7 @@ def apply_to_batch(func, iterator, n_input=1, hook=None):
     >>> # model.predict([img]) -> ([pred_bbox], [pred_label], [pred_score])
     >>> model = FasterRCNNVGG16(pretrained_model='voc07')
     >>>
-    >>> in_values, out_values, rest_values = apply_to_batch(
+    >>> in_values, out_values, rest_values = apply_to_iterator(
     ...     model.predict, iterator)
     >>>
     >>> # in_values contains one iterator
diff --git a/docs/source/reference/utils.rst b/docs/source/reference/utils.rst
index 4e863715b8..2d6a9b83d4 100644
--- a/docs/source/reference/utils.rst
+++ b/docs/source/reference/utils.rst
@@ -51,9 +51,9 @@ write_image
 Iterator Utilities
 ------------------
 
-apply_to_batch
-~~~~~~~~~~~~~~
-.. autofunction:: apply_to_batch
+apply_to_iterator
+~~~~~~~~~~~~~~~~~
+.. autofunction:: apply_to_iterator
 
 ProgressHook
 ~~~~~~~~~~~~
diff --git a/examples/classification/eval_imagenet.py b/examples/classification/eval_imagenet.py
index c6bdece997..79e8ba7025 100644
--- a/examples/classification/eval_imagenet.py
+++ b/examples/classification/eval_imagenet.py
@@ -11,7 +11,7 @@
 from chainercv.links import FeaturePredictor
 from chainercv.links import VGG16
 
-from chainercv.utils import apply_to_batch
+from chainercv.utils import apply_to_iterator
 from chainercv.utils import ProgressHook
 
 
@@ -42,7 +42,7 @@ def main():
         model.to_gpu()
 
     print('Model has been prepared. Evaluation starts.')
-    in_values, out_values, rest_values = apply_to_batch(
+    in_values, out_values, rest_values = apply_to_iterator(
         model.predict, iterator, hook=ProgressHook(len(dataset)))
     del in_values
 
diff --git a/examples/detection/eval_voc07.py b/examples/detection/eval_voc07.py
index e8fe6fef6b..c6c182a03f 100644
--- a/examples/detection/eval_voc07.py
+++ b/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_to_batch
+from chainercv.utils import apply_to_iterator
 from chainercv.utils import ProgressHook
 
 
@@ -56,7 +56,7 @@ def main():
     iterator = iterators.SerialIterator(
         dataset, args.batchsize, repeat=False, shuffle=False)
 
-    in_values, out_values, rest_values = apply_to_batch(
+    in_values, out_values, rest_values = apply_to_iterator(
         model.predict, iterator, hook=ProgressHook(len(dataset)))
     # delete unused iterators explicitly
     del in_values
diff --git a/examples/segnet/eval_camvid.py b/examples/segnet/eval_camvid.py
index 7fcf91ed33..683eb416ef 100644
--- a/examples/segnet/eval_camvid.py
+++ b/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_to_batch
+from chainercv.utils import apply_to_iterator
 from chainercv.utils import ProgressHook
 
 
@@ -60,7 +60,7 @@ def main():
     it = chainer.iterators.SerialIterator(test, batch_size=args.batchsize,
                                           repeat=False, shuffle=False)
 
-    in_values, out_values, rest_values = apply_to_batch(
+    in_values, out_values, rest_values = apply_to_iterator(
         model.predict, it, hook=ProgressHook(len(test)))
     # Delete an iterator of images to save memory usage.
     del in_values
diff --git a/tests/utils_tests/iterator_tests/test_apply_to_batch.py b/tests/utils_tests/iterator_tests/test_apply_to_iterator.py
similarity index 91%
rename from tests/utils_tests/iterator_tests/test_apply_to_batch.py
rename to tests/utils_tests/iterator_tests/test_apply_to_iterator.py
index ccde2ae094..00ef717bf2 100644
--- a/tests/utils_tests/iterator_tests/test_apply_to_batch.py
+++ b/tests/utils_tests/iterator_tests/test_apply_to_iterator.py
@@ -6,7 +6,7 @@
 from chainer.iterators import SerialIterator
 from chainer import testing
 
-from chainercv.utils import apply_to_batch
+from chainercv.utils import apply_to_iterator
 
 
 @testing.parameterize(*testing.product({
@@ -15,9 +15,9 @@
     'with_rest_values': [False, True],
     'with_hook': [False, True],
 }))
-class TestApplyPredictionToIterator(unittest.TestCase):
+class TestApplyToIterator(unittest.TestCase):
 
-    def test_apply_to_batch(self):
+    def test_apply_to_iterator(self):
         if self.multi_in_values:
             n_input = 2
         else:
@@ -84,7 +84,7 @@ def hook(in_values, out_values, rest_values):
         else:
             hook = None
 
-        in_values, out_values, rest_values = apply_to_batch(
+        in_values, out_values, rest_values = apply_to_iterator(
             func, iterator, n_input=n_input, hook=hook)
 
         self.assertEqual(len(in_values), n_input)
@@ -108,9 +108,9 @@ def hook(in_values, out_values, rest_values):
                     self.assertEqual(rest_val, rest_val_expect)
 
 
-class TestApplyPredictionToIteratorWithInfiniteIterator(unittest.TestCase):
+class TestApplyToIteratorWithInfiniteIterator(unittest.TestCase):
 
-    def test_apply_to_batch_with_infinite_iterator(self):
+    def test_apply_to_iterator_with_infinite_iterator(self):
         def func(*in_values):
             n_sample = len(in_values[0])
             return [np.random.uniform(size=(48, 64)) for _ in range(n_sample)]
@@ -122,7 +122,7 @@ def func(*in_values):
 
         iterator = SerialIterator(dataset, 2)
 
-        in_values, out_values, rest_values = apply_to_batch(func, iterator)
+        in_values, out_values, rest_values = apply_to_iterator(func, iterator)
 
         for _ in range(10):
             next(in_values[0])
diff --git a/tests/utils_tests/iterator_tests/test_progress_hook.py b/tests/utils_tests/iterator_tests/test_progress_hook.py
index 96fc657b0a..2ef4f43599 100644
--- a/tests/utils_tests/iterator_tests/test_progress_hook.py
+++ b/tests/utils_tests/iterator_tests/test_progress_hook.py
@@ -4,7 +4,7 @@
 from chainer.iterators import SerialIterator
 from chainer import testing
 
-from chainercv.utils import apply_to_batch
+from chainercv.utils import apply_to_iterator
 from chainercv.utils import ProgressHook
 
 
@@ -25,7 +25,7 @@ def func(*in_values):
     def test_progress_hook(self):
         iterator = SerialIterator(self.dataset, 2, repeat=False)
 
-        in_values, out_values, rest_values = apply_to_batch(
+        in_values, out_values, rest_values = apply_to_iterator(
             self.func, iterator,
             hook=ProgressHook(n_total=len(self.dataset)))
 
@@ -36,7 +36,7 @@ def test_progress_hook(self):
     def test_progress_hook_with_infinite_iterator(self):
         iterator = SerialIterator(self.dataset, 2)
 
-        in_values, out_values, rest_values = apply_to_batch(
+        in_values, out_values, rest_values = apply_to_iterator(
             self.func, iterator, hook=ProgressHook())
 
         for _ in range(10):

From 2d3cb55eeb2fe6f364618bd17d43bbb06fa000e8 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Thu, 1 Mar 2018 12:07:11 +0900
Subject: [PATCH 10/18] update docs

---
 chainercv/utils/iterator/apply_to_iterator.py | 40 +++++++++++++------
 1 file changed, 27 insertions(+), 13 deletions(-)

diff --git a/chainercv/utils/iterator/apply_to_iterator.py b/chainercv/utils/iterator/apply_to_iterator.py
index 67e8a35ef2..11b6bdf655 100644
--- a/chainercv/utils/iterator/apply_to_iterator.py
+++ b/chainercv/utils/iterator/apply_to_iterator.py
@@ -2,41 +2,55 @@
 
 
 def apply_to_iterator(func, iterator, n_input=1, hook=None):
-    """Apply a function/method to an iterator of batches.
+    """Apply a function/method to batches from an iterator.
 
     This function applies a function/method to an iterator of batches.
-    It assumes that the iterator returns a batch of input data or
-    a batch of tuples whose first elements ara input data.
+
+    It assumes that the iterator iterates over a collection of tuples
+    that contain inputs to :func:`func`.
+    Additionally, the tuples may contain values that are not used by func.
+    For convenience, we allow the iterator to iterate over a collection of
+    inputs that are not tuple.
+    Here is an illustration of the expected behavior of the iterator.
 
     >>> batch = next(iterator)
     >>> # batch: [in_val]
     or
-    >>> # batch: [(in_val0, in_val1, ...)]
+    >>> # batch: [(in_val0, ..., in_val{n_input})]
     or
-    >>> # batch: [(in_val0, in_val1, ..., rest_val0, rest_val1, ...)]
+    >>> # batch: [(in_val0, ..., in_val{n_input}, rest_val0, ...)]
 
-    This function applies :func:`func` to batch(es) of input data and gets
-    computed value(s). :func:`func` should take a batch of data and
-    return a batch of computed values
-    or a tuple of batches of computed values.
+    :func:`func` should take batch(es) of data and
+    return batch(es) of computed values.
+    Here is an illustration of the expected behavior of the function.
 
-    >>> out_vals = func(in_val0, in_val1, ...)
+    >>> out_vals = func(in_val0, ..., in_val{n_input})
     >>> # out_vals: [out_val]
     or
-    >>> out_vals0, out_vals1, ... = func(in_val0, in_val1)
+    >>> out_vals0, out_vals1, ... = func(in_val0, ..., in_val{n_input})
     >>> # out_vals0: [out_val0]
     >>> # out_vals1: [out_val1]
 
+    With :func:`apply_to_iterator`, users can get iterator(s) of values
+    returned by :func:`func`. It also returns iterator(s) of input values and
+    values that are not used for computation.
+
+    >>> in_values, out_values, rest_values = apply_to_iterator(
+    >>>     func, iterator, n_input)
+    >>> # in_values: (iter of in_val0, ..., iter of in_val{n_input})
+    >>> # out_values: (iter of out_val0, ...)
+    >>> # rest_values: (iter of rest_val0, ...)
+
     Here is an exmple, which applies a pretrained Faster R-CNN to
     PASCAL VOC dataset.
 
     >>> from chainer import iterators
     >>>
-    >>> from chainercv.datasets import VOCDetectionDataset
+    >>> from chainercv.datasets import VOCBBoxDataset
     >>> from chainercv.links import FasterRCNNVGG16
     >>> from chainercv.utils import apply_to_iterator
     >>>
-    >>> dataset = VOCDetectionDataset(year='2007', split='test')
+    >>> dataset = VOCBBoxDataset(year='2007', split='test')
     >>> # next(iterator) -> [(img, gt_bbox, gt_label)]
     >>> iterator = iterators.SerialIterator(
     ...     dataset, 2, repeat=False, shuffle=False)

From a4e7c2e8dbe4e158d7830d80a97592dbb6122c32 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Thu, 1 Mar 2018 12:15:24 +0900
Subject: [PATCH 11/18] fix docs

---
 chainercv/utils/iterator/apply_to_iterator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/chainercv/utils/iterator/apply_to_iterator.py b/chainercv/utils/iterator/apply_to_iterator.py
index 11b6bdf655..2cbc6dfbd2 100644
--- a/chainercv/utils/iterator/apply_to_iterator.py
+++ b/chainercv/utils/iterator/apply_to_iterator.py
@@ -24,10 +24,10 @@ def apply_to_iterator(func, iterator, n_input=1, hook=None):
     return batch(es) of computed values.
     Here is an illustration of the expected behavior of the function.
 
-    >>> out_vals = func(in_val0, ..., in_val{n_input})
+    >>> out_vals = func([in_val0], ..., [in_val{n_input}])
     >>> # out_vals: [out_val]
     or
-    >>> out_vals0, out_vals1, ... = func(in_val0, ..., in_val{n_input})
+    >>> out_vals0, out_vals1, ... = func([in_val0], ..., [in_val{n_input}])
     >>> # out_vals0: [out_val0]
     >>> # out_vals1: [out_val1]
 

From 2e052a40ab46b5f09ec197686c32631cfbaa9229 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Thu, 1 Mar 2018 12:18:05 +0900
Subject: [PATCH 12/18] n_input - 1

---
 chainercv/utils/iterator/apply_to_iterator.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/chainercv/utils/iterator/apply_to_iterator.py b/chainercv/utils/iterator/apply_to_iterator.py
index 2cbc6dfbd2..68405e5981 100644
--- a/chainercv/utils/iterator/apply_to_iterator.py
+++ b/chainercv/utils/iterator/apply_to_iterator.py
@@ -16,18 +16,18 @@ def apply_to_iterator(func, iterator, n_input=1, hook=None):
     >>> batch = next(iterator)
     >>> # batch: [in_val]
     or
-    >>> # batch: [(in_val0, ..., in_val{n_input})]
+    >>> # batch: [(in_val0, ..., in_val{n_input - 1})]
     or
-    >>> # batch: [(in_val0, ..., in_val{n_input}, rest_val0, ...)]
+    >>> # batch: [(in_val0, ..., in_val{n_input - 1}, rest_val0, ...)]
 
     :func:`func` should take batch(es) of data and
     return batch(es) of computed values.
     Here is an illustration of the expected behavior of the function.
 
-    >>> out_vals = func([in_val0], ..., [in_val{n_input}])
+    >>> out_vals = func([in_val0], ..., [in_val{n_input - 1}])
     >>> # out_vals: [out_val]
     or
-    >>> out_vals0, out_vals1, ... = func([in_val0], ..., [in_val{n_input}])
+    >>> out_vals0, out_vals1, ... = func([in_val0], ..., [in_val{n_input - 1}])
     >>> # out_vals0: [out_val0]
     >>> # out_vals1: [out_val1]
 
@@ -37,7 +37,7 @@ def apply_to_iterator(func, iterator, n_input=1, hook=None):
 
     >>> in_values, out_values, rest_values = apply_to_iterator(
     >>>     func, iterator, n_input)
-    >>> # in_values: (iter of in_val0, ..., iter of in_val{n_input})
+    >>> # in_values: (iter of in_val0, ..., iter of in_val{n_input - 1})
     >>> # out_values: (iter of out_val0, ...)
     >>> # rest_values: (iter of rest_val0, ...)
 

From f920744891c707ccace90317bcdd852a20eebfc1 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Thu, 1 Mar 2018 12:35:26 +0900
Subject: [PATCH 13/18] fix tuple init

---
 chainercv/utils/iterator/apply_to_iterator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/chainercv/utils/iterator/apply_to_iterator.py b/chainercv/utils/iterator/apply_to_iterator.py
index 68405e5981..71b5d06222 100644
--- a/chainercv/utils/iterator/apply_to_iterator.py
+++ b/chainercv/utils/iterator/apply_to_iterator.py
@@ -141,7 +141,7 @@ def _apply(func, iterator, n_input, hook):
                 rest_values.append(sample[n_input:])
             else:
                 in_values.append((sample,))
-                rest_values.append(tuple())
+                rest_values.append(())
 
         # in_values: [(in_val0, in_val1, ...)]
         #     ->  ([in_val0], [in_val1], ...)

From cc911960eac169971fb68437282fb005eab4bf77 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Thu, 1 Mar 2018 12:42:19 +0900
Subject: [PATCH 14/18] fix tuple init

---
 tests/utils_tests/iterator_tests/test_apply_to_iterator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/utils_tests/iterator_tests/test_apply_to_iterator.py b/tests/utils_tests/iterator_tests/test_apply_to_iterator.py
index 00ef717bf2..969d9c47f2 100644
--- a/tests/utils_tests/iterator_tests/test_apply_to_iterator.py
+++ b/tests/utils_tests/iterator_tests/test_apply_to_iterator.py
@@ -59,7 +59,7 @@ def func(*in_values):
             dataset = chainer.datasets.TupleDataset(
                 *(in_values_expect + rest_values_expect))
         else:
-            rest_values_expect = tuple()
+            rest_values_expect = ()
             n_rest = 0
 
             dataset = list(zip(*in_values_expect))

From 0bd2f6d970aeaca436e6858b0e53661e364c1fd6 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Thu, 1 Mar 2018 14:28:30 +0900
Subject: [PATCH 15/18] use :func:

---
 chainercv/utils/iterator/apply_to_iterator.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/chainercv/utils/iterator/apply_to_iterator.py b/chainercv/utils/iterator/apply_to_iterator.py
index 71b5d06222..abf6d74dc6 100644
--- a/chainercv/utils/iterator/apply_to_iterator.py
+++ b/chainercv/utils/iterator/apply_to_iterator.py
@@ -8,7 +8,8 @@ def apply_to_iterator(func, iterator, n_input=1, hook=None):
 
     It assumes that the iterator iterates over a collection of tuples
     that contain inputs to :func:`func`.
-    Additionally, the tuples may contain values that are not used by func.
+    Additionally, the tuples may contain values
+    that are not used by :func:`func`.
     For convenience, we allow the iterator to iterate over a collection of
     inputs that are not tuple.
     Here is an illustration of the expected behavior of the iterator.

From 2e449a3d3fa13544de893cd6b34a346a346dba6d Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Thu, 1 Mar 2018 14:33:46 +0900
Subject: [PATCH 16/18] do not assume chainer.Iterator

---
 chainercv/utils/iterator/apply_to_iterator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/chainercv/utils/iterator/apply_to_iterator.py b/chainercv/utils/iterator/apply_to_iterator.py
index abf6d74dc6..77f6eae9c9 100644
--- a/chainercv/utils/iterator/apply_to_iterator.py
+++ b/chainercv/utils/iterator/apply_to_iterator.py
@@ -72,7 +72,7 @@ def apply_to_iterator(func, iterator, n_input=1, hook=None):
     Args:
         func: A callable that takes batch(es) of input data and returns
             computed data.
-        iterator (chainer.Iterator): An iterator of batch.
+        iterator (iterator): An iterator of batch.
             The first :obj:`n_input` elements in each sample are
             treated as input values. They are passed to :obj:`func`.
         n_input (int): The number of input data. The default value is :obj:`1`.

From 583ed87ebd62a6aa687556e5059f6f2500b6c097 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Thu, 1 Mar 2018 14:34:53 +0900
Subject: [PATCH 17/18] batch -> batches

---
 chainercv/utils/iterator/apply_to_iterator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/chainercv/utils/iterator/apply_to_iterator.py b/chainercv/utils/iterator/apply_to_iterator.py
index 77f6eae9c9..a86577cee8 100644
--- a/chainercv/utils/iterator/apply_to_iterator.py
+++ b/chainercv/utils/iterator/apply_to_iterator.py
@@ -72,7 +72,7 @@ def apply_to_iterator(func, iterator, n_input=1, hook=None):
     Args:
         func: A callable that takes batch(es) of input data and returns
             computed data.
-        iterator (iterator): An iterator of batch.
+        iterator (iterator): An iterator of batches.
             The first :obj:`n_input` elements in each sample are
             treated as input values. They are passed to :obj:`func`.
         n_input (int): The number of input data. The default value is :obj:`1`.

From 09f7ade47f6544d43251c3d4513a2e7badcb6099 Mon Sep 17 00:00:00 2001
From: Toru Ogawa <Hakuyume@users.noreply.github.com>
Date: Thu, 1 Mar 2018 18:13:57 +0900
Subject: [PATCH 18/18] add note

---
 chainercv/utils/iterator/apply_to_iterator.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/chainercv/utils/iterator/apply_to_iterator.py b/chainercv/utils/iterator/apply_to_iterator.py
index a86577cee8..cd0073010a 100644
--- a/chainercv/utils/iterator/apply_to_iterator.py
+++ b/chainercv/utils/iterator/apply_to_iterator.py
@@ -13,6 +13,7 @@ def apply_to_iterator(func, iterator, n_input=1, hook=None):
     For convenience, we allow the iterator to iterate over a collection of
     inputs that are not tuple.
     Here is an illustration of the expected behavior of the iterator.
+    This behaviour is the same as :class:`chainer.Iterator`.
 
     >>> batch = next(iterator)
     >>> # batch: [in_val]