Merge pull request #277 from yuyu2172/change-eval-detection

Change eval_detection_voc_ap to eval_detection_voc

chainercv/evaluations/__init__.py

@@ -1,6 +1,6 @@
-from chainercv.evaluations.eval_detection_voc_ap import calc_detection_voc_ap  # NOQA
-from chainercv.evaluations.eval_detection_voc_ap import calc_detection_voc_prec_rec  # NOQA
-from chainercv.evaluations.eval_detection_voc_ap import eval_detection_voc_ap  # NOQA
+from chainercv.evaluations.eval_detection_voc import calc_detection_voc_ap  # NOQA
+from chainercv.evaluations.eval_detection_voc import calc_detection_voc_prec_rec  # NOQA
+from chainercv.evaluations.eval_detection_voc import eval_detection_voc  # NOQA
 from chainercv.evaluations.eval_semantic_segmentation_iou import calc_semantic_segmentation_confusion  # NOQA
 from chainercv.evaluations.eval_semantic_segmentation_iou import calc_semantic_segmentation_iou  # NOQA
 from chainercv.evaluations.eval_semantic_segmentation_iou import eval_semantic_segmentation_iou  # NOQA

chainercv/evaluations/eval_detection_voc_ap.py → chainercv/evaluations/eval_detection_voc.py

@@ -8,7 +8,7 @@
 from chainercv.utils.bbox.bbox_iou import bbox_iou
 
 
-def eval_detection_voc_ap(
+def eval_detection_voc(
         pred_bboxes, pred_labels, pred_scores, gt_bboxes, gt_labels,
         gt_difficults=None,
         iou_thresh=0.5, use_07_metric=False):
@@ -56,12 +56,17 @@ def eval_detection_voc_ap(
             :obj:`False`.
 
     Returns:
-        ~numpy.ndarray:
-        This function returns an array of average precisions.
-        The :math:`l`-th value corresponds to the average precision
-        for class :math:`l`. If class :math:`l` does not exist in
-        either :obj:`pred_labels` or :obj:`gt_labels`, the corresponding
-        value is set to :obj:`numpy.nan`.
+        dict:
+
+        The keys, value-types and the description of the values are listed
+        below.
+
+        * **ap** (*numpy.ndarray*): An array of average precisions. \
+            The :math:`l`-th value corresponds to the average precision \
+            for class :math:`l`. If class :math:`l` does not exist in \
+            either :obj:`pred_labels` or :obj:`gt_labels`, the corresponding \
+            value is set to :obj:`numpy.nan`.
+        * **map** (*float*): The average of Average Precisions over classes.
 
     """
 
@@ -72,7 +77,7 @@ def eval_detection_voc_ap(
 
     ap = calc_detection_voc_ap(prec, rec, use_07_metric=use_07_metric)
 
-    return ap
+    return {'ap': ap, 'map': np.nanmean(ap)}
 
 
 def calc_detection_voc_prec_rec(

chainercv/extensions/detection/detection_voc_evaluator.py

@@ -4,7 +4,7 @@
 from chainer import reporter
 import chainer.training.extensions
 
-from chainercv.evaluations import eval_detection_voc_ap
+from chainercv.evaluations import eval_detection_voc
 from chainercv.utils import apply_prediction_to_iterator
 
 
@@ -85,17 +85,17 @@ def evaluate(self):
             gt_bboxes, gt_labels = gt_values
             gt_difficults = None
 
-        ap = eval_detection_voc_ap(
+        result = eval_detection_voc(
             pred_bboxes, pred_labels, pred_scores,
             gt_bboxes, gt_labels, gt_difficults,
             use_07_metric=self.use_07_metric)
 
-        report = {'map': np.nanmean(ap)}
+        report = {'map': result['map']}
 
         if self.label_names is not None:
             for l, label_name in enumerate(self.label_names):
                 try:
-                    report['ap/{:s}'.format(label_name)] = ap[l]
+                    report['ap/{:s}'.format(label_name)] = result['ap'][l]
                 except IndexError:
                     report['ap/{:s}'.format(label_name)] = np.nan
 

docs/source/reference/evaluations.rst

@@ -7,9 +7,9 @@ Evaluations
 Detection VOC
 -------------
 
-eval_detection_voc_ap
-~~~~~~~~~~~~~~~~~~~~~
-.. autofunction:: eval_detection_voc_ap
+eval_detection_voc
+~~~~~~~~~~~~~~~~~~
+.. autofunction:: eval_detection_voc
 
 calc_detection_voc_ap
 ~~~~~~~~~~~~~~~~~~~~~

examples/detection/eval_voc07.py

@@ -1,7 +1,6 @@
 from __future__ import division
 
 import argparse
-import numpy as np
 import sys
 import time
 
@@ -10,7 +9,7 @@
 
 from chainercv.datasets import voc_detection_label_names
 from chainercv.datasets import VOCDetectionDataset
-from chainercv.evaluations import eval_detection_voc_ap
+from chainercv.evaluations import eval_detection_voc
 from chainercv.links import FasterRCNNVGG16
 from chainercv.links import SSD300
 from chainercv.links import SSD512
@@ -86,17 +85,16 @@ def main():
     pred_bboxes, pred_labels, pred_scores = pred_values
     gt_bboxes, gt_labels, gt_difficults = gt_values
 
-    ap = eval_detection_voc_ap(
+    result = eval_detection_voc(
         pred_bboxes, pred_labels, pred_scores,
         gt_bboxes, gt_labels, gt_difficults,
         use_07_metric=True)
-    map_ = np.nanmean(ap)
 
     print()
-    print('mAP: {:f}'.format(map_))
+    print('mAP: {:f}'.format(result['map']))
     for l, name in enumerate(voc_detection_label_names):
-        if ap[l]:
-            print('{:s}: {:f}'.format(name, ap[l]))
+        if result['ap'][l]:
+            print('{:s}: {:f}'.format(name, result['ap'][l]))
         else:
             print('{:s}: -'.format(name))
 

tests/evaluations_tests/test_eval_detection_voc_ap.py → tests/evaluations_tests/test_eval_detection_voc.py

@@ -9,7 +9,7 @@
 
 from chainercv.evaluations import calc_detection_voc_ap
 from chainercv.evaluations import calc_detection_voc_prec_rec
-from chainercv.evaluations import eval_detection_voc_ap
+from chainercv.evaluations import eval_detection_voc
 
 
 @testing.parameterize(*(
@@ -190,7 +190,7 @@ def setUpClass(cls):
             base_url,
             'voc_detection_result_2007_test_truncated_2017_06_06.npz'))[0])
 
-    def test_eval_detection_voc_ap(self):
+    def test_eval_detection_voc(self):
         pred_bboxes = self.result['bboxes']
         pred_labels = self.result['labels']
         pred_scores = self.result['scores']
@@ -199,7 +199,7 @@ def test_eval_detection_voc_ap(self):
         gt_labels = self.dataset['labels']
         gt_difficults = self.dataset['difficults']
 
-        ap = eval_detection_voc_ap(
+        result = eval_detection_voc(
             pred_bboxes, pred_labels, pred_scores,
             gt_bboxes, gt_labels, gt_difficults,
             use_07_metric=True)
@@ -228,4 +228,6 @@ def test_eval_detection_voc_ap(self):
             0.654545,
         ]
 
-        np.testing.assert_almost_equal(ap, expected, decimal=5)
+        np.testing.assert_almost_equal(result['ap'], expected, decimal=5)
+        np.testing.assert_almost_equal(
+            result['map'], np.nanmean(expected), decimal=5)