add VOC instance segmentation evaluation

chainercv/evaluations/__init__.py

@@ -1,6 +1,8 @@
 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_instance_segmentation_voc import calc_instance_segmentation_voc_prec_rec  # NOQA
+from chainercv.evaluations.eval_instance_segmentation_voc import eval_instance_segmentation_voc  # NOQA
 from chainercv.evaluations.eval_semantic_segmentation import calc_semantic_segmentation_confusion  # NOQA
 from chainercv.evaluations.eval_semantic_segmentation import calc_semantic_segmentation_iou  # NOQA
 from chainercv.evaluations.eval_semantic_segmentation import eval_semantic_segmentation  # NOQA

chainercv/evaluations/eval_instance_segmentation_voc.py

@@ -0,0 +1,206 @@
+from __future__ import division
+
+from collections import defaultdict
+import numpy as np
+import six
+
+from chainercv.evaluations import calc_detection_voc_ap
+from chainercv.utils.mask.mask_iou import mask_iou
+
+
+def eval_instance_segmentation_voc(
+        pred_masks, pred_labels, pred_scores,
+        gt_masks, gt_labels,
+        iou_thresh=0.5, use_07_metric=False):
+    """Calculate average precisions based on evaluation code of PASCAL VOC.
+
+    This function evaluates predicted masks obtained from a dataset
+    which has :math:`N` images by using average precision for each class.
+    The code is based on the evaluation code used in `FCIS`_.
+
+    .. _`FCIS`: https://arxiv.org/abs/1611.07709
+
+    Args:
+        pred_masks (iterable of numpy.ndarray): An iterable of :math:`N`
+            sets of masks. Its index corresponds to an index for the base
+            dataset. Each element of :obj:`pred_masks` is an object mask
+            and is an array whose shape is :math:`(R, H, W)`,
+            where :math:`R` corresponds
+            to the number of masks, which may vary among images.
+        pred_labels (iterable of numpy.ndarray): An iterable of labels.
+            Similar to :obj:`pred_masks`, its index corresponds to an
+            index for the base dataset. Its length is :math:`N`.
+        pred_scores (iterable of numpy.ndarray): An iterable of confidence
+            scores for predicted masks. Similar to :obj:`pred_masks`,
+            its index corresponds to an index for the base dataset.
+            Its length is :math:`N`.
+        gt_masks (iterable of numpy.ndarray): An iterable of ground truth
+            masks whose length is :math:`N`. An element of :obj:`gt_masks` is
+            an object mask whose shape is :math:`(R, H, W)`. Note that the
+            number of masks :math:`R` in each image does not need to be
+            same as the number of corresponding predicted masks.
+        gt_labels (iterable of numpy.ndarray): An iterable of ground truth
+            labels which are organized similarly to :obj:`gt_masks`. Its
+            length is :math:`N`.
+        iou_thresh (float): A prediction is correct if its Intersection over
+            Union with the ground truth is above this value.
+        use_07_metric (bool): Whether to use PASCAL VOC 2007 evaluation metric
+            for calculating average precision. The default value is
+            :obj:`False`.
+
+    Returns:
+        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.
+
+    """
+
+    prec, rec = calc_instance_segmentation_voc_prec_rec(
+        pred_masks, pred_labels, pred_scores,
+        gt_masks, gt_labels, iou_thresh)
+
+    ap = calc_detection_voc_ap(prec, rec, use_07_metric=use_07_metric)
+
+    return {'ap': ap, 'map': np.nanmean(ap)}
+
+
+def calc_instance_segmentation_voc_prec_rec(
+        pred_masks, pred_labels, pred_scores,
+        gt_masks, gt_labels, iou_thresh):
+    """Calculate precision and recall based on evaluation code of PASCAL VOC.
+
+    This function calculates precision and recall of
+    predicted masks obtained from a dataset which has :math:`N` images.
+    The code is based on the evaluation code used in `FCIS`_.
+
+    .. _`FCIS`: https://arxiv.org/abs/1611.07709
+
+    Args:
+        pred_masks (iterable of numpy.ndarray): An iterable of :math:`N`
+            sets of masks. Its index corresponds to an index for the base
+            dataset. Each element of :obj:`pred_masks` is an object mask
+            and is an array whose shape is :math:`(R, H, W)`,
+            where :math:`R` corresponds
+            to the number of masks, which may vary among images.
+        pred_labels (iterable of numpy.ndarray): An iterable of labels.
+            Similar to :obj:`pred_masks`, its index corresponds to an
+            index for the base dataset. Its length is :math:`N`.
+        pred_scores (iterable of numpy.ndarray): An iterable of confidence
+            scores for predicted masks. Similar to :obj:`pred_masks`,
+            its index corresponds to an index for the base dataset.
+            Its length is :math:`N`.
+        gt_masks (iterable of numpy.ndarray): An iterable of ground truth
+            masks whose length is :math:`N`. An element of :obj:`gt_masks` is
+            an object mask whose shape is :math:`(R, H, W)`. Note that the
+            number of masks :math:`R` in each image does not need to be
+            same as the number of corresponding predicted masks.
+        gt_labels (iterable of numpy.ndarray): An iterable of ground truth
+            labels which are organized similarly to :obj:`gt_masks`. Its
+            length is :math:`N`.
+        iou_thresh (float): A prediction is correct if its Intersection over
+            Union with the ground truth is above this value.
+
+    Returns:
+        tuple of two lists:
+        This function returns two lists: :obj:`prec` and :obj:`rec`.
+
+        * :obj:`prec`: A list of arrays. :obj:`prec[l]` is precision \
+            for class :math:`l`. If class :math:`l` does not exist in \
+            either :obj:`pred_labels` or :obj:`gt_labels`, :obj:`prec[l]` is \
+            set to :obj:`None`.
+        * :obj:`rec`: A list of arrays. :obj:`rec[l]` is recall \
+            for class :math:`l`. If class :math:`l` that is not marked as \
+            difficult does not exist in \
+            :obj:`gt_labels`, :obj:`rec[l]` is \
+            set to :obj:`None`.
+
+    """
+
+    pred_masks = iter(pred_masks)
+    pred_labels = iter(pred_labels)
+    pred_scores = iter(pred_scores)
+    gt_masks = iter(gt_masks)
+    gt_labels = iter(gt_labels)
+
+    n_pos = defaultdict(int)
+    score = defaultdict(list)
+    match = defaultdict(list)
+
+    for pred_mask, pred_label, pred_score, gt_mask, gt_label in \
+            six.moves.zip(
+                pred_masks, pred_labels, pred_scores,
+                gt_masks, gt_labels):
+
+        for l in np.unique(np.concatenate((pred_label, gt_label)).astype(int)):
+            pred_keep_l = pred_label == l
+            pred_mask_l = pred_mask[pred_keep_l]
+            pred_score_l = pred_score[pred_keep_l]
+            # sort by score
+            order = pred_score_l.argsort()[::-1]
+            pred_mask_l = pred_mask_l[order]
+            pred_score_l = pred_score_l[order]
+
+            gt_keep_l = gt_label == l
+            gt_mask_l = gt_mask[gt_keep_l]
+
+            n_pos[l] += gt_keep_l.sum()
+            score[l].extend(pred_score_l)
+
+            if len(pred_mask_l) == 0:
+                continue
+            if len(gt_mask_l) == 0:
+                match[l].extend((0,) * pred_mask_l.shape[0])
+                continue
+
+            iou = mask_iou(pred_mask_l, gt_mask_l)
+            gt_index = iou.argmax(axis=1)
+            # set -1 if there is no matching ground truth
+            gt_index[iou.max(axis=1) < iou_thresh] = -1
+            del iou
+
+            selec = np.zeros(gt_mask_l.shape[0], dtype=bool)
+            for gt_idx in gt_index:
+                if gt_idx >= 0:
+                    if not selec[gt_idx]:
+                        match[l].append(1)
+                    else:
+                        match[l].append(0)
+                    selec[gt_idx] = True
+                else:
+                    match[l].append(0)
+
+    for iter_ in (pred_masks, pred_labels, pred_scores, gt_masks, gt_labels):
+        if next(iter_, None) is not None:
+            raise ValueError('Length of input iterables need to be same.')
+
+    n_fg_class = max(n_pos.keys()) + 1
+    prec = [None] * n_fg_class
+    rec = [None] * n_fg_class
+
+    for l in n_pos.keys():
+        score_l = np.array(score[l])
+        match_l = np.array(
+            match[l], dtype=np.int8)
+
+        order = score_l.argsort()[::-1]
+        match_l = match_l[order]
+
+        tp = np.cumsum(match_l == 1)
+        fp = np.cumsum(match_l == 0)
+
+        # If an element of fp + tp is 0,
+        # the corresponding element of prec[l] is nan.
+        prec[l] = tp / (fp + tp)
+        # If n_pos[l] is 0, rec[l] is None.
+        if n_pos[l] > 0:
+            rec[l] = tp / n_pos[l]
+
+    return prec, rec

docs/source/reference/evaluations.rst

@@ -20,6 +20,17 @@ calc_detection_voc_prec_rec
 .. autofunction:: calc_detection_voc_prec_rec
 
 
+Instance Segmentation VOC
+-------------------------
+
+eval_instance_segmentation_voc
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autofunction:: eval_instance_segmentation_voc
+
+calc_instance_segmentation_voc_prec_rec
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autofunction:: calc_instance_segmentation_voc_prec_rec
+
 Semantic Segmentation IoU
 -------------------------