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"""Pascal VOC Detection evaluation."""
from __future__ import division
from collections import defaultdict
import numpy as np
import mxnet as mx
from ..bbox import bbox_iou
class VOCMApMetric(mx.metric.EvalMetric):
"""
Calculate mean AP for object detection task
Parameters:
---------
iou_thresh : float
IOU overlap threshold for TP
class_names : list of str
optional, if provided, will print out AP for each class
"""
def __init__(self, iou_thresh=0.5, class_names=None):
super(VOCMApMetric, self).__init__('VOCMeanAP')
if class_names is None:
self.num = None
else:
assert isinstance(class_names, (list, tuple))
for name in class_names:
assert isinstance(name, str), "must provide names as str"
num = len(class_names)
self.name = list(class_names) + ['mAP']
self.num = num + 1
self.reset()
self.iou_thresh = iou_thresh
self.class_names = class_names
def reset(self):
"""Clear the internal statistics to initial state."""
if getattr(self, 'num', None) is None:
self.num_inst = 0
self.sum_metric = 0.0
else:
self.num_inst = [0] * self.num
self.sum_metric = [0.0] * self.num
self._n_pos = defaultdict(int)
self._score = defaultdict(list)
self._match = defaultdict(list)
def get(self):
"""Get the current evaluation result.
Returns
-------
name : str
Name of the metric.
value : float
Value of the evaluation.
"""
self._update() # update metric at this time
if self.num is None:
if self.num_inst == 0:
return (self.name, float('nan'))
else:
return (self.name, self.sum_metric / self.num_inst)
else:
names = ['%s'%(self.name[i]) for i in range(self.num)]
values = [x / y if y != 0 else float('nan') \
for x, y in zip(self.sum_metric, self.num_inst)]
return (names, values)
# pylint: disable=arguments-differ, too-many-nested-blocks
def update(self, pred_bboxes, pred_labels, pred_scores,
gt_bboxes, gt_labels, gt_difficults=None):
"""Update internal buffer with latest prediction and gt pairs.
Parameters
----------
pred_bboxes : mxnet.NDArray or numpy.ndarray
Prediction bounding boxes with shape `B, N, 4`.
Where B is the size of mini-batch, N is the number of bboxes.
pred_labels : mxnet.NDArray or numpy.ndarray
Prediction bounding boxes labels with shape `B, N`.
pred_scores : mxnet.NDArray or numpy.ndarray
Prediction bounding boxes scores with shape `B, N`.
gt_bboxes : mxnet.NDArray or numpy.ndarray
Ground-truth bounding boxes with shape `B, M, 4`.
Where B is the size of mini-batch, M is the number of ground-truths.
gt_labels : mxnet.NDArray or numpy.ndarray
Ground-truth bounding boxes labels with shape `B, M`.
gt_difficults : mxnet.NDArray or numpy.ndarray, optional, default is None
Ground-truth bounding boxes difficulty labels with shape `B, M`.
"""
def as_numpy(a):
"""Convert a (list of) mx.NDArray into numpy.ndarray"""
if isinstance(a, (list, tuple)):
out = [x.asnumpy() if isinstance(x, mx.nd.NDArray) else x for x in a]
try:
out = np.concatenate(out, axis=0)
except ValueError:
out = np.array(out)
return out
elif isinstance(a, mx.nd.NDArray):
a = a.asnumpy()
return a
if gt_difficults is None:
gt_difficults = [None for _ in as_numpy(gt_labels)]
for pred_bbox, pred_label, pred_score, gt_bbox, gt_label, gt_difficult in zip(
*[as_numpy(x) for x in [pred_bboxes, pred_labels, pred_scores,
gt_bboxes, gt_labels, gt_difficults]]):
# strip padding -1 for pred and gt
valid_pred = np.where(pred_label.flat >= 0)[0]
pred_bbox = pred_bbox[valid_pred, :]
pred_label = pred_label.flat[valid_pred].astype(int)
pred_score = pred_score.flat[valid_pred]
valid_gt = np.where(gt_label.flat >= 0)[0]
gt_bbox = gt_bbox[valid_gt, :]
gt_label = gt_label.flat[valid_gt].astype(int)
if gt_difficult is None:
gt_difficult = np.zeros(gt_bbox.shape[0])
else:
gt_difficult = gt_difficult.flat[valid_gt]
for l in np.unique(np.concatenate((pred_label, gt_label)).astype(int)):
pred_mask_l = pred_label == l
pred_bbox_l = pred_bbox[pred_mask_l]
pred_score_l = pred_score[pred_mask_l]
# sort by score
order = pred_score_l.argsort()[::-1]
pred_bbox_l = pred_bbox_l[order]
pred_score_l = pred_score_l[order]
gt_mask_l = gt_label == l
gt_bbox_l = gt_bbox[gt_mask_l]
gt_difficult_l = gt_difficult[gt_mask_l]
self._n_pos[l] += np.logical_not(gt_difficult_l).sum()
self._score[l].extend(pred_score_l)
if len(pred_bbox_l) == 0:
continue
if len(gt_bbox_l) == 0:
self._match[l].extend((0,) * pred_bbox_l.shape[0])
continue
# VOC evaluation follows integer typed bounding boxes.
pred_bbox_l = pred_bbox_l.copy()
pred_bbox_l[:, 2:] += 1
gt_bbox_l = gt_bbox_l.copy()
gt_bbox_l[:, 2:] += 1
iou = bbox_iou(pred_bbox_l, gt_bbox_l)
gt_index = iou.argmax(axis=1)
# set -1 if there is no matching ground truth
gt_index[iou.max(axis=1) < self.iou_thresh] = -1
del iou
selec = np.zeros(gt_bbox_l.shape[0], dtype=bool)
for gt_idx in gt_index:
if gt_idx >= 0:
if gt_difficult_l[gt_idx]:
self._match[l].append(-1)
else:
if not selec[gt_idx]:
self._match[l].append(1)
else:
self._match[l].append(0)
selec[gt_idx] = True
else:
self._match[l].append(0)
def _update(self):
""" update num_inst and sum_metric """
aps = []
recall, precs = self._recall_prec()
for l, rec, prec in zip(range(len(precs)), recall, precs):
ap = self._average_precision(rec, prec)
aps.append(ap)
if self.num is not None and l < (self.num - 1):
self.sum_metric[l] = ap
self.num_inst[l] = 1
if self.num is None:
self.num_inst = 1
self.sum_metric = np.nanmean(aps)
else:
self.num_inst[-1] = 1
self.sum_metric[-1] = np.nanmean(aps)
def _recall_prec(self):
""" get recall and precision from internal records """
n_fg_class = max(self._n_pos.keys()) + 1
prec = [None] * n_fg_class
rec = [None] * n_fg_class
for l in self._n_pos.keys():
score_l = np.array(self._score[l])
match_l = np.array(self._match[l], dtype=np.int32)
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.
with np.errstate(divide='ignore', invalid='ignore'):
prec[l] = tp / (fp + tp)
# If n_pos[l] is 0, rec[l] is None.
if self._n_pos[l] > 0:
rec[l] = tp / self._n_pos[l]
return rec, prec
def _average_precision(self, rec, prec):
"""
calculate average precision
Params:
----------
rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
Returns:
----------
ap as float
"""
if rec is None or prec is None:
return np.nan
# append sentinel values at both ends
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], np.nan_to_num(prec), [0.]))
# compute precision integration ladder
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# look for recall value changes
i = np.where(mrec[1:] != mrec[:-1])[0]
# sum (\delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
class VOC07MApMetric(VOCMApMetric):
""" Mean average precision metric for PASCAL V0C 07 dataset
Parameters:
---------
iou_thresh : float
IOU overlap threshold for TP
class_names : list of str
optional, if provided, will print out AP for each class
"""
def __init__(self, *args, **kwargs):
super(VOC07MApMetric, self).__init__(*args, **kwargs)
def _average_precision(self, rec, prec):
"""
calculate average precision, override the default one,
special 11-point metric
Params:
----------
rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
Returns:
----------
ap as float
"""
if rec is None or prec is None:
return np.nan
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(np.nan_to_num(prec)[rec >= t])
ap += p / 11.
return ap