DetectionMAP(input, gt_label, gt_box, gt_difficult=None, class_num=None, background_label=0, overlap_threshold=0.5, evaluate_difficult=True, ap_version='integral')
Calculate the detection mean average precision (mAP).
The general steps are as follows:
calculate the true positive and false positive according to the input of detection and labels.
calculate mAP value, support two versions: ‘11 point’ and ‘integral’. 11point: the 11-point interpolated average precision. integral: the natural integral of the precision-recall curve.
Please get more information from the following articles:
input (Variable) – LoDTensor, The detection results, which is a LoDTensor with shape [M, 6]. The layout is [label, confidence, xmin, ymin, xmax, ymax]. The data type is float32 or float64.
gt_label (Variable) – LoDTensor, The ground truth label index, which is a LoDTensor with shape [N, 1].The data type is float32 or float64.
gt_box (Variable) – LoDTensor, The ground truth bounding box (bbox), which is a LoDTensor with shape [N, 4]. The layout is [xmin, ymin, xmax, ymax]. The data type is float32 or float64.
gt_difficult (Variable|None) – LoDTensor, Whether this ground truth is a difficult bounding bbox, which can be a LoDTensor [N, 1] or not set. If None, it means all the ground truth labels are not difficult bbox.The data type is int.
class_num (int) – The class number.
background_label (int) – The index of background label, the background label will be ignored. If set to -1, then all categories will be considered, 0 by default.
overlap_threshold (float) – The threshold for deciding true/false positive, 0.5 by default.
evaluate_difficult (bool) – Whether to consider difficult ground truth for evaluation, True by default. This argument does not work when gt_difficult is None.
ap_version (str) – The average precision calculation ways, it must be ‘integral’ or ‘11point’. Please check https://sanchom.wordpress.com/tag/average-precision/ for details.
import paddle.fluid as fluid batch_size = None # can be any size image_boxs_num = 10 bounding_bboxes_num = 21 pb = fluid.data(name='prior_box', shape=[image_boxs_num, 4], dtype='float32') pbv = fluid.data(name='prior_box_var', shape=[image_boxs_num, 4], dtype='float32') loc = fluid.data(name='target_box', shape=[batch_size, bounding_bboxes_num, 4], dtype='float32') scores = fluid.data(name='scores', shape=[batch_size, bounding_bboxes_num, image_boxs_num], dtype='float32') nmsed_outs = fluid.layers.detection_output(scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv) gt_box = fluid.data(name="gt_box", shape=[batch_size, 4], dtype="float32") gt_label = fluid.data(name="gt_label", shape=[batch_size, 1], dtype="float32") difficult = fluid.data(name="difficult", shape=[batch_size, 1], dtype="float32") exe = fluid.Executor(fluid.CUDAPlace(0)) map_evaluator = fluid.metrics.DetectionMAP(nmsed_outs, gt_label, gt_box, difficult, class_num = 3) cur_map, accum_map = map_evaluator.get_map_var()
- Returns: mAP variable of current mini-batch and
accumulative mAP variable cross mini-batches.
Reset metric states at the begin of each pass/user specified batch. :param executor: a executor for executing
reset_program (Program|None) – a single Program for reset process. If None, will create a Program.