- paddle.fluid.contrib.layers.nn. multiclass_nms2 ( bboxes, scores, score_threshold, nms_top_k, keep_top_k, nms_threshold=0.3, normalized=True, nms_eta=1.0, background_label=0, return_index=False, name=None )
This operator is to do multi-class non maximum suppression (NMS) on boxes and scores. In the NMS step, this operator greedily selects a subset of detection bounding boxes that have high scores larger than score_threshold, if providing this threshold, then selects the largest nms_top_k confidences scores if nms_top_k is larger than -1. Then this operator pruns away boxes that have high IOU (intersection over union) overlap with already selected boxes by adaptive threshold NMS based on parameters of nms_threshold and nms_eta. Aftern NMS step, at most keep_top_k number of total bboxes are to be kept per image if keep_top_k is larger than -1.
bboxes (Variable) – Two types of bboxes are supported: 1. (Tensor) A 3-D Tensor with shape [N, M, 4 or 8 16 24 32] represents the predicted locations of M bounding bboxes, N is the batch size. Each bounding box has four coordinate values and the layout is [xmin, ymin, xmax, ymax], when box size equals to 4. 2. (LoDTensor) A 3-D Tensor with shape [M, C, 4] M is the number of bounding boxes, C is the class number
scores (Variable) – Two types of scores are supported: 1. (Tensor) A 3-D Tensor with shape [N, C, M] represents the predicted confidence predictions. N is the batch size, C is the class number, M is number of bounding boxes. For each category there are total M scores which corresponding M bounding boxes. Please note, M is equal to the 2nd dimension of BBoxes. 2. (LoDTensor) A 2-D LoDTensor with shape [M, C]. M is the number of bbox, C is the class number. In this case, input BBoxes should be the second case with shape [M, C, 4].
background_label (int) – The index of background label, the background label will be ignored. If set to -1, then all categories will be considered. Default: 0
score_threshold (float) – Threshold to filter out bounding boxes with low confidence score. If not provided, consider all boxes.
nms_top_k (int) – Maximum number of detections to be kept according to the confidences after the filtering detections based on score_threshold.
nms_threshold (float) – The threshold to be used in NMS. Default: 0.3
nms_eta (float) – The threshold to be used in NMS. Default: 1.0
keep_top_k (int) – Number of total bboxes to be kept per image after NMS step. -1 means keeping all bboxes after NMS step.
normalized (bool) – Whether detections are normalized. Default: True
return_index (bool) – Whether return selected index. Default: False
name (str) – Name of the multiclass nms op. Default: None.
(Out, Index) if return_index is True, otherwise, a tuple with one Variable(Out) is returned. Out: A 2-D LoDTensor with shape [No, 6] represents the detections. Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax] or A 2-D LoDTensor with shape [No, 10] represents the detections. Each row has 10 values: [label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the total number of detections. If all images have not detected results, all elements in LoD will be 0, and output tensor is empty (None). Index: Only return when return_index is True. A 2-D LoDTensor with shape [No, 1] represents the selected index which type is Integer. The index is the absolute value cross batches. No is the same number as Out. If the index is used to gather other attribute such as age, one needs to reshape the input(N, M, 1) to (N * M, 1) as first, where N is the batch size and M is the number of boxes.
- Return type
A tuple with two Variables
import paddle.fluid as fluid boxes = fluid.layers.data(name='bboxes', shape=[81, 4], dtype='float32', lod_level=1) scores = fluid.layers.data(name='scores', shape=, dtype='float32', lod_level=1) out, index = fluid.layers.multiclass_nms2(bboxes=boxes, scores=scores, background_label=0, score_threshold=0.5, nms_top_k=400, nms_threshold=0.3, keep_top_k=200, normalized=False, return_index=True)