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@ -49,6 +49,7 @@ __all__ = [
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'box_coder',
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'polygon_box_transform',
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'yolov3_loss',
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'multiclass_nms',
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]
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@ -262,8 +263,10 @@ def detection_output(loc,
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number is N + 1, N is the batch size. The i-th image has
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`LoD[i + 1] - LoD[i]` detected results, if it is 0, the i-th image
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has no detected results. If all images have not detected results,
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all the elements in LoD are 0, and output tensor only contains one
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LoD will be set to {1}, and output tensor only contains one
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value, which is -1.
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(After version 1.3, when no boxes detected, the lod is changed
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from {0} to {1}.)
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Examples:
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.. code-block:: python
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@ -1960,3 +1963,119 @@ def generate_proposals(scores,
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rpn_roi_probs.stop_gradient = True
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return rpn_rois, rpn_roi_probs
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def multiclass_nms(bboxes,
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scores,
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score_threshold,
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nms_top_k,
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keep_top_k,
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nms_threshold=0.3,
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normalized=True,
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nms_eta=1.,
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background_label=0,
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name=None):
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"""
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**Multiclass NMS**
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This operator is to do multi-class non maximum suppression (NMS) on
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boxes and scores.
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In the NMS step, this operator greedily selects a subset of detection bounding
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boxes that have high scores larger than score_threshold, if providing this
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threshold, then selects the largest nms_top_k confidences scores if nms_top_k
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is larger than -1. Then this operator pruns away boxes that have high IOU
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(intersection over union) overlap with already selected boxes by adaptive
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threshold NMS based on parameters of nms_threshold and nms_eta.
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Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
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per image if keep_top_k is larger than -1.
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Args:
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bboxes (Variable): Two types of bboxes are supported:
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1. (Tensor) A 3-D Tensor with shape
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[N, M, 4 or 8 16 24 32] represents the
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predicted locations of M bounding bboxes,
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N is the batch size. Each bounding box has four
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coordinate values and the layout is
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[xmin, ymin, xmax, ymax], when box size equals to 4.
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2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
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M is the number of bounding boxes, C is the
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class number
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scores (Variable): Two types of scores are supported:
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1. (Tensor) A 3-D Tensor with shape [N, C, M]
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represents the predicted confidence predictions.
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N is the batch size, C is the class number, M is
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number of bounding boxes. For each category there
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are total M scores which corresponding M bounding
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boxes. Please note, M is equal to the 2nd dimension
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of BBoxes.
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2. (LoDTensor) A 2-D LoDTensor with shape [M, C].
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M is the number of bbox, C is the class number.
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In this case, input BBoxes should be the second
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case with shape [M, C, 4].
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background_label (int): The index of background label, the background
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label will be ignored. If set to -1, then all
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categories will be considered. Default: 0
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score_threshold (float): Threshold to filter out bounding boxes with
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low confidence score. If not provided,
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consider all boxes.
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nms_top_k (int): Maximum number of detections to be kept according to
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the confidences aftern the filtering detections based
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on score_threshold.
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nms_threshold (float): The threshold to be used in NMS. Default: 0.3
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nms_eta (float): The threshold to be used in NMS. Default: 1.0
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keep_top_k (int): Number of total bboxes to be kept per image after NMS
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step. -1 means keeping all bboxes after NMS step.
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normalized (bool): Whether detections are normalized. Default: True
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name(str): Name of the multiclass nms op. Default: None.
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Returns:
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Out: A 2-D LoDTensor with shape [No, 6] represents the detections.
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Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
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or A 2-D LoDTensor with shape [No, 10] represents the detections.
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Each row has 10 values:
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[label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the
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total number of detections. If there is no detected boxes for all
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images, lod will be set to {1} and Out only contains one value
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which is -1.
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(After version 1.3, when no boxes detected, the lod is changed
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from {0} to {1})
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Examples:
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.. code-block:: python
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boxes = fluid.layers.data(name='bboxes', shape=[81, 4],
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dtype='float32', lod_level=1)
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scores = fluid.layers.data(name='scores', shape=[81],
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dtype='float32', lod_level=1)
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out = fluid.layers.multiclass_nms(bboxes=boxes,
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scores=scores,
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background_label=0,
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score_threshold=0.5,
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nms_top_k=400,
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nms_threshold=0.3,
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keep_top_k=200,
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normalized=False)
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"""
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helper = LayerHelper('multiclass_nms', **locals())
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output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
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helper.append_op(
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type="multiclass_nms",
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inputs={'BBoxes': bboxes,
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'Scores': scores},
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attrs={
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'background_label': background_label,
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'score_threshold': score_threshold,
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'nms_top_k': nms_top_k,
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'nms_threshold': nms_threshold,
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'nms_eta': nms_eta,
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'keep_top_k': keep_top_k,
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'nms_eta': nms_eta,
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'normalized': normalized
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},
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outputs={'Out': output})
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output.stop_gradient = True
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return output
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jerrywgz
7 years ago
committed by
GitHub