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move nms2 to contrib, test=develop (#20709)

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revert-20712-fix_depthwise_conv
wangguanzhong 6 years ago committed by GitHub
parent 80c97e560d
commit 9a3e22aad4
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3 changed files with 134 additions and 138 deletions
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131
python/paddle/fluid/contrib/layers/nn.py
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@ -31,6 +31,7 @@ __all__ = [
'match_matrix_tensor',
'tree_conv',
'fused_embedding_seq_pool',
'multiclass_nms2',
]
@ -494,3 +495,133 @@ def fused_embedding_seq_pool(input,
'padding_idx': padding_idx
})
return out
def multiclass_nms2(bboxes,
scores,
score_threshold,
nms_top_k,
keep_top_k,
nms_threshold=0.3,
normalized=True,
nms_eta=1.,
background_label=0,
return_index=False,
name=None):
"""
**Multiclass NMS2**
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.
Args:
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 aftern 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.
Returns:
A tuple with two Variables: (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.
Examples:
.. code-block:: python
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=[81],
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)
"""
helper = LayerHelper('multiclass_nms2', **locals())
output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
index = helper.create_variable_for_type_inference(dtype='int')
helper.append_op(
type="multiclass_nms2",
inputs={'BBoxes': bboxes,
'Scores': scores},
attrs={
'background_label': background_label,
'score_threshold': score_threshold,
'nms_top_k': nms_top_k,
'nms_threshold': nms_threshold,
'nms_eta': nms_eta,
'keep_top_k': keep_top_k,
'nms_eta': nms_eta,
'normalized': normalized
},
outputs={'Out': output,
'Index': index})
output.stop_gradient = True
index.stop_gradient = True
if return_index:
return output, index
return output

136
python/paddle/fluid/layers/detection.py
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@ -53,7 +53,6 @@ __all__ = [
'yolo_box',
'box_clip',
'multiclass_nms',
'multiclass_nms2',
'retinanet_detection_output',
'distribute_fpn_proposals',
'box_decoder_and_assign',
@ -3148,141 +3147,6 @@ def multiclass_nms(bboxes,
return output
def multiclass_nms2(bboxes,
scores,
score_threshold,
nms_top_k,
keep_top_k,
nms_threshold=0.3,
normalized=True,
nms_eta=1.,
background_label=0,
return_index=False,
name=None):
"""
**Multiclass NMS2**
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.
Args:
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 aftern 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.
Returns:
A tuple with two Variables: (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.
Examples:
.. code-block:: python
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=[81],
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)
"""
helper = LayerHelper('multiclass_nms2', **locals())
output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
index = helper.create_variable_for_type_inference(dtype='int')
helper.append_op(
type="multiclass_nms2",
inputs={'BBoxes': bboxes,
'Scores': scores},
attrs={
'background_label': background_label,
'score_threshold': score_threshold,
'nms_top_k': nms_top_k,
'nms_threshold': nms_threshold,
'nms_eta': nms_eta,
'keep_top_k': keep_top_k,
'nms_eta': nms_eta,
'normalized': normalized
},
outputs={'Out': output,
'Index': index})
output.stop_gradient = True
index.stop_gradient = True
if return_index:
return output, index
return output
def distribute_fpn_proposals(fpn_rois,
min_level,
max_level,

5
python/paddle/fluid/tests/test_detection.py
Unescape View File

@ -557,8 +557,9 @@ class TestMulticlassNMS2(unittest.TestCase):
bboxes = layers.data(
name='bboxes', shape=[-1, 10, 4], dtype='float32')
scores = layers.data(name='scores', shape=[-1, 10], dtype='float32')
output = layers.multiclass_nms2(bboxes, scores, 0.3, 400, 200, 0.7)
output2, index = layers.multiclass_nms2(
output = fluid.contrib.multiclass_nms2(bboxes, scores, 0.3, 400,
200, 0.7)
output2, index = fluid.contrib.multiclass_nms2(
bboxes, scores, 0.3, 400, 200, 0.7, return_index=True)
self.assertIsNotNone(output)
self.assertIsNotNone(output2)

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