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paddle.fluid / layers / retinanet_detection_output
retinanet_detection_output¶
-
paddle.fluid.layers.
retinanet_detection_output
( bboxes, scores, anchors, im_info, score_threshold=0.05, nms_top_k=1000, keep_top_k=100, nms_threshold=0.3, nms_eta=1.0 ) [源代码] ¶
在 RetinaNet 中,有多个 FPN 层会输出用于分类的预测值和位置回归的预测值,该OP通过执行以下步骤将这些预测值转换成最终的检测结果:
在每个FPN层上,先剔除分类预测值小于score_threshold的anchor,然后按分类预测值从大到小排序,选出排名前nms_top_k的anchor,并将这些anchor与其位置回归的预测值做解码操作得到检测框。
合并全部FPN层上的检测框,对这些检测框进行非极大值抑制操作(NMS)以获得最终的检测结果。
- 参数:
-
bboxes (List) – 由来自不同FPN层的Tensor组成的列表,表示全部anchor的位置回归预测值。列表中每个元素是一个维度为 \([N, Mi, 4]\) 的3-D Tensor,其中,第一维N表示批量训练时批量内的图片数量,第二维Mi表示每张图片第i个FPN层上的anchor数量,第三维4表示每个anchor有四个坐标值。数据类型为float32或float64。
scores (List) – 由来自不同FPN层的Tensor组成的列表,表示全部anchor的分类预测值。列表中每个元素是一个维度为 \([N, Mi, C]\) 的3-D Tensor,其中第一维N表示批量训练时批量内的图片数量,第二维Mi表示每张图片第i个FPN层上的anchor数量,第三维C表示类别数量( 不包括背景类 )。数据类型为float32或float64。
anchors (List) – 由来自不同FPN层的Tensor组成的列表,表示全部anchor的坐标值。列表中每个元素是一个维度为 \([Mi, 4]\) 的2-D Tensor,其中第一维Mi表示第i个FPN层上的anchor数量,第二维4表示每个anchor有四个坐标值([xmin, ymin, xmax, ymax])。数据类型为float32或float64。
im_info (Variable) – 维度为 \([N, 3]\) 的2-D Tensor,表示输入图片的尺寸信息。 其中,第一维N表示批量训练时各批量内的图片数量,第二维3表示各图片的尺寸信息,分别是网络输入尺寸的高和宽,以及原图缩放至网络输入大小时的缩放比例。数据类型为float32或float64。
score_threshold (float32) – 在NMS步骤之前,用于滤除每个FPN层的检测框的阈值,默认值为0.05。
nms_top_k (int32) – 在NMS步骤之前,保留每个FPN层的检测框的数量,默认值为1000。
keep_top_k (int32) – 在NMS步骤之后,每张图像要保留的检测框数量,默认值为100,若设为-1,则表示保留NMS步骤后剩下的全部检测框。
nms_threshold (float32) – NMS步骤中用于剔除检测框的Intersection-over-Union(IoU)阈值,默认为0.3。
nms_eta (float32) – NMS步骤中用于调整nms_threshold的参数。默认值为1.,表示nms_threshold的取值在NMS步骤中一直保持不变,即其设定值。若nms_eta小于1.,则表示当nms_threshold的取值大于0.5时,每保留一个检测框就调整一次nms_threshold的取值,即nms_threshold = nms_threshold * nms_eta,直到nms_threshold的取值小于等于0.5后结束调整。
注意:在模型输入尺寸特别小的情况,此时若用score_threshold滤除anchor,可能会导致没有任何检测框剩余。为避免这种情况出现,该OP不会对最高FPN层上的anchor做滤除。因此,要求bboxes、scores、anchors中最后一个元素是来自最高FPN层的Tensor 。
返回:维度是 \([No, 6]\) 的2-D LoDTensor,表示批量内的检测结果。第一维No表示批量内的检测框的总数,第二维6表示每行有六个值:[label, score,xmin,ymin,xmax,ymax]。该LoDTensor的LoD中存放了每张图片的检测框数量,第i张图片的检测框数量为 \(LoD[i + 1] - LoD[i]\) 。如果 \(LoD[i + 1] - LoD[i]\) 为0,则第i个图像没有检测结果。 如果批量内的全部图像都没有检测结果,则LoD中所有元素被设置为0,LoDTensor被赋为空(None)。
返回类型:变量(Variable),数据类型为float32或float64。
代码示例
import paddle.fluid as fluid
bboxes_low = fluid.data(
name='bboxes_low', shape=[1, 44, 4], dtype='float32')
bboxes_high = fluid.data(
name='bboxes_high', shape=[1, 11, 4], dtype='float32')
scores_low = fluid.data(
name='scores_low', shape=[1, 44, 10], dtype='float32')
scores_high = fluid.data(
name='scores_high', shape=[1, 11, 10], dtype='float32')
anchors_low = fluid.data(
name='anchors_low', shape=[44, 4], dtype='float32')
anchors_high = fluid.data(
name='anchors_high', shape=[11, 4], dtype='float32')
im_info = fluid.data(
name="im_info", shape=[1, 3], dtype='float32')
nmsed_outs = fluid.layers.retinanet_detection_output(
bboxes=[bboxes_low, bboxes_high],
scores=[scores_low, scores_high],
anchors=[anchors_low, anchors_high],
im_info=im_info,
score_threshold=0.05,
nms_top_k=1000,
keep_top_k=100,
nms_threshold=0.45,
nms_eta=1.0)
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