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paddle.fluid / layers / ssd_loss
ssd_loss¶
-
paddle.fluid.layers.
ssd_loss
( location, confidence, gt_box, gt_label, prior_box, prior_box_var=None, background_label=0, overlap_threshold=0.5, neg_pos_ratio=3.0, neg_overlap=0.5, loc_loss_weight=1.0, conf_loss_weight=1.0, match_type='per_prediction', mining_type='max_negative', normalize=True, sample_size=None ) [源代码] ¶
该OP用于SSD物体检测算法的多窗口损失层
该层用于计算SSD的损失,给定位置偏移预测,置信度预测,候选框和真实框标签,以及难样本挖掘的类型。通过执行以下步骤,返回的损失是本地化损失(或回归损失)和置信度损失(或分类损失)的加权和:
1、通过二分匹配算法查找匹配的边界框。
1.1、计算真实框与先验框之间的IOU相似度。
1.2、通过二分匹配算法计算匹配的边界框。
2、计算难分样本的置信度
2.1、根据匹配的索引获取目标标签。
2.2、计算置信度损失。
3、应用难样本挖掘算法来获取负样本索引并更新匹配的索引。
4、分配分类和回归目标
4.1、根据生成的候选框bbox进行编码。
4.2、分配回归目标。
4.3、分配分类目标。
5、计算总体的物体损失。
5.1计算置信度(confidence)损失。
5.1计算回归(location)损失。
5.3计算总体加权损失。
- 参数:
-
location (Variable)- 位置预测,具有形状[N,Np,4]的3D-Tensor,N是batch大小,Np是每个实例的预测总数。 4是坐标的维数,布局是[xmin,ymin,xmax,ymax],xmin,ymin代表box左上坐标,xmax,ymax代表box右下坐标,数据类型为float32或float64。
confidence (Variable) - 置信度(分类)预测,具有形状[N,Np,C]的3D-Tensor,N是batch大小,Np是每个实例的预测总数,C是类别数量,数据类型为float32或float64。
gt_box (Variable)- 真实框(bbox),具有形状[Ng,4]的2D LoDTensor,Ng是mini-batch输入的真实框(bbox)的总数,4是坐标的维数,布局是[xmin,ymin,xmax,ymax],xmin,ymin代表box左上坐标,xmax,ymax代表box右下坐标,数据类型为float32或float64。
gt_label (Variable)- ground-truth标签, 具有形状[Ng,1]的2D LoDTensor,Ng是mini-batch输入的真实框(bbox)的总数,1表示类别号,数据类型为float32或float64。
prior_box (Variable)- 检测网络生成的候选框, 具有形状[Np,4]的2D-Tensor,Np是生成的候选框总数,4是坐标的维数,布局是[xmin,ymin,xmax,ymax],xmin,ymin代表box左上坐标,xmax,ymax代表box右下坐标,数据类型为float32或float64。。
prior_box_var (Variable)- 候选框的方差, 具有形状[Np,4]的2D张量,形状及数据类型同
prior_box
。background_label (int)- background标签的索引,默认为0。
overlap_threshold (float)- 额外匹配的bbox阈值,当找到匹配的框,如果
match_type
为'per_prediction',使用overlap_threshold
确定额外匹配的bbox。默认为0.5。neg_pos_ratio (float)- 负框与正框的比率,仅在
mining_type
为'max_negative'时使用,默认为3.0。neg_overlap (float)- 不匹配预测的负重叠上限。仅当
mining_type
为'max_negative'时使用,默认为0.5。loc_loss_weight (float)- 回归损失的权重,默认为1.0。
conf_loss_weight (float)- 置信度损失的权重,默认为1.0。
match_type (str)- 训练期间匹配方法的类型应为'bipartite'或'per_prediction',默认为'per_prediction'。
mining_type (str)- 难样本挖掘类型,分为'hard_example'或'max_negative',目前只支持'max_negative'。
normalize (bool)- 是否通过输出位置的总数将SSD损失标准化,默认为True。
sample_size (int)- 负样本框的最大样本大小,仅在
mining_type
为'hard_example'时使用。
返回: Variable(Tensor) 定位损失和置信度损失的加权和, 具有形状[N * Np,1], N是batch大小,Np是每个实例的预测总数,数据类型为float32或float64。
抛出异常: ValueError
- 如果 mining_type
是'hard_example',目前只支持 max_negative
的挖掘类型。
代码示例
import paddle.fluid as fluid
pb = fluid.layers.data(
name='prior_box',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
pbv = fluid.layers.data(
name='prior_box_var',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
loc = fluid.layers.data(name='target_box', shape=[10, 4], dtype='float32')
scores = fluid.layers.data(name='scores', shape=[10, 21], dtype='float32')
gt_box = fluid.layers.data(
name='gt_box', shape=[4], lod_level=1, dtype='float32')
gt_label = fluid.layers.data(
name='gt_label', shape=[1], lod_level=1, dtype='float32')
loss = fluid.layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
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