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paddle.vision / ops / yolo_loss
yolo_loss¶
-
paddle.vision.ops.
yolo_loss
( x, gt_box, gt_label, anchors, anchor_mask, class_num, ignore_thresh, downsample_ratio, gt_score=None, use_label_smooth=True, name=None, scale_x_y=1.0 ) [源代码] ¶
该运算通过给定的预测结果和真实框计算yolov3损失。
yolov3 loss前的网络输出形状为[N,C,H,W],H和W应该相同,用来指定网格(grid)大小。每个网格点预测S个边界框(bounding boxes),S由每个尺度中 anchors
簇的个数指定。在第二维(表示通道的维度)中,C的值应为S *(class_num + 5),class_num是源数据集的对象种类数(如coco中为80),另外,除了存储4个边界框位置坐标x,y,w,h,还包括边界框以及每个anchor框的one-hot关键字的置信度得分。 假设有四个表征位置的坐标为 \(t_x, t_y, t_w, t_h\) ,那么边界框的预测将会如下定义:
$$ b_x = \sigma(t_x) + c_x $$ $$ b_y = \sigma(t_y) + c_y $$ $$ b_w = p_w e^{t_w} $$ $$ b_h = p_h e^{t_h} $$
在上面的等式中, \(c_x, c_y\) 是当前网格的左上角, \(p_w, p_h\) 由anchors指定。 置信度得分是anchor框和真实框之间的IoU的逻辑回归值,anchor框的得分最高为1,此时该anchor框对应着最大IoU。 如果anchor框之间的IoU大于忽略阀值ignore_thresh,则该anchor框的置信度评分损失将会被忽略。
因此,yolov3损失包括三个主要部分,框位置损失,目标性损失,分类损失。L1损失用于 框坐标(w,h),同时,sigmoid交叉熵损失用于框坐标(x,y),目标性损失和分类损失。
每个真实框将在所有anchor中找到最匹配的anchor,对该anchor的预测将会计算全部(三种)损失,但是没有匹配GT box(ground truth box真实框)的anchor的预测只会产生目标性损失。
为了权衡大框(box)和小(box)之间的框坐标损失,框坐标损失将与比例权重相乘而得。即:
$$ weight_{box} = 2.0 - t_w * t_h $$
最后的loss值将如下计算:
$$ loss = (loss_{xy} + loss_{wh}) * weight_{box} + loss_{conf} + loss_{class} $$
当 use_label_smooth
为 True
时,在计算分类损失时将平滑分类目标,将正样本的目标平滑到1.0-1.0 / class_num,并将负样本的目标平滑到1.0 / class_num。
GTScore
(如果存在)表示真实框的mixup得分,那么真实框所产生的所有损失需要乘上GTScore。
- 参数:
-
x (Tensor) : YOLOv3损失运算的输入张量,这是一个形状为[N,C,H,W]的四维Tensor。H和W应该相同,第二维(C)存储框的位置信息,以及每个anchor box的置信度得分和one-hot分类。数据类型为float32或float64。
gt_box (Tensor) : 真实框,应该是[N,B,4]的形状。第三维用来承载x、y、w、h,其中 x, y是真实框的中心坐标,w, h是框的宽度和高度,且x、y、w、h将除以输入图片的尺寸,缩放到[0,1]区间内。 N是batch size,B是图像中所含有的的最多的box数目。数据类型为float32或float64。
gt_label (Tensor) : 真实框的类id,应该形为[N,B]。数据类型为int32。
anchors (list|tuple) : 指定anchor框的宽度和高度,它们将逐对进行解析
anchor_mask (list|tuple) : 当前YOLOv3损失计算中使用anchor的mask索引
class_num (int) : 要预测的类别数
ignore_thresh (float) : 一定条件下忽略某框置信度损失的忽略阈值
downsample_ratio (int) : 网络输入到YOLOv3 loss输入的下采样率,因此第一,第二和第三个 loss 的下采样率应分别为32,16,8
gt_score (Tensor): 真实框的混合得分,形为[N,B]。 默认None。数据类型为float32或float64。
use_label_smooth (bool): 是否使用平滑标签。 默认为True
name (str,可选): 操作的名称(可选,默认值为None)。更多信息请参见 Name 。
scale_x_y (float, 可选): 缩放解码边界框的中心点。 默认值1.0 。
返回¶
Tensor, yolov3损失的值,具有形状[N]的1-D Tensor。
代码示例¶
import paddle
import numpy as np
x = np.random.random([2, 14, 8, 8]).astype('float32')
gt_box = np.random.random([2, 10, 4]).astype('float32')
gt_label = np.random.random([2, 10]).astype('int32')
x = paddle.to_tensor(x)
gt_box = paddle.to_tensor(gt_box)
gt_label = paddle.to_tensor(gt_label)
loss = paddle.vision.ops.yolo_loss(x,
gt_box=gt_box,
gt_label=gt_label,
anchors=[10, 13, 16, 30],
anchor_mask=[0, 1],
class_num=2,
ignore_thresh=0.7,
downsample_ratio=8,
use_label_smooth=True,
scale_x_y=1.)
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