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paddle.vision / ops / yolo_box
yolo_box¶
-
paddle.vision.ops.
yolo_box
( x, img_size, anchors, class_num, conf_thresh, downsample_ratio, clip_bbox=True, name=None, scale_x_y=1.0 ) [源代码] ¶
该运算符基于YOLOv3网络的输出结果,生成YOLO检测框。
连接 yolo_box 网络的输出形状应为[N,C,H,W],其中 H 和 W 相同,用来指定网格大小。对每个网格点预测给定的数目的框,这个数目记为 S ,由 anchor 的数量指定。 在第二维(通道维度)中,C应该等于S *(5 + class_num),class_num是源数据集中对象类别数目(例如coco数据集中的80),此外第二个(通道)维度中还有4个框位置坐标x,y,w,h,以及anchor box的one-hot key的置信度得分。
假设4个位置坐标是 \(t_x\) ,\(t_y\) ,\(t_w\) , \(t_h\) ,则框的预测算法为:
在上面的等式中, \(c_x\) , \(c_x\) 是当前网格的左上角顶点坐标。 \(p_w\) , \(p_h\) 由anchors指定。
每个anchor预测框的第五通道的逻辑回归值表示每个预测框的置信度得分,并且每个anchor预测框的最后class_num通道的逻辑回归值表示分类得分。 应忽略置信度低于conf_thresh的框。另外,框最终得分是置信度得分和分类得分的乘积。
参数¶
x (Tensor): YoloBox的输入张量是一个4-D张量,形状为[N,C,H,W]。第二维(C)存储每个anchor box位置坐标,每个anchor box的置信度分数和one hot key。通常,X应该是YOLOv3网络的输出。数据类型为float32或float64
img_size (Tensor): YoloBox的图像大小张量,这是一个形状为[N,2]的二维张量。该张量保持每个输入图像的高度和宽度,用于对输出图像按输入图像比例调整输出框的大小。数据类型为int32。
anchors (list | tuple) : anchor的宽度和高度,它将逐对解析
class_num (int): 要预测的类数
conf_thresh (float): 检测框的置信度得分阈值。置信度得分低于阈值的框应该被忽略
downsample_ratio (int): 从网络输入到YoloBox操作输入的下采样率,因此应依次为第一个,第二个和第三个YoloBox运算设置该值为32,16,8
clip_bbox (bool, 可选): 是否将输出的bbox裁剪到
img_size
范围内,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): 放缩解码边界框的中心点,默认值:1.0 .
返回¶
框的坐标,形为[N,M,4]的三维张量
框的分类得分, 形为 [N,M,class_num]的三维张量
代码示例¶
import paddle
import numpy as np
x = np.random.random([2, 14, 8, 8]).astype('float32')
img_size = np.ones((2, 2)).astype('int32')
x = paddle.to_tensor(x)
img_size = paddle.to_tensor(img_size)
boxes, scores = paddle.vision.ops.yolo_box(x,
img_size=img_size,
anchors=[10, 13, 16, 30],
class_num=2,
conf_thresh=0.01,
downsample_ratio=8,
clip_bbox=True,
scale_x_y=1.)
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