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paddle.fluid / layers / generate_mask_labels
generate_mask_labels¶
-
paddle.fluid.layers.
generate_mask_labels
( im_info, gt_classes, is_crowd, gt_segms, rois, labels_int32, num_classes, resolution ) [源代码] ¶
为Mask-RCNN生成mask标签
对于给定的 RoI (Regions of Interest) 和 输入ground truth的分类标签和分割的坐标标签,采样出前景RoI,并返回其在输入 rois
中索引位置,并对每个RoI生成 \(K*M^{2}\) 的二值mask标签。K为类别个数,M是RoI特征图大小。这些输出目标一般用于计算mask分支的损失。
请注意分割groud-truth(真实标签,下简称GT)数据格式,这里要求分割标签为坐标信息,假如,第一个实例有两个GT对象。 第二个实例有一个GT对象,该GT分割标签是两段(例如物体中间被隔开),输入标签格式组织如下:
#[
# [[[229.14, 370.9, 229.14, 370.9, ...]],
# [[343.7, 139.85, 349.01, 138.46, ...]]], # 第0个实例对象
# [[[500.0, 390.62, ...],[115.48, 187.86, ...]]] # 第1个实例对象
#]
batch_masks = []
for semgs in batch_semgs:
gt_masks = []
for semg in semgs:
gt_segm = []
for polys in semg:
gt_segm.append(np.array(polys).reshape(-1, 2))
gt_masks.append(gt_segm)
batch_masks.append(gt_masks)
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=feeds)
feeder.feed(batch_masks)
- 参数:
-
im_info (Variable) – 维度为[N,3]的2-D Tensor,数据类型为float32。 N是batch size,其每个元素是图像的高度、宽度、比例,比例是图片预处理时resize之后的大小和原始大小的比例 \(\frac{target\_size}{original\_size}\) 。
gt_classes (Variable) – 维度为[M,1]的2-D LoDTensor,数据类型为int32,LoD层数为1。 M是的groud-truth总数,其每个元素是类别索引。
is_crowd (Variable) – 维度和
gt_classes
相同的 LoDTensor,数据类型为int32,每个元素指示一个ground-truth是否为crowd(crowd表示一组对象的集合)。gt_segms (Variable) – 维度为[S,2]的2D LoDTensor,它的LoD层数为3,数据类型为float32。通常用户不需要理解LoD,但用户应该在Reader中返回正确的数据格式。LoD[0]表示每个实例中GT对象的数目。 LoD[1]表示每个GT对象的标签分段数。LoD[2]表示每个分段标签多边形(polygon)坐标点的个数。S为所有示例的标签的多边形坐标点的总数。每个元素是(x,y)坐标点。
rois (Variable) – 维度维度[R,4]的2-D LoDTensor,LoD层数为1,数据类型为float32。 R是RoI的总数,其中每个元素是在原始图像范围内具有(xmin,ymin,xmax,ymax)格式的bounding box。
labels_int32 (Variable) – 形为[R,1]且类型为int32的2-D LoDTensor,数据类型为int32。 R与
rois
中的R含义相同。每个元素表示RoI框的一个类别标签索引。num_classes (int) – 类别数目。
resolution (int) – 特征图分辨率大小。
- 返回:
-
mask_rois (Variable): 维度为[P,4]的2-D LoDTensor,数据类型为float32。P是采样出的RoI总数,每个元素都是在原始图像大小范围内具有[xmin,ymin,xmax,ymax]格式的bounding box。
mask_rois_has_mask_int32(Variable):维度为[P,1]的2-D LoDTensor,数据类型为int32。每个元素表示采样出的RoI在输入
rois
内的位置索引。mask_int32(Variable):维度为[P,K * M * M]的2-D LoDTensor,数据类型为int32。K为种类数,M为特征图的分辨率大小,每个元素都是二值mask标签。
返回类型:tuple(Variable)
代码示例:
import paddle.fluid as fluid
im_info = fluid.data(name="im_info", shape=[None, 3], dtype="float32")
gt_classes = fluid.data(name="gt_classes", shape=[None, 1],
dtype="float32", lod_level=1)
is_crowd = fluid.data(name="is_crowd", shape=[None, 1],
dtype="float32", lod_level=1)
gt_masks = fluid.data(name="gt_masks", shape=[None, 2],
dtype="float32", lod_level=3)
# rois, roi_labels 可以是fluid.layers.generate_proposal_labels的输出
rois = fluid.data(name="rois", shape=[None, 4],
dtype="float32", lod_level=1)
roi_labels = fluid.data(name="roi_labels", shape=[None, 1],
dtype="int32", lod_level=1)
mask_rois, mask_index, mask_int32 = fluid.layers.generate_mask_labels(
im_info=im_info,
gt_classes=gt_classes,
is_crowd=is_crowd,
gt_segms=gt_masks,
rois=rois,
labels_int32=roi_labels,
num_classes=81,
resolution=14)
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