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体验新版 GitCode,发现更多精彩内容 >>
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33c8607e
编写于
3月 11, 2019
作者:
D
dengkaipeng
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差异文件
fix doc. test=develop
上级
00e822d2
变更
2
隐藏空白更改
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2 changed file
with
8 addition
and
8 deletion
+8
-8
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
paddle/fluid/operators/detection/yolo_box_op.cc
paddle/fluid/operators/detection/yolo_box_op.cc
+7
-7
未找到文件。
paddle/fluid/API.spec
浏览文件 @
33c8607e
...
...
@@ -328,7 +328,7 @@ paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=Non
paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '032d0f4b7d8f6235ee5d91e473344f0e'))
paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0e5ac2507723a0b5adec473f9556799b'))
paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '991e934c3e09abf0edec7c9c978b4691'))
paddle.fluid.layers.yolo_box (ArgSpec(args=['x', 'img_size', 'anchors', 'class_num', 'conf_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '
991e934c3e09abf0edec7c9c978b469
1'))
paddle.fluid.layers.yolo_box (ArgSpec(args=['x', 'img_size', 'anchors', 'class_num', 'conf_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '
170091cef6ebfcba6e54c55b496d002
1'))
paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '397e9e02b451d99c56e20f268fa03f2e'))
paddle.fluid.layers.multiclass_nms (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)), ('document', 'ca7d1107b6c5d2d6d8221039a220fde0'))
paddle.fluid.layers.distribute_fpn_proposals (ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7bb011ec26bace2bc23235aa4a17647d'))
...
...
paddle/fluid/operators/detection/yolo_box_op.cc
浏览文件 @
33c8607e
...
...
@@ -83,7 +83,7 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"ImgSize"
,
"The image size tensor of YoloBox operator, "
"This is a 2-D tensor with shape of [N, 2]. This tensor holds "
"height and width of each input image us
ing for resize
output "
"height and width of each input image us
ed for resizing
output "
"box in input image scale."
);
AddOutput
(
"Boxes"
,
"The output tensor of detection boxes of YoloBox operator, "
...
...
@@ -117,9 +117,9 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, H and W specify the grid size, each grid point predict
given number boxes, this given number, which following will be represented as S,
is specified by the number of anchors
,
In the second dimension(the channel
dimension), C should be equal to S * (
class_num + 5
), class_num is the object
category number of source dataset(such as 80 in coco dataset), so
in
the
is specified by the number of anchors
.
In the second dimension(the channel
dimension), C should be equal to S * (
5 + class_num
), class_num is the object
category number of source dataset(such as 80 in coco dataset), so the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
also includes confidence score of the box and class one-hot key of each anchor
box.
...
...
@@ -143,10 +143,10 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
in the equation above, :math:`c_x, c_y` is the left top corner of current grid
and :math:`p_w, p_h` is specified by anchors.
The logistic regression value of the 5
rd
channel of each anchor prediction boxes
represent the confidence score of each prediction box, and the logistic
The logistic regression value of the 5
th
channel of each anchor prediction boxes
represent
s
the confidence score of each prediction box, and the logistic
regression value of the last :attr:`class_num` channels of each anchor prediction
boxes represent the classifcation scores. Boxes with confidence scores less than
boxes represent
s
the classifcation scores. Boxes with confidence scores less than
:attr:`conf_thresh` should be ignored, and box final scores is the product of
confidence scores and classification scores.
...
...
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