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3f5a4416
编写于
4月 17, 2019
作者:
X
XiaoguangHu
提交者:
GitHub
4月 17, 2019
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Merge pull request #16926 from tink2123/cherry-pick-yolov3
[Cherry-pick] polish yolov3_loss annotation
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d8326ec6
36b22bb6
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-4
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
paddle/fluid/operators/detection/yolov3_loss_op.cc
paddle/fluid/operators/detection/yolov3_loss_op.cc
+3
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paddle/fluid/API.spec
浏览文件 @
3f5a4416
...
@@ -348,7 +348,7 @@ paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes',
...
@@ -348,7 +348,7 @@ paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes',
paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '587845f60c5d97ffdf2dfd21da52eca1'))
paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '587845f60c5d97ffdf2dfd21da52eca1'))
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.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.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0e5ac2507723a0b5adec473f9556799b'))
paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '
059021025283ad1ee6f4d32228cf3e4e
'))
paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '
4d170807a13d33925d1049d2892832bf
'))
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', '5566169a5ab993d177792c023c7fb340'))
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', '5566169a5ab993d177792c023c7fb340'))
paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '397e9e02b451d99c56e20f268fa03f2e'))
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.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/operators/detection/yolov3_loss_op.cc
浏览文件 @
3f5a4416
...
@@ -171,8 +171,8 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -171,8 +171,8 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
The output of previous network is in shape [N, C, H, W], while H and W
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
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,
given number bo
unding bo
xes, this given number, which following will be represented as S,
is specified by the number of anchor
s,
In the second dimension(the channel
is specified by the number of anchor
clusters in each scale.
In the second dimension(the channel
dimension), C should be equal to S * (class_num + 5), class_num is the object
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
category number of source dataset(such as 80 in coco dataset), so in the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
...
@@ -203,7 +203,7 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -203,7 +203,7 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
thresh, the confidence score loss of this anchor box will be ignored.
thresh, the confidence score loss of this anchor box will be ignored.
Therefore, the yolov3 loss consist of three major parts, box location loss,
Therefore, the yolov3 loss consist of three major parts, box location loss,
confidence score loss, and classification loss. The L
2
loss is used for
confidence score loss, and classification loss. The L
1
loss is used for
box coordinates (w, h), and sigmoid cross entropy loss is used for box
box coordinates (w, h), and sigmoid cross entropy loss is used for box
coordinates (x, y), confidence score loss and classification loss.
coordinates (x, y), confidence score loss and classification loss.
...
...
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