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体验新版 GitCode,发现更多精彩内容 >>
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144016fc
编写于
2月 22, 2019
作者:
D
dengkaipeng
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电子邮件补丁
差异文件
fix adaptive_pool and yolov3_loss. test=develop
上级
eb65b4e4
变更
4
隐藏空白更改
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Showing
4 changed file
with
131 addition
and
79 deletion
+131
-79
paddle/fluid/operators/detection/yolov3_loss_op.cc
paddle/fluid/operators/detection/yolov3_loss_op.cc
+20
-14
paddle/fluid/operators/pool_op.cc
paddle/fluid/operators/pool_op.cc
+69
-56
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+10
-9
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+32
-0
未找到文件。
paddle/fluid/operators/detection/yolov3_loss_op.cc
浏览文件 @
144016fc
...
...
@@ -144,30 +144,36 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
"The ignore threshold to ignore confidence loss."
)
.
SetDefault
(
0.7
);
AddComment
(
R"DOC(
This operator generate
yolov3 loss by
given predict result and ground
This operator generate
s yolov3 loss based on
given predict result and ground
truth boxes.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same,
specify the grid size, each grid point predict given
number boxes, this given number is specified by anchors, it should be
half anchors length, which following will be represented as S. In the
second dimention(the channel dimention), C should be S * (class_num + 5),
c
lass_num is the box categoriy number of source dataset(such as coco),
s
o in the second dimention, stores 4 box location coordinates x, y, w, h
a
nd
confidence score of the box and class one-hot key of each anchor box.
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
c
ategory number of source dataset(such as 80 in coco dataset), so in the
s
econd(channel) dimension, apart from 4 box location coordinates x, y, w, h,
a
lso includes
confidence score of the box and class one-hot key of each anchor box.
While the 4 location coordinates if $$tx, ty, tw, th$$
, the box predictions
correspnd to
:
Assume the 4 location coordinates is :math:`t_x, t_y, t_w, t_h`
, the box predictions
should be following
:
$$
b_x = \sigma(t_x) + c_x
b_y = \sigma(t_y) + c_y
b_x = \\sigma(t_x) + c_x
$$
$$
b_y = \\sigma(t_y) + c_y
$$
$$
b_w = p_w e^{t_w}
$$
$$
b_h = p_h e^{t_h}
$$
While $$c_x, c_y$$ is the left top corner of current grid and $$p_w, p_h$$
is specified by anchors.
In the equaltion above, :math:`c_x, c_y` is the left top corner of current grid
and :math:`p_w, p_h`
is specified by anchors.
As for confidence score, it is the logistic regression value of IoU between
anchor boxes and ground truth boxes, the score of the anchor box which has
...
...
paddle/fluid/operators/pool_op.cc
浏览文件 @
144016fc
...
...
@@ -260,34 +260,39 @@ Example:
$$
For exclusive = false:
.. math::
hstart &= i * strides[0] - paddings[0] \\
hend &= hstart + ksize[0] \\
wstart &= j * strides[1] - paddings[1] \\
wend &= wstart + ksize[1] \\
Output(i ,j) &= \frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
$$
hstart = i * strides[0] - paddings[0]
$$
$$
hend = hstart + ksize[0]
$$
$$
wstart = j * strides[1] - paddings[1]
$$
$$
wend = wstart + ksize[1]
$$
$$
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
$$
For exclusive = true:
$$
hstart = max(0, i * strides[0] - paddings[0])
$$
$$
hend = min(H, hstart + ksize[0])
$$
$$
wstart = max(0, j * strides[1] - paddings[1])
$$
$$
wend = min(W, wstart + ksize[1])
$$
$$
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
.. math::
hstart &= max(0, i * strides[0] - paddings[0]) \\
hend &= min(H, hstart + ksize[0]) \\
wstart &= max(0, j * strides[1] - paddings[1]) \\
wend &= min(W, wstart + ksize[1]) \\
Output(i ,j) &= \frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
For adaptive = true:
.. math::
hstart &= floor(i * H_{in} / H_{out}) \\
hend &= ceil((i + 1) * H_{in} / H_{out}) \\
wstart &= floor(j * W_{in} / W_{out}) \\
wend &= ceil((j + 1) * W_{in} / W_{out}) \\
Output(i ,j) &= \frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
)DOC"
);
}
...
...
@@ -417,39 +422,47 @@ Example:
$$
For exclusive = false:
.. math::
dstart &= i * strides[0] - paddings[0] \\
dend &= dstart + ksize[0] \\
hstart &= j * strides[1] - paddings[1] \\
hend &= hstart + ksize[1] \\
wstart &= k * strides[2] - paddings[2] \\
wend &= wstart + ksize[2] \\
Output(i ,j, k) &= \frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$
dstart = i * strides[0] - paddings[0]
$$
$$
dend = dstart + ksize[0]
$$
$$
hstart = j * strides[1] - paddings[1]
$$
$$
hend = hstart + ksize[1]
$$
$$
wstart = k * strides[2] - paddings[2]
$$
$$
wend = wstart + ksize[2]
$$
$$
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$
For exclusive = true:
.. math::
dstart &= max(0, i * strides[0] - paddings[0]) \\
dend &= min(D, dstart + ksize[0]) \\
hend &= min(H, hstart + ksize[1]) \\
wstart &= max(0, k * strides[2] - paddings[2]) \\
wend &= min(W, wstart + ksize[2]) \\
Output(i ,j, k) &= \frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
For adaptive = true:
.. math::
dstart &= floor(i * D_{in} / D_{out}) \\
dend &= ceil((i + 1) * D_{in} / D_{out}) \\
hstart &= floor(j * H_{in} / H_{out}) \\
hend &= ceil((j + 1) * H_{in} / H_{out}) \\
wstart &= floor(k * W_{in} / W_{out}) \\
wend &= ceil((k + 1) * W_{in} / W_{out}) \\
Output(i ,j, k) &= \frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
dstart = max(0, i * strides[0] - paddings[0])
$$
$$
dend = min(D, dstart + ksize[0])
$$
$$
hend = min(H, hstart + ksize[1])
$$
$$
wstart = max(0, k * strides[2] - paddings[2])
$$
$$
wend = min(W, wstart + ksize[2])
$$
$$
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
)DOC"
);
}
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
144016fc
...
...
@@ -545,15 +545,16 @@ def yolov3_loss(x,
TypeError: Attr ignore_thresh of yolov3_loss must be a float number
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchors = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80, anchors=anchors,
ignore_thresh=0.5, downsample_ratio=32)
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchor_mask = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, gtlabel=gtlabel, anchors=anchors,
anchor_mask=anchor_mask, class_num=80,
ignore_thresh=0.7, downsample_ratio=32)
"""
helper
=
LayerHelper
(
'yolov3_loss'
,
**
locals
())
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
144016fc
...
...
@@ -2577,6 +2577,20 @@ def adaptive_pool2d(input,
represent height and width, respectively. Also the H and W dimensions of output(Out)
is same as Parameter(pool_size).
For average adaptive pool2d:
.. math::
hstart &= floor(i * H_{in} / H_{out})
hend &= ceil((i + 1) * H_{in} / H_{out})
wstart &= floor(j * W_{in} / W_{out})
wend &= ceil((j + 1) * W_{in} / W_{out})
Output(i ,j) &=
\\
frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
Args:
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCHW, where N is batch size, C is
...
...
@@ -2675,6 +2689,24 @@ def adaptive_pool3d(input,
three elements which represent height and width, respectively. Also the D, H and W
dimensions of output(Out) is same as Parameter(pool_size).
For average adaptive pool3d:
.. math::
dstart &= floor(i * D_{in} / D_{out})
dend &= ceil((i + 1) * D_{in} / D_{out})
hstart &= floor(j * H_{in} / H_{out})
hend &= ceil((j + 1) * H_{in} / H_{out})
wstart &= floor(k * W_{in} / W_{out})
wend &= ceil((k + 1) * W_{in} / W_{out})
Output(i ,j, k) &=
\\
frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
Args:
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCDHW, where N is batch size, C is
...
...
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