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1c9fc655
编写于
6月 15, 2018
作者:
Q
qiaolongfei
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3 changed file
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48 addition
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45 deletion
+48
-45
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+39
-34
python/paddle/fluid/layers/learning_rate_scheduler.py
python/paddle/fluid/layers/learning_rate_scheduler.py
+5
-7
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+4
-4
未找到文件。
python/paddle/fluid/layers/detection.py
浏览文件 @
1c9fc655
...
...
@@ -603,7 +603,7 @@ def prior_box(input,
offset
=
0.5
,
name
=
None
):
"""
**Prior
box o
perator**
**Prior
Box O
perator**
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
Each position of the input produce N prior boxes, N is determined by
...
...
@@ -632,26 +632,30 @@ def prior_box(input,
name(str): Name of the prior box op. Default: None.
Returns:
boxes(Variable): the output prior boxes of PriorBox.
The layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input,
num_priors is the total
box count of each position of input.
Variances(Variable): the expanded variances of PriorBox.
The layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input
num_priors is the total
box count of each position of input
tuple: A tuple with two Variable (boxes, variances)
boxes: the output prior boxes of PriorBox.
The layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input,
num_priors is the total
box count of each position of input.
variances: the expanded variances of PriorBox.
The layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input
num_priors is the total
box count of each position of input
Examples:
.. code-block:: python
box, var = prior_box(
input=conv1,
image=images,
min_sizes=[100.],
flip=True,
clip=True)
box, var = fluid.layers.prior_box(
input=conv1,
image=images,
min_sizes=[100.],
flip=True,
clip=True)
"""
helper
=
LayerHelper
(
"prior_box"
,
**
locals
())
dtype
=
helper
.
input_dtype
()
...
...
@@ -721,11 +725,9 @@ def multi_box_head(inputs,
stride
=
1
,
name
=
None
):
"""
**Prior_boxes**
Generate prior boxes for SSD(Single Shot MultiBox Detector)
algorithm. The details of this algorithm, please refer the
section 2.2 of SSD paper
(SSD: Single Shot MultiBox Detector)
section 2.2 of SSD paper
`SSD: Single Shot MultiBox Detector
<https://arxiv.org/abs/1512.02325>`_ .
Args:
...
...
@@ -766,24 +768,27 @@ def multi_box_head(inputs,
name(str): Name of the prior box layer. Default: None.
Returns:
mbox_loc(Variable): The predicted boxes' location of the inputs.
The layout is [N, H*W*Priors, 4]. where Priors
is the number of predicted boxes each position of each input.
mbox_conf(Variable): The predicted boxes' confidence of the inputs.
The layout is [N, H*W*Priors, C]. where Priors
is the number of predicted boxes each position of each input
and C is the number of Classes.
boxes(Variable): the output prior boxes of PriorBox.
The layout is [num_priors, 4]. num_priors is the total
box count of each position of inputs.
Variances(Variable): the expanded variances of PriorBox.
The layout is [num_priors, 4]. num_priors is the total
box count of each position of inputs
tuple: A tuple with four Variables. (mbox_loc, mbox_conf, boxes, variances)
mbox_loc: The predicted boxes' location of the inputs. The layout
is [N, H*W*Priors, 4]. where Priors is the number of predicted
boxes each position of each input.
mbox_conf: The predicted boxes' confidence of the inputs. The layout
is [N, H*W*Priors, C]. where Priors is the number of predicted boxes
each position of each input and C is the number of Classes.
boxes: the output prior boxes of PriorBox. The layout is [num_priors, 4].
num_priors is the total box count of each position of inputs.
variances: the expanded variances of PriorBox. The layout is
[num_priors, 4]. num_priors is the total box count of each position of inputs
Examples:
.. code-block:: python
mbox_locs, mbox_confs, box, var = layers.multi_box_head(
mbox_locs, mbox_confs, box, var = fluid.layers.multi_box_head(
inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
image=images,
num_classes=21,
...
...
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @
1c9fc655
...
...
@@ -163,8 +163,6 @@ def polynomial_decay(learning_rate,
power
=
1.0
,
cycle
=
False
):
"""
**Polynomial Decay**
Applies polynomial decay to the initial learning rate.
.. code-block:: python
...
...
@@ -178,14 +176,14 @@ def polynomial_decay(learning_rate,
Args:
learning_rate(Variable|float32): A scalar float32 value or a Variable. This
will be the initial learning rate during training
will be the initial learning rate during training
.
decay_steps(int32): A Python `int32` number.
end_learning_rate(float
, Default: 0.0001
): A Python `float` number.
power(float
, Default: 1.0): A Python `float` number
cycle(bool
, Default: False): Boolean.
If set true, decay the learning rate every decay_steps.
end_learning_rate(float): A Python `float` number.
power(float
): A Python `float` number.
cycle(bool
):
If set true, decay the learning rate every decay_steps.
Returns:
The decayed learning rate
Variable:
The decayed learning rate
"""
global_step
=
_decay_step_counter
()
...
...
python/paddle/fluid/layers/tensor.py
浏览文件 @
1c9fc655
...
...
@@ -40,14 +40,14 @@ __all__ = [
def
create_tensor
(
dtype
,
name
=
None
,
persistable
=
False
):
"""
**Create a Tensor**
Create an variable, which will hold a LoDTensor with data type dtype.
Args:
dtype
(string): 'float32'|'int32'|..., the data type of the
dtype(string): 'float32'|'int32'|..., the data type of the
created tensor.
name
(string, Default: None
): The name of the created tensor, if not set,
name
(string
): The name of the created tensor, if not set,
the name will be a random unique one.
persistable
(bool, Default: False
): Set the persistable flag of the create tensor.
persistable
(bool
): Set the persistable flag of the create tensor.
Returns:
Variable: The tensor variable storing the created tensor.
...
...
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