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bcae8729
编写于
6月 15, 2018
作者:
F
fengjiayi
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差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into dev_add_doc
上级
29ddf6c5
566a9402
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
221 addition
and
61 deletion
+221
-61
paddle/fluid/operators/activation_op.cc
paddle/fluid/operators/activation_op.cc
+1
-1
paddle/fluid/operators/detection/box_coder_op.cc
paddle/fluid/operators/detection/box_coder_op.cc
+27
-14
paddle/fluid/operators/gaussian_random_batch_size_like_op.cc
paddle/fluid/operators/gaussian_random_batch_size_like_op.cc
+6
-3
python/paddle/fluid/initializer.py
python/paddle/fluid/initializer.py
+100
-2
python/paddle/fluid/layers/io.py
python/paddle/fluid/layers/io.py
+8
-8
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+44
-31
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+18
-2
python/paddle/fluid/tests/unittests/test_initializer.py
python/paddle/fluid/tests/unittests/test_initializer.py
+17
-0
未找到文件。
paddle/fluid/operators/activation_op.cc
浏览文件 @
bcae8729
...
...
@@ -112,7 +112,7 @@ $$out = \frac{1}{1 + e^{-x}}$$
__attribute__
((
unused
))
constexpr
char
LogSigmoidDoc
[]
=
R"DOC(
Logsigmoid Activation Operator
$$out = \
log
\frac{1}{1 + e^{-x}}$$
$$out = \
\log \
\frac{1}{1 + e^{-x}}$$
)DOC"
;
...
...
paddle/fluid/operators/detection/box_coder_op.cc
浏览文件 @
bcae8729
...
...
@@ -106,23 +106,36 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
"and M represents the number of deocded boxes."
);
AddComment
(
R"DOC(
Bounding Box Coder Operator.
Bounding Box Coder.
Encode/Decode the target bounding box with the priorbox information.
The Encoding schema described below:
ox = (tx - px) / pw / pxv
oy = (ty - py) / ph / pyv
ow = log(abs(tw / pw)) / pwv
oh = log(abs(th / ph)) / phv
ox = (tx - px) / pw / pxv
oy = (ty - py) / ph / pyv
ow = log(abs(tw / pw)) / pwv
oh = log(abs(th / ph)) / phv
The Decoding schema described below:
ox = (pw * pxv * tx * + px) - tw / 2
oy = (ph * pyv * ty * + py) - th / 2
ow = exp(pwv * tw) * pw + tw / 2
oh = exp(phv * th) * ph + th / 2
where tx, ty, tw, th denote the target box's center coordinates, width and
height respectively. Similarly, px, py, pw, ph denote the priorbox's(anchor)
center coordinates, width and height. pxv, pyv, pwv, phv denote the variance
of the priorbox and ox, oy, ow, oh denote the encoded/decoded coordinates,
width and height.
ox = (pw * pxv * tx * + px) - tw / 2
oy = (ph * pyv * ty * + py) - th / 2
ow = exp(pwv * tw) * pw + tw / 2
oh = exp(phv * th) * ph + th / 2
where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates, width
and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote the
priorbox's (anchor) center coordinates, width and height. `pxv`, `pyv`, `pwv`,
`phv` denote the variance of the priorbox and `ox`, `oy`, `ow`, `oh` denote the
encoded/decoded coordinates, width and height.
)DOC"
);
}
};
...
...
paddle/fluid/operators/gaussian_random_batch_size_like_op.cc
浏览文件 @
bcae8729
...
...
@@ -36,11 +36,12 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
void
Apply
()
override
{
AddAttr
<
float
>
(
"mean"
,
"(float, default 0.0) "
"
mean of random tensor
."
)
"
The mean (or center) of the gaussian distribution
."
)
.
SetDefault
(
.0
f
);
AddAttr
<
float
>
(
"std"
,
"(float, default 1.0) "
"std of random tensor."
)
"The standard deviation (std, or spread) of the "
"gaussian distribution."
)
.
SetDefault
(
1.0
f
);
AddAttr
<
int
>
(
"seed"
,
"(int, default 0) "
...
...
@@ -55,9 +56,11 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
.
SetDefault
(
framework
::
proto
::
VarType
::
FP32
);
AddComment
(
R"DOC(
GaussianRandom Operator.
Used to initialize tensors with gaussian random generator.
The defalut mean of the distribution is 0. and defalut standard
deviation (std) of the distribution is 1.. Uers can set mean and std
by input arguments.
)DOC"
);
}
};
...
...
python/paddle/fluid/initializer.py
浏览文件 @
bcae8729
...
...
@@ -15,11 +15,13 @@
import
framework
import
numpy
as
np
import
contextlib
from
framework
import
convert_np_dtype_to_dtype_
from
core
import
VarDesc
__all__
=
[
'Constant'
,
'Uniform'
,
'Normal'
,
'Xavier'
,
'force_init_on_cpu'
,
'Constant'
,
'Uniform'
,
'Normal'
,
'Xavier'
,
'
Bilinear'
,
'
force_init_on_cpu'
,
'init_on_cpu'
,
'ConstantInitializer'
,
'UniformInitializer'
,
'NormalInitializer'
,
'XavierInitializer'
'NormalInitializer'
,
'XavierInitializer'
,
'BilinearInitializer'
]
_force_init_on_cpu_
=
False
...
...
@@ -422,6 +424,101 @@ class MSRAInitializer(Initializer):
return
op
class
BilinearInitializer
(
Initializer
):
"""Implements the bilinear initializer.
This initializer can be used in transposed convolution operator to
act as upsampling. Users can upsample a feature map with shape of
(B, C, H, W) by any integer factor. The usage is:
>>> factor = 2
>>> w_attr = ParamAttr(learning_rate=0., regularizer=L2Decay(0.),
>>> initializer=Bilinear())
>>> conv_up = fluid.layers.conv2d_transpose(
>>> input,
>>> num_filters=C,
>>> output_size=None,
>>> filter_size=2 * factor - factor % 2,
>>> padding=ceil((factor - 1) / 2.),
>>> stride=factor,
>>> groups=C,
>>> param_attr=w_attr,
>>> bias_attr=False)
Where, `num_filters=C` and `groups=C` means this is channel-wise tranposed
convolution. The filter shape will be (C, 1, K, K) where K is `filer_size`,
This initializer will set a (K, K) interpolation kernel for every channel
of the filter identically. The resulting shape of the output feature map
will be (B, C, factor * H, factor * W). Note that the learning rate and the
weight decay are set to 0 in order to keep coefficient values of bilinear
interpolation unchanged during training.
"""
def
__init__
(
self
):
"""Constructor for BilinearInitializer.
"""
super
(
BilinearInitializer
,
self
).
__init__
()
def
__call__
(
self
,
var
,
block
):
"""Add biliear initialization ops for a variable
Args:
var (Variable): Variable that needs to be initialized.
block (Block): The block in which initialization ops should
be added.
Returns:
the initialization op
Raises:
ValueError: If type of `var` and `block` is not right.
If the shape of `var` size is not 4 and
var.shape[2] != var.shape[3].
"""
if
not
isinstance
(
var
,
framework
.
Variable
):
raise
ValueError
(
"var must be framework.Variable."
)
if
not
isinstance
(
block
,
framework
.
Block
):
raise
ValueError
(
"block must be framework.Block."
)
shape
=
var
.
shape
if
len
(
shape
)
!=
4
:
raise
ValueError
(
"the length of shape must be 4."
)
if
shape
[
2
]
!=
shape
[
3
]:
raise
ValueError
(
"shape[2] must be equal to shape[3]."
)
weight
=
np
.
zeros
(
np
.
prod
(
var
.
shape
),
dtype
=
'float32'
)
size
=
shape
[
3
]
# factor
f
=
np
.
ceil
(
size
/
2.
)
# center
c
=
(
2
*
f
-
1
-
f
%
2
)
/
(
2.
*
f
)
for
i
in
range
(
np
.
prod
(
shape
)):
x
=
i
%
size
y
=
(
i
/
size
)
%
size
weight
[
i
]
=
(
1
-
abs
(
x
/
f
-
c
))
*
(
1
-
abs
(
y
/
f
-
c
))
weight
=
np
.
reshape
(
weight
,
shape
)
if
var
.
dtype
==
VarDesc
.
VarType
.
FP32
:
value_name
=
"fp32_values"
values
=
[
float
(
v
)
for
v
in
weight
.
flat
]
else
:
raise
ValueError
(
"Unsupported dtype %s"
,
input
.
dtype
)
if
np
.
prod
(
shape
)
>
1024
*
1024
:
raise
ValueError
(
"The size of input is too big. "
)
op
=
block
.
append_op
(
type
=
'assign_value'
,
outputs
=
{
'Out'
:
[
var
]},
attrs
=
{
'dtype'
:
var
.
dtype
,
'shape'
:
list
(
shape
),
value_name
:
values
})
var
.
op
=
op
return
op
# We short the class name, since users will use the initializer with the package
# name. The sample code:
#
...
...
@@ -436,3 +533,4 @@ Uniform = UniformInitializer
Normal
=
NormalInitializer
Xavier
=
XavierInitializer
MSRA
=
MSRAInitializer
Bilinear
=
BilinearInitializer
python/paddle/fluid/layers/io.py
浏览文件 @
bcae8729
...
...
@@ -378,16 +378,16 @@ def random_data_generator(low, high, shapes, lod_levels, for_parallel=True):
Variable: A Reader Variable from which we can get random data.
Examples:
.. code-block:: python
reader = fluid.layers.io.random_data_generator(
low=0.0,
high=1.0,
shapes=[(3,224,224), (1)],
lod_levels=[0, 0])
.. code-block:: python
# Via the reader, we can use 'read_file' layer to get data:
image, label = fluid.layers.io.read_file(reader)
reader = fluid.layers.random_data_generator(
low=0.0,
high=1.0,
shapes=[[3,224,224], [1]],
lod_levels=[0, 0])
# Via the reader, we can use 'read_file' layer to get data:
image, label = fluid.layers.read_file(reader)
"""
dtypes
=
[
core
.
VarDesc
.
VarType
.
FP32
]
*
len
(
shapes
)
shape_concat
=
[]
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
bcae8729
...
...
@@ -364,8 +364,7 @@ def dynamic_lstm(input,
cell_activation(str): The activation for cell output. Choices = ["sigmoid",
"tanh", "relu", "identity"], default "tanh".
candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh",
"relu", "identity"],
Choices = ["sigmoid", "tanh", "relu", "identity"],
default "tanh".
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
name(str|None): A name for this layer(optional). If set None, the layer
...
...
@@ -540,27 +539,31 @@ def dynamic_lstmp(input,
cell_activation(str): The activation for cell output. Choices = ["sigmoid",
"tanh", "relu", "identity"], default "tanh".
candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh",
"relu", "identity"],
Choices = ["sigmoid", "tanh", "relu", "identity"],
default "tanh".
proj_activation(str): The activation for projection output.
Choices = ["sigmoid", "tanh",
"relu", "identity"],
Choices = ["sigmoid", "tanh", "relu", "identity"],
default "tanh".
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
tuple: The projection of hidden state, and cell state of LSTMP. The
\
shape of projection is (T x P), for the cell state which is
\
(T x D), and both LoD is the same with the `input`.
tuple: A tuple of two output variable: the projection of hidden state,
\
and cell state of LSTMP. The shape of projection is (T x P),
\
for the cell state which is (T x D), and both LoD is the same
\
with the `input`.
Examples:
.. code-block:: python
dict_dim, emb_dim = 128, 64
data = fluid.layers.data(name='sequence', shape=[1],
dtype='int32', lod_level=1)
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
hidden_dim, proj_dim = 512, 256
fc_out = fluid.layers.fc(input=
input_seq
, size=hidden_dim * 4,
fc_out = fluid.layers.fc(input=
emb
, size=hidden_dim * 4,
act=None, bias_attr=None)
proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
size=hidden_dim * 4,
...
...
@@ -626,10 +629,10 @@ def dynamic_gru(input,
candidate_activation
=
'tanh'
,
h_0
=
None
):
"""
**
Dynamic GRU
Layer**
**
Gated Recurrent Unit (GRU)
Layer**
Refer to `Empirical Evaluation of Gated Recurrent Neural Networks on
Sequence Modeling <https://arxiv.org/abs/1412.3555>`_
Sequence Modeling <https://arxiv.org/abs/1412.3555>`_
.
The formula is as follows:
...
...
@@ -676,17 +679,25 @@ def dynamic_gru(input,
Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid".
candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
h_0 (Variable): The hidden output of the first time step.
h_0 (Variable): This is initial hidden state. If not set, default is
zero. This is a tensor with shape (N x D), where N is the number of
total time steps of input mini-batch feature and D is the hidden
size.
Returns:
Variable: The hidden state of GRU. The shape is :math:`(T
\\
times D)`,
\
and
lod
is the same with the input.
and
sequence length
is the same with the input.
Examples:
.. code-block:: python
dict_dim, emb_dim = 128, 64
data = fluid.layers.data(name='sequence', shape=[1],
dtype='int32', lod_level=1)
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
hidden_dim = 512
x = fluid.layers.fc(input=
data
, size=hidden_dim * 3)
x = fluid.layers.fc(input=
emb
, size=hidden_dim * 3)
hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
"""
...
...
@@ -924,13 +935,13 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
Drop or keep each element of `x` independently. Dropout is a regularization
technique for reducing overfitting by preventing neuron co-adaption during
training. The dropout operator randomly set (according to the given dropout
training. The dropout operator randomly set
s
(according to the given dropout
probability) the outputs of some units to zero, while others are remain
unchanged.
Args:
x (Variable): The input tensor.
dropout_prob (float): Probability of setting units to zero.
x (Variable): The input tensor
variable
.
dropout_prob (float): Probability of setting units to zero.
is_test (bool): A flag indicating whether it is in test phrase or not.
seed (int): A Python integer used to create random seeds. If this
parameter is set to None, a random seed is used.
...
...
@@ -940,13 +951,14 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
will be named automatically.
Returns:
Variable: A tensor variable.
Variable: A tensor variable
is the shape with `x`
.
Examples:
.. code-block:: python
x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
droped = fluid.layers.dropout(input=x, dropout_rate
=0.5)
x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
droped = fluid.layers.dropout(x, dropout_prob
=0.5)
"""
helper
=
LayerHelper
(
'dropout'
,
**
locals
())
...
...
@@ -3012,32 +3024,33 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
norm. For a 1-D tensor (`dim` is fixed to 0), this layer computes
.. math::
y =
\f
rac{x}{ \sqrt{\sum {x^2} + epsion }}
y =
\\
frac{x}{ \sqrt{\sum {x^2} + epsion }}
For `x` with more dimensions, this layer independently normalizes each 1-D
slice along dimension `axis`.
Args:
x(Variable|list): The input tensor to l2_normalize layer.
axis(int): The axis on which to apply normalization. If `axis < 0`,
axis(int): The axis on which to apply normalization. If `axis < 0`,
\
the dimension to normalization is rank(X) + axis. -1 is the
last dimension.
epsilon(float): The epsilon value is used to avoid division by zero,
epsilon(float): The epsilon value is used to avoid division by zero,
\
the defalut value is 1e-10.
name(str|None): A name for this layer(optional). If set None, the layer
name(str|None): A name for this layer(optional). If set None, the layer
\
will be named automatically.
Returns:
Variable: The output tensor variable.
Variable: The output tensor variable
is the same shape with `x`
.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data",
shape=(3, 17, 13),
dtype="float32")
normed = fluid.layers.l2_normalize(x=data, axis=1)
data = fluid.layers.data(name="data",
shape=(3, 17, 13),
dtype="float32")
normed = fluid.layers.l2_normalize(x=data, axis=1)
"""
if
len
(
x
.
shape
)
==
1
:
...
...
python/paddle/fluid/layers/tensor.py
浏览文件 @
bcae8729
...
...
@@ -513,11 +513,27 @@ def save_combine(x, file_path, overwrite=True):
Saves a list of variables into a single file.
Args:
x(list): A list of Tensor/LoDTensor to be saved together in a single file.
x(list): A list of Tensor/LoDTensor variables to be saved together in
a single file.
file_path(str): The file path where variables will be saved.
overwrite(bool): Whether or not cover the given file when it has already
overwrite(bool): Whether or not cover the given file when it has already
existed. If it's set 'False' and the file is existed, a runtime
error will be thrown.
Returns:
There is no return value.
Examples:
.. code-block:: python
v1 = fluid.layers.data(name="data",
shape=(4, 6),
dtype="float32")
v2 = fluid.layers.data(name="data",
shape=(6, 8, 4),
dtype="float32")
normed = fluid.layers.save_combine([v1, v2], file_path="output")
"""
helper
=
LayerHelper
(
"save_combine"
,
**
locals
())
helper
.
append_op
(
...
...
python/paddle/fluid/tests/unittests/test_initializer.py
浏览文件 @
bcae8729
...
...
@@ -364,5 +364,22 @@ class TestMSRAInitializer(unittest.TestCase):
self
.
assertEqual
(
init_op
.
attr
(
'seed'
),
134
)
class
TestMSRAInitializer
(
unittest
.
TestCase
):
def
test_bilinear_initializer
(
self
):
"""Test the bilinear initializer with supplied arguments
"""
program
=
framework
.
Program
()
block
=
program
.
global_block
()
block
.
create_parameter
(
dtype
=
"float32"
,
shape
=
[
8
,
1
,
3
,
3
],
lod_level
=
0
,
name
=
"param"
,
initializer
=
initializer
.
BilinearInitializer
())
self
.
assertEqual
(
len
(
block
.
ops
),
1
)
init_op
=
block
.
ops
[
0
]
self
.
assertEqual
(
init_op
.
type
,
'assign_value'
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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