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体验新版 GitCode,发现更多精彩内容 >>
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45f18b00
编写于
6月 14, 2018
作者:
D
dzhwinter
浏览文件
操作
浏览文件
下载
差异文件
Merge remote-tracking branch 'origin/develop' into doc/api1
上级
16a3d88a
e42e6ea1
变更
11
隐藏空白更改
内联
并排
Showing
11 changed file
with
575 addition
and
26 deletion
+575
-26
AUTHORS.md
AUTHORS.md
+1
-0
cmake/inference_lib.cmake
cmake/inference_lib.cmake
+10
-1
doc/fluid/api/layers.rst
doc/fluid/api/layers.rst
+19
-0
paddle/fluid/operators/concat_op.cc
paddle/fluid/operators/concat_op.cc
+8
-2
paddle/fluid/operators/concat_op.cu.cc
paddle/fluid/operators/concat_op.cu.cc
+8
-2
paddle/fluid/operators/get_places_op.cc
paddle/fluid/operators/get_places_op.cc
+1
-1
paddle/fluid/operators/split_op.cc
paddle/fluid/operators/split_op.cc
+4
-1
paddle/fluid/operators/split_op.cu.cc
paddle/fluid/operators/split_op.cu.cc
+4
-1
python/paddle/fluid/layers/control_flow.py
python/paddle/fluid/layers/control_flow.py
+28
-0
python/paddle/fluid/layers/io.py
python/paddle/fluid/layers/io.py
+20
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+472
-18
未找到文件。
AUTHORS.md
浏览文件 @
45f18b00
...
...
@@ -22,6 +22,7 @@
| jczaja | Jacek Czaja |
| JiayiFeng | Jia-Yi Feng |
| kbinias | Krzysztof Binias |
| kexinzhao | Ke-Xin Zhao |
| kuke | Yi-Bing Liu |
| lcy-seso | Ying Cao |
| lipeng-unisound | Peng Li |
...
...
cmake/inference_lib.cmake
浏览文件 @
45f18b00
...
...
@@ -39,7 +39,7 @@ function(copy TARGET)
message
(
FATAL_ERROR
"
${
TARGET
}
source numbers are not equal to destination numbers"
)
endif
()
math
(
EXPR len
"
${
copy_lib_SRCS_len
}
- 1"
)
add_custom_target
(
${
TARGET
}
DEPENDS
${
copy_lib_DEPS
}
)
foreach
(
index RANGE
${
len
}
)
list
(
GET copy_lib_SRCS
${
index
}
src
)
...
...
@@ -155,6 +155,15 @@ copy(inference_lib DEPS paddle_fluid_shared paddle_fluid
DSTS
${
dst_dir
}
/
${
module
}
${
dst_dir
}
/
${
module
}
)
if
(
WITH_CONTRIB
)
set
(
contrib_dst_dir
"
${
FLUID_INSTALL_DIR
}
/contrib/inference"
)
copy
(
contrib_inference_lib DEPS paddle_inference_api
SRCS
${
PADDLE_SOURCE_DIR
}
/paddle/contrib/inference/paddle_inference_api.h
${
PADDLE_BINARY_DIR
}
/paddle/contrib/inference/libpaddle_inference_api.*
DSTS
${
contrib_dst_dir
}
${
contrib_dst_dir
}
)
endif
()
set
(
module
"platform"
)
copy
(
platform_lib DEPS profiler_py_proto
SRCS
${
src_dir
}
/
${
module
}
/*.h
${
src_dir
}
/
${
module
}
/dynload/*.h
${
src_dir
}
/
${
module
}
/details/*.h
...
...
doc/fluid/api/layers.rst
浏览文件 @
45f18b00
...
...
@@ -342,6 +342,12 @@ conv2d
.. autofunction:: paddle.fluid.layers.conv2d
:noindex:
conv3d
------
.. autofunction:: paddle.fluid.layers.conv3d
:noindex:
sequence_pool
-------------
...
...
@@ -366,6 +372,12 @@ pool2d
.. autofunction:: paddle.fluid.layers.pool2d
:noindex:
pool3d
------
.. autofunction:: paddle.fluid.layers.pool3d
:noindex:
batch_norm
----------
...
...
@@ -384,6 +396,13 @@ conv2d_transpose
.. autofunction:: paddle.fluid.layers.conv2d_transpose
:noindex:
conv3d_transpose
----------------
.. autofunction:: paddle.fluid.layers.conv2d_transpose
:noindex:
sequence_expand
---------------
...
...
paddle/fluid/operators/concat_op.cc
浏览文件 @
45f18b00
...
...
@@ -107,7 +107,13 @@ REGISTER_OPERATOR(concat, ops::ConcatOp, ops::ConcatOpMaker,
false
>
/* set false to disable empty grad */
);
REGISTER_OPERATOR
(
concat_grad
,
ops
::
ConcatOpGrad
);
REGISTER_OP_CPU_KERNEL
(
concat
,
ops
::
ConcatKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
concat
,
ops
::
ConcatKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
ConcatKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ConcatKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
,
ops
::
ConcatKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
);
REGISTER_OP_CPU_KERNEL
(
concat_grad
,
ops
::
ConcatGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
ops
::
ConcatGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
ConcatGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ConcatGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
,
ops
::
ConcatGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
);
paddle/fluid/operators/concat_op.cu.cc
浏览文件 @
45f18b00
...
...
@@ -15,7 +15,13 @@ limitations under the License. */
#include "paddle/fluid/operators/concat_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
concat
,
ops
::
ConcatKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
concat
,
ops
::
ConcatKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
ConcatKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ConcatKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
,
ops
::
ConcatKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
);
REGISTER_OP_CUDA_KERNEL
(
concat_grad
,
ops
::
ConcatGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
ops
::
ConcatGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
ConcatGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ConcatGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
,
ops
::
ConcatGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
);
paddle/fluid/operators/get_places_op.cc
浏览文件 @
45f18b00
...
...
@@ -85,7 +85,7 @@ class GetPlacesOpProtoMaker : public framework::OpProtoAndCheckerMaker {
.
InEnum
({
"CUDA"
,
"CPU"
,
"AUTO"
})
.
SetDefault
(
"AUTO"
);
AddComment
(
R"DOC(
Returns a list of places based on
flag
s. The list will be used for parallel
Returns a list of places based on
argument
s. The list will be used for parallel
execution.
)DOC"
);
}
...
...
paddle/fluid/operators/split_op.cc
浏览文件 @
45f18b00
...
...
@@ -115,4 +115,7 @@ USE_CPU_ONLY_OP(concat);
REGISTER_OPERATOR
(
split
,
ops
::
SplitOp
,
ops
::
SplitOpMaker
,
ops
::
SplitGradMaker
);
REGISTER_OP_CPU_KERNEL
(
split
,
ops
::
SplitOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
SplitOpKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
,
ops
::
SplitOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
SplitOpKernel
<
paddle
::
platform
::
CPUPlace
,
int64_t
>
,
ops
::
SplitOpKernel
<
paddle
::
platform
::
CPUPlace
,
int
>
);
paddle/fluid/operators/split_op.cu.cc
浏览文件 @
45f18b00
...
...
@@ -15,4 +15,7 @@ limitations under the License. */
#include "paddle/fluid/operators/split_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
split
,
ops
::
SplitOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
split
,
ops
::
SplitOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SplitOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SplitOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
,
ops
::
SplitOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
);
python/paddle/fluid/layers/control_flow.py
浏览文件 @
45f18b00
...
...
@@ -1210,6 +1210,34 @@ class IfElseBlockGuard(object):
class
IfElse
(
object
):
"""
if-else control flow.
Args:
cond (Variable): condition used to compare.
name (str, default None): The name of this layer.
Examples:
.. code-block:: python
limit = fluid.layers.fill_constant_batch_size_like(
input=label, dtype='int64', shape=[1], value=5.0)
cond = fluid.layers.less_than(x=label, y=limit)
ie = fluid.layers.IfElse(cond)
with ie.true_block():
true_image = ie.input(image)
hidden = fluid.layers.fc(input=true_image, size=100, act='tanh')
prob = fluid.layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
with ie.false_block():
false_image = ie.input(image)
hidden = fluid.layers.fc(
input=false_image, size=200, act='tanh')
prob = fluid.layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
prob = ie()
"""
OUT_IF_ELSE_BLOCKS
=
0
IN_IF_ELSE_TRUE_BLOCKS
=
1
IN_IF_ELSE_FALSE_BLOCKS
=
2
...
...
python/paddle/fluid/layers/io.py
浏览文件 @
45f18b00
...
...
@@ -587,6 +587,26 @@ def read_file(file_obj):
class
Preprocessor
(
object
):
"""
A block for data pre-processing in reader.
Args:
reader (Variable): A reader variable.
name (str, default None): The name of the reader.
Examples:
.. code-block:: python
preprocessor = fluid.layers.io.Preprocessor(reader=reader)
with preprocessor.block():
img, lbl = preprocessor.inputs()
img_out = img / 2
lbl_out = lbl + 1
preprocessor.outputs(img_out, lbl_out)
data_file = fluid.layers.io.double_buffer(preprocessor())
"""
BEFORE_SUB_BLOCK
=
0
IN_SUB_BLOCK
=
1
AFTER_SUB_BLOCK
=
2
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
45f18b00
...
...
@@ -25,20 +25,72 @@ import utils
import
random
__all__
=
[
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'dynamic_lstmp'
,
'dynamic_gru'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'sequence_pool'
,
'sequence_softmax'
,
'softmax'
,
'pool2d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'sequence_expand'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'reduce_prod'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
,
'ctc_greedy_decoder'
,
'edit_distance'
,
'l2_normalize'
,
'matmul'
,
'topk'
,
'warpctc'
,
'sequence_reshape'
,
'transpose'
,
'im2sequence'
,
'nce'
,
'beam_search'
,
'row_conv'
,
'multiplex'
,
'layer_norm'
,
'softmax_with_cross_entropy'
,
'smooth_l1'
,
'one_hot'
,
'autoincreased_step_counter'
,
'reshape'
,
'lod_reset'
,
'lrn'
,
'pad'
,
'label_smooth'
,
'roi_pool'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'gather'
,
'random_crop'
,
'mean_iou'
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'dynamic_lstmp'
,
'dynamic_gru'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'conv3d'
,
'sequence_pool'
,
'sequence_softmax'
,
'softmax'
,
'pool2d'
,
'pool3d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'conv3d_transpose'
,
'sequence_expand'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'reduce_prod'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
,
'ctc_greedy_decoder'
,
'edit_distance'
,
'l2_normalize'
,
'matmul'
,
'topk'
,
'warpctc'
,
'sequence_reshape'
,
'transpose'
,
'im2sequence'
,
'nce'
,
'beam_search'
,
'row_conv'
,
'multiplex'
,
'layer_norm'
,
'softmax_with_cross_entropy'
,
'smooth_l1'
,
'one_hot'
,
'autoincreased_step_counter'
,
'reshape'
,
'lod_reset'
,
'lrn'
,
'pad'
,
'label_smooth'
,
'roi_pool'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'gather'
,
'random_crop'
,
'mean_iou'
,
]
...
...
@@ -1275,8 +1327,6 @@ def conv2d(input,
conv2d = fluid.layers.conv2d(
input=data, num_filters=2, filter_size=3, act="relu")
"""
if
stride
is
None
:
stride
=
[
1
,
1
]
num_channels
=
input
.
shape
[
1
]
...
...
@@ -1339,6 +1389,171 @@ def conv2d(input,
return
helper
.
append_activation
(
pre_act
)
def
conv3d
(
input
,
num_filters
,
filter_size
,
stride
=
1
,
padding
=
0
,
dilation
=
1
,
groups
=
None
,
param_attr
=
None
,
bias_attr
=
None
,
use_cudnn
=
True
,
use_mkldnn
=
False
,
act
=
None
,
name
=
None
):
"""
**Convlution3D Layer**
The convolution3D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and
Output(Output) are in NCDHW format. Where N is batch size C is the number of
channels, D is the depth of the feature, H is the height of the feature,
and W is the width of the feature. Convlution3D is similar with Convlution2D
but adds one dimension(depth). If bias attribution and activation type are
provided, bias is added to the output of the convolution, and the
corresponding activation function is applied to the final result.
For each input :math:`X`, the equation is:
.. math::
Out = \sigma (W
\\
ast X + b)
In the above equation:
* :math:`X`: Input value, a tensor with NCDHW format.
* :math:`W`: Filter value, a tensor with MCDHW format.
* :math:`
\\
ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:`
\\
sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
Example:
- Input:
Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)`
- Output:
Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
Where
.. math::
D_{out}&=
\\
frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1
\\\\
H_{out}&=
\\
frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1
\\\\
W_{out}&=
\\
frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
Args:
input (Variable): The input image with [N, C, D, H, W] format.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride (int|tuple): The stride size. If stride is a tuple, it must
contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
stride_D = stride_H = stride_W = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must
contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
padding_D = padding_H = padding_W = padding. Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
groups (int): The groups number of the Conv3d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1
param_attr (ParamAttr): The parameters to the Conv3d Layer. Default: None
bias_attr (ParamAttr): Bias parameter for the Conv3d layer. Default: None
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not.
act (str): Activation type. Default: None
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution and
\
non-linearity activation result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 12, 32, 32], dtype='float32')
conv2d = fluid.layers.conv3d(
input=data, num_filters=2, filter_size=3, act="relu")
"""
l_type
=
'conv3d'
helper
=
LayerHelper
(
l_type
,
**
locals
())
dtype
=
helper
.
input_dtype
()
num_channels
=
input
.
shape
[
1
]
if
groups
is
None
:
num_filter_channels
=
num_channels
else
:
if
num_channels
%
groups
!=
0
:
raise
ValueError
(
"num_channels must be divisible by groups."
)
num_filter_channels
=
num_channels
/
groups
filter_size
=
utils
.
convert_to_list
(
filter_size
,
3
,
'filter_size'
)
stride
=
utils
.
convert_to_list
(
stride
,
3
,
'stride'
)
padding
=
utils
.
convert_to_list
(
padding
,
3
,
'padding'
)
dilation
=
utils
.
convert_to_list
(
dilation
,
3
,
'dilation'
)
if
not
isinstance
(
use_cudnn
,
bool
):
raise
ValueError
(
"use_cudnn should be True or False"
)
input_shape
=
input
.
shape
filter_shape
=
[
num_filters
,
num_filter_channels
]
+
filter_size
def
_get_default_param_initializer
():
std
=
(
2.0
/
(
filter_size
[
0
]
**
3
*
num_channels
))
**
0.5
return
Normal
(
0.0
,
std
,
0
)
filter_param
=
helper
.
create_parameter
(
attr
=
helper
.
param_attr
,
shape
=
filter_shape
,
dtype
=
dtype
,
default_initializer
=
_get_default_param_initializer
())
pre_bias
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
l_type
,
inputs
=
{
'Input'
:
input
,
'Filter'
:
filter_param
,
},
outputs
=
{
"Output"
:
pre_bias
},
attrs
=
{
'strides'
:
stride
,
'paddings'
:
padding
,
'dilations'
:
dilation
,
'groups'
:
groups
,
'use_cudnn'
:
use_cudnn
,
'use_mkldnn'
:
use_mkldnn
})
pre_act
=
helper
.
append_bias_op
(
pre_bias
,
dim_start
=
1
,
dim_end
=
2
)
return
helper
.
append_activation
(
pre_act
)
def
sequence_pool
(
input
,
pool_type
):
"""
This function add the operator for sequence pooling.
...
...
@@ -1526,12 +1741,84 @@ def pool2d(input,
if
not
isinstance
(
use_cudnn
,
bool
):
raise
ValueError
(
"use_cudnn should be True or False"
)
helper
=
LayerHelper
(
'pool2d'
,
**
locals
())
l_type
=
'pool2d'
helper
=
LayerHelper
(
l_type
,
**
locals
())
dtype
=
helper
.
input_dtype
()
pool_out
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"pool2d"
,
type
=
l_type
,
inputs
=
{
"X"
:
input
},
outputs
=
{
"Out"
:
pool_out
},
attrs
=
{
"pooling_type"
:
pool_type
,
"ksize"
:
pool_size
,
"global_pooling"
:
global_pooling
,
"strides"
:
pool_stride
,
"paddings"
:
pool_padding
,
"use_cudnn"
:
use_cudnn
,
"ceil_mode"
:
ceil_mode
,
"use_mkldnn"
:
use_mkldnn
})
return
pool_out
def
pool3d
(
input
,
pool_size
=-
1
,
pool_type
=
"max"
,
pool_stride
=
1
,
pool_padding
=
0
,
global_pooling
=
False
,
use_cudnn
=
True
,
ceil_mode
=
False
,
use_mkldnn
=
False
,
name
=
None
):
"""
This function adds the operator for pooling in 3-dimensions, using the
pooling configurations mentioned in input parameters.
Args:
input (Variable): ${input_comment}
pool_size (int): ${ksize_comment}
pool_type (str): ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
use_mkldnn (bool): ${use_mkldnn_comment}
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: output of pool3d layer.
"""
if
pool_type
not
in
[
"max"
,
"avg"
]:
raise
ValueError
(
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'."
,
str
(
pool_type
))
if
global_pooling
is
False
and
pool_size
==
-
1
:
raise
ValueError
(
"When the global_pooling is False, pool_size must be passed "
"and be a valid value. Received pool_size: "
+
str
(
pool_size
))
pool_size
=
utils
.
convert_to_list
(
pool_size
,
3
,
'pool_size'
)
pool_padding
=
utils
.
convert_to_list
(
pool_padding
,
3
,
'pool_padding'
)
pool_stride
=
utils
.
convert_to_list
(
pool_stride
,
3
,
'pool_stride'
)
if
not
isinstance
(
use_cudnn
,
bool
):
raise
ValueError
(
"use_cudnn should be True or False"
)
l_type
=
"pool3d"
helper
=
LayerHelper
(
l_type
,
**
locals
())
dtype
=
helper
.
input_dtype
()
pool_out
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
l_type
,
inputs
=
{
"X"
:
input
},
outputs
=
{
"Out"
:
pool_out
},
attrs
=
{
...
...
@@ -1952,6 +2239,173 @@ def conv2d_transpose(input,
return
out
def
conv3d_transpose
(
input
,
num_filters
,
output_size
=
None
,
filter_size
=
None
,
padding
=
0
,
stride
=
1
,
dilation
=
1
,
groups
=
None
,
param_attr
=
None
,
bias_attr
=
None
,
use_cudnn
=
True
,
act
=
None
,
name
=
None
):
"""
**Convlution3D transpose layer**
The convolution3D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
are in NCDHW format. Where N is batch size, C is the number of channels,
D is the depth of the feature, H is the height of the feature, and W
is the width of the feature. Parameters(dilations, strides, paddings) are
two elements. These two elements represent height and width, respectively.
The details of convolution transpose layer, please refer to the following
explanation and references `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
For each input :math:`X`, the equation is:
.. math::
Out = W
\\
ast X
In the above equation:
* :math:`X`: Input value, a tensor with NCDHW format.
* :math:`W`: Filter value, a tensor with MCDHW format.
* :math:`
\\
ast` : Convolution transpose operation.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
Example:
- Input:
Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$
- Output:
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
Where
.. math::
D_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1
\\\\
H_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1
\\\\
W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
Args:
input(Variable): The input image with [N, C, D, H, W] format.
num_filters(int): The number of the filter. It is as same as the output
image channel.
output_size(int|tuple|None): The output image size. If output size is a
tuple, it must contain three integers, (image_D, image_H, image_W). This
parameter only works when filter_size is None.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
Otherwise, the filter will be a square. None if use output size to
calculate filter_size.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
padding_D = padding_H = padding_W = padding. Default: padding = 0.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
stride_D = stride_H = stride_W = stride. Default: stride = 1.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv3d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
when group=2, the first half of the filters is only connected to the
first half of the input channels, while the second half of the
filters is only connected to the second half of the input channels.
Default: groups=1
param_attr(ParamAttr): The parameters to the Conv3d_transpose Layer.
Default: None
bias_attr(ParamAttr): Bias parameter for the Conv3d layer. Default: None
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act(str): Activation type. Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution transpose result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 12, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv3d_transpose(
input=data, num_filters=2, filter_size=3)
"""
l_type
=
"conv3d_transpose"
helper
=
LayerHelper
(
l_type
,
**
locals
())
if
not
isinstance
(
input
,
Variable
):
raise
TypeError
(
"Input of conv3d_transpose must be Variable"
)
input_channel
=
input
.
shape
[
1
]
padding
=
utils
.
convert_to_list
(
padding
,
3
,
'padding'
)
stride
=
utils
.
convert_to_list
(
stride
,
3
,
'stride'
)
dilation
=
utils
.
convert_to_list
(
dilation
,
3
,
'dilation'
)
if
not
isinstance
(
use_cudnn
,
bool
):
raise
ValueError
(
"use_cudnn should be True or False"
)
if
filter_size
is
None
:
if
output_size
is
None
:
raise
ValueError
(
"output_size must be set when filter_size is None"
)
if
isinstance
(
output_size
,
int
):
output_size
=
[
output_size
,
output_size
]
d_in
=
input
.
shape
[
2
]
h_in
=
input
.
shape
[
3
]
w_in
=
input
.
shape
[
4
]
filter_size_d
=
(
output_size
[
0
]
-
(
d_in
-
1
)
*
stride
[
0
]
+
2
*
padding
[
0
]
-
1
)
/
dilation
[
0
]
+
1
filter_size_h
=
(
output_size
[
1
]
-
(
h_in
-
1
)
*
stride
[
1
]
+
2
*
padding
[
1
]
-
1
)
/
dilation
[
1
]
+
1
filter_size_w
=
(
output_size
[
2
]
-
(
w_in
-
1
)
*
stride
[
2
]
+
2
*
padding
[
2
]
-
1
)
/
dilation
[
2
]
+
1
filter_size
=
[
filter_size_d
,
filter_size_h
,
filter_size_w
]
else
:
filter_size
=
utils
.
convert_to_list
(
filter_size
,
3
,
'conv3d_transpose.filter_size'
)
groups
=
1
if
groups
is
None
else
groups
filter_shape
=
[
input_channel
,
num_filters
/
groups
]
+
filter_size
img_filter
=
helper
.
create_parameter
(
dtype
=
input
.
dtype
,
shape
=
filter_shape
,
attr
=
helper
.
param_attr
)
pre_bias
=
helper
.
create_tmp_variable
(
dtype
=
input
.
dtype
)
helper
.
append_op
(
type
=
l_type
,
inputs
=
{
'Input'
:
[
input
],
'Filter'
:
[
img_filter
]},
outputs
=
{
'Output'
:
pre_bias
},
attrs
=
{
'strides'
:
stride
,
'paddings'
:
padding
,
'dilations'
:
dilation
,
'groups'
:
groups
,
'use_cudnn'
:
use_cudnn
})
pre_act
=
helper
.
append_bias_op
(
pre_bias
,
dim_start
=
1
,
dim_end
=
2
)
out
=
helper
.
append_activation
(
pre_act
)
return
out
def
sequence_expand
(
x
,
y
,
ref_level
=-
1
,
name
=
None
):
"""Sequence Expand Layer. This layer will expand the input variable **x**
according to specified level lod of **y**. Please note that lod level of
...
...
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