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938319bb
编写于
7月 12, 2018
作者:
C
chenweihang
提交者:
GitHub
7月 12, 2018
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' into unsqueeze_op
上级
b8ea7a08
092d6201
变更
28
隐藏空白更改
内联
并排
Showing
28 changed file
with
948 addition
and
219 deletion
+948
-219
paddle/fluid/inference/analysis/data_flow_graph.cc
paddle/fluid/inference/analysis/data_flow_graph.cc
+85
-1
paddle/fluid/inference/analysis/data_flow_graph.h
paddle/fluid/inference/analysis/data_flow_graph.h
+36
-0
paddle/fluid/inference/analysis/data_flow_graph_tester.cc
paddle/fluid/inference/analysis/data_flow_graph_tester.cc
+67
-2
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+1
-0
paddle/fluid/operators/detection/rpn_target_assign_op.cc
paddle/fluid/operators/detection/rpn_target_assign_op.cc
+3
-2
paddle/fluid/operators/im2sequence_op.cc
paddle/fluid/operators/im2sequence_op.cc
+18
-7
paddle/fluid/operators/im2sequence_op.h
paddle/fluid/operators/im2sequence_op.h
+91
-34
paddle/fluid/operators/math/im2col.cc
paddle/fluid/operators/math/im2col.cc
+0
-26
paddle/fluid/operators/math/im2col.cu
paddle/fluid/operators/math/im2col.cu
+0
-30
paddle/fluid/operators/squeeze_op.cc
paddle/fluid/operators/squeeze_op.cc
+202
-0
paddle/fluid/platform/CMakeLists.txt
paddle/fluid/platform/CMakeLists.txt
+1
-1
python/paddle/fluid/annotations.py
python/paddle/fluid/annotations.py
+38
-0
python/paddle/fluid/backward.py
python/paddle/fluid/backward.py
+40
-25
python/paddle/fluid/layers/device.py
python/paddle/fluid/layers/device.py
+3
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+40
-12
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+12
-12
python/paddle/fluid/tests/book/notest_understand_sentiment.py
...on/paddle/fluid/tests/book/notest_understand_sentiment.py
+2
-2
python/paddle/fluid/tests/book/test_recognize_digits.py
python/paddle/fluid/tests/book/test_recognize_digits.py
+10
-8
python/paddle/fluid/tests/book/test_word2vec.py
python/paddle/fluid/tests/book/test_word2vec.py
+2
-1
python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py
.../tests/book_memory_optimization/test_memopt_fit_a_line.py
+5
-4
python/paddle/fluid/tests/unittests/test_calc_gradient.py
python/paddle/fluid/tests/unittests/test_calc_gradient.py
+0
-2
python/paddle/fluid/tests/unittests/test_get_places_op.py
python/paddle/fluid/tests/unittests/test_get_places_op.py
+2
-1
python/paddle/fluid/tests/unittests/test_im2sequence_op.py
python/paddle/fluid/tests/unittests/test_im2sequence_op.py
+147
-33
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+8
-3
python/paddle/fluid/tests/unittests/test_optimizer.py
python/paddle/fluid/tests/unittests/test_optimizer.py
+11
-11
python/paddle/fluid/tests/unittests/test_parallel_op.py
python/paddle/fluid/tests/unittests/test_parallel_op.py
+2
-1
python/paddle/fluid/tests/unittests/test_squeeze_op.py
python/paddle/fluid/tests/unittests/test_squeeze_op.py
+114
-0
python/setup.py.in
python/setup.py.in
+8
-0
未找到文件。
paddle/fluid/inference/analysis/data_flow_graph.cc
浏览文件 @
938319bb
...
...
@@ -90,6 +90,20 @@ std::string DataFlowGraph::DotString() const {
return
dot
.
Build
();
}
std
::
string
DataFlowGraph
::
HumanReadableInfo
(
bool
show_values
,
bool
show_functions
)
const
{
std
::
stringstream
values
,
functions
;
for
(
auto
&
n
:
nodes
.
nodes
())
{
if
(
show_values
&&
n
->
IsValue
())
{
values
<<
n
->
repr
()
<<
"
\n
"
;
}
if
(
show_functions
&&
n
->
IsFunction
())
{
functions
<<
n
->
repr
()
<<
"
\n
"
;
}
}
return
"Values:
\n
"
+
values
.
str
()
+
"
\n\n
"
+
"Functions:
\n
"
+
functions
.
str
();
}
//
// NodesBFSIterator
//
...
...
@@ -146,7 +160,7 @@ bool GraphTraits<DataFlowGraph>::NodesBFSIterator::operator==(
if
((
!
queue_
.
empty
())
&&
(
!
other
.
queue_
.
empty
()))
{
return
queue_
.
front
()
==
other
.
queue_
.
front
()
&&
visited_
.
size
()
==
other
.
visited_
.
size
();
// here need to check the
// equality of queue and
// equality of queue and
// visited. Just a light but week implementation.
}
return
false
;
...
...
@@ -208,6 +222,76 @@ Node *GraphTraits<DataFlowGraph>::NodesDFSIterator::operator->() {
return
stack_
.
top
();
}
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
::
NodesTSIterator
(
const
std
::
vector
<
Node
*>
&
source
)
{
PADDLE_ENFORCE
(
!
source
.
empty
(),
"Start points of topological sorting should not be empty!"
);
std
::
unordered_set
<
Node
*>
visited
;
std
::
unordered_set
<
Node
*>
to_visit
{
source
.
begin
(),
source
.
end
()};
std
::
vector
<
Node
*>
inlink_visited
;
while
(
!
to_visit
.
empty
())
{
std
::
vector
<
Node
*>
queue
(
to_visit
.
begin
(),
to_visit
.
end
());
for
(
auto
*
p
:
queue
)
{
inlink_visited
.
clear
();
std
::
copy_if
(
p
->
inlinks
.
begin
(),
p
->
inlinks
.
end
(),
std
::
back_inserter
(
inlink_visited
),
[
&
](
Node
*
x
)
{
return
visited
.
count
(
x
);
});
if
(
inlink_visited
.
size
()
==
p
->
inlinks
.
size
())
{
sorted_
.
push_back
(
p
);
for
(
auto
*
_
:
p
->
outlinks
)
{
if
(
!
visited
.
count
(
_
))
{
to_visit
.
insert
(
_
);
}
}
to_visit
.
erase
(
p
);
visited
.
insert
(
p
);
}
}
}
}
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
::
NodesTSIterator
(
const
paddle
::
inference
::
analysis
::
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
&
other
)
:
sorted_
(
other
.
sorted_
),
cursor_
(
other
.
cursor_
)
{}
Node
&
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
::
operator
*
()
{
PADDLE_ENFORCE_LT
(
cursor_
,
sorted_
.
size
());
return
*
sorted_
[
cursor_
];
}
paddle
::
inference
::
analysis
::
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
&
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
::
operator
++
()
{
if
(
++
cursor_
>=
sorted_
.
size
())
{
sorted_
.
clear
();
cursor_
=
0
;
}
return
*
this
;
}
paddle
::
inference
::
analysis
::
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
&
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
::
operator
=
(
const
paddle
::
inference
::
analysis
::
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
&
other
)
{
cursor_
=
other
.
cursor_
;
sorted_
=
other
.
sorted_
;
return
*
this
;
}
bool
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
::
operator
==
(
const
paddle
::
inference
::
analysis
::
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
&
other
)
{
return
sorted_
==
other
.
sorted_
&&
cursor_
==
other
.
cursor_
;
}
Node
*
GraphTraits
<
DataFlowGraph
>::
NodesTSIterator
::
operator
->
()
{
PADDLE_ENFORCE_LT
(
cursor_
,
sorted_
.
size
());
return
sorted_
[
cursor_
];
}
}
// namespace analysis
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/analysis/data_flow_graph.h
浏览文件 @
938319bb
...
...
@@ -48,6 +48,9 @@ struct DataFlowGraph {
// Output a DOT graph file for debug.
std
::
string
DotString
()
const
;
std
::
string
HumanReadableInfo
(
bool
show_values
=
true
,
bool
show_functions
=
true
)
const
;
private:
// Remove duplicate edges and so on.
void
Clean
();
...
...
@@ -107,6 +110,32 @@ struct GraphTraits<DataFlowGraph> {
std
::
unordered_set
<
Node
*>
visited_
;
};
// Topological sorting iterator on nodes.
struct
NodesTSIterator
:
public
std
::
iterator
<
std
::
forward_iterator_tag
,
Node
*>
{
NodesTSIterator
()
=
default
;
explicit
NodesTSIterator
(
const
std
::
vector
<
Node
*>
&
source
);
NodesTSIterator
(
NodesTSIterator
&&
other
)
:
sorted_
(
std
::
move
(
other
.
sorted_
)),
cursor_
(
other
.
cursor_
)
{
other
.
cursor_
=
0
;
}
NodesTSIterator
(
const
NodesTSIterator
&
other
);
Node
&
operator
*
();
NodesTSIterator
&
operator
++
();
// TODO(Superjomn) current implementation just compare the first
// element, need to compare the graph and all the elements in the queue and
// set.
NodesTSIterator
&
operator
=
(
const
NodesTSIterator
&
other
);
bool
operator
==
(
const
NodesTSIterator
&
other
);
bool
operator
!=
(
const
NodesTSIterator
&
other
)
{
return
!
(
*
this
==
other
);
}
Node
*
operator
->
();
private:
std
::
vector
<
Node
*>
sorted_
;
int
cursor_
{
0
};
};
explicit
GraphTraits
(
DataFlowGraph
*
graph
)
:
graph_
(
graph
)
{}
// default use BFS to visit the nodes.
...
...
@@ -119,17 +148,24 @@ struct GraphTraits<DataFlowGraph> {
iterator_range
<
NodesDFSIterator
>
nodes_in_DFS
()
{
return
iterator_range
<
NodesDFSIterator
>
(
nodes_dfs_begin
(),
nodes_dfs_end
());
}
iterator_range
<
NodesTSIterator
>
nodes_in_TS
()
{
return
iterator_range
<
NodesTSIterator
>
(
nodes_ts_begin
(),
nodes_ts_end
());
}
private:
NodesBFSIterator
nodes_bfs_begin
()
{
return
NodesBFSIterator
(
graph_
->
inputs
);
}
NodesBFSIterator
nodes_bfs_end
()
{
return
NodesBFSIterator
();
}
NodesDFSIterator
nodes_dfs_begin
()
{
return
NodesDFSIterator
(
graph_
->
inputs
);
}
NodesDFSIterator
nodes_dfs_end
()
{
return
NodesDFSIterator
();
}
NodesTSIterator
nodes_ts_begin
()
{
return
NodesTSIterator
(
graph_
->
inputs
);
}
NodesTSIterator
nodes_ts_end
()
{
return
NodesTSIterator
();
}
private:
DataFlowGraph
*
graph_
;
};
...
...
paddle/fluid/inference/analysis/data_flow_graph_tester.cc
浏览文件 @
938319bb
...
...
@@ -24,11 +24,11 @@ TEST(DataFlowGraph, BFS) {
auto
dfg
=
ProgramDescToDFG
(
desc
);
dfg
.
Build
();
for
(
auto
*
in
:
dfg
.
inputs
)
{
for
(
auto
*
in
:
dfg
.
inputs
)
{
LOG
(
INFO
)
<<
"inputs: "
<<
in
->
name
()
<<
" "
<<
static_cast
<
int
>
(
in
->
type
());
}
for
(
auto
*
out
:
dfg
.
outputs
)
{
for
(
auto
*
out
:
dfg
.
outputs
)
{
LOG
(
INFO
)
<<
"outputs: "
<<
out
->
name
()
<<
" "
<<
static_cast
<
int
>
(
out
->
type
());
}
...
...
@@ -57,6 +57,71 @@ TEST(DataFlowGraph, DFS) {
ASSERT_EQ
(
count
,
dfg
.
nodes
.
size
());
}
// Topological sorting.
/*
* Graph topology
* inputs: 0, 1, 2
* 0 -> 4
* 0 -> 5
* 1 -> 6
* 2 -> 7
* 4 -> 5
* 4 -> 7
* 4 -> 3
* 7 -> 3
*/
TEST
(
DataFlowGraph
,
TS
)
{
DataFlowGraph
graph
;
for
(
int
i
=
0
;
i
<
8
;
i
++
)
{
auto
*
node
=
graph
.
nodes
.
Create
(
Node
::
Type
::
kValue
);
node
->
SetName
(
"node-"
+
std
::
to_string
(
i
));
}
auto
add_link
=
[
&
](
int
i
,
int
j
)
{
Node
*
source
=
graph
.
nodes
.
GetMutable
(
i
);
Node
*
target
=
graph
.
nodes
.
GetMutable
(
j
);
target
->
inlinks
.
push_back
(
source
);
source
->
outlinks
.
push_back
(
target
);
};
graph
.
inputs
.
push_back
(
graph
.
nodes
.
GetMutable
(
0
));
graph
.
inputs
.
push_back
(
graph
.
nodes
.
GetMutable
(
1
));
graph
.
inputs
.
push_back
(
graph
.
nodes
.
GetMutable
(
2
));
add_link
(
0
,
4
);
add_link
(
0
,
5
);
add_link
(
1
,
6
);
add_link
(
2
,
7
);
add_link
(
4
,
5
);
add_link
(
4
,
7
);
add_link
(
4
,
3
);
add_link
(
7
,
3
);
auto
its
=
GraphTraits
<
DataFlowGraph
>
(
&
graph
).
nodes_in_TS
();
std
::
vector
<
int
>
sorted_ids
;
for
(
auto
it
=
its
.
begin
();
it
!=
its
.
end
();
++
it
)
{
LOG
(
INFO
)
<<
it
->
name
();
sorted_ids
.
push_back
(
it
->
id
());
}
// Assert a occurs prior to b in the sorted_ids.
auto
assert_positive_sequence_pair
=
[
&
](
int
a
,
int
b
)
{
auto
a_offset
=
std
::
find
(
sorted_ids
.
begin
(),
sorted_ids
.
end
(),
a
);
auto
b_offset
=
std
::
find
(
sorted_ids
.
begin
(),
sorted_ids
.
end
(),
b
);
ASSERT_LT
(
a_offset
,
b_offset
);
};
assert_positive_sequence_pair
(
2
,
7
);
assert_positive_sequence_pair
(
7
,
3
);
assert_positive_sequence_pair
(
4
,
3
);
assert_positive_sequence_pair
(
0
,
4
);
assert_positive_sequence_pair
(
0
,
5
);
assert_positive_sequence_pair
(
1
,
6
);
assert_positive_sequence_pair
(
4
,
5
);
assert_positive_sequence_pair
(
4
,
7
);
}
}
// namespace analysis
}
// namespace inference
}
// namespace paddle
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
938319bb
...
...
@@ -266,6 +266,7 @@ op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
op_library
(
cos_sim_op DEPS cos_sim_functor
)
op_library
(
parallel_do_op DEPS executor
)
op_library
(
unsqueeze_op DEPS reshape_op
)
op_library
(
squeeze_op DEPS reshape_op
)
if
(
WITH_GPU
)
op_library
(
conv_op DEPS vol2col depthwise_conv im2col
)
...
...
paddle/fluid/operators/detection/rpn_target_assign_op.cc
浏览文件 @
938319bb
...
...
@@ -86,8 +86,9 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
std
::
minstd_rand
engine
,
std
::
vector
<
int
>*
inds
)
const
{
std
::
uniform_real_distribution
<
float
>
uniform
(
0
,
1
);
if
(
inds
->
size
()
>
num
)
{
for
(
int
i
=
num
;
i
<
inds
->
size
();
++
i
)
{
const
int64_t
size
=
static_cast
<
int64_t
>
(
inds
->
size
());
if
(
size
>
num
)
{
for
(
int64_t
i
=
num
;
i
<
size
;
++
i
)
{
int
rng_ind
=
std
::
floor
(
uniform
(
engine
)
*
i
);
if
(
rng_ind
<
num
)
std
::
iter_swap
(
inds
->
begin
()
+
rng_ind
+
offset
,
...
...
paddle/fluid/operators/im2sequence_op.cc
浏览文件 @
938319bb
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/im2sequence_op.h"
#include <string>
#include <vector>
namespace
paddle
{
...
...
@@ -28,20 +29,19 @@ class Im2SequenceOp : public framework::OperatorWithKernel {
"Input(X) of Im2SequenceOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of Im2SequenceOp op should not be null."
);
auto
in_dim
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_EQ
(
in_dim
.
size
(),
4
,
"Input(X) format must be 4D tensor, eg., NCHW."
);
auto
kernels
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"kernels"
);
auto
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
auto
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
int
batch_size
=
in_dim
[
0
];
int
img_channels
=
in_dim
[
1
];
int
img_height
=
in_dim
[
2
];
int
img_width
=
in_dim
[
3
];
auto
kernels
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"kernels"
);
auto
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
auto
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
int
output_height
=
Im2SeqOutputSize
(
img_height
,
kernels
[
0
],
paddings
[
0
],
paddings
[
2
],
strides
[
0
]);
int
output_width
=
Im2SeqOutputSize
(
img_width
,
kernels
[
1
],
paddings
[
1
],
...
...
@@ -61,6 +61,10 @@ class Im2SequenceOpMaker : public framework::OpProtoAndCheckerMaker {
"C: channels"
"H: height"
"W: width"
);
AddInput
(
"Y"
,
"(Tensor) The input tensor of image real size(H, W)."
"2-D with shape [batchsize, 2]"
)
.
AsDispensable
();
AddOutput
(
"Out"
,
"(LodTensor) The output data of im2sequence op,"
);
AddAttr
<
std
::
vector
<
int
>>
(
"kernels"
,
"(vector<int>), the "
...
...
@@ -73,6 +77,13 @@ class Im2SequenceOpMaker : public framework::OpProtoAndCheckerMaker {
"(vector<int> default:{0, 0, 0, 0}), the "
"paddings(up_pad, left_pad, down_pad, right_pad)"
)
.
SetDefault
({
0
,
0
,
0
,
0
});
AddAttr
<
std
::
vector
<
int
>>
(
"out_stride"
,
"the attribute is valid only when input(Y)"
"is not NULL.this attribute represents the"
"scaling of the pic through the CNN"
"(vector<int> dedault:{1,1}),the out_stride"
" (out_stride_height, out_stride_width)"
)
.
SetDefault
({
1
,
1
});
AddComment
(
R"DOC(
This op uses kernels to scan images and converts these images to sequences.
After expanding, The number of time steps are output_height * output_width
...
...
@@ -123,7 +134,7 @@ output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.]
[ 7. 1. 7. 9. 2. 1. 3. 5.]
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8,
9
}
output.dims = {8,
8
}
output.lod = [[0, 4, 8]]
)DOC"
);
...
...
paddle/fluid/operators/im2sequence_op.h
浏览文件 @
938319bb
...
...
@@ -13,6 +13,7 @@
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/eigen.h"
...
...
@@ -39,50 +40,106 @@ class Im2SequenceKernel : public framework::OpKernel<T> {
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
Tensor
*
in
=
ctx
.
Input
<
Tensor
>
(
"X"
);
LoDTensor
*
out
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
// TODO(wanghaoshuang): Add layout checker after 'set_layout'
// being available for python API
// PADDLE_ENFORCE_EQ(in->layout(), framework::DataLayout::kNCHW,
// "Input(X) layout must be NCHW");
auto
in_dim
=
in
->
dims
();
int
batch_size
=
in_dim
[
0
];
int
img_channels
=
in_dim
[
1
];
int
img_height
=
in_dim
[
2
];
int
img_width
=
in_dim
[
3
];
auto
kernels
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"kernels"
);
auto
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
auto
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
int
output_height
=
Im2SeqOutputSize
(
img_height
,
kernels
[
0
],
paddings
[
0
],
paddings
[
2
],
strides
[
0
]);
int
output_width
=
Im2SeqOutputSize
(
img_width
,
kernels
[
1
],
paddings
[
1
],
paddings
[
3
],
strides
[
1
]);
const
std
::
vector
<
int
>
dilations
({
1
,
1
});
auto
out_dims
=
out
->
dims
();
out
->
Resize
({
batch_size
,
out
->
numel
()
/
batch_size
});
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
const
Tensor
src
=
in
->
Slice
(
i
,
i
+
1
).
Resize
({
img_channels
,
img_height
,
img_width
});
Tensor
dst
=
out
->
Slice
(
i
,
i
+
1
).
Resize
(
{
output_height
,
output_width
,
img_channels
,
kernels
[
0
],
kernels
[
1
]});
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kOCF
,
DeviceContext
,
T
>
f
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
f
(
dev_ctx
,
src
,
dilations
,
strides
,
paddings
,
&
dst
);
}
out
->
Resize
(
out_dims
);
// set lod information
// TODO(wanghaoshuang): Move this to InferShape
framework
::
LoD
lod
(
1
);
lod
[
0
].
reserve
(
batch_size
+
1
);
for
(
int
i
=
0
,
offset
=
0
;
i
<
batch_size
+
1
;
++
i
)
{
if
(
ctx
.
HasInput
(
"Y"
)
&&
batch_size
>
1
)
{
const
Tensor
*
imgrealsize
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
out_stride
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"out_stride"
);
Tensor
cpu_shape_tensor
;
TensorCopySync
(
*
imgrealsize
,
platform
::
CPUPlace
(),
&
cpu_shape_tensor
);
std
::
vector
<
int
>
imgreal_h
;
std
::
vector
<
int
>
imgreal_w
;
std
::
vector
<
int
>
output_height
;
std
::
vector
<
int
>
output_width
;
int
result
=
0
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
int
tmp_real_h
=
static_cast
<
int
>
((
cpu_shape_tensor
.
data
<
T
>
())[
2
*
i
]);
int
tmp_real_w
=
static_cast
<
int
>
((
cpu_shape_tensor
.
data
<
T
>
())[
2
*
i
+
1
]);
if
(
tmp_real_h
%
out_stride
[
0
]
==
0
)
{
tmp_real_h
=
tmp_real_h
/
out_stride
[
0
];
}
else
{
tmp_real_h
=
tmp_real_h
/
out_stride
[
0
]
+
1
;
}
if
(
tmp_real_w
%
out_stride
[
1
]
==
0
)
{
tmp_real_w
=
tmp_real_w
/
out_stride
[
1
];
}
else
{
tmp_real_w
=
tmp_real_w
/
out_stride
[
1
]
+
1
;
}
imgreal_h
.
push_back
(
tmp_real_h
);
imgreal_w
.
push_back
(
tmp_real_w
);
output_height
.
push_back
(
Im2SeqOutputSize
(
imgreal_h
[
i
],
kernels
[
0
],
paddings
[
0
],
paddings
[
2
],
strides
[
0
]));
output_width
.
push_back
(
Im2SeqOutputSize
(
imgreal_w
[
i
],
kernels
[
1
],
paddings
[
1
],
paddings
[
3
],
strides
[
1
]));
result
+=
output_height
[
i
]
*
output_width
[
i
];
}
out
->
mutable_data
<
T
>
({
result
,
img_channels
*
kernels
[
0
]
*
kernels
[
1
]},
ctx
.
GetPlace
());
const
std
::
vector
<
int
>
dilations
({
1
,
1
});
int
offset_out
=
0
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
const
Tensor
src
=
in
->
Slice
(
i
,
i
+
1
).
Resize
({
img_channels
,
img_height
,
img_width
});
Tensor
dst
=
out
->
Slice
(
offset_out
,
offset_out
+
output_height
[
i
]
*
output_width
[
i
])
.
Resize
({
output_height
[
i
],
output_width
[
i
],
img_channels
,
kernels
[
0
],
kernels
[
1
]});
offset_out
+=
output_height
[
i
]
*
output_width
[
i
];
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kOCF
,
DeviceContext
,
T
>
f
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
f
(
dev_ctx
,
src
,
dilations
,
strides
,
paddings
,
&
dst
);
}
framework
::
LoD
lod
(
1
);
lod
[
0
].
reserve
(
batch_size
+
1
);
int
offset
=
0
;
lod
[
0
].
push_back
(
offset
);
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
offset
+=
output_height
[
i
]
*
output_width
[
i
];
lod
[
0
].
push_back
(
offset
);
}
out
->
set_lod
(
lod
);
}
else
{
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
output_height
=
Im2SeqOutputSize
(
img_height
,
kernels
[
0
],
paddings
[
0
],
paddings
[
2
],
strides
[
0
]);
int
output_width
=
Im2SeqOutputSize
(
img_width
,
kernels
[
1
],
paddings
[
1
],
paddings
[
3
],
strides
[
1
]);
const
std
::
vector
<
int
>
dilations
({
1
,
1
});
auto
out_dims
=
out
->
dims
();
out
->
Resize
({
batch_size
,
out
->
numel
()
/
batch_size
});
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
const
Tensor
src
=
in
->
Slice
(
i
,
i
+
1
).
Resize
({
img_channels
,
img_height
,
img_width
});
Tensor
dst
=
out
->
Slice
(
i
,
i
+
1
).
Resize
({
output_height
,
output_width
,
img_channels
,
kernels
[
0
],
kernels
[
1
]});
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kOCF
,
DeviceContext
,
T
>
f
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
f
(
dev_ctx
,
src
,
dilations
,
strides
,
paddings
,
&
dst
);
}
out
->
Resize
(
out_dims
);
framework
::
LoD
lod
(
1
);
lod
[
0
].
reserve
(
batch_size
+
1
);
int
offset
=
0
;
lod
[
0
].
push_back
(
offset
);
offset
+=
output_height
*
output_width
;
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
offset
+=
output_height
*
output_width
;
lod
[
0
].
push_back
(
offset
);
}
out
->
set_lod
(
lod
);
}
out
->
set_lod
(
lod
);
}
};
...
...
paddle/fluid/operators/math/im2col.cc
浏览文件 @
938319bb
...
...
@@ -43,21 +43,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int
col_height
=
col
->
dims
()[
3
];
int
col_width
=
col
->
dims
()[
4
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
[
0
]
+
padding
[
2
]
-
((
dilation
[
0
]
*
(
filter_height
-
1
)
+
1
)))
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding
[
1
]
+
padding
[
3
]
-
((
dilation
[
1
]
*
(
filter_width
-
1
)
+
1
)))
/
stride
[
1
]
+
1
,
col_width
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
int
channels_col
=
im_channels
*
filter_height
*
filter_width
;
const
T
*
im_data
=
im
.
data
<
T
>
();
...
...
@@ -178,17 +163,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
int
col_height
=
col
->
dims
()[
0
];
int
col_width
=
col
->
dims
()[
1
];
PADDLE_ENFORCE_EQ
(
(
im_height
+
padding
[
0
]
+
padding
[
2
]
-
filter_height
)
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
(
(
im_width
+
padding
[
1
]
+
padding
[
3
]
-
filter_width
)
/
stride
[
1
]
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
"inconsistent."
);
const
T
*
im_data
=
im
.
data
<
T
>
();
T
*
col_data
=
col
->
data
<
T
>
();
...
...
paddle/fluid/operators/math/im2col.cu
浏览文件 @
938319bb
...
...
@@ -77,21 +77,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int
col_height
=
col
->
dims
()[
3
];
int
col_width
=
col
->
dims
()[
4
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
[
0
]
+
padding
[
2
]
-
(
dilation
[
0
]
*
(
filter_height
-
1
)
+
1
))
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding
[
1
]
+
padding
[
3
]
-
(
dilation
[
1
]
*
(
filter_width
-
1
)
+
1
))
/
stride
[
1
]
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
"inconsistent."
);
int
num_outputs
=
im_channels
*
col_height
*
col_width
;
int
blocks
=
(
num_outputs
+
1024
-
1
)
/
1024
;
int
block_x
=
512
;
...
...
@@ -274,21 +259,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
int
col_height
=
col
->
dims
()[
0
];
int
col_width
=
col
->
dims
()[
1
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
[
0
]
+
padding
[
2
]
-
(
dilation
[
0
]
*
(
filter_height
-
1
)
+
1
))
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding
[
1
]
+
padding
[
3
]
-
(
dilation
[
1
]
*
(
filter_width
-
1
)
+
1
))
/
stride
[
1
]
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
"inconsistent."
);
int
block_dim_x
=
0
;
int
block_dim_y
=
0
;
if
(
filter_height
<=
4
&&
filter_width
<=
4
)
{
...
...
paddle/fluid/operators/squeeze_op.cc
0 → 100644
浏览文件 @
938319bb
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
class
SqueezeOpInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SqueezeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SqueezeOp should not be null."
);
const
auto
&
x_dims
=
ctx
->
GetInputDim
(
"X"
);
// Check input tensor dims (<6) Eigen limit.
PADDLE_ENFORCE
(
x_dims
.
size
()
<=
6
,
"Invalid dimnesions, the rank of Input(X) "
"should be in the range of [1, 6] (Eigen limit)."
);
const
auto
&
axes
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"axes"
);
for
(
int
a
:
axes
)
{
PADDLE_ENFORCE_LT
(
a
,
x_dims
.
size
(),
"The squeeze axis should be less than input "
"tensor's rank."
);
}
auto
out_dims
=
GetOutputShape
(
axes
,
x_dims
);
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
if
(
x_dims
[
0
]
==
out_dims
[
0
])
{
// Only pass LoD when the first dimension of output and Input(X)
// are the same.
ctx
->
ShareLoD
(
"X"
,
"Out"
);
}
}
static
framework
::
DDim
GetOutputShape
(
const
std
::
vector
<
int
>
squeeze_dims
,
const
framework
::
DDim
&
in_dims
)
{
size_t
num_squeeze_dims
=
squeeze_dims
.
size
();
int
cnt_squeezed_dims
=
0
;
bool
should_squeeze
[
9
]
=
{
false
};
// Determines number of dimensions of output tensor after squeeze.
// Mark and count the dimensions need to be squeezed
if
(
num_squeeze_dims
==
0
)
{
for
(
int
idx
=
0
;
idx
<
in_dims
.
size
();
++
idx
)
{
if
(
in_dims
[
idx
]
==
1
)
{
should_squeeze
[
idx
]
=
true
;
++
cnt_squeezed_dims
;
}
}
}
else
{
for
(
size_t
idx
=
0
;
idx
<
num_squeeze_dims
;
++
idx
)
{
int
current
=
squeeze_dims
[
idx
]
<
0
?
squeeze_dims
[
idx
]
+
in_dims
.
size
()
:
squeeze_dims
[
idx
];
// Check current index, the upper limit has beed checked in line 36.
PADDLE_ENFORCE
(
current
>=
0
,
"Invalid axis, the negative axis is out of range."
);
PADDLE_ENFORCE
(
in_dims
[
current
]
==
1
,
"Invalid axis index, the axis that will be squeezed "
"should be equal to 1."
);
if
(
!
(
should_squeeze
[
current
]))
{
++
cnt_squeezed_dims
;
}
should_squeeze
[
current
]
=
true
;
}
}
// Make output dimensions
std
::
vector
<
int64_t
>
output_shape
(
in_dims
.
size
()
-
cnt_squeezed_dims
,
0
);
for
(
int
in_idx
=
0
,
out_idx
=
0
;
in_idx
<
in_dims
.
size
();
++
in_idx
)
{
if
(
!
should_squeeze
[
in_idx
])
{
output_shape
[
out_idx
++
]
=
in_dims
[
in_idx
];
}
}
return
framework
::
make_ddim
(
output_shape
);
}
};
class
SqueezeOp
:
public
framework
::
OperatorBase
{
public:
using
OperatorBase
::
OperatorBase
;
private:
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
{
auto
&
axes
=
Attr
<
std
::
vector
<
int
>>
(
"axes"
);
auto
x_dims
=
scope
.
FindVar
(
Input
(
"X"
))
->
Get
<
framework
::
LoDTensor
>
().
dims
();
auto
out_dims
=
SqueezeOpInferShape
::
GetOutputShape
(
axes
,
x_dims
);
framework
::
AttributeMap
attrs
;
attrs
[
"shape"
]
=
framework
::
vectorize2int
(
out_dims
);
attrs
[
"inplace"
]
=
Attr
<
bool
>
(
"inplace"
);
// Invoke Reshape Op
auto
reshape_op
=
framework
::
OpRegistry
::
CreateOp
(
"reshape"
,
{{
"X"
,
{
Input
(
"X"
)}},
{
"Shape"
,
{}}},
{{
"Out"
,
{
Output
(
"Out"
)}}},
attrs
);
reshape_op
->
Run
(
scope
,
place
);
}
};
class
SqueezeOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor). The input tensor of squeeze operator."
);
AddOutput
(
"Out"
,
"(Tensor). The output tensor of squeeze operator."
);
AddAttr
<
std
::
vector
<
int
>>
(
"axes"
,
"(std::vector<int>). List of integers,"
" indicating the dimensions to squeeze."
)
.
SetDefault
({});
AddAttr
<
bool
>
(
"inplace"
,
"(default: false) Squeeze the source tensor's shape without "
"memory copy. When Attr(inplace) is set true, the output "
"tensor shares memory with Input(X), otherwise, a new output "
"tensor is created, and its data are copied from Input(x)."
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Squeeze Operator.
Remove single-dimensional entries from the shape of a tensor.
Takes a parameter axes with a list of axes to squeeze.
If axes is not provided, all the single dimensions will be removed from the shape.
If an axis is selected with shape entry not equal to one, an error is raised.
Examples:
Case 1:
Given
X.shape = (1, 3, 1, 5)
and
axes = [0]
we get:
Out.shape = (3, 1, 5)
Case 2:
Given
X.shape = (1, 3, 1, 5)
and
axes = []
we get:
Out.shape = (3, 5)
)DOC"
);
}
};
class
SqueezeGradInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
context
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
context
->
GetInputDim
(
"X"
));
context
->
ShareLoD
(
"X"
,
framework
::
GradVarName
(
"X"
));
}
};
class
SqueezeGradOp
:
public
framework
::
OperatorBase
{
public:
using
OperatorBase
::
OperatorBase
;
private:
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
{
auto
dx_name
=
Output
(
framework
::
GradVarName
(
"X"
));
auto
dout_name
=
Input
(
framework
::
GradVarName
(
"Out"
));
auto
x_dims
=
scope
.
FindVar
(
Input
(
"X"
))
->
Get
<
framework
::
LoDTensor
>
().
dims
();
framework
::
AttributeMap
attrs
;
attrs
[
"shape"
]
=
framework
::
vectorize2int
(
x_dims
);
attrs
[
"inplace"
]
=
Attr
<
bool
>
(
"inplace"
);
auto
reshape_op
=
framework
::
OpRegistry
::
CreateOp
(
"reshape"
,
{{
"X"
,
{
dout_name
}},
{
"Shape"
,
{}}},
{{
"Out"
,
{
dx_name
}}},
attrs
);
reshape_op
->
Run
(
scope
,
place
);
}
};
}
// namespace operators
}
// namespace paddle
// Tell linker to use reshape op
USE_OP
(
reshape
);
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
squeeze
,
ops
::
SqueezeOp
,
ops
::
SqueezeOpMaker
,
ops
::
SqueezeOpInferShape
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
squeeze_grad
,
ops
::
SqueezeGradOp
,
ops
::
SqueezeGradInferShape
);
paddle/fluid/platform/CMakeLists.txt
浏览文件 @
938319bb
...
...
@@ -46,7 +46,7 @@ ENDIF()
# memcpy depends on device_context, here add deps individually for
# avoiding cycle dependencies
cc_library
(
device_context SRCS device_context.cc init.cc DEPS malloc
place eigen3 stringpiece cpu_helper
${
GPU_CTX_DEPS
}
${
MKLDNN_CTX_DEPS
}
)
place eigen3 stringpiece cpu_helper
framework_proto
${
GPU_CTX_DEPS
}
${
MKLDNN_CTX_DEPS
}
)
nv_test
(
device_context_test SRCS device_context_test.cu DEPS device_context gpu_info
)
cc_test
(
init_test SRCS init_test.cc DEPS device_context
)
...
...
python/paddle/fluid/annotations.py
0 → 100644
浏览文件 @
938319bb
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
functools
import
sys
__all__
=
[
'deprecated'
]
def
deprecated
(
since
,
instead
,
extra_message
=
""
):
def
decorator
(
func
):
err_msg
=
"API {0} is deprecated since {1}. Please use {2} instead."
.
format
(
func
.
__name__
,
since
,
instead
)
if
len
(
extra_message
)
!=
0
:
err_msg
+=
"
\n
"
err_msg
+=
extra_message
@
functools
.
wraps
(
func
)
def
wrapper
(
*
args
,
**
kwargs
):
print
>>
sys
.
stderr
,
err_msg
return
func
(
*
args
,
**
kwargs
)
wrapper
.
__doc__
+=
"
\n
"
wrapper
.
__doc__
+=
err_msg
return
wrapper
return
decorator
python/paddle/fluid/backward.py
浏览文件 @
938319bb
...
...
@@ -18,10 +18,7 @@ import collections
import
copy
import
unique_name
__all__
=
[
'append_backward'
,
'calc_gradient'
,
]
__all__
=
[
'append_backward'
]
def
_rename_arg_
(
op_descs
,
old_name
,
new_name
,
begin_idx
=
None
,
end_idx
=
None
):
...
...
@@ -123,7 +120,8 @@ def _append_grad_suffix_(name):
def
_addup_repetitive_outputs_
(
op_descs
):
"""
In backward part, an variable may be the output of more than one ops.
In this case, the variable should be the accumulation of all the outputs.
And one op may yield its multiple outputs to the same variable.
In these cases, the variable should be the accumulation of all the outputs.
`sum_op`s are added to implement the accumulate.
"""
pending_sum_ops
=
[]
...
...
@@ -136,29 +134,46 @@ def _addup_repetitive_outputs_(op_descs):
"sum"
,
{
"X"
:
renamed_vars
[
var_name
]},
{
"Out"
:
[
var_name
]},
{
"use_mkldnn"
:
False
}),
idx
))
renamed_vars
[
var_name
]
=
[
var_name
]
for
var_name
in
op_desc
.
output_arg_names
():
if
var_name
==
core
.
empty_var_name
(
)
or
var_name
in
op_desc
.
input_arg_names
():
# empty variable or inplace op
continue
if
len
(
renamed_vars
[
var_name
])
==
0
:
# it's the first time we get the variable
renamed_vars
[
var_name
]
=
[
var_name
]
else
:
if
len
(
renamed_vars
[
var_name
])
==
1
:
for
param_idx
,
param_name
in
enumerate
(
op_desc
.
output_names
()):
arg_names
=
op_desc
.
output
(
param_name
)
for
arg_idx
,
var_name
in
enumerate
(
arg_names
):
if
var_name
==
core
.
empty_var_name
(
)
or
var_name
in
op_desc
.
input_arg_names
():
# empty variable or inplace op
continue
if
len
(
renamed_vars
[
var_name
])
==
0
:
# it's the first time we get the variable
renamed_vars
[
var_name
]
=
[
var_name
]
else
:
if
len
(
renamed_vars
[
var_name
])
==
1
:
new_name
=
var_name
+
"@RENAME@"
+
\
str
(
var_rename_count
[
var_name
])
var_rename_count
[
var_name
]
+=
1
# rename original var_name
renamed_vars
[
var_name
][
0
]
=
new_name
_rename_arg_
(
op_descs
,
var_name
,
new_name
,
0
,
idx
)
_rename_arg_
(
pending_sum_ops
,
var_name
,
new_name
)
for
p
in
op_desc
.
output_names
()[:
param_idx
]:
p_arg_names
=
op_desc
.
output
(
p
)
if
var_name
in
p_arg_names
:
op_desc
.
set_output
(
p
,
[
new_name
if
x
==
var_name
else
x
for
x
in
p_arg_names
])
arg_names
=
[
new_name
if
x
==
var_name
else
x
for
x
in
arg_names
[:
arg_idx
]
]
+
arg_names
[
arg_idx
:]
new_name
=
var_name
+
"@RENAME@"
+
\
str
(
var_rename_count
[
var_name
])
var_rename_count
[
var_name
]
+=
1
# rename original var_name
renamed_vars
[
var_name
][
0
]
=
new_name
_rename_arg_
(
op_descs
,
var_name
,
new_name
,
0
,
idx
)
_rename_arg_
(
pending_sum_ops
,
var_name
,
new_name
)
new_name
=
var_name
+
"@RENAME@"
+
\
str
(
var_rename_count
[
var_name
])
var_rename_count
[
var_name
]
+=
1
op_desc
.
rename_output
(
var_name
,
new_name
)
renamed_vars
[
var_name
].
append
(
new_name
)
arg_names
[
arg_idx
]
=
new_name
op_desc
.
set_output
(
param_name
,
arg_names
)
renamed_vars
[
var_name
].
append
(
new_name
)
for
var_name
,
inputs
in
renamed_vars
.
iteritems
():
if
len
(
inputs
)
>
1
:
pending_sum_ops
.
append
(
...
...
python/paddle/fluid/layers/device.py
浏览文件 @
938319bb
...
...
@@ -18,10 +18,12 @@ All util layers.
from
layer_function_generator
import
autodoc
from
..framework
import
unique_name
from
..layer_helper
import
LayerHelper
from
..annotations
import
deprecated
__all__
=
[
'get_places'
]
__all__
=
[]
@
deprecated
(
since
=
'0.15.0'
,
instead
=
"ParallelExecutor"
)
@
autodoc
()
def
get_places
(
device_count
=
None
,
device_type
=
None
):
helper
=
LayerHelper
(
'get_places'
,
**
locals
())
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
938319bb
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Copyright (c ) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
...
...
@@ -3900,7 +3914,13 @@ def transpose(x, perm, name=None):
return
out
def
im2sequence
(
input
,
filter_size
=
1
,
stride
=
1
,
padding
=
0
,
name
=
None
):
def
im2sequence
(
input
,
filter_size
=
1
,
stride
=
1
,
padding
=
0
,
input_image_size
=
None
,
out_stride
=
1
,
name
=
None
):
"""
Extracts image patches from the input tensor to form a tensor of shape
{input.batch_size * output_height * output_width, filter_size_H *
...
...
@@ -3937,6 +3957,15 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
padding_up = padding_down = padding_left = padding_right = padding
Default: padding = 0.
input_image_size(Variable): the input contains image real size.It's dim
is [batchsize, 2]. It is dispensable.It is just for batch inference.
out_stride(int|tuple): The scaling of image through CNN. It is
dispensable. It is valid only when input_image_size is not null.
If out_stride is tuple, it must contain two intergers,
(out_stride_H, out_stride_W). Otherwise,
the out_stride_H = out_stride_W = out_stride.
name (int): The name of this layer. It is optional.
Returns:
...
...
@@ -3987,7 +4016,7 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8,
9
}
output.dims = {8,
8
}
output.lod = [[4, 4]]
...
...
@@ -4009,18 +4038,17 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
if
len
(
padding
)
==
2
:
padding
.
append
(
padding
[
0
])
padding
.
append
(
padding
[
1
])
inputs
=
{
"X"
:
input
}
attrs
=
{
"kernels"
:
filter_size
,
"strides"
:
stride
,
"padding"
:
padding
}
if
input_image_size
:
if
isinstance
(
out_stride
,
int
):
out_stride
=
[
out_stride
,
out_stride
]
inputs
[
"Y"
]
=
input_image_size
attrs
[
"out_stride"
]
=
out_stride
helper
=
LayerHelper
(
'im2sequence'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
())
helper
.
append_op
(
type
=
'im2sequence'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'kernels'
:
filter_size
,
'strides'
:
stride
,
'paddings'
:
padding
,
})
type
=
'im2sequence'
,
inputs
=
inputs
,
outputs
=
{
'Out'
:
out
},
attrs
=
attrs
)
return
out
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
938319bb
...
...
@@ -29,7 +29,7 @@ __all__ = [
'SGD'
,
'Momentum'
,
'Adagrad'
,
'Adam'
,
'Adamax'
,
'DecayedAdagrad'
,
'Ftrl'
,
'SGDOptimizer'
,
'MomentumOptimizer'
,
'AdagradOptimizer'
,
'AdamOptimizer'
,
'AdamaxOptimizer'
,
'DecayedAdagradOptimizer'
,
'RMSPropOptimizer'
,
'FtrlOptimizer'
,
'Adadelta'
,
'ModelAverage'
,
'
Optimizer'
,
'
RMSPropOptimizer'
'FtrlOptimizer'
,
'Adadelta'
,
'ModelAverage'
,
'RMSPropOptimizer'
]
...
...
@@ -67,7 +67,7 @@ class Optimizer(object):
self
.
_LARS_weight_decay
=
LARS_weight_decay
def
_create_global_learning_rate
(
self
):
lr
=
self
.
global_learning_rate
()
lr
=
self
.
_
global_learning_rate
()
if
isinstance
(
lr
,
framework
.
Variable
):
return
...
...
@@ -86,7 +86,7 @@ class Optimizer(object):
dtype
=
'float32'
if
self
.
_dtype
==
None
else
self
.
_dtype
,
persistable
=
True
)
def
global_learning_rate
(
self
,
program
=
None
):
def
_
global_learning_rate
(
self
,
program
=
None
):
"""
get global decayed learning rate
:return:
...
...
@@ -110,9 +110,9 @@ class Optimizer(object):
return
param_lr
else
:
if
param_lr
==
1.0
:
return
self
.
global_learning_rate
()
return
self
.
_
global_learning_rate
()
else
:
return
self
.
global_learning_rate
()
*
param_lr
return
self
.
_
global_learning_rate
()
*
param_lr
def
_create_accumulators
(
self
,
block
,
parameters
):
"""Create all accumulators needed by the parameters
...
...
@@ -185,10 +185,10 @@ class Optimizer(object):
format
(
name
,
param
.
name
))
return
self
.
_accumulators
[
name
][
param
.
name
]
def
create_optimization_pass
(
self
,
parameters_and_grads
,
loss
,
startup_program
=
None
):
def
_
create_optimization_pass
(
self
,
parameters_and_grads
,
loss
,
startup_program
=
None
):
"""Add optimization operators to update gradients to variables.
Args:
...
...
@@ -221,7 +221,7 @@ class Optimizer(object):
self
.
_create_global_learning_rate
()
if
self
.
_LARS_weight_decay
>
0.0
:
layers
.
append_LARS
(
parameters_and_grads
,
self
.
global_learning_rate
(),
self
.
_
global_learning_rate
(),
self
.
_LARS_weight_decay
)
optimize_ops
=
[]
...
...
@@ -262,8 +262,8 @@ class Optimizer(object):
params_grads
=
append_regularization_ops
(
params_grads
,
self
.
regularization
)
optimize_ops
=
self
.
create_optimization_pass
(
params_grads
,
loss
,
startup_program
)
optimize_ops
=
self
.
_
create_optimization_pass
(
params_grads
,
loss
,
startup_program
)
return
optimize_ops
,
params_grads
...
...
python/paddle/fluid/tests/book/notest_understand_sentiment.py
浏览文件 @
938319bb
...
...
@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
from
paddle.fluid.layers.device
import
get_places
import
unittest
import
paddle.fluid
as
fluid
import
paddle
...
...
@@ -144,7 +144,7 @@ def train(word_dict,
cost
,
acc_out
,
prediction
=
net_method
(
data
,
label
,
input_dim
=
dict_dim
,
class_dim
=
class_dim
)
else
:
places
=
fluid
.
layers
.
get_places
()
places
=
get_places
()
pd
=
fluid
.
layers
.
ParallelDo
(
places
)
with
pd
.
do
():
cost
,
acc
,
_
=
net_method
(
...
...
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @
938319bb
...
...
@@ -12,15 +12,17 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
argparse
import
paddle.fluid
as
fluid
import
paddle
import
sys
import
numpy
import
unittest
import
math
import
sys
import
os
import
sys
import
unittest
import
numpy
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid.layers.device
import
get_places
BATCH_SIZE
=
64
...
...
@@ -76,7 +78,7 @@ def train(nn_type,
net_conf
=
conv_net
if
parallel
:
places
=
fluid
.
layers
.
get_places
()
places
=
get_places
()
pd
=
fluid
.
layers
.
ParallelDo
(
places
)
with
pd
.
do
():
img_
=
pd
.
read_input
(
img
)
...
...
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @
938319bb
...
...
@@ -14,6 +14,7 @@
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid.layers.device
import
get_places
import
unittest
import
os
import
numpy
as
np
...
...
@@ -80,7 +81,7 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
avg_cost
,
predict_word
=
__network__
(
[
first_word
,
second_word
,
third_word
,
forth_word
,
next_word
])
else
:
places
=
fluid
.
layers
.
get_places
()
places
=
get_places
()
pd
=
fluid
.
layers
.
ParallelDo
(
places
)
with
pd
.
do
():
avg_cost
,
predict_word
=
__network__
(
...
...
python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py
浏览文件 @
938319bb
...
...
@@ -12,12 +12,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
import
math
import
sys
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid.layers.device
import
get_places
# need to fix random seed and training data to compare the loss
# value accurately calculated by the default and the memory optimization
# version.
...
...
@@ -34,7 +35,7 @@ if fluid.core.is_compiled_with_cuda():
use_nccl
=
False
place
=
fluid
.
CUDAPlace
(
0
)
places
=
fluid
.
layers
.
get_places
(
device_count
=
0
,
device_type
=
device_type
)
places
=
get_places
(
device_count
=
0
,
device_type
=
device_type
)
pd
=
fluid
.
layers
.
ParallelDo
(
places
,
use_nccl
=
use_nccl
)
with
pd
.
do
():
x_
=
pd
.
read_input
(
x
)
...
...
python/paddle/fluid/tests/unittests/test_calc_gradient.py
浏览文件 @
938319bb
...
...
@@ -16,8 +16,6 @@ import unittest
import
paddle.fluid
as
fluid
import
paddle.fluid.layers
as
layers
import
paddle.fluid.framework
as
framework
import
paddle.fluid.optimizer
as
optimizer
from
paddle.fluid.backward
import
calc_gradient
...
...
python/paddle/fluid/tests/unittests/test_get_places_op.py
浏览文件 @
938319bb
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
import
paddle.fluid
as
fluid
from
paddle.fluid.layers.device
import
get_places
import
decorators
import
unittest
...
...
@@ -20,7 +21,7 @@ import unittest
class
TestGetPlaces
(
unittest
.
TestCase
):
@
decorators
.
prog_scope
()
def
test_get_places
(
self
):
places
=
fluid
.
layers
.
get_places
()
places
=
get_places
()
cpu
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
cpu
)
exe
.
run
(
fluid
.
default_main_program
())
...
...
python/paddle/fluid/tests/unittests/test_im2sequence_op.py
浏览文件 @
938319bb
...
...
@@ -16,23 +16,48 @@ import numpy as np
from
op_test
import
OpTest
def
get_output_shape
(
attrs
,
in_shape
):
def
get_output_shape
(
attrs
,
in_shape
,
img_real_size
):
batchsize
=
in_shape
[
0
]
img_height
=
in_shape
[
2
]
img_width
=
in_shape
[
3
]
paddings
=
np
.
array
(
attrs
[
'paddings'
]).
astype
(
"int32"
)
kernels
=
np
.
array
(
attrs
[
'kernels'
]).
astype
(
"int32"
)
strides
=
np
.
array
(
attrs
[
'strides'
]).
astype
(
"int32"
)
output_height
=
np
.
zeros
((
1
,
batchsize
)).
astype
(
"int32"
)
output_width
=
np
.
zeros
((
1
,
batchsize
)).
astype
(
"int32"
)
if
len
(
img_real_size
):
out_stride
=
np
.
array
(
attrs
[
'out_stride'
]).
astype
(
"int32"
)
imgreal_h
=
0
imgreal_w
=
0
for
index
in
range
(
batchsize
):
if
img_real_size
[
index
,
0
]
%
out_stride
[
0
]
==
0
:
imgreal_h
=
img_real_size
[
index
,
0
]
/
out_stride
[
0
]
else
:
imgreal_h
=
img_real_size
[
index
,
0
]
/
out_stride
[
0
]
+
1
if
img_real_size
[
index
,
0
]
%
out_stride
[
1
]
==
0
:
imgreal_w
=
img_real_size
[
index
,
1
]
/
out_stride
[
1
]
else
:
imgreal_w
=
img_real_size
[
index
,
0
]
/
out_stride
[
1
]
+
1
output_height
[
0
,
index
]
=
\
1
+
\
(
imgreal_h
+
paddings
[
0
]
+
paddings
[
2
]
-
kernels
[
0
]
+
strides
[
0
]
-
1
)
/
\
strides
[
0
]
paddings
=
attrs
[
'paddings'
]
kernels
=
attrs
[
'kernels'
]
strides
=
attrs
[
'strides'
]
output_width
[
0
,
index
]
=
\
1
+
\
(
imgreal_w
+
paddings
[
1
]
+
paddings
[
3
]
-
kernels
[
1
]
+
strides
[
1
]
-
1
)
/
\
strides
[
1
]
else
:
for
index
in
range
(
batchsize
):
output_height
[
0
,
index
]
=
\
1
+
\
(
img_height
+
paddings
[
0
]
+
paddings
[
2
]
-
kernels
[
0
]
+
strides
[
0
]
-
1
)
/
\
strides
[
0
]
output_height
=
\
1
+
\
(
img_height
+
paddings
[
0
]
+
paddings
[
2
]
-
kernels
[
0
]
+
strides
[
0
]
-
1
)
/
\
strides
[
0
]
output_width
=
\
1
+
\
(
img_width
+
paddings
[
1
]
+
paddings
[
3
]
-
kernels
[
1
]
+
strides
[
1
]
-
1
)
/
\
strides
[
1
]
output_width
[
0
,
index
]
=
\
1
+
\
(
img_width
+
paddings
[
1
]
+
paddings
[
3
]
-
kernels
[
1
]
+
strides
[
1
]
-
1
)
/
\
strides
[
1
]
return
output_height
,
output_width
...
...
@@ -75,22 +100,25 @@ def im2col(attrs, im, col):
im_row_offset
][
im_col_offset
]
def
Im2Sequence
(
inputs
,
attrs
):
output_height
,
output_width
=
get_output_shape
(
attrs
,
inputs
.
shape
)
def
Im2Sequence
(
inputs
,
img_real_size
,
attrs
):
output_height
,
output_width
=
get_output_shape
(
attrs
,
inputs
.
shape
,
img_real_size
)
img_channels
=
inputs
.
shape
[
1
]
batch_size
=
inputs
.
shape
[
0
]
out
=
np
.
zeros
([
batch_size
,
output_height
,
output_width
,
img_channels
,
attrs
[
'kernels'
][
0
],
attrs
[
'kernels'
][
1
]
]).
astype
(
"float32"
)
for
i
in
range
(
len
(
inputs
)):
im2col
(
attrs
,
inputs
[
i
],
out
[
i
])
out
=
out
.
reshape
([
batch_size
*
output_height
*
output_width
,
img_channels
*
attrs
[
'kernels'
][
0
]
*
attrs
[
'kernels'
][
1
]
])
out
=
[]
for
index
in
range
(
batch_size
):
tmp
=
np
.
zeros
([
output_height
[
0
,
index
],
output_width
[
0
,
index
],
img_channels
,
attrs
[
'kernels'
][
0
],
attrs
[
'kernels'
][
1
]
]).
astype
(
"float32"
)
out
.
append
(
tmp
)
for
index
in
range
(
len
(
inputs
)):
im2col
(
attrs
,
inputs
[
index
],
out
[
index
])
out
[
index
]
=
out
[
index
].
reshape
([
output_height
[
0
,
index
]
*
output_width
[
0
,
index
],
img_channels
*
attrs
[
'kernels'
][
0
]
*
attrs
[
'kernels'
][
1
]
])
out
=
np
.
concatenate
(
out
,
axis
=
0
)
return
out
...
...
@@ -103,7 +131,7 @@ class TestBlockExpandOp(OpTest):
self
.
attrs
=
{
'kernels'
:
[
2
,
2
],
'strides'
:
[
1
,
1
],
'paddings'
:
[
1
,
1
,
1
,
1
]
'paddings'
:
[
1
,
1
,
1
,
1
]
,
}
def
setUp
(
self
):
...
...
@@ -113,7 +141,8 @@ class TestBlockExpandOp(OpTest):
self
.
batch_size
,
self
.
img_channels
,
self
.
img_height
,
self
.
img_width
]).
astype
(
"float32"
)
out
=
Im2Sequence
(
x
,
self
.
attrs
)
real_size
=
np
.
array
([]).
astype
(
"float32"
)
out
=
Im2Sequence
(
x
,
real_size
,
self
.
attrs
)
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'Out'
:
out
}
...
...
@@ -133,20 +162,20 @@ class TestBlockExpandOpCase2(TestBlockExpandOp):
self
.
attrs
=
{
'kernels'
:
[
2
,
1
],
'strides'
:
[
2
,
1
],
'paddings'
:
[
2
,
1
,
2
,
1
]
'paddings'
:
[
2
,
1
,
2
,
1
]
,
}
class
TestBlockExpandOpCase3
(
TestBlockExpandOp
):
def
config
(
self
):
self
.
batch_size
=
3
self
.
batch_size
=
2
self
.
img_channels
=
1
self
.
img_height
=
4
self
.
img_width
=
5
self
.
attrs
=
{
'kernels'
:
[
2
,
1
],
'strides'
:
[
2
,
1
],
'paddings'
:
[
2
,
0
,
2
,
0
]
'paddings'
:
[
2
,
0
,
2
,
0
]
,
}
...
...
@@ -159,9 +188,94 @@ class TestBlockExpandOpCase4(TestBlockExpandOp):
self
.
attrs
=
{
'kernels'
:
[
2
,
2
],
'strides'
:
[
1
,
1
],
'paddings'
:
[
0
,
0
,
0
,
0
]
'paddings'
:
[
0
,
0
,
0
,
0
],
}
class
TestBlockExpandOpCase5
(
OpTest
):
def
config
(
self
):
self
.
batch_size
=
1
self
.
img_channels
=
3
self
.
img_height
=
4
self
.
img_width
=
5
self
.
attrs
=
{
'kernels'
:
[
2
,
1
],
'strides'
:
[
2
,
1
],
'paddings'
:
[
2
,
1
,
2
,
1
],
'out_stride'
:
[
2
,
2
],
}
def
setUp
(
self
):
self
.
config
()
self
.
op_type
=
"im2sequence"
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
self
.
batch_size
,
self
.
img_channels
,
self
.
img_height
,
self
.
img_width
]).
astype
(
"float32"
)
real_size
=
np
.
array
([[
8
,
10
],
[
5
,
8
]]).
astype
(
"float32"
)
out
=
np
.
array
(
Im2Sequence
(
x
,
real_size
,
self
.
attrs
))
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
real_size
}
#l ??
self
.
outputs
=
{
'Out'
:
out
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestBlockExpandOpCase6
(
OpTest
):
def
config
(
self
):
self
.
batch_size
=
3
self
.
img_channels
=
1
self
.
img_height
=
4
self
.
img_width
=
5
self
.
attrs
=
{
'kernels'
:
[
2
,
1
],
'strides'
:
[
1
,
1
],
'paddings'
:
[
0
,
0
,
0
,
0
],
'out_stride'
:
[
1
,
1
],
}
def
setUp
(
self
):
self
.
config
()
self
.
op_type
=
"im2sequence"
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
self
.
batch_size
,
self
.
img_channels
,
self
.
img_height
,
self
.
img_width
]).
astype
(
"float32"
)
real_size
=
np
.
array
([[
8
,
10
],
[
5
,
8
],
[
5
,
8
]]).
astype
(
"float32"
)
out
=
np
.
array
(
Im2Sequence
(
x
,
real_size
,
self
.
attrs
))
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
real_size
}
#l ??
self
.
outputs
=
{
'Out'
:
out
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestBlockExpandOpCase7
(
OpTest
):
def
config
(
self
):
self
.
batch_size
=
2
self
.
img_channels
=
2
self
.
img_height
=
3
self
.
img_width
=
3
self
.
attrs
=
{
'kernels'
:
[
2
,
2
],
'strides'
:
[
1
,
1
],
'paddings'
:
[
1
,
0
,
1
,
0
],
'out_stride'
:
[
2
,
2
],
}
def
setUp
(
self
):
self
.
config
()
self
.
op_type
=
"im2sequence"
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
self
.
batch_size
,
self
.
img_channels
,
self
.
img_height
,
self
.
img_width
]).
astype
(
"float32"
)
real_size
=
np
.
array
([[
6
,
6
],
[
4
,
4
]]).
astype
(
"float32"
)
out
=
np
.
array
(
Im2Sequence
(
x
,
real_size
,
self
.
attrs
))
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
real_size
}
self
.
outputs
=
{
'Out'
:
out
}
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
'__main__'
:
unittest
.
main
()
#set shiftwidth=4 set expandtab set tabstop=4
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
938319bb
...
...
@@ -16,6 +16,7 @@ from __future__ import print_function
import
unittest
import
paddle.fluid.layers
as
layers
from
paddle.fluid.layers.device
import
get_places
import
paddle.fluid.nets
as
nets
from
paddle.fluid.framework
import
Program
,
program_guard
,
default_main_program
from
paddle.fluid.param_attr
import
ParamAttr
...
...
@@ -238,7 +239,7 @@ class TestBook(unittest.TestCase):
def
test_get_places
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
get_places
(
device_count
=
4
)
x
=
get_places
(
device_count
=
4
)
self
.
assertIsNotNone
(
x
)
print
(
str
(
program
))
...
...
@@ -251,12 +252,16 @@ class TestBook(unittest.TestCase):
print
(
str
(
program
))
def
test_im2sequence
(
self
):
print
(
"test_im2sequence"
)
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
3
,
128
,
128
],
dtype
=
'float32'
)
y
=
layers
.
data
(
name
=
'y'
,
shape
=
[],
dtype
=
'float32'
)
output
=
layers
.
im2sequence
(
input
=
x
,
stride
=
[
1
,
1
],
filter_size
=
[
2
,
2
])
input
=
x
,
input_image_size
=
y
,
stride
=
[
1
,
1
],
filter_size
=
[
2
,
2
],
out_stride
=
[
1
,
1
])
self
.
assertIsNotNone
(
output
)
print
(
str
(
program
))
...
...
python/paddle/fluid/tests/unittests/test_optimizer.py
浏览文件 @
938319bb
...
...
@@ -97,7 +97,7 @@ class TestMomentumOptimizer(unittest.TestCase):
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
momentum_optimizer
.
get_accumulators
()),
0
)
opts
=
momentum_optimizer
.
create_optimization_pass
(
opts
=
momentum_optimizer
.
_
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
self
.
assertEqual
(
len
(
opts
),
3
)
sgd_op
=
opts
[
-
1
]
...
...
@@ -151,7 +151,7 @@ class TestMomentumOptimizer(unittest.TestCase):
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
momentum_optimizer
.
get_accumulators
()),
0
)
opts
=
momentum_optimizer
.
create_optimization_pass
(
opts
=
momentum_optimizer
.
_
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
self
.
assertEqual
(
len
(
opts
),
3
)
sgd_op
=
opts
[
-
1
]
...
...
@@ -214,8 +214,8 @@ class TestAdagradOptimizer(unittest.TestCase):
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
adagrad_optimizer
.
get_accumulators
()),
0
)
opts
=
adagrad_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
opts
=
adagrad_optimizer
.
_create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
self
.
assertEqual
(
len
(
opts
),
3
)
self
.
assertEqual
([
op
.
type
for
op
in
opts
],
[
"fill_constant"
,
"elementwise_mul"
,
"adagrad"
])
...
...
@@ -278,8 +278,8 @@ class TestAdamOptimizer(unittest.TestCase):
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
adam_optimizer
.
get_accumulators
()),
0
)
opts
=
adam_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
opts
=
adam_optimizer
.
_
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
self
.
assertEqual
(
len
(
opts
),
5
)
self
.
assertEqual
(
[
op
.
type
for
op
in
opts
],
...
...
@@ -345,8 +345,8 @@ class TestAdamaxOptimizer(unittest.TestCase):
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
adamax_optimizer
.
get_accumulators
()),
0
)
opts
=
adamax_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
opts
=
adamax_optimizer
.
_
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
self
.
assertEqual
(
len
(
opts
),
4
)
self
.
assertEqual
(
[
op
.
type
for
op
in
opts
],
...
...
@@ -409,7 +409,7 @@ class TestDecayedAdagradOptimizer(unittest.TestCase):
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
decayed_adagrad_optimizer
.
get_accumulators
()),
0
)
opts
=
decayed_adagrad_optimizer
.
create_optimization_pass
(
opts
=
decayed_adagrad_optimizer
.
_
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
self
.
assertEqual
(
len
(
opts
),
3
)
self
.
assertEqual
(
...
...
@@ -475,8 +475,8 @@ class TestFtrlOptimizer(unittest.TestCase):
params_grads
=
append_backward
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
ftrl_optimizer
.
get_accumulators
()),
0
)
opts
=
ftrl_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
opts
=
ftrl_optimizer
.
_
create_optimization_pass
(
params_grads
,
mul_out
,
init_program
)
self
.
assertEqual
(
len
(
opts
),
3
)
self
.
assertEqual
([
op
.
type
for
op
in
opts
],
[
"fill_constant"
,
"elementwise_mul"
,
"ftrl"
])
...
...
python/paddle/fluid/tests/unittests/test_parallel_op.py
浏览文件 @
938319bb
...
...
@@ -15,6 +15,7 @@
import
unittest
import
paddle.fluid
as
fluid
from
paddle.fluid.layers.device
import
get_places
import
paddle.fluid.profiler
as
profiler
import
numpy
...
...
@@ -115,7 +116,7 @@ class BaseParallelForTest(unittest.TestCase):
if
use_parallel
:
thread_num
=
fluid
.
core
.
get_cuda_device_count
(
)
if
use_gpu
else
8
places
=
fluid
.
layers
.
get_places
(
thread_num
)
places
=
get_places
(
thread_num
)
pd
=
fluid
.
layers
.
ParallelDo
(
places
,
use_nccl
=
use_nccl
)
data
=
next
(
generator
)
...
...
python/paddle/fluid/tests/unittests/test_squeeze_op.py
0 → 100644
浏览文件 @
938319bb
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
# Correct: General.
class
TestSqueezeOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"squeeze"
self
.
init_test_case
()
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)}
self
.
init_attrs
()
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
new_shape
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Out"
)
def
init_test_case
(
self
):
self
.
ori_shape
=
(
1
,
3
,
1
,
5
)
self
.
axes
=
(
0
,
2
)
self
.
new_shape
=
(
3
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
False
}
# Correct: There is mins axis.
class
TestSqueezeOp1
(
TestSqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
1
,
3
,
1
,
5
)
self
.
axes
=
(
0
,
-
2
)
self
.
new_shape
=
(
3
,
5
)
# Correct: No axes input.
class
TestSqueezeOp2
(
TestSqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
1
,
3
,
1
,
5
)
self
.
axes
=
()
self
.
new_shape
=
(
3
,
5
)
# Correct: Just part of axes be squeezed.
class
TestSqueezeOp3
(
TestSqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
3
,
1
,
5
,
1
,
4
,
1
)
self
.
axes
=
(
1
,
-
1
)
self
.
new_shape
=
(
3
,
5
,
1
,
4
)
# Correct: Inplace.
class
TestSqueezeOpInplace1
(
TestSqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
1
,
3
,
1
,
5
)
self
.
axes
=
(
0
,
2
)
self
.
new_shape
=
(
3
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
# Correct: Inplace. There is mins axis.
class
TestSqueezeOpInplace2
(
TestSqueezeOp
):
def
inti_test_case
(
self
):
self
.
ori_shape
=
(
1
,
3
,
1
,
5
)
self
.
axes
=
(
0
,
-
2
)
self
.
new_shape
=
(
3
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
# Correct: Inplace. No axes input.
class
TestSqueezeOpInplace3
(
TestSqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
1
,
3
,
1
,
5
)
self
.
axes
=
()
self
.
new_shape
=
(
3
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
# Correct: Inpalce. Just part of axes be squeezed.
class
TestSqueezeOpInplace4
(
TestSqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
3
,
1
,
5
,
1
,
4
,
1
)
self
.
axes
=
(
1
,
-
1
)
self
.
new_shape
=
(
3
,
5
,
1
,
4
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
if
__name__
==
"__main__"
:
unittest
.
main
()
python/setup.py.in
浏览文件 @
938319bb
...
...
@@ -181,6 +181,14 @@ else:
command = "patchelf --set-rpath '$ORIGIN/../libs/' ${PADDLE_BINARY_DIR}/python/paddle/fluid/core.so"
if os.system(command) != 0:
raise Exception("patch core.so failed, command: %s" % command)
if '${WITH_FLUID_ONLY}'== 'OFF':
# change rpath of _swig_paddle.so.
if "@APPLE@" == "1":
command = "install_name_tool -id \"@loader_path/../paddle/libs/\" ${PADDLE_BINARY_DIR}/python/py_paddle/_swig_paddle.so"
else:
command = "patchelf --set-rpath '$ORIGIN/../paddle/libs/' ${PADDLE_BINARY_DIR}/python/py_paddle/_swig_paddle.so"
if os.system(command) != 0:
raise Exception("patch _swig_paddle.so failed, command: %s" % command)
setup(name='${PACKAGE_NAME}',
version='${PADDLE_VERSION}',
...
...
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