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体验新版 GitCode,发现更多精彩内容 >>
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f373269d
编写于
9月 30, 2020
作者:
Q
Qi Li
提交者:
GitHub
9月 30, 2020
浏览文件
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电子邮件补丁
差异文件
update histogram op for performance optimization, test=develop (#24912)
上级
4d5ddbf1
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
90 addition
and
45 deletion
+90
-45
paddle/fluid/operators/histogram_op.cu
paddle/fluid/operators/histogram_op.cu
+29
-20
python/paddle/fluid/tests/unittests/test_histogram_op.py
python/paddle/fluid/tests/unittests/test_histogram_op.py
+56
-2
python/paddle/tensor/linalg.py
python/paddle/tensor/linalg.py
+5
-23
未找到文件。
paddle/fluid/operators/histogram_op.cu
浏览文件 @
f373269d
...
...
@@ -12,8 +12,6 @@ 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. */
#define EIGEN_USE_GPU
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/operators/histogram_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
...
...
@@ -32,28 +30,38 @@ inline int GET_BLOCKS(const int N) {
}
template
<
typename
T
,
typename
IndexType
>
__device__
static
IndexType
GetBin
(
T
bVal
,
T
minvalue
,
T
max
value
,
__device__
static
IndexType
GetBin
(
T
input_value
,
T
min_value
,
T
max_
value
,
int64_t
nbins
)
{
IndexType
bin
=
static_cast
<
int
>
((
bVal
-
minvalue
)
*
nbins
/
(
maxvalue
-
min
value
));
if
(
bin
==
nbins
)
bin
-=
1
;
return
bin
;
IndexType
bin
=
static_cast
<
int
>
((
input_value
-
min_value
)
*
nbins
/
(
max_value
-
min_
value
));
IndexType
output_index
=
bin
<
nbins
-
1
?
bin
:
nbins
-
1
;
return
output_index
;
}
template
<
typename
T
,
typename
IndexType
>
__global__
void
KernelHistogram
(
const
T
*
input
,
const
int
totalElements
,
const
int64_t
nbins
,
const
T
minvalue
,
const
T
maxvalue
,
int64_t
*
output
)
{
CUDA_KERNEL_LOOP
(
linearIndex
,
totalElements
)
{
const
IndexType
inputIdx
=
threadIdx
.
x
+
blockIdx
.
x
*
blockDim
.
x
;
const
auto
inputVal
=
input
[
inputIdx
];
if
(
inputVal
>=
minvalue
&&
inputVal
<=
maxvalue
)
{
const
IndexType
bin
=
GetBin
<
T
,
IndexType
>
(
inputVal
,
minvalue
,
maxvalue
,
nbins
);
const
IndexType
outputIdx
=
bin
<
nbins
-
1
?
bin
:
nbins
-
1
;
paddle
::
platform
::
CudaAtomicAdd
(
&
output
[
outputIdx
],
1
);
__global__
void
KernelHistogram
(
const
T
*
input
,
const
int
total_elements
,
const
int64_t
nbins
,
const
T
min_value
,
const
T
max_value
,
int64_t
*
output
)
{
extern
__shared__
int64_t
buf_hist
[];
for
(
int
i
=
threadIdx
.
x
;
i
<
nbins
;
i
+=
blockDim
.
x
)
{
buf_hist
[
i
]
=
0
;
}
__syncthreads
();
CUDA_KERNEL_LOOP
(
input_index
,
total_elements
)
{
// const IndexType input_index = threadIdx.x + blockIdx.x * blockDim.x;
const
auto
input_value
=
input
[
input_index
];
if
(
input_value
>=
min_value
&&
input_value
<=
max_value
)
{
const
IndexType
output_index
=
GetBin
<
T
,
IndexType
>
(
input_value
,
min_value
,
max_value
,
nbins
);
paddle
::
platform
::
CudaAtomicAdd
(
&
buf_hist
[
output_index
],
1
);
}
}
__syncthreads
();
for
(
int
i
=
threadIdx
.
x
;
i
<
nbins
;
i
+=
blockDim
.
x
)
{
paddle
::
platform
::
CudaAtomicAdd
(
&
output
[
i
],
buf_hist
[
i
]);
}
}
template
<
typename
DeviceContext
,
typename
T
>
...
...
@@ -125,8 +133,9 @@ class HistogramCUDAKernel : public framework::OpKernel<T> {
auto
stream
=
context
.
template
device_context
<
platform
::
CUDADeviceContext
>().
stream
();
KernelHistogram
<
T
,
IndexType
><<<
GET_BLOCKS
(
input_numel
),
PADDLE_CUDA_NUM_THREADS
,
0
,
stream
>>>
(
KernelHistogram
<
T
,
IndexType
><<<
GET_BLOCKS
(
input_numel
),
PADDLE_CUDA_NUM_THREADS
,
nbins
*
sizeof
(
int64_t
),
stream
>>>
(
input_data
,
input_numel
,
nbins
,
output_min
,
output_max
,
out_data
);
}
};
...
...
python/paddle/fluid/tests/unittests/test_histogram_op.py
浏览文件 @
f373269d
...
...
@@ -58,12 +58,66 @@ class TestHistogramOpAPI(unittest.TestCase):
msg
=
'histogram output is wrong, out ='
+
str
(
actual
.
numpy
()))
class
TestHistogramOpError
(
unittest
.
TestCase
):
"""Test histogram op error."""
def
run_network
(
self
,
net_func
):
main_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_program
,
startup_program
):
net_func
()
exe
=
fluid
.
Executor
()
exe
.
run
(
main_program
)
def
test_bins_error
(
self
):
"""Test bins should be greater than or equal to 1."""
def
net_func
():
input_value
=
paddle
.
fill_constant
(
shape
=
[
3
,
4
],
dtype
=
'float32'
,
value
=
3.0
)
paddle
.
histogram
(
input
=
input_value
,
bins
=-
1
,
min
=
1
,
max
=
5
)
with
self
.
assertRaises
(
fluid
.
core
.
EnforceNotMet
):
self
.
run_network
(
net_func
)
def
test_min_max_error
(
self
):
"""Test max must be larger or equal to min."""
def
net_func
():
input_value
=
paddle
.
fill_constant
(
shape
=
[
3
,
4
],
dtype
=
'float32'
,
value
=
3.0
)
paddle
.
histogram
(
input
=
input_value
,
bins
=
1
,
min
=
5
,
max
=
1
)
with
self
.
assertRaises
(
fluid
.
core
.
EnforceNotMet
):
self
.
run_network
(
net_func
)
def
test_min_max_range_error
(
self
):
"""Test range of min, max is not finite"""
def
net_func
():
input_value
=
paddle
.
fill_constant
(
shape
=
[
3
,
4
],
dtype
=
'float32'
,
value
=
3.0
)
paddle
.
histogram
(
input
=
input_value
,
bins
=
1
,
min
=-
np
.
inf
,
max
=
5
)
with
self
.
assertRaises
(
fluid
.
core
.
EnforceNotMet
):
self
.
run_network
(
net_func
)
def
test_type_errors
(
self
):
with
program_guard
(
Program
()):
# The input type must be Variable.
self
.
assertRaises
(
TypeError
,
paddle
.
histogram
,
1
,
bins
=
5
,
min
=
1
,
max
=
5
)
# The input type must be 'int32', 'int64', 'float32', 'float64'
x_bool
=
fluid
.
data
(
name
=
'x_bool'
,
shape
=
[
4
,
3
],
dtype
=
'bool'
)
self
.
assertRaises
(
TypeError
,
paddle
.
histogram
,
x_bool
,
bins
=
5
,
min
=
1
,
max
=
5
)
class
TestHistogramOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"histogram"
self
.
init_test_case
()
np_input
=
np
.
random
.
randint
(
low
=
0
,
high
=
20
,
size
=
self
.
in_shape
,
dtype
=
np
.
int64
)
np_input
=
np
.
random
.
uniform
(
low
=
0.0
,
high
=
20.0
,
size
=
self
.
in_shape
)
self
.
inputs
=
{
"X"
:
np_input
}
self
.
init_attrs
()
Out
,
_
=
np
.
histogram
(
...
...
python/paddle/tensor/linalg.py
浏览文件 @
f373269d
...
...
@@ -862,41 +862,23 @@ def histogram(input, bins=100, min=0, max=0):
If min and max are both zero, the minimum and maximum values of the data are used.
Args:
input (
Variable
): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor
input (
Tensor
): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor
should be float32, float64, int32, int64.
bins (int): number of histogram bins
min (int): lower end of the range (inclusive)
max (int): upper end of the range (inclusive)
Returns:
Variable: Tensor or LoDTensor calculated by histogram layer. The data type is int64
.
Tensor: data type is int64, shape is (nbins,)
.
Code Example 1:
.. code-block:: python
import paddle
import numpy as np
startup_program = paddle.static.Program()
train_program = paddle.static.Program()
with paddle.static.program_guard(train_program, startup_program):
inputs = paddle.data(name='input', dtype='int32', shape=[2,3])
output = paddle.histogram(inputs, bins=5, min=1, max=5)
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(startup_program)
img = np.array([[2, 4, 2], [2, 5, 4]]).astype(np.int32)
res = exe.run(train_program,
feed={'input': img},
fetch_list=[output])
print(np.array(res[0])) # [0,3,0,2,1]
Code Example 2:
Examples:
.. code-block:: python
import paddle
paddle.disable_static(paddle.CPUPlace())
inputs = paddle.to_tensor([1, 2, 1])
result = paddle.histogram(inputs, bins=4, min=0, max=3)
print(result) # [0, 2, 1, 0]
paddle.enable_static()
"""
if
in_dygraph_mode
():
return
core
.
ops
.
histogram
(
input
,
"bins"
,
bins
,
"min"
,
min
,
"max"
,
max
)
...
...
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