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dc62e16d
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dc62e16d
编写于
4月 12, 2016
作者:
A
Andrew Harp
提交者:
TensorFlower Gardener
4月 12, 2016
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差异文件
Standalone benchmark for Tensorflow models that runs on desktop and Android.
Change: 119706066
上级
1b06f752
变更
3
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3 changed file
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348 addition
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0 deletion
+348
-0
tensorflow/tools/benchmark/BUILD
tensorflow/tools/benchmark/BUILD
+66
-0
tensorflow/tools/benchmark/README.md
tensorflow/tools/benchmark/README.md
+57
-0
tensorflow/tools/benchmark/benchmark_model.cc
tensorflow/tools/benchmark/benchmark_model.cc
+225
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未找到文件。
tensorflow/tools/benchmark/BUILD
0 → 100644
浏览文件 @
dc62e16d
# Description:
# Benchmark utility that can run on desktop and Android.
package
(
default_visibility
=
[
"//visibility:public"
])
licenses
([
"notice"
])
# Apache 2.0
load
(
"//tensorflow:tensorflow.bzl"
,
"tf_copts"
)
exports_files
([
"LICENSE"
])
cc_library
(
name
=
"benchmark_model_lib"
,
srcs
=
[
"benchmark_model.cc"
,
],
copts
=
tf_copts
(),
visibility
=
[
"//visibility:public"
],
deps
=
select
({
"//tensorflow:android"
:
[
"//tensorflow/core:android_tensorflow_lib"
,
],
"//conditions:default"
:
[
"//tensorflow/core:core_cpu"
,
"//tensorflow/core:lib"
,
"//tensorflow/core:framework"
,
"//tensorflow/core:framework_internal"
,
"//tensorflow/core:protos_all_cc"
,
"//tensorflow/core:tensorflow"
,
],
}),
)
# This binary may be built for either desktop or Android.
# A typical Android build command will look like the following:
# bazel build -c opt tensorflow/core:android_tensorflow_lib \
# --crosstool_top=//external:android/crosstool \
# --cpu=armeabi-v7a \
# --host_crosstool_top=@bazel_tools//tools/cpp:toolchain
#
# NOTE: currently '-pthread' must be removed from the LINK_OPTS variable
# in google/protobuf/BUILD to sucessfully build for Android. This is temporary
# pending an update of the version of the protobuf library that Tensorflow
# uses.
cc_binary
(
name
=
"benchmark_model"
,
copts
=
tf_copts
(),
linkopts
=
select
({
"//tensorflow:android"
:
[
"-pie"
,
"-s"
,
"-landroid"
,
"-ljnigraphics"
,
"-llog"
,
"-lm"
,
"-z defs"
,
"-s"
,
"-Wl,--icf=all"
,
# Identical Code Folding
"-Wl,--exclude-libs,ALL"
,
# Exclude syms in all libs from auto export
],
"//conditions:default"
:
[],
}),
linkstatic
=
1
,
visibility
=
[
"//visibility:public"
],
deps
=
[
":benchmark_model_lib"
],
)
tensorflow/tools/benchmark/README.md
0 → 100644
浏览文件 @
dc62e16d
# Tensorflow Model Benchmark Tool
## Description
A simple C++ binary to benchmark a compute graph and its individual operators,
both on desktop machines and on Android.
## To build/install/run
### On Android:
(1) build for your specific platform, e.g.:
```
bash
$bazel
build
-c
opt
\
--crosstool_top
=
//external:android/crosstool
\
--cpu
=
armeabi-v7a
\
--host_crosstool_top
=
@bazel_tools//tools/cpp:toolchain
\
tensorflow/tools/benchmark:benchmark_model
```
(2) Connect your phone. Push the binary to your phone with adb push
(make the directory if required):
```
bash
$adb
push bazel-bin/tensorflow/tools/benchmark/benchmark_model /data/local/tmp
```
(3) Push the compute graph that you need to test. For example:
adb push tensorflow_inception_graph.pb /data/local/tmp
(4) Run the benchmark. For example:
```
bash
$adb
shell
"/data/local/tmp/benchmark_model
\
--graph=/data/local/tmp/tensorflow_inception_graph.pb
\
--input_layer="
input:0
"
\
--input_layer_shape="
1,224,224,3
"
\
--input_layer_type="
float
"
\
--output_layer="
output:0
"
```
### On desktop:
(1) build the binary
```
bash
$bazel
build
-c
opt tensorflow/tools/benchmark:benchmark_model
```
(2) Run on your compute graph, similar to the Android case but without the need of adb shell.
For example:
```
bash
$bazel
-bin
/tensorflow/tools/benchmark/benchmark_model
\
--graph
=
tensorflow_inception_graph.pb
\
--input_layer
=
"input:0"
\
--input_layer_shape
=
"1,224,224,3"
\
--input_layer_type
=
"float"
\
--output_layer
=
"output:0"
```
The Inception graph used as an example here may be downloaded from
https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
\ No newline at end of file
tensorflow/tools/benchmark/benchmark_model.cc
0 → 100644
浏览文件 @
dc62e16d
/* Copyright 2016 Google Inc. 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.
==============================================================================*/
// A C++ binary to benchmark a compute graph and its individual operators,
// both on desktop machines and on Android.
//
// See README.md for usage instructions.
#include <cstdlib>
#include <memory>
#include <string>
#include <unordered_set>
#include <vector>
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/graph/algorithm.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/graph_constructor.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/util/command_line_flags.h"
#include "tensorflow/core/util/stat_summarizer.h"
namespace
tensorflow
{
// Global variables that holds the Tensorflow classifier.
static
std
::
unique_ptr
<
tensorflow
::
Session
>
session
;
static
StatSummarizer
g_stats
;
struct
Flags
{
string
graph
=
"/data/local/tmp/tensorflow_inception_graph.pb"
;
string
input_layer
=
"input:0"
;
string
input_layer_shape
=
"1,224,224,3"
;
string
input_layer_type
=
"float"
;
string
output_layer
=
"output:0"
;
int
num_runs
=
50
;
string
run_delay
=
"-1.0"
;
int
num_threads
=
-
1
;
};
static
Flags
*
flags
;
// Filled in by main()
static
bool
InitializeBenchmark
()
{
g_stats
.
Reset
();
LOG
(
INFO
)
<<
"Loading Tensorflow."
;
tensorflow
::
SessionOptions
options
;
tensorflow
::
ConfigProto
&
config
=
options
.
config
;
if
(
flags
->
num_threads
>
0
)
{
config
.
set_intra_op_parallelism_threads
(
flags
->
num_threads
);
}
LOG
(
INFO
)
<<
"Got config, "
<<
config
.
device_count_size
()
<<
" devices"
;
session
.
reset
(
tensorflow
::
NewSession
(
options
));
tensorflow
::
GraphDef
tensorflow_graph
;
Status
s
=
ReadBinaryProto
(
Env
::
Default
(),
flags
->
graph
,
&
tensorflow_graph
);
if
(
!
s
.
ok
())
{
LOG
(
ERROR
)
<<
"Could not create Tensorflow Graph: "
<<
s
;
return
false
;
}
s
=
session
->
Create
(
tensorflow_graph
);
if
(
!
s
.
ok
())
{
LOG
(
ERROR
)
<<
"Could not create Tensorflow Session: "
<<
s
;
return
false
;
}
// Clear the proto to save memory space.
tensorflow_graph
.
Clear
();
return
true
;
}
static
bool
RunBenchmark
()
{
DataType
input_data_type
;
CHECK
(
DataTypeFromString
(
flags
->
input_layer_type
,
&
input_data_type
))
<<
flags
->
input_layer_type
<<
" was an invalid type"
;
std
::
vector
<
int32
>
sizes
;
CHECK
(
str_util
::
SplitAndParseAsInts
(
flags
->
input_layer_shape
,
','
,
&
sizes
))
<<
"Incorrect size string specified: "
<<
flags
->
input_layer_shape
;
TensorShape
input_shape
;
for
(
int
i
=
0
;
i
<
sizes
.
size
();
++
i
)
{
input_shape
.
AddDim
(
sizes
[
i
]);
}
Tensor
input_tensor
(
input_data_type
,
input_shape
);
switch
(
input_data_type
)
{
case
DT_INT32
:
{
auto
int_tensor
=
input_tensor
.
flat
<
int32
>
();
int_tensor
=
int_tensor
.
constant
(
0.0
);
break
;
}
case
DT_FLOAT
:
{
auto
float_tensor
=
input_tensor
.
flat
<
float
>
();
float_tensor
=
float_tensor
.
constant
(
0.0
);
break
;
}
case
DT_QUINT8
:
{
auto
int_tensor
=
input_tensor
.
flat
<
quint8
>
();
int_tensor
=
int_tensor
.
constant
(
0.0
);
break
;
}
default:
LOG
(
FATAL
)
<<
"Unsupported input type: "
<<
flags
->
input_layer_type
;
}
std
::
vector
<
std
::
pair
<
string
,
tensorflow
::
Tensor
>
>
input_tensors
(
{{
flags
->
input_layer
,
input_tensor
}});
std
::
vector
<
tensorflow
::
Tensor
>
output_tensors
;
std
::
vector
<
string
>
output_names
({
flags
->
output_layer
});
tensorflow
::
Status
s
;
RunOptions
run_options
;
run_options
.
set_trace_level
(
RunOptions
::
FULL_TRACE
);
RunMetadata
run_metadata
;
s
=
session
->
Run
(
run_options
,
input_tensors
,
output_names
,
{},
&
output_tensors
,
&
run_metadata
);
assert
(
run_metadata
.
has_step_stats
());
const
StepStats
&
stats
=
run_metadata
.
step_stats
();
g_stats
.
ProcessStepStats
(
stats
);
if
(
!
s
.
ok
())
{
LOG
(
ERROR
)
<<
"Error during inference: "
<<
s
;
return
false
;
}
return
true
;
}
}
// namespace tensorflow
int
main
(
int
argc
,
char
**
argv
)
{
tensorflow
::
flags
=
new
tensorflow
::
Flags
();
const
bool
parse_result
=
tensorflow
::
ParseFlags
(
&
argc
,
argv
,
{
tensorflow
::
Flag
(
"graph"
,
&
tensorflow
::
flags
->
graph
),
tensorflow
::
Flag
(
"input_layer"
,
&
tensorflow
::
flags
->
input_layer
),
tensorflow
::
Flag
(
"input_layer_shape"
,
&
tensorflow
::
flags
->
input_layer_shape
),
tensorflow
::
Flag
(
"input_layer_type"
,
&
tensorflow
::
flags
->
input_layer_type
),
tensorflow
::
Flag
(
"output_layer"
,
&
tensorflow
::
flags
->
output_layer
),
tensorflow
::
Flag
(
"num_runs"
,
&
tensorflow
::
flags
->
num_runs
),
tensorflow
::
Flag
(
"run_delay"
,
&
tensorflow
::
flags
->
run_delay
),
tensorflow
::
Flag
(
"num_threads"
,
&
tensorflow
::
flags
->
num_threads
),
});
if
(
!
parse_result
)
{
LOG
(
ERROR
)
<<
"Error parsing command-line flags."
;
return
-
1
;
}
::
tensorflow
::
port
::
InitMain
(
argv
[
0
],
&
argc
,
&
argv
);
if
(
argc
>
1
)
{
LOG
(
ERROR
)
<<
"Unknown argument "
<<
argv
[
1
];
return
-
1
;
}
LOG
(
INFO
)
<<
"Graph: ["
<<
tensorflow
::
flags
->
graph
<<
"]"
;
LOG
(
INFO
)
<<
"Input layer: ["
<<
tensorflow
::
flags
->
input_layer
<<
"]"
;
LOG
(
INFO
)
<<
"Input shape: ["
<<
tensorflow
::
flags
->
input_layer_shape
<<
"]"
;
LOG
(
INFO
)
<<
"Input type: ["
<<
tensorflow
::
flags
->
input_layer_type
<<
"]"
;
LOG
(
INFO
)
<<
"Output layer: ["
<<
tensorflow
::
flags
->
output_layer
<<
"]"
;
LOG
(
INFO
)
<<
"Num runs: ["
<<
tensorflow
::
flags
->
num_runs
<<
"]"
;
LOG
(
INFO
)
<<
"Inter-run delay (seconds): ["
<<
tensorflow
::
flags
->
run_delay
<<
"]"
;
LOG
(
INFO
)
<<
"Num threads: ["
<<
tensorflow
::
flags
->
num_threads
<<
"]"
;
if
(
!
tensorflow
::
InitializeBenchmark
())
{
return
-
1
;
}
// Convert the run_delay string into a timespec.
const
double
sleep_seconds
=
std
::
strtod
(
tensorflow
::
flags
->
run_delay
.
c_str
(),
nullptr
);
timespec
req
;
req
.
tv_sec
=
static_cast
<
time_t
>
(
sleep_seconds
);
req
.
tv_nsec
=
(
sleep_seconds
-
req
.
tv_sec
)
*
1000000000
;
LOG
(
INFO
)
<<
"Running benchmark"
;
for
(
int
i
=
0
;
i
<
tensorflow
::
flags
->
num_runs
;
++
i
)
{
if
(
!
tensorflow
::
RunBenchmark
())
{
LOG
(
INFO
)
<<
"Failed on run "
<<
i
;
return
-
1
;
}
// If requested, sleep between runs for an arbitrary amount of time.
// This can be helpful to determine the effect of mobile processor
// scaling and thermal throttling.
if
(
sleep_seconds
>
0.0
)
{
nanosleep
(
&
req
,
nullptr
);
}
}
tensorflow
::
g_stats
.
PrintStepStats
();
return
0
;
}
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