Version 22.0.2 of the firebase-ml-model-interpreter
library introduces a new getLatestModelFile()
method, which gets the location on the device of custom
models. You can use this method to directly instantiate a TensorFlow Lite Interpreter
object, which you can use instead of the FirebaseModelInterpreter
wrapper.
Going forward, this is the preferred approach. Because the TensorFlow Lite interpreter version is no longer coupled with the Firebase library version, you have more flexibility to upgrade to new versions of TensorFlow Lite when you want, or more easily use custom TensorFlow Lite builds.
This page shows how you can migrate from using FirebaseModelInterpreter
to the
TensorFlow Lite Interpreter
.
1. Update project dependencies
Update your project's dependencies to include version 22.0.2 of the firebase-ml-model-interpreter
library (or newer) and the tensorflow-lite
library:
Before
implementation
(
"com.google.firebase:firebase-ml-model-interpreter:22.0.1"
)
After
implementation
(
"com.google.firebase:firebase-ml-model-interpreter:22.0.2"
)
implementation
(
"org.tensorflow:tensorflow-lite:2.0.0"
)
2. Create a TensorFlow Lite interpreter instead of a FirebaseModelInterpreter
Instead of creating a FirebaseModelInterpreter
, get the model's location on
device with getLatestModelFile()
and use it to create a TensorFlow Lite Interpreter
.
Before
Kotlin
val
remoteModel
=
FirebaseCustomRemoteModel
.
Builder
(
"your_model"
).
build
()
val
options
=
FirebaseModelInterpreterOptions
.
Builder
(
remoteModel
).
build
()
val
interpreter
=
FirebaseModelInterpreter
.
getInstance
(
options
)
Java
FirebaseCustomRemoteModel
remoteModel
=
new
FirebaseCustomRemoteModel
.
Builder
(
"your_model"
).
build
();
FirebaseModelInterpreterOptions
options
=
new
FirebaseModelInterpreterOptions
.
Builder
(
remoteModel
).
build
();
FirebaseModelInterpreter
interpreter
=
FirebaseModelInterpreter
.
getInstance
(
options
);
After
Kotlin
val
remoteModel
=
FirebaseCustomRemoteModel
.
Builder
(
"your_model"
).
build
()
FirebaseModelManager
.
getInstance
().
getLatestModelFile
(
remoteModel
)
.
addOnCompleteListener
{
task
-
>
val
modelFile
=
task
.
getResult
()
if
(
modelFile
!=
null
)
{
// Instantiate an org.tensorflow.lite.Interpreter object.
interpreter
=
Interpreter
(
modelFile
)
}
}
Java
FirebaseCustomRemoteModel
remoteModel
=
new
FirebaseCustomRemoteModel
.
Builder
(
"your_model"
).
build
();
FirebaseModelManager
.
getInstance
().
getLatestModelFile
(
remoteModel
)
.
addOnCompleteListener
(
new
OnCompleteListener<File>
()
{
@Override
public
void
onComplete
(
@NonNull
Task<File>
task
)
{
File
modelFile
=
task
.
getResult
();
if
(
modelFile
!=
null
)
{
// Instantiate an org.tensorflow.lite.Interpreter object.
Interpreter
interpreter
=
new
Interpreter
(
modelFile
);
}
}
});
3. Update input and output preparation code
With FirebaseModelInterpreter
, you specify the model's input and output shapes
by passing a FirebaseModelInputOutputOptions
object to the interpreter when
you run it.
For the TensorFlow Lite interpreter, you instead allocate ByteBuffer
objects
with the right size for your model's input and output.
For example, if your model has an input shape of [1 224 224 3] float
values
and an output shape of [1 1000] float
values, make these changes:
Before
Kotlin
val
inputOutputOptions
=
FirebaseModelInputOutputOptions
.
Builder
()
.
setInputFormat
(
0
,
FirebaseModelDataType
.
FLOAT32
,
intArrayOf
(
1
,
224
,
224
,
3
))
.
setOutputFormat
(
0
,
FirebaseModelDataType
.
FLOAT32
,
intArrayOf
(
1
,
1000
))
.
build
()
val
input
=
ByteBuffer
.
allocateDirect
(
224
*
224
*
3
*
4
).
order
(
ByteOrder
.
nativeOrder
())
// Then populate with input data.
val
inputs
=
FirebaseModelInputs
.
Builder
()
.
add
(
input
)
.
build
()
interpreter
.
run
(
inputs
,
inputOutputOptions
)
.
addOnSuccessListener
{
outputs
-
>
// ...
}
.
addOnFailureListener
{
// Task failed with an exception.
// ...
}
Java
FirebaseModelInputOutputOptions
inputOutputOptions
=
new
FirebaseModelInputOutputOptions
.
Builder
()
.
setInputFormat
(
0
,
FirebaseModelDataType
.
FLOAT32
,
new
int
[]
{
1
,
224
,
224
,
3
})
.
setOutputFormat
(
0
,
FirebaseModelDataType
.
FLOAT32
,
new
int
[]
{
1
,
1000
})
.
build
();
float
[][][][]
input
=
new
float
[
1
][
224
][
224
][
3
]
;
// Then populate with input data.
FirebaseModelInputs
inputs
=
new
FirebaseModelInputs
.
Builder
()
.
add
(
input
)
.
build
();
interpreter
.
run
(
inputs
,
inputOutputOptions
)
.
addOnSuccessListener
(
new
OnSuccessListener<FirebaseModelOutputs>
()
{
@Override
public
void
onSuccess
(
FirebaseModelOutputs
result
)
{
// ...
}
})
.
addOnFailureListener
(
new
OnFailureListener
()
{
@Override
public
void
onFailure
(
@NonNull
Exception
e
)
{
// Task failed with an exception
// ...
}
});
After
Kotlin
val
inBufferSize
=
1
*
224
*
224
*
3
*
java
.
lang
.
Float
.
SIZE
/
java
.
lang
.
Byte
.
SIZE
val
inputBuffer
=
ByteBuffer
.
allocateDirect
(
inBufferSize
).
order
(
ByteOrder
.
nativeOrder
())
// Then populate with input data.
val
outBufferSize
=
1
*
1000
*
java
.
lang
.
Float
.
SIZE
/
java
.
lang
.
Byte
.
SIZE
val
outputBuffer
=
ByteBuffer
.
allocateDirect
(
outBufferSize
).
order
(
ByteOrder
.
nativeOrder
())
interpreter
.
run
(
inputBuffer
,
outputBuffer
)
Java
int
inBufferSize
=
1
*
224
*
224
*
3
*
java
.
lang
.
Float
.
SIZE
/
java
.
lang
.
Byte
.
SIZE
;
ByteBuffer
inputBuffer
=
ByteBuffer
.
allocateDirect
(
inBufferSize
).
order
(
ByteOrder
.
nativeOrder
());
// Then populate with input data.
int
outBufferSize
=
1
*
1000
*
java
.
lang
.
Float
.
SIZE
/
java
.
lang
.
Byte
.
SIZE
;
ByteBuffer
outputBuffer
=
ByteBuffer
.
allocateDirect
(
outBufferSize
).
order
(
ByteOrder
.
nativeOrder
());
interpreter
.
run
(
inputBuffer
,
outputBuffer
);
4. Update output handling code
Finally, instead of getting the model's output with the FirebaseModelOutputs
object's getOutput()
method, convert the ByteBuffer
output to whatever
structure is convenient for your use case.
For example, if you're doing classification, you might make changes like the following:
Before
Kotlin
val
output
=
result
.
getOutput
(
0
)
val
probabilities
=
output
[
0
]
try
{
val
reader
=
BufferedReader
(
InputStreamReader
(
assets
.
open
(
"custom_labels.txt"
)))
for
(
probability
in
probabilities
)
{
val
label
:
String
=
reader
.
readLine
()
println
(
"
$
label
:
$
probability
"
)
}
}
catch
(
e
:
IOException
)
{
// File not found?
}
Java
float
[][]
output
=
result
.
getOutput
(
0
);
float
[]
probabilities
=
output
[
0
]
;
try
{
BufferedReader
reader
=
new
BufferedReader
(
new
InputStreamReader
(
getAssets
().
open
(
"custom_labels.txt"
)));
for
(
float
probability
:
probabilities
)
{
String
label
=
reader
.
readLine
();
Log
.
i
(
TAG
,
String
.
format
(
"%s: %1.4f"
,
label
,
probability
));
}
}
catch
(
IOException
e
)
{
// File not found?
}
After
Kotlin
modelOutput
.
rewind
()
val
probabilities
=
modelOutput
.
asFloatBuffer
()
try
{
val
reader
=
BufferedReader
(
InputStreamReader
(
assets
.
open
(
"custom_labels.txt"
)))
for
(
i
in
probabilities
.
capacity
())
{
val
label
:
String
=
reader
.
readLine
()
val
probability
=
probabilities
.
get
(
i
)
println
(
"
$
label
:
$
probability
"
)
}
}
catch
(
e
:
IOException
)
{
// File not found?
}
Java
modelOutput
.
rewind
();
FloatBuffer
probabilities
=
modelOutput
.
asFloatBuffer
();
try
{
BufferedReader
reader
=
new
BufferedReader
(
new
InputStreamReader
(
getAssets
().
open
(
"custom_labels.txt"
)));
for
(
int
i
=
0
;
i
<
probabilities
.
capacity
();
i
++
)
{
String
label
=
reader
.
readLine
();
float
probability
=
probabilities
.
get
(
i
);
Log
.
i
(
TAG
,
String
.
format
(
"%s: %1.4f"
,
label
,
probability
));
}
}
catch
(
IOException
e
)
{
// File not found?
}
