Version 0.20.0 of the Firebase/MLModelInterpreter
library introduces a new getLatestModelFilePath()
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 Firebase's ModelInterpreter
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 ModelInterpreter
to the
TensorFlow Lite Interpreter
.
1. Update project dependencies
Update your project's Podfile to include version 0.20.0 of the Firebase/MLModelInterpreter
library (or newer) and the TensorFlow Lite
library:
Before
Swift
pod
' Firebase
/
MLModelInterpreter
' ,
' 0.19
.
0
'
Objective-C
pod
' Firebase
/
MLModelInterpreter
' ,
' 0.19.0
'
After
Swift
pod
' Firebase
/
MLModelInterpreter
' ,
' ~
> 0.20
.
0
' pod
' TensorFlowLiteSwift
'
Objective-C
pod
' Firebase
/
MLModelInterpreter
' ,
' ~
>
0.20.0
' pod
' TensorFlowLiteObjC
'
2. Create a TensorFlow Lite interpreter instead of a Firebase ModelInterpreter
Instead of creating a Firebase ModelInterpreter
, get the model's location on
device with getLatestModelFilePath()
and use it to create a TensorFlow Lite Interpreter
.
Before
Swift
let
remoteModel
=
CustomRemoteModel
(
name
:
" your_remote_model
" // The name you assigned in the Firebase
console.
)
interpreter
=
ModelInterpreter
.
modelInterpreter
(
remoteModel
:
remoteModel
)
Objective-C
// Initialize using the name you assigned in the Firebase
console.
FIRCustomRemoteModel
*
remoteModel
=
[[
FIRCustomRemoteModel
alloc
]
initWithName
:
@
" your_remote_model
" ];
interpreter
=
[
FIRModelInterpreter
modelInterpreterForRemoteModel
:
remoteModel
];
After
Swift
let
remoteModel
=
CustomRemoteModel
(
name
:
" your_remote_model
" // The name you assigned in the Firebase
console.
)
ModelManager
.
modelManager
().
getLatestModelFilePath
(
remoteModel
)
{
(
remoteModelPath
,
error
)
in
guard
error
==
nil
,
let
remoteModelPath
=
remoteModelPath
else
{
return
}
do
{
interpreter
=
try
Interpreter
(
modelPath
:
remoteModelPath
)
}
catch
{
// Error?
}
}
Objective-C
FIRCustomRemoteModel
*
remoteModel
=
[[
FIRCustomRemoteModel
alloc
]
initWithName
:
@
" your_remote_model
" ];
[[
FIRModelManager
modelManager
]
getLatestModelFilePath
:
remoteModel
completion
:
^
(
NSString
*
_Nullable
filePath
,
NSError
*
_Nullable
error
)
{
if
(
error
!=
nil
||
filePath
==
nil
)
{
return
;
}
NSError
*
tfError
=
nil
;
interpreter
=
[[
TFLInterpreter
alloc
]
initWithModelPath
:
filePath
error
:
& tfError
];
}];
3. Update input and output preparation code
With ModelInterpreter
, you specify the model's input and output shapes
by passing a ModelInputOutputOptions
object to the interpreter when you run
it.
For the TensorFlow Lite interpreter, you instead call allocateTensors()
to
allocate space for the model's input and output, then copy your input data to
the input tensors.
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
Swift
let
ioOptions
=
ModelInputOutputOptions
()
do
{
try
ioOptions
.
setInputFormat
(
index
:
0
,
type
:
.
float32
,
dimensions
:
[
1
,
224
,
224
,
3
]
)
try
ioOptions
.
setOutputFormat
(
index
:
0
,
type
:
.
float32
,
dimensions
:
[
1
,
1000
]
)
}
catch
let
error
as
NSError
{
print
(
" Failed
to
set
input
or
output
format
with
error
:
\
(
error
.
localizedDescription
)")
}
let
inputs
=
ModelInputs
()
do
{
let
inputData
=
Data
()
// Then populate with input data.
try
inputs
.
addInput
(
inputData
)
}
catch
let
error
{
print
(
" Failed
to
add
input
:
\
(
error
)")
}
interpreter
.
run
(
inputs
:
inputs
,
options
:
ioOptions
)
{
outputs
,
error
in
guard
error
==
nil
,
let
outputs
=
outputs
else
{
return
}
// Process outputs
// ...
}
Objective-C
FIRModelInputOutputOptions
*
ioOptions
=
[[
FIRModelInputOutputOptions
alloc
]
init
];
NSError
*
error
;
[
ioOptions
setInputFormatForIndex
:
0
type
:
FIRModelElementTypeFloat32
dimensions
:
@[
@1
,
@224
,
@224
,
@3
]
error
:
& error
];
if
(
error
!=
nil
)
{
return
;
}
[
ioOptions
setOutputFormatForIndex
:
0
type
:
FIRModelElementTypeFloat32
dimensions
:
@[
@1
,
@1000
]
error
:
& error
];
if
(
error
!=
nil
)
{
return
;
}
FIRModelInputs
*
inputs
=
[[
FIRModelInputs
alloc
]
init
];
NSMutableData
*
inputData
=
[[
NSMutableData
alloc
]
initWithCapacity
:
0
];
// Then populate with input data.
[
inputs
addInput
:
inputData
error
:
& error
];
if
(
error
!=
nil
)
{
return
;
}
[
interpreter
runWithInputs
:
inputs
options
:
ioOptions
completion
:
^
(
FIRModelOutputs
*
_Nullable
outputs
,
NSError
*
_Nullable
error
)
{
if
(
error
!=
nil
||
outputs
==
nil
)
{
return
;
}
// Process outputs
// ...
}];
After
Swift
do
{
try
interpreter
.
allocateTensors
()
let
inputData
=
Data
()
// Then populate with input data.
try
interpreter
.
copy
(
inputData
,
toInputAt
:
0
)
try
interpreter
.
invoke
()
}
catch
let
err
{
print
(
err
.
localizedDescription
)
}
Objective-C
NSError
*
error
=
nil
;
[
interpreter
allocateTensorsWithError
:
& error
];
if
(
error
!=
nil
)
{
return
;
}
TFLTensor
*
input
=
[
interpreter
inputTensorAtIndex
:
0
error
:
& error
];
if
(
error
!=
nil
)
{
return
;
}
NSMutableData
*
inputData
=
[[
NSMutableData
alloc
]
initWithCapacity
:
0
];
// Then populate with input data.
[
input
copyData
:
inputData
error
:
& error
];
if
(
error
!=
nil
)
{
return
;
}
[
interpreter
invokeWithError
:
& error
];
if
(
error
!=
nil
)
{
return
;
}
4. Update output handling code
Finally, instead of getting the model's output with the ModelOutputs
object's output()
method, get the output tensor from the interpreter and convert its
data to whatever structure is convenient for your use case.
For example, if you're doing classification, you might make changes like the following:
Before
Swift
let
output
=
try
?
outputs
.
output
(
index
:
0
)
as
?
[[
NSNumber
]]
let
probabilities
=
output
?[
0
]
guard
let
labelPath
=
Bundle
.
main
.
path
(
forResource
:
" custom_labels
" ,
ofType
:
" txt
" )
else
{
return
}
let
fileContents
=
try
?
String
(
contentsOfFile
:
labelPath
)
guard
let
labels
=
fileContents
?.
components
(
separatedBy
:
" \
n
" )
else
{
return
}
for
i
in
0
..
< labels
.
count
{
if
let
probability
=
probabilities
?[
i
]
{
print
(
" \
(
labels
[
i
]):
\
(
probability
)")
}
}
Objective-C
// Get first and only output of inference with a batch size of 1
NSError
*
error
;
NSArray
*
probabilites
=
[
outputs
outputAtIndex
:
0
error
:
& error
][
0
];
if
(
error
!=
nil
)
{
return
;
}
NSString
*
labelPath
=
[
NSBundle
.
mainBundle
pathForResource
:
@
" retrained_labels
"
ofType
:@
" txt
" ];
NSString
*
fileContents
=
[
NSString
stringWithContentsOfFile
:
labelPath
encoding
:
NSUTF8StringEncoding
error
:
& error
];
if
(
error
!=
nil
||
fileContents
==
NULL
)
{
return
;
}
NSArray<NSString
*>
*
labels
=
[
fileContents
componentsSeparatedByString
:
@
" \
n
" ];
for
(
int
i
=
0
;
i
<
labels
.
count
;
i
++
)
{
NSString
*
label
=
labels
[
i
];
NSNumber
*
probability
=
probabilites
[
i
];
NSLog
(@
" %
@
:
%
f
" ,
label
,
probability
.
floatValue
);
}
After
Swift
do
{
// After calling interpreter.invoke():
let
output
=
try
interpreter
.
output
(
at
:
0
)
let
probabilities
=
UnsafeMutableBufferPointer<Float32>
.
allocate
(
capacity
:
1000
)
output
.
data
.
copyBytes
(
to
:
probabilities
)
guard
let
labelPath
=
Bundle
.
main
.
path
(
forResource
:
" custom_labels
" ,
ofType
:
" txt
" )
else
{
return
}
let
fileContents
=
try
?
String
(
contentsOfFile
:
labelPath
)
guard
let
labels
=
fileContents
?.
components
(
separatedBy
:
" \
n
" )
else
{
return
}
for
i
in
labels
.
indices
{
print
(
" \
(
labels
[
i
]):
\
(
probabilities
[
i
])")
}
}
catch
let
err
{
print
(
err
.
localizedDescription
)
}
Objective-C
NSError
*
error
=
nil
;
TFLTensor
*
output
=
[
interpreter
outputTensorAtIndex
:
0
error
:
& error
];
if
(
error
!=
nil
)
{
return
;
}
NSData
*
outputData
=
[
output
dataWithError
:
& error
];
if
(
error
!=
nil
)
{
return
;
}
Float32
probabilities
[
outputData
.
length
/
4
];
[
outputData
getBytes
:
& probabilities
length
:
outputData
.
length
];
NSString
*
labelPath
=
[
NSBundle
.
mainBundle
pathForResource
:
@
" custom_labels
"
ofType
:@
" txt
" ];
NSString
*
fileContents
=
[
NSString
stringWithContentsOfFile
:
labelPath
encoding
:
NSUTF8StringEncoding
error
:
& error
];
if
(
error
!=
nil
||
fileContents
==
nil
)
{
return
;
}
NSArray<NSString
*>
*
labels
=
[
fileContents
componentsSeparatedByString
:
@
" \
n
" ];
for
(
int
i
=
0
;
i
<
labels
.
count
;
i
++
)
{
NSLog
(@
" %
@
:
%
f
" ,
labels
[
i
],
probabilities
[
i
]);
}
