tensorflow/lite/core/subgraph.cc BytesRequired number of elements overflowed. Node number 1 (CONV_2D) failed to prepare. tflite - flutter

Question

I am trying to convert a CNN model into tflite model. I converted it successfully, but this error happens when I try to load and run the model.
I am building a flutter app.
It initializes the Tensorflow Lite runtime but then raises this error.
I/tflite (27856): Initialized TensorFlow Lite runtime.
E/flutter (27856): [ERROR:flutter/lib/ui/ui_dart_state.cc(166)] Unhandled Exception: PlatformException(Failed to load model, Internal error: Unexpected failure when preparing tensor allocations: tensorflow/lite/core/subgraph.cc BytesRequired number of elements overflowed.
E/flutter (27856):
E/flutter (27856): Node number 1 (CONV_2D) failed to prepare.

Answer 1

I think I have figured out the problem.
After spending days trying to solve this problem. I found out that the model I was using to convert was an ImagNet pretrained model which is InceptionV3. The problem is may be there are some layers could not converted.
I used the following and they worked perfectly fine.
MobileNet and MobileNetV2.
NasNet Mobile version.
OR if you are new to deep learning and don't want to train or skip the deep learning part you can use Teachable Machine then convert it easly.
I hope this could help you guys!! Thank you

Answer 2

I ran into the exact same issue the last few days. I tried to load and run a tflite model on Android. I finally figured out how to solve the problem.
I was creating my model using:
model = Xception(include_top=False)
The important part here is include_top=False, together with the default argument input_shape=None.
If you look at the source code of Xception, Inception, MobileNet, or whatever (that you can find here), you will see that at some point before creating the first layer they call
input_shape = imagenet_utils.obtain_input_shape(
input_shape,
default_size=<default_size>,
min_size=<min_size>,
data_format=backend.image_data_format(),
require_flatten=include_top,
weights=weights)
which is implemented here, with the most important part for us being:
if input_shape:
...
else:
if require_flatten:
input_shape = default_shape
else:
if data_format == 'channels_first':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
Thus, if I am not mistaken, when we set include_top to False, instead of getting the default shape we end up with undefined number of rows and columns. I am not sure how this is converted to tflite, although there is no error raised during conversion, but it really seems that Android cannot work with that (probably this is equivalent to setting an infinite image size). Hence this error when initializing the interpreter:
BytesRequired number of elements overflowed
When I set the proper input_shape argument in the constructor, i.e.
model = Xception(include_top=False, weights=None, input_shape=(rows, cols, channels))
then the converted model was working fine on Android.
As for why it is initializing correctly with MobileNetV2 in the same situation, i.e. by creating the model like so:
model = MobileNetV2(include_top=False)
I cannot explain...
Hope this brings an answer to your original question.

Answer 3

In fact, this is specified in the documentation, for instance in Xception:
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(299, 299, 3)`.
It should have exactly 3 inputs channels,
and width and height should be no smaller than 71.
E.g. `(150, 150, 3)` would be one valid value.
Whilst for MobileNetV2:
input_shape: Optional shape tuple, to be specified if you would
like to use a model with an input image resolution that is not
(224, 224, 3).
It should have exactly 3 inputs channels (224, 224, 3).
You can also omit this option if you would like
to infer input_shape from an input_tensor.
If you choose to include both input_tensor and input_shape then
input_shape will be used if they match, if the shapes
do not match then we will throw an error.
E.g. `(160, 160, 3)` would be one valid value.
Although it is not crystal clear.