Input images with dynamic dimensions in Tensorflow-lite

Input images with dynamic dimensions in Tensorflow-lite

Yes, you can use dynamic tensors in TF-Lite. The reason why you can’t
directly set the shape to

[None, 128, None, 1]

is because this way, you can
easily support more languages in the future. Furthermore, it makes the best
use of static memory allocation scheme. This is a smart design choice for a
framework that is intended to be used on small devices with low computation
power. Here are the steps on how to dynamically set the tensor’s size:

0. Freezing

It seems like you’re converting from a frozen GraphDef, i.e. a

*.pb

file.
Suppose your frozen model has input shape

[None, 128, None, 1]

.

1. Conversion step.

During this step, set the input size to any valid one that can be accepted
by your model. For example:

tflite_convert   --graph_def_file='model.pb'   --output_file='model.tflite'   --input_shapes=1,128,80,1      # <-- here, you set an #     arbitrary valid shape  --input_arrays='input' --output_arrays='Softmax'

2. Inference step

The trick is to use the function

interpreter::resize_tensor_input(...)

of
the TF-Lite API in real time during inference. I will provide a python
implementation of it. The Java and C++ implementation should be the same (as
they have similar API):

from tensorflow.contrib.lite.python import interpreter# Load the *.tflite model and get input detailsmodel = Interpreter(model_path='model.tflite')input_details = model.get_input_details()# Your network currently has an input shape (1, 128, 80 , 1),# but suppose you need the input size to be (2, 128, 200, 1).model.resize_tensor_input(    input_details[0]['index'], (2, 128, 200, 1))model.allocate_tensors()

That’s it. You can now use that model for images with shape

(2, 128, 200,1)

, as long as your network architecture allows such an input shape. Beware
that you will have to do

model.allocate_tensors()

every time you do such a
reshape, so it will be very inefficient. It is strongly recommended to
avoid using this function too much in your program.