ValueError: None is only supported in the 1st dimension. Tensor 'flatbuffer_data' has invalid shape '[None, None, 1, 512]'

Question

I am trying to convert my tensorflow model (2.0) into tensorflow lite format. My model has two input layers as follows:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Lambda, Input, add, Dot, multiply, dot 
from tensorflow.keras.backend import dot, transpose, expand_dims
from tensorflow.keras.models import Model

r1 = Input(shape=[None, 1, 512], name='flatbuffer_data') # I want to take a variable amount of 
# 512 float embeddings from my flatbuffer, if the flatbuffer has 4, embeddings then it would
# be inferred as shape=[4, 1, 512], if it has a 100 embeddings, then it is [100, 1, 512].
r2 = Input(shape=[1, 512], name='query_embedding')

#Example code

minus_r1 = Lambda(lambda x: -x, name='invert_value')(r1)
subtracted = add([r2, minus_r1], name='embeddings_diff')

out1 = tf.argsort(subtracted)
out2 = tf.sort(subtracted)

model = Model([r1, r2], [out1, out2])

I am then doing some tensor operations on the layers and saving the models as follows (there is no training and hence no trainable parameters, just some linear algebra ops which I want to port to android)

model.save('combined_model.h5')

I get my tensorflow .h5 model , thus but then when I try to convert it into, tensorflow lite, I get the following error:

import tensorflow as tf
model = tf.keras.models.load_model('combined_model.h5')
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

#Error
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/aspiring1/.virtualenvs/faiss/lib/python3.6/site-packages/tensorflow_core/lite/python/lite.py", line 446, in convert
    "invalid shape '{1}'.".format(_get_tensor_name(tensor), shape_list))
ValueError: None is only supported in the 1st dimension. Tensor 'flatbuffer_data' has invalid shape '[None, None, 1, 512]'.

I know that we had dynamic and static shape inference in tensorflow 1.x using tensorflow placeholders. Is there an analogue here in tensorflow 2.x. Also, I'd appreciate a solution in tensorflow 1.x too.

Some answers and blogs I've read that might help: Tensorflow: how to save/restore a model?

Understand dynamic and static shape in tensorflow

Understanding tensorflow shapes

Using the first link above I also tried creating a tensorflow 1.x graph and tried saving it using the saved model format, but I don't get the desired results.

You can find my code for the same here: tensorflow 1.x gist code

Answer 1

Full code: https://drive.google.com/file/d/1MN4-FX_-hz3y-UAuf7OTj_XYuVTlsSTP/view?usp=sharing

---

Why doesn't this work?

I know that we had dynamic and static shape inference in tensorflow 1.x using tensorflow placeholders. Is there an analogue here in tensorflow 2.x

That all still works fine. I think the problem is that tf.lite doesn't handle dynamic shapes. I think it prealocates all it's tensors, once and re-uses them (I could be wrong).

So, first of all that extra dimension:

[None, None, 1, 512]

keras.Input always includes a batch dimension, which tf.lite can handle being unknown (this restriction seems relaxed in tf-nightly).

But lite seems to prefer a batch dimension of 1. If you switch to:

r1 = Input(shape=[4], batch_size=None, name='flatbuffer_data')
r2 = Input(shape=[4], batch_size=1, name='query_embedding')

Passes the conversion, but still fails when you try to execute the tflite model, because the model wants all unknown dimensions to be 1:

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

i = tf.lite.Interpreter(model_content=tflite_model)
i.allocate_tensors()
i.get_input_details()

i.set_tensor(0, tf.constant([[0.,0,0,0],[1,1,1,1],[2,2,2,2]]))
i.set_tensor(1, tf.constant([[0.,0,0,0]]))

ValueError: Cannot set tensor: Dimension mismatch. Got 3 but expected 1 for dimension 0 of input 0.

With tf-nightly you can convert the model as you've written it, but that also fails to run since the unknown dimension assumed to be 1:

r1 = Input(shape=[None, 4], name='flatbuffer_data') 
r2 = Input(shape=[1, 4], name='query_embedding')

...

import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

i = tf.lite.Interpreter(model_content=tflite_model)
i.allocate_tensors()
print(i.get_input_details())

i.set_tensor(0, tf.constant([[[0.,0,0,0],[1,1,1,1],[2,2,2,2]]]))
i.set_tensor(1, tf.constant([[[0.,0,0,0]]]))

ValueError: Cannot set tensor: Dimension mismatch. Got 3 but expected 1 for dimension 1 of input 0.

Solution? No. Almost.

I think you need to give that array a size larger than you expect it to be, and pass an int telling your model how many elements to slice out:

n = Input(shape=(), dtype=tf.int32, name='num_inputs')
r1 = Input(shape=[1000, 4], name='flatbuffer_data')
r2 = Input(shape=[4], name='query_embedding')

#Example code
x = tf.reshape(r1, [1000,4])
x = tf.gather(x, tf.range(tf.squeeze(n)))
minus_r1 = Lambda(lambda x: -x, name='invert_value')(x)
subtracted = add([r2, minus_r1], name='embeddings_diff')

out1 = tf.argsort(subtracted, name='argsort')
out2 = tf.sort(subtracted, name="sorted")

model = Model([r1, r2, n], [out1, out2])

Then it works:

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

i = tf.lite.Interpreter(model_content=tflite_model)
i.allocate_tensors()

for d in i.get_input_details():
  print(d)

a = np.zeros([1000, 4], dtype=np.float32)
a[:3] = [
          [0.,0,0,0],
          [1,1,1,1],
          [2,2,2,2]]

i.set_tensor(0, tf.constant(a[np.newaxis,...], dtype=tf.float32))
i.set_tensor(1, tf.constant([[0.,0,0,0]]))
i.set_tensor(2, tf.constant([3], dtype=tf.int32))

i.invoke()

print()
for d in i.get_output_details():
  print(i.get_tensor(d['index']))

[[ 0.  0.  0.  0.]
 [-1. -1. -1. -1.]
 [-2. -2. -2. -2.]]
[[0 1 2 3]
 [0 1 2 3]
 [0 1 2 3]]

OP tried this in a java interpreter and got:

java.lang.IllegalArgumentException: Internal error: Failed to apply delegate: Attempting to use a delegate that only supports static-sized tensors with a graph that has dynamic-sized tensors.

So we're not quite done yet.