Tensorflow: Slicing a Tensor into overlapping blocks

Question

I have a 1D tensor that I wish to partition into overlapping blocks. I'm thinking of something like: tensor = tf.constant([1, 2, 3, 4, 5, 6, 7])

overlapping_blocker(tensor,block_size=3,stride=2)
=> [[1 2 3], [3, 4, 5], [5, 6, 7]]

So far I've only found ways to partition a tensor into non-overlapping blocks. Anybody knows of a way to solve this?

This needs to work for arbitrary input dimension (i.e. my input is like a tf.placeholder([None])

Answer 1

You can achieve the same using tf.extract_image_patches.

tensor = tf.placeholder(tf.int32, [None])

def overlapping_blocker(tensor,block_size=3,stride=2):
   return tf.squeeze(tf.extract_image_patches(tensor[None,...,None, None], ksizes=[1, block_size, 1, 1], strides=[1, stride, 1, 1], rates=[1, 1, 1, 1], padding='VALID'))

result = overlapping_blocker(tensor,block_size=3,stride=2)
sess = tf.InteractiveSession()
print(result.eval({tensor:np.array([1, 2, 3, 4, 5, 6, 7], np.int32)}))

#[[1 2 3]
#[3 4 5]
#[5 6 7]]