CODEWITHSUNDEEP
pythontensorflowkerasdeep-learning
Rms45
Rms45

Reputation: 23

Creating masks for duplicate elements in Tensorflow/Keras

I am trying to write a custom loss function for a person-reidentification task which is trained in a multi-task learning setting along with object detection. The filtered label values are of the shape (batch_size, num_boxes). I would like to create a mask such that only the values which repeat in dim 1 are considered for further calculations. How do I do this in TF/Keras-backend?

Short Example:

Input labels = [[0,0,0,0,12,12,3,3,4], [0,0,10,10,10,12,3,3,4]]
Required output: [[0,0,0,0,1,1,1,1,0],[0,0,1,1,1,0,1,1,0]]

(Basically I want to filter out only duplicates and discard unique identities for the loss function).

I guess a combination of tf.unique and tf.scatter could be used but I do not know how.

Upvotes: 0

Views: 186

Answers (1)

Andrey
Andrey

Reputation: 6377

This code works:

x = tf.constant([[0,0,0,0,12,12,3,3,4], [0,0,10,10,10,12,3,3,4]])
def mark_duplicates_1D(x):
  y, idx, count = tf.unique_with_counts(x)
  comp = tf.math.greater(count, 1)
  comp = tf.cast(comp, tf.int32)
  res = tf.gather(comp, idx)
  mult = tf.math.not_equal(x, 0)
  mult = tf.cast(mult, tf.int32)
  res *= mult
  return res
res = tf.map_fn(fn=mark_duplicates_1D, elems=x)

Upvotes: 1

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