Performance issues in the definition of pre_process_binary_cross_entropy, DexiNed-TF2/model.py(P1)

[DLPerf]

Hello, I found a performance issue in the definition of pre_process_binary_cross_entropy, DexiNed-TF2/model.py, tf.cast(y, dtype=tf.float32) will be created repeatedly during program execution, resulting in reduced efficiency. I think it should be created before the loop.

The same issues exist in:

• tf.dtypes.cast(tmp_y > 0., tf.float32)
• tf.math.reduce_sum(mask, axis=[1, 2, 3], keepdims=True)
• tf.where(tf.equal(y, 0.0), beta, beta2)

Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.

[xavysp]

Hi @DLPerf thanks, I'll fix next week because now I am finishing the last version of DexiNed-Pythorch.