Add tensorflow adapted ASL

src/loss_functions/tf_losses.py

@@ -0,0 +1,42 @@
+import tensorflow as tf
+
+def AsymmetricLoss(gamma_neg=4.0, gamma_pos=1.0): # Wrapper for ASL function
+  """"
+  Tensorflow adaptation of "Official Pytorch Implementation of: 'Asymmetric Loss For Multi-Label Classification'(ICCV, 2021) paper" --> https://github.com/Alibaba-MIIL/ASL/blob/main/src/loss_functions/losses.py
+  Returns a loss function with asymmetric, specifiable emphases for false negatives & false positives. Output can be passed in as loss function for model.compile().
+  ----------
+  Parameters
+  ----------
+  gamma_neg: asymmetric emphasis on false negatives
+  gamma_pos: assymetric emphasis on false positives
+  """
+
+  # Return ASL function with custom emphases
+  def ASL_func(y, x):
+    """"
+    Parameters
+    ----------
+    x: input logits (y hat)
+    y: targets (multi-label binarized vector)
+    """
+
+    # Calculating Probabilities
+    xs_pos = x
+    xs_neg = 1 - x
+
+    # Basic CE calculation
+    los_pos = y * tf.math.log(xs_pos)
+    los_neg = (1 - y) * tf.math.log(xs_neg)
+    loss = los_pos + los_neg
+
+    # Asymmetric Focusing
+    if gamma_neg > 0 or gamma_pos > 0:
+        pt0 = xs_pos * y
+        pt1 = xs_neg * (1 - y)  # pt = p if t > 0 else 1-p
+        pt = pt0 + pt1
+        one_sided_gamma = gamma_pos * y + gamma_neg * (1 - y)
+        one_sided_w = tf.math.pow(1 - pt, one_sided_gamma)
+        loss *= one_sided_w
+
+    return -tf.math.reduce_sum(loss)
+  return ASL_func