def _preprocess_Y(self, Y):
"""Convert Y to T-dim lists of soft labels if necessary"""
# If not a list, convert to a singleton list
if not isinstance(Y, list):
if self.T != 1:
msg = "For T > 1, Y must be a list of N-dim or [N, K_t] tensors"
raise ValueError(msg)
Y = [Y]
if not len(Y) == self.T:
msg = "Expected Y to be a list of length T ({self.T}), not {len(Y)}"
raise ValueError(msg)
Y = [Y_t.clone() for Y_t in Y]
return [EndModel._preprocess_Y(self, Y_t) for Y_t in Y]
def _preprocess_Y(self, Y):
"""Convert Y to t-length list of soft labels if necessary"""
# If not a list, convert to a singleton list
if not isinstance(Y, list):
if self.t != 1:
msg = "For t > 1, Y must be a list of n-dim or [n, K_t] tensors"
raise ValueError(msg)
Y = [Y]
if not len(Y) == self.t:
msg = f"Expected Y to be a t-length list (t={self.t}), not {len(Y)}"
raise ValueError(msg)
return [
EndModel._preprocess_Y(self, Y_t, self.K[t])
for t, Y_t in enumerate(Y)
]