device=self.device,
)
- #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
- self.save_quizzes(
- result_dir,
- f"DEBUG_input_{n_epoch}_{result.size(0):04d}",
- quizzes=input[:72],
- prediction=True,
- )
- self.save_quizzes(
- result_dir,
- f"DEBUG_result_{n_epoch}_{result.size(0):04d}",
- quizzes=result[:72],
- prediction=True,
- )
- #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-
if self.back_accuracy:
+ # If back_accuracy is True, we compute the accuracy on
+ # the backward quizzes not by counting how many time
+ # the real prompt A is equal to the reconstructed
+ # prompt A*, but how many time the answers B* computed
+ # from A* is equal to the correct answer. So we look
+ # for the accuracy of A->B*=B for the forward, but for
+ # the backward we look at B->A*->B*=B instead of B->A*=A
+
n_forward = input[:, 0] == self.token_forward
nb_total = input[n_forward].size(0)
nb_correct = (
.item()
)
- self.logger(
- f"back_accuracy {n_epoch=} {model.id=} {nb_correct=} {nb_total=}"
- )
-
n_backward = input[:, 0] == self.token_backward
back_input = self.reverse_time(result[n_backward])
n_backward, 1 : 1 + self.answer_len
]
back_nb_total, back_nb_correct = compute_accuracy(back_input)
+
+ self.logger(
+ f"accuracy {n_epoch=} {model.id=} {nb_correct} / {nb_total}"
+ )
self.logger(
- f"back_accuracy {n_epoch=} {model.id=} {back_nb_correct=} {back_nb_total=}"
+ f"back_accuracy {n_epoch=} {model.id=} {back_nb_correct} / {back_nb_total}"
)
+
nb_total += back_nb_total
nb_correct += back_nb_correct
+ else:
+ self.logger(
+ f"accuracy {n_epoch=} {model.id=} {nb_correct} / {nb_total}"
+ )
else:
nb_total = input.size(0)
nb_correct = (input == result).long().min(dim=1).values.sum()
- exit(0)
-
return nb_total, nb_correct
train_nb_total, train_nb_correct = compute_accuracy(self.train_w_quizzes[:nmax])