X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=0858282fa3b5cab185d14025d5ad758de44411c2;hb=16e7952b7cc32ca21498fa3a12fb79f679ea8c21;hp=44599f7db31bd7abe2b971afeb6ff5b795875682;hpb=4aa7e109b4c712643cdddc2480b66d8799f71d3f;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index 44599f7..0858282 100755
--- a/tasks.py
+++ b/tasks.py
@@ -110,13 +110,14 @@ class SandBox(Task):
 
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
+
         # A bit of paranoia never hurts
         assert (
             self.nb_codes <= max_nb_codes
             and self.train_input.min() >= 0
             and self.test_input.min() >= 0
-            and tuple(self.train_ar_mask.unique()) == (0, 1)
-            and tuple(self.test_ar_mask.unique()) == (0, 1)
+            and tuple(x.item() for x in self.train_ar_mask.unique()) in { (0,), (1,), (0,1) }
+            and tuple(x.item() for x in self.test_ar_mask.unique()) in { (0,), (1,), (0,1) }
         )
 
     def batches(self, split="train", nb_to_use=-1, desc=None):
@@ -160,8 +161,10 @@ class SandBox(Task):
                         f"               {n_epoch} ground truth {self.problem.seq2str(st)}"
                     )
 
-            nb_total = ar_mask.sum().item()
-            nb_correct = ((result == input).long() * ar_mask).sum().item()
+            nb_total, nb_correct = self.problem.compute_nb_correct(input, ar_mask, result)
+
+            # nb_total = ar_mask.sum().item()
+            # nb_correct = ((result == input).long() * ar_mask).sum().item()
 
             return nb_total, nb_correct
 
@@ -1588,13 +1591,19 @@ class QMLP(Task):
 
         self.train_input = seq[:nb_train_samples]
         self.train_q_test_set = q_test_set[:nb_train_samples]
+        self.train_ref_test_errors = test_error[:nb_train_samples]
         self.test_input = seq[nb_train_samples:]
         self.test_q_test_set = q_test_set[nb_train_samples:]
-        self.ref_test_errors = test_error
+        self.test_ref_test_errors = test_error[nb_train_samples:]
+
+        filename = os.path.join(result_dir, f"train_errors_ref.dat")
+        with open(filename, "w") as f:
+            for e in self.train_ref_test_errors:
+                f.write(f"{e}\n")
 
         filename = os.path.join(result_dir, f"test_errors_ref.dat")
         with open(filename, "w") as f:
-            for e in self.ref_test_errors:
+            for e in self.test_ref_test_errors:
                 f.write(f"{e}\n")
 
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1