X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=0858282fa3b5cab185d14025d5ad758de44411c2;hb=16e7952b7cc32ca21498fa3a12fb79f679ea8c21;hp=066f1bbec05fcc0d65365823ea60931010e118cb;hpb=26ef53ee3769c3b6b92b85d15b5a43cbd18ede07;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index 066f1bb..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
 
@@ -1555,7 +1558,6 @@ import qmlp
 
 
 class QMLP(Task):
-
     ######################
 
     def __init__(
@@ -1563,6 +1565,7 @@ class QMLP(Task):
         nb_train_samples,
         nb_test_samples,
         batch_size,
+        result_dir,
         logger=None,
         device=torch.device("cpu"),
     ):
@@ -1577,19 +1580,31 @@ class QMLP(Task):
                 f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
             )
 
-        seq, q_test_set = generate_sequence_and_test_set(
-            nb_mlps=nb_train_samples+nb_test_samples,
+        seq, q_test_set, test_error = qmlp.generate_sequence_and_test_set(
+            nb_mlps=nb_train_samples + nb_test_samples,
             nb_samples=self.nb_samples_per_mlp,
             device=self.device,
             batch_size=64,
             nb_epochs=250,
-            nb_mlps_per_batch=1024
+            nb_mlps_per_batch=1024,
         )
 
         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.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.test_ref_test_errors:
+                f.write(f"{e}\n")
 
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
@@ -1599,7 +1614,7 @@ class QMLP(Task):
         for batch in tqdm.tqdm(
             input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
         ):
-            yield self.trim(batch)
+            yield batch
 
     def vocabulary_size(self):
         return self.nb_codes
@@ -1609,14 +1624,13 @@ class QMLP(Task):
     ):
         correct = self.test_input[:1000]
         result = correct.clone()
-        ar_mask = torch.arange(result.size(1)) > self.nb_samples_per_mlp * 3 + 1
+        ar_mask = (
+            torch.arange(result.size(1), device=result.device)
+            > self.nb_samples_per_mlp * 3 + 1
+        ).long()[None, :]
+        ar_mask = ar_mask.expand_as(result)
         result *= 1 - ar_mask  # paraaaaanoiaaaaaaa
 
-        logger(f"----------------------------------------------------------")
-
-        for e in self.tensor2str(result[:10]):
-            logger(f"test_before {e}")
-
         masked_inplace_autoregression(
             model,
             self.batch_size,
@@ -1626,18 +1640,14 @@ class QMLP(Task):
             device=self.device,
         )
 
-        logger(f"----------------------------------------------------------")
-
-        for e in self.tensor2str(result[:10]):
-            logger(f"test_after  {e}")
-
-        logger(f"----------------------------------------------------------")
-
-        q_train_set = result[:, : nb_samples * 3]
-        q_params = result[:, nb_samples * 3 + 1 :]
-        error_test = evaluate_q_params(q_params, q_test_set, nb_mlps_per_batch=17)
+        q_train_set = result[:, : self.nb_samples_per_mlp * 3]
+        q_params = result[:, self.nb_samples_per_mlp * 3 + 1 :]
+        error_test = qmlp.evaluate_q_params(q_params, self.test_q_test_set)
 
-        logger(f"{error_test=}")
+        filename = os.path.join(result_dir, f"test_errors_{n_epoch:04d}.dat")
+        with open(filename, "w") as f:
+            for e in error_test:
+                f.write(f"{e}\n")
 
 
 ######################################################################