Update.

[culture.git] / main.py

diff --git a/main.py b/main.py
index 524715a..7f9d521 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -211,7 +211,7 @@ assert args.nb_train_samples % args.batch_size == 0
 assert args.nb_test_samples % args.batch_size == 0
 
 quizz_machine = quizz_machine.QuizzMachine(
-    sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2),
+    problem=sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2),
     nb_train_samples=args.nb_train_samples,
     nb_test_samples=args.nb_test_samples,
     batch_size=args.physical_batch_size,
@@ -349,44 +349,51 @@ def run_tests(model, quizz_machine, deterministic_synthesis):
 
 
 def create_c_quizzes(
-    model,
-    other_models,
+    models,
     quizz_machine,
     nb_for_train=1000,
     nb_for_test=100,
     min_ave_seq_logproba=None,
 ):
     kept = []
-
+    model_indexes = []
     sum_logits, sum_nb_c_quizzes = 0, 0
 
     while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
-        nb_to_generate = 4 * (nb_for_train + nb_for_test)
+        nb_to_generate = nb_for_train + nb_for_test
+
+        if len(model_indexes) == 0:
+            model_indexes = [i.item() for i in torch.randperm(len(models))]
+
+        model = models[model_indexes.pop()]
 
         new_c_quizzes, nb_correct, ave_seq_logproba = quizz_machine.create_c_quizzes(
+            nb=nb_to_generate,
+            model_for_generation=model,
+            models_for_validation=models,
+            min_ave_seq_logproba=min_ave_seq_logproba,
             n_epoch=n_epoch,
             result_dir=args.result_dir,
             logger=log_string,
-            nb=nb_to_generate,
-            model=model,
-            other_models=other_models,
-            min_ave_seq_logproba=min_ave_seq_logproba,
         )
 
         sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
         sum_nb_c_quizzes += new_c_quizzes.size(0)
 
-        to_keep = new_c_quizzes[nb_correct == len(other_models) - 1]
+        to_keep = new_c_quizzes[nb_correct == len(models) - 1]
 
         if args.dirty_debug:
-            to_keep = new_c_quizzes
+            to_keep = new_c_quizzes[
+                torch.randint(3, (new_c_quizzes.size(0),), device=new_c_quizzes.device)
+                == 0
+            ]
+
+        kept.append(to_keep)
 
         log_string(
-            f"keep {to_keep.size(0)}/{new_c_quizzes.size(0)} c_quizzes ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%)"
+            f"keep c_quizzes {to_keep.size(0)}/{new_c_quizzes.size(0)} ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%) total {sum([ x.size(0) for x in kept])}/{nb_to_generate}"
         )
 
-        kept.append(to_keep)
-
     new_c_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
 
     quizz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
@@ -396,7 +403,6 @@ def create_c_quizzes(
         new_c_quizzes[:72],
         args.result_dir,
         f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}",
-        log_string,
     )
 
     return sum_logits / sum_nb_c_quizzes
@@ -463,12 +469,8 @@ for n_epoch in range(args.nb_epochs):
     )
 
     if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_c_quizzes:
-        other_models = models.copy()
-        other_models.remove(model)
-
         ave_seq_logproba = create_c_quizzes(
-            model,
-            other_models,
+            models,
             quizz_machine,
             nb_for_train=nb_new_c_quizzes_for_train,
             nb_for_test=nb_new_c_quizzes_for_test,