Update.

[culture.git] / main.py

diff --git a/main.py b/main.py
index 45fa68c..8033836 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -73,7 +73,7 @@ parser.add_argument("--deterministic_synthesis", action="store_true", default=Fa
 
 parser.add_argument("--nb_gpts", type=int, default=5)
 
-parser.add_argument("--check", action="store_true", default=False)
+parser.add_argument("--dirty_debug", action="store_true", default=False)
 
 ######################################################################
 
@@ -182,8 +182,8 @@ for n in vars(args):
 
 ######################################################################
 
-if args.check:
-    args.nb_train_samples = 500
+if args.dirty_debug:
+    args.nb_train_samples = 2500
     args.nb_test_samples = 100
 
 if args.physical_batch_size is None:
@@ -335,23 +335,37 @@ def create_quizzes(
     task,
     nb_for_train=1000,
     nb_for_test=100,
+    desired_average_logits=None,
 ):
     kept = []
 
+    sum_logits, sum_nb_quizzes = 0, 0
+
     while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
-        new_quizzes, nb_correct = task.create_new_quizzes(
+        nb_to_generate = 4 * (nb_for_train + nb_for_test)
+
+        new_quizzes, nb_correct, average_logits = task.create_new_quizzes(
             n_epoch=n_epoch,
             result_dir=args.result_dir,
             logger=log_string,
-            nb=4 * (nb_for_train + nb_for_test),
+            nb=nb_to_generate,
             model=model,
             other_models=other_models,
+            desired_average_logits=desired_average_logits,
         )
 
-        print(nb_correct)
+        sum_logits += new_quizzes.size(0) * average_logits
+        sum_nb_quizzes += new_quizzes.size(0)
 
         to_keep = new_quizzes[nb_correct == len(other_models) - 1]
-        log_string(f"keep {to_keep.size(0)} quizzes")
+
+        if args.dirty_debug:
+            to_keep = new_quizzes
+
+        log_string(
+            f"keep {to_keep.size(0)}/{new_quizzes.size(0)} quizzes ({to_keep.size(0)*100/new_quizzes.size(0):.02f}%)"
+        )
+
         kept.append(to_keep)
 
     new_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
@@ -366,6 +380,8 @@ def create_quizzes(
         log_string,
     )
 
+    return sum_logits / sum_nb_quizzes
+
 
 ######################################################################
 
@@ -398,13 +414,17 @@ accuracy_to_make_quizzes = 0.975
 nb_new_quizzes_for_train = 1000
 nb_new_quizzes_for_test = 100
 
-if args.check:
+if args.dirty_debug:
     accuracy_to_make_quizzes = 0.0
-    nb_new_quizzes_for_train = 10
+    nb_new_quizzes_for_train = 100
     nb_new_quizzes_for_test = 10
 
+desired_average_logits = None
+
 for n_epoch in range(args.nb_epochs):
-    a = [(model.id, model.main_test_accuracy) for model in models]
+    log_string(f"--- epoch {n_epoch} ----------------------------------------")
+
+    a = [(model.id, float(model.main_test_accuracy)) for model in models]
     a.sort(key=lambda p: p[0])
     log_string(f"current accuracies {a}")
 
@@ -419,6 +439,8 @@ for n_epoch in range(args.nb_epochs):
     # improve it
     one_epoch(model, task)
 
+    task.renew_samples(args.nb_train_samples // args.nb_gpts)
+
     log_string(
         f"train_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
     )
@@ -426,18 +448,31 @@ for n_epoch in range(args.nb_epochs):
     # test it
     run_tests(model, task, deterministic_synthesis=False)
 
-    if model.main_test_accuracy >= accuracy_to_make_quizzes:
+    log_string(
+        f"test_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
+    )
+
+    if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_quizzes:
         other_models = models.copy()
         other_models.remove(model)
 
-        create_quizzes(
+        average_logits = create_quizzes(
             model,
             other_models,
             task,
             nb_for_train=nb_new_quizzes_for_train,
             nb_for_test=nb_new_quizzes_for_test,
+            desired_average_logits=desired_average_logits,
         )
 
+        # We keep the first average logits as a reference
+        if desired_average_logits is None:
+            desired_average_logits = average_logits
+        else:
+            log_string(
+                f"desired_average_logits {desired_average_logits} average_logits {average_logits}"
+            )
+
         # We update everyone
         for model in models:
             run_tests(model, task, deterministic_synthesis=False)