Update.

[culture.git] / main.py

diff --git a/main.py b/main.py
index 2afe61b..ebecad8 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -14,6 +14,14 @@ from torch.nn import functional as F
 import ffutils
 import mygpt, tasks
 
+# world quizzes vs. culture quizzes
+
+######################################################################
+
+accuracy_to_make_c_quizzes = 0.975
+nb_new_c_quizzes_for_train = 1000
+nb_new_c_quizzes_for_test = 100
+
 ######################################################################
 
 if torch.cuda.is_available():
@@ -73,7 +81,7 @@ 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)
 
 ######################################################################
 
@@ -84,6 +92,13 @@ if args.result_dir is None:
 
 ######################################################################
 
+if args.dirty_debug:
+    accuracy_to_make_c_quizzes = 0.0
+    nb_new_c_quizzes_for_train = 100
+    nb_new_c_quizzes_for_test = 10
+
+######################################################################
+
 default_args = {
     "model": "37M",
     "batch_size": 100,
@@ -182,7 +197,7 @@ for n in vars(args):
 
 ######################################################################
 
-if args.check:
+if args.dirty_debug:
     args.nb_train_samples = 2500
     args.nb_test_samples = 100
 
@@ -329,7 +344,7 @@ def run_tests(model, task, deterministic_synthesis):
 ######################################################################
 
 
-def create_quizzes(
+def create_c_quizzes(
     model,
     other_models,
     task,
@@ -338,11 +353,13 @@ def create_quizzes(
     desired_average_logits=None,
 ):
     kept = []
-    nb_generated_tokens, sum_logits = 0, 0
+
+    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)
-        new_quizzes, nb_correct, average_logits = task.create_new_quizzes(
+
+        new_c_quizzes, nb_correct, average_logits = task.create_c_quizzes(
             n_epoch=n_epoch,
             result_dir=args.result_dir,
             logger=log_string,
@@ -352,28 +369,33 @@ def create_quizzes(
             desired_average_logits=desired_average_logits,
         )
 
-        nb_generated_tokens += new_quizzes.numel()
-        sum_logits += average_logits * new_quizzes.numel()
+        sum_logits += new_c_quizzes.size(0) * average_logits
+        sum_nb_c_quizzes += new_c_quizzes.size(0)
+
+        to_keep = new_c_quizzes[nb_correct == len(other_models) - 1]
+
+        if args.dirty_debug:
+            to_keep = new_c_quizzes
 
-        to_keep = new_quizzes[nb_correct == len(other_models) - 1]
         log_string(
-            f"keep {to_keep.size(0)}/{new_quizzes.size(0)} quizzes ({to_keep.size(0)*100/new_quizzes.size(0):.02f}%)"
+            f"keep {to_keep.size(0)}/{new_c_quizzes.size(0)} c_quizzes ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%)"
         )
+
         kept.append(to_keep)
 
-    new_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
+    new_c_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
 
-    task.store_new_quizzes(new_quizzes[:nb_for_train], for_train=True)
-    task.store_new_quizzes(new_quizzes[nb_for_train:], for_train=False)
+    task.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
+    task.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
 
-    task.save_image(
-        new_quizzes[:72],
+    task.save_quizzes(
+        new_c_quizzes[:72],
         args.result_dir,
-        f"world_quiz_{n_epoch:04d}_{model.id:02d}.png",
+        f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}",
         log_string,
     )
 
-    return sum_logits / nb_generated_tokens
+    return sum_logits / sum_nb_c_quizzes
 
 
 ######################################################################
@@ -403,15 +425,6 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 
 ######################################################################
 
-accuracy_to_make_quizzes = 0.975
-nb_new_quizzes_for_train = 1000
-nb_new_quizzes_for_test = 100
-
-if args.check:
-    accuracy_to_make_quizzes = 0.0
-    nb_new_quizzes_for_train = 10
-    nb_new_quizzes_for_test = 10
-
 desired_average_logits = None
 
 for n_epoch in range(args.nb_epochs):
@@ -432,29 +445,29 @@ for n_epoch in range(args.nb_epochs):
     # improve it
     one_epoch(model, task)
 
-    task.renew_samples(args.nb_train_samples // args.nb_gpts)
+    task.renew_w_quizzes(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}"
+        f"train_set_composition w_quizzes {task.nb_batch_w_quizzes} c_quizzes {task.nb_batch_c_quizzes}"
     )
 
     # test it
     run_tests(model, task, deterministic_synthesis=False)
 
     log_string(
-        f"test_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
+        f"test_set_composition w_quizzes {task.nb_batch_w_quizzes} c_quizzes {task.nb_batch_c_quizzes}"
     )
 
-    if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_quizzes:
+    if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_c_quizzes:
         other_models = models.copy()
         other_models.remove(model)
 
-        average_logits = create_quizzes(
+        average_logits = create_c_quizzes(
             model,
             other_models,
             task,
-            nb_for_train=nb_new_quizzes_for_train,
-            nb_for_test=nb_new_quizzes_for_test,
+            nb_for_train=nb_new_c_quizzes_for_train,
+            nb_for_test=nb_new_c_quizzes_for_test,
             desired_average_logits=desired_average_logits,
         )