X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=43241dd79a96da7d7a1f902f8be95541d0ab56c7;hb=cc241681730e50ad149a68c612e3a06f2d4a71be;hp=7b8b6422fdc9ce18bac4c0981b80850220fa020a;hpb=08d4ba04f038318080fc2815d85843c4873c896f;p=culture.git

diff --git a/main.py b/main.py
index 7b8b642..43241dd 100755
--- a/main.py
+++ b/main.py
@@ -13,7 +13,7 @@ from torch.nn import functional as F
 
 import ffutils
 import mygpt
-import sky, wireworld, quizz_machine
+import sky, reasoning, quizz_machine
 
 # world quizzes vs. culture quizzes
 
@@ -79,20 +79,26 @@ parser.add_argument("--dropout", type=float, default=0.1)
 
 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
 
-parser.add_argument("--both_directions", action="store_true", default=False)
-
 parser.add_argument("--problem", type=str, default="sky")
 
 parser.add_argument("--nb_gpts", type=int, default=5)
 
-parser.add_argument("--min_to_validate", type=int, default=4)
+parser.add_argument("--min_to_validate", type=int, default=None)
 
-parser.add_argument("--max_to_validate", type=int, default=4)
+parser.add_argument("--max_to_validate", type=int, default=None)
 
 parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
 
+parser.add_argument("--generation_temperature", type=float, default=2.0)
+
+parser.add_argument("--deterministic_validation", action="store_true", default=False)
+
+parser.add_argument("--bidirectional_validation", action="store_true", default=False)
+
 parser.add_argument("--dirty_debug", action="store_true", default=False)
 
+######################################################################
+
 parser.add_argument("--sky_height", type=int, default=6)
 
 parser.add_argument("--sky_width", type=int, default=8)
@@ -107,6 +113,12 @@ parser.add_argument("--sky_speed", type=int, default=3)
 
 args = parser.parse_args()
 
+if args.min_to_validate is None:
+    args.min_to_validate = args.nb_gpts - 1
+
+if args.max_to_validate is None:
+    args.max_to_validate = args.nb_gpts - 1
+
 if args.result_dir is None:
     args.result_dir = f"results_culture"
 
@@ -237,8 +249,10 @@ if args.problem == "sky":
         nb_iterations=args.sky_nb_iterations,
         speed=args.sky_speed,
     )
-elif args.problem == "wireworld":
-    problem = wireworld.Wireworld(height=8, width=10, nb_iterations=2, speed=5)
+    back_accuracy = False
+elif args.problem == "reasoning":
+    problem = reasoning.Reasoning(device=device)
+    back_accuracy = True
 else:
     raise ValueError
 
@@ -246,6 +260,7 @@ quizz_machine = quizz_machine.QuizzMachine(
     problem=problem,
     nb_train_samples=args.nb_train_samples,
     nb_test_samples=args.nb_test_samples,
+    back_accuracy=back_accuracy,
     batch_size=args.physical_batch_size,
     result_dir=args.result_dir,
     logger=log_string,
@@ -411,10 +426,14 @@ def create_c_quizzes(
             c_quizzes = quizz_machine.generate_quizzes(
                 nb_to_create,
                 model_for_generation=model_for_generation,
+                temperature=args.generation_temperature,
             )
 
             nb_correct, seq_logproba = quizz_machine.compute_correctness(
-                c_quizzes, models, both_directions=args.both_directions
+                c_quizzes,
+                models,
+                bidirectional_validation=args.bidirectional_validation,
+                deterministic_validation=args.deterministic_validation,
             )
 
             for n, l in zip(nb_correct, seq_logproba):
@@ -492,6 +511,9 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 for n_epoch in range(args.nb_epochs):
     log_string(f"--- epoch {n_epoch} ----------------------------------------")
 
+    cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
+    log_string(f"current_test_accuracies {cta}")
+
     # Select, improve, and eval the worst model
 
     weakest_model = min(models, key=lambda m: float(m.main_test_accuracy))
@@ -512,9 +534,6 @@ for n_epoch in range(args.nb_epochs):
         f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
     )
 
-    cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
-    log_string(f"current_test_accuracies {cta}")
-
     # Replace a fraction of the w_quizzes with fresh ones
 
     quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)