X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=0f1fbb5386be975f5853e35575b742fdcee5d622;hb=16cb07f99cf770fb4e97824f874a68cbddd4c1cf;hp=ba8843b313993062d3dd4b8f1020de77b5bf71e3;hpb=f38523dc7bcd791867b45423babb8ddb3358b31e;p=picoclvr.git

diff --git a/main.py b/main.py
index ba8843b..0f1fbb5 100755
--- a/main.py
+++ b/main.py
@@ -12,7 +12,7 @@ from torch import nn
 from torch.nn import functional as F
 
 import ffutils
-import mygpt, tasks
+import mygpt, tasks, problems
 
 ######################################################################
 
@@ -81,13 +81,15 @@ parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
 ##############################
 # rpl options
 
-parser.add_argument("--rpl-nb_starting_values", type=int, default=5)
+parser.add_argument("--rpl_nb_starting_values", type=int, default=5)
 
-parser.add_argument("--rpl-max_input", type=int, default=9)
+parser.add_argument("--rpl_max_input", type=int, default=9)
 
-parser.add_argument("--rpl-prog_len", type=int, default=10)
+parser.add_argument("--rpl_prog_len", type=int, default=10)
 
-parser.add_argument("--rpl-nb_runs", type=int, default=8)
+parser.add_argument("--rpl_nb_runs", type=int, default=8)
+
+parser.add_argument("--rpl_no_prog", action="store_true", default=False)
 
 ##############################
 # sandbox options
@@ -333,19 +335,19 @@ picoclvr_pruner_eval = (
 
 if args.task == "sandbox":
     if args.sandbox_level == 0:
-        problem = tasks.ProblemLevel0(
+        problem = problems.ProblemLevel0(
             nb_sentences=args.sandbox_levels_nb_items,
             len_prompt=args.sandbox_levels_len_source,
             len_result=args.sandbox_levels_len_result,
         )
     elif args.sandbox_level == 1:
-        problem = tasks.ProblemLevel1(
+        problem = problems.ProblemLevel1(
             nb_operators=args.sandbox_levels_nb_items,
             len_source=args.sandbox_levels_len_source,
             len_result=args.sandbox_levels_len_result,
         )
     elif args.sandbox_level == 2:
-        problem = tasks.ProblemLevel2(
+        problem = problems.ProblemLevel2(
             len_source=args.sandbox_levels_len_source,
             len_result=args.sandbox_levels_len_result,
         )
@@ -354,7 +356,7 @@ if args.task == "sandbox":
 
     task = tasks.SandBox(
         problem,
-        # tasks.ProblemAddition(zero_padded=False, inverted_result=False),
+        # problems.ProblemAddition(zero_padded=False, inverted_result=False),
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
         batch_size=args.batch_size,
@@ -442,6 +444,7 @@ elif args.task == "rpl":
         max_input=args.rpl_max_input,
         prog_len=args.rpl_prog_len,
         nb_runs=args.rpl_nb_runs,
+        no_prog=args.rpl_no_prog,
         logger=log_string,
         device=device,
     )
@@ -515,12 +518,12 @@ else:
 
 if args.task == "expr" and args.expr_input_file is not None:
     task.produce_results(
-        nb_epochs_finished,
-        model,
-        args.result_dir,
-        log_string,
-        args.deterministic_synthesis,
-        args.expr_input_file,
+        n_epoch=nb_epochs_finished,
+        model=model,
+        result_dir=args.result_dir,
+        logger=log_string,
+        deterministic_synthesis=args.deterministic_synthesis,
+        input_file=args.expr_input_file,
     )
 
     exit(0)
@@ -596,11 +599,11 @@ nb_samples_seen = 0
 
 if nb_epochs_finished >= nb_epochs:
     task.produce_results(
-        nb_epochs_finished,
-        model,
-        args.result_dir,
-        log_string,
-        args.deterministic_synthesis,
+        n_epoch=nb_epochs_finished,
+        model=model,
+        result_dir=args.result_dir,
+        logger=log_string,
+        deterministic_synthesis=args.deterministic_synthesis,
     )
 
 for n_epoch in range(nb_epochs_finished, nb_epochs):
@@ -654,7 +657,11 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         )
 
         task.produce_results(
-            n_epoch, model, args.result_dir, log_string, args.deterministic_synthesis
+            n_epoch=n_epoch,
+            model=model,
+            result_dir=args.result_dir,
+            logger=log_string,
+            deterministic_synthesis=args.deterministic_synthesis,
         )
 
     checkpoint = {