From: François Fleuret <francois@fleuret.org>
Date: Thu, 20 Jul 2023 23:12:17 +0000 (+0200)
Subject: Update.
X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=27dd45177a8552a8b8c9e3a4d9388844fa1d4d27;p=picoclvr.git

Update.
---

diff --git a/main.py b/main.py
index ba8843b..5506bbd 100755
--- a/main.py
+++ b/main.py
@@ -89,6 +89,8 @@ parser.add_argument("--rpl-prog_len", type=int, default=10)
 
 parser.add_argument("--rpl-nb_runs", type=int, default=8)
 
+parser.add_argument("--rpl-no-prog", action="store_true", default=False)
+
 ##############################
 # sandbox options
 
@@ -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,
     )
diff --git a/tasks.py b/tasks.py
index da39a83..ca71182 100755
--- a/tasks.py
+++ b/tasks.py
@@ -1059,6 +1059,7 @@ class RPL(Task):
         max_input=9,
         prog_len=6,
         nb_runs=5,
+        no_prog=False,
         logger=None,
         device=torch.device("cpu"),
     ):
@@ -1066,6 +1067,7 @@ class RPL(Task):
 
         self.batch_size = batch_size
         self.device = device
+        self.no_prog = no_prog
 
         train_sequences = [
             rpl.generate(
@@ -1100,13 +1102,42 @@ class RPL(Task):
         self.id2token = dict([(n, c) for c, n in self.token2id.items()])
 
         self.t_nul = self.token2id["<nul>"]
-        self.t_prog = self.token2id["<prog>"]
         self.t_input = self.token2id["<input>"]
         self.t_output = self.token2id["<output>"]
+        self.t_prog = self.token2id["<prog>"]
+        self.t_end = self.token2id["<end>"]
 
         self.train_input = self.tensorize(train_sequences)
         self.test_input = self.tensorize(test_sequences)
 
+        if no_prog:
+            k = torch.arange(self.train_input.size(1), device=self.train_input.device)[
+                None, :
+            ]
+            p = (
+                ((self.train_input == self.t_prog).long() * k)
+                .max(1, keepdim=True)
+                .values
+            )
+            self.train_input = (
+                self.train_input * (k <= p).long()
+                + self.t_end * (k == p + 1).long()
+                + self.t_nul * (k > p + 1).long()
+            )
+            k = torch.arange(self.test_input.size(1), device=self.test_input.device)[
+                None, :
+            ]
+            p = (
+                ((self.test_input == self.t_prog).long() * k)
+                .max(1, keepdim=True)
+                .values
+            )
+            self.test_input = (
+                self.test_input * (k <= p).long()
+                + self.t_end * (k == p + 1).long()
+                + self.t_nul * (k > p + 1).long()
+            )
+
         if logger is not None:
             logger(f"value_max {val_max}")
             for x in self.train_input[:25]:
@@ -1221,13 +1252,14 @@ class RPL(Task):
 
         # --------------------------------------------------------------------
 
-        test_nb_total, test_nb_errors = compute_nb_errors_prog(
-            self.test_input[:1000].to(self.device), nb_to_log=10
-        )
+        if not self.no_prog:
+            test_nb_total, test_nb_errors = compute_nb_errors_prog(
+                self.test_input[:1000].to(self.device), nb_to_log=10
+            )
 
-        logger(
-            f"accuracy_prog_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
-        )
+            logger(
+                f"accuracy_prog_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
+            )
 
         test_nb_total, test_nb_errors = compute_nb_errors_output(
             self.test_input[:1000].to(self.device), nb_to_log=10