X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=622cd567ae5d1d845dfe1b0b186fb1851ae0c80e;hb=17267a244c31be85db250706fead811f20158810;hp=1a6c41572ee8c5e2f5e8ce65b9d8cbfea3834f8a;hpb=9e3211bab93700003ff835e346ef413044147b73;p=culture.git

diff --git a/tasks.py b/tasks.py
index 1a6c415..622cd56 100755
--- a/tasks.py
+++ b/tasks.py
@@ -2100,7 +2100,7 @@ import world
 
 class World(Task):
     def save_image(self, input, result_dir, filename, logger):
-        img = world.sample2img(self.train_input.to("cpu"), self.height, self.width)
+        img = world.sample2img(input.to("cpu"), self.height, self.width)
         image_name = os.path.join(result_dir, filename)
         torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=8, padding=2)
         logger(f"wrote {image_name}")
@@ -2208,7 +2208,8 @@ class World(Task):
             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
         )
 
-        logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
+        main_test_accuracy = test_nb_correct / test_nb_total
+        logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
 
         ##############################
 
@@ -2225,58 +2226,66 @@ class World(Task):
             device=self.device,
         )
 
-        self.save_image(result, result_dir, f"world_result_{n_epoch:04d}.png", logger)
+        self.save_image(
+            result[:96], result_dir, f"world_result_{n_epoch:04d}.png", logger
+        )
+
+        return main_test_accuracy
+
+    def store_new_quizzes(self, new_quizzes, for_train=True):
+        input = self.train_input if for_train else self.test_input
 
-    def store_new_problems(self, new_problems):
-        nb_current = self.train_input.size(0)
-        nb_new = new_problems.size(0)
+        nb_current = input.size(0)
+        nb_new = new_quizzes.size(0)
         if nb_new >= nb_current:
-            self.train_input[...] = new_problems[:nb_current]
+            input[...] = new_quizzes[:nb_current]
         else:
             nb_kept = nb_current - nb_new
-            self.train_input[:nb_kept] = self.train_input[-nb_kept:].clone()
-            self.train_input[nb_kept:] = new_problems
+            input[:nb_kept] = input[-nb_kept:].clone()
+            input[nb_kept:] = new_quizzes
 
-    def create_new_problems(self, n_epoch, result_dir, logger, nb, model, nb_runs):
-        new_problems = torch.empty(
+    def create_new_quizzes(self, n_epoch, result_dir, logger, nb, model, nb_runs):
+        new_quizzes = torch.empty(
             nb, self.height * self.width * 2 + 1, device=self.device, dtype=torch.int64
         )
-        ar_mask = torch.full(new_problems.size(), 1, device=self.device)
+        ar_mask = torch.full(new_quizzes.size(), 1, device=self.device)
 
         masked_inplace_autoregression(
             model,
             self.batch_size,
-            new_problems,
+            new_quizzes,
             ar_mask,
             deterministic_synthesis=False,
-            progress_bar_desc="new problems",
+            progress_bar_desc="new quizzes",
             device=self.device,
         )
 
-        nb_correct = torch.empty(nb, device=self.device, dtype=torch.int64)
+        input = (
+            new_quizzes[:, None, :]
+            .expand(-1, nb_runs, -1)
+            .clone()
+            .reshape(-1, new_quizzes.size(-1))
+        )
+        result = input.clone()
 
-        for n in tqdm.tqdm(
-            range(new_problems.size(0)), dynamic_ncols=True, desc="checking problems"
-        ):
-            result = new_problems[n][None, :].expand(nb_runs, -1).clone()
-            ar_mask = (
-                (torch.arange(result.size(1), device=self.device) > result.size(1) // 2)
-                .long()[None, :]
-                .expand_as(result)
-            )
+        ar_mask = (
+            (torch.arange(result.size(1), device=self.device) > result.size(1) // 2)
+            .long()[None, :]
+            .expand_as(result)
+        )
 
-            masked_inplace_autoregression(
-                model,
-                self.batch_size,
-                result,
-                ar_mask,
-                deterministic_synthesis=False,
-                progress_bar_desc=None,
-                device=self.device,
-            )
+        masked_inplace_autoregression(
+            model,
+            self.batch_size,
+            result,
+            ar_mask,
+            deterministic_synthesis=False,
+            progress_bar_desc=None,
+            device=self.device,
+        )
 
-            nb_correct[n] = (
-                (new_problems[n][None, :] == result).long().min(dim=1).values.sum()
-            )
+        nb_correct = (
+            (input == result).long().min(dim=-1).values.reshape(-1, nb_runs).sum(dim=-1)
+        )
 
-        return new_problems, nb_correct
+        return new_quizzes, nb_correct