X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=622cd567ae5d1d845dfe1b0b186fb1851ae0c80e;hb=17267a244c31be85db250706fead811f20158810;hp=1b28108079905addada818ceab335183688434b3;hpb=b9924ef4309a69af2af150dba73125ed5fed0093;p=culture.git

diff --git a/tasks.py b/tasks.py
index 1b28108..622cd56 100755
--- a/tasks.py
+++ b/tasks.py
@@ -2099,11 +2099,18 @@ import world
 
 
 class World(Task):
+    def save_image(self, input, result_dir, filename, logger):
+        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}")
+
     def __init__(
         self,
         nb_train_samples,
         nb_test_samples,
         batch_size,
+        result_dir=None,
         logger=None,
         device=torch.device("cpu"),
     ):
@@ -2141,6 +2148,11 @@ class World(Task):
 
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
+        if result_dir is not None:
+            self.save_image(
+                self.train_input[:96], result_dir, f"world_train.png", logger
+            )
+
     def batches(self, split="train", nb_to_use=-1, desc=None):
         assert split in {"train", "test"}
         input = self.train_input if split == "train" else self.test_input
@@ -2196,11 +2208,12 @@ 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}")
 
         ##############################
 
-        input, ar_mask = self.test_input[:64], self.test_ar_mask[:64]
+        input, ar_mask = self.test_input[:96], self.test_ar_mask[:96]
         result = input.clone() * (1 - ar_mask)
 
         masked_inplace_autoregression(
@@ -2213,56 +2226,66 @@ class World(Task):
             device=self.device,
         )
 
-        img = world.sample2img(result.to("cpu"), self.height, self.width)
-        image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")
-        torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=8, padding=2)
-        logger(f"wrote {image_name}")
+        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 create_new_problems(self, n_epoch, result_dir, logger, nb, model, nb_runs):
-        new_problems = torch.empty(
+        nb_current = input.size(0)
+        nb_new = new_quizzes.size(0)
+        if nb_new >= nb_current:
+            input[...] = new_quizzes[:nb_current]
+        else:
+            nb_kept = nb_current - nb_new
+            input[:nb_kept] = input[-nb_kept:].clone()
+            input[nb_kept:] = new_quizzes
+
+    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,
         )
 
-        img = world.sample2img(new_problems[:64].to("cpu"), self.height, self.width)
-        image_name = os.path.join(result_dir, f"world_new_{n_epoch:04d}.png")
-        torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=8, padding=2)
-        logger(f"wrote {image_name}")
-
-        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