X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=8b57cb2056462d8265bb38c1dffb49c4d59dc41a;hb=e3a8032a070175ece08fc79c77312d5f2f59150e;hp=f8fb9b93ace534d6a225558f82b7d2d61211031a;hpb=2ac9d1299a84f96228f49fbdac02d5a7017445e5;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index f8fb9b9..8b57cb2 100755
--- a/tasks.py
+++ b/tasks.py
@@ -60,6 +60,69 @@ class Task:
         pass
 
 
+######################################################################
+
+
+class Problem:
+    def generate(nb):
+        pass
+
+    def perf(seq, logger):
+        pass
+
+
+class ProblemByheart(Problem):
+    def __init__(self):
+        pass
+
+
+class SandBox(Task):
+    def __init__(
+        self,
+        nb_train_samples,
+        nb_test_samples,
+        batch_size,
+        logger=None,
+        device=torch.device("cpu"),
+    ):
+        super().__init__()
+
+        self.batch_size = batch_size
+
+        def generate_sequences(nb_samples):
+            problem_indexes = torch.randint(len(problems), (nb_samples,))
+            nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0)
+            print(f"{nb_samples_per_problem}")
+
+        self.train_input = generate_sequences(nb_train_samples)
+        self.test_input = generate_sequences(nb_test_samples)
+
+        self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+
+    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
+        if nb_to_use > 0:
+            input = input[:nb_to_use]
+        if desc is None:
+            desc = f"epoch-{split}"
+        for batch in tqdm.tqdm(
+            input.split(self.batch_size), dynamic_ncols=True, desc=desc
+        ):
+            yield batch
+
+    def vocabulary_size(self):
+        return self.nb_codes
+
+    def produce_results(
+        self, n_epoch, model, result_dir, logger, deterministic_synthesis
+    ):
+        # logger(
+        # 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}%"
+        # )
+        pass
+
+
 ######################################################################
 
 import picoclvr
@@ -108,6 +171,8 @@ class PicoCLVR(Task):
         pruner_train=None,
         pruner_eval=None,
     ):
+        super().__init__()
+
         def generate_descr(nb, cache_suffix, pruner):
             return picoclvr.generate(
                 nb,
@@ -296,6 +361,8 @@ class MNIST(Task):
     def __init__(
         self, nb_train_samples, nb_test_samples, batch_size, device=torch.device("cpu")
     ):
+        super().__init__()
+
         self.nb_train_samples = (nb_train_samples,)
         self.nb_test_samples = (nb_test_samples,)
         self.batch_size = batch_size
@@ -366,6 +433,8 @@ class Maze(Task):
         nb_walls,
         device=torch.device("cpu"),
     ):
+        super().__init__()
+
         self.batch_size = batch_size
         self.height = height
         self.width = width
@@ -537,6 +606,8 @@ class Snake(Task):
         prompt_length,
         device=torch.device("cpu"),
     ):
+        super().__init__()
+
         self.batch_size = batch_size
         self.height = height
         self.width = width
@@ -635,6 +706,8 @@ class Stack(Task):
         fraction_values_for_train=None,
         device=torch.device("cpu"),
     ):
+        super().__init__()
+
         self.batch_size = batch_size
         self.nb_steps = nb_steps
         self.nb_stacks = nb_stacks
@@ -782,6 +855,8 @@ class Expr(Task):
         batch_size,
         device=torch.device("cpu"),
     ):
+        super().__init__()
+
         self.batch_size = batch_size
         self.device = device
 
@@ -961,6 +1036,8 @@ class World(Task):
         device=torch.device("cpu"),
         device_storage=torch.device("cpu"),
     ):
+        super().__init__()
+
         self.batch_size = batch_size
         self.device = device