X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=eef84af68d4393b2a0b9adb1efb00743fb0318ee;hb=5366dfd7bd57ec3298d1030f7d5327ff26bc5aad;hp=f8fb9b93ace534d6a225558f82b7d2d61211031a;hpb=2ac9d1299a84f96228f49fbdac02d5a7017445e5;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index f8fb9b9..eef84af 100755
--- a/tasks.py
+++ b/tasks.py
@@ -62,6 +62,112 @@ class Task:
 
 ######################################################################
 
+
+class Problem:
+    def generate_sequences(self, nb):
+        pass
+
+    def log_performance(self, sequences, logger):
+        pass
+
+
+class ProblemByheart(Problem):
+    def __init__(self):
+        nb_seq, len_prompt, len_result = 100, 5, 5
+        self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result))
+        self.seq[:, len_prompt] = 10
+
+    def generate_sequences(self, nb):
+        sequences = self.seq[torch.randint(self.seq.size(0), (nb,))]
+        ar_mask = (sequences==10).long()
+        ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+        return sequences, ar_mask
+
+        # problems = [ProblemByheart()]
+        # nb_common_codes = 100
+
+        # 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}")
+            # all_seq = []
+            # for nb, p in zip(nb_samples_per_problem, problems):
+                # all_seq.append(p.generate_sequences(nb_samples_per_problem[nb]))
+            # return all_seq
+
+        # for strain, stest in zip(train_seq, test_seq):
+            # s = torch.cat((strain, stest), 0)
+
+class SandBox(Task):
+    def __init__(
+        self,
+        problem,
+        nb_train_samples,
+        nb_test_samples,
+        batch_size,
+        logger=None,
+        device=torch.device("cpu"),
+    ):
+        super().__init__()
+
+        self.batch_size = batch_size
+        self.device = device
+
+        self.train_input, self.train_ar_mask = problem.generate_sequences(nb_train_samples)
+        self.test_input, self.test_ar_mask = problem.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
+    ):
+
+        def compute_accuracy(input, ar_mask):
+            result = input.clone() * (1-ar_mask)
+            masked_inplace_autoregression(
+                model,
+                self.batch_size,
+                result,
+                ar_mask,
+                deterministic_synthesis,
+                progress_bar_desc=None,
+                device=self.device,
+            )
+
+            nb_total = ar_mask.sum().item()
+            nb_correct = ((result==input).long() * ar_mask).sum().item()
+
+            return nb_total, nb_correct
+
+        train_nb_total, train_nb_correct = compute_accuracy(self.train_input, self.train_ar_mask)
+
+        logger(
+            f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
+        )
+
+        test_nb_total, test_nb_correct = compute_accuracy(self.test_input, self.test_ar_mask)
+
+        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}%"
+        )
+
+######################################################################
+
 import picoclvr
 
 
@@ -108,6 +214,8 @@ class PicoCLVR(Task):
         pruner_train=None,
         pruner_eval=None,
     ):
+        super().__init__()
+
         def generate_descr(nb, cache_suffix, pruner):
             return picoclvr.generate(
                 nb,
@@ -296,6 +404,8 @@ 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 +476,8 @@ class Maze(Task):
         nb_walls,
         device=torch.device("cpu"),
     ):
+        super().__init__()
+
         self.batch_size = batch_size
         self.height = height
         self.width = width
@@ -537,6 +649,8 @@ class Snake(Task):
         prompt_length,
         device=torch.device("cpu"),
     ):
+        super().__init__()
+
         self.batch_size = batch_size
         self.height = height
         self.width = width
@@ -635,6 +749,8 @@ 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 +898,8 @@ class Expr(Task):
         batch_size,
         device=torch.device("cpu"),
     ):
+        super().__init__()
+
         self.batch_size = batch_size
         self.device = device
 
@@ -961,6 +1079,8 @@ class World(Task):
         device=torch.device("cpu"),
         device_storage=torch.device("cpu"),
     ):
+        super().__init__()
+
         self.batch_size = batch_size
         self.device = device