From a2ffcd9b27aa0f3cc0b56090a32e88b73dfa0a54 Mon Sep 17 00:00:00 2001
From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= <francois@fleuret.org>
Date: Tue, 18 Jul 2023 08:49:15 +0200
Subject: [PATCH] Update.

---
 tasks.py | 47 ++++++++++++++++++++++++++++++++---------------
 world.py |  7 ++++---
 2 files changed, 36 insertions(+), 18 deletions(-)

diff --git a/tasks.py b/tasks.py
index eef84af..fb85576 100755
--- a/tasks.py
+++ b/tasks.py
@@ -79,7 +79,7 @@ class ProblemByheart(Problem):
 
     def generate_sequences(self, nb):
         sequences = self.seq[torch.randint(self.seq.size(0), (nb,))]
-        ar_mask = (sequences==10).long()
+        ar_mask = (sequences == 10).long()
         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
         return sequences, ar_mask
 
@@ -87,16 +87,17 @@ class ProblemByheart(Problem):
         # 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
+        # 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)
+        # s = torch.cat((strain, stest), 0)
+
 
 class SandBox(Task):
     def __init__(
@@ -107,17 +108,29 @@ class SandBox(Task):
         batch_size,
         logger=None,
         device=torch.device("cpu"),
+        max_nb_codes=1024,
     ):
         super().__init__()
 
         self.batch_size = batch_size
         self.device = device
 
-        self.train_input, self.train_ar_mask = problem.generate_sequences(nb_train_samples)
+        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
 
+        # A bit of paranoia never hurts
+        assert (
+            self.nb_codes <= max_nb_codes
+            and self.train_input.min() >= 0
+            and self.test_input.min() >= 0
+            and tuple(self.train_ar_mask.unique()) == (0, 1)
+            and tuple(self.test_ar_mask.unique()) == (0, 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
@@ -136,9 +149,8 @@ class SandBox(Task):
     def produce_results(
         self, n_epoch, model, result_dir, logger, deterministic_synthesis
     ):
-
         def compute_accuracy(input, ar_mask):
-            result = input.clone() * (1-ar_mask)
+            result = input.clone() * (1 - ar_mask)
             masked_inplace_autoregression(
                 model,
                 self.batch_size,
@@ -150,22 +162,27 @@ class SandBox(Task):
             )
 
             nb_total = ar_mask.sum().item()
-            nb_correct = ((result==input).long() * 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)
+        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)
+        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
diff --git a/world.py b/world.py
index 12c6553..1d64fa3 100755
--- a/world.py
+++ b/world.py
@@ -61,12 +61,13 @@ class SignSTE(nn.Module):
         else:
             return s
 
+
 class DiscreteSampler2d(nn.Module):
     def __init__(self):
         super().__init__()
 
     def forward(self, x):
-        s = (x >= x.max(-3,keepdim=True).values).float()
+        s = (x >= x.max(-3, keepdim=True).values).float()
 
         if self.training:
             u = x.softmax(dim=-3)
@@ -96,7 +97,6 @@ def train_encoder(
     logger=None,
     device=torch.device("cpu"),
 ):
-
     mu, std = train_input.float().mean(), train_input.float().std()
 
     def encoder_core(depth, dim):
@@ -459,7 +459,8 @@ if __name__ == "__main__":
         frame2seq,
         seq2frame,
     ) = create_data_and_processors(
-        25000, 1000,
+        25000,
+        1000,
         nb_epochs=5,
         mode="first_last",
         nb_steps=20,
-- 
2.20.1