X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=15d97b84ceedbc884fd64ab01288e796ce88dfd9;hb=bf48dc69f7f57ad391481c8917570e35f661cc4a;hp=82d965b040becefc4f4933e055fc9e19d3a6976e;hpb=994d2408781ebaed6da16b10b2b3ebedeff82756;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index 82d965b..15d97b8 100755
--- a/tasks.py
+++ b/tasks.py
@@ -590,8 +590,6 @@ class Snake(Task):
             )
             result *= 1 - ar_mask
 
-            # snake.solver(result,ar_mask)
-
             masked_inplace_autoregression(
                 model,
                 self.batch_size,
@@ -605,19 +603,8 @@ class Snake(Task):
 
             nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
 
-            # nb_total = result.size(0)
-            # nb_correct = ((result - input).abs().sum(1) == 0).sum()
-
             return nb_total, nb_correct
 
-        # train_nb_total, train_nb_correct = compute_nb_correct(
-        # self.train_input, self.train_prior_visits
-        # )
-
-        # logger(
-        # f"accuracy_train 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_nb_correct(
             self.test_input[:1000], self.test_prior_visits[:1000]
         )
@@ -880,17 +867,22 @@ class Expr(Task):
             values_input = expr.extract_results([self.seq2str(s) for s in input])
             values_result = expr.extract_results([self.seq2str(s) for s in result])
 
-            for i, r in zip(values_input, values_result):
-                for n, vi in i.items():
-                    vr = r.get(n)
-                    if vr is None or vr < 0:
-                        nb_missed += 1
-                    else:
-                        d = abs(vr - vi)
-                        if d >= nb_delta.size(0):
+            filename = os.path.join(result_dir, f"expr_result_{n_epoch:04d}.txt")
+
+            with open(filename, "w") as f:
+                for i, r in zip(values_input, values_result):
+                    for n, vi in i.items():
+                        vr = r.get(n)
+                        f.write(f"{vi} {-1 if vr is None else vr}\n")
+
+                        if vr is None or vr < 0:
                             nb_missed += 1
                         else:
-                            nb_delta[d] += 1
+                            d = abs(vr - vi)
+                            if d >= nb_delta.size(0):
+                                nb_missed += 1
+                            else:
+                                nb_delta[d] += 1
 
             ######################################################################
 
@@ -951,4 +943,57 @@ class Expr(Task):
         ##############################################################
 
 
+######################################################################
+import world
+
+
+class World(Task):
+    def __init__(
+        self,
+        nb_train_samples,
+        nb_test_samples,
+        batch_size,
+        device=torch.device("cpu"),
+    ):
+        self.batch_size = batch_size
+        self.device = device
+
+        (
+            self.train_input,
+            self.train_actions,
+            self.test_input,
+            self.test_actions,
+            self.frame2seq,
+            self.seq2frame,
+        ) = world.create_data_and_processors(
+            nb_train_samples,
+            nb_test_samples,
+            mode="first_last",
+            nb_steps=30,
+            nb_epochs=2,
+        )
+
+        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
+    ):
+        pass
+
+
 ######################################################################