Update.

diff --git a/main.py b/main.py
index 79b4b56..3b29d01 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -404,7 +404,7 @@ if args.check:
     nb_new_quizzes_for_test = 10
 
 for n_epoch in range(args.nb_epochs):
-    a = [(model.id, model.main_test_accuracy) for model in models]
+    a = [(model.id, float(model.main_test_accuracy)) for model in models]
     a.sort(key=lambda p: p[0])
     log_string(f"current accuracies {a}")
 

diff --git a/tasks.py b/tasks.py
index ad95237..1254323 100755 (executable)
--- a/tasks.py
+++ b/tasks.py
@@ -104,11 +104,11 @@ class World(Task):
         self.height = 7
         self.width = 9
 
-        self.train_input = world.generate(
+        self.train_input = world.generate_seq(
             nb_train_samples, height=self.height, width=self.width
         ).to(device)
 
-        self.test_input = world.generate(
+        self.test_input = world.generate_seq(
             nb_test_samples, height=self.height, width=self.width
         ).to(device)
 

diff --git a/world.py b/world.py
index 68f46de..0d3509f 100755 (executable)
--- a/world.py
+++ b/world.py
@@ -18,26 +18,16 @@ from torch.nn import functional as F
 colors = torch.tensor(
     [
         [255, 255, 255],
-        [255, 20, 147],
-        [0, 0, 255],
+        [255, 0, 0],
         [0, 192, 0],
+        [0, 0, 255],
+        [255, 192, 0],
         [0, 255, 255],
+        [255, 0, 255],
+        [192, 255, 192],
+        [255, 192, 192],
+        [192, 192, 255],
         [192, 192, 192],
-        [106, 90, 205],
-        [255, 0, 0],
-        [220, 20, 60],
-        [65, 105, 225],
-        [255, 200, 0],
-        # [255, 182, 193],
-        # [75, 0, 130],
-        # [128, 0, 128],
-        # [30, 144, 255],
-        # [135, 206, 235],
-        # [0, 255, 0],
-        # [64, 224, 208],
-        # [250, 128, 114],
-        # [255, 165, 0],
-        # [0, 255, 255],
     ]
 )
 
@@ -50,87 +40,93 @@ token_backward = token_forward + 1
 token2char = "_" + "".join([chr(ord("A") + n) for n in range(len(colors) - 1)]) + "><"
 
 
-def generate(
+def generate_seq(
     nb,
     height,
     width,
     nb_birds=3,
-    nb_iterations=1,
+    nb_iterations=2,
 ):
     pairs = []
 
     for _ in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
-        f_start = torch.zeros(height, width, dtype=torch.int64)
+        while True:
+            f_start = torch.zeros(height, width, dtype=torch.int64)
+
+            i, j, vi, vj = (
+                torch.empty(nb_birds, dtype=torch.int64),
+                torch.empty(nb_birds, dtype=torch.int64),
+                torch.empty(nb_birds, dtype=torch.int64),
+                torch.empty(nb_birds, dtype=torch.int64),
+            )
+
+            col = torch.randperm(colors.size(0) - 1)[:nb_birds].sort().values + 1
 
-        i, j, vi, vj = (
-            torch.empty(nb_birds, dtype=torch.int64),
-            torch.empty(nb_birds, dtype=torch.int64),
-            torch.empty(nb_birds, dtype=torch.int64),
-            torch.empty(nb_birds, dtype=torch.int64),
-        )
-
-        col = torch.randperm(colors.size(0) - 1)[:nb_birds].sort().values + 1
-
-        for n in range(nb_birds):
-            c = col[n]
-
-            while True:
-                i[n], j[n] = (
-                    torch.randint(height, (1,))[0],
-                    torch.randint(width, (1,))[0],
-                )
-                vm = torch.randint(4, (1,))[0]
-                vi[n], vj[n] = (vm % 2) * 2 - 1, (vm // 2) * 2 - 1
-                if (
-                    i[n] - vi[n] >= 0
-                    and i[n] - vi[n] < height
-                    and j[n] - vj[n] >= 0
-                    and j[n] - vj[n] < width
-                    and f_start[i[n], j[n]] == 0
-                    and f_start[i[n] - vi[n], j[n]] == 0
-                    and f_start[i[n], j[n] - vj[n]] == 0
-                ):
-                    break
-
-            f_start[i[n], j[n]] = c
-            f_start[i[n] - vi[n], j[n]] = c
-            f_start[i[n], j[n] - vj[n]] = c
-
-        f_end = f_start.clone()
-
-        for l in range(nb_iterations):
             for n in range(nb_birds):
                 c = col[n]
-                f_end[i[n], j[n]] = 0
-                f_end[i[n] - vi[n], j[n]] = 0
-                f_end[i[n], j[n] - vj[n]] = 0
 
-                pi, pj, pvi, pvj = i[n].item(), j[n].item(), vi[n].item(), vj[n].item()
+                while True:
+                    i[n], j[n] = (
+                        torch.randint(height, (1,))[0],
+                        torch.randint(width, (1,))[0],
+                    )
+                    vm = torch.randint(4, (1,))[0]
+                    vi[n], vj[n] = (vm % 2) * 2 - 1, (vm // 2) * 2 - 1
+                    if (
+                        i[n] - vi[n] >= 0
+                        and i[n] - vi[n] < height
+                        and j[n] - vj[n] >= 0
+                        and j[n] - vj[n] < width
+                        and f_start[i[n], j[n]] == 0
+                        and f_start[i[n] - vi[n], j[n]] == 0
+                        and f_start[i[n], j[n] - vj[n]] == 0
+                    ):
+                        break
+
+                f_start[i[n], j[n]] = c
+                f_start[i[n] - vi[n], j[n]] = c
+                f_start[i[n], j[n] - vj[n]] = c
+
+            f_end = f_start.clone()
 
-                assert (
-                    f_end[i[n], j[n]] == 0
-                    and f_end[i[n] - vi[n], j[n]] == 0
-                    and f_end[i[n], j[n] - vj[n]] == 0
-                )
-
-                if (i[n] == 0 and vi[n] == -1) or (i[n] == height - 1 and vi[n] == 1):
-                    vi[n] = -vi[n]
-                if (j[n] == 0 and vj[n] == -1) or (j[n] == width - 1 and vj[n] == 1):
-                    vj[n] = -vj[n]
-
-                i[n] += vi[n]
-                j[n] += vj[n]
-
-                if not (
-                    f_end[i[n], j[n]] == 0
-                    and f_end[i[n] - vi[n], j[n]] == 0
-                    and f_end[i[n], j[n] - vj[n]] == 0
-                ):
-                    i[n], j[n], vi[n], vj[n] = pi, pj, pvi, pvj
-
-                f_end[i[n], j[n]] = c
-                f_end[i[n] - vi[n], j[n]] = c
-                f_end[i[n], j[n] - vj[n]] = c
+            for l in range(nb_iterations):
+                f_end[...] = 0
+                nb_collisions = 0
+                for n in range(nb_birds):
+                    c = col[n]
+
+                    pi, pj, pvi, pvj = (
+                        i[n].item(),
+                        j[n].item(),
+                        vi[n].item(),
+                        vj[n].item(),
+                    )
+
+                    if (i[n] == 0 and vi[n] == -1) or (
+                        i[n] == height - 1 and vi[n] == 1
+                    ):
+                        vi[n] = -vi[n]
+                    if (j[n] == 0 and vj[n] == -1) or (
+                        j[n] == width - 1 and vj[n] == 1
+                    ):
+                        vj[n] = -vj[n]
+
+                    i[n] += vi[n]
+                    j[n] += vj[n]
+
+                    if not (
+                        f_end[i[n], j[n]] == 0
+                        and f_end[i[n] - vi[n], j[n]] == 0
+                        and f_end[i[n], j[n] - vj[n]] == 0
+                    ):
+                        nb_collisions += 1
+
+                    f_end[i[n], j[n]] = c
+                    f_end[i[n] - vi[n], j[n]] = c
+                    f_end[i[n], j[n] - vj[n]] = c
+
+            if nb_collisions == 0:
+                break
 
         pairs.append((f_start, f_end))
 
@@ -154,7 +150,10 @@ def generate(
     return torch.cat(result, dim=0)
 
 
-def generate_(
+######################################################################
+
+
+def generate_seq_(
     nb,
     height,
     width,
@@ -303,7 +302,7 @@ if __name__ == "__main__":
 
     height, width = 6, 8
     start_time = time.perf_counter()
-    seq = generate(nb=90, height=height, width=width)
+    seq = generate_seq(nb=90, height=height, width=width)
     delay = time.perf_counter() - start_time
     print(f"{seq.size(0)/delay:02f} samples/s")