X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=world.py;h=36aa1e97638403195c0d711fde941be83ef17a7a;hb=fc1de19bf86b2cfd09264dfc6fbda1937248a40a;hp=89833e6582349727a9372f3ba15ea66eeb95c3b5;hpb=e8e9b3941f150b20aa9585f7fa0a1f5e2fe6f547;p=culture.git

diff --git a/world.py b/world.py
index 89833e6..36aa1e9 100755
--- a/world.py
+++ b/world.py
@@ -18,32 +18,165 @@ from torch.nn import functional as F
 colors = torch.tensor(
     [
         [255, 255, 255],
-        [0, 0, 0],
         [255, 0, 0],
-        [0, 128, 0],
+        [0, 192, 0],
         [0, 0, 255],
-        [255, 200, 0],
+        [255, 192, 0],
+        [0, 255, 255],
+        [255, 0, 255],
+        [192, 255, 192],
+        [255, 192, 192],
+        [192, 192, 255],
         [192, 192, 192],
     ]
 )
 
-token2char = "_X" + "".join([str(n) for n in range(len(colors) - 2)]) + ">"
+token_background = 0
+first_bird_token = 1
+nb_bird_tokens = colors.size(0) - 1
+token_forward = first_bird_token + nb_bird_tokens
+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=2, return_iterations=False
+):
+    pairs = []
+    kept_iterations = []
+
+    for _ in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
+        while True:
+            iterations = []
+
+            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
+
+            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):
+                iterations.append(f_end.clone())
+                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
+
+            iterations.append(f_end.clone())
+
+            if nb_collisions == 0:
+                break
+
+        kept_iterations.append(iterations)
+        pairs.append((f_start, f_end))
+
+    result = []
+    for p in pairs:
+        if torch.rand(1) < 0.5:
+            result.append(
+                torch.cat(
+                    [p[0].flatten(), torch.tensor([token_forward]), p[1].flatten()],
+                    dim=0,
+                )[None, :]
+            )
+        else:
+            result.append(
+                torch.cat(
+                    [p[1].flatten(), torch.tensor([token_backward]), p[0].flatten()],
+                    dim=0,
+                )[None, :]
+            )
+
+    if return_iterations:
+        # iterations = torch.cat([ torch.cat([ x[None, None] for x in l], dim = 1) for l in kept_iterations ], dim=0)
+        return torch.cat(result, dim=0), kept_iterations
+    else:
+        return torch.cat(result, dim=0)
+
+
+######################################################################
+
+
+def generate_seq_old(
     nb,
     height,
     width,
-    max_nb_obj=2,
+    nb_birds=3,
     nb_iterations=2,
 ):
-    f_start = torch.zeros(nb, height, width, dtype=torch.int64)
-    f_end = torch.zeros(nb, height, width, dtype=torch.int64)
-    n = torch.arange(f_start.size(0))
+    pairs = []
 
-    for n in range(nb):
-        nb_fish = torch.randint(max_nb_obj, (1,)).item() + 1
-        for c in torch.randperm(colors.size(0) - 2)[:nb_fish].sort().values:
+    for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
+        f_start = torch.zeros(height, width, dtype=torch.int64)
+        f_end = torch.zeros(height, width, dtype=torch.int64)
+        n = torch.arange(f_start.size(0))
+
+        for c in (
+            (torch.randperm(nb_bird_tokens) + first_bird_token)[:nb_birds].sort().values
+        ):
             i, j = (
                 torch.randint(height - 2, (1,))[0] + 1,
                 torch.randint(width - 2, (1,))[0] + 1,
@@ -51,10 +184,10 @@ def generate(
             vm = torch.randint(4, (1,))[0]
             vi, vj = (vm // 2) * (2 * (vm % 2) - 1), (1 - vm // 2) * (2 * (vm % 2) - 1)
 
-            f_start[n, i, j] = c + 2
-            f_start[n, i - vi, j - vj] = c + 2
-            f_start[n, i + vj, j - vi] = c + 2
-            f_start[n, i - vj, j + vi] = c + 2
+            f_start[i, j] = c
+            f_start[i - vi, j - vj] = c
+            f_start[i + vj, j - vi] = c
+            f_start[i - vj, j + vi] = c
 
             for l in range(nb_iterations):
                 i += vi
@@ -66,42 +199,102 @@ def generate(
                     i += vi
                     j += vj
 
-            f_end[n, i, j] = c + 2
-            f_end[n, i - vi, j - vj] = c + 2
-            f_end[n, i + vj, j - vi] = c + 2
-            f_end[n, i - vj, j + vi] = c + 2
+            f_end[i, j] = c
+            f_end[i - vi, j - vj] = c
+            f_end[i + vj, j - vi] = c
+            f_end[i - vj, j + vi] = c
 
-    return torch.cat(
-        [
-            f_end.flatten(1),
-            torch.full((f_end.size(0), 1), len(colors)),
-            f_start.flatten(1),
-        ],
-        dim=1,
-    )
+        pairs.append((f_start, f_end))
+
+    result = []
+    for p in pairs:
+        if torch.rand(1) < 0.5:
+            result.append(
+                torch.cat(
+                    [p[0].flatten(), torch.tensor([token_forward]), p[1].flatten()],
+                    dim=0,
+                )[None, :]
+            )
+        else:
+            result.append(
+                torch.cat(
+                    [p[1].flatten(), torch.tensor([token_backward]), p[0].flatten()],
+                    dim=0,
+                )[None, :]
+            )
 
+    return torch.cat(result, dim=0)
 
-def sample2img(seq, height, width):
-    f_start = seq[:, : height * width].reshape(-1, height, width)
-    f_start = (f_start >= len(colors)).long() + (f_start < len(colors)).long() * f_start
-    f_end = seq[:, height * width + 1 :].reshape(-1, height, width)
-    f_end = (f_end >= len(colors)).long() + (f_end < len(colors)).long() * f_end
 
-    img_f_start, img_f_end = colors[f_start], colors[f_end]
+def frame2img(x, height, width, upscale=15):
+    x = x.reshape(-1, height, width)
+    m = torch.logical_and(x >= 0, x < first_bird_token + nb_bird_tokens).long()
+    x = colors[x * m].permute(0, 3, 1, 2)
+    s = x.shape
+    x = x[:, :, :, None, :, None].expand(-1, -1, -1, upscale, -1, upscale)
+    x = x.reshape(s[0], s[1], s[2] * upscale, s[3] * upscale)
 
-    img = torch.cat(
+    x[:, :, :, torch.arange(0, x.size(3), upscale)] = 0
+    x[:, :, torch.arange(0, x.size(2), upscale), :] = 0
+    x = x[:, :, 1:, 1:]
+
+    for n in range(m.size(0)):
+        for i in range(m.size(1)):
+            for j in range(m.size(2)):
+                if m[n, i, j] == 0:
+                    for k in range(2, upscale - 2):
+                        x[n, :, i * upscale + k, j * upscale + k] = 0
+                        x[n, :, i * upscale + upscale - 1 - k, j * upscale + k] = 0
+
+    return x
+
+
+def seq2img(seq, height, width, upscale=15):
+    f_first = seq[:, : height * width].reshape(-1, height, width)
+    f_second = seq[:, height * width + 1 :].reshape(-1, height, width)
+    direction = seq[:, height * width]
+
+    direction_symbol = torch.full((direction.size(0), height * upscale - 1, upscale), 0)
+    direction_symbol = colors[direction_symbol].permute(0, 3, 1, 2)
+    separator = torch.full((direction.size(0), 3, height * upscale - 1, 1), 0)
+
+    for n in range(direction_symbol.size(0)):
+        if direction[n] == token_forward:
+            for k in range(upscale):
+                direction_symbol[
+                    n,
+                    :,
+                    (height * upscale) // 2 - upscale // 2 + k,
+                    3 + upscale // 2 - abs(k - upscale // 2),
+                ] = 0
+        elif direction[n] == token_backward:
+            for k in range(upscale):
+                direction_symbol[
+                    n,
+                    :,
+                    (height * upscale) // 2 - upscale // 2 + k,
+                    3 + abs(k - upscale // 2),
+                ] = 0
+        else:
+            for k in range(2, upscale - 2):
+                direction_symbol[
+                    n, :, (height * upscale) // 2 - upscale // 2 + k, k
+                ] = 0
+                direction_symbol[
+                    n, :, (height * upscale) // 2 - upscale // 2 + k, upscale - 1 - k
+                ] = 0
+
+    return torch.cat(
         [
-            img_f_start,
-            torch.full(
-                (img_f_start.size(0), img_f_start.size(1), 1, img_f_start.size(3)), 1
-            ),
-            img_f_end,
+            frame2img(f_first, height, width, upscale),
+            separator,
+            direction_symbol,
+            separator,
+            frame2img(f_second, height, width, upscale),
         ],
-        dim=2,
+        dim=3,
     )
 
-    return img.permute(0, 3, 1, 2)
-
 
 def seq2str(seq):
     result = []
@@ -117,13 +310,30 @@ if __name__ == "__main__":
 
     height, width = 6, 8
     start_time = time.perf_counter()
-    seq = generate(nb=64, height=height, width=width, max_nb_obj=3)
+    seq, it = generate_seq(
+        nb=64, height=height, width=width, nb_iterations=100, return_iterations=True
+    )
     delay = time.perf_counter() - start_time
     print(f"{seq.size(0)/delay:02f} samples/s")
 
     print(seq2str(seq[:4]))
 
-    img = sample2img(seq, height, width)
+    for t in range(len(it[0])):
+        img = torch.cat([frame2img(f[t], height, width) for f in it], dim=0)
+        torchvision.utils.save_image(
+            img.float() / 255.0,
+            f"/tmp/frame_{t:03d}.png",
+            nrow=8,
+            padding=6,
+            pad_value=0,
+        )
+
+    # m = (torch.rand(seq.size()) < 0.05).long()
+    # seq = (1 - m) * seq + m * 23
+
+    img = seq2img(seq, height, width)
     print(img.size())
 
-    torchvision.utils.save_image(img.float() / 255.0, "world.png", nrow=8, padding=2)
+    torchvision.utils.save_image(
+        img.float() / 255.0, "/tmp/world.png", nrow=6, padding=6, pad_value=0
+    )