X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=sky.py;h=11641853d8e8081f1cc7d0cf63d112f1ba30b518;hb=6b4e192557e03528ffd10364123de454aa9c9f08;hp=36aa1e97638403195c0d711fde941be83ef17a7a;hpb=559137879125c5fdc652e69787d3d78a922b450e;p=culture.git

diff --git a/sky.py b/sky.py
index 36aa1e9..1164185 100755
--- a/sky.py
+++ b/sky.py
@@ -5,7 +5,7 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
-import math, sys, tqdm
+import math, sys, tqdm, os
 
 import torch, torchvision
 
@@ -14,293 +14,286 @@ from torch.nn import functional as F
 
 ######################################################################
 
+import problem
 
-colors = torch.tensor(
-    [
-        [255, 255, 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],
-    ]
-)
 
-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
+class Sky(problem.Problem):
+    colors = torch.tensor(
+        [
+            [255, 255, 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],
+        ]
+    )
 
-token2char = "_" + "".join([chr(ord("A") + n) for n in range(len(colors) - 1)]) + "><"
+    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_seq(
-    nb, height, width, nb_birds=3, nb_iterations=2, return_iterations=False
-):
-    pairs = []
-    kept_iterations = []
+    def __init__(
+        self,
+        height=6,
+        width=8,
+        nb_birds=3,
+        speed=2,
+        nb_iterations=2,
+        avoid_collision=True,
+    ):
+        self.height = height
+        self.width = width
+        self.nb_birds = nb_birds
+        self.speed = speed
+        self.nb_iterations = nb_iterations
+        self.avoid_collision = avoid_collision
+
+    def direction_tokens(self):
+        return self.token_forward, self.token_backward
+
+    def generate_frame_sequences(self, nb):
+        frame_sequences = []
+
+        for _ in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
+            i, j, vi, vj = (
+                torch.empty(self.nb_birds, dtype=torch.int64),
+                torch.empty(self.nb_birds, dtype=torch.int64),
+                torch.empty(self.nb_birds, dtype=torch.int64),
+                torch.empty(self.nb_birds, dtype=torch.int64),
+            )
 
-    for _ in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
-        while True:
-            iterations = []
+            def collision_okay():
+                if not self.avoid_collision:
+                    return True
 
-            f_start = torch.zeros(height, width, dtype=torch.int64)
+                count = torch.zeros(self.height, self.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),
-            )
+                for n in range(self.nb_birds):
+                    count[i[n], j[n]] += 1
+                    count[i[n] - vi[n], j[n]] += 1
+                    count[i[n], j[n] - vj[n]] += 1
 
-            col = torch.randperm(colors.size(0) - 1)[:nb_birds].sort().values + 1
+                return count.max() <= 1
 
-            for n in range(nb_birds):
-                c = col[n]
+            col = (
+                torch.randperm(self.colors.size(0) - 1)[: self.nb_birds].sort().values
+                + 1
+            )
 
+            while True:
                 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
-                    ):
+                    for n in range(self.nb_birds):
+                        while True:
+                            i[n] = torch.randint(self.height, (1,))
+                            j[n] = torch.randint(self.width, (1,))
+                            vm = torch.randint(4, (1,))
+                            vi[n], vj[n] = (vm % 2) * 2 - 1, (vm // 2) * 2 - 1
+                            if (
+                                i[n] - vi[n] >= 0
+                                and i[n] - vi[n] < self.height
+                                and j[n] - vj[n] >= 0
+                                and j[n] - vj[n] < self.width
+                            ):
+                                break
+
+                    if collision_okay():
                         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, :]
-            )
+                result = torch.zeros(
+                    self.nb_iterations * self.speed,
+                    self.height,
+                    self.width,
+                    dtype=torch.int64,
+                )
 
-    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)
+                fine = torch.empty(self.nb_iterations * self.speed)
 
+                t_to_keep = (
+                    torch.arange(self.nb_iterations, device=result.device) * self.speed
+                )
 
-######################################################################
+                for l in range(self.nb_iterations * self.speed):
+                    fine[l] = collision_okay()
+                    for n in range(self.nb_birds):
+                        c = col[n]
+                        result[l, i[n], j[n]] = c
+                        result[l, i[n] - vi[n], j[n]] = c
+                        result[l, i[n], j[n] - vj[n]] = c
 
+                        if (i[n] == 0 and vi[n] == -1) or (
+                            i[n] == self.height - 1 and vi[n] == 1
+                        ):
+                            vi[n] = -vi[n]
 
-def generate_seq_old(
-    nb,
-    height,
-    width,
-    nb_birds=3,
-    nb_iterations=2,
-):
-    pairs = []
-
-    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,
-            )
-            vm = torch.randint(4, (1,))[0]
-            vi, vj = (vm // 2) * (2 * (vm % 2) - 1), (1 - vm // 2) * (2 * (vm % 2) - 1)
-
-            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
-                j += vj
-                if i < 0 or i >= height or j < 0 or j >= width:
-                    i -= vi
-                    j -= vj
-                    vi, vj = -vi, -vj
-                    i += vi
-                    j += vj
-
-            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
-
-        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 (j[n] == 0 and vj[n] == -1) or (
+                            j[n] == self.width - 1 and vj[n] == 1
+                        ):
+                            vj[n] = -vj[n]
 
-    return torch.cat(result, dim=0)
-
-
-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)
-
-    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(
-        [
-            frame2img(f_first, height, width, upscale),
-            separator,
-            direction_symbol,
-            separator,
-            frame2img(f_second, height, width, upscale),
-        ],
-        dim=3,
-    )
+                        i[n] += vi[n]
+                        j[n] += vj[n]
+
+                result = result[t_to_keep]
+                fine = fine[t_to_keep]
+
+                if fine[-1]:
+                    break
+
+            frame_sequences.append(result)
+
+        return frame_sequences
+
+    ######################################################################
+
+    def generate_token_sequences(self, nb):
+        frame_sequences = self.generate_frame_sequences(nb)
 
+        result = []
 
-def seq2str(seq):
-    result = []
-    for s in seq:
-        result.append("".join([token2char[v] for v in s]))
-    return result
+        for frame_sequence in frame_sequences:
+            a = []
+            if torch.rand(1) < 0.5:
+                for frame in frame_sequence:
+                    if len(a) > 0:
+                        a.append(torch.tensor([self.token_forward]))
+                    a.append(frame.flatten())
+            else:
+                for frame in reversed(frame_sequence):
+                    if len(a) > 0:
+                        a.append(torch.tensor([self.token_backward]))
+                    a.append(frame.flatten())
+
+            result.append(torch.cat(a, dim=0)[None, :])
+
+        return torch.cat(result, dim=0)
+
+    ######################################################################
+
+    def frame2img(self, x, scale=15):
+        x = x.reshape(-1, self.height, self.width)
+        m = torch.logical_and(
+            x >= 0, x < self.first_bird_token + self.nb_bird_tokens
+        ).long()
+        x = self.colors[x * m].permute(0, 3, 1, 2)
+        s = x.shape
+        x = x[:, :, :, None, :, None].expand(-1, -1, -1, scale, -1, scale)
+        x = x.reshape(s[0], s[1], s[2] * scale, s[3] * scale)
+
+        x[:, :, :, torch.arange(0, x.size(3), scale)] = 0
+        x[:, :, torch.arange(0, x.size(2), scale), :] = 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, scale - 2):
+                            for l in [0, 1]:
+                                x[n, :, i * scale + k, j * scale + k - l] = 0
+                                x[
+                                    n, :, i * scale + scale - 1 - k, j * scale + k - l
+                                ] = 0
+
+        return x
+
+    def seq2img(self, seq, scale=15):
+        all = [
+            self.frame2img(
+                seq[:, : self.height * self.width].reshape(-1, self.height, self.width),
+                scale,
+            )
+        ]
+
+        separator = torch.full((seq.size(0), 3, self.height * scale - 1, 1), 0)
+
+        t = self.height * self.width
+
+        while t < seq.size(1):
+            direction_tokens = seq[:, t]
+            t += 1
+
+            direction_images = self.colors[
+                torch.full(
+                    (direction_tokens.size(0), self.height * scale - 1, scale), 0
+                )
+            ].permute(0, 3, 1, 2)
+
+            for n in range(direction_tokens.size(0)):
+                if direction_tokens[n] == self.token_forward:
+                    for k in range(scale):
+                        for l in [0, 1]:
+                            direction_images[
+                                n,
+                                :,
+                                (self.height * scale) // 2 - scale // 2 + k - l,
+                                3 + scale // 2 - abs(k - scale // 2),
+                            ] = 0
+                elif direction_tokens[n] == self.token_backward:
+                    for k in range(scale):
+                        for l in [0, 1]:
+                            direction_images[
+                                n,
+                                :,
+                                (self.height * scale) // 2 - scale // 2 + k - l,
+                                3 + abs(k - scale // 2),
+                            ] = 0
+                else:
+                    for k in range(2, scale - 2):
+                        for l in [0, 1]:
+                            direction_images[
+                                n,
+                                :,
+                                (self.height * scale) // 2 - scale // 2 + k - l,
+                                k,
+                            ] = 0
+                            direction_images[
+                                n,
+                                :,
+                                (self.height * scale) // 2 - scale // 2 + k - l,
+                                scale - 1 - k,
+                            ] = 0
+
+            all += [
+                separator,
+                direction_images,
+                separator,
+                self.frame2img(
+                    seq[:, t : t + self.height * self.width].reshape(
+                        -1, self.height, self.width
+                    ),
+                    scale,
+                ),
+            ]
+
+            t += self.height * self.width
+
+        return torch.cat(all, dim=3)
+
+    def seq2str(self, seq):
+        result = []
+        for s in seq:
+            result.append("".join([self.token2char[v] for v in s]))
+        return result
+
+    def save_image(self, input, result_dir, filename):
+        img = self.seq2img(input.to("cpu"))
+        image_name = os.path.join(result_dir, filename)
+        torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=6, padding=4)
+
+    def save_quizzes(self, input, result_dir, filename_prefix):
+        self.save_image(input, result_dir, filename_prefix + ".png")
 
 
 ######################################################################
@@ -308,31 +301,31 @@ def seq2str(seq):
 if __name__ == "__main__":
     import time
 
-    height, width = 6, 8
+    sky = Sky(height=6, width=8, speed=4, nb_iterations=2)
+
     start_time = time.perf_counter()
-    seq, it = generate_seq(
-        nb=64, height=height, width=width, nb_iterations=100, return_iterations=True
-    )
+    token_sequences = sky.generate_token_sequences(nb=64)
     delay = time.perf_counter() - start_time
-    print(f"{seq.size(0)/delay:02f} samples/s")
+    print(f"{token_sequences.size(0)/delay:02f} seq/s")
 
-    print(seq2str(seq[:4]))
+    # print(sky.seq2str(seq[:4]))
 
-    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,
-        )
+    # for t in range(len(it[0])):
+    # img = torch.cat([sky.frame2img(f[t]) 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())
+    # print(seq.size())
+    img = sky.seq2img(token_sequences)
+    # print(img.size())
 
     torchvision.utils.save_image(
         img.float() / 255.0, "/tmp/world.png", nrow=6, padding=6, pad_value=0