X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=world.py;h=68f46de76b1bea49fd25936696a604d000d48600;hb=908351dd77e8a703fb55b32a209c2fca4f551669;hp=e76c07f7c5e75185bc89acc3ed23a419ec7a0d2e;hpb=9e62722596c40655041a0a812512115f1036c6fc;p=culture.git

diff --git a/world.py b/world.py
index e76c07f..68f46de 100755
--- a/world.py
+++ b/world.py
@@ -1,328 +1,320 @@
 #!/usr/bin/env python
 
-import math
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
+import math, sys, tqdm
 
 import torch, torchvision
 
 from torch import nn
 from torch.nn import functional as F
-import cairo
-
-
-class Box:
-    def __init__(self, x, y, w, h, r, g, b):
-        self.x = x
-        self.y = y
-        self.w = w
-        self.h = h
-        self.r = r
-        self.g = g
-        self.b = b
-
-    def collision(self, scene):
-        for c in scene:
-            if (
-                self is not c
-                and max(self.x, c.x) <= min(self.x + self.w, c.x + c.w)
-                and max(self.y, c.y) <= min(self.y + self.h, c.y + c.h)
-            ):
-                return True
-        return False
-
-
-def scene2tensor(xh, yh, scene, size=64):
-    width, height = size, size
-    pixel_map = torch.ByteTensor(width, height, 4).fill_(255)
-    data = pixel_map.numpy()
-    surface = cairo.ImageSurface.create_for_data(
-        data, cairo.FORMAT_ARGB32, width, height
-    )
-
-    ctx = cairo.Context(surface)
-    ctx.set_fill_rule(cairo.FILL_RULE_EVEN_ODD)
-
-    for b in scene:
-        ctx.move_to(b.x * size, b.y * size)
-        ctx.rel_line_to(b.w * size, 0)
-        ctx.rel_line_to(0, b.h * size)
-        ctx.rel_line_to(-b.w * size, 0)
-        ctx.close_path()
-        ctx.set_source_rgba(b.r, b.g, b.b, 1.0)
-        ctx.fill()
-
-    hs = size * 0.1
-    ctx.set_source_rgba(0.0, 0.0, 0.0, 1.0)
-    ctx.move_to(xh * size - hs / 2, yh * size - hs / 2)
-    ctx.rel_line_to(hs, 0)
-    ctx.rel_line_to(0, hs)
-    ctx.rel_line_to(-hs, 0)
-    ctx.close_path()
-    ctx.fill()
-
-    return pixel_map[None, :, :, :3].flip(-1).permute(0, 3, 1, 2).float() / 255
-
-
-def random_scene():
-    scene = []
-    colors = [
-        (1.00, 0.00, 0.00),
-        (0.00, 1.00, 0.00),
-        (0.60, 0.60, 1.00),
-        (1.00, 1.00, 0.00),
-        (0.75, 0.75, 0.75),
-    ]
-
-    for k in range(10):
-        wh = torch.rand(2) * 0.2 + 0.2
-        xy = torch.rand(2) * (1 - wh)
-        c = colors[torch.randint(len(colors), (1,))]
-        b = Box(
-            xy[0].item(), xy[1].item(), wh[0].item(), wh[1].item(), c[0], c[1], c[2]
-        )
-        if not b.collision(scene):
-            scene.append(b)
-
-    return scene
-
-
-def sequence(nb_steps=10, all_frames=False):
-    delta = 0.1
-    effects = [
-        (False, 0, 0),
-        (False, delta, 0),
-        (False, 0, delta),
-        (False, -delta, 0),
-        (False, 0, -delta),
-        (True, delta, 0),
-        (True, 0, delta),
-        (True, -delta, 0),
-        (True, 0, -delta),
-    ]
-
-    while True:
-        frames = []
-
-        scene = random_scene()
-        xh, yh = tuple(x.item() for x in torch.rand(2))
-
-        frames.append(scene2tensor(xh, yh, scene))
-
-        actions = torch.randint(len(effects), (nb_steps,))
-        change = False
-
-        for a in actions:
-            g, dx, dy = effects[a]
-            if g:
-                for b in scene:
-                    if b.x <= xh and b.x + b.w >= xh and b.y <= yh and b.y + b.h >= yh:
-                        x, y = b.x, b.y
-                        b.x += dx
-                        b.y += dy
-                        if (
-                            b.x < 0
-                            or b.y < 0
-                            or b.x + b.w > 1
-                            or b.y + b.h > 1
-                            or b.collision(scene)
-                        ):
-                            b.x, b.y = x, y
-                        else:
-                            xh += dx
-                            yh += dy
-                            change = True
-            else:
-                x, y = xh, yh
-                xh += dx
-                yh += dy
-                if xh < 0 or xh > 1 or yh < 0 or yh > 1:
-                    xh, yh = x, y
-
-            if all_frames:
-                frames.append(scene2tensor(xh, yh, scene))
-
-        if not all_frames:
-            frames.append(scene2tensor(xh, yh, scene))
-
-        if change:
-            break
-
-    return frames, actions
-
 
 ######################################################################
 
 
-# ||x_i - c_j||^2 = ||x_i||^2 + ||c_j||^2 - 2<x_i, c_j>
-def sq2matrix(x, c):
-    nx = x.pow(2).sum(1)
-    nc = c.pow(2).sum(1)
-    return nx[:, None] + nc[None, :] - 2 * x @ c.t()
-
-
-def update_centroids(x, c, nb_min=1):
-    _, b = sq2matrix(x, c).min(1)
-    b.squeeze_()
-    nb_resets = 0
+colors = torch.tensor(
+    [
+        [255, 255, 255],
+        [255, 20, 147],
+        [0, 0, 255],
+        [0, 192, 0],
+        [0, 255, 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],
+    ]
+)
+
+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(
+    nb,
+    height,
+    width,
+    nb_birds=3,
+    nb_iterations=1,
+):
+    pairs = []
+
+    for _ in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
+        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),
+        )
 
-    for k in range(0, c.size(0)):
-        i = b.eq(k).nonzero(as_tuple=False).squeeze()
-        if i.numel() >= nb_min:
-            c[k] = x.index_select(0, i).mean(0)
+        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()
+
+                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
+
+        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:
-            n = torch.randint(x.size(0), (1,))
-            nb_resets += 1
-            c[k] = x[n]
-
-    return c, b, nb_resets
-
-
-def kmeans(x, nb_centroids, nb_min=1):
-    if x.size(0) < nb_centroids * nb_min:
-        print("Not enough points!")
-        exit(1)
-
-    c = x[torch.randperm(x.size(0))[:nb_centroids]]
-    t = torch.full((x.size(0),), -1)
-    n = 0
-
-    while True:
-        c, u, nb_resets = update_centroids(x, c, nb_min)
-        n = n + 1
-        nb_changes = (u - t).sign().abs().sum() + nb_resets
-        t = u
-        if nb_changes == 0:
-            break
+            result.append(
+                torch.cat(
+                    [p[1].flatten(), torch.tensor([token_backward]), p[0].flatten()],
+                    dim=0,
+                )[None, :]
+            )
 
-    return c, t
+    return torch.cat(result, dim=0)
 
 
-######################################################################
+def generate_(
+    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))
 
-def patchify(x, factor, invert_size=None):
-    if invert_size is None:
-        return (
-            x.reshape(
-                x.size(0),  # 0
-                x.size(1),  # 1
-                factor,  # 2
-                x.size(2) // factor,  # 3
-                factor,  # 4
-                x.size(3) // factor,  # 5
+        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,
             )
-            .permute(0, 2, 4, 1, 3, 5)
-            .reshape(-1, x.size(1), x.size(2) // factor, x.size(3) // factor)
-        )
-    else:
-        return (
-            x.reshape(
-                invert_size[0],  # 0
-                factor,  # 1
-                factor,  # 2
-                invert_size[1],  # 3
-                invert_size[2] // factor,  # 4
-                invert_size[3] // factor,  # 5
+            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, :]
             )
-            .permute(0, 3, 1, 4, 2, 5)
-            .reshape(invert_size)
-        )
-
-
-def train_encoder(input, device=torch.device("cpu")):
-    class SomeLeNet(nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
-            self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
-            self.fc1 = nn.Linear(256, 200)
-            self.fc2 = nn.Linear(200, 10)
-
-        def forward(self, x):
-            x = F.relu(F.max_pool2d(self.conv1(x), kernel_size=3))
-            x = F.relu(F.max_pool2d(self.conv2(x), kernel_size=2))
-            x = x.view(x.size(0), -1)
-            x = F.relu(self.fc1(x))
-            x = self.fc2(x)
-            return x
-
-    ######################################################################
-
-    model = SomeLeNet()
 
-    nb_parameters = sum(p.numel() for p in model.parameters())
+    return torch.cat(result, dim=0)
+
+
+def sample2img(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]
+
+    def mosaic(x, upscale):
+        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
+
+    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(
+        [
+            mosaic(f_first, upscale),
+            separator,
+            direction_symbol,
+            separator,
+            mosaic(f_second, upscale),
+        ],
+        dim=3,
+    )
 
-    print(f"nb_parameters {nb_parameters}")
 
-    optimizer = torch.optim.SGD(model.parameters(), lr=lr)
-    criterion = nn.CrossEntropyLoss()
+def seq2str(seq):
+    result = []
+    for s in seq:
+        result.append("".join([token2char[v] for v in s]))
+    return result
 
-    model.to(device)
-    criterion.to(device)
 
-    train_input, train_targets = train_input.to(device), train_targets.to(device)
-    test_input, test_targets = test_input.to(device), test_targets.to(device)
+######################################################################
 
-    mu, std = train_input.mean(), train_input.std()
-    train_input.sub_(mu).div_(std)
-    test_input.sub_(mu).div_(std)
+if __name__ == "__main__":
+    import time
 
+    height, width = 6, 8
     start_time = time.perf_counter()
+    seq = generate(nb=90, height=height, width=width)
+    delay = time.perf_counter() - start_time
+    print(f"{seq.size(0)/delay:02f} samples/s")
 
-    for k in range(nb_epochs):
-        acc_loss = 0.0
-
-        for input, targets in zip(
-            train_input.split(batch_size), train_targets.split(batch_size)
-        ):
-            output = model(input)
-            loss = criterion(output, targets)
-            acc_loss += loss.item()
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-        nb_test_errors = 0
-        for input, targets in zip(
-            test_input.split(batch_size), test_targets.split(batch_size)
-        ):
-            wta = model(input).argmax(1)
-            nb_test_errors += (wta != targets).long().sum()
-        test_error = nb_test_errors / test_input.size(0)
-        duration = time.perf_counter() - start_time
-
-        print(f"loss {k} {duration:.02f}s {acc_loss:.02f} {test_error*100:.02f}%")
+    print(seq2str(seq[:4]))
 
+    # m = (torch.rand(seq.size()) < 0.05).long()
+    # seq = (1 - m) * seq + m * 23
 
-######################################################################
-
-if __name__ == "__main__":
-    import time
+    img = sample2img(seq, height, width)
+    print(img.size())
 
-    all_frames = []
-    nb = 1000
-    start_time = time.perf_counter()
-    for n in range(nb):
-        frames, actions = sequence(nb_steps=31)
-        all_frames += frames
-    end_time = time.perf_counter()
-    print(f"{nb / (end_time - start_time):.02f} samples per second")
-
-    input = torch.cat(all_frames, 0)
-
-    # x = patchify(input, 8)
-    # y = x.reshape(x.size(0), -1)
-    # print(f"{x.size()=} {y.size()=}")
-    # centroids, t = kmeans(y, 4096)
-    # results = centroids[t]
-    # results = results.reshape(x.size())
-    # results = patchify(results, 8, input.size())
-
-    print(f"{input.size()=} {results.size()=}")
-
-    torchvision.utils.save_image(input[:64], "orig.png", nrow=8)
-    torchvision.utils.save_image(results[:64], "qtiz.png", nrow=8)
-
-    # frames, actions = sequence(nb_steps=31, all_frames=True)
-    # frames = torch.cat(frames, 0)
-    # torchvision.utils.save_image(frames, "seq.png", nrow=8)
+    torchvision.utils.save_image(
+        img.float() / 255.0, "/tmp/world.png", nrow=6, padding=6, pad_value=0
+    )