X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=world.py;h=43126d5d63466e948317839e727a410e2b267c62;hb=6917d3d52a4b473d31121a471ab98fa114bdb1a6;hp=a93684b0f6797d86a2b8d227abedd3c0b5610672;hpb=4b7407bbbd9636b89f663a6a9124e078a16aaef8;p=culture.git

diff --git a/world.py b/world.py
index a93684b..43126d5 100755
--- a/world.py
+++ b/world.py
@@ -1,459 +1,129 @@
 #!/usr/bin/env python
 
+# 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:
-    nb_rgb_levels = 10
-
-    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):
-    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 / (Box.nb_rgb_levels - 1),
-            b.g / (Box.nb_rgb_levels - 1),
-            b.b / (Box.nb_rgb_levels - 1),
-            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)
-        .long()
-        .mul(Box.nb_rgb_levels)
-        .floor_divide(256)
-    )
-
-
-def random_scene():
-    scene = []
-    colors = [
-        ((Box.nb_rgb_levels - 1), 0, 0),
-        (0, (Box.nb_rgb_levels - 1), 0),
-        (0, 0, (Box.nb_rgb_levels - 1)),
-        ((Box.nb_rgb_levels - 1), (Box.nb_rgb_levels - 1), 0),
-        (
-            (Box.nb_rgb_levels * 2) // 3,
-            (Box.nb_rgb_levels * 2) // 3,
-            (Box.nb_rgb_levels * 2) // 3,
-        ),
-    ]
-
-    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 generate_episode(nb_steps=10, size=64):
-    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, size=size))
-
-        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
-
-            frames.append(scene2tensor(xh, yh, scene, size=size))
-
-        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
-
-    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)
-        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
-
-    return c, t
-
-
-######################################################################
-
-
-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
-            )
-            .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
-            )
-            .permute(0, 3, 1, 4, 2, 5)
-            .reshape(invert_size)
-        )
-
-
-class Normalizer(nn.Module):
-    def __init__(self, mu, std):
-        super().__init__()
-        self.register_buffer("mu", mu)
-        self.register_buffer("log_var", 2 * torch.log(std))
-
-    def forward(self, x):
-        return (x - self.mu) / torch.exp(self.log_var / 2.0)
-
-
-class SignSTE(nn.Module):
-    def __init__(self):
-        super().__init__()
+colors = torch.tensor(
+    [
+        [255, 255, 255],
+        [0, 0, 0],
+        [255, 0, 0],
+        [0, 128, 0],
+        [0, 0, 255],
+        [255, 255, 0],
+        [192, 192, 192],
+    ]
+)
 
-    def forward(self, x):
-        # torch.sign() takes three values
-        s = (x >= 0).float() * 2 - 1
-        if self.training:
-            u = torch.tanh(x)
-            return s + u - u.detach()
-        else:
-            return s
+token2char = "_X" + "".join([str(n) for n in range(len(colors) - 2)]) + ">"
 
 
-def train_encoder(
-    train_input,
-    test_input,
-    depth=2,
-    dim_hidden=48,
-    nb_bits_per_token=10,
-    lr_start=1e-3,
-    lr_end=1e-4,
-    nb_epochs=10,
-    batch_size=25,
-    device=torch.device("cpu"),
+def generate(
+    nb,
+    height,
+    width,
+    max_nb_obj=2,
+    nb_iterations=2,
 ):
-    mu, std = train_input.float().mean(), train_input.float().std()
-
-    def encoder_core(depth, dim):
-        l = [
-            [
-                nn.Conv2d(
-                    dim * 2**k, dim * 2**k, kernel_size=5, stride=1, padding=2
-                ),
-                nn.ReLU(),
-                nn.Conv2d(dim * 2**k, dim * 2 ** (k + 1), kernel_size=2, stride=2),
-                nn.ReLU(),
-            ]
-            for k in range(depth)
-        ]
-
-        return nn.Sequential(*[x for m in l for x in m])
-
-    def decoder_core(depth, dim):
-        l = [
-            [
-                nn.ConvTranspose2d(
-                    dim * 2 ** (k + 1), dim * 2**k, kernel_size=2, stride=2
-                ),
-                nn.ReLU(),
-                nn.ConvTranspose2d(
-                    dim * 2**k, dim * 2**k, kernel_size=5, stride=1, padding=2
-                ),
-                nn.ReLU(),
-            ]
-            for k in range(depth - 1, -1, -1)
-        ]
-
-        return nn.Sequential(*[x for m in l for x in m])
-
-    encoder = nn.Sequential(
-        Normalizer(mu, std),
-        nn.Conv2d(3, dim_hidden, kernel_size=1, stride=1),
-        nn.ReLU(),
-        # 64x64
-        encoder_core(depth=depth, dim=dim_hidden),
-        # 8x8
-        nn.Conv2d(dim_hidden * 2**depth, nb_bits_per_token, kernel_size=1, stride=1),
-    )
-
-    quantizer = SignSTE()
-
-    decoder = nn.Sequential(
-        nn.Conv2d(nb_bits_per_token, dim_hidden * 2**depth, kernel_size=1, stride=1),
-        # 8x8
-        decoder_core(depth=depth, dim=dim_hidden),
-        # 64x64
-        nn.ConvTranspose2d(dim_hidden, 3 * Box.nb_rgb_levels, kernel_size=1, stride=1),
-    )
-
-    model = nn.Sequential(encoder, decoder)
-
-    nb_parameters = sum(p.numel() for p in model.parameters())
-
-    print(f"nb_parameters {nb_parameters}")
-
-    model.to(device)
-
-    g5x5 = torch.exp(-torch.tensor([[-2.0, -1.0, 0.0, 1.0, 2.0]]) ** 2 / 2)
-    g5x5 = (g5x5.t() @ g5x5).view(1, 1, 5, 5)
-    g5x5 = g5x5 / g5x5.sum()
-
-    for k in range(nb_epochs):
-        lr = math.exp(
-            math.log(lr_start) + math.log(lr_end / lr_start) / (nb_epochs - 1) * k
-        )
-        optimizer = torch.optim.Adam(model.parameters(), lr=lr)
-
-        acc_train_loss = 0.0
-
-        for input in train_input.split(batch_size):
-            z = encoder(input)
-            zq = z if k < 1 else quantizer(z)
-            output = decoder(zq)
-
-            output = output.reshape(
-                output.size(0), -1, 3, output.size(2), output.size(3)
-            )
-
-            train_loss = F.cross_entropy(output, input)
-
-            acc_train_loss += train_loss.item() * input.size(0)
-
-            optimizer.zero_grad()
-            train_loss.backward()
-            optimizer.step()
-
-        acc_test_loss = 0.0
-
-        for input in test_input.split(batch_size):
-            z = encoder(input)
-            zq = z if k < 1 else quantizer(z)
-            output = decoder(zq)
-
-            output = output.reshape(
-                output.size(0), -1, 3, output.size(2), output.size(3)
+    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))
+
+    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:
+            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[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
+
+            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[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
+
+    return torch.cat(
+        [
+            f_end.flatten(1),
+            torch.full((f_end.size(0), 1), len(colors)),
+            f_start.flatten(1),
+        ],
+        dim=1,
+    )
 
-            test_loss = F.cross_entropy(output, input)
-
-            acc_test_loss += test_loss.item() * input.size(0)
-
-        train_loss = acc_train_loss / train_input.size(0)
-        test_loss = acc_test_loss / test_input.size(0)
-
-        print(f"train_ae {k} lr {lr} train_loss {train_loss} test_loss {test_loss}")
-        sys.stdout.flush()
-
-    return encoder, quantizer, decoder
-
-def generate_episodes(nb):
-    all_frames = []
-    for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world-data"):
-        frames, actions = generate_episode(nb_steps=31)
-        all_frames += [ frames[0], frames[-1] ]
-    return torch.cat(all_frames, 0).contiguous()
-
-def create_data_and_processors(nb_train_samples, nb_test_samples):
-    train_input = generate_episodes(nb_train_samples)
-    test_input = generate_episodes(nb_test_samples)
-    encoder, quantizer, decoder = train_encoder(train_input, test_input, nb_epochs=2)
-
-    input = test_input[:64]
-
-    z = encoder(input.float())
-    height, width = z.size(2), z.size(3)
-    zq = quantizer(z).long()
-    pow2=(2**torch.arange(zq.size(1), device=zq.device))[None,None,:]
-    seq = (zq.permute(0,2,3,1).clamp(min=0).reshape(zq.size(0),-1,zq.size(1)) * pow2).sum(-1)
-    print(f"{seq.size()=}")
-
-    ZZ=zq
 
-    zq = ((seq[:,:,None] // pow2)%2)*2-1
-    zq = zq.reshape(zq.size(0), height, width, -1).permute(0,3,1,2)
+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
 
-    print(ZZ[0])
-    print(zq[0])
+    img_f_start, img_f_end = colors[f_start], colors[f_end]
 
-    print("CHECK", (ZZ-zq).abs().sum())
+    img = 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,
+        ],
+        dim=2,
+    )
 
-    results = decoder(zq.float())
-    T = 0.1
-    results = results.reshape(
-        results.size(0), -1, 3, results.size(2), results.size(3)
-    ).permute(0, 2, 3, 4, 1)
-    results = torch.distributions.categorical.Categorical(logits=results / T).sample()
+    return img.permute(0, 3, 1, 2)
 
 
-    torchvision.utils.save_image(
-        input.float() / (Box.nb_rgb_levels - 1), "orig.png", nrow=8
-    )
-
-    torchvision.utils.save_image(
-        results.float() / (Box.nb_rgb_levels - 1), "qtiz.png", nrow=8
-    )
+def seq2str(seq):
+    result = []
+    for s in seq:
+        result.append("".join([token2char[v] for v in s]))
+    return result
 
 
 ######################################################################
 
 if __name__ == "__main__":
-    create_data_and_processors(250,100)
+    import time
 
-    # train_input = generate_episodes(2500)
-    # test_input = generate_episodes(1000)
+    height, width = 6, 8
+    start_time = time.perf_counter()
+    seq = generate(nb=64, height=height, width=width, max_nb_obj=3)
+    delay = time.perf_counter() - start_time
+    print(f"{seq.size(0)/delay:02f} samples/s")
 
-    # encoder, quantizer, decoder = train_encoder(train_input, test_input)
+    print(seq2str(seq[:4]))
 
-    # input = test_input[torch.randperm(test_input.size(0))[:64]]
-    # z = encoder(input.float())
-    # zq = quantizer(z)
-    # results = decoder(zq)
+    img = sample2img(seq, height, width)
+    print(img.size())
 
-    # T = 0.1
-    # results = torch.distributions.categorical.Categorical(logits=results / T).sample()
+    torchvision.utils.save_image(img.float() / 255.0, "world.png", nrow=8, padding=2)