Update.

[culture.git] / world.py

diff --git a/world.py b/world.py
index b35a08e..36aa1e9 100755 (executable)
--- a/world.py
+++ b/world.py
@@ -1,476 +1,339 @@
 #!/usr/bin/env python
 
 #!/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 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
-

 
 ######################################################################
 
 
 
 ######################################################################
 
 

-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__()
-
-    def forward(self, x):
-        # torch.sign() takes three values
-        s = (x >= 0).float() * 2 - 1
+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],
+    ]
+)

 
 

-        if self.training:
-            u = torch.tanh(x)
-            return s + u - u.detach()
-        else:
-            return s
+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 DiscreteSampler2d(nn.Module):
-    def __init__(self):
-        super().__init__()
+token2char = "_" + "".join([chr(ord("A") + n) for n in range(len(colors) - 1)]) + "><"

 
 

-    def forward(self, x):
-        s = (x >= x.max(-3,keepdim=True).values).float()

 
 

-        if self.training:
-            u = x.softmax(dim=-3)
-            return s + u - u.detach()
-        else:
-            return s
-
-
-def loss_H(binary_logits, h_threshold=1):
-    p = binary_logits.sigmoid().mean(0)
-    h = (-p.xlogy(p) - (1 - p).xlogy(1 - p)) / math.log(2)
-    h.clamp_(max=h_threshold)
-    return h_threshold - h.mean()
-
-
-def train_encoder(
-    train_input,
-    test_input,
-    depth,
-    nb_bits_per_token,
-    dim_hidden=48,
-    lambda_entropy=0.0,
-    lr_start=1e-3,
-    lr_end=1e-4,
-    nb_epochs=10,
-    batch_size=25,
-    logger=None,
-    device=torch.device("cpu"),
+def generate_seq(
+    nb, height, width, nb_birds=3, nb_iterations=2, return_iterations=False

 ):
 ):

-    if logger is None:
-        logger = lambda s: print(s)
-
-    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),
-    )
+    pairs = []
+    kept_iterations = []

 
 

-    quantizer = SignSTE()
+    for _ in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
+        while True:
+            iterations = []

 
 

-    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())
-
-    logger(f"nb_parameters {nb_parameters}")
-
-    model.to(device)
-
-    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 tqdm.tqdm(train_input.split(batch_size), desc="vqae-train"):
-            input = input.to(device)
-            z = encoder(input)
-            zq = quantizer(z)
-            output = decoder(zq)
+            f_start = torch.zeros(height, width, dtype=torch.int64)

 
 

-            output = output.reshape(
-                output.size(0), -1, 3, output.size(2), output.size(3)
+            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),

             )
 
             )
 

-            train_loss = F.cross_entropy(output, input)
-
-            if lambda_entropy > 0:
-                train_loss = train_loss + lambda_entropy * loss_H(z, h_threshold=0.5)
-
-            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 tqdm.tqdm(test_input.split(batch_size), desc="vqae-test"):
-            input = input.to(device)
-            z = encoder(input)
-            zq = quantizer(z)
-            output = decoder(zq)
-
-            output = output.reshape(
-                output.size(0), -1, 3, output.size(2), output.size(3)
+            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, :]

             )
 
             )
 

-            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)
-
-        logger(f"train_ae {k} lr {lr} train_loss {train_loss} test_loss {test_loss}")
-        sys.stdout.flush()
-
-    return encoder, quantizer, decoder
-
-
-######################################################################
-
-
-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(nb_insert_attempts=3):
-    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(nb_insert_attempts):
-        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(steps, 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))
-
-        actions = torch.randint(len(effects), (len(steps),))
-        nb_changes = 0
-
-        for s, a in zip(steps, actions):
-            if s:
-                frames.append(scene2tensor(xh, yh, scene, size=size))
-
-            grasp, dx, dy = effects[a]
-
-            if grasp:
-                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
-                            nb_changes += 1
-            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 nb_changes > len(steps) // 3:
-            break
-
-    return frames, actions
+    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_episodes(nb, steps):
-    all_frames, all_actions = [], []
-    for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world-data"):
-        frames, actions = generate_episode(steps)
-        all_frames += frames
-        all_actions += [actions[None, :]]
-    return torch.cat(all_frames, 0).contiguous(), torch.cat(all_actions, 0)
-
-
-def create_data_and_processors(
-    nb_train_samples,
-    nb_test_samples,
-    mode,
-    nb_steps,
-    depth=3,
-    nb_bits_per_token=8,
-    nb_epochs=10,
-    device=torch.device("cpu"),
-    device_storage=torch.device("cpu"),
-    logger=None,
+def generate_seq_old(
+    nb,
+    height,
+    width,
+    nb_birds=3,
+    nb_iterations=2,

 ):
 ):

-    assert mode in ["first_last"]
-
-    if mode == "first_last":
-        steps = [True] + [False] * (nb_steps + 1) + [True]
+    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, :]
+            )

 
 

-    train_input, train_actions = generate_episodes(nb_train_samples, steps)
-    train_input, train_actions = train_input.to(device_storage), train_actions.to(
-        device_storage
-    )
-    test_input, test_actions = generate_episodes(nb_test_samples, steps)
-    test_input, test_actions = test_input.to(device_storage), test_actions.to(
-        device_storage
+    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,

     )
 
     )
 

-    encoder, quantizer, decoder = train_encoder(
-        train_input,
-        test_input,
-        depth=depth,
-        nb_bits_per_token=nb_bits_per_token,
-        lambda_entropy=1.0,
-        nb_epochs=nb_epochs,
-        logger=logger,
-        device=device,
-    )
-    encoder.train(False)
-    quantizer.train(False)
-    decoder.train(False)
-
-    z = encoder(train_input[:1].to(device))
-    pow2 = (2 ** torch.arange(z.size(1), device=device))[None, None, :]
-    z_h, z_w = z.size(2), z.size(3)
-
-    def frame2seq(input, batch_size=25):
-        seq = []
-        p = pow2.to(device)
-        for x in input.split(batch_size):
-            x = x.to(device)
-            z = encoder(x)
-            ze_bool = (quantizer(z) >= 0).long()
-            output = (
-                ze_bool.permute(0, 2, 3, 1).reshape(
-                    ze_bool.size(0), -1, ze_bool.size(1)
-                )
-                * p
-            ).sum(-1)
-
-            seq.append(output)
-
-        return torch.cat(seq, dim=0)
-
-    def seq2frame(input, batch_size=25, T=1e-2):
-        frames = []
-        p = pow2.to(device)
-        for seq in input.split(batch_size):
-            seq = seq.to(device)
-            zd_bool = (seq[:, :, None] // p) % 2
-            zd_bool = zd_bool.reshape(zd_bool.size(0), z_h, z_w, -1).permute(0, 3, 1, 2)
-            logits = decoder(zd_bool * 2.0 - 1.0)
-            logits = logits.reshape(
-                logits.size(0), -1, 3, logits.size(2), logits.size(3)
-            ).permute(0, 2, 3, 4, 1)
-            output = torch.distributions.categorical.Categorical(
-                logits=logits / T
-            ).sample()
-
-            frames.append(output)
-
-        return torch.cat(frames, dim=0)
-
-    return train_input, train_actions, test_input, test_actions, frame2seq, seq2frame
+
+def seq2str(seq):
+    result = []
+    for s in seq:
+        result.append("".join([token2char[v] for v in s]))
+    return result

 
 
 ######################################################################
 
 if __name__ == "__main__":
 
 
 ######################################################################
 
 if __name__ == "__main__":

-    (
-        train_input,
-        train_actions,
-        test_input,
-        test_actions,
-        frame2seq,
-        seq2frame,
-    ) = create_data_and_processors(
-        25000, 1000,
-        nb_epochs=5,
-        mode="first_last",
-        nb_steps=20,
-    )
+    import time

 
 

-    input = test_input[:256]
+    height, width = 6, 8
+    start_time = time.perf_counter()
+    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]))
+
+    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,
+        )

 
 

-    seq = frame2seq(input)
-    output = seq2frame(seq)
+    # m = (torch.rand(seq.size()) < 0.05).long()
+    # seq = (1 - m) * seq + m * 23

 
 

-    torchvision.utils.save_image(
-        input.float() / (Box.nb_rgb_levels - 1), "orig.png", nrow=16
-    )
+    img = seq2img(seq, height, width)
+    print(img.size())

 
     torchvision.utils.save_image(
 
     torchvision.utils.save_image(

-        output.float() / (Box.nb_rgb_levels - 1), "qtiz.png", nrow=16
+        img.float() / 255.0, "/tmp/world.png", nrow=6, padding=6, pad_value=0

     )
     )