Update.

diff --git a/quizz_machine.py b/quizz_machine.py
index daa8a54..43fd868 100755 (executable)
--- a/quizz_machine.py
+++ b/quizz_machine.py
@@ -64,28 +64,12 @@ def masked_inplace_autoregression(
         model.train(t)
 
 
-######################################################################
-
-
-class Task:
-    def batches(self, split="train", nb_to_use=-1, desc=None):
-        pass
-
-    def vocabulary_size(self):
-        pass
-
-    def produce_results(
-        self, n_epoch, model, result_dir, logger, deterministic_synthesis
-    ):
-        pass
-
-
 ######################################################################
 
 import sky
 
 
-class QuizzMachine(Task):
+class QuizzMachine:
     def save_image(self, input, result_dir, filename, logger):
         img = sky.seq2img(input.to("cpu"), self.height, self.width)
         image_name = os.path.join(result_dir, filename)

diff --git a/sky.py b/sky.py
index 36aa1e9..3458d85 100755 (executable)
--- a/sky.py
+++ b/sky.py
@@ -40,267 +40,284 @@ 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 = []
+class Sky:
+    def __init__(self, height, width):
+        self.heigh = heigh
+        self.width = width
 
-    for _ in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
-        while True:
-            iterations = []
+    def generate_seq(
+        nb, height, width, nb_birds=3, nb_iterations=2, return_iterations=False
+    ):
+        pairs = []
+        kept_iterations = []
 
-            f_start = torch.zeros(height, width, dtype=torch.int64)
+        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
 
-            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)
+                    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,
+        nb_birds=3,
+        nb_iterations=2,
+    ):
+        pairs = []
 
-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
+        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, :]
+                )
 
-            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, :]
-            )
-
-    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,
-    )
+        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,
+        )
 
-def seq2str(seq):
-    result = []
-    for s in seq:
-        result.append("".join([token2char[v] for v in s]))
-    return result
+    def seq2str(seq):
+        result = []
+        for s in seq:
+            result.append("".join([token2char[v] for v in s]))
+        return result
 
 
 ######################################################################