Update.

[culture.git] / sky.py

diff --git a/sky.py b/sky.py
index 3458d85..ec476a6 100755 (executable)
--- a/sky.py
+++ b/sky.py
@@ -5,7 +5,7 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
 
 # Written by Francois Fleuret <francois@fleuret.org>
 

-import math, sys, tqdm
+import math, sys, tqdm, os

 
 import torch, torchvision
 
 
 import torch, torchvision
 


@@ -14,40 +14,46 @@ from torch.nn import functional as F
 
 ######################################################################
 
 
 ######################################################################
 

+import problem
+
+
+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],
+        ]
+    )
+
+    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)]) + "><"
+    )

 
 

-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
-
-token2char = "_" + "".join([chr(ord("A") + n) for n in range(len(colors) - 1)]) + "><"
-
-
-class Sky:
-    def __init__(self, height, width):
-        self.heigh = heigh
+    def __init__(self, height=6, width=8, nb_birds=3, nb_iterations=2):
+        self.height = height

         self.width = width
         self.width = width

+        self.nb_birds = nb_birds
+        self.nb_iterations = nb_iterations

 
 

-    def generate_seq(
-        nb, height, width, nb_birds=3, nb_iterations=2, return_iterations=False
-    ):
+    def direction_tokens(self):
+        return self.token_forward, self.token_backward
+
+    def generate_seq(self, nb, return_iterations=False):

         pairs = []
         kept_iterations = []
 
         pairs = []
         kept_iterations = []
 


@@ -55,32 +61,37 @@ class Sky:
             while True:
                 iterations = []
 
             while True:
                 iterations = []
 

-                f_start = torch.zeros(height, width, dtype=torch.int64)
+                f_start = torch.zeros(self.height, self.width, dtype=torch.int64)

 
                 i, j, vi, vj = (
 
                 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),
+                    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),

                 )
 
                 )
 

-                col = torch.randperm(colors.size(0) - 1)[:nb_birds].sort().values + 1
+                col = (
+                    torch.randperm(self.colors.size(0) - 1)[: self.nb_birds]
+                    .sort()
+                    .values
+                    + 1
+                )

 
 

-                for n in range(nb_birds):
+                for n in range(self.nb_birds):

                     c = col[n]
 
                     while True:
                         i[n], j[n] = (
                     c = col[n]
 
                     while True:
                         i[n], j[n] = (

-                            torch.randint(height, (1,))[0],
-                            torch.randint(width, (1,))[0],
+                            torch.randint(self.height, (1,))[0],
+                            torch.randint(self.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
                         )
                         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 i[n] - vi[n] < self.height

                             and j[n] - vj[n] >= 0
                             and j[n] - vj[n] >= 0

-                            and j[n] - vj[n] < width
+                            and j[n] - vj[n] < self.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
                             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


@@ -93,11 +104,11 @@ class Sky:
 
                 f_end = f_start.clone()
 
 
                 f_end = f_start.clone()
 

-                for l in range(nb_iterations):
+                for l in range(self.nb_iterations):

                     iterations.append(f_end.clone())
                     f_end[...] = 0
                     nb_collisions = 0
                     iterations.append(f_end.clone())
                     f_end[...] = 0
                     nb_collisions = 0

-                    for n in range(nb_birds):
+                    for n in range(self.nb_birds):

                         c = col[n]
 
                         pi, pj, pvi, pvj = (
                         c = col[n]
 
                         pi, pj, pvi, pvj = (


@@ -108,11 +119,11 @@ class Sky:
                         )
 
                         if (i[n] == 0 and vi[n] == -1) or (
                         )
 
                         if (i[n] == 0 and vi[n] == -1) or (

-                            i[n] == height - 1 and vi[n] == 1
+                            i[n] == self.height - 1 and vi[n] == 1

                         ):
                             vi[n] = -vi[n]
                         if (j[n] == 0 and vj[n] == -1) or (
                         ):
                             vi[n] = -vi[n]
                         if (j[n] == 0 and vj[n] == -1) or (

-                            j[n] == width - 1 and vj[n] == 1
+                            j[n] == self.width - 1 and vj[n] == 1

                         ):
                             vj[n] = -vj[n]
 
                         ):
                             vj[n] = -vj[n]
 


@@ -143,7 +154,11 @@ class Sky:
             if torch.rand(1) < 0.5:
                 result.append(
                     torch.cat(
             if torch.rand(1) < 0.5:
                 result.append(
                     torch.cat(

-                        [p[0].flatten(), torch.tensor([token_forward]), p[1].flatten()],
+                        [
+                            p[0].flatten(),
+                            torch.tensor([self.token_forward]),
+                            p[1].flatten(),
+                        ],

                         dim=0,
                     )[None, :]
                 )
                         dim=0,
                     )[None, :]
                 )


@@ -152,7 +167,7 @@ class Sky:
                     torch.cat(
                         [
                             p[1].flatten(),
                     torch.cat(
                         [
                             p[1].flatten(),

-                            torch.tensor([token_backward]),
+                            torch.tensor([self.token_backward]),

                             p[0].flatten(),
                         ],
                         dim=0,
                             p[0].flatten(),
                         ],
                         dim=0,


@@ -168,27 +183,26 @@ class Sky:
     ######################################################################
 
     def generate_seq_old(
     ######################################################################
 
     def generate_seq_old(

+        self,

         nb,
         nb,

-        height,
-        width,
-        nb_birds=3,
-        nb_iterations=2,

     ):
         pairs = []
 
         for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
     ):
         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)
+            f_start = torch.zeros(self.height, self.width, dtype=torch.int64)
+            f_end = torch.zeros(self.height, self.width, dtype=torch.int64)

             n = torch.arange(f_start.size(0))
 
             for c in (
             n = torch.arange(f_start.size(0))
 
             for c in (

-                (torch.randperm(nb_bird_tokens) + first_bird_token)[:nb_birds]
+                (torch.randperm(self.nb_bird_tokens) + self.first_bird_token)[
+                    : self.nb_birds
+                ]

                 .sort()
                 .values
             ):
                 i, j = (
                 .sort()
                 .values
             ):
                 i, j = (

-                    torch.randint(height - 2, (1,))[0] + 1,
-                    torch.randint(width - 2, (1,))[0] + 1,
+                    torch.randint(self.height - 2, (1,))[0] + 1,
+                    torch.randint(self.width - 2, (1,))[0] + 1,

                 )
                 vm = torch.randint(4, (1,))[0]
                 vi, vj = (vm // 2) * (2 * (vm % 2) - 1), (1 - vm // 2) * (
                 )
                 vm = torch.randint(4, (1,))[0]
                 vi, vj = (vm // 2) * (2 * (vm % 2) - 1), (1 - vm // 2) * (


@@ -200,10 +214,10 @@ class Sky:
                 f_start[i + vj, j - vi] = c
                 f_start[i - vj, j + vi] = c
 
                 f_start[i + vj, j - vi] = c
                 f_start[i - vj, j + vi] = c
 

-                for l in range(nb_iterations):
+                for l in range(self.nb_iterations):

                     i += vi
                     j += vj
                     i += vi
                     j += vj

-                    if i < 0 or i >= height or j < 0 or j >= width:
+                    if i < 0 or i >= self.height or j < 0 or j >= self.width:

                         i -= vi
                         j -= vj
                         vi, vj = -vi, -vj
                         i -= vi
                         j -= vj
                         vi, vj = -vi, -vj


@@ -222,7 +236,11 @@ class Sky:
             if torch.rand(1) < 0.5:
                 result.append(
                     torch.cat(
             if torch.rand(1) < 0.5:
                 result.append(
                     torch.cat(

-                        [p[0].flatten(), torch.tensor([token_forward]), p[1].flatten()],
+                        [
+                            p[0].flatten(),
+                            torch.tensor([self.token_forward]),
+                            p[1].flatten(),
+                        ],

                         dim=0,
                     )[None, :]
                 )
                         dim=0,
                     )[None, :]
                 )


@@ -231,7 +249,7 @@ class Sky:
                     torch.cat(
                         [
                             p[1].flatten(),
                     torch.cat(
                         [
                             p[1].flatten(),

-                            torch.tensor([token_backward]),
+                            torch.tensor([self.token_backward]),

                             p[0].flatten(),
                         ],
                         dim=0,
                             p[0].flatten(),
                         ],
                         dim=0,


@@ -240,10 +258,12 @@ class Sky:
 
         return torch.cat(result, dim=0)
 
 
         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)
+    def frame2img(self, x, upscale=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, upscale, -1, upscale)
         x = x.reshape(s[0], s[1], s[2] * upscale, s[3] * upscale)
         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)


@@ -262,81 +282,93 @@ class Sky:
 
         return x
 
 
         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]
+    def seq2img(self, seq, upscale=15):
+        f_first = seq[:, : self.height * self.width].reshape(
+            -1, self.height, self.width
+        )
+        f_second = seq[:, self.height * self.width + 1 :].reshape(
+            -1, self.height, self.width
+        )
+        direction = seq[:, self.height * self.width]

 
         direction_symbol = torch.full(
 
         direction_symbol = torch.full(

-            (direction.size(0), height * upscale - 1, upscale), 0
+            (direction.size(0), self.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)
+        direction_symbol = self.colors[direction_symbol].permute(0, 3, 1, 2)
+        separator = torch.full((direction.size(0), 3, self.height * upscale - 1, 1), 0)

 
         for n in range(direction_symbol.size(0)):
 
         for n in range(direction_symbol.size(0)):

-            if direction[n] == token_forward:
+            if direction[n] == self.token_forward:

                 for k in range(upscale):
                     direction_symbol[
                         n,
                         :,
                 for k in range(upscale):
                     direction_symbol[
                         n,
                         :,

-                        (height * upscale) // 2 - upscale // 2 + k,
+                        (self.height * upscale) // 2 - upscale // 2 + k,

                         3 + upscale // 2 - abs(k - upscale // 2),
                     ] = 0
                         3 + upscale // 2 - abs(k - upscale // 2),
                     ] = 0

-            elif direction[n] == token_backward:
+            elif direction[n] == self.token_backward:

                 for k in range(upscale):
                     direction_symbol[
                         n,
                         :,
                 for k in range(upscale):
                     direction_symbol[
                         n,
                         :,

-                        (height * upscale) // 2 - upscale // 2 + k,
+                        (self.height * upscale) // 2 - upscale // 2 + k,

                         3 + abs(k - upscale // 2),
                     ] = 0
             else:
                 for k in range(2, upscale - 2):
                     direction_symbol[
                         3 + abs(k - upscale // 2),
                     ] = 0
             else:
                 for k in range(2, upscale - 2):
                     direction_symbol[

-                        n, :, (height * upscale) // 2 - upscale // 2 + k, k
+                        n, :, (self.height * upscale) // 2 - upscale // 2 + k, k

                     ] = 0
                     direction_symbol[
                         n,
                         :,
                     ] = 0
                     direction_symbol[
                         n,
                         :,

-                        (height * upscale) // 2 - upscale // 2 + k,
+                        (self.height * upscale) // 2 - upscale // 2 + k,

                         upscale - 1 - k,
                     ] = 0
 
         return torch.cat(
             [
                         upscale - 1 - k,
                     ] = 0
 
         return torch.cat(
             [

-                frame2img(f_first, height, width, upscale),
+                self.frame2img(f_first, upscale),

                 separator,
                 direction_symbol,
                 separator,
                 separator,
                 direction_symbol,
                 separator,

-                frame2img(f_second, height, width, upscale),
+                self.frame2img(f_second, upscale),

             ],
             dim=3,
         )
 
             ],
             dim=3,
         )
 

-    def seq2str(seq):
+    def seq2str(self, seq):

         result = []
         for s in seq:
         result = []
         for s in seq:

-            result.append("".join([token2char[v] for v in s]))
+            result.append("".join([self.token2char[v] for v in s]))

         return result
 
         return result
 

+    def save_image(self, input, result_dir, filename, logger):
+        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)
+        logger(f"wrote {image_name}")
+
+    def save_quizzes(self, input, result_dir, filename_prefix, logger):
+        self.save_image(input, result_dir, filename_prefix + ".png", logger)
+

 
 ######################################################################
 
 if __name__ == "__main__":
     import time
 
 
 ######################################################################
 
 if __name__ == "__main__":
     import time
 

-    height, width = 6, 8
+    sky = Sky(height=6, width=8, nb_iterations=100)
+

     start_time = time.perf_counter()
     start_time = time.perf_counter()

-    seq, it = generate_seq(
-        nb=64, height=height, width=width, nb_iterations=100, return_iterations=True
-    )
+    seq, it = sky.generate_seq(nb=64, return_iterations=True)

     delay = time.perf_counter() - start_time
     print(f"{seq.size(0)/delay:02f} samples/s")
 
     delay = time.perf_counter() - start_time
     print(f"{seq.size(0)/delay:02f} samples/s")
 

-    print(seq2str(seq[:4]))
+    print(sky.seq2str(seq[:4]))

 
     for t in range(len(it[0])):
 
     for t in range(len(it[0])):

-        img = torch.cat([frame2img(f[t], height, width) for f in it], dim=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",
         torchvision.utils.save_image(
             img.float() / 255.0,
             f"/tmp/frame_{t:03d}.png",


@@ -348,7 +380,7 @@ if __name__ == "__main__":
     # m = (torch.rand(seq.size()) < 0.05).long()
     # seq = (1 - m) * seq + m * 23
 
     # m = (torch.rand(seq.size()) < 0.05).long()
     # seq = (1 - m) * seq + m * 23
 

-    img = seq2img(seq, height, width)
+    img = sky.seq2img(seq)

     print(img.size())
 
     torchvision.utils.save_image(
     print(img.size())
 
     torchvision.utils.save_image(