[
[255, 255, 255],
[255, 0, 0],
- [0, 128, 0],
+ [0, 192, 0],
[0, 0, 255],
- [255, 200, 0],
+ [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_fish_token = 1
-nb_fish_tokens = len(colors) - 1
-token_forward = first_fish_token + nb_fish_tokens
+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([str(n) for n in range(len(colors) - 1)]) + "><"
+token2char = "_" + "".join([chr(ord("A") + n) for n in range(len(colors) - 1)]) + "><"
-def generate(
+def generate_seq(
nb,
height,
width,
- max_nb_obj=2,
+ nb_birds=3,
+ nb_iterations=2,
+):
+ pairs = []
+
+ for _ in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
+ while True:
+ 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
+
+ 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):
+ 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
+
+ if nb_collisions == 0:
+ break
+
+ 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 generate_seq_(
+ nb,
+ height,
+ width,
+ nb_birds=3,
nb_iterations=2,
):
pairs = []
f_end = torch.zeros(height, width, dtype=torch.int64)
n = torch.arange(f_start.size(0))
- nb_fish = torch.randint(max_nb_obj, (1,)).item() + 1
for c in (
- (torch.randperm(nb_fish_tokens) + first_fish_token)[:nb_fish].sort().values
+ (torch.randperm(nb_bird_tokens) + first_bird_token)[:nb_birds].sort().values
):
i, j = (
torch.randint(height - 2, (1,))[0] + 1,
def sample2img(seq, height, width, upscale=15):
- f_start = seq[:, : height * width].reshape(-1, height, width)
- f_end = seq[:, height * width + 1 :].reshape(-1, height, width)
+ 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_fish_token + nb_fish_tokens).long()
+ 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)):
return x
- return torch.cat([mosaic(f_start, upscale), mosaic(f_end, upscale)], dim=3)
+ 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,
+ )
def seq2str(seq):
height, width = 6, 8
start_time = time.perf_counter()
- seq = generate(nb=64, height=height, width=width, max_nb_obj=3)
+ seq = generate_seq(nb=90, height=height, width=width)
delay = time.perf_counter() - start_time
print(f"{seq.size(0)/delay:02f} samples/s")
print(img.size())
torchvision.utils.save_image(
- img.float() / 255.0, "/tmp/world.png", nrow=8, padding=2
+ img.float() / 255.0, "/tmp/world.png", nrow=6, padding=6, pad_value=0
)