[255, 0, 0],
[0, 128, 0],
[0, 0, 255],
- [255, 255, 0],
+ [255, 200, 0],
[192, 192, 192],
]
)
-token2char = "_X01234>"
+token2char = "_X" + "".join([str(n) for n in range(len(colors) - 2)]) + ">"
def generate(
nb,
height,
width,
- obj_length=6,
- mask_height=3,
- mask_width=3,
- nb_obj=3,
+ max_nb_obj=2,
+ nb_iterations=2,
):
- intact = torch.zeros(nb, height, width, dtype=torch.int64)
- n = torch.arange(intact.size(0))
-
- for n in range(nb):
- for c in torch.randperm(colors.size(0) - 2)[:nb_obj] + 2:
- z = intact[n].flatten()
- m = (torch.rand(z.size()) * (z == 0)).argmax(dim=0)
- i, j = m // width, m % width
+ 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 tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
+ 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)
- for l in range(obj_length):
- intact[n, i, j] = c
+
+ 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 or intact[n, i, j] != 0:
+ if i < 0 or i >= height or j < 0 or j >= width:
i -= vi
j -= vj
- vi, vj = -vj, vi
+ vi, vj = -vi, -vj
i += vi
j += vj
- if (
- i < 0
- or i >= height
- or j < 0
- or j >= width
- or intact[n, i, j] != 0
- ):
- break
-
- masked = intact.clone()
-
- for n in range(nb):
- i = torch.randint(height - mask_height + 1, (1,))[0]
- j = torch.randint(width - mask_width + 1, (1,))[0]
- masked[n, i : i + mask_height, j : j + mask_width] = 1
+
+ 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(
[
- masked.flatten(1),
- torch.full((masked.size(0), 1), len(colors)),
- intact.flatten(1),
+ f_end.flatten(1),
+ torch.full((f_end.size(0), 1), len(colors)),
+ f_start.flatten(1),
],
dim=1,
)
def sample2img(seq, height, width):
- intact = seq[:, : height * width].reshape(-1, height, width)
- masked = seq[:, height * width + 1 :].reshape(-1, height, width)
- img_intact, img_masked = colors[intact], colors[masked]
+ 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
+
+ img_f_start, img_f_end = colors[f_start], colors[f_end]
img = torch.cat(
[
- img_intact,
+ img_f_start,
torch.full(
- (img_intact.size(0), img_intact.size(1), 1, img_intact.size(3)), 1
+ (img_f_start.size(0), img_f_start.size(1), 1, img_f_start.size(3)), 1
),
- img_masked,
+ img_f_end,
],
dim=2,
)
height, width = 6, 8
start_time = time.perf_counter()
- seq = generate(nb=64, height=height, width=width)
+ 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")