def generate_seq(
- nb,
- height,
- width,
- nb_birds=3,
- nb_iterations=2,
+ nb, height, width, nb_birds=3, nb_iterations=2, return_iterations=False
):
pairs = []
+ kept_iterations = []
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 = (
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):
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 = []
)[None, :]
)
- return torch.cat(result, dim=0)
+ 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_(
+def generate_seq_old(
nb,
height,
width,
return torch.cat(result, dim=0)
-def sample2img(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 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)
- def mosaic(x, upscale):
- 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:]
- 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
- 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
- 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)
return torch.cat(
[
- mosaic(f_first, upscale),
+ frame2img(f_first, height, width, upscale),
separator,
direction_symbol,
separator,
- mosaic(f_second, upscale),
+ frame2img(f_second, height, width, upscale),
],
dim=3,
)
height, width = 6, 8
start_time = time.perf_counter()
- seq = generate_seq(nb=90, height=height, width=width)
+ 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,
+ )
+
# m = (torch.rand(seq.size()) < 0.05).long()
# seq = (1 - m) * seq + m * 23
- img = sample2img(seq, height, width)
+ img = seq2img(seq, height, width)
print(img.size())
torchvision.utils.save_image(