return self.token_forward, self.token_backward
def generate_frame_sequences(self, nb):
+ result = []
+ N = 100
+ for _ in tqdm.tqdm(
+ range(0, nb + N, N), dynamic_ncols=True, desc="world generation"
+ ):
+ result.append(self.generate_frame_sequences_hard(100))
+ return torch.cat(result, dim=0)[:nb]
+
+ def generate_frame_sequences_hard(self, nb):
frame_sequences = []
+ nb_frames = (self.nb_iterations - 1) * self.speed + 1
result = torch.full(
- (nb * 4, self.nb_iterations * self.speed, self.height, self.width),
+ (nb * 4, nb_frames, self.height, self.width),
self.token_empty,
)
result[n, 0, i + vi, j + vj] = self.token_tail
break
- if torch.rand(1) < 0.75:
- break
+ # if torch.rand(1) < 0.75:
+ break
weight = torch.full((1, 1, 3, 3), 1.0)
- # mask = (torch.rand(result[:, 0].size()) < 0.01).long()
- # rand = torch.randint(4, mask.size())
- # result[:, 0] = mask * rand + (1 - mask) * result[:, 0]
+ mask = (torch.rand(result[:, 0].size()) < 0.01).long()
+ rand = torch.randint(4, mask.size())
+ result[:, 0] = mask * rand + (1 - mask) * result[:, 0]
# empty->empty
# head->tail
# tail->conductor
# conductor->head if 1 or 2 head in the neighborhood, or remains conductor
- for l in range(self.nb_iterations * self.speed - 1):
+ nb_heads = (result[:, 0] == self.token_head).flatten(1).long().sum(dim=1)
+ valid = nb_heads > 0
+
+ for l in range(nb_frames - 1):
nb_head_neighbors = (
F.conv2d(
input=(result[:, l] == self.token_head).float()[:, None, :, :],
+ (1 - mask_1_or_2_heads) * self.token_conductor
)
)
+ pred_nb_heads = nb_heads
+ nb_heads = (
+ (result[:, l + 1] == self.token_head).flatten(1).long().sum(dim=1)
+ )
+ valid = torch.logical_and(valid, (nb_heads >= pred_nb_heads))
+
+ result = result[valid]
result = result[
:, torch.arange(self.nb_iterations, device=result.device) * self.speed
i = (result[:, -1] == self.token_head).flatten(1).max(dim=1).values > 0
result = result[i]
- print(f"{result.size(0)=} {nb=}")
+ # print(f"{result.size(0)=} {nb=}")
if result.size(0) < nb:
# print(result.size(0))
if __name__ == "__main__":
import time
- wireworld = Wireworld(height=10, width=15, nb_iterations=2, speed=5)
+ wireworld = Wireworld(height=8, width=10, nb_iterations=5, speed=1)
start_time = time.perf_counter()
frame_sequences = wireworld.generate_frame_sequences(nb=96)
# print(wireworld.seq2str(seq[:4]))
- # for t in range(frame_sequences.size(1)):
- # img = wireworld.seq2img(frame_sequences[:, t])
- # torchvision.utils.save_image(
- # img.float() / 255.0,
- # f"/tmp/frame_{t:03d}.png",
- # nrow=8,
- # padding=6,
- # pad_value=0,
- # )
+ for t in range(frame_sequences.size(1)):
+ img = wireworld.seq2img(frame_sequences[:, t])
+ 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
+ wireworld = Wireworld(height=8, width=10, nb_iterations=2, speed=5)
token_sequences = wireworld.generate_token_sequences(32)
wireworld.save_quizzes(token_sequences, "/tmp", "seq")
# img = wireworld.seq2img(frame_sequences[:60])