return states, actions, rewards
+def seq2str(seq):
+ def token2str(t):
+ if t >= first_state_code and t < first_state_code + nb_state_codes:
+ return " #@$"[t - first_state_code]
+ elif t >= first_actions_code and t < first_actions_code + nb_actions_codes:
+ return "ISNEW"[t - first_actions_code]
+ elif t >= first_rewards_code and t < first_rewards_code + nb_rewards_codes:
+ return "-0+"[t - first_rewards_code]
+ elif (
+ t >= first_lookahead_rewards_code
+ and t < first_lookahead_rewards_code + nb_lookahead_rewards_codes
+ ):
+ return "n.p"[t - first_lookahead_rewards_code]
+ else:
+ return "?"
+
+ return ["".join([token2str(x.item()) for x in row]) for row in seq]
+
+
######################################################################
def status_bar(a, r, lr=None):
a, r = a.item(), r.item()
sb_a = "ISNEW"[a] if a >= 0 and a < 5 else "?"
- sb_r = " " + ("- +"[r + 1] if r in {-1, 0, 1} else "?")
- if lr is not None:
+ sb_r = "- +"[r + 1] if r in {-1, 0, 1} else "?"
+ if lr is None:
+ sb_lr = ""
+ else:
lr = lr.item()
- sb_r = sb_r + "/" + ("- +"[lr + 1] if lr in {-1, 0, 1} else "?")
- return sb_a + " " * (states.size(-1) - len(sb_a) - len(sb_r)) + sb_r
+ sb_lr = "n p"[lr + 1] if lr in {-1, 0, 1} else "?"
+ return (
+ sb_a
+ + "/"
+ + sb_r
+ + " " * (states.size(-1) - 1 - len(sb_a + sb_r + sb_lr))
+ + sb_lr
+ )
if lookahead_rewards is None:
result += (
seq = episodes2seq(states, actions, rewards, lookahead_delta=T)
s, a, r, lr = seq2episodes(seq, height, width, lookahead=True)
print(episodes2str(s, a, r, lookahead_rewards=lr, unicode=True, ansi_colors=True))
+ print()
+ for s in seq2str(seq):
+ print(s)
self.width = width
states, actions, rewards = escape.generate_episodes(
- nb_train_samples + nb_test_samples, height, width, T
+ nb_train_samples + nb_test_samples, height, width, 3 * T
)
- seq = escape.episodes2seq(states, actions, rewards, lookahead_delta=5)
+ seq = escape.episodes2seq(states, actions, rewards, lookahead_delta=T)
+ seq = seq[:, seq.size(1) // 3 : 2 * seq.size(1) // 3]
self.train_input = seq[:nb_train_samples].to(self.device)
self.test_input = seq[nb_train_samples:].to(self.device)
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
- # if logger is not None:
- # for s, a in zip(self.train_input[:100], self.train_ar_mask[:100]):
- # logger(f"train_sequences {self.problem.seq2str(s)}")
- # a = "".join(["01"[x.item()] for x in a])
- # logger(f" {a}")
-
def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
):
result = self.test_input[:100].clone()
+
+ # Saving the ground truth
+
+ s, a, r, lr = escape.seq2episodes(
+ result, self.height, self.width, lookahead=True
+ )
+ str = escape.episodes2str(
+ s, a, r, lookahead_rewards=lr, unicode=True, ansi_colors=True
+ )
+
+ filename = os.path.join(result_dir, f"test_true_seq_{n_epoch:04d}.txt")
+ with open(filename, "w") as f:
+ f.write(str)
+ logger(f"wrote {filename}")
+
+ # Re-generating from the first frame
+
ar_mask = (
torch.arange(result.size(1), device=result.device)
> self.height * self.width + 2
device=self.device,
)
+ # Saving the generated sequences
+
s, a, r, lr = escape.seq2episodes(
result, self.height, self.width, lookahead=True
)